Discovering Novel LLM Experts via Task-Capability Coevolution
Authors: Andrew Dai
Boris Meinardus
Ciaran Regan
Yingtao Tian
Yujin Tang
Venue: ICLR 2026 Poster (2026)
Keywords: Large Language Model
LLMs
Minimal Criterion Coevolution
Evolutionary Model Merging
Synthetic Data
Quality-Diversity
Open-endedness
Primary area: foundation or frontier models, including LLMs
TL;DR: Open-ended coevolution of LLMs and synthetic data (without explicit optimization) leads to the discovery of a superior population of LLMs than baselines.

Abstract: Frontier model developers aim to train models continually to possess emergent, diverse capabilities. 
To extend capabilities, the current pre-training and post-training paradigm requires manually starting training runs with static datasets or reward functions every time.
Addressing this limitation, our work pursues the insight that open-endedness (via the coevolution of models and tasks) can discover models with increasingly novel skills in a single run.
We introduce a new model development framework that extends coevolution to large language model (LLM) discovery, open-ended \textit{Assessment Coevolving with Diverse Capabilities} (AC/DC).
AC/DC evolves both LLMs via model merging and natural language tasks via synthetic data generation.
AC/DC discovers growing archives of LLMs that surpass the capabilities of larger LLMs while taking up less GPU memory.
In particular, our LLM populations achieve a broader Coverage of expertise than other curated models or baselines on downstream benchmarks, without \textit{any} explicit benchmark optimization.
Furthermore, AC/DC improves Coverage over time, continually innovates on tasks and models, and improves performance in multi-agent best-of-N selection.
Our findings highlight the potential of coevolution as a means of discovering broader sets of capabilities from base LLMs.
Overall, AC/DC brings us one step closer to a profoundly new paradigm of LLM development, where continual improvements to the diversity of model capabilities can be accelerated by leveraging existing models as stepping stones to increasingly powerful models.

Published as a conference paper at ICLR 2026
DISCOVERING NOVEL LLM EXPERTS VIA TASK-CAPABILITY COEVOLUTION
Andrew Dai Boris Meinardus' Ciaran Regan Yingtao Tian Yujin Tang
Sakana Al
ABSTRACT
Frontier model developers aim to train models continually to possess emergent, di- verse capabilities. To extend capabilities, the current pre-training and post-training paradigm requires manually starting training runs with static datasets or reward functions every time. Addressing this limitation, our work pursues the insight that open-endedness (via the coevolution of models and tasks) can discover models with inereasingly novel skills in single run. We introduce a new model devel- opment framework that extends coevolution to large language model (LLM) dis- covery, open-ended Assessment Coevolving with Diverse Capabilities (AC/DC), AC/DC evolves both LLMs via model merging and natural language tasks via syn- thetic data generation. AC/DC discovers growing archives of LLMs that surpass the capabilities of larger LLMs while taking up less GPU memory. In particular, our L.LM populations achieve a broader Coverage of expertise than other curated models baselines downstream benchmarks, without any explicit benchmark optimization. Furthermore, AC/DC improves Coverage over time, continually in- novates on tasks and models, and improves performance in multi-agent best-of-N selection. Our findings highlight the potential of coevolution as a means of dis- covering broader sets of capabilities from base LLMs. Overall, AC/DC brings us one step closer to profoundly new paradigm of LLM development, where con- tinual improvements to the diversity of model capabilities can accelerated by leveraging existing models as stepping stones to increasingly powerful models.
INTRODUCTION
LLMs and foundation models (Brown et al. 2020 Bommasani et al., 2021 Kaddour et al. 2023)
underpin key advances in AI for open-ended discovery and innovation Nguyen al.] 2016 Lehman eet al. 2023) Zhang et al. 2023 Lu et al.) 2024). Such innovation capacity in future Al systems, innate human civilization, would not only have profound implications for automated scientific discovery, but would also accelerate AI research itself. How do we get closer to LLMs as engines of knowledge accumulation and serendipitous discovery, with the ability to stumble upon greatness (Stanley & Lehman 2015) and drive paradigm shifts (e.g., the Transformer (Vaswani et al. 2017))? Additionally, how do we imbue LLMMs with innovation capacity and broader capabilities, given the prohibitively expensive costs and inaccessibility of running bigger and bigger models (Pan & Wang 2025 or obtaining more data (Muennighoff et al., 2023), especially for typical ML researchers?
While excitement grows around LLMs for scientific discovery Ciomera-Paredes et al., 2023 Novikov et al.) 2025), the current paradigm of LLM development struggles to keep up with the accumulation of knowledge
learnable discovered data. Developers must continually adapt to
incremental improvements in static datasets (Albalak et al.) 2024 Kandpal et al. 2025). environ-
ments (Intellect 2025: Lambert et al., 2024 et al. 2025). learning algorithms (Shao al.)
2024 Liu et al., 2025b), and architectures (Yang et al. 2025 Muennighotf et al., 2024). to push the
boundaries of frontier models. Continually training synthetic data (Wang al. [023d:] Xu al.
2023 Maini et al.) 2024 Havrilla et al. 2024) and
reward signals (Zhao et al. 2025 brings us closer to self self-improving LLMs. Still, only one model produced at a time. Trusting single big static model to solve all real-world problems would therefore be challenging, due to concerns about fractured entangled representations (Kumar al. (025) and costs (Li et al. 2025).
"Equal contribution. Correspondence: adai [at] tcd. boris s m.m hardus00 [at]ggma com

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Published as a conference paper at ICLR 2026
Model-Task Coevolution
Final Task Fircs-Diversity Matters
Coverage Improvement over Baselines
Improved Single Model Performance MMLU
Improvement
Over Time
10.19%
total
99%
Absolute Caverage
Imprrtvee improvement memeent Pha
62.41%
Coverage (%)
humanitled
2.90%
Expert
2.04%
scences
Control
Experts
Model
GPF40
Time
Figure I: Method Overview. AC/DC coevolves an increasing set of diverse LLMs alongside an increasingly diverse and complex set of tasks, measuring the discovered models" capabilities. Our discovered collective of models (across different model familics tested) covers more skills than baselines across wide range of benchmarks. Morcover, AC/DC discovers improved single model performance (as seen by MMLU (Hendrycks et al. 202 performance) and demonstrates improve- ment over time (shown average of MMLU and MMLU Pro (Wang et al. 2024) accuracy).
In contrast to individual models, collective intelligence (CI) (exemplified by human civilization) is capable of endlessly achieving feats far greater than any single human could (Mitchell 2009). CI has even inspired new paradigms in AI (Ha & Tang 2022> and multi-agent LLM systems (Liang et al. 2023 Inoue et al. 2025), making them more robust during test-time scaling. By discovering whole collectives of small and accessible LLMs with diverse capabilities, we can overcome the limitations
and weaknesses of any single trained model or the need to train different models separately.
To overcome the challenge of CI discovery, open-endedness (OE) an emerging paradigm aiming for never-ending discovery via open-ended algorithms (Stanley al. (017). Pursuing such AI- generating algorithms (Clune) 2020), open-ended coevolution takes inspiration from the creativity explosion of natural evolution and human innovation, and pursues ever-changing learning environ- ments for populations of increasingly intelligent agents to gain diverse capabilities (Wang et al. 2019 Dennis et al.) (020). Leveraging recent advances in OE through LLMs (Faldor et al.) [0244 Aki et al., 2024 Nisioti et al. 2024), we introduce new framework to discover a whole popu- lation of expert L.MMs through open-ended Assessment Coevolving with (/w) Diverse Capabilities (AC/DC). AC/DC combines evolutionary model merging (Akiba et al.) (025) and synthetic data generation (Lu et al., 2025) to enable LLM populations to continually adapt to novel challenges that generated, while satisfying minimal criteria for model and task quality (Brant & Stanley 2017). all without explicit benchmark optimization (Lehman & Stanley 201
Following coevolution via AC/DC, discovered broad array of LLMs with diverse expertise and response styles that solve synthetic tasks spanning engineering, the sciences, and creative writ- ing. When we selected fixed-size subset of LLMS that make up the broadest skill Coverage synthetic tasks, and then evaluated them on various LLM benchmarks that are -itrrf-distributionn (OOD) synthetic tasks at test time, found that our population of smaller evolved LLMs (with a combined lower LLM parameter count than compared baseline models) was able to solve and cover more benchmark tasks than bigger LLMs of the same model family, well as the initial seed LLM population. Our results also suggest that our LLM collectives surpass or reach GPT-4o (Hurst et al., (024) levels of knowledge covered with a significantly lower proportion of collective model parameters. Furthermore, a single evolved model achieved better MMLU (Hendrycks et al.) 2021b) performance than the best starting seed model (cf.. Fig. [ more iterations of coevolution led to continually improved model population performance test time, and cooperative final answer (Best-of-N) selection was more often successful with our LLM collectives than with baselines.
In short, our main contributions are (1) the AC/DC method applying coevolution discovery framework, (2) a demonstration of autonomous discovery of di- verse LLM experts solving OOD tasks more broadly than baseline methods (some directly optimiz- ing for benchmarks) and the-thh-hhefff models, and (3) evidence a path to open-ended improvement of LLMss without explicit benchmark optimization, through analysis of AC/DC.
novel joint
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2 BACKGROUND AND PRELIMINARIES
This section introduces key concepts central to AC/DC: model merging operations that enable evo- lutionary discovery LLM populations, Coverage metrics that quantify collective capabilities, and quality-diversity (QD) principles that guide our coevolutionary process.
Evolutionary Model Merging. Model merging combines multiple existing LLMs produce new models with lower resource requirements than training from scratch (Wortsman et al. 2022 II- harco et al., 2023). Akiba et al. (2025) introduced evolutionary model merge (EvoMerge), which automates the merging process through evolutionary optimization using CMA-ES (Hansen & Os- termeier 200T). Building on this foundation, we employ two key evolutionary operations:
Crossover: We sample two parent LLMs randomly and merge them using weighted linear inter- polation of their task vectors, as done in CycleQD (CQD) (Kuroki al.) 2025). The task vector Tpi O parent, Obase represents the difference between parent LLM and a base LLM (see Ap- pendix App. M for more details).
Mutation: We generalize existing mutation operations by applying noise to the singular values of weight matrices in merged LLMs. For each weight matrix W, we compute its singular value decom- position W UEV* and apply perturbations to the first & singular values in E before reconstruc- tion, loosely inspired by [Sun et al. (2025). This approach modifies the representational structure while preserving the overall weight matrix geometry (see Appendix App. M for more details).
Coverage Metric. Coverage measures the collective problem-solvingg capacity of LLM populations. Given Q total number of questions and N number of LLMs, Coverage is:
N V
Coverage = Q 9=1 1=1
Σ
( vy))
(I)
where Tg.s is the output of LLM i for question q, My is the ground truth answer for question q. and V denotes the logical OR operation over all LLMs. Coverage quantifies whether least
one LLM in the population solves each problem, capturing the collective intelligence potential of
diverse LLM ensembles. Unlike individual LLM accuracy, Coverage emphasizes complementary capabilities that emerge from LLM diversity.
Skill Vectors. We represent LLM capabilities through binary skill vectors, where each indexed element indicates task completion status. They serve as behavioral signatures that enable direct comparison of LLMs without predefining niches (as in MAP-Elites (Mouret & Clune (0155)). The distance between skill vectors informs the diversity of complementary LLM capabilities.
Quality-Diversiny (QD). QD generates collections of diverse, high-quality solutions (Pugh et al., 2016 Lehman & Stanley 201 unlike traditional optimization, which seeks single optimal solution. For model selection, we apply Dominated Novelty Search (DNS) (Bahlous- -Boldi et al.) 2025). recent OD algorithm particularly suited skill vector representations (similar Meyerson & Miikkulainen (017)). DNS computes local competition fitness f. by measuring each solution's distance from better-performing solutions in the descriptor space. For solution i, fa is computed as:
1 Ejex di +00
if [ > 0
J.
otherwise
where D, contains solutions fitter than solution i, KG contains indices of l solutions in Dj with small- est distances di,, between solutions and j. Local competition encourages diversity by rewarding solutions that are distant from higher-performing neighbors in the behavioral space.
Open-Ended Coevolution. Brant Stanley (2017 2020) demonstrate that defining minimal cri- teria (MC) for both agents and environments enables more open-ended outcomes in coevolution, filtering out undesired outcomes while enabling exploration to flourish. AC/DC coevolves popula- tions of LLMs and synthetic tasks, where models must satisfy quality thresholds while maximizing quality and behavioral diversity through their skill vector representations. This creates a dynamic environment where increasingly sophisticated capabilities can emerge through the interplay between model evolution and task complexity. Related work discussion in App. G highlights AC/DC as a novel system combining concepts from various fields.
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3
AC/DC: ASSESSMENT COEVOLVING WITH DIVERSE CAPABILITIES
Select Update
Model Archive
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Figure 2: Algorithm Overview. AC/DC continuously coevolves a model (LLM) archive and a synthetic task archive. LLMs are evolved using model merging crossover, and weight noising as a mutation operation. Tasks are evolved using a large scientist LLM that transforms existing task descriptions to generate increasingly novel and complex tasks. Models are evaluated on this data. We then compute a skill vector (i.e., signature of quality and diversity) for each model and a pass rate for each task. Based on those, we first apply minimal criterion (MC) filters (gibberish LLM filter, impossible task filter) and then select the models and tasks to update the archives, respectively.
We describe an open-ended algorithm for automatically discovering diverse LLMs that can collec- tively cover wide range of skills. AC/DC coevolves two archives: an LLM archive A& selected via DNS on skill vectors, and a synthetic active task archive Aq containing an increasingly complex and novel set of challenges that the L.M archive must solve. We illustrate the algorithm in Fig. and provide the pseudocode below (Algorithm[] D Inspired by Brant & Stanley (2017). we highlight minimal criteria (MC) for both LLMs and tasks. For further details, see Appendix App. N
Algorithm AC/DC: Assessment Cocvolving with Diverse Capabilities
1: Initialize: Model archive Aas seed and init models, Task archive Aq <- seed and init tasks
2: for = to G do
Modee Evolution Phase
3: P f- SELECTPARENTS(AAI
Select P parents
4:
0 <- CROSSOVERMUTTATE(P...
> Generate offspring
5:
E < EVALUATE(O,, Aq)
> Evaluate skill vectors, get responses to tasks; App.
F22
6:
T + GIBBERISIIFILTER((E)
> Trim away/discard degenerate models; App.
F.5
7:
AM DNSUPDATEEAM,
Select top M models; App. A.3.
8:
if mod Grask = 0 then
> Task Evolution Phase
9:
Q GENNEAAEEAASKS(sceniiss LLM)
Generate N Ngen tasks; App.
F.
10:
Qnon & NOVELTYFILIER(()
> Remove similar tasks; App.
F.4
Quatid +- VALIDATETAAKSSCQ...
Reflection & validation; App.
F.
12:
Aq.Aq, + UPDATEETASKARCHIVE/
AQ Q Qvalia) b Aq, is global task archive B Update skill vectors with new task pool
13:
REDDALUUTEAACIIVVEAAM Caliii.Qvaaai Qwatid)
14:
end if
15:
SAVEARCHIVES(AM. Aq. end for
16:
17:
return SELECTTTASKFFRRCEAANN Aq.)
D Select N₂ diverse models
Model Archive Evolution. Evolution begins with at least three seed LLMs, representing fine-tuned versions of the same base architecture. We maintain M active LLMs i.e., LLMs considered as parents for the next generation (as in DNS). We also save a historical archive of LLMs every Grask generations (before task adaptation), as candidates for a future task force. Given the existing parent population in M, apply crossover and mutation operators described in Sec. producing
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N offspring and yielding M + N candidates for evaluation (Lines 3-4). Each candidate LLM is evaluated on our synthetic task archive and assigned skill vectors (Line 5), We apply a novel MC filter called the "gibberish filter" to eliminate degenerate models by sampling outputs from the LLM for three random tasks each and employing a judge I.LM to assess text coherence (Line 6). For the remaining models, we employ DNS to compute adjusted local competition scores f (cf. App.A.3.1 We retain the highest-fitness model and select the remaining P 1 models with top f scores such that we have most M active models (Line 7).
Task Archive Evolution. Alongside LLM agents, we coevolve an increasingly challenging and diverse set of tasks. We employ large scientist LLM to synthesize tasks in line with METR Task Standard Team (2024) (but simplified), where each task comprises a question-answer pair with an accompanying scoring function defined in Python (Line 9; App.E.Ip. We extend et al. (2025) with a code extraction tool that enables robust evaluation of tasks requiring code generation. allowing the scientist LLM to programmatically parse and execute subject model responses. We maintain at most mma active tasks per generation that are used to evaluate the current generation of models, alongside a global task archive updated every Gtask generations. We employ two vector databases for efficient similarity search: one for active tasks (newest) and another for the global archive.
Task evolution begins with Naced manually curated seed tasks (ef. App. E. and generates Ninit initial tasks through our evolution pipeline (Lines 9-13): (1) Task Proposal Phase samples parent task and three random tasks from the active task database. Based on the parent task's s difficulty pro- determened by average pass rates across the current model population- we classify adaptation requirements as: increase difficulty, decrease difficulty, or generate a novel variant. The scientist LLMM receives the parent task, three random reference tasks, and an adaptation type to generate candidate task. (2) Novelty Filtering retrieves the three most similar tasks from the global archive using cosine similarity in embedding space. judge LLM then determines whether the proposed task introduces sufficient novelty relative to existing tasks. (3) Reflection and Validation applies iterative refinement through celf-evaluation cycles where the scientist LLM first attempts to solve its own generated task, and we execute the scoring function to identify implementation issues. Compi- lation errors trigger automatic correction with error feedback, while logic-based errors prompt task refinement. (4) Quality Assurance and MC implements additional filtering to remove impossible tasks that no LLM was able to solve, replacing them with their parent tasks. Accepted tasks are added to both the global archive and the active tasks. See App. E.4 for generated example tasks.
Model Selection for Downstream Evaluation. After coevolution over multiple generations, we se- lect Nef models (from the historical archive) for our force that maximize the number of correctly solved tasks across our synthetic task distribution Aq, (Line 17) (see [D2][ for experiments with different selection strategies). This selection process operates independently of downstream bench- marks, avoiding optimization pressure and maintaining model generalization for OOD domains.
4 QUANTITATIVE RESULTS
We compare our task force Coverage (Eq. 1T) against several baseline approaches. We evaluate on a diverse set of benchmarks covering general knowledge, math, STEM, and code (see App. A for details). See App. A.2 for model specifications and App. for hyperparameters.
A
Baselines Setup. We compare against four basclines: (1) Experts (N=3): Hand-selected instruct models (code, math, general) prompted once each with temperature 0 (for a discussion on N=8 experts, see App. D.4p. (2) Control (N=3/N=8): The general instruct model prompted 3 or 8 times with temperature 0.7. (3) Big Model: A single large instruct model prompted once with temperature 0. (4) GPT-4o: Prompted once as the Big Model.
Best-of-N Selection Setup. Next to Coverage, we also evaluate Best-of-N (BoN) single-answer selection from multiple candidates using standard benchmark versions, testing whether Coverage improvements translate to practical deployment scenarios. We implement three techniques for the three benchmark types (MCQ, math, code). For further details, see App.
Coverage. Tab. D presents Coverage results across four base model families (see App. B.1 for details), revealing important patterns in AC/DC's performance across different architectures and scales. AC/DC works on mulitple model families, achieving positive improvements on average across all model families and configurations (+2.04% +10.199 19% across comparisons). Qwen 2.5
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Table I: AC/DC (ours) Coverage improvement across different models. Results show average performance improvement across all benchmarks for N=3 and N=8 configurations over the respec- tive baseline. Gains are significant in most individual and aggregated (cf. App. K).
vs Experts
vs Control (%)
vs Big Model (%)
vs GPT-4o (%)
Base Model
N=3 (%)
N=3
N=8
N=3
N=8
N=3
N=8
Qwen2 7B
+2.06
-0.45
-1.04
+0.69
+8.83
-6.08
+2.05
Qwen2.5 7B
+4.40
+0.40
+0.61
+3.85
+9.78
+1.02
+6,95
Qwen3 14B
-0.21
+0.49
+1.54
+4,22
+9.48
+5.45
+10.71
DeepSeek V1 7B
+9.69
+9.35
+7.04
+1.96
+12.69
-18.46
-7.72
Average
+3.99
+2.45
+2.04
+2.68
+10.19
-4.52
+2.99
and DeepSeek numbers show consistent improvements across expert and control baselines, indi- cating effective discovery of complementary capabilities. Qwen3 14B exhibits sealing-dependde behavior where N=3 configurations underperform expert baselines but demonstrate improvement over the control baselines. Qwen2 demonstrates strong improvement over the three experts, but slightly lower coverage against control. Still, in Fig. |
Qwen2 coverage increase over time for N=8.
AC/DC also achieves substantial parameter efficiency for example, Qwen2.5 7B achieves 3.85% improvement over a 72B model using only 29% of the parameters N=3, growing to 9.78% im- provement at N=8, suggesting that distributed specialization benefits compound with scale. Our re- sults show that AC/DC successfully discovers complementary capabilities that extend beyond what can be achieved through either manual expert selection parameter scaling.
Finally, comparing our task force to GPT-40, we demonstrate that our N=8 collective of models achieves broader Coverage. This is especially interesting considering that our task forces require very little compute to merge and then serve them in consideration the potential costs of GPT- 4o. Moreover, at N=3, our Qwen 2.5 task force of 7B models outperforms GPT-4o. This finding suggests that a collective of smaller, diverse, and capable models possesses the knowledge of single frontier model, which can be leveraged given advances in BoN section methods.
Table 2: AC/DC (ours) Best-of-N improvement across different models. Results show average performance improvement across all benchmarks for N=3 and N=8 configurations over the respec- tive baseline. Gains are significant in several individual and aggregated cases (cf. App. K.
Experts N=3 (%)
vs Control ( (%)
vs Big Model ( (%)
vs GPT-4o
Base Model
N=3
N=8
N=3
N=8
N=3
N=8
Qwen2 7B
-1.31
+2.33
+0.34
-6.32
-2.19
-12997
-8.84
Qwen2.5 7B
-1.26
+0,27
-0.83
-3,22
-1.11
-6.32
-4.11
Qwen3 14B
-0.49
+0.29
+0.50
-0.78
+1.37
-3.17
-1.02
DeepSeek V1 7B
+11.73
+4.49
+7.92
-1.27
+4.94
-20.83
-14.62
Average
+1.34
+0.99
+1.05
-3.89
-0.25
-10.82
-7.17
Best-of-N (BoN). Tab. 2 presents BoN selection results across four base model families (see App. B.2 for details), revealing how Coverage improvements can translate into practical single- answer scenarios. AC/DC maintains strong performance when restricted to best-of-N, achieving positive improvements on average across representative comparisons (+0.99% to +1.34% vs control and experts). Most base model groups show positive improvements over control baselines, with particularly strong performance from Qwen2 7B, and DeepSeek VI 7B. Compared against the expert baselines, the DeepSeek task force demonstrates exceptional improvements, whereas on the Owen-based model families, we observe room for improvement.
Most notably, AC/DC sometimes achieves improved parameter efficiency against big models. For example, DeepSeek 7B reaches within 1.27% of the 67B model's performance using only 17% of the parameters at N=3, and surpasses it by 4. 4.94% at N=8 while using 16% fewer parameters.
Comparing against GPT-4o, observe that our 8 Qwen2.5 7B and Qwen3 14B models come close to GPT-4o's 's performance, indicating that with improved BoN methods, the collective smaller models is within reach of outperforming the significantly larger proprietary model.

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These results suggest that AC/DC can narrow the gap between single big models and multiple small models, even in rudimentary Best-of-N response setups. AC/DC can also scale up to larger collec- tives. Closing the gap remains a general challenge for future research focused on Best-of-N whole (as acknowledged in Sec. 6. but can leverage complementary gains via AC/DC.
Ablations. We examine the contribution of individual algorithmic components by systematically removing each from AC/DC's evolutionary process (detailed results in App. [.I]. The ablation reveals that QD selection (via DNS) and the gibberish filter are the most critical components, with their removal causing the largest absolute performance drops (2.39% and 2.46% at N=3, 0.88% and 1.18% at N=8, respectively). Removing individual components like mutation or novelty filtering causes modest decreases ranging from 0.50%- 1.16% at N=3 and 0.37% 1.1% at N=8.
Most importantly, removing all evolutionary components simultancously causes substantial perfor- mance degradation (2.36% drop at N=3, 7.02% drop at N=8). Overall, AC/DC is more often sig- nificantly better if none of these components are removed (cf. App. KK.. Performance drop severe when all the components are removed, especially for larger task forces. In App. . we demonstrate that including coevolution improves performance over model evolution on a static synthetic dataset (by 3.62% for N=8). In App. D.7 we demonstrate the effect of seed task selection, and in App. D.8 the effect of changing the scientist model.
AC/DC (ours) outper- forms prior QD methods. Avg. Coverage across benchmarks.
Table 3:
Finally, Tab. compares AC/DC to prior QD methods (DNS, CQD) that optimize for benchmark-speciffe datasets (see App. D.5 for implementation details and extended tables). In contrast, AC/DC does not optimize for any benchmark and achieves the highest benchmark Coverage N=8 models, demonstrating that AC/DC discovers more diverse and capable
Configuration
N=3
N=8
AC/DC (ours)
60.82
69.00
DNS
60.18
66.48
LLMs. Concurrently, DNS improves on Coverage over CQD, justifying its usage in AC/DC.
CQD
59.85
65.42
QUALITATIVE CASE STUDY
5.1
EMERGENT SPECIALIZATION MERGED MODELS
Fig. illustrates how our eight discovered models develop distinct performance profiles, with each model excelling in specific categories while performing differently across others, enabling them to function as complementary components of collective intelligence. This specialization creates valu- able Coverage patterns where models contribute unique capabilities to the ensemble. For instance, Model may not achieve the highest overall accuracy. but provides correct answers chemistry questions that no other model in the population can solve.
Similarly, Model 6 performs better in business and computer science do- mains, while Model leads in biol- ogy. These specialized capabilities ensure the task force can collectively address questions across diverse do- mains, even when individual mod- els show weaknesses in certain ar- eas. In contrast, the control base- line exhibits minimal variance across categories and overall weaker perfor- mance. Moreover, Fig. [ demon- strates that AC/DC discovers im- proved single LLMs, as evidenced by superior MMLU Pro performance compared to baseline models.
Merged Model Yack Force
Control Baseline
Figure 3: Specialization of Merged Models on MMLU Pro Categories. Accuracy distribution across subjects for AC/DC (left) and Control baseline (right) (N=8). For clarity, we hide the instruct seed model in the AC/DC task force.
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Figure 4: Merged models unlock new capabilities. Higher Coverage means that our models solve tasks that baselines didn't. These examples show a sample from MMLU, GSM8K, and GPQA, respectively, where none of the baseline models (math expert, code expert, reprompting the instruct model 8x, and the 72B model) solved the task, whereas at least one of our models did.
5.2 RESPONSE EXAMPLES AND DIVERSITY FROM MERGED MODELS
Merged Models vs. Baselines on Benchmark Tasks. AC/DC demonstrates superior Coverage by solving tasks that baseline methods cannot address. Fig. 4 presents examples where none of the baseline poodlls-inclddig math experts, code experts, repeated instruct model sampling, and the 72B model successuuly solve the task, while at least one of our discovered models provides the correct solution.
The MMLU example (left) illustrates multiple advantages of AC/DC beyond correctness. While baseline models fail to identify the correct answer, the 72B model additionally demonstrates poor re- sponse formatting, incorrectly placing square brackets around the main reasoning rather than the step headers. Our model not only provides the correct answer but also delivers concise, well-formatted reasoning that adheres to the expected structure.
The GSM8K arithmetic example (middle) showcases improved mathematical reasoning capabilities that likely emerge from our model merging process. By combining the potentially overfitted math expert with the general instruct model, AC/DC appears to broaden narrow mathematical skills to handle wider range of instruction formats, resulting in more robust problem-solving abilities.
The GPQA physics example (right) reveals an important advantage related to output length con- straints. During evaluation, responses exceeding the 1024-token limit often result in incorrect sessments, and most baseline responses violate this constraint. AC/DC implements a 512-token maximum during training, creating selection pressure for models that produce concise answers. Qualitative analysis suggests our discovered models find solutions using fewer tokens, leading to higher accuracy within the evaluation framework's 's constraints. This demonstrates how evolutionary pressure can optimize for practical deployment considerations beyond raw capability.
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Figure 5: Models in our Task Force give diverse answers. Two examples of synthetic tasks generated by AC/DC and the answers of 3 models in our Task Force. In the left example, we can see how all three models give different analogies. Moreover, Model structures the analogy in Python function. For the right example, we can see that our models provide 3 different implementations of the same optimal algorithm.
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Merged Model Response Diversity on Synthetic Tasks. Building on the quantitative capability distribution demonstrated in Sec. [5.1, we examine qualitatively whether our models generate diverse responses by analyzing their outputs two synthetic tasks (Fig. 5
The creative writing task (left) requires both analogical reasoning and computer science knowledge, revealing distinct approaches across three models. Each model proposes completely different mmalogy-librand navigation, urban directions, and maze solving- demionsannmst genuine diversity in conceptual frameworks rather than superficial variations. Notably, one model presents its analogy as a Python function, likely reflecting its ancestry from a code expert model and illustrating how evolutionary merging preserves specialized formatting preferences even non-coding contexts (for more details on model evolution analysis, see App. E.2).
The algorithm implementation task (right) shows diversity in coding style and approach while main- taining algorithmic correctness. These variations demonstrate that AC/DC produces models with different coding philosophies and defensive programming practices, suggesting genuine stylistic diversity beyond mere surface-level differences.
This qualitative analysis confirms that our discovered models exhibit meaningful diversity both creative reasoning and technical implementation, supporting the quantitative evidence of broad capa- bility distributions and validating that AC/DC generates truly complementary rather than redundant model behaviors. More qualitative analyses on cocvolution are in App.
Additionally, Appendix App. [ we demonstrate quantitative and qualitative analysis comparing the three expert seed models to discovered merged models, investigating how challenging our syn- thetic tasks are to the expert models compared to our merged models. We find that our merged models, average and individual models, perform better ing further evidence for the complexity of our synthetic data and the capabilities emerging through AC/DC, potentially beyond those present in off-the-shelf models.
synthetic data, demonstrat-
5.3 QUALITY AND DIVERSITY OF SYNTHETIC TASKS A HUMAN STUDY
To validate the quality and novelty of our synthetically generated tasks, we conducted a human study where three expert reviewers evaluated 47 synthetic tasks and 49 benchmark tasks across three dimensions: correctness, out-of-distribution (OOD) nature relative to standard benchmarks, and creativity. Full methodology and results details are provided in Appendix App. H
Table 4: Human evaluation results for synthetic tasks. Values show mean + standard error across all labels.
Creativity 97.8% 2.2% 68.9% + 6.9% 37.8% + 7.2%
Correctness
Out-of-Distribution
Results demonstrate that AC/DC generates high-quality tasks with strong novelty characteristics. The 97.8% correctness rate confirms that synthetic tasks are well-formed and solvable. Critically, nearly 70% were rated out-of-distribution compared to established benchmarks, providing evi- dence that AC/DC successfully generates novel task types beyond existing evaluation datasets, sup- porting our claims for OOD training. Over one-third were rated as creative, indicating exploration of problem-solving approaches not commonly tested by standard benchmarks.
As a validation baseline, we also evaluated tasks from eight standard benchmarks. These showed substantially lower OOD (10.2%) and creativity (6.1%) ratings, with the few exceptions con- centrated exclusively in complex graduate-Ievel benchmarks (MMLU-Pro, GPQA). This pattern confirms that reviewers appropriately distinguished between novel synthetic tasks and established benchmark content. Statistical analysis reveals strong inter-rater agreement on objective metrics (correctness: p 0.46, OOD: p 0.57), demonstrating robust and reliable findings.
6 CONCLUSION, LIMITATIONS, AND FUTURE WORK
This work introduces AC/DC, a framework for automatically discovering diverse LLM collectives through open-ended coevolution of models and synthetic tasks. AC/DC demonstrates that extending

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EvoMerge to novel innovation-driver pipeline can create task forces that outperform both larger monolithic models (while using fewer parameters) and manually curated expert ensembles. AC/DC does not optimize for any downstream benchmark and achieves consistent improvements across multiple model families, with evolved populations showing a wider coverage of capabilities and emergent specializations that validate the discovery of complementary skills.
We highlight limitations with AC/DC that motivate further work. Firstly, successful merge outcomes can depend on empirically testing seed model combinations; for example, strongly fine-tuned mod- els with divergent parameter spaces merges poorly, potentially limiting performance gains (Horoi al. 2025) (e.g., see results with Llama3, App. C). The framework relies on fixed scientist LLM for task generation, constraining exploration potential. AC/DC primarily discovers emergent skills through crossover rather than candidate models themselves acquiring new knowledge, bounded by the initial seed models' capabilities, which could be addressed through mutation (e.g., our mutation operator). Finally, an inherited limitation from EvoMerge is that it requires seed models that are fine-tuned versions of the same base model.
Key future work directions include developing recursive self-improving scientist models using evolved model populations for task generation. Furthermore, as with all prior attempts towards un- bounded open- open-endedness, extending runs well beyond an arbitrary limit on coevolution steps would enable investigation of longer-term open-ended dynamics and whether innovation rates remain stable over extended time horizons (as we observe promising signs of continual task and model innova- tions in Appendix App- D.3 and App. [D66]. Moreover, similar to how lot of research focuses on developing base LLMs suitable for subsequent post-training, research on the understanding of model merging compatibility of seed models is a relevant future research direction. We investigate potential ad-hoc predictors for the compatibility of seed models for evolutionary model merging in Appendix App. J which can be an interesting starting point for future research. A complementary challenge to determining seed conditions for coevolution is determining the right minimal criteria to facilitate the discovery of ideal model behaviors that are non-trivial to obtain via loss or objective functions. We investigate another way in which the criteria settings we have set for AC/DC can lead to better performance in different evaluation settings, for example, under constrained response length limits (cf. Appendix App. D.9.. Additionally, expanding scientist LLM tools (c.g.. adding web search capabilities) for task generation would enhance the correctness and scope of novel tasks (Lu et al., 2024]. Integrating model fine-tuning could enable more efficient knowledge acquisition beyond crossover-basee discovery. Moreover, advanced merging techniques such as M2N2 (Abrantes et al. 2025) could provide saaherrdegrreeooffrreedo model combinations. Finally, implementing model collaboration during training and test-time inference could enhance population-level performance. Nevertheless, independently developing more sophisticated multi-agent best-of-N extraction meth- ods could be valuable complementary research direction (Inoue et al., (2225) (cf. App. G on multi-agent systems). Finally, the creativity of standalone LLMS remains fundamental bottleneck that necessitates further innovations to Al model architectures or open-ended discovery pipelines
that leverage Al models for search/exploration (Budd& & Scarfe 2026 Zahavy 2026 Franceschelli
& Musolesi 2025a).
In conclusion, AC/DC represents a paradigm shift from scaling individual models toward deliber- ately developing complementary agent collectives. We hint at possible new directions to further address the limitations of the norm, monolithic model development, by introducing a more open- ended model population discovery approach. This distributed specialization approach offers path to parameter-efficient AI systems that achieve sophisticated capabilities without the computational costs of ever-larger monolithic (frontier) models (ef. App. [L] By automatically discovering novel LLM experts and continually advancing a population of diversely capable LLMs, LLMs may one day embody the engine that drives both knowledge acquisition and transformative creativity, en- abling discoveries that improve both its own inner workings, and the outer loop environment that it may transform and adapt to in tandem with humans and other AI systems. After all, natural evolution on Earth actively produces a rich phylogeny that has enabled lifeforms (c.g., trees, coral) that also serve as challenges and opportunities for others (e-g., giraffes, fish), a successful instance of open- ended coevolution since over a billion years ago. Or, through cultural (co)evolution, even leaps of serendipitous invention from the vacuum tube to the computer (Stanley & Lehman 2015 Stanley et al. 2017). With AC/DC, we demonstrate first step towards this vision, bringing closer to discovering collective AI that is as open-ended, complex, and creative as human civilization.
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ETHICS STATEMENT
AC/DC focuses on automatically coevolving LLMs and synthetic tasks. As this work only encom- passes the evaluation of models synthetic and benchmark tasks without involving sensitive data, human subjects, or potential misuse applications, we identify no ethical concerns.
REPRODUCIBILITY STATEMENT
To ensure reproducibility of results, provide source code and configs, showing the details of the algorithm, run setup, seed tasks, and LLM prompts. All base models and evaluation benchmarks used this work are publicly available.
AUTHOR CONTRIBUTIONS
In the following, we list the contributions of the authors to the paper.
Andrew Dai: Proposed the initial idea. Equal main contribution the development of the AC/DC framework and conducted the experiments. Equal main contribution to the writing of the paper.
Boris Meinardus: Equal main contribution to the development of the AC/DC framework and conducted the experiments. Equal main contribution to the writing of the paper.
Ciaran Regan: Assisted with the experiments and contributed to the writing of the paper.
Yingtao Tian: Advised on the project and the writing of the paper.
Yujin Tang: Advised the writing of the paper.
ACKNOWLEDGMENTT
We thank the Sakana AI research team, in particular (in alphabetical order), Johannes Ackermann, Takuya Akiba, Sam Farle, Simon Guo, David Ha, Shengran Hu, Yuichi Inoue, Llion Jones, Akarsh Kumar, Robert Lange, Sebastian Risi, and Alex I.. Zhang. for helpful discussions and feedback. We also thank Koshi Eguchi and Kou Misaki for providing technical support and maintenance during our experimental runs on compute cluster.
REFERENCES
João Abrantes, Robert Lange, and Yujin Tang. Competition and attraction improve model fusion. In Proceedings of the Genetic and Evolutionary Computation Conference, pp. 1217-1225, 2025.
Johannes Ackermann, Oliver Richter, and Roger Wattenhofer. Unsupervised task clustering for multi-task reinforcement learning. In Joint European Conference on Muchine Learning and Knowledge Discovery in Databuses, pp. 222-237. Springer, 2021.
Fuma Aki, Riku Ikeda, Takumi Saito, Ciaran Regan, and Mizuki Oka. LIm-poet: Evolving complex environments using large language models, 2024.
Takuya Akiba, Makoto Shing, Yujin Tang, Qi Sun, and David Ha. Evolutionary optimization of model merging recipes. Nature Machine Intelligence, 7(2):195-206 2025.
Alon Albalak, Yanai Elazar, Sang Michael Xie, Shayne Longpre, Nathan Lambert, Xinyi Wang, Niklas Muennighoff, Bairu Hou, Liangming Pan, Haewon Jeong, et al. A survey on data selection for language models. arXiv preprint arXiv:2402.1683 2024.
Jacob Austin, Augustus Odena, Maxwell Nye. Maarten Bosma, Henryk Michalewski. David Dohan, Ellen Jiang. Carrie Cai, Michael Terry. Quoc Le, al. Program synthesis with large language models. arXiv preprint arXiv: /00.///.07770 07732, 2021.
Ryan Bahlous-Boll Boldi, Maxence Faldor, Luca Grillotti, Hannah Janmohamed, Lisa Coiffard, Lee Spector, and Antoine Cully. Dominated novelty search: Rethinking local competition in quality- diversity. In Proceedings of the Genetic and Evolutionary Computation Conference, pp. 104-112, 2025.
11

---

Published as a conference paper at ICLR 2026
Yuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion, Andy Jones, Anna Chen, Anna Goldie, Azalia Mirhoseini, Cameron McKinnon, et al. Constitutional ai: Harm- lessness from ai feedback. arXiv preprint arXiv:2212.08073, 2022.
Varun Bhatt, Bryon Tjanaka, Matthew Fontaine, and Stefanos Nikolaidis. Deep surrogate assisted generation of environments. Advances in Neural Information Processing Systems, 35:37762-
37777, 2022.
Xiao Bi, Deli Chen, Guanting Chen, Shanhuang Chen, Damai Dai, Chengqi Deng, Honghui Ding, Kai Dong, Qiushi Du, Zhe Fu, et al. Deepseek llm: Scaling open-source language models with longtermism. arXiv preprint arXiv:2401.0222 240.02944, 2024.
Rishi Bommasani, Drew A Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, et al. On the opportu- nities and risks of foundation models. arXiv preprint arXiv:2 07258, 2021.
David M Bossens and Danesh Tarapore. Quality- diverssty meta-cvolution: Customizing behavior spaces meta-objective. IEEE Transactions Evolutionary Computation, 26(5):111-11181, 2022.
Herbie Bradley, Andrew Dai, Hannah Teufel, Jenny Zhang, Koen Oostermeijer, Marco Bellagente, Jeff Clune, Kenneth Stanley, Grégory Schott, and Joel Lehman. Quality-diversity through ai feedback, 2023.
Jonathan C. Brant and Kenneth O. Stanley. Minimal criterion coevolution: a new approach to open- ended search. Proceedings of the Genetic and Evolutionary Computation Conference, GECCO '17, pp. 67-74, New York, NY, USA, 2017. Association for Computing Machinery. ISBN 9781450349208. doi: 10.1145/3071 178111788301010 30711017188
URL wttp:///ee...r//..11111111
3071178 3071186
Jonathan C. Brant and Kenneth O. Stanley. Diversity preservation in minimal criterion coevolution through resource limitation. Proceedings of the 2020 Genetic and Evolutionary Computation Conference, GECCO 200 pp. 58-66, New York, NY, USA, 2020. Association for Computing Machinery. ISBN 978 97837550712888 doi: 10.1 10.1145/377933.33389999 URL httpss://oo:
org/10.1145. 3377 930 3389809
Bradley Brown, Jordan Juravsky, Ryan Ehrlich, Ronald Clark, Quoc V. Le, Christopher Ré, and Azalia Mirhoseini. Large language monkeys: Scaling inference compute with repeated sampling. arXiv preprint arXiv:2407.2 1787, 2024.
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot learners. Advances in neural information processing systems, 33:1877-1901, 2020.
Dr. Jeremy Budd and Dr. Tim Scarfe. lated. MLST Archive, 2026. URL why htty--rrativity-caaeee
Why creativity cannot be interpo- wws...iii..ms.tta....
be wwt.ineeeeeeteee..
Louis Castricato, Nathan Lile, Rafael Rafailov, Phillpp Fränken, and Chelsea Finn. Persona: A reproducible testbed for pluralistic alignment. In Proceedings of the 31st International Conference on Computational Linguistics, pp. 11348-11361 2025.
Konstantinos Chatzilygeroudis. Antoine Cully, Vassilis Vassiliades, and Jean-Baptiste Mouret. -uility-diversity optimization: novel branch of stochastic optimization. Black Box Opti- mization, Machine Learning, and No-Free Lunch Theorems, pp. 109-135. Springer, 2021.
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, Alex Ray, Raul Puri, Gretchen Krueger, Michael Petrov, Heidy Khlaaf, Girish Sastry, Pamela Mishkin, Brooke Chan, Scott Gray, Nick Ryder, Mikhail Pavlov, Alethea Power, Lukasz Kaiser, Mohammad Bavarian, Clemens Winter, Philippe Tillet, Felipe Petroski Such, Dave Cummings, Matthias Plappert, Fo- tios Chantzis, Elizabeth Barnes, Ariel Herbert- Voss, William Hebgen Guss, Alex Nichol, Alex Paino, Nikolas Tezak, Jie Tang, Igor Babuschkin, Suchir Balaji, Shantanu Jain, William Saunders,
12

---

Published as a conference paper at ICLR 2026
Christopher Hesse, Andrew N. Carr, Jan Leike, Joshua Achiam, Vedant Misra, Evan Morikawa, Alec Radford, Matthew Knight, Miles Brundage, Mira Murati, Katie Mayer, Peter Welinder, Bob McGrew, Dario Amodei, Sam McCandlish, llya Sutskever, and Wojeiech Zaremba. Evaluating large language models trained on code. arXiv preprint arXiv:2107.03:3 03374, 2021a.
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde De Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al. Evaluating large language models trained code. arXiv preprint arXiv:2107.03333 0033444 2021b.
John Joon Young Chung, Vishakh Padmakumar, Melissa Roemmele, Yuqian Sun, and Max Kreminski. Modifying large language model post-training for diverse creative writing. arXiv preprint arXiv:2503. 17111 2025.
Jeff Clune. Ai-gas: Ai-generating algorithms, alternate paradigm for producing general artificial intelligence, 2020.
wttp://rrr.raa......... 10985
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, et al. Training verifiers to solve math word problems, arXiv preprint 1rXiv:2110.14100 14168, 2021.
Cédric Colas, Vashisht Madhavan, Joost Huizinga, and Jeff Clune. Scaling map-elites to deep neu- roevolution. In Proceedings of the 2020 Genetic and Evolutionary Computation Conference, pp. 67-75, 2020.
Edoardo Conti, Vashisht Madhavan, Felipe Petroski Such, Joel Lehman, Kenneth Stanley, and Jeff Clune. Improving exploration in evolution strategies for deep reinforcement learning via popu- lation of novelty-seeking agents. Advances in neural information processing systems, 31, 2018.
Antoine Cully and Yiannis Demiris. Quality and diversity optimization: A unifying modular frame- work. IEEE Transactions on Evolutionary Computation, 22(2):245-25 259,, 2017.
Antoine Cully, Jeff Clune, Danesh Tarapore, and Jean- Saan-Bpptiste Mouret. Robots that can adapt like animals. Nature, 521((555):503-5007 2015.
Michael Dennis, Natasha Jaques, Eugene Vinitsky, Alexandre Bayen, Stuart Russell, Andrew Critch, and Sergey Levine. Emergent complexity and zero-shot transfer via unsupervised environment design. Advances in neural information processing systems, 33: 13099-13061, 2020.
Aaron Dharna, Cong Lu, and Jeff Clune. Foundation model self eelf-play: Open-ended strategy innova- tion via foundation models. arXiv preprini arXiv:2507.06466, 2025.
Stephane Doncieux, Alban Laftaquière, and Alexandre Coninx. Novelty search: a theoretical per- spective. In Proceedings of the Genetic and Evolutionary Computation Conference, pp. 99-106, 2019.
Yilun Du, Shuang Li, Antonio Torralba, Joshua B. Tenenbaum, and Igor Mordatch. Improving fac- tuality and reasoning language models through multiagent debate. In International Conference Machine Learning (ICML), 2024. Ntl:23355.13255
Mayalen Etcheverry, Bert Wang-Chak Chan, Clément Moulin-Frier, and Pierre Yves Oudeyer. Pierre-Yves Meta-diversity search in complex systems, recipe for artificial open-endedness?? 2021.
Maxence Faldor, Jenny Zhang, Antoine Cully, and Jeff Clune. Omni-epic: Open-endedness via models of human notions of interestingness with environments programmed in code, 2024.
Manon Flageat, Bryan Lim, and Antoine Cully. Enhancing map-elites with multiple parallel evo- lution strategies. In Proceedings of the Genetic and Evolutionary Computation Conference, pp- 1082-1090, 2024.
Matthew C Fontaine, Julian Togelius, Stefanos Nikolaidis, and Amy K Hoover. Covariance matrix adaptation for the rapid illumination of behavior space. Proceedings of the 2020 genetic and evolutionary computation conference, pp. 942 2020.
13

---

Published as a conference paper at ICLR 2026
Giorgio Franceschelli and Mirco Musolesi. Creative beam search: Lim-as-a-judge for improving response generation. arXiv preprint wxxv:2445500099) 2024.
Giorgio Franceschelli and Mirco Musolesi. On the creativity of large language models. A/ & society, 40(5):3785-377 2025a.
Giorgio Franceschelli and Mirco Musolesi, Diffsampling: Enhancing diversity and accuracy in neural text generation. arXiv preprint wrXiv:2502.14037 2025b.
Dan Friedman and Adji Bousso Dieng. The vendi score: diversity evaluation metric for machine learning. arXiv preprint vr.iv:2200.02/111 2022.
Adam Gaier, Alexander Asteroth, and Jean-Baptiste Mouret. Data-efficient design exploration
through surrogate-assisted illumination. Evolutionary computation, 26(3):381-410, 2018.
Adam Gaier, Alexander Asteroth, and Jean-Baptiste Mouret. Are quality diversity algorithms better at generating stepping stones than objective-based search? Proceedings of the Genetic and Evolutionary Computation Conference Companion, pp. 115-116, 2019.
Leo Gao. Jonathan Tow, Baber Abbasi, Stella Biderman, Sid Black, Anthony DiPofi, Charles Fos- ter, Laurence Golding, Jeffrey Hsu, Alain Le Noac'h, Haonan Li, Kyle McDonell, Niklas Muen- nighoff, Chris Ociepa, Jason Phang. Laria Reynolds, Hailey Schoelkopf, Aviya Skowron, Lintang Sutawika, Eric Tang, Anish Thite, Ben Wang, Kevin Wang, and Andy Zou. The language model evaluation harness, 2024. URL htttt::::///// wttp:/eeoo........oeeeoeeeee
Aaron Grattafior, Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Alex Vaughan, al. The Ilama 3 herd of models. arXiv preprint arXiv:440.221783 2024.
Luca Grillotti, Maxence Faldor, Borja González León, and Antoine Cully. Quality actor- critic: Learning perfoperfoniing and diverse behaviors via value and successor features critics. In International Conference on Machine Learning. PMLR, 2024.
David Ha and Yujin Tang. Collective intelligence for deep learning: A survey of recent develop- 2022.
ments. Collective Intelligence,
Nikolaus Hansen and Andreas Ostermeier. Completely derandomized self-adaptation in evolution strategies. Evolutionary Computation, 9:159-195, 06 2001. doi: 10. 0..1166//1666601177 753190398.
Alex Havrilla, Andrew Dai, Laura O' O'Mahony, Koen Oostermeijer, Vera Zisler, Alon Albalak, Fab- rizio Milo, Sharath Chandra Raparthy, Kanishk Gandhi, Baber Abbasi, al. Surveying the effects of quality, diversity, and complexity in synthetic data from large language models. arXiv preprint arXiv:2412.0298/ 2024.
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Ja- cob Steinhardt. Measuring massive multitask language understanding. In International Confer- ence on Learning Representations, 2021a.
/otttpps:///ppeevviie ew. net. wwwe/forrit forum? i
d7KB 1m13mmm3mmmmm 3GmQ.
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. Measuring massive multitask language understanding. Proceedings of the Interna- tional Conference on Learning Representations (ICLR), 2021b.
Stefan Horoi, Guy Wolf, Eugene Belilovsky, and Gintare Karolina Dziugaite. Less is more: Under- training experts improves model upeycling. arXiv preprint arXiv:2506.14120 2025.
Shengran Hu, Cong Lu, and Jeff Clune. Automated design of agentic systems. In The Thirteenth International Conference on Learning Representations, 2025. URL Utt::tttp:/ppeeeevvve net / Eorum? wicrummiiiiie
Siyuan Huang, Zhiyuan Ma, Jintao Du, Changhua Meng, Weiqiang Wang, and Zhouhan Lin. Mirror- consistency: Harnessing inconsistency in majority voting. arXiv preprint arXiv:2410. 10857, 2024.
14

---

Published as a conference paper at ICLR 2026
Edward Hughes, Michael Dennis, Jack Parker-Holder, Feryal Behbahani, Aditi Mavalankar, Yuge Shi, Tom Schaul, and Tim Rocktaschel. Open-endedness is essential for artificial superhuman intelligence, 2024.
Aaron Hurst, Adam Lerer, Adam Goucher, Adam Perelman, Aditya Ramesh, Aidan Clark, AJ Os- trow, Akila Welihinda, Alan Hayes, Alec Radford, et al. Gpt-4o system card. arXiv preprint arXiv:2410.21276 2024.
Gabriel Ilharco, Marco Tulio Ribeiro, Mitchell Wortsman, Suchin Gururangan, Ludwig Schmidt, Hannaneh Hajishirzi, and Ali Farhadi. Editing models with task arithmetic. arXiv preprint arXiv:2212.04089, 2022.
Gabriel Ilharco, Marco Tulio Ribeiro, Mitchell Wortsman, Ludwig Schmidt, Hannaneh Hajishirzi, and Ali Farhadi. Editing models with task arithmetic. In The Eleventh International Confer- ence on Learning Representations, 2023. URL httph:tp////// http:ss////eereeeee.rro net hwwp://wwwee/..ottt//e/ forum?id-
6tCKwf8- jri
Yuichi Inoue, Kou Misaki, Yuki Imajuku, So Kuroki, Taishi Nakamura, and Takuya Akiba. Wider or deeper? scaling IIm inference-time compute with adaptive branching tree search. arXiv preprint
arXiv:2503.04412, 2025.
Prime Intellect.
Prime-envroomment, 2025.
URL https://githur: com/
PrimeIntellect santelllet-ai/primee at/pprineeenviirommett-
Geoffrey Irving, Paul Christiano, and Dario Amodei. Al safety via debate. arXiv preprint
arXiv:1805.00880 00899, 2018.
Mete Ismayilzada, Antonio Laverghetta Jr, Simone A Luchini, Reet Patel, Antoine Bosselut, Lonneke Van Der Plas, and Roger Beaty. Creative preference optimization. arXiv preprint S.Kv:22005.1442.. 2025.
Minqi Jiang, Tim Rocktäschel, and Edward Grefenstette. General intelligence requires rethinking exploration. Royal Society Open Science, 10(6):230539, 2023.
Yuu Jinnai, Jonathan Uesato, Ankit Mahajan, and Markus N. Rabe. Regularized best-of-n sampling to mitigate reward hacking for language model alignment. In Proceedings of the Annual Confer- ence of the North American Chapter of the Association for Computational Linguisties (NAACL). 2025. arXiv:240.00005.
Jean Kaddour, Joshua Harris, Maximilian Mozes, Herbie Bradley, Roberta Raileanu, and Robert McHardy, Challenges and applications of large language models. arXiv preprint vrXiv:2307.I0169 2023.
Nikhil Kandpal, Brian Lester, Colin Raffel, Sebastian Majstorovic. Stella Biderman, Baber Abbasi, Luca Soldaini, Enrico Shippole, A Feder Cooper, Aviya Skowron, et al. The common pile vO. I: An 8tb dataset of public domain and openly licensed text. arXiv preprins arXiv:2506.05209, 2025.
Leon Keller, Daniel Tanneberg. Svenja Stark, and Jan Peters. Model-based quality-diversiiy search for efficient robot learning. In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 9675-9680. IEEE, 2020.
Paul Kent, Adam Gaier, Captittt Mouret, and Juergen Branke, Bayesian optimisation for quality diversity search with coupled descriptor functions. IEEE Transactions on Evolutionary Computation, 2024.
Devvri Khatri, Lovish Madaan, Rishabh Tiwari, Rachit Bansal, Sai Surya Duvvuri, Manzil Zaheer, Inderjit S Dhillon, David Brandfonbrener, and Rishabh Agarwal. The art of scaling reinforcement learning compute for lms. arXiv preprint arXiv:2510. 13786, 2025.
Robert Kirk, Ishita Mediratta, Christoforos Nalmpantis, Jelena Luketina, Eric Hambro, Edward Grefenstette, and Roberta Raileamu. Understanding the effects rlhf on Ilm generalisation and diversity. arXiv preprint arXiv:2310.06455 064552, 2023.
15

---

Published as a conference paper at ICLR 2026
Akarsh Kumar, Jeff Clune, Joel Lehman, and Kenneth O Stanley. Questioning representational optimism in deep learning: The fractured entangled representation hypothesis. arXiv preprint 10XY::2505.11881, 2025.
So Kuroki, Taishi Nakamura, Takuya Akiba, and Yujin Tang. Agent skill acquisition for large language models via cycleQD. In The Thirteenth International Conference Learning Repre- sentations, 2025.
nee //oppps:////errree htt.eewwee//ooorrrrto forum? did.uummmidddddiiiooe d=Kvdh
2wGCO
Nathan Lambert, Jacob Morrison, Valentina Pyatkin, Shengyi Huang, Hamish Ivison, Faeze Brah- man, Lester James V Miranda, Alisa Liu, Nouha Dziri, Shane Lyu, et al. Tulu 3: Pushing frontiers in open language model post-training. arXiv preprint arXiv:24 1.151115244.. 2024.
Jack Lanchantin, Angelica Chen, Shehzaad Dhuliawala, Ping Yu, Jason Weston, Sainbayar Sukhbaatar, and Ilia Kulikov. Diverse preference optimization. arXiv preprint arXiv:2501.11001, 2025.
Joel Lehman and Kenneth O Stanley. Revising the evolutionary computation abstraction: minimal criteria novelty search. In Proceedings the computation, pp. 103-110, 2010.
the 12th annual conference on Genetic and evolutionary
Joel Lehman and Kenneth Stanley. Abandoning objectives: Evolution through the search for novelty alone. Evolutionary computation, 1992):189-223, 201 la.
Joel Lehman and Kenneth Stanley, Evolving diversity of virtual creatures through novelty search and local competition. In Proceedings of the 13th annual conference Genetic and evolutionary computation, pp. 21 1-218, 2011b.
Joel Lehman, Kenneth O Stanley, al. Exploiting open-endedness to solve problems through the search for novelty. In ALIFE, pp. 329-336, 2008
Joel Lehman, Jonathan Gordon, Shawn Jain, Kamal Ndousse, Cathy Ych, and Kenneth O Stanley. Evolution through large models. In Handbook of Evolutionary Machine Learning, pp. 331-366. Springer, 2023.
Aitor Lewkowycz, Anders Andreassen, David Dohan, Ethan Dyer, Henryk Michalewski, Vinay Ra- masesh, Ambrose Slone, Cem Anil, Imanol Schlag, Theo Gutman-Sole et al. Solving quantitative reasoning problems with language models. Advances in neural information processing systems, 35:3843-3857, 2022.
Pengfei Li, Jianyi Yang. Mohammad A Islam, and Shaolei Ren. Making ai less' thirsty'. Communi- cations of the ACM, 68(7):54-611 2025.
Tian Liang Zhiwei He, Wenxiang Jiao, Xing Wang. Yan Wang. Rui Wang, Yujiu Yang, Shuming Shi, and Zhaopeng Tu. Encouraging divergent thinking in large language models through multi- agent debate. arXiv preprint arXiv:2305. 19118, 2023.
Tian Liang, Zhiwei He, Wenxiang Jiao, Xing Wang, Yan Wang. Rui Wang. Yujiu Yang, Shuming Shi, and Zhaopeng Tu. Encouraging divergent thinking in large language models through multi- agent debate. In Proceedings of the 2024 conference empirical methods in natural language processing, pp. 17889-17904, 2024.
Hunter Lightman, Vineet Kosaraju, Yura Burda, Harri Edwards, Bowen Baker, Teddy Lee, Jan Leike, John Schulman, Ilya Sutskever, and Karl Cobbe. Let's verify step by step. arXiv preprint wrXiv:2305.20050 2023. OpenAl.
Bryan Lim, Manon Flageat, and Antoine Cully. Efficient exploration using model- based quality- diversity with gradients. In Artificial Life Conference Proceedings 35, volume 2023, pp. 4. MIT Press One Rogers Street, Cambridge, MA 02142-1209, USA journals-info 2023.
Chris Yuhao Liu, Liang Zeng, Yuzhen Xiao, Jujie He, Jiacai Liu, Chaojie Wang, Rui Yan, Wei Shen, Fuxiang Zhang. Jiacheng Xu. Yang Liu, and Yahui Zhou. rewadrk- Scaling preference data curation via human-ai synergy. arXiv preprint arXiv:2507.01355, 2025a.
16

---

Published as a conference paper at ICLR 2026
Jingyuan Liu, Jianlin Su, Xingeheng Yao, Zhejun Jiang, Guokun Lai, Yulun Du, Yidao Qin, Weixin Xu, Enzhe Lu, Junjie Yan, et al. Muon is scalable for Ilm training. arXiv preprint 12X2222502..6982., 2025b.
Ruibo Liu, Jerry Wei, Fangyu Liu, Chenglei Si, Yanzhe Zhang. Jinmeng Rao, Steven Zheng, Daiyi Peng, Diyi Yang, Denny Zhou, et al. Best practices and lessons learned on synthetic data. arXiv 2024.
preprint arXiv:2404.07555
Xiao Liu, Hao Yu, Hanchen Zhang, Yifan Xu, Xuanyu Lei, Hanyu Lai, Yu Gu, Hangliang Ding, Kaiwen Men, Kejuan Yang, Shudan Zhang, Xiang Deng. Aohan Zeng, Zhengxiao Du, Chenhui Zhang, Sheng Shen, Tianjun Zhang, Yu Su, Huan Sun, Minlie Huang, Yuxiao Dong, and Jie Tang. Agentbench: Evaluating ilms as agents. arXiv preprint arXiv: 2308.03688. 2023.
Chris Lu, Cong Lu, Robert Lange. Jakob N Foerster, Jeff Clune, and David Ha. The ai scientist: To- wards fully automated open- ended scientific discovery. arXiv preprint S.div:2408.062992 2024.
Cong Lu, Shengran Hu, and Jeff Clune. Automated capability discovery via foundation model self-exploration. arXiv preprint arXiv:2502.07577 2025.
Pratyush Maini, Skyler Seto, He Bai, David Grangier, Yizhe Zhang, and Navdeep Jaitly. Rephras- ing the web: A recipe for compute and data-efficient language modeling. arXiv preprint arXiv:2401. 16380, 2024.
Leland McInnes, John Healy, and Steve Astels. hdbscan: Hierarchical density based clustering. Journal of Open Source Software, 2(11):205, 2017. doi: 10.21 10211005/oss.00005. URL https:
//dbi.org/10.211055.3
00205
METR Task Standard Team. Metr task standard, 2024. URL http:://gitubb.cmm
STANDARD mc..
Elliot Meyerson and Risto Mükkulainen. Discovering evolutionary stepping stones through behavior domination. In Proceedings of the Genetic and Evolutionary Computation Conference, pp. 139- 146, 2017.
Elliot Meyerson, Joel Lehman, and Risto Miikkulainen. Learning behavior characterizations for novelty search. In Proceedings of the Genetic and Evolutionary Computation Conference 2016, pp. 149-156, 2016.
Melanie Mitchell. Complexity: A guided tour. Oxford university press, 2009.
Jean-Baptiste Mouret and Jeff Clune. Illuminating search spaces by mapping elites. arXiv preprint arXiv:1504.04999 2015a.
Jean-Baptiste Mouret and Jeff Clune. Illuminating search spaces by mapping elites. arXiv preprint arXiv:1504.04999 20155.
Niklas Muennighoff, Alexander Rush, Boaz Barak, Teven Le Scao, Nouamane Tazi, Aleksandra Piktus, Sampo Pyysalo, Thomas Wolf, and Colin A Raffel. Scaling cons-conntr language models. Advances in Neural Information Processing Systems, 36:50358-5033 2023.
Niklas Muennighoff, SU Hongjin, Liang Wang, Nan Yang, Furu Wei, Tao Yu, Amanpreet Singh, and Douwe Kiela. Generative representational instruction tuning. In The Thirteenth International Conference on Learning Representutions, 2024.
Anh Nguyen, Jason Yosinski, and Jeff Clune. Understanding innovation engines: Automated cre- ativity and improved stochastic optimization via deep learning. Evolutionary computation, 24(3): 545-572, 2016.
Eleni Nisioti, Claire Glanois, Elias Najarro, Andrew Dai, Elliot Meyerson, Joachim Winther Peder- sen, Laetitia Teodorescu, Conor F Hayes, Shyam Sudhakaran, and Sebastian Risi. From text to life: On the reciprocal relationship between artificial life and large language models. In Artificial Life Conference Proceedings 36, volume 2024, pp. 39. MIT Press One Rogers Street, Cambridge, MA 02142-1209, USA journals-info... ... 2024.
17

---

Published as a conference paper at ICLR 2026
Alexander Novikov, Ngån Vu, Marvin Eisenberger, Emilien Dupont, Po-Sen Huang, Adam Zsolt Wagner, Sergey Shirobokov, Borislav Kozlovskii, Francisco JR Ruiz, Abbas Mehrabian, et al. Alphaevolve: A coding agent for scientific and algorithmic discovery. Google DeepMind, 2025.
Laura O' O'Mahony, Leo Grinsztajn, Hailey Schoelkopf, and Stella Biderman. Autribtting mode col- lapse in the fine-tuning of large language models. In ICLR 2024 Workshop on Mathematical and Empirical Understanding of Foundation Models, volume 2, pp. 2, 2024.
Davide Paglieri, Logan Cross, William Cunningham, Joel Z. Leibo, and Alexander Sasha Vezh- nevets. Persona generators: Generating diverse synthetic personas at scale. arXiv preprint arXiv:2602.03545, 2026.
Guanzhong Pan and Haibo Wang. A cost-benefit analysis of on-premise large language model deployment: Breaking even with commercial Ilm services, 2025. URLhttts::/axxiv..om
0ba/22009.180
Giuseppe Paolo, Alban Laftaquiere, Alexandre Coninx, and Stephane Doncieux. Unsupervised learning and exploration of reachable outcome space. In 2020 IEEE International Conference on Robotics and Automation (ICRA), pp.
IEEE, 2020.
Giuseppe Paolo, Miranda Coninx, Alban Laflaquière, and Stephane Doncieux. Discovering and exploiting sparse rewards in learned behavior space. Evolutionary Compulation, 32(3):275- 305, 2024.
Ethan Perez, Sam Ringer, Kamile Lukosiute, Karina Nguyen, Edwin Chen, Scott Heiner, Craig Pettit, Catherine Olsson, Sandipan Kundu, Saurav Kadavath, et al. Discovering language model behaviors with model-written evaluations. In Findings of the association for computational lin- guistics: ACI. 2023, pp. 13387-13434, 2023.
Justin K Pugh, Lisa B Soros. and Kenneth O Stanley, Quality diversity: A new frontier for evolu- tionary computation. Frontiers in Robotics and AI, 3:40, 2016.
Qwen, An Yang, Baosong Yang, Beichen Zhang, Binyuan Hui, Zheng, Bowen Yu, Chengyuan Li, Dayiheng Liu, Fei Huang, Haoran Wei, Huan Lin, Jian Yang, Jianhong Tu, Jianwei Zhang, Jianxin Yang. Jiaxi Yang, Jingren Zhou, Junyang Lin, Kai Dang, Keming Lu, Keqin Bao, Kexin Yang, Le Yu, Mei Li, Mingfeng Xue, Pei Zhang, Qin Zhu, Rui Men, Runji Lin, Tianhao Li, Tianyi Tang, Tingyu Xia, Xingzhang Ren, Xuancheng Ren, Yang Fan, Yang Su, Yichang Zhang, Yu Wan, Yuqiong Liu, Zeyu Cui, Zhenru Zhang, and Zihan Qiu. Qwen2.5 technical report. arXiv preprint arxiv: 24 15/ 15, 2025. URL htt.t:ttt///xaiiii
abs /2112.42222 15115
David Rein, Betty Li Hou, Asa Cooper Stickland, Jackson Petty, Richard Yuanzhe Pang, Julien Di- rani, Julian Michael, and Samuel R Bowman. Gpqa: A graduate-level google-proof q&a bench- mark. First Conference on Language Modeling, 2024.
Bernardino Romera-Paredes, Mohammadamin Barekatain, Alexander Novikov, Matej Balog, M. Pawan Kumar, Emilien Dupont, Francisco J. R. Ruiz, Jordan Ellenberg, Pengming Wang, Omar Fawzi, Pushmeet Kohli, and Alhussein Fawzi. Mathematical discoveries from program search with large language models. Nature, 2023. doi:
Mikayel Samvelyan, Sharath Chandra Raparthy, Andrei Lupu, Eric Hambro, Aram Markosyan, Manish Bhatt, Yuning Mao, Minqi Jiang, Jack Parker-Holder, Jakob Foerster, et al. Rainbow teaming: Open-ended generation of diverse adversarial prompts. Advances in Neural Information Processing Systems, 37:69747-69788 2024.
Zhihong Shao, Peiyi Wang. Qihao Zhu, Runxin Xu, Junxiao Song. Xiao Bi, Haowei Zhang, Mingchuan Zhang, YK Li, Yang Wu, et al. Deepseekmath: Pushing the limits of mathemati- cal reasoning in open language models. arXiv preprint uxXiv:2402.03300, 2024.
Lin Shi, Chiyu Ma. Wenhua Liang. Xingjian Diao, Weicheng Ma. and Soroush Vosoughi. Judging the judges: A systematic study of position bias in auudee- arXiv preprint 27X7794006.07911, 2024.
18

---

Published as a conference paper at ICLR 2026
Olivier Sigaud, Gianluca Baldassarre, Cedrie Colas, Stephane Doncieux, Richard Duro, Nicolas Perrin-Gilbert, and Vieri-Giuliano Santucci. A definition of open-ended learning problems for goal-conditioned agents. arXiv preprint arXiv:2311.0034, 2023.
Andries Smit, Paul Duckworth, Nathan Grinsztajn, Thomas Barrett, and Amu Pretorius. Should we be going mad? a look at multi-agent debate strategies for llms. arXiv preprint arXiv:2311.17377 173371, 2023.
Charlie Snell, Jaehoon Lee, Kelvin Xu, and Aviral Kumar. Scaling LLM test-time compute opti- mally can be more effective than scaling model parameters. arXiv preprint arXiv:2408.03314, 2024. August 2024.
L. B. Soros and Kenneth O. Stanley. Identifying necessary conditions for open-ended evolution through the artificial life world of chromaria. In Proc. Int. Conf. on the Sythesis and Simulation of Living Systems (ALIFE), pp. 793-800, Cambridge, MA, 2014. MIT Press.
Kenneth O Stanley. Why open-endedness matters. Artificial life, 25(3):232 -33, 2019.
Kenneth O. Stanley and Joel Lehman. Why greatness cannot be planned: The myth of the objective. Springer, 2015.
Kenneth O Stanley, Joel Lehman, and Lisa Soros. Open-endedness: The last grand challenge you'l 've never heard of. While open-ente endeenndssssss could be force for discovering intelligence, could also be component of Al üsseff. 2017.
Qi Sun, Edoardo Cetin, and Yujin Tang. Transformer-squared Self-adaptive LLMs. In The Thirteenth International Conference on Learning Representations, 2025. URL https:/7
openreview www.:ee//ffo foreet/oorumf///o ddd=dhtt:hhh Sqmcvk,
Mirac Suzgun, Nathan Scales, Nathanael Schärli, Sebastian Gehrmann, Yi Tay, Hyung Won Chung, Aakanksha Chowdhery, Quoc V Le, Ed H Chi, Denny Zhou, , and Jason Wei. Challenging big- bench tasks and whether chain-of-thought can solve them. arXiv preprint arXiv:2210.09266) 2022.
Yashar Talebirad, Ali Parsaee, Vishwajeet Ohal, Amirhossein Nadiri, Csongor Szepesvari, Yash Mouje, and Eden Redman. Wisdom of the machines: Exploring collective intelligence in LLM crowds. In First Workshop on Social Simulation with L.MMs, 2025. URL. https:
net. Excroooxxxxxxxxxxu.xu
Laurens der Maaten and Geoffrey Hinton. Visualizing data using t-sne. Journal of Ma- chine Learning Research, 986))2579-2605, 2008. URL http: jmlr wwg. wttppppers.vg/////
WaadermaateenDe html
Vassilis Vassiliades and Jean-Baptiste Mouret. Discovering the elite hypervolume by leveraging in- terspecies correlation. In Proceedings of the Genetic and Evolutionary Computation Conference, GECCO '18, pp. 149-156, New York, NY, USA, 2018. Association for Computing Machin- ery. ISBN
doi: 10.1145/3205555 3205602. https: http:/do.cco httpp:s//doo..or//////
1145/3205453 3205602]
Vassilis Vassiliades, Konstantinos Chatzilygerouddis, and Jean-Baptiste Mouret. Using centroidal Voronoi tessellations scale up the multidimensional archive of phenotypic elites algorithm. IEEE Transactions on Evolutionary Computation, 22(4):623 630, 2017.
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, łukasz Kaiser, and Illia Polosukhin. Attention all you need. Advances in neural informa- fion processing systems, 30, 2017.
Ashwin Vijayakumar, Michael Cogswell, Ramprasath R Selvaraju, Qing Sun, Stefan Lee, David Crandall, and Dhruv Batra. Diverse beam search: Decoding diverse solutions from neural se- quence models. arXiv preprint arXiv:/6/0.02: 2016.
Liang Wang. Nan Yang, Xiaolong Huang, Linjun Yang, Rangan Majumder, and Furu Wei. Improv- ing text embeddings with large language models. arXiv preprint arXiv:2401. 000668, 2023a.
19

---

Published as a conference paper at ICLR 2026
Rui Wang, Joel Lehman, Jeff Clune, and Kenneth O. Stanley. Paired open-ended trailblazer (POET): endlessly generating increasingly complex and diverse learning environments and their solutions. CoRR, abs/1901.01753, 2019. /RLhttp::// arxiv. httgg//bs/sgag 01753
Rui Wang, Joel Lehman, Aditya Rawal, Jiale Zhi, Yulun Li, Jeffrey Clune, and Kenneth Stanley. Enhanced poet: Open-ended reinforcement learning through unbounded invention of learning challenges and their solutions. In International conference on machine learning, pp. 9940-9951. PMLR, 2020.
Weiqin Wang. Yile Wang, and Hui Huang. Ranked voting based self-consistency of large language models. arXiv preprint arXiv:2505. 2025.
Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoe Le, Ed Chi, Sharan Narang, Aakanksha Chowdh- cry, and Denny Zhou, Self-consistency improves chain of thought reasoning in language models. In International Conference on Learning Representations (ICLR), 2023b. arXiv:2203.1117.
Yizhong Wang, Yeganeh Kordi, Swaroop Mishra, Alisa Liu, Noah A. Smith, Daniel Khashabi, and Hannanch Hajishirzi. Self-instruct: Aligning language models with self-generated instructions. In Anna Rogers, Jordan Boyd-Graber, and Naoaki Okazaki (eds.). Proceedings of the 6l Annual Meeting of the Association for Computational Linguistics (Volume I: Long Papers), pp. 13484- 13508, Toronto, Canada, July 2023c. Association for Computational Linguistics. doi: 10.18653/ 11/203.aclllong.
wwt.orggg///. 2023 www.:.....cooono... 754/
Yizhong Wang, Yeganeh Kordi, Swaroop Mishra, Alisa Liu, Noah A. Smith, Daniel Khashabi, and Hannaneh Hajishirzi. Self-instruct: Aligning language models with self-generated instructions, 2023d.
Yubo Wang, Xueguang Ma. Ge Zhang. Yuansheng Ni, Abhranil Chandra, Shiguang Guo, Weim- ing Ren, Aaran Arulraj, Xuan He, Ziyan Jiang, Tianle Li, Max Ku, Kai Wang. Alex Zhuang, Rongqi Fan, Xiang Yuc, and Wenhu Chen. MMLU-pro: A more robust and challenging multi- task language understanding benchmark. In The Thirty-eight Conference on Neural Information Processing Systems Datasets and Benchmarks Track, 2024. URL htttss:/ inet/ ddd1md111111101123333 0DM6R2r3
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoe V Le, Denny Zhou, et al. Chain-of- of-thought prompting elicits reasoning in large language models. Advances in neural information processing systems, 0224824-24837 2022.
Mitchell Wortsman, Gabriel Ilharco, Samir Ya Gadre, Rebecca Roelofs, Raphael Gontijo-Loope Ari S Morcos, Hongseok Namkoong, Ali Farhadi, Yair Carmon, Simon Kornblith, et al. Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time. In International conference on machine learning, pp. 23965-23998. PMLR, 2022.
Can Xu, Qingfeng Sun, Kai Zheng, Xiubo Geng, Pu Zhao, Jiazhan Feng, Chongyang Tao, and Daxin Jiang. WizardIm: Empowering large language models to follow complex instructions, 2023.
Prateek Yadav, Derek Tam, Leshem Choshen, Colin A Raffel, and Mohit Bansal. Ties-merging: Re- solving interference when merging models. Advances in Neural Information Processing Systems, 36:7093-7115, 2023.
An Yang, Baosong Yang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Zhou, Chengpeng Li, Chengyuan Li, Dayiheng Liu, Fei Huang, Guanting Dong, Haoran Wei, Huan Lin, Jialong Tang, Jialin Wang, Jian Yang, Jianhong Tu, Jianwei Zhang, Jianxin Ma, Jin Xu, Jingren Zhou, Jinze Bai, Jinzheng He, Junyang Lin, Kai Dang, Keming Lu, Ke- Ke-Yang Chen, Kexin Yang. Mei Li, Min Xue, Na Ni, Pei Zhang, Peng Wang, Ru Peng. Rui Men, Ruize Gao, Runji Lin, Shijie Wang. Shuai Bai, Sinan Tan, Tianhang Zhu, Tianhao Li, Tianyu Liu, Wenbin Ge, Xiaodong Deng, Xiaohuan Zhou, Xingzhang Ren, Xinyu Zhang, Xipin Wei, Xuancheng Ren, Yang Fan, Yang Yao, Yichang Zhang, Yunyang Wan, Yunfci Chu, Zeyu Cui, Zhenru Zhang, and Zhi- Wei Fan. Qwen2 technical report. arXiv preprint axxiv:240..1m 240.. 10671, 2024.
An Yang, Anfeng Li, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Gao. Chengen Huang, Chenxu Lv, et al. Qwen3 technical report. arXiv preprint (5l.:250500988.. 2025.
20

---

Published as a conference paper at ICLR 2026
Le Yu, Bowen Yu, Haiyang Yu, Fei Huang, and Yongbin Li. Language models super mario: Ab- sorbing abilities from homologous models as a free lunch. In Forty-first International Conference on Machine Learning. 2024.
Yang Yu. Pass@ k metric for rlvr: A diagnostic tool of exploration, but an objective. arXiv preprint arXiv:2511.162.) 2025.
Tom Zahavy. Lims can't jump. 2026.
Jenny Zhang, Joel Lehman, Kenneth Stanley, and Jeff Clune. Omni: Open-endedness via models of human notions of interestingness. arXiv preprint arXiv:2306.0171/ 2023.
Jenny Zhang, Shengran Hu, Cong Lu, Robert Lange, and Jeff Clune. Darwin godel machine: Open- ended evolution of self-improving agents. arXiv preprint arXiv:2505. 222954,, 2025a.
Yiqun Zhang, Peng Ye, Xiaocui Yang. Shi Feng, Shufei Zhang. Lei Bai, Wanli Ouyang, and Shuyue Hu. Nature-insping population-bast based evolution of large language models. arXiv preprint arXiv:2503.01/55 155, 2025b.
Yulun Zhang, Matthew C Fontaine, Amy Hoover, and Stefanos Nikolaidis. Deep surrogate as- sisted map-elites for automated hearthstone deckbuilding. In Proceedings of the Genetic and Evolutionary Computation Conference, pp. 158-167, 2022.
Andrew Zhao, Yiran Wu, Yang Yue, Tong Wu, Quentin Xu, Matthicu Lin, Shenzhi Wang, Qingyun Wu, Zilong Zheng, and Gao Huang. Absolute zero: Reinforced self-play reasoning with zero data. arXiv preprint arXiv:2505.033:
2025.
Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric P. Xing. Hao Zhang, Joseph Gonzalez, and lon Stoica. Judging LLM-as-a-judge with MT-Bench and chatbot arena. In Advances in Neural Information Processing Systems (NeurIPS), 2023. arXiv:2306 20..230660668..
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APPENDIX CONTENTS
Setup Details
23
Detailed Quantitative Results
28
Extended Discussion on Limitations
32
Additional Results
33
Additional Qualitative Results
43
AC/DC Prompts
58
Extended Related Work
67
H Human Study
70
Comparison of Seed Models and Merged Models Synthetic Data
72
J Merging Compatibility Analysis
81
Statistical Significance Analysis
83
L Computational Cost Analysis
89
M LLM Parameter Update Details
90
Justification of Open-endedness Design Choices
LLM Usage During Paper Writing
95
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A SETUP DETAILS
A.1 TRAINING HYPERPARAMETERSS
This section provides the hyperparameters used for all experiments unless otherwise specified.
Table AC/DC hyperparameters.
Component
Parameter
Value
Number of generations
50
Active models per gen
16
General
New offssring per gen
Active tasks per gen Hist. archive gen interval Scientist Model
250
First k singular values mutation rate
256
Mutation
0.25
Crossover
standard deviation
0.5
Evaluation
max tokens temperature
512
(synthetic
tasks)
top-p
1.0
Task difficulty threshold
0.5
Task
Max reflections Embedding Model
3
Generator
n components
perplexity
50
learning_rate
200
I-SNE
n-iter
3000
init
pca
random_star state karyyexxaggeattion
42
6.0
min min_cluster_size min mampsss
16
HDBSCAN
cluster selection epsilon
2
cluster selection method metric
eom
euclidean
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A.2 MODELS USED
For our experiments with different model families, we use the following models from Hugging Face:
Qwen2 7B Yang et al. 2024)
Control:
Experts:
Qhvet//oeen2-Mattth----nnntttttt
Oweet/Owen2277--nnstrueet
Big Model: (ween//oee22-778-IIISSe
Qwen2.5 7B (Qwen et al. 2025
Control:
Experts:
prrtthii/Murote///emmmn------hhh tn rnst
Overr/wwen2.5-75-nnnttrrre
Big Model:
ruct
Qwen3 14B (Yang et al. 2025)
Control: Qwen/Qwen 2wenn/0wen3113
Experts:
Qwen/Qwen3-14B
Big (ote:///ee2222335--222----------
DeepSeek V1 7B Bi al. 2024
Control: begggel-aaa/daapaee------------
Experts:
deeppex-e//aappeeeerttttt------s
base -1.
Big Model: d
Llama3 8B (Grattafiori al. 2024
Control: meta 4rt
Experts:
Marlfennse/Atttlodde22-1--------
Big Model: http::/c...aaa//tteeeeeeaaaaea--
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A.3 ALGORITHM DETAILS
A.3.1 DNS NOVELTY SCORE COMPUTATION
The Dominated Novelty Score is a key component of our selection mechanism that balances quality and diversity in the model archive. For each model, we compute its novelty relative models with higher fitness, encouraging retention of models that solve unique subsets of tasks. Algorithm provides the detailed computation.
Algorithm 2 Dominated Novelty Score Computation for One Solution
1: Input: Solution Archive A, Parameters k, Odoms (difficulty weights)
2: F < {s' E AA: fitness(s") > {itn
Find fitter solutions
3: i then
4:
return Otdom
D Maximum score if no fitter solutions exist
5: end if
6: scores [
7: for each E F
8: Us + skill vector of
Binary vector of task successes
9:
U/* skill vector of s'
10:
// Compute weighted unique skills: tasks solved by s but not by s
11:
mmique_weighte
Σ, W/ (v. A (t)
12:
total weighted f- Σ; Wt
b Sum of all difficulty weights
13:
Normalize by total skill vector to measure contribution to coverage
14:
skill_score <- unique weighted x 100
weighte
15:
scores.append (ssilpleeddllllosoorrre
16: end for
17: scores.sort()
Sort ascending Use at most & neighbors
18: k +- min(k, (scores))
return _ E scores[i]
Mean of lowest scores
The algorithm identifies models that complement the existing archive by solving tasks that higher- fitness models fail on. A model receives high novelty score when it uniquely solves many tasks that fitter models cannot solve. The skill score is computed as the ratio of weighted unique skills to the total weighted skill vector, measuring the model's contribution overall task coverage relative to fitter solutions. Key configuration parameters include:
Oldom (default: 999): Maximum novelty score assigned when a model has no fitter com- petitors
k (default: 3): Number of nearest neighbors for novelty computation
Wy; Difficulty weights for task i, computed as the fraction of current model population failing that task
When using difficulty weights, harder tasks (those failed by more models) contribute more to the novelty score, encouraging retention models that solve challenging problems. This mechanism ensures diversity in the archive while maintaining a preference for higher overall fitness. This is also related to what metric Abrantes et al. 2025) used for diversity maintenance in M2N2. AC/DC directly leverages this part of explicit distance measurements between model behaviors to support the use of QD algorithms in AC/DC.
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A.4 EXPERIMENTAL SETUP DETAILS
This section provides details the experimental setup such as the henchmarks used and how we aug- ment them (App. A.4.1) and details on our best-of-N single-asser-seleccion methods (App. A.4.3
A.4.1 BENCHMARK DETAILS
We evaluate on MMLU (eendrycks et al. 2021a), MMLU Pro Wang et al. 2024 GPQA (Rein et al. 2024), BBH (Suzgun et al. (2022). GSM8K (Cobbe et al. (021). Minerva MATH (ewkkwyez et al.
2022). Humaneval (Chen al. 2021b), and MBPP (Austin al. 2021).
from EleutherAl (Gao et al. 2024) evaluate on these benchmarks, with task names mmlu cot.cot... wwt.cotolllaaaa. 11ama, bbb.cot.zeroshoc
We use Language Model Evaluation Harness LLn-evallatioon-arneee
mmlu.pro Lama, gpqa wtt.:mminn.otttzeesose
gsm8k 1.111 ama,
minerva maatt,thh humaneva 1 .ii.siitttttt..... and and mbpp_inste ruct respectively. We use the origi- nal evaluation config settings for these benchmarks from the repo with the last commit on September 21 (UTC-00).
Since multiple-choice question (MCQ) benchmarks are prone to "cheating" when evaluating Cover- age (randomly sampling each option once will lead to 100% accuracy), we design new, open-ended versions of these benchmarks, which then refer to as MMLU judge, MMLU Pro judge, GPQA judge, and BBH judge. We design these judge-evaluated benchmarks such that we do not provide multiple-choice options in the questions, relying only on the absolute knowledge/capability of the subject model.
To generate these new datasets, we prompt LLM filter out all samples that not possible to answer without the multiple choice options (e.g., questions in the form of "Which of the following [...]"). The remaining questions that are self-consistent are prompted to the sub- ject model.
To evaluate the correctness of a subject's answer, we prompt an LLM judge to determine whether the candidate solution is correct given the ground truth answer.
We provide the datasct filtering and LLM judge prompts in App.
For output generation, we set the output token length for the judge-evaluated benchmarks to 256 to- kens. A shorter response assesses the capability of LLMs to return concise and direct answers within their first response statements, and forces them to rely on knowledge recall for these knowledge- based tasks instead of guessing through excessive reasoning. This also reduces the compute time for LLM judge calls. For the other four benchmarks, we set the output token length to 2048 tokens.
A.4.2 BASELINE DETAILS
Fundamentally, our proposal is to rethink model development, moving from developing one large, monolithic LLM, to a population based approach, automatically developing collective of diverse smaller LLMs.
To evaluate the effectiveness of this approach, we target improving the Coverage over large scale open-source and even proprietary LLMs. Nevertheless, one may ask, whether our discovered col- lective is better than simply re- re-prompting the same sized general instruct model. This is our control baseline. Moreover, one might ask, if we start of with the three experts as seed models, we would want to achieve higher Coverage than those.
In Tab. I we observe that, compared to the big models, we achieve noticeable Coverage improve- ments, and surpass or get close to the GPT-4o performance, demonstrating that our collective of smaller models fundamentally possess the capabilities of much larger models to answer the re- spective questions.
The Experts N=8 baseline emerged from the question of "What if we resample the three experts N times". Although argue that this approach of manually selecting experts is unscalable and it being even worse to tune the sampling distribution, it an interesting comparison. To establish this baseline and ensure consistency across model families, we arbitrarily select the 3-3-2 (instruct-code-
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math), which was selected based observations that the code models achieved higher scores wider rage of benchmarks than the math model, arguably, making the baseline stronger.
we compare against prior quality-diversity work, demonstrating that AC/DC discovers a task force that achieves higher Coverage while not optimizing for any downstream benchmark (which the other methods actively do).
In addition to these "sanity check" baselines, in Tab. 3
A.4.3 BEST-OF-N
AEEEEEANAWWWEEEEEEEEEEEEEEEEEEEE SELECTION METHODS
In this section, we elaborate our single-answer-slletton methods used in Sec. For the two LLM-judge-based methods, we only provide the final subject model answers without the reasoning trace.
Divide and Conquer. For this method, we prompt a judge LLM to decide on the correct answer for two candidate solution. If the model deems both wrong, we ask it to provide the answer that "more correct". We apply this process in a devide-and-conquer approach, first, grouping all candidate solutions into pairs, then selecting the "winners", and repeating this process until only one answers remains.
Monarchical LLM. We provide all N answers LLM judge at once and prompt it select the correct answer.
RM-based. We leverage scalar reward model (Liu et al. 2025a) each full candidate submission and select the candidate with the highest score.
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B DETAILED QUANTITATIVE RESULTS
B.1 COVERAGE RESULTS
B.I.1 RESULTS FOR ALL MODELS ALL BENCHMARKS
Table 6: AC/DC (ours) Coverage performance comparison across different benchmarks and model configurations for all model families used (see App. A.2 Key findings are discussed in the next subsection in App. B.1.2
Method
MMLU judge
MMLUPM Pro
GPQA judge
BBH judge
GSM8K
Minerva
HumanEval
MBPP
Avg.
judge
Qwen 2
Big Model
50.44
47.12
4.36
78.32
90.83
66.18
82.93
70.40
61.32
= 3
Control Experts AC/DC
54.58
50.97
7.72
73.01
92.34
67.28
87.80
66.00
62.46
47.05
41.78
6.38
65.29
91.28
75.08
85.98
66.80
59.96
55.42
51.28
9.06
69.44
89.99
65.76
86.59
68.60
62.02
= 8
Control AC/DC
67.69
64.27
12.75
84.54
95.68
76.92
92.68
75.00
71.19
67.20
63.03
15.44
82.03
94.77
75.32
89.02
74.40
70.15
Qwen 2.5
Big Model
49.30
47.00
0.00
82.30
91.70
81.96
89.63
80.20
65.26
=
Control Experts
58.17
56.96
8.05
83.18
93.33
83.32
92.68
74.00
68.71
48.33
50.40
5.03
68.24
97.42
82.44
89.63
76.20
64.71
AC/DC
62.46
59.45
5.37
84.34
93.71
83.10
89.02
75.40
69.11
Control AC/DC
69.35
67.95
12.42
89.29
95.53
88.58
93.90
78.40
74.43
73.31
69.63
11.41
90.29
96.44
88.24
89.63
81.40
75.04
Qwen 3
Big Model
$7.30
53.60
0.30
83.70
94.70
84.80
96.34
83.80
69.32
=
Control Experts AC/DC
63.59
64.95
10.74
87.93
94.77
91.78
92.07
78.60
73.05
63.77
65.09
9.06
89.67
95.38
90.32
95.12
81.60
73.75
62.74
64.60
12.75
87.71
94.24
90.74
94.51
81.00
73.54
N 8
Control AC/DC
73.01
73.55
13.42
92.0
95.83
94.78
93.90
81.60
77.26
73.17
75.09
17.45
92.94
96.29
94.32
95.12
86.00
78.80
DeepSeek VI
Big Model
40.10
31.70
3.00
62.20
80.50
29.80
70.12
64.00
47.68
=
Control Experts AC/DC
39.85
29.39
2.68
48.21
74.15
22.90
56.
49,00
40.29
27.34
18.14
1.34
34.01
54.97
49.48
71.34
63.00
39.95
37.22
32.74
1.68
51.43
84.00
49.52
72.56
68.00
49.64
= 8
Control AC/DC
55.94
43.74
8.39
65.57
85.67
34.94
73.
59.20
53.33
49.35
46.54
6.71
69.34
64.00
80.49
76.60
60.37
GPTT-40
Big Model
54.31
53.64
7.72
83.08
94.69
74.32
90.20
86.80
68.10
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B.1.2 ANALYSIS AND KEY INSIGHTS
Evolutionary Discovery Outperforms Manual Curation. Despite expert models' superior perfor- mance in specialized domains, AC/DC discovers model combinations with broader skill Coverage. This suggests that the space of useful model combinations extends beyond obvious Nomain--specinne specializations.
Model Diversity Beats Parameter Scaling, Across both task force sizes, our distributed approach outperforms larger monolithic models while using fewer total parameters. This finding supports the hypothesis that specialized model populations can achieve superior Coverage compared to scaling individual models.
Complementary vs. Overlapping Skills. The consistent improvements over control baselines demonstrate that our evolved models develop genuinely complementary capabilities rather than re- dundant expertise. This validation supports our core hypothesis about automatic discovery of col- lective intelligence through evolutionary pressure for both quality and diversity.
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B.2 BEST-OF-N RESULTS
B.2.1 RESULTS FOR ALL MODELS ON ALL BENCHMARKS
Table 7: AC/DC (ours) Best-of-N performance comparison across different benchmarks and model
configurations for all model families (see App. A.2). Key findings are discussed the next subsec- tion App. B.2.2
Method
MMLU
MMLU Pro
GPQA
BBH
GSM8K
Minerva
HumanEval
MBPP
Avg.
Qwen 2
Big Model
82.04
63.15
10.27
68.42
90.83
66.18
82.93
70.40
66.78
Divide and Conquer
Monarchical LLM
RM-based
=
Experts
72.16
46.33
22.54
63.97
88.70
67.46
75.61
57.40
61.77
Control AC/DC
59.81
42.07
16.07
60.01
89.54
60.90
78.05
58.60
58.13
71.31
50.20
19.64
63.11
86.58
59.46
73.78
59.60
60.46
= 8
Control AC/DC
71.62
51.45
27.68
67.72
90.22
64.00
80.49
60.80
64.25
76.14
56.84
25.22
69.73
88.86
61.46
78.66
59.80
64.59
Qwen 2.5
Big Model
84.65
70.64
10.71
53.14
91.74
81.96
89.63
80.20
70.33
Divide and Conquer
Monarchical LLM
RM-based
==3
Experts
77.97
60.72
19.86
65.40
90.22
77.84
85,98
69.00
68.37
Control AC/DC
76.29
59.99
17.41
60.91
91.21
76.74
83.54
68.60
66.84
78.22
60.34
18.53
62.20
88.55
77.68
84.15
67.20
67.11
N 8
Control AC/DC
78.21
64.40
21.65
67.87
91.13
79.36
86.59
71.20
70.05
79.41
63.50
22.10
70.60
89.91
77.86
83.54
66.80
69.22
Qwen 3
Big Model
88.81
73.11
8.48
38.30
94.69
84.80
96.34
83.80
71.04
Divide and Conquer
Monarchical L.M
RM-based
=
Experts Control AC/DC
81.81
67.99
17.86
55.51
92.65
88.14
87,20
74.80
70.75
81.36
68.86
13.62
51.10
93.10
86.70
90.85
74.20
69.97
81.17
69.31
14.06
52.10
92.12
86.86
91.46
75.00
70.26
Control AC/DC
82.03
70.99
18.75
59.12
92.95
89.20
87.80
74.40
71.91
82.33
71.47
20.31
62.05
91.58
88.10
89.02
74.40
72.41
DeepSeek VI
Big Model
70.81
43.12
14.73
57.86
80.52
29.80
70.12
64.00
53.87
Divide and Conquer
Monarchical T.M
RM-hosed
=
Experts Control AC/DC
51.30
29.96
27.90
50.22
54.81
19.34
50.00
43.40
40.87
55.35
33.55
22.54
54.03
73.62
42.72
54.88
48.20
48.11
59.76
37.87
29.24
55.03
81.20
43.80
57.32
56.60
52.60
= 8
Control AC/DC
66.46
41.79
25.22
64.11
81.20
25.84
56.10
46.40
50.89
67.70
47.17
33.26
63.86
85.06
50.64
62.20
60.60
58.81
GPTT-4
Big Model
54.31
53.64
7.72
83.08
94.69
74.32
90.20
86.80
68.10
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B.2.2 ANALYSIS AND KEY INSIGHTS
Coverag--to-Selctton Translation. The consistent improvements in single answer selection val- idate that our Coverage gains reflect genuine complementary capabilities rather than statistical ar- tifacts. Models that cover diverse skills collectively also contribute effectively when aggregated through selection mechanisms.
Parameter Efficiency. Our results support the hypothesis that distributed specialized models can (given sophisticated selection/collahoratio strategies) achieve superior performance compared to parameter scaling. For example, the N=8 configuration for our DeepSeek models outperform model with 14% more parameters within the same model family, while the N=3 configuration nearly matches model with 210% more parameters.
Selection Method Optimization. The benchmark-specific selection strategies highlight the impor- tance of matching aggregation methods to task characteristics. This finding suggests that future work collective intelligence should consider exploring task-aware selection mechanisms rather than universal approaches.
Generalization Beyond Benchmarks. These findings reinforce evidence from App. B. that di- verse collectives smaller models can outperform larger monolithic models. Importantly, our open- ended evolutionary algorithm achieves these results without optimizing for any specific downstream benchmark, supporting the hypothesis that diversity-drive evolution discovers broadly applicable complementary skills rather than benchmark-speciffe adaptations.
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C
EXTENDED DISCUSSION ON LIMITATIONS
Table 8: AC/DC (ours) Coverage performance comparison across different benchmarks for the
Llama 3 family of models (see App. A.2
2 for details the model used).
Method
MMLU judge
MMLU Pro
GPQA judge
BBH judge
GSMSK
Minerva
HumanEval
MBPP
Avg.
judge
Llama
Big Model
47.50
43.40
3.70
78.70
92.20
50.32
81.71
68.20
58.22
=
Control
51.12
45.27
6.04
75.96
89.77
43.48
70.73
63.80
55.77
Experts
45.9
38.58
8.05
66.47
94.47
47.88
68.90
65.00
54.41
AC/DC
47.74
40.25
8.39
71.91
87.26
37.82
67.68
64.20
53.16
N
Control AC/DC
64.44
59.49
12.08
87.09
95.45
56.40
78.05
71.20
65.53
59.22
52.13
13.09
83.58
90.98
46.
75.61
70.80
61.44
GPT-4o
Big Model
54.31
53.64
7.72
83.08
94.69
74.32
90.20
86.80
68.10
Table 9: AC/DC (ours) Best-of-N performance comparison across different benchmarks for the Llama 3 family of models (see App. A.2 for details on the model used).
Method
MMLU
MMLU Pro
GPQA
BBH
GSM8K
Minerva
HumanEval
MBPP
Avg.
Big Model
60.67
59.23
17.19
69.21
92.19
50.32
81.71
68.20
62.34
Divide and Conquer
Monarchical LLM
RM-based
3
Experts
71.46
51.44
24.78
61.02
82.03
37.38
67.07
56.60
56.47
Control AC/DC
68.47
50.22
24.55
65.73
86.05
41.70
62.20
56.60
56.94
69.94
47.58
20.76
62.71
82.79
34.00
62.20
$6.00
54.50
Control AC/DC
71.86
55.46
28.35
73.25
89.16
42.92
66.46
58.80
60.78
74.58
51.51
30.80
69.67
84.23
36.58
61.59
55.80
58.10
GPT--o
Big Model
54.31
53.64
7.72
83.08
94.69
74.32
90.20
86.80
68.10
In Sec. we discuss several limitations which we provide further discussion on in this section. As mentioned, the process of model merging is strongly reliant on the compatability of the seed models used, also observed in previous work (Horoi et al. 2025). A supporting example of this observation are our experiments on the Llama3 family models, where Tab, S and Tab, 9 present Coverage and BoN results, respectively. Merged models typically inherit both the strengths and the limitations of parent models, producing models that are more prone to response degradation even the same benchmark where other kinds of merged models solve benchmark tasks without fail.
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D ADDITIONAL RESULTS
The experiments for additional results were performed using the Qwen 2 family models.
D.1 IMPACT OF ALGORITHM COMPONENTS
Table 10: Ablation study of AC/DC (ours), showing the impact of different components of the evolutionary algorithm. Coverage performance across benchmarks (excluding Minerva) for N=3 and N=8 configurations with individual components removed.
Configuration
N=3
N=8
AC/DC
60.82
69.00
Fitness Only
58.36
67.82
No Mutation
59.66
67.81
No Novelty Filter
60.32
68.63
No Gibberish Filter
58.43
68.12
W/o all components
58.46
61.98
Tab. 10 presents the results of an ablation where we remove one component of AC/DC's algorithm time. We discuss the observations in Sec. Moreover, we present the average across all benchmarks excluding Minerva Math, which we discuss in App. D.5.I
D.2 IMPACT OF TASK FORCE SELECTION STRATEGIES
Table 11: Impact of Task Force selection strategies. Results showing average Coverage across all benchmarks and all models.
Method
N=3
N=8
Global Skill Vector (Coverage)
60.47
68.24
Global Skill Vector (Fitness)
59.94
67.44
Random
57.38
65.74
After letting AC/DC run for multiple generations, we populate an extensive global archive of models and synthetic tasks. Several approaches to selecting our N models for our Task Force exist. this section, we discuss the following three:
Global skill vector (Coverage). For this strategy, we evaluate all our models in our global model archive on all tasks in our global task archive. We then select the N models that maximize the Coverage on our synthetic data archive, "optimizing" for complementary skills.
Global skill vector (fitness). Here, we also perform the complete global task archive evaluation above, but now select the N fittest models.
Random We here randomly select N models from global model archive.
Tab. I1 presents the results of these three selection strategies. We evaluate model families (Qwen 2, Qwen 2.5, Qwen 3, DeepSeek) on all benchmarks (see App. using the llm-as-a-judge vari- ants of the respective benchmarks), except for Minerva Math, due compute constraints for this ablation. We observe that random selection performs the worst across both N = and N = scales, demonstrating that intelligent task force composition based on our synthetic dataset trans- lates improved downstream performance. Selecting based on maximizing Coverage and selecting the highest fitness individuals on our global task archive (the global skill vector) both demonstrate superior performance, yet, optimizing for synthetic data Coverage yields the highest accuracy gains.
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Evolution of Adaptation Types and Vendi Scores Over Generations
adeplation adaptation, harder global Vendi Score
60
60
40
Vendi Score
Number
20
10
Generation
Figure 6: Adaptation types and Vendi score over time. For this experiment, we only enabled adaptations types to be making task more difficult completely novel. Moreover, we show the global Vendi Score (Vendi score of the global task archive) over time demosntreating increasing diversity in our task archive.
D.3 TASK ARCHIVE NOVELTY OVER TIME
Fig. 6 presents the adaptation types at each task adaptation cycle and the global Vendi score (Fried- man & Dieng 2022), i.e., the Vendi score for our global task archive. The Vendi score is a measure for diversity in task embedding space. For this experiment, we only enable tasks to be evolved to be either more difficult, novel. We observe that over the course of training,
mostly gen- erate more difficult tasks, highlighting the increasing difficult of our task archive over time, until we discover the capability limits of population at generation 35, 40, and, especially, generation 45, where we see a stark decrease in adaptations for harder tasks and an increase in adaptations for more novel tasks.
Moreover, we observe a steady increase in the Vendi score, demonstrating an increasing diversity within our global task archive. Nevertheless, observe that with each adaptation cycle, the increase of the Vendi score gradually decreases, compared to the early generations.
The Vendi score measures diversity within the task embedding space. This has the advantage of capturing the spread across diverse problem formulations and wording, which can be more easily separated using embeddings. Nevertheless, this also means that the Vendi score is subject limitation of embedding based similarity/spread measures. Although the context can be structurally similar, the semantics can be very different. For this reason, following prior work (Lu et al., 2025). we implement two-layered novelty filter. First, we assess embedding- based similarity, and then we evaluate semantic novelty using an LLM judge. Because of the latter, we can have a more fine- grained assessment of novel tasks, which is reflected in the fact that, although the increase in Vendi score seems to slow down at around generations 40/45, we still observe that we add 71 new tasks (23 harder 48 novel), which is comparable earlier generations.
core
In fact, analyzing the task difficulty dynamics, we observe the generation 45's decreased "harder" adaptations reflect the system discovering current capability limits of the model population and piv- oting to novel exploration (48 novel tasks). This suggests continued innovation task space, espe- cially considering that the space of possible tasks may be incomprehensible, rather than task novelty saturation. Fig. [ shows continued downstream performance improvement through generation 50, confirming that capability advancement continues.
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D.4
DISCUSSION ON EXPERTS N=8 BASELINE
Table 12: Coverage comparison between Experts and AC/DC (ours) with N model families.
across different
Method
MMLU judge
MMLU Pro judge
GPQA judge
BBH judge
GSM8K
Minerva
HumanEval
MBPP
Avg.
Qwen 2
Experts AC/DC
72.06
69.14
9.73
86.83
95.91
82.52
91.46
75.20
72.86
67.20
63.03
15.44
82.03
94.77
75.32
89.02
74.40
70.15
Qwn 2.5
Experts AC/DC
72.97
67.86
10.74
89.55
96.97
88.80
95.12
81.80
75.48
73.31
69.63
11.41
90.29
96.44
88.24
89.63
81.40
75.04
Qwen 3
Experts AC/DC
72.61
73.51
14.77
92.88
97.27
94.36
95.73
95.40
79.57
73.17
75.09
17.45
92.94
96.29
94.32
95.12
86.00
78.80
DeepSeek VI
Experts 47.24
36.15
77.48
60.34
76.22
70.20
53.63
AC/DC
49.35
46.54
6.71
89.92
64.00
76.60
60.37
AC/DC demonstrates consistent improvements However, one might consider alternative configurations that maximize the utilization of expert mod- els within our inference budget. To address this, we evaluate configuration that distributes inference calls across our three expert models (3 instruct, code, 2 math calls), shown in Tab. 12
primary baselines across model families.
While this "Experts 8" baseline achieves competitive performance in some cases, we note several important distinctions from AC/DC: (1) the 3-3-2 distribution represents manually tuned configu- ration rather than principled allocation strategy, (2) it relies on multiple sampling from limited set of models rather than leveraging diverse evolved capabilities, and (3) the comparison conflates inference-time scaling with AC/DC's model discovery process.
The arbitrary nature of this baseline becomes apparent when considering alternative distributions: other configurations such as 4-2-2 or 2-4-2 would yield different results, and in the extreme case of 8-0-0 (using only the instruct model), we recover Control 8 baseline, which we have already demonstrated that AC/DC consistently outperforms across all model families.
Notably, AC/DC maintains competitive superior performance while discovering genuinely diverse models through evolution, rather than simply increasing inference calls to existing models. The mixed results across benchmarks suggest that raw inference scaling and evolved model diversity offer complementary but distinct advantages.
In particular, the judge-evaluated benchmarks (MMLU Judge, MMLU Pro Judge, GPQA Judge, BBH Judge), AC/DC consistently outperforms Experts 8 across Qwen 2.5, Qwen 3, and DeepSeek VI all four metrics, and achieves notably higher GPQA scores across all model families (+5.71, +0.67, +2.68, +1.01 percentage points respectively), suggesting that our evolved task force espe- cially beneficial for tasks requiring open-ended reasoning where the answers are not provided in multiple-choice format.
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D.5 COMPARISON TO PRIOR QD METHODS
To compare AC/DC (with coevolution and QD) with prior quality-diversity approaches without coevolution (DNS (Bahlous-Boldi et al., 2025) and CycleQD (CQD) (Kuroki al. 2025)), conduct experiments using identical training conditions. For all methods discussed, we train on the same model family. Qwen 2. Both baseline methods are trained on the same four benchmarks used in our evaluation, with 50 fixed training samples drawn from each of GSM8K (Cobbe et al. 202T MBPP (Austin al. 2021), AgentBench OS, and AgentBench DB (Liu et al. 2023).
For the task force selection strategies:
CQD: Following their approach, after evolution, we select the top models from each task-specific archive (2 models x 4 tasks models total for N=8). For N=3, we collect the top models for GSM8K, MBPP, and AgentBench OS.
We select the top-8 models based on local competition scores, which measure per- formance against local neighborhoods in the behavior space. For N=3, we select the top-3 models.
DNS:
The key distinction is that while both DNS and CycleQD directly optimize for performance on these specific benchmarks during training, AC/DC evolves models on synthetically generated tasks without any benchmark- sppecfff optimization. Despite this apparent disadvantage, AC/DC achieves superior performance at N-8, demonstrating that evolution diverse synthetic tasks can discover more capable and complementary models than direct benchmark optimization.
We also analyzed train-set coverage for DNS and CQD. On the 200 tasks total (4 sets of 50 training examples across the tasks), we evaluated the top-20 models and top-5 models for DNS (based on local competition score). For CQD, select the top-5 from the top-2 in GSM8K, and top models for the three other tasks, and for top-20, we get the five best models for each of the tasks. For top-5 coverage, DNS beats CQD, obtaining 60.5% versus 56.5%. For top-20, DNS again beats COD, obtaining 70% versus 65%. Results provide even more evidence for the suitability of DNS as a QD algorithm for AC/DC over CQD.
D.5.1 FULL RESULTS FOR ALL METHODS ON ALI. BENCHMARKS
Table 13: Comparison of AC/DC (ours) to prior QD methods. Results showing average Coverage across all benchmarks. Average excluding Minerva benchmark.
Method
MMLU judge
MMLU Pro
GPQA judge
BBH judge
GSM8K
Minerva
HumanEval
MBPP
Avg.
Avg.
judge
AC/C
55,42
51,28
10.07
69.44
90.00
39.88
84.15
65.40
$8.21
60.82
DNS
52.26
48.98
9.40
69.08
90.37
45.88
84.15
67.00
58.39
60.18
CQD
51.94
48.43
9.06
67.40
91.81
46.08
83.54
66.80
58.13
59.85
AC/DC
66.78
62.72
15.77
81.67
95.07
51.86
88.41
72.60
66.86
69.00
DNS
62.37
59.69
14.77
79.33
93.03
54.38
86.59
69.60
64,97
66.48
CQD
60.19
57.49
13.76
78.82
93.71
51.52
85.37
68.60
63.68
65.42
Tab. 13 we present the full results per benchmark. Additionally, we show the average perfor- mance across all benchmarks and the average across all except Minerva. In Tab. 3 we present the average results without Minerva, because, due to computational constraints, we restricted to running the benchmark with the default aon-eval-harness settings, which have lower maximum output token length. For other comparisons (and for our main results), we compute results that more accurately reflect the models/populations' true capabilities.
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D.6 DNS ON STATIC SYNTHETIC DATASET
To estimate the effects of an ablation where we execute our pipeline on static synthetic dataset, consider the progress made up until generation 5 of our existing AC/DC run on Qwen 2.5 as representative performance.
In other words, it is reasonable expect that the performance would stagnate around that of our task force at generation 5, as up to that point, our synthetic dataset is static and would then be updated.
To support this argument, we analyze the newly added models per generation. With static dataset, we expect the number of new (fit and diverse) models discovered to significantly reduce early in the evolution process. We observe this behaviour when looking at the number of new models in our DNS baseline (Fig. (a)), where we evolve models on the static dataset downstream benchmark training sets. In contrast, looking the number of models discovered when running our AC/DC algorithm (Fig. (b)), we detect constant influx of new models.
Humber
(a) DNS
(b) AC/DC
Figure 7: New models added archive per generation.
Considering this, we find that the average performance on our LLL--s-a-judge tasks, our generation 5 task force achieves 59.66% accuracy, whereas our final task force, achieved through a dynamically coevolving synthetic dataset achieves 61.19% (1.53% absolute improvement).
Table 14: Static vs. Coevolving Synthetic Dataset. Coverage comparison between task forces evolved on static synthetic dataset versus our full coevolution approach on Qwen 2.5. The static baseline shows a performance plateau when the synthetic dataset does not dynamically coevolve with the model population. The values for "AC/DC (Coevolving)" are the average scores across two training runs with two different RNG seeds.
Method
MMLU judge
MMLU Pro judge
GPQA judge
BBH judge
GSMSK
Minerva
HumanEval
Avg.
N=3
Static Datasct
53.15
51.74
3.69
80.97
92.27
51.48
87.80
60.16
AC/DC (Coevolving)
56.67
55.72
5.37
81.65
93.10
40.69
88.72
60.27
Improvement
+3.52
+3.98
+1.68
+0.68
+0.83
-10.79
+0.92
+0.11
N=8
Static Dataset
59.38
57.37
4.36
85.18
94.01
59.62
90.24
64.31
AC/DC (Coevolving)
67.80
66.30
7.72
88.40
95.64
56.33
93.29
67.93
Improvement +8.42
+3.36
+3.22
+1.63
-3.29
+3.05
+3.62
Additionally, Tab. 14 presents the quantitative comparison between task forces evolved a static synthetic dataset (i.e. the initialized task pool without further adaptation) versus our full coevolution approach Qwen 2.5. The static dataset bascline achieves 60. 60.16% average Coverage for N=3 and 64.31% % for N=8. In contrast, our coevolving approach achieves 60.27% and 67.93%, respectively, representing absolute improvements of +0.1 1119 % and +3.62%.
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The improvements particularly pronounced showledgg-intensivee benchmarks (MMLU; +3.52% MMLU Pro: +3.98% and reasoning tasks (GPQA: +1.68%/+3.36%% BBH: +0.68%/43.229%) While the static baseline shows higher performance Minerva Math, this is likely due to the early-stage synthetic dataset being biased toward mathematical reasoning tasks, which expect to be subsequently diversified through coevolution.
These results demonstrate that dynamic coevolution of the synthetic task archive is critical for dis- covering diverse and complementary model capabilities. The findings strengthen our hypothesis that open-ended evolution requires continuous expansion of the challenge space and going beyond optimizing for the affinity between a population of models and fixed distribution of diverse tasks Ackerrann et al. 2021).
D.7
EFFECT OF SEED TASKS
In App. B33 we present the four seed tasks used initiate the cocvolution process.
Impact of Seed Tasks. Coverage comparison between coevolution runs with all seed tasks versus without code generation seed tasks Qwen 2.5, The values for "All Seed Tasks" are the average scores across two training runs with two different RNG seeds.
Table 15:
Method
MMLU judge
MMLU Pro judge
GPQA judge
BBH judge
GSM8K
Minerva
HumanEval
Avg.
N=3
W/o Code Seed Task Seed Tasks
62.46
59.45
5.37
84.34
93.71
41.02 40.69
89.02 88.72
62.20
55.72
5.37
81.65
93.10
60.27
Wio Code Seed Task All Sced Tasks
73.31
69.63
11.41
90.29
96.44
53.84 56,33
90.24 93.29
69.31
67.80
66.30
7.72
88.40
95.64
67.93
ablate the effect of different compositions of seed tasks. Once All Seed Tasks, where we use all four seed tasks, and once W/o Code Seed Task, where we use the three seed tasks, excluding App. E.3.2
In Tab. 15
We can observe that the selection of seed tasks can have a big effect on the performance of the final task force. Leveraging all four seed tasks, including the coding seed task, can improve the coding benchmark performance (at N=8, HumanEval performance being 3.25% better than without the coding task), but, in effect, can hurt downstream performance on other benchmarks (e.g., at N=8, on MMLU judge and MMLU Pro judge, the performance being -5.51% % and -3.33%).
D.8 EFFECT OF SCIENTIST MODEL
To explore the generalizability of AC/DC different scientist models, conducted experiment the existing prompts and hyper-
using Qwen3-235B-A22B as the scientist model while maintaining parameters optimized for Qwen2.5-72B. This experiment highlights both the flexibility of AC/DC and the importance of model-specific prompt engineering.
Using the original generation settings, we observed that the Qwen3-235B-A22D scientist model fre- quently produced incomplete task implementations within our generation token limit, yielding 555 total synthetic tasks compared to over 1000 tasks generated by the Qwen2.5-72B scientist model. We evaluated the resulting task forces using the same Qwen2 7B subject model across both condi- tions.
Scientist Model
Avg. Coverage (N =3 Avg. Coverage (N 8)
Qwen2.5-72B -35e33-33BB-A2B
58.21
66.86
57.57
65.46
Table 16: Task force performance comparison for two runs using two different scientist models. Both configurations use Qwen2 7B as the subject model.
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As shown in T6 the task forces generated with the Qwen3-235B-A223 scientist model achieve slightly lower Coverage scores than those generated with Qwen2.5-72B. We attribute this perfor- mance gap primarily to the reduced size of the synthetic task pool.
Importantly, the lower task force scores do not necessarily indicate inferior capability of the Qwen3- 235B-A22B scientist model itself. Rather, this case study illustrates the practical considerations when adapting AC/DC to different scientist models. We expect that with appropriate prompt engi- neering, adjusted generation limits, and hyperparameter tuning specific to the Qwen3-235B-A22B model, the framework could effectively leverage its potentially superior capabilities to generate higher-quality synthetic tasks and achieve improved downstream performance.
D.9 EFFECTS OF RESPONSE LENGTH CRITERIA FOR EVOLVED MODEL SELECTION
We may want to discover models that follow desired criteria that are non-trivial to train for via loss functions. Setting the desired criteria for candidate models to follow during coevolution can be a simple approach to get such desired model performance.
We find that the configuration setting of output token length during cocvolution can aid discover- ing models that are more suited to different response length limits (e.g. 256 output tokens) than off- the-shelf models in some cases. Interestingly still, evolved models are flexible in providing correct responses to benchmark tasks when the default response length setting for benchmark at test-time (2048) is longer than what was used during coevolution (512). Setting additional minimal criteria for models during coevolution can be a simpler approach of discovering more suitable models for solving tasks in desired ways than defining custom loss functions or architecture adaptations.
We present and discuss the results for Coverage and Best-of-N against baselines under the short response length benchmark setting for code and math benchmarks, and compare the performance of AC/DC against baselines in this setting against the default response length setting.
To show if performance gaps between the AC/DC task force and baselines are wider or narrower in the short response setting in comparison to the default response setting, we calculate the change in performance gap (going from the default setting to the short response setting). This is done for the code and math benchmarks. We then show this for both Coverage and Best-of-N in Tabs. 17 and [9 respectively. The full benchmark results for the short response setting used as part of the calculations are shown in Tabs. [8 and 20
D.9.1 COVERAGE COMPARISON AGAINST DEFAULT RESPONSE LENGTH SETTING
Change in coverage performance gap: short vs. long response benchmarking. A positive value indicates that AC/DC's advantage over the baseline is larger in the short-response setting than in the default setting.
Table 17:
vs Experts N=3 (%)
vs Control (%)
vs Big Model ( (%)
Base Model
N=3
N=8
N=3
N=8
Qwen2 7B
+0.43
+2.34
+2.84
+9.76
+11.53
Qwen2.5 7B
-1.47
+4.43
+6.19
+3.90
+6.32
Qwen3 14B
-0.89
-0.37
+0.65
+2.63
+3.43
DeepSeek V1 7B
-1.96
-6.12
-5.39
+0.72
+1.50
Average
-0.97
+0.07
+1.07
+4.25
+5.70
Coverage gains by AC/DC against the Big Model baseline are wider for the short response setting, when compared to the performance gap observed in the default response setting. The change in performance gap is seen across all four model family runs, as shown Tab, I7
These findings suggest that typical larger off-the-shelf models are even weaker by default at giving more concise responses to the prompt than coevolved models that have been selected to give more correct responses given a shorter response length limit. This observation is grounded in the concrete Coverage gains by AC/DC over baselines in the main table. In general, selection of desired criteria for evolved models can be a simple approach to adapt model behavior for better Coverage under different settings, as an alternative to multi-objective optimization.
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Table 18: AC/DC (ours) Coverage performance comparison in the short response length evalua- tion setup across different benchmarks and model configurations for all model families used (see App. A.2 The short response setting has been applied evaluation on math and code benchmarks.
Method
MMLU judge
MMLU Pro
GPQA judge
BBH judge
GSM8K
Minerva
HumanEval
MBPP
Avg.
judge
Qwen 2
Big Model
50.44
47.12
4.36
78.32
90.83
35.56
49.39
64.00
52.50
3
Control
54.58
50.97
7.72
73.01
92.34
36. 10
79.88
64.20
57.35
Experts
47.05
41.78
6.38
65.29
91.28
47.50
81.10
66.00
55.80
AC/DC
55.42
51.28
9.06
69.44
89.99
39.88
84.15
65.40
58.08
N
Control AC/DC
67.69
64.27
12.75
84.54
95.68
46.10
92.07
70.60
66.71
67.20
63.03
15.44
82.03
94.77
53.26
89.02
72.00
67.09
Qwen 2.5
Big Model
49.30
47.00
0.00
82.30
91.70
28.30
89.60
75.40
57.95
N =
Control
58.17
56.96
8.05
83.18
93.33
31.94
90.85
66.60
61.14
Experts AC/DC
48.33
50.40
5.03
68.24
97.42
46.22
90.85
74.2
60.09
62.46
59.45
5.37
84.34
93.71
41.02
89.02
74.60
63.75
8
Control AC/DC
69.35
67.95
12.42
89.29
95.53
39.84
92.68
70.4
67.18
73.31
69.63
11.41
90.29
96.44
53.84
90.24
82.00
70.90
Qwen 3
Big Model
57.30
53.60
0.30
83.70
94.70
23.
94.50
74.60
60.23
Control Experts AC/DC
63.59
64.95
10.74
87.93
94.77
32.02
93.29
76.40
16.46
65.09
9.06
89.67
37.48
93,90
77.00
66.42
62.74
64.60
12.75
87.71
94.24
31.32
93,90
78.80
65.76
N = 8
Control AC/DC
73.01
73.55
13.42
92.0
95.83
37.76
93.29
77.6
69.56
73.17
75.09
17.45
92.94
96.29
39.28
95.73
81.4
71.42
DeepSeck V1
Big Model
40.10
31.70
3.00
62.20
80.50
25.20
26.20
56.40
40.66
N =
Control Experts
39.85
29.39
2.68
48.21
74.15
20.94
29.27
49,00
36.69
27.34
18.14
1.34
34.01
54.97
36.44
60.98
41.00
34.28
AC/DC
37.22
32.74
1.68
51.43
84.0
39.08
58.54
39.20
42.99
=
Control AC/DC
55.94 49.35
43.74
8.39
65.57
85.67
33.02
45.73
60.00
49.76
46.54
69.34
89.92
51.02
72.56
47.40
54.11
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D.9.2 BEST-OF-N COMPARISON AGAINST DEFAULT RESPONSE LENGTH SETTING
Change in Best-of-N performance gap: short long response benchmarking. A positive value indicates that AC/DC's advantage over the baseline is larger in the short-response setting than in the default setting.
Table 19:
Experts N-3 (%)
vs Control (%)
vs Big Model (%)
Base Model
N=3
N=8
N=3
N=8
Qwen2 7B
+4.40
+2.50
+1.67
+11.92
+11.89
Qwen2.5 7B
-1.48
+2.21
+5.45
+2.80
+6.08
Qwen3 14B
+4.16
+0.11
-1.36
43.10
+2.45
DeepSeek V1 7B
-8.61
+4.72
-6.12
-6.17
+0.72
Average
-0.38
+2.38
-0.09
+2.91
+5.28
Best-of-N gains by AC/DC against the Big Model baseline wider for the short response setting compared to the default response setting, as shown in Tab. [9 mirroring the pattern observed for Coverage. The widening performance gap against the Big Model baseline consistent across three of the four model families, with an average increase of +2.91%% at N=3 and +5.28% at N=8. All four model families show a positive increase N=8.
Our findings suggests that further gains under different Best-of-N evaluation constraint settings can be achieved in coevolved models solely through the design of the right of selection criteria dur- ing coevolution. The right selection criteria could even lead to better Best-of-N performance than standalone answers from big off-the fffelf models, under different (e.g., shorter) response settings. Additionally, further gains in the short response setting can facilitate more efficient Best-of-N se- lection by reducing the context length for models that need to select the best answer out multiple candidates.
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Table 20: AC/DC (ours) Best-of-N performance comparison in the short response length evaluation setup across different benchmarks and model configurations for all model families (see App. A.2 The short response setting has been applied evaluation on math and code benchmarks.
Method
MMLU
MMLU Pro
GPQA
BBH
GSM8K
Minerva
HumanEval
MBPP
Avg.
Qwen 2
Rig Model
82.04
63.15
10.27
68.42
90.83
35.56
49.39
64.00
57.96
Divide and Conquer
Monarchical LLM
RM RM-based
N
Experts 72.16
46.33
22.54
63.97
88.70
43.70
66.46
49.80
56.71
Control AC/DC
59.81
42.07
16.07
60.01
89.54
30.84
75.61
58.20
54.02
71.31
50.20
19.64
63.11
86.58
35.28
78.66
56.00
57.60
=
Control AC/DC
71.62
$1.45
27.68
67.72
90.22
37.12
79.88
58.60
60.
76.14
56.84
25.22
69.73
88.86
41.46
79.27
56.20
61.72
Qwen 2.5
Big Model
84.65
70.64
10.71
53.14
91.74
28.28
89.63
75.40
63.02
Divide and Conquer
Monarchical LLM
RM- RM-based
=
Experts
77.97
60.72
19.86
65.40
90.22
44.12
81.10
66.26
63.21
Control AC/DC
76.29
59.99
17.41
60.91
91.21
28.30
80.49
64.00
59.83
78.22
60.34
18.53
62.20
88.55
33.64
82.32
65.80
61.20
N
Control AC/DC
78.21
64.40
21.65
67.87
91.13
31.00
85,98
64.20
79.41
63.50
22.10
70.60
89.91
45.92
81.71
66.40
64.94
Qwen 3
Big Model
88.81
73.11
8.48
38.30
94.69
23.12
94,51
74.60
61.95
Divide and Conquer
Monarchical LLM
RM-based
Experts Control AC/DC
81.81
67.99 68.86
17.86
55,51
92.65
34.68
82.32
56.20
61.13
81.36
13.62
51.10
93.10
28.58
89.02
73.40
62.38
81.17
69.31
14.06
52.10
92.12
28.
89.63
75.26
62.72
N 8
Control AC/DC
82,03
70.99
18.75
59.12
92.95
30.92
89.63
73.40
64.72
82.33
71.47
20.31
62.05
91.58
32.96
87.20
68.46
64.55
DeepSeek
Big Model
70.81
43.12
14.73
57.86
80.52
25.20
26.22
56.40
46.86
Divide and Conquer
Monarchical LLM
RM-based
3
Experts
51.30
29.96
27.90
50.22
54.81
31.40
48.78
24.60
39.87
Control
55.35
33.55
22.54
54.03
73.62
17.80
20.73
46,00
40.45
AC/DC
59.76
37.87
29.24
55.03
81.20
34.52
50.00
30.80
47.30
N
Control
66.46
41.79
25.22
64.11
$1.20
23.94
26.83
48.80
47.29
AC/DC
67.70
47.17
33.26
63.86
85.06
40.30
56.10
23.80
52.16
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E
ADDITIONAL QUALITATIVE RESULTS
E.1 CASE STUDY ON SYNTHETIC TASK DIVERSITY
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Figure 8: Analysis of global task archive embedding space generated by AC/DC with Qwen 2. We represent each task by structuring its metadata using the template in App. F.3 and then embedding it using an embedding model (see Tab. 5). We then reduce the dimensionality of embeddings using t-SNE (van der Maaten & Hinton 2008 The clusters are automatically generated using HDBSCAN (McInnes et 2017).
Fig. 8 presents the global task embedding space evolved through AC/DC with Qwen2-based pop- ulation. In the left half of the embedding space, we can find tasks in the writing space, such as
generating short story based a prompt and generating analogy for abstract concept, requiring creative writing abilities
human-ai- iinua--i-interacttinn requiring emotional intelligence and alignment
writing accurate historical text, testing for historical knowledge
developing legal argument given a case, examining legal knowledge and persuasion abilities
The right half of the embedding space presents more technical challenges, for example
constructing regular expressions
solving complex math and pattern recognition problems
proving mathematical theorems
implementing code such as SQL queries
These example tasks demonstrate the diversity of synthetic tasks generated by AC/DC (many of which might not have been created by human annotators), presenting the breadth of knowledge examined by system to discover unique capabilities.
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Model Dvolution Thee Generation
Figure 9: Evolution tree of AC/DC evolving the Qwen2-based seed model. Highlighted models are those selected for the task force by maximizing Coverage global task archive.
E.2
CASE STUDY ON MODEL LINEAGES
E.2. 1 INSPECTING TASK FORCE SELECTION
Fig. presents the evolution tree produced by AC/DC applied to our three Qwen 2 seed models (see App. A.2). highlighting the models selected for our task force. We observe that task force contains models with wide fitness range, ranging from 0.53 to 0.62. Nevertheless, as described in Sec. 3 this task force is selected for optimizing Coverage across synthetic data, meaning that we don't t always want the highest fitness indiiiuuls, but individuals that have complementary skills (which we discuss in App. D.2).
For instance, we observe that our model discovered in generation 30 (presented as (en_30-ind.1) achieves the highest global fitness, i.e., fitness across our entire synthetic task archive, yet the instruct model, which is part of our task force, does not achieve the highest fitness, but is part of our task force.
E.2.2 INSPECTING LINEAGE OF SPECIFIC TASK FORCE MODEL
Fig. presents three example lineages of our discovered LLMs, demonstrating complex histories of skill merging of parent models, embedding the knowledge of all three seed models into the weights of the observed models.
The first tree (top) presents the lineage of the model gen_17_ind.6, the sixth model evolved in generation 17. As observed in prior work leveraging evolutionary search techniques (Zhang et al. 2025a), we can see that, to reach the model that was part of the task force, we don t only leverage high fitness individuals. To discover model gen_17 1i.iiddd..... 6, AC/DC first discovered pen_14_ind 6, with a local fitness f of 0.476, which is weaker lower than that of its parents (gen_12_ind_5, with / 0.528 and gen_ 1 0_ind_2, f 0.488). This highlights that (locally) weaker solutions are relevant for discovering models for global task force with complementary skills, by potentially providing unique capabilities, not captured by the simple fitness metric.
The second (middle) and third (bottom) tree present the lineages of the models ven_28-ind.5 and gen.29.ind..5 respectively. Both models are accentors of the just discussed model gen_17.ind.6 yet, we can see that sen_29_ind_5 has a more complex lineage than gen 28 ind 6. gen 28 ind 5 only adds one new ancector its lineage in addition the lineage of gen 17 ind 6, namely gen. 20 ind 3, demonstrating that even only two additional relevant crossover operations can lead to the discovery capabilities beneficial for the final task force. the other hand, although the models gen_2 9_ind_5 and gen.28_ind.e -ind_6 and 1nn_2__ind_5 only one generation apart, we can observe that gen.29 introduces more novel and complex lineages.
These observations demonstrate core advantage evolutionary search, (1) discovering novel solu- tions by building top of prior ones that don't necessarily seem to be the best performing and (2) the emergent complexity of (2.1) intricate lineages or (2.2) sophisticated capabilities through simple but relevant combinations of prior solutions.
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Gansotion
Gemertton
Figure 10: Lineages of AC/DC evolved Qwen2-based models. All presented lineages are of models that are part of the selected task force.
E.3 SEED TASKS USED
E.3.1 SEED TASK 1: CIRCULAR QUEUE LENGTH
class TaskFamilyy Astaricmechod def get_Lasks dict[ste, diet]:
return [
*1"
"queation": "Given that the storage space for aircular queve is the array A pointing
position becore the tail element, assuming the current values of frent and cear are 8 and 3, respectively length queue Q).,, "options": (*A*: "5*, "B"; "6*, "c": "16", *p": *17*), "enswer"; "c",
[21], with front pointing
Astaricmechod
int__instruction it: dict) str:
return **** "quastion " (adz 15 (5/"op ["ooptttnne (**"") [188; it "opt fons") [*8"]) [ (ncs (el* options "["e") lag: (t (t("options" [*8"]] in)nneturn the letter of the correct option.
estazicmechon
diet, return 1.0 if t t("answe=") llower = submissicn. submissicn.lower// () (strip() else 0.0
stz)
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E.3.2 SEED TASK 2: FIRST REPEATED CHARACTER
class TaskFomi ly: Estat Estaticme: lomethod
get_tasks dict(str, dict]:
return {
"1"
"questien"; "Write pythen function called "iiesseepesseeeehhrraaee"" to find the first rapeated charscter in a given string.",
"test_ "rest_casea"; [("aboabe", *a"), (Mabe", (M123123", *1*)), "expected_ repcst_ HeassLLeeeeeeeeeLccaaaaaeeer
@stazicmethod
def get_.instructions it: dict) => str:
p*"*[t "questionn")
Astaricmechod
def scora (t: diet, sutmission: str) -> flost None:
from sandbox_eval_helpes import iet__fentionnneeee to
Teat cases from get_taaks
Lest_cases L["Lest_cases""
correci_count num_test__caee len (test_caaed)
furction collable mapping sinotionn_ame_t__alllaao get_funottion_amee caceeeett__aalalaaaee lable ssionsssion)
if len
== 0:
0.0
Get function names furo_names curottoo__name to__e_ttaall aalllaal.ekes
iE [["expected_ "umee_nee_Fueeemmmeee return 0.0
func_names:
Run test cases for tast_inpurt eypactad_catput in teat_caaes:
try: Execute the function
submiseion_sesult funetion_naze name celeeabbbe t( [""xpaacto__func_aaae [ittst_input]
Compare with expected cutput
if submission_resuu
expected_outpiti
correct_count +-
except Exception:
If function faiia on test case, skip contiaue
pass_rale correct__oune nun_Lest_cases mir_passs_aate 1.0
return 1.0 if pass_rate >e mir_pass_rax else 0.0
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House Published as a conference paper at ICLR 2026
E.3.3 SEED TASK 3: LAW MULTIPLE CHOICE TREE DAMAGE
class TaskFomi ly: Estat #staticme:hos homechod
get_taske dict(str, dict]:
return [
"1"
the lavn in fxent of his home and within five feet of the public sidawalk there was a large tree. The roots of the tree caused the sidewalk buckle severely requirea jacent landownera to keep sidewalks in safe condition. Ilouse engaged Contractor repair the sidewalk, leaving should be made. Contracter dug the sidewalk, cut back the roots of the tree, and laid a new sidewalk. Two days after Bouse had paid Contractor the agreed price of the repsir, the
"questicn"; "House his home
becone dangerous. ordinancs of
Contractor
hew
repair
the fell had asserted claims against House and contractor, and both defendanta admit that cutting briver have any recourse against Contrector?", "options";
and damaged parked belonging briver. briver
Judgment
"A": "No, if payment by Bousa was an acceptancs of the work.", "g": "No, becsuse House selected Contracter to the work.", "o"; "Yes, if judgmant against Heuse based vicarious liability.
Hlouse's conduct was factual
harm.",
"enswer":
Estat tomechod get_inntte
diet)
st::
return gx"nfe (qqueqquunnn" Vaaz [er (t(" ["" Annn (e it/"options"] [ [*="]) unct fer" options 1 (**") (anz (t ("options") (*o*)] (n)nSeturn the the correct option,
@staricmerhed
diot, ssionssionn atr) flost
return 1.0 if [["anewe:") "..ee:"]]oooo llower () submiseion. lower sttcip() else 0.0
E.3.4 SEED TASK 4: MATI WORD PROBLEM CLIPS
class TaskF amily: @staricmathes vet_tasks (str, dfct]: return {
"question"; "Natalia sold elips to 48 her frienda In April, and then she sold half as many clips in May, low many clips did Natalis sell altogether in April and Nay?"
Eat tomechod
def insti diet) str:
zeturn g
@atat Iata:lomeshod
diet, aubmission: stx)
try:
submission_val (eubnission)
based the given ressoning may_sales april_eales
Compute the correct
april_ssles 48
Catculate May sales: haif of April's sales
Calculate total saies for both monthe total_ssles april_sales may_sales
1.0 if submisalon_ve total_sales alse 0.0 except ValueBrcoz: 0.0
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E.4 EXAMPLES GENERATED TASKS
E.4.1 EXAMPLE I: LOGICAL REASONING FOR LEGAL FALLACIES
class TackF amily: @staricmethod def () -> dict (str, dict):
return [
*1"
"arqument"; *Tf you're re not with us, you're re against us. Therefore, you must
support policies.
"fallacy": "False Dilemma"
22
"azgument ; "Jaan says that clinate change is real, but hefes just scientist his opinion doesn't count.", "fallacy": "Ad Nominen"
Estat Estacicmezhon Lemezhod
def iet_instruction dict) str:
ta"*Tdentify logical facy following argusent, explain why is failacy, construct logically sound counter azgument that addresses the same lssue, critique strength the counter- argumeen...
Argument: [t] (t('argument")
Format your zesponse as follows:
Fallacy: [Name of fallacy]
Explanation: [Sxpienation of why it is fallscy]
Counter-Argumeat: (Logically sound counter-argumett]
Critique: (Critique of the counter-argumenr, inclvaing potential weaknasses)
#stacicmechod
def it: dist, sutmission: str) flost None:
sandbox_eval_helpe impost iun
instructions aakkfamily.gge.iisereeeeeee
expected_fallade [['Tallacy']
[bbmitted_fallaeee None
submit:ed_explanntee
None
cubritzed_critiine = None
parts [part.stript for part In parta:
part cubmicsion..trripp..pppl.i
if part.alartaaitti/aaaaleey '):
submitter_Fallecyy (len''alyyyy ():).strip()
elif part. (ttpartttwttthhsssssssttttttttt ():
aubmi subml:ttoo_expllnstttii
(len ():].str(p()
elif (trrssttrtssithh(coootete part [Ient"ooottrrrArrgmenn st].strip()
();
submi care___outttr_rrgmeeeee
ellf Sartt.startewitthhCrttte '):
aubmisted_erittquue (len "):).strip()
if not (aubmitted_ fallacy and sahmittad_oxpllnnee and gubmitted_oritiquue return 0.0
criteria -1
E"The identified fallacy should be fexpecyed_
"The explenation should correctly describe why the identified fallacy
fall rallacy,",
"The aaautee-arqueett should logically sound the ort original argument.",
"The critique should identify potential weaknesses in the counter-azqument and provide balanced evaluation."
1.0 if eval_with aaath_lln__udeeee Judgo (instructions, submission, criteria) clse 0.0
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E.4.2
EXAMPLE 2: LOGICAL UNDERSTANDING OF MUTUAL KNOWLEDGE
import
TaskFamily: @staricmezhod
def get_Laske () dict(str, dict]: {
"1':
eevents'
'Agent & puts red ball in box.",
'Agent tells Agent that box contains ball.',
'Agent C opens the box and sees the red ball.'
2
eevents'
'Agent & puts blue ball in box.',
'Agent Agent
box contains blue ball.',
'Agent C opens the box and sees the blue ball.'
Rstaricme:hod
get_instructions dict)
events = Ann join ( ('vvent'))
return gaaagiven the following sequence of events, determine the final knowledge the contento of the box.
each agent
Sequence
(events)
Provide your answer in the foilowing format:
**Agent knowsee: [What Agent A knows] [What Agent knows]
**Agant
*'geett C knowa**: [Mhat Agent C knows]
@stacicme:had scora diet, submissionn str) flost | Nona:
from Landbox_eval_helper import wwa_wwthh_ln__jdee instructions
oriteria
"The response should clearly state the final knowledge of each agent (A, B, C)."
"The response should indicate that Agent A knows the box contains the ball and know this.", "The response should indicate that Agent knows the box contains the ball and that C knows this.",
that both
response should indicate Agent knows contains the ball."
1
return 1.0 wval_with_llm_ppee (instructions, submission, criteria)
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E.4.3 EXAMPLE 3: CALCULATE ARE OF SEGMENT OF CIRCLE
import math
TaskFamily:
@staricmezhod def get_laske() > dictiste, dict]:
'1': ('radius's 5, "angla': 60), '2 ("radiua's 10, tanglefs 120]
@stacicmethad
def get ine__instuuttons it: dict) str:
radius t('zadius']
angle tt"angle")
I'Calculate
aegment cirole with radius (radlus)
central angle (angle) degzees. Provide the answer es flosting point number rounded to places, without any additionsl text explanations."
Asta:icmechod
def diet, submission: str) try:
flost None:
L['rrdi
angle =l'angle']
Convert angle radians
angle_rad rath (argla;
Calculate the area of the sector sector_area langle 360) math.pi (radius Calculate the area of the triangle triangle_ares
math.
Calculate the area of the segment
segment_ sector_aras -triangleaares Rouna the correct answer pereec__answe round cong area, 2) submittad_answen float faubml sttomm/sioo.ttrpp..) if aubmilLed_answe =- corzect_answe::
decimal places
else:
return except ValueBrror:
return 0,0
except Exception
peint (f' E:ror: {e}*) 0.0
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E.4.4 EXAMPLE 4: PROVE ALGORITHMIC CORRECTNESS OF THE A ALGORITHM
import вутру
TaskFamily: #stacicmezhod def get_Laske dictistr, dict]:
{
*1"
"a "algoriths"; "** algorithm pathfinding with consistent heuristic.", "Leet_cased":
(f'A: MB': rel: 4h, <ere (1or: 2b Ob "ahortest_pathhhn
()
(EW:: Per: 10, ror: 3hr 1Bts Mror: 1h, rera (TDr: 2)1, "shortest_pathhl, ((AA) Mere 1, r 100), rer (Mcr -1501, ror Ob, 'shortest_pathhi c
6
4491)
b, "expected oprove___ster__llee
), "2"
"sigorithm"; "Floyd-Warshall algorithm for finding the shortest patha between all paira of vertices in a graph.",
"Lest_oasee";
(E'A: prer: 1, re:: 4h rer: (for) 2b rere Ob 'shortest (A, B, C)
31),
((AA Merr: 10, fot 3), far (rota 1), rets (rots 2)1, "shortest_patha 6))
B, C, D)
plrove_tlayd Laagooiiiiaa
Setaticmeched def ins_iinstuccioonsi it: dict) <> str:
for carrectneas lowlnning ahgorithm: ("algorithm") H. Your proof should be cleas, logical, and well-stzucture and should cover both case address the properties of consistent heuristic and how it ensures the optimality of the solution.'
formal
inductive step applicable. the sigorithm,
@ataricmached
diet, submission: atx) from. sandbox_eval_hell import waal_wthh_lm__jpp instructions gesttamily.ggr_.nszzueeee.
criteriz =
"The proof should logically sound wel well-structured."
"The proof should correctly prove the correctness of the given algorithn.
"The proof should cover beth the base case and the inductive step if applicable.
"For the Ar algerithm, the proef should address the properties of consistent and ensures aptimality the solution."
return 1.0 it eval_with_ 11m__mmm______ judge (instructions, aubmission, criteria) else 0.0
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E.4.5
EXAMPLE 5: SYNTHESIZE POSITION PapER ON GENETIC ENGINEERING
import
class Tasks amily: 8sta: sstaiicmehood def get_tasks dict(str, dict]:
return {
"debate_ "The debate over genetic engineezing has become increseingly polarized. Proponents argue that genetic engineering can lead significent advancements in medicine, agriculeure, and bistechnclogy. They highlight the petential to cure genetic diseases, incresse crep yields, and develop new materials. Critics, howevez, raise serious ethical, sacial, enginearing could lead to unintended consequences, such as the creation of "designer babies'
environmental
They azgue that
social inequalities. ecologicel impact, including the potentis. isl for gesetically modified organisne (GMDs) to disrupt naturel ecosystems. comprchensive regulatory frameworka and the need for piblic engagement and transparency. Both sides acknowledge the importance of belancing innovation with respensible governance but they differ the apprepriate balance and the role of government in oversseing ganet engineering."
are
debate further cemplicated by
}r *2":
ethical implications genetic engineering are subject of interse debate. Supporters argue that gonetie engineering can al leviate suffering by treating genetis disorders and improving quality of life. They alse contend that it can contribute globel food security by develaping cropa climate change. Opponents, nowever, warn of the potential for genetic engineering to be used
"debate_text":
resistant pests
enhancing physical cognitive traits. They raise concerns about the commedification of human life and the potential for genetic discrimination. Ethicista and the rights of future generations. The debate is further complicated by the lack of international consensus and the varying cultural and religious perspectives on genetic enginearing."
therapeutic purposes,
dehate the
stetus genetically medified orgeniams
@ataricmachod det get_instructionalte dict) str:
return f*Given the folloxing debate on genatic engingering, summarize the dchata, evaluate key arguments counterarquments, that Lakes clear staace on the issue. The position paper should be auppozted by at
synthestze position paper
peer-reviesed sources, and should maintain caherence and logical slows/n)nnt/// d'ebart"doe (xxtt)]]see [/nn///nnnaarrrrr and Evalustion: ianiioonnnnnn \nlaPosition Paper: Treorporate least three peer-reviewed sources support your arguments. example, might refezence atudles the elfectivenese of therapy or the envirenment impact of GMOs."
evidence
#sta:icme:hod
score diet, from sandbox_eval, helper import aval_with_ lm____tth____________________ paaax__vaa_helper instructions
str)
Nene:
criteria
"The sunmery should capture the main points and key arguments.", include "The summary should provide a criticel anelysis of ethical, social, and
summary
counteraggumetts.
scientific considerations.*
*The aummary should maintain coherence and logical flou.",
"The summary ahould not contain unnecessary details or Langents.",
"The summery should accurately reflect the content of the debste.",
"The position paper should take clear stance en the issue.", position paper
supported by evidence
debate
three peer-reviewed sources.",
*The position paper should maintain ccharence and logical flow.",
*The position paper ahould addreas potential counterarquments and provide well
rounded view."
return 1.0 if ewal_with, 11m judga (instructions, submission, critaria) clse 0.0
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E.4.6 EXAMPLE 6: SIMPLIFY ALGEBRAIC EXPRESSION
class TaskFomily: Estat Estacicmes lomethod get_tasks dict(str, dict]: return [
"1" "expression": *(x 3) (x 3)
"expressioa": *2 Ix 1 4) 1x 21"
@stazicmethod def get_instructions it: dict) <> str:
I'Simplity the following algebraic expresaion: [tI'expression]]] Provide your anawer in the simplest Ierm, without any additional explanatien or stepe."
@staricmachad
def score(t: dist, submission: str) -> flest None: from аympy
symbols, simplify
x = symbola
cozrec__answer: simplity (L[ exxpresssoon))) try:
Semove
delimiters preseat
submlasion submi weppaaeee.... "))stripp) ")..trrp
Simplify the submitted answer submittad_aneeee
(submissioo))))
if aubmi anawer == correct_answer:
else:
except Exceptions
return 0.0
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E.4.7 EXAMPLE 7: IMPLEMENT FUNCTION TO GENERATE FIBONACCI SEQUENCE
class TaskFomi Estat setaricme get_tasks dict(str, dict]: return
'1": ('n_terma': 13/,
'2' ('n_terms' 15}
Estat icme-hod
get_instruction dict)
return & Write funetien called +iibonacci_seeuenee that takes integer
aLterms as input and returns a 1ist conteining the Pibonseci sequence up to the specified number of For example, 'n_terms" is 5, the function should return [0, 1, 1, 2, 3)."
The fanction should be implemented using lcop recursion,
score diet, autmission: str) from baabbox_vval_hhlppe import got_ffnetiion cow.ooooootttt_oollli
| None:
Function generate the expested Fibenaces sequence def ganerate_ fibonacc [n_terms]:
n_terms
retura !]
b_terms
return [0]
elif a_terms 2:
[0, tib_sequence = 10, 1]
in sange :_terms]:
Fib_sequence(-E)
fib_sequence
Test cases from get_ n_terna tt'n__terss' oup
(ibonacci (n_terma)
Get function name to callable mapping caaertoonnn_____callaalt get__funatton_name_p paaae_tt______ ( submission
Run test 2ase
try: Execute the function Submlaston_reau
mnctioo_name_____ @ame_to_call labla(' tthoaacti_equeenee (r_terms)
Compare with expected output 1f aubmiasion_result expected_output: retura 1.0
except Excaption:
If Tunction falls on test case, return 0.0 return 0.0
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E.4.8
EXAMPLE 8: IMPLEMENT FUNCTION TO COMPUTE LONGEST COMMON SUBSEQUENCE
class TackFamilly
Astaricmechod
def get_tasks -> dict(str, dict]:
return
'I:
"prcapt'; 'Cemplete the function "Jongest_commoo_ lists of chorsctars and returns the longest common subsequance (IC8). 'test_casea': [(('A' 'R', 'C', 'D', (R"), (MA', 'R', 'D', 'P", (G"), (MAY
that takes two
([',, 'B', 'C"), ['X,, "y, (2)), []. (1'2', "B', 'C', '5', 'D', ',, 'B L'B", "D', 1 12 " "Ir ('B ror 'B, Wil,, Expectee_Suns__.tee
'prompt': 'Cmmletee function 'longest, common wtt._//oooooaaaaaaeeeeee subsequence that two
Hats of charsctars and returns the longest common subsequence (TC5)
"Lest_casea'' [((',', "B', 'C', 'D', (B",, ('M 'B', 'D', 'F', ( (AA
3', (D))),
((', 'B', 'c', '0 (g), ('e fpr, 'C', 's', (K), (MW, 1st,
rol, (D', (R))),
(['2', 'B', 'C', 'B' 'D' '8', 'B',, ('B', 'D', iC', 'M, 'B', "I, ('B', fe', (B', "A]),,, "expected_tune_aanee Langest_comoon_suaeee
Estat homethod def iot_innttuettins it: diet) > str:
return C*ommp lete the lowing Python funct ooneioonnnnttttt ('prompt' An
Asta:icme:hod
def score(t: diet, submission: str) flest None:
from Landbox_aval_bet leelpp import wonetttt_nnme_eeeeeee labble
Test cases from get_tasks test_cases t(' "lat_aaaeeee""
Get function name to cellable mapping seaetioo_nam__t__aee submiasion
pot_ffunctionnamm_eerr l
Run test cases
for segl, seg2, expected_oatput in test_cases: try:
Pxecute the function
submlasion_reeult funct Loweoonnnnnooooeooeeeeeoeeeeeeeo how.rooonnnnnntcccllllllllllllle (eepeeeedeeeeeeeee__uuuun Tunc_name"" (aeql,
seq2)
Compare expected output
if submission_resule expectes_output: raturn 0.0
except Exception:
function fails on case, return 0.0
1.0
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E.4.9 EXAMPLE 9: CALCULATE FREE-FALL TIME
class TaskFomi Estat Estacicme:ha lomethod
get_tasks dict(str, dict]: return [
'1": "height"; 45,
"gravity": 9.81, "expacteo_tine"" 3.03
"height"; 100,
"gravity"; 9.81, "expected_cimee" 4.52
#stacicmethod
def set_insttuctionaie it: dict) +> str:
return d'Cllculate the time it takes for an object to fall from a height (t(' the formula g), whare is the height and e is the acce eration due to gravity (9.81 m/a")), AnRound your
haight']l
free-fall motion under grovity: \nt (2 h
decimal places,"
@staricmathed
def acora dict, submissioon atr) flost None:
import math
Calenlate the expected time using free-fall formula expected_time math, aart (2 + tt'hhighh") ) t (rrgravity ]) zoundlexpected__ingg
expected_time__ee
try:
Convert
to
submiaston_tiie float (awumiiaaoo..trripp
Check if the submission matches the expected time with a small tolerance expeated_ tim___toode_r_dd 3.05:
1f
retura 1.0
else:
retura 0.0
except ValueRrror:
return 0.0
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E.4.10
EXAMPLE 10: PROVIDE EXPLANATION OF CHEMICAL INTERACTIONS
class TaskFomilyy Esta: iomethod
get_tasks dict(str, dict]: return [
"1" "preapt": "Provide detailed explenatien of the chemical interactions in water, including: inl. sond typela2, Elactronentivity differenceskn3, Xelecular geonetry) Interrolecu ions Potertial reactionskn", "Leet_casea";
(") "*)
expeccte
2" "prompt"; "Jaing knowledge
previous taak, explain role water in the process of photosynthesis. Specifically, describe how water is involved in light-dapendeat reactions and overall chemical equaticn paotosynthesis. sure to mention the products and reactanta of the light-dopendant reactions and how they relate so the overell photosyathesis equation.A"', An",
("", "*)
Dexpeecte__uuc_itee
@ataricmathed
def get_Instructiona it: dict) >> str:
p/'pronpt']
@atoricmathed
def (ts diet, submission: atr) flost Mone: from sandbox_oval_heii, import aval weelwwih__ln__jdee instructions TaskFam! iysget_
criteria -
chemical interactions in water, covering bond type, electroneçativity differences, molecular geonetry, intermalecula- interactions,
response should include detailed explanation
potential reactions.",
*The response should explain the role of water in the proceas of photosynthesis, specifically describing how water is involved in the Hight-depondent resetions and the chemical equation photosyntheais. Mention light depeadent reections and how they relate to the overall photosyathesis equation."
products reactants the
return 1.0 if eval_with_ 11m judga (instrections, submission, criteria) clse 0.0
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F AC/DC PROMPTS
We build upon and extend the system prompts used by Lu et (2025).
F.1 TASK CREATION PROMPTS
The system prompt for task creation instructs the scientist model to propose novel, diverse task families to uncover the subject model's capabilities:
Task Creation System Prompt
You are expert in designing task families to assess the capabilities of particular new large language model (LLM). Your goal is create novel, diverse task familics that can reveal the breadth and depth the model's skills.
We especially want you to uncover tasks that could reveal surprising capabilities or failures. Success- fully written task families will be added to task archive.
In each generation, previously generated task families will be provided context that you generate different tasks. Be creative, explore different domains, and design tasks that can distinguish between models with varying levels of expertise. Make sure each task is coherent and does not try to combine too many concepts into one.
Rach task family should be designed according the METR Standard, which requires the following Python format:
class TaskF amii::mi @staticmethod
def get_tasks() -> dict istr, dict
Returns mapping from task names to arbitrary data that specific each task.
Each task family should contain tasks in the dicticnary, labelled "1" and "2"
implementation
@staticmethed
def get_instructions (t: dict) -> str:
This method should return a string containing initial task instructions for the agent.
... implementation
@stat cmethod
def score(t: dict, submissien: str) -> float None: 'score' is called after the agent subnits solution. on success. Do not give partial scores
Return on failure
Threwing an exception will result in a score of 0. For math code related tasks, implements the step-by- step code compute the correct answer given the task instructions.
... implementation
When applicable, for example, for any math or code related tasks, the score function MUST imple- ment the code get the correct answer given the task instructions. You complex computations. Don't 't forget forget that sympy returns fractions by default, so you need to convert the results returns to floats. Make sure to add comments to your code to explain what you are doing. Make sure to handle different answer types, latex style answers such sqrta
the standard python
operations, but also libraries like numpy sympy to import them. Also,
fracab, or text based answers, such as p q. or + q.
The score function may optionally call a helper function that calls GPT-4 based LLM judge.
# Automated L.I. judge helper function def eval with 11m judge
instructions: str, The instructions for the task submission: str, & The subrission to evaluate
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criteria: Optional [str]] None, Optional
additional criteria
) -> bool:
# Returns a boolean indicating whether the agent is deemed to have succeeded
the task, and meets any additional criteria.
You should use this function unless the task is better evaluated through code. DO NOT RE- IMPLEMENT THIS FUNCTION! There is need to repeat any criteria that already in the in- structions. One possible use of the criteria field if you already have an answer in mind task. An example usage in score with an optional criteria is as follows:
i Dstaticmett cmethod
def score (t: dict, submi ssion: str) -> float None: from sandbox eval helper import
Lim_judge
instructions TaskFamily itw.yrggeetnnstttttinnssss (t.
criteria "TThe response should include the name Alice. return 1.0 if Ival_with_ (in (instructions, submission, criteria) else 0.0
Additionally, the score function may call the get mapping from function names to their callables.
@staticmethed
def get__fucction_nane
func_string: str, string containing multiple python function definitions.
) -> dict|ste, Callablel:
Returns dictionary mapping function names to their callables. You should use this function if the task instruction requests the agent to write code. You use this example function referenee for how implement the score function. Reimplement it for simple tasks, extend for more complex tasks. An example usage score follows:
def score dict, submission str) float None: from sandbox_eval_hete
import get_funotion__am__tooco
Test cases from get_tasks
test_input, expected_outpll = ["test_cases"] [0]
Get function name to callable mapping
function name_tate_eeeeeee callable get function name to callable submission
Run test case try: Execute the function
submission_resiis resuulttreesee___e
canction_name_to_calll
expected_func_net [unc_name"]]]
expected func
test_input
Compare with expected cutput
submission_resull expected_outputs return 1.0
except Exception:
IE function fails on test case, return 0.0 return 0.0
Respond precisely the following format including the JSON start and end markers:
THOUGHT:
<THOUGHT>
RESPONSE ISON: <JSON>
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In <THOUGHT>, first briefty think and reason about what kind of task family you want to propose. Thoughts may also include (but not limited to): your motivation for investigating the capability, whether you think the model will succeed fail, its novelty relative what you have already gener- ated, how to ensure the tasks are valid, whether is suitable invoke an LLM judge for scoring. In <JSON>, provide a JSON response with the following fields:
"name of task": A concise, descriptive label (lowercase,
spaces, e.g..
"aane.capitaLcctt""
"description.of.task A clear explanation of what the task entails (e.g. "Return the capital city of country").
sereinngmmasurddd.. The specific LLM capability being evaluated (e.g.. knowledge, reasoning, creativity,
on 1-5 scale. = very easy (simple factual recall), 2 = casy (basic understanding, some inference), = moderate (application of knowledge, multiple steps), 4 difficult (analysis, synthesis, creative problem-solving 5 very difficult (highly specialized knowledge, complex reason- ing).
"est wwtttimmuaaaaa...... tiicultyy An estimate
difficulty
"done": By default, this is set to "False". You will have (num_rounds} rounds to refine the task family but do not need to use them all. Tasks will only be saved if they are flagged "done" " by the end. Do not return "True" until you satisfied with and have received feedback on the task family.
"= "cask_ 1y": The fully implemented Python code for the TaskF ami 1y class. Write good human-readable code.
"examplo catamplc.iine cenllll.iinst ruction" An example instruction for the task that we would expect from the output of get.instructien
This should be string..
All values JSON should strings. You may only use standard Python packages and libraries implement the tasks. Required library imports should included either at the top of the or in the class method where they are used. An import at the start of the class has no effect. DO NOT download additional data from the internet, access the file system. Your response will be automatically parsed and used for evaluation, ensure all components MUST be fully implemented and adhere to the METR standard.
In the initial round of task generation (starting from just the seed tasks). we generate first batch of tasks. We hereby define probability that the task is supposed to be completely novel, or novel, but still related to the seed task.
For generating completely novel task, we use this user prompt for the scientist (alongside the system prompt above):
Initial Task Prompt Completely Novel
A previous generated task family is provided below (with code):
[prev_json)
Generate the next interestingly new task family.
For generating novel but similar task, the following prompt:
Initial Task Prompt Adapt Similar
A previous generated task family is provided below (with oode):
(prev_json)
Generate new task family that is inspired by the previous task family, so that provides a more interesting challenge that is more complex explores beyond what the current task family is evaluating in terms of model capabilities.
Similarly, for new tasks after the initial generation phase, we adapt a task given its difficulty level (see Sec.
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For creating novel task, when the parent task difficulty was "appropriately difficult", again the same probability as above for generating a completely novel task, or novel one that still similar to the parent task.
Task Prompt Completely Novel
Consider the following task family;
(original_task__ tas____s_______
Summaries of other previously generated tasks for context are:
[other_task_json
Generate a new task family that is interestingly different, aiming to explore diverse capabilities. You can draw inspiration from the provided task, but prioritize novelty in terms of:
The specific capability being measured.
The domain context of the task.
The format or style of the interaction.
Ensure the new task is coherent, adheres to the METR standard, and is distinct from existing tasks. Respond in the standard JSON format with THOUGHT and RESPONSE JSON sections. Set "done" to "False" initially, as this task will be validated.
Task Prompt Adapt Similar
Consider the following task family:
(original_task_595
Summaries of other previously generated tasks for inspiration are:
Sother_task___0
Generate a new task family that inspired by the previous task family, so that provides a more interesting challenge that is more complex explores beyond what the current task family is evaluating in terms of model capabilities. Draw inspiration from the provided task and implement novelty in terms of:
The specific capability heing measured.
The contextual setting the task.
The format or style of the interaction.
Ensure the new task is coherent, adheres the METR standard, and is distinct from existing tasks. Respond in the standard JSON format with THOUGHT and RESPONSE ISON sections. Set "done" to "Fal se" initially, as this task will he validated.
For creating tasks that are inspired by the parent task are more difficult or easier, we leverage following prompts:
Make Task Harder Prompt
The following task family was found to be too easy for the current models:
(original_ Baanal___ss______ _ask_
Summaries of other previously generated tasks for context are:
(other_taske) satherrtassk_jsons
Generate a new task family that is conceptually related hut significantly more challenging. This could involve:
Requiring deeper reasoning multi-step problem solving.
Introducing more complex constraints edge cases.
Using more advanced concepts within the same domain.
Increasing the required precision or detail in the answer.
Ensure the new task remains coherent and adheres to the METR standard. Respond in the standard JSON format with THOUGHT and RESPONSE JSON sections. Set "done" " to "False" initially. this will be validated.
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Make Task Easier Prompt
The following task family was found to be too difficult (or impossible) for the current models:
Summaries of other previously generated tasks for context are:
tother_task_jsee
Generate a new task family that conceptually related but significantly easier. This could involve:
Breaking the prohlem down into simpler steps.
Reducing the complexity of the required reasoning or knowledge.
Providing scaffolding clearer instructions.
Focusing on more fundamental aspect of the capability.
Ensure the new task remains coherent and adheres the METR standard. Respond in the standard JSON format with THOUGHT RESPONSE ISON sections. Set "done" to "False" initially. as this task will be validated.
If the generated task has implementation or logic errors and needs refinement, the scientist sees:
Task Creation Reflexion Prompt
Current round = (current wrwprrenttouudddmmuum..... (ol/dddsss.........o
In your thoughts, first carefully consider the correctness of the task family just created. Factors may include but not limited to:
Whether sufficient synthetic data has been given the instructions to challenge the models.
Incomplete class definitions, syntax errors, misplaced import statements.
Feasibility of the evaluation criteria.
Any ambiguity issues in the clarity of the tasks.
The consistency and accuracy of all the fields in the JSON.
Whether precisely entries in get atsks have been defined.
Whether agents can get a successful score in an unintended manner.
Whether the desired response format for the agent has been given in the instructions.
Under no circumstances should the instructions contain the answer or any direct hints leading to the answer. Ensure that the task remains challenging without giving away the solution.
If the agent fails the task, OK as long as the task valid and the agent's failure due to the difficulty the task. You are also trying to find surprising failures! Here the result of attempting to evaluate the tasks the given model: [eval_respense)
In the next attempt, try and improve the implementation. Respond in the same JSON format before. there is nothing improve, simply repeat the previous response EXACTLY after the thought and set the "done" flag to "True", Do not set the "done" flag to "Truc" if you are still improving the task on the current turn. Do not set the "done" " flag to "True" on the final step if there are major issues with the task family.
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F.2 EVALUATION PROMPTS
We can configure the subject model to provide an answer using chain-of-thought prompting (Wei al.
)
Chain-of- Thoughtt Evaluation System Prompt
You are helpful assistant. You are given task to complete. First, reason about the problem and your plan for solving the task, and then provide your final answer starting with Answer:
you are asked the following format: [[START OF FINAL ANSWER TEMPLATE]] Answer:
code, write code after the Answer:
def Functicn_ii name (youur_aguueetts) # Add the relevant arguments here.
implementation
return your reuurreturn..m result
[[END FINAL ANSWER TEMPLATE]] Make sure to define the function with its arguments and return the result of the function in the format specified in the task prompt. Don't forget to import the necessary libraries.
Only the text after Answer: will be evaluated. This will be automatically parsed you follow the format exactly. Respect any other output constraints given task prompt after Answer:
F.3 TASK EMBEDDING PROMPT
We generate embeddings using intfloat/e5 - to then stored in our vector databases for cosine similarity-baad based retrieval. Before embedding, encode each task in the following format:
(Wang al. 2023a
Task Embedding Prompt
Name of task: {name_of_tet _t_sssskkk
Description of task: (descripticn.of.
Capability being measured: (capability weiiityybiinnng www.b.iinnnnee. meaggmmesrerdd Estimated human difficulty: {eat imated human di difficulty) Example instruction: Lexampleeiisttuutee
F.4 NOVELTY ASSESSMENT PROMPTS
To determine whether newly generated task is interestingly new compared to the three most similar existing tasks in the archive, we use the following system prompt:
Interestingly New System Prompt
You an expert in designing task families model (LLM). Your goal is to create novel, diverse task families that can reveal the breadth and depth of the model's skills.
the capabilities of particular large language
You will be given task family that you just generated along with its closest saved neighbours (i.e., the task families that most similar the newly generated one). Determine whether the generated task family intercstingly new compared the closest task families. so, it should added the task archive. Factors may include but are not limited to:
The novelty of the subject area
The difficulty the tasks
The capabilities being measured
Whether the agent succeeded the task
First, briefly think and reason about the task family in relation to the existing closest task families.
Then, provide your decision 'Deci on: Yes' Deci sion: No indicate whether the task family should he added to task archive. Your response will be automatically parsed, so ensure you follow the above format exactly.
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We then prompt with:
Interestingly New Prompt
You have just generated the task family:
{new_task}
The closest task families that have already been created are:
{c] selossst_tassss
the generated task family interestingly new compared to closest task families? yes, should be added to the task archive.
See Tab. for details which specific scientist model use.
F,5 GIBBERISHI FILTER PROMPTS
We propose novel minimal criterion filter for determining whether a newly discovered model is valid solution, or not. Hereby, we define valid solution to be an LLM that produces coherent and sensible text and not gibberish.
To detect these degenerate solutions, we prompt an LLM judge with the following system and user prompts:
Gibberish Filter System Prompt
are helpful assistant.
Gibberish Filter User Prompt
Consider the following tasks shown in the numbered list:
ITASK 1
(instructionl)
[TASK 2]
(instruction2)
[TASK 31
(instructicn3)
and the following three responses to the task from LLM (in numbered list):
IRESPONSE 1
(outputsl|
IRESPONSE 2]
(outputs2)
IRESPONSE 31
(outputs3)
Your task is to determine the LLM responded with gibberish, incoherent sentences, or not. Factors may include but are not limited to:
Does the LLM form coherent sentences they responding with jumbled words and strings?
Does the LLM use punctuation in sensible way, or spam random symbol characters?
Does the LLM up repeating sentences non-stop?
Does the LLM fail to return any output response?
Does the LLM respond in way that completely ignores the task or goes completely off-topic, in reasonable way of attempting to solve the task?
Does the LLM format its response in way that highly unexpected in the context of the tasks?
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The responses don't t have to be correct! But they should be at least somewhat coherent, considering the factors gibberish responses mentioned above well any other reasonable expectations how LLMs may respond in gibberish manner.
Firstly, very briefly think step-by-step reason about the responses. Then, provide your answer as Answer: Yes' if the responses are gibberish, or Answer: No' if they are not.
Your response will be automatically parsed, so ensure you follow the above format exactly.
F.6 LLM-AS-A-JUDGH TOOL PROMPTS
For tasks where score calls an LLM-based judge (which we set to be the same as the general- purpose scientist model), use the following system and user prompts.
LLM Judge System Prompt
You impartial judge evaluating the quality of responses provided by Al assistant. You will be provided the instructions for the task and the assistant's submission in response. You will decide as helpfulness, relevance,
whether not agent succeeded at task. Please consider factors accuracy, depth, creativity, and level detail the response. You may additionally given a set of criteria to evaluate the response against. Do not allow the length of the response to influence your decision.
Respond precisely in the following format:
THOUGHT: <THOUGHT>
DECISION: <DECISION>
<THOUGHT>, briefly reason about the agent's response whether meets the additional criteria. In <DRCTS STON>, provide your answer as either YYe"" " or "No".
LLM Judge Prompt
Instruction: (instruct ons}
Submission: {submi snicn] Additional Evaluation Criteria:
(criteria)
F.7 OPEN-ENDED CELTIPLE-CHOICE BENCHMARK LLM JUDGE PROMPTS
We leverage new, open ppen-ended versions of the common multiple-choice benchmarks MMLU, MMLU Pro, GPQA, and BBH. We discuss the details these benchmarks in App. A.4.1 and provide the prompts used here.
For filtering the dataset, we use the following system and user prompts:
MCQ Benchmark Filtering System Prompt
You professional educator. Your job evaluate whether question is unambiguous and can be answered without the multiple choice options. You need determine whether is clear what the question asking.
You will be given the parsed question that you need to evaluate.
A valid question here means:
The standalone question can answered without the multiple choice options.
It is clear what the question is asking.
Even question is posed text continuation task, the continuation can be generated without the context of multiple choice options, then it valid.
If the question contains anything along the lines of "Which of the following...". then the question is not valid.
Respond precisely in the following format:
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THOUGHT:
<THOUGHT>
DECISION: <DECISION>
<THOUGHT>, briefly reason about question whether it can answered without multiple choice answers.
In <DECISION>> provide your answer as either "Yes" or "No".
MCQ Benchmark Filtering User Prompt
Question:
(question) on
To evaluate subject model's candidate solution, we leverage the following system and user prompts:
Benchmark Eval LLM Judge System Prompt
You impartial judge evaluating the quality of responses provided an Al assistant. You will be provided the question
task, multiple choice amswer options, correct ground truth solution, and the assistant's 's response. You will decide whether or not the AI assistant succeeded at the task. Please consider factors such helpfulness, relevance, accuracy, depth, creativity, and level of detail of the response. Do not allow the length of the response influence your decision. However, the Al assistant should respond clear manner and try to answer the question directly. Even the Al assistant uses slightly different wording the ground truth answer, as long as provides the equivalent right answer, the response should be evaluated as correct.
The question for the task is inserted between [START CF QUESTION]] and [END OF QUESTION]
multiple choice answer options are inserted between [[STRT OF MULTIPLE CHOICE OFTIONS] ] and [END OF MULTIPLE CHOICE OPTIONS]] 1. The correct ground truth solution is inserted between [[START OF GROUND TRUTH ANSWER] and [END OF GROUND TRUTH ANSWER]
The assistant's response is inserted between [SSART OF AT ASSISTANT RESPONSE]] and IIRND OF ASS ASSTSTANT RESPONSE]] ]. Respond precisely in the following format:
THOUGHT: <THOUGHT>
DECISION: <DECISION>
<THOUGHT>, briefly reason about the Al assistant's response and whether meets the criteria the ground truth solution. In <DECISION>, provide your answer either " "Yes" " "No".
Benchmark Eval LLM Judge User Prompt
[[START OF QUESTION]]
!question)
IEND OF QUESTION]
[|START OF MULTIPLE CHOICE OPTIONS]
(choices)
[END OF MULTIPLE CHOICE OPTIONS]
[|START GROUND TRUTH ANSWER]
[target)
[EED OF GROUND TRUTH ANSWER]
[[START OF AI ASSISTANT RESPONSE]] ]
(submission)
HEND OF AI ASSISTANT RESPONSZ]
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G EXTENDED RELATED WORK
Coevolution and Open-Enden Beee-endessess through LLMs. Building Al capable of unbounded innova- tion is a grand challenge of open-endedness (Stanley et al. 2017), which seeks to generate endless sequences of artifacts and interactions that are both novel (Sigaud et al. 2023) and learnable/inter- esting (Hughes et al.) 2024). By studying how life coevolves with an ever-changing environment, we see a complexity explosion emerge through local competition (Lehman & Stanley 101 and the coevolution of agents and environments (Wang et al.
2019 2020). One important question
on how to ensure the necessary minimal criteria (MC) and filters that enable exploration to flourish,
while avoiding completely undesired outcomes from dominating (Lehman & Stanley 2010 Soros &
Stanley, 2014 Jiang al. 2023). Brant & Stanley (2017) 2020) (MCC) show that defining filters or
MC's for both agents and environments to satisfy enables more open-ended outcomes in coevolution.
Recent advances demonstrate the potential for open-endedness through LLMS (Zhang et al.) 2023
Aki et al. 2024 Faldor et al. 2024 Dharna et al., (025), where language models can generate di- verse environments and challenges. AC/DC takes first step towards discovering LLMS themselves via more open-ended search, which could subsequently power open-ended agentic search (Hu et al.)
2025 Zhang al. 2025a). Building on established principles of minimal criteria and coevolutionary dynamics, AC/DC applies these concepts to the joint evolution of model populations and synthetic task distributions. Additionally, while Dharna al. (2025) combines QD with self-play, AC/DC combines QD with population-based coevolution, which is related to MCC (Brant& & Stanley 2017)
Evolutionary Model Merging. Model merging can produce L.LMs by combining multiple existing LLMss. Merging and testing resulting models is computationally cheaper than training models. Ap- proaches include linear interpolation of weights (Wortsman et al. [022] [Tharco et al. 2023) or TIES Yadav et al.) 2023) and DARE (Yu et al. 2024). Akiba et al. (2025) introduced evolutionary model merge MevgM,rge). automating merging by presenting a model benchmark optimization approach with CMA-ES (Hansen & Ostermeier 2001). Subsequent works extend EvoMerge to discover LLM populations that optimize for benchmark performance (Zhang et al.] 2025b) while maintaining di- versity via CycleQD (Kuroki al.) (2025) competition/titness sharing (M2N2) (Abrantes al.) 2025). Unlike these approaches, AC/DC discovers LLMs that generalize to tasks without any ex- plicit objective optimization on benchmarks. Furthermore, AC/DC can compute both quality and BCs for any LLM evaluated on synthetic task pools of any size and composition/topic (via DNS Boldi et al., 2025)). allowing QD for the coevolution of LLMs and tasks without the lim- itations or rigidity of fixed niche or measurement bin interval sizes, given that skill vectors are consistent in order and size within the same generation/iteraioo of coevolution. Additionally, we solve the limitation of behavior characteristics (BCs) used in CQD (Kuroki et al. 2025) through the fine-gra fine-grained behavior signature represented skill vectors, which uniquely distinguish models with different expertises (based on synthetic tasks solved), unlike CQD that uses binned intervals over aggregated benchmark accuracies (i.c., distinguishing model niches as unique when percentage accuracies benchmark for science tasks are different when aggregated, but discarding models with similar accuracies on science tasks even when such models are unique in being specialized in subfields such as physics or biology).
Novelty Search. Both Novelty Search and QD methods explicitly incorporate diversity of behavior into evolutionary search, i.e., encourage solutions that do things that are different and new. These approaches move beyond traditional optimization by encouraging continuous exploration and gener- ation of diverse and novel artifacts. Novelty Search (Lehman et al. 2008 Lehman & Stanley 201 Doncieux et al. 2019) explicitly ranks and selects artifacts based solely on their novelty relative to previously discovered solutions, promoting continual discovery without direct reliance on prede- fined objectives. This approach successfully mitigates deception in search spaces and highlights the strength of novelty as a guiding principle for exploration, inspiring broader discussions about open- ended innovation (Stanley & Lehman 2015). Adaptive approaches for evaluating novelty were introduced to allow for more open-ended exploration of different spaces of novelty (Meyerson et al.) 2016 Paolo et al. 2020 Etcheverry et al. 2021). Unlike existing frameworks, our approach enables AC/DC to adapt to different definitions of behavior during search by leveraging the skill vector as a means of flexibly measuring behavioral diversity during task adaptation and coevolution.
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Quality-Diversity (QD). QD (Pugh et al. 2016; Cully & Demiris 2017 Chatzilygeroudis et al.,
202T) explicitly optimizes both diversity and high-quality performance, while maintaining a struc- tured collection (archive) of diverse high-quality solutions with unique behavior characteristics (BCs). Influential algorithms such as MAP-Elites (Mouret & Clune 2015a Cully et al.) 2015) em-
phasize local competition within niches (Lehman & Stanley] [201 Tb) systematically explore and optimize throughout& a diverse behavior space. Extensions include methods for efficiently handling higher-dimensional descriptor spaces and novel mutation operators (Vassiliades et al. [017]] Vassil- iades & Mouret, 2018 Fontaine et al. 2020 Conti et al., 2018 Colas et al.] 2020] Flageat et al. 2024j. Existing methods handle the complexity and adaptation of diverse high-quality search spaces through either a meta-adaptation approach (Bossens & Tarapore 2022) or learned parametrized be-
havior functions (Gaier et al. 2018 Kent et al.] [024 Keller et al.] 2020; Bhatt et al., 2022; Lim
et al. 2023 Zhang et al., 2022, Paolo et al.] 2024] Grillotti et al., 2024). Different to existing paradigms of search space adaptation and known QD applications, skill vectors in our framework,
AC/DC, naturally represent both quality and behavior (for diversity), while being simple to adapt to the composition and size of synthetic task pools during coevolution.
Synthetic Data for LLMs. In the direction of synthetic data for evaluating LLM capabilities, early work has explored prompting LLMs to rewrite existing LLM benchmark tasks into variants of those tasks, as well having LLMSs generate example yes/no questions (Perez al. 2023). They demon- strated that model-written evaluations can discover LLM capabilities and weaknesses that manually curated benchmarks fail to reveal. Instead of being constrained to training on internet data, many works show that LLMs can generate effective text training data (Wang et al. 2023c| Maini et al.) 2024 Liu et al.
2024) of increasing quality. diversity, and complexity, starting from seed text data
20233 Samvelyan et al. 2024 Havrilla et al. (024) (where QD approaches help). Synthetic training signals can also be obtained through LLM -.M--gnerated preference data (Bai et al.] 2022) or task solution reward (Zhao et al. 2025). [u et al. (2025), a method we build upon, show that principles of open-ended search can generate surprisingly new tasks that reveal unexpected ca- pabilities or weaknesses in LLMs. To the best of our understanding, AC/DC leverages synthetic data to demonstrate the first instance of diverse LLM-generated tasks cocvolving with a population of L.Mss, rather than fine-tuning just one LLM.
(Bradley et al.
Multi-Agent Systems and Best-of-N Strategies. The gap between coverage metrics (pass@k, ora- cle accuracy, our proposed Coverage) and practical single-answer selection represents a fundamental challenge in LLM evaluation. The pass @K k metric (Chen et 2021a) measures whether at least one correct answer exists among samples, with coverage scaling log-linearly over orders of magnitude (Brown et al., 2024), yet recent work demonstrates pass @k serves as a diagnostic tool rather than an effective optimization objective (Yu) 2025), highlighting that generating correct answers and select- ing them are fundamentally different problems. Recent advances in test-time compute scaling have shown that optimal strategies are problem- dependen with adaptive allocation of inference com- pute outperforming fixed sampling budgets (Snell et al.] 2022), and tree search methods enabling principled exploration-exploii improving individual model reasoning through extended inference, AC/DC generates diverse model populations with complementary capabilities scaling on the number of model axis. Self-consistency via majority voting (Wang al. 2023b) has become the standard baseline for aggregating multiple samples from a single model. However, majority voting assumes samples from a single model with homogeneous capabilities and has been shown to overlook informative minority responses (Huang let al. 2024) and can even degrade performance in certain settings (Wang et al. 2025). In contrast, our work explicitly seeks evolve distinct specialist models whose complementary capabilities should not be aggregated via simple majority rule, which would suppress the unique perspectives we aim to discover. Beyond voting methods, reward model-based selection suffers from reward hacking when models have imperfect alignment with true objectives (Lightman et al. 2023 Jinnai
trade-offss (Inoue et al. 2025); while these approaches focus on
al. 2025). while LLM judge- based approaches exhibit systematic biases including position bias and verbosity bias (Zheng et 2023 Shi et al. 2024). We explore three simple selection strategies- tournament-style judge selection, single-prompt judge selection, and reward model scoring-finding ffndinng that all cxhibit substantial gaps between Coverage and single-answe performance. Multi-agent de- bate and collaborative systems (Liang et al.) 2024 Irving et al. 2018 Du et al., 2024) represent alternative approaches, though recent work questions their reliability compared to simpler methods (Smit et al. 2023), while ensemble diversity from heterogeneous models shows promise (Talebirad let al. 2025); these collaborative techniques are orthogonal to our evolutionary discovery process and represent promising future directions for answer aggregation from evolved populations.
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Alternative Model Output Diversity Methods. Model output diversity (of meaningful quality) remains a challenge that, if unsolved, would impede the ability of LLMs to return diverse responses that may solve different problems (Bradley et al. 2023 Kirk al.
2023; O'Mahony et al. 2024] Liang et al., 2023). To facilitate more divergent outputs from LLMs,, prior work introduced meth- ods to facilitate diverse outputs through approaches such encouraging semantic diversity during output sampling VVijayakumar et al., 2016,] Franceschelli & Musolesi [2024] 2025b Havrilla et al.
2024).
diversity-inducing optimization of a single model (Lanchantin et al. 2025 Chung et al,
2025 Ismayilzada et al. 2025), or even facilitating diverse persona generation (Paglieri et al.) 2026 Castricato aal]) as a possible approach of introducing input diversity prompting for more diverse outputs. While these methods can facilitate model (output) diversity for the sake of primarily diversity, our method, AC/DC, approaches the problem with a fundamentally different solution, by automatically generating multiple models with diverse task-solving capabilities. Our approach takes a direct step towards tackling the challenge of facilitating model diversity for the sake facilitating both diverse and useful outputs, that can lead to correct solutions for knowledge-basce tasks.
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H
HUMAN STUDY
H.1 HUMAN STUDY METHODOLOGY
We conducted human evaluation study with 94 independent assessments (45 synthetic, 49 bench- mark tasks) across three expert reviewers to validate task quality and novelty. Our protocol incorpo- rated multiple bias mitigation strategies:
Blind Evaluation Protocol: Task sources (synthetic vs. benchmark) were completely hidden from reviewers during evaluation, preventing confirmation bias and ensuring objective assessment.
Balanced Sampling Design: Tasks were sampled using controlled 50/50 distribution (50% syn- thetic, 50% distributed across 8 our benchmarks), ensuring fair comparison without overrepresenta- tion.
Similarity-Bane -Based Calibration: For each task, reviewers were shown the 3 most similar benchmark tasks (via embedding -aased retrieval) to establish concrete reference point when assessing out-of- distribution characteristics this grounds the OOD metric in actual distributional differences rather than subjective perception.
Standardized Evaluation Criteria: All reviewers received detailed written guidelines (see App. H.3 defining correctness, creativity, and OOD characteristics, ensuring consistent interpre- tation across annotators.
We verified inter-rater reliability using chi-square tests, confirming strong agreement on objective metrics (correctness: 0.46, OOD: p 0.57), which validates that our findings are not driven by individual annotator biases.
Statistical Validation:
This multi-layere approach ensures our human evaluation provides reliable, unbiased evidence for synthetic task quality and distributional novelty.
H.2 HUMAN STUDY RESULTS
Our evaluation demonstrates that synthetically generated tasks achieve high quality while exhibiting noticeable distributional novelty as shown in the results of Tab.
The 97.8% correctness rate demonstrates that our generation approach is capable of producing high-quality, well-formed, solvable, and meaningful tasks. Nearly 70% of synthetic tasks were rated as out-of-distribution compared to established benchmarks, providing strong evidence that our approach successfully generates novel task types beyond existing evaluation datasets. Notably, we expect a certain percentage of "in-distribution" tasks, as core math and code tasks are likely to be similar. Over one-third (37.8 37.8% were rated as creative, indicating they explore problem-solving approaches not commonly tested by standard benchmarks.
Synthetic Task Validation:
Benchmark Baseline Validation: As expected, benchmark tasks showed substantially lower OOD (10.2%) and creativity (6.1%) ratings, confirming that reviewers correctly identified established benchmark tasks in-distribution. Notably, the benchmark tasks rated as OOD creative were concentrated exclusively in the most challenging benchmarks-MMLLU creative) and GPQA (16.77 7% OOD, 16.7% creative)-while all other benchmarks (BBH, GSM8K, MATH, HumanEval, MBPP, MMLU) received 0% OOD and 0% creative ratings. This pattern val- idates our evaluation protocol: reviewers appropriately recognized that graduate-Ievel and expert- domain questions may appear novel due to their complexity, while correctly identifying that standard benchmarks are in-distribution. This serves as a sanity check confirming the reliability of our human evaluation.
(33.3% OOD, 16.7%
Inter-Rater Reliability: Statistical analysis confirms strong inter-rater agreement on objective met- rics (correctness:
0.46, OOD: 0.57), demonstrating that our findings are robust and not driven by individual annotator biases. While the creativity metric showed expected variability (p 0.02) due to its inherently subjective nature, the pattern remained consistent: synthetic tasks received substantially higher creativity ratings across all three reviewers (ranging from 21.4% 11.4% to 69.2%) compared to benchmark tasks (0% to 17.6%).
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H.3
LABELING GUIDELINES
H.3.1 CORRECTNESS
CORRECTNESS EVALUATION GUIDELINES
A task is considered CORRECT if:
The instruction clear and unambiguous The instruction can be understood and executed by human or Al it's question, it has well-defined answer The task does not contain logical contradictions task specification is internally consistent The requirements are feasible to implement/answer
A task is considered INCORRECT if:
The instruction unclear, ambiguous, or confusing The task contains logical errors or contradictions The task is impossible to solve implement Critical information is missing task specification self-contradictory
Note: Focus on the task definition itself, not potential implementation details.
H.3.2 OUT-OF-DISTRIBUTION
OUTIOF-DISTRRIUUTION (OOD) EVALUATION GUIDELINES
A task considered OUT-OF-DISTTIBUTION (OOD) if:
It's unlikely to appear in standard Al benchmarks or datasets
It requires knowledge or skills not commonly tested
It involves unusual domain combinations It requires novel reasoning patterns
It would be difficult to find similar examples typical training data It tests capabilities in unexpected or underexplored ways
A task is considered D-DISTRIBUTION (not OOD) if:
It resembles common benchmark tasks (MMLU, GSM8K, HumanEval, etc.)
It's standard problem type from textbooks or courses It follows well-known problem patterns Similar examples abundant in typical training datasets tests standard, rrequemtl--vallutte capabilities
Key question to ask:
"Would this task likely appear in existing Al benchmarks training data?"
NO - likely OOD
If YES - likely in-distribution
Note: task can be creative but still in in-distribution, vice
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H.3.3 CREATIVITY
CREATIVITY EVALUATION GUIDELINES
A task is considered CREATIVE if:
It presents novel or unusual problem formulation It combines concepts in interesting or unexpected ways It requires non-trivial reasoning or problem-solving It goes beyond simple variations of common tasks It demonstrates originality approach or domain It would be interesting or engaging to solve
A task is considered NOT CREATIVE if:
It's a straightforward, routine task It's a simple variation of a very common problem It requires only basic, mechanical operations It's standard texthook-style problem It lacks novelty or originality
Examples of creative tasks:
Novel combinations of domains (e.g., applying game theory to music composition)
Tasks requiring multi-step ereative reasoning
Problems with interesting constraints or twists
Examples of non-creative tasks:
Simple arithmetic calculations
Basic data structure operations
Standard classification problems
I COMPARISON OF SEED MODELS AND MERGED MODELS ON SYNTHETIC DATA
1.1 QUANTITATIVE ANALYSIS
L.1.1 PERFORMANCE ACROSS MODEL. FAMILIES
We conducted analysis comparing the fitness of seed models and merged models across four model families (Qwen2, Qwen2.5, Qwen3, and DeepSeek) on their respective complete synthetic datasets. Table 21 summarizes the fitness scores and improvements for each family.
Table 21: Model merging performance across different model families. Improvements are calculated percentage change from the seed model performance.
Family
Tasks
Seed Models
Top-3 Merged Max
Avg Imp-
Avg
Max
Avg
Qwen2.5
1,094
0.5253
0.5622
0.6252
0.6353
+19.0%
Qwen3
1,044
0.5361
0.5661
0.6255
0.6255
+16.7%
Qwen2
1,117
0.3405
0,5058
0.6085
0.6132
+78.7%
DeepSeek
1,005
0.3032
0.4418
0.4163
0.4179
+37.3%
Fig. 11 further illustrates the fitness distributions for seed models versus merged models across all four families. The fitness improvements vary significantly across families, with Qwen2 showing the highest average improvement (+78.7%) starting from the generally weakest seed model (0.34), while Qwen2.5 and Qwen3, despite having higher seed baselines (0.53 and 0.54, respectively), show more moderate improvements (+19.0% and + +16.7%).
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Figure 11: Comparison of three seed models the three fittest merged models on the global syn- thetic task pool.
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Figure 12: Confusion matrix of synthetic tasks where all models merged and seed models failed and at least one model succeeded. We show the confusion matrices for the experiments with all model families.
In Fig. 11 we can clearly observe that our three merged models per model family, all exhibit high fitness scores the diverse synthetic data, whereas all seed models, especially the heavily finetuned and specialized seed models, such as the math and code experts, perform weaker on the diverse data. Notably, the general-purpose instruct model is also weaker the synthetic data than all our merged models, except for the DeepSeek experiment, where it is roughly on par.
To understand the complementarity between seed and merged models, analyzed confusion matri- ces showing task-level success patterns, Fig. 12 presents these matrices using the following criteria: (1) All seeds fail, merged succeed: all seed models fail (score=0.0) and least one merged model succeeds (score=1.0); (2) Seeds succeed, all merged fail: at least one seed succeeds and all merged models fail; (3) Both succeed: least one seed and one merged model succeed; (4) Both fail: all models fail.
The confusion matrices reveal complementarity patterns. For Qwen 2, merged models succeed on 153 of tasks where all seeds fail, while completely failing only 23 of tasks where seeds succeed. Improvement patterns emerge across Qwen 2.5 and Qwen demonstrating that merging preserves most seed capabilities while adding new ones. Nevertheless, for DeepSeek, see that this pattern is reversed, where seed models succeed on 149 tasks where merged models fail whilst merged models succeed only 100 tasks where seed models fail.
These findings demonstrate that our individual merged models improve over the individual seed models.
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1.1.2 ENHANCED PERFORMANCE ON OUT-OF-DISTRIBUTE DISTRIBUTION TASKS
To investigate whether model merging provides differential benefits for challenging tasks, we eval- uated Qwen2.5 on the set of 31 iiftof-diitribution (OOD) synthetic tasks identified through our human study (see Appendix App. H). Tab. 22 compares performance on the full task set versus the OOD subset.
Table 22: Fitness improvement of Qwen2.5 merged models vs seed expert models on all synthetic tasks and those labeled as OOD. The improvement differential demonstrates stronger benefits on challenging tasks that are not commonly evaluated in standard benchmarks, which the seed models optimized for.
Metric
All Tasks
OOD+Synthetic (31 tasks)
Difference
(1,094 tasks)
Avg fitness improvement
+19.0%
+44.0%
+25.0pp
Max fitness improvement
+13.0%
+31.6%
+18.6pp
The results reveal that merged models show 2.3x stronger average improvement on OOD+synthetic tasks compared to the full task set.
Fig. 13 visualizes the fitness distributions for the OOD task subset. The fitness comparison shows merged models achieving substantially higher scores (avg 0.7742, max 0.8065) compared to seeds (avg 0.5376, max 0.6129).
Fitness Comparison: qwen2.5 (31 tasks)
1.0
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Figure 13: Qwen2.5 performance on 31 human-labeled OOD and synthetic tasks (see App. showing improved performance compared to seed models.
These findings suggest that model merging is particularly valuable for aut-of-distributinn scenarios requiring diverse skill integration, where the emergent capabilities of merged models discover via coevolution on a diverse set of synthetic tasks, provides novel capabilities not captured by individual seed models.
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1.2 QUALITATIVE ANALYSIS
To understand the types of tasks where merging provides benefits, we examine specific examples where least one merged model succeeds while all seed models fail. We present representative cases from both in-distribution and OOD task categories.
1,2.1 IN-DISTRIBUTION TASKS
(qwen2.5) task 663 Sel.laavvadddpoolll and combinatories
Instruction
A box contains red, blue, and 2 green balls. Two balls drawn without replacement. What is the probability that both balls are red, given least one ball is red? Provide your answer as a simplified fraction. Provide your answer in the specified format. Do not include any additional text or explanations.
Merged Model
Number of red balls number of blue balls = 3
number of green balls = 2 total number of halls 5 + 2 10
Prohability of drawing at least one hall:
Case 1: Drawing two red balls Probability (5/10)* (4/9)= 20/90 2/9
Case 2: Drawing one red and one non-red ball Probability = (5/10) (5/9) + (5/10) (5/9) = 50/90 5/9
Total probability of least red ball 2/9 5/9 7/9
Probability of drawing two red balls given that at least one red: Total probability of at least one red ball = 7/9
Probability of drawing two red balls 2/9
Conditional probability = (2/9) (7/9) 2/7
Answer: 2/7
Math Expert
Answer:
The merged model correctly applies Bayes' theorem. In contrast, the math expert does not provide any reasoning trace and provides an incorrect answer
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(qwen2.5) busk_549 ad www.mmaticalllppppppmmmm proofs
Instruction
Generate step-by-step mathematical proof for following statement: Prove that for any integer и > 1, there exists a prime number such that n < < 2n (Bertrand's Postulate).
Merged Model
To prove Bertrand's Postulate, will the Prime Number Theorem and some properties of the factorial function. Prime Number Theorem states that the number primes than or equal log x as i approaches infinity. However, for our purposes, we will more elementary approach.
æ, denoted by (xx). is approximately
1. Step 1: Consider the factorial function and its properties.
The factorial function n! (n factorial) is the product of all positive integers up to For example, 5! 5 4 3 x 120.
2. Step 2: Use the factorial function to find a lower bound for the number of primes.
We will use the fact that (n!)² (the square n!) can expressed in terms factorials smaller numbers. Specifically, we have:
(n!)) = n! x n! n (n 1) x (n 2) x ww...xxxxxxxxxxx (n 1) (n 2) x x x2xx
This can be rewritten
(ml)² = n (n!) x (n - 1) x (n 2) x
x (n 2) x
Notice that each term in the product is multiple of prime number less than or equal to Therefore, (n!)² divisible by all primes less than equal n.
3. Step 3: Use the properties of the factorial function to find prime between n and 2n.
Consider the number 2n!. We can write:
2n! 2 n! x x) (n
2n
Notice that 2n! divisible by all primes less than or equal to n (as shown Step 2). Now, consider the number 2n! - 1. This number is not divisible by any prime less than or equal to bocause 2n! is divisible by all such primes, and subtracting from multiple of prime p results in number that not divisible by p.
4. Step 4: Conclude the proof using the properties of 2n! 1.
Since 2n! - is not divisible by any prime less than or equal to n, must be divisible by a prime p such that < p < 2n. This is because the only primes that could divide 2n! are those greater than n and less than equal 2n.
Therefore, we have shown that for integer 1. there exists prime number such that n < 2n. This completes the proof of Bertrand's s Postulate. Answer: Bertrand's Postulate proven by showing that for any integer n >> there exists prime number such that n <, <, 2n.
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Code Expert
To prove Bertrand's Postulate, which stales that for any integer ri >> 1, there exists prime number p such that < 2ni, we can combination of mathematical induction and properties of prime numbers. The proof is quite involved and requires carcful analysis. Let's proceed step by step.
APPROACH
1. Base Case:
Verify the statement for small values of
2. Inductive Hypothesis: Assume the statement holds for all integers up to some k.
3. Inductive Step: Prove the statement for using the inductive hypothesis and other propertics primes.
SOLUTION CODE
def pertrands postulstteeees... erttraanddppstulaaeeiiieee (n)
if <= 1:
return False
Check for primes in the range (n, 2n) for p in range (n 1, 2 n 1);
if is is_prime(p): return True
return False
def is_prime (num) if num <= 1: return False for i range (2, int (num**0.5) 1) if num return False
0:
return True
Test the function with some values
print (bortrands_potuuattolll (2)) # True
print (bortrands_postulatee (3)) $ True
print (bertrands_
# True
print (bertrandsspostuulatee (10)) # True
EXPLANATION
1. Base Case: For 2, primes between
are 3, which satisfies the condition. For n 3. primes between 3 and are and 5, but is in the range. For 5, primes between and 10 are 7, which is valid. For 10, primes between 10 and 20 include 11, 13, 17, 19, etc.
2. Inductive Hypothesis: Assume that for all integers where 2 s < m, there exists a prime p such that < < 2k.
3. Inductive Step:
We need show that for m, there exists prime p such that m p < 2m. do this, we consider the product primes to derive a contradiction if no such prime exists.
primes up to m and use properties of factorials
detailed proof involves showing that product primes up to m multiplied by m less than (2m)m//4 leading contradiction if there the proof and requires careful analysis inequalities
primes between and 2m. This is complex part properties of prime numbers.
The code provided uses a brute-fore approach to check for primes in the range (n, 2n) for given n. This is practical way to verify the statement for small values but is not proof. The actual proof of Bertrand's Postulate is more theoretical and involves deeper mathematical concepts.
Thus, the code serves verification tool, hut the proof itself beyond the scope of this example. The code can he used to test the hypothesis for specific values, but the general proof requires a more rigorous approach.
The merged model correctly solves the task, following the instruction to prove Bertrand's Postulate. Two of the three seed models returned random symbols, and the code expert provided answer that implements a brute-foree approach to check for primes in the range (n, 2n) for a given n and does not formally prove the statement.
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1.2.2 OUT-OF-DISTRIBUTION TASKS
(qwen2.5) task 3772.comlee__ogiie segaelxxog reasoning
Instruction
Given the following logical statements: For all x, if is bird, then x can fly. For all if can fly, then has wings. Penguin bird.
Penguin cannot fly.
Deduce the most consistent conclusion and provide detailed explanation of the reasoning process, even if the premises contain contradictions. Do not include any additional other than conclusion and the explanation.
Merged Model
The conclusion that penguins have wings.
Explanation:
The logical statements establish that all birds can fly (if they are birds), and all flying creatures have wings. However, the statement about penguins cannot fly introduces contradiction because it implies penguins birds but contradicts the general rule that birds fly. Despite this contradiction, the conclusion that penguins have wings is consistent with second statement that all flying creatures have wings. Since penguins cannot fly, they do not fall under the category of flying creatures and thus do not have wings. However. the conclusion is based on the given premises and the logical structure provided, leading the conclusion that penguins have wings due the conditional statements about birds and flying creatures.
Instruct Model
The premises contain a contradiction because according to the first and second statements, all birds can fly and all that can fly have wings. However, the penguin is bird that cannot fly, which violates the first statement. Despite this contradiction, the most consistent conclusion penguins, despite being birds, do not have wings based on the given information.
draw is that
Explanation: The first two statements establish a logical chain where all birds can fly and all that can fly have wings. The penguin being bird and not heing able fly directly contradicts the first statement. However, the third statement explicitly states that penguin is bird, and the fourth statement penguin cannot fly. Given these facts, we can infer the penguin does not have wings, as it cannot fly and the second statement asserts that only those that can fly have wings. This conclusion is consistent with the given information, even though the premises contain logical contradiction.
that
The merged model recognizes the contradiction and provides nuanced reasoning about the logical structure, concluding that despite the contradiction, the conditional statements lead to penguins hav- ing wings. The general purpose instruct model incorrectly concludes penguins do not have wings, failing properly navigate the logical dependencies despite explicitly identifying the contradiction.
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(qwen2.5) task sentrexeeeetttennetttttttrtronne
Instruction
Transform the following sentence according to the rule:
Original Sentence: If she writes letter every day, then her friends will happy.
Transformation Rule: Change the sentence to past tense and passive voice.
Provide only the transformed sentence without any additional text. Ensure that the transformed sen- tence maintains the original meaning and follows the specified grammatical rules precisely, especially the conditional structure and the past perfect tense.
Merged Model
letter was written by her every day, then her friends would have been happy.
Instruct Model
If' letter is written by her every day, then her friends will have been happy.
The merged model properly applies both passive voice transformation and past perfect tense in the consequent. The seed model fails to fully transform the past tense in the antecedent ("is written" instead of "was written"), demonstrating the merged model's 's superior grasp of complex grammatical transformations.
These examples illustrate that merged models exhibit improved performance on tasks requiring pre- cise mathematical reasoning, possibly extending beyond the standard training on math expert data and careful instruction-folowiin for spatial tasks, and perhaps generalizing the logical reasoning abilities of code and math experts. The diversity of successful tasks suggests that merging could enable the integration of complementary skills beyond what individual seed models provide.
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J MERGING COMPATIBILITY ANALYSIS
In standard LLM development, expensive pretraining is typically conducted with an eye toward subsequent mid- and post-training stages. In the space of (evolutionary) model merging, developing core models specifically suited for merging has received comparatively less attention. Only very recently have efforts been made to deliberately conduct research on this challenge (Horoi et al. 2025).
We evaluate AC/DC across 5 model families, seed model configurations, and different model sizes and observe that out of demonstrate improvements over baselines, with absolute improvements varying across these configurations.
In Sec. of the main paper, we briefly address this limitation. Nevertheless, the question remains whether there are diagnostic predictors of successful seed model merging and how practitioners can know beforehand.
To address this question, we analyzed differences in weight space among different seed model con- figurations, identifying relevant correlations to assess their compatibility (which points to interesting future research). Moreover, we propose an additional technique to evaluate early in training whether the coevolution process will successfully produce merged models.
J.1 ANALYSIS APPROACH AND FINDINGS
We conducted analysis of weight-space geometry across five seed model compositions across dif- ferent model families to identify potential predictors of compatibility. Our investigation proceeded in tWo stages:
Stage 1: Expert-to-Base Analysis - We first measured L2 norm distances between each expert model and its base model across all layers (embedding, transformer blocks, output head). While this analysis revealed differences in specialization magnitude (c.g., some experts diverged substantially from the base while others remained close), failed predict merging success. Most critically, the Llama3 family exhibited excellent uniformity metrics (low coefficient of variation across experts and tight 1.2 norm ranges), yet this seed model choice did not lead to models that demonstrably outperform baselines in our experiments. This suggests that measuring expert deviation from shared reference point (the base model) is insufficient for predicting merge compatibility.
Stage 2: Pairwise Expert Geometry -
We then computed direct pairwise distances between ex- perts (comparing experts to each other rather than the base). For three experts, A, B, and C, we measured the mean L2 norm between all pairs:
N N
1
d(x,))
|X, Yill2
where N is the number of parameters and X,, Y are corresponding parameter values. We then computed similarity ratio characterize the geometric structure:
similarity ratio minld(A,BB),dAAA))dd....8 (Ix
This metric quantifies whether a "clear pair" of similar experts exists (low ratio) versus all experts being roughly equidistant (high ratio approaching 1.0).
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Key Observations:
The pairwise analysis correctly predicted merging outcomes for 4 out of families (80% accuracy). The similarity ratio appears to correlate with merge success in many cases: families with low ratios (< 0.1), indicating a clear pair structure where two experts are substantially closer to each other than to the third, generally merged well. Conversely, families with high ratios (> 0.7), indicating equidistant configurations where all experts are roughly equally separated, generally merged poorly. Importantly, even within the same base model family (Qwen2.5), different expert selections yielded dramatically different outcomes-the seed models used in our paper (here referred to as Qwen2.5 ALT distinguish them) achieved excellent merging performance (similarity ratio 0.097), while the native Qwen2.5 instruct/code/math experts (Qwen2.5 Official) produced very poor merging results (similarity ratio 0.834). This suggests that seed model selection, even from the same pretrained base, affects merge compatibility.
Breaking down the specific geometric structures observed:
Clear pair structure (Qwen2.5 ALT: ratio 0.097, Qwen 2.0: ratio 0.047): Two experts very close to each other with the third more distant - these families merged well
Equidistant structure (Llama3: ratio 0.786, Qwen2.5 Official: ratio 0.834): All three experts are roughly equally distant from each other + these families merged poorly
Specialist cluster (DeepSeek: ratio 0.642): The specialist experts (code, math) closest to each other, despite both being far from base + merged best of all families
However, we emphasize caution in generalization: The DeepSeek family, which achieved the best merging performance, does not conform to the "clear pair" pattern and instead exhibits a moderate similarity ratio (0.642) with considerable absolute distances (maximum pairwise distance 182.76). Notably, in DeepSeek, the two specialist experts (code and math) are closest to each other-in contrast to Qwen2.5 Official (which merged poorly), where the specialist experts are furthest apart (distance 75.85). This suggests that which experts form the closest pair may matter as much as overall geometric structure, but this hypothesis rests limited data points and requires validation.
Finally, as demonstrated in Fig. [4 one predictor we used during development was tracking the number of gibberish models during coevolution. We found that if we observed many gibberish models being detected within the first few generations, we could confidently assume that the final performance would be poor.
dmcigibberahhmmodeee wwtps//mmdddldttttttteee counter
dro/iihbbershhmmoo counter
N
Llama3 8B
(b) Qwen2 7B
Figure 14: Gibberish models detected via our gibberish filter for experiments with (a) Llama3 8B and (b) Qwen2 7B model families. We observe that for the experiment with Llama, we detect significantly more gibberish models.
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K STATISTICAL SIGNIFICANCE ANALYSIS
To rigorously assess the significance of Coverage and Best-of-N (BoN) improvements achieved by AC/DC, we conducted comprehensive statistical lesting across all experimental conditions. This section describes our bootstrap-based methodology and presents detailed results demonstrating the statistical reliability of our findings.
K.1 METHODOLOGY
K.1.1 SCORE NORMALIZATION
To ensure fair comparisons across benchmarks with different difficulty levels and score distributions, we applied min-max normalization to all scores. For each benchmark b, we linearly mapped scores the range [0, 1]:
Sb.m. millym ,f' Sb,m' .
f maX4m/ .f' 8b,mn 3 minlmmm ,f' 8h,m',
where ff.m., the raw score for benchmark b, method m, and model family f. and s/ sb.m.. is the normalized score. The minimum and maximum values are computed across all methods and model families for each benchmark independently.
We performed normalization separately for two groups: (1) main baselines and model merging baselines, and (2) ablation experiments. This grouping ensures adequate range of data points from which we can get clear performance differentials within controlled experiment groups, that would then lead to more informative relative benchmark score gains comparisons.
K.1.2 BOOTSTRAP HYPOTHESIS TESTING
We employed bootstrap resampling to test the significance of performance differences between AC/DC and baseline methods. For each comparison,
Computed pairwise performance differences A; ACDDC. Soweeine, across all n benchmarks for given model family (or aggregated across multiple model families).
2. Generated bootstrap distribution resampling the differences {A.)= with replacement 50,000 times, computing the mean difference for each resample.
3. Calculated the bootstrapped mean A and 95% confidence intervals using the percentile method.
4. Computed one-tailed p-values to test whether AC/DC shows consistent improvement (i.e., Ho Â < 0 vs. H, A 0). Lower p-values indicate higher confidence that AC/DC achieves meaningful performance gains.
This approach accounts for variance across benchmarks while providing robust statistical evidence for performance improvements.
K.2
COVERAGE RESULTS
K.2.1
MAIN BASELINES: TASK FORCE SIZE N 3
Aggregated testing across benchmarks and model families demonstrates that AC/DC signifi- cantly outperforms these baselines at N = 3:
vs. Control baseline: p 0.0127 (CI: 0.006 0.053 0.107)
vs. Experts baseline: p < 0.0001 (CI: 0.048
0.104 0.167)
Ys. Big Model baseline: 0.0017 (CI: 0.026 : 0.095 : 0.177)
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For the DeepSeek model family specifically,, AC/DC shows particularly strong performance:
vs. Experts baseline: p 0.0001 (CI: 0.088 0.221 0.380)
Control baseline: p 0.0045 (CI: 0.044 0.189 0.336)
Additionally, the Qwen 3 model family significantly outperforms GPT-4o at = 3:
Qwen vs. GPT--4: p 0.0080 (CI: 0.026 0.140 0.246)
K.2.2 MAIN BASELINES: TASK FORCE SIZE = 8
At the larger task force size, aggregated across 4 model families and 8 benchmarks, AC/DC signifi- cantly outperforms the following baselines:
vs. Big Model baseline: 0.0001 (CI: 0.234 0.312 0.396)
vs. Control baseline: p 0.0175 (CI: 0.003 0.052 0.106)
vs. Experts baseline: p = 0.1 126 (CI: -0.022: : 0.035 0.098) [more often outperforms]
Notably, AC/DC with Qwen 2.5 and Qwen 3 model families significantly outperforms GPT-4o in Coverage, while Qwen more often outperforms GPT-4o:
Qwen 2.5 GPT-4o: p 0.0041 (CI: 0.044 0.185 0.333)
Qwen 3 vs. GPT-4o: < 0.0001 (CI: 0.160 0.305 0.445)
Qwen vs. GPT-40: p = 0.1698 (CI: -0.087 0.081 0.248) [more often outperforms]
For DeepSeek at N 8, AC/DC significantly outperforms all baselines except GPT-4o:
vs. Big Model baseline: < 0.0001 (CI: 0.272 : 0.346 0.423)
Experts baseline: p < 0.0001 (CI: 0.109 0.219 0.359)
vs. Control baseline: p = 0.0214 (CI: 0.005 0.162 0.317)
K.2.3 KNOWLEDGE RECALL BENCHMARKS
When isolating tests to MMLU judge, MMLU Pro judge, and GPQA judge benchmarks- which test general and scientific knowledge recall without multiple-choice options- the N 8 AC/DC Qwen task force outperforms the following baselines:
vs. Big Model: p 0.0001; paired I-test p 0.0294 (CI: 0.478 0.652 0.983)
vs. GPT-4o: p < 0.0001; paired t-test 0.0009 (CI: 0.494 0.540 0.568)
vs. Control: p < 0.0001; paired t-test 0.1649 (CI: 0.005 0.090 0.231)
vs. Experts: p < 0.0001: paired t-test p 0.1238 (CI: 0.017 0.069 0.154)
For Qwen 2 on these shooledge-focuseee benchmarks:
vs. Big Model (N = 3): < 0.0001; paired I-test p = 0.0746 (CI: 0.089 0.198 0.369)
Big Model (N 8): 0.0001; paired t-test p 0.0115 (CI: 0.367 0.502 0.635)
vs. Experts (N 3): p < 0.0001; paired t-test 0.0007 (CI: 0.202 0.214 : 0.230)
vs. Experts (N 8): p = 0.30; paired t-test = 0.47 (CI: -0.146 0.013 0.327)
vs. Control (N 3): p < 0.0001; paired t-test 0.1391 (CI: 0.007 0.045 0.105)
VS. Control (N 8): p 0.30; paired t-test = 0.2968 (CI: -0.029 0.087 0.154)
Aggregating across these knowledge benchmarks and model families at N
vs. Big Model: < 0.0001 (CI: 0.407 0.516 0.636)
vs. Experts: p = 0.0586 (CI: -0.013 0.058 : 0.134) [borderline significant]
Control: p 0.2573 (CI: -0.042 0.021 0.084) [more often improves]
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K.2.4 SUMMARY: COVERAGE
The statistical analysis confirms that AC/DC reliably produces more diverse, specialized model task forces with broader Coverage than baseline methods. Key findings include:
AC/DC significantly outperforms all three main baselines (Control, Experts, Big Model) at N
when aggregated across model families, and outperforms Control and Big Model baselines at N = 8; the Experts baseline at ments do not reach significance (more often outperforms).
remains the only case where improve-
Strong per-family results are observed for DeepSeek and Qwen 3 at both task force sizes, with Qwen 2.5 and Qwen 3 also significantly outperforming GPT-4o at N 8, and Qwen 3 significantly outperforming GPT-4o even at 3.
The method achieves particularly strong results on knowledge recall tasks, with Qwen significantly outperforming both the Big Model baseline and GPT-4o, and Qwen 2 showing significant gains over Big Model and Experts baselines at smaller task force sizes.
K.3 BEST-OF-N SELECTION RESULTS
K.3.1 TASK FORCE SIZE N =
For Best-of-N selection with N 3 models, AC/DC aggregated across 4 model families outper- forms the following baselines:
* vs. Control baseline: p 0.0002 (CI: 0.022 0.053 0.088) [highly significant]
Experts baseline: p 0.0552 (CI: -0.008 0.043 0.103) [borderline significant]
For DeepSeek VI specifically at N = 3:
vs. Experts: p < 0.0001 (CI: 0.151 0.257 0.392)
vs. Control: p < 0.0001 (CI: 0.064 0.125 0.194)
Model familf-specific results for N 3:
Qwen 2 vs. Control: p 0.0578 (CI: -0.015 0.071 0.164) [borderline significant]
Qwen 2.5 vs. Control: 0.2560 (CI: -0.019 0.009 : 0.033) [more often outperforms]
Qwen vs. Control: 0.0951 (CI: -0.004 0.007 0.016) [marginally better]
K.3.2 TASK FORCE SIZE N
At N 8, AC/DC significantly outperforms the control baseline in aggregate:
vs. Control baseline: p 0.0114 (CI: 0.007 0.050 0.097) [significant]
DeepSeek V1 demonstrates particularly strong performance at 8, significantly outperforming both the control and big model baselines:
DeepSeek V1 results:
vs. Control: p < 0.0001 (CI: 0.114 0.210 0.303) [highly significant]
vs. Big Model: = 0.0457 (CI: -0.022 0.158 0.360) [significant]
Qwen 3 more likely outperforms GPT-4o at N = 8:
Qwen 3 results:
GPT-40: p 0.1940 (CI: -0.160 0.197 0.433)
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K.3.3 SUMMARY: BEST-OF-N SELECTION
The BoN analysis demonstrates that AC/DC's Coverage improvements can translate into practical single-answer selection scenarios:
At N = 3, AC/DC significantly outperforms the control baseline; DeepSeek VI shows particularly strong individual gains over control and expert baselines.
At N 8, AC/DC significantly outperforms the control baseline in aggregate; DeepSeek VI shows particularly strong gains over both the control and big model baselines. and Qwen 3, the best model family task force against GPT-4o, more likely outperforms GPT-4o than the inverse, in spite of the absolute average performance difference being lower.
K.4 COMPARISON TO ALTERNATIVE QD METHODS
We compared AC/DC against two alternative quality-diversity approaches: CycleQD (CQD) and standard Dominated Novelty Search (DNS). Additionally, we test DNS against CQD to justify its integration part of AC/DC.
At N = 3, AC/DC shows marginally significant improvements over both alternatives, while DNS and CQD remain statistically indistinguishable:
AC/DC vs. CQD: = 0.0356 (bootstrap; Cl: -0.002 : 0.033 : 0.066; wins: 5/7)
(marginally significant]
AC/DC vs. DNS: = 0.0431 (bootstrap; CI: -0.003 :
0.023 0.052; wins: 4/6)
[marginally significant]
DNS vs. CQD: 0.1065 (bootstrap: CI: -0.007 0.010 0.023; wins: 6/7) [more often
outperforms]
At N 8, AC/DC demonstrates substantially stronger and highly significant advantages:
AC/DC vs. CQD: p < 0.0001 (bootstrap; Cl: 0.076 0.104 0.140; wins: 7/7) [highly
significant]
AC/DC vs. DNS: < 0.0001 (bootstrap; CI: 0.055 0.075 0.098; wins: 7/7) [highly
significant]
DNS vs. CQD: p 0.0130 (bootstrap; CI: 0.004 0.029 0.051; wins: 6/7) [significant]
These results demonstrate that AC/DC outperforms alternative QD methods even at small population sizes, with these advantages growing substantially scale increases. Furthermore, DNS as the QD algorithm of choice is shown to be more effective at selecting more diverse, specialized models than CQD, making it clear choice for model selection in AC/DC.
K.5 ABLATION STUDY RESULTS
To understand the contribution of each algorithmic component, we conducted ablation experiments where individual components were systematically removed.
K.5.1
TASK FORCE SIZE N 3
At N 3, removing most components leads to statistically significant performance degradation:
Fitness-only selection: < 0.0001 (bootstrap; Cl: 0.393 0.632 0.856; wins 7/7)
[significantly worse]
No gibberish filter: p 0.0001 (bootstrap; CI: 0.385 0.628 0.854; wins 7/7) [signifi- cantly worse]
No (task) novelty filter: = 0.1246 (bootstrap; -0.064 0.112 0.304; wins 4/7) [more often worse]
None of the above (fitness-only selection, no novelty filter, no gibberish filter, no muta-
tion): p < 0.0001 (bootstrap: Cl: 0.293 0.549 08788; wins 7/7) [significantly worse]
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K.5.2 TASK FORCE SIZE N-
At N 8, the importance of the complete method becomes substantially more evident:
None the above: p < 0.0001 (bootstrap; Cl: 0.677 0.852 : 1.014; wins 7/7) [highly significant degradation]
No gibherish filter: p < 0.0001 (bootstrap; CI: 0.067 : 0.123 0.181; wins 6/6) [signifi- cantly worse]
Fitness-only selection: = 0.0036 (bootstrap; Cl: 0.050 0.169 : 0.272; wins 6/7) [significantly worse]
No novelty filter: = 0.0207 (bootstrap; CI: 0.002 0.064 0.126; wins 5/7) [signifi- cantly worse]
No mutation: p 0.0358 (bootstrap; CI: -0.012 0.214 0.503; wins 5/7) [significantly worse]
K.5.3 SUMMARY: ABLATIONS
The ablation studies confirm that each component of AC/DC contributes meaningfully to overall performance:
At N 3, using fitness-based selection only, removing the gibberish filter, or removing all components simultaneously results in significant performance degradation. The novelty filter shows positive but non-significant effect at this scale (p- 0.125).
At N = 8, the importance of the full method becomes dramatically more evident, with the complete ablation (removing all components) showing highly significant performance drops (p 0.0001, mean difference 0.85). Notably, the novelty filter reaches statistical significance at this scale (p = 0.021), as does mutation removal (p = 0.036).
These results demonstrate that AC/DC's algorithmic innovations work synergistically, with benefits becoming more pronounced at larger population sizes.
K.6 REPRODUCIBILITY ANALYSIS: VARIANCE ACROSS RE-RUNS
To assess the reproducibility and stability of AC/DC, we analyzed the variance in performance across multiple independent We report standard deviations and ranges for the Qwen 2.5 model family benchmark scores at both N = 3 and N = 8 task force sizes, comparing AC/DC against the control baseline.
K.6.1
TASK FORCE SIZE N 3
For the control baseline (3 runs), we observed low variance across benchmarks with mean standard deviation of 0.94 points and median of 0. points in raw scores. The highest variance occurred on GPQA judge (std dev: 0.78 points, range: 1.35 points) and Minerva (std dev: 1.43 points, range: 2.82 points), while most other benchmarks showed standard deviations below 0.36 points.
For AC/DC (2 runs), variance was comparable, with mean standard deviation of 0.64 points and median of 0.43 points. The benchmarks with highest variance
GPQA judge (std dev: 0.95 points, range: 1.34 points) and Minerva (std dev: 1.32 points, range: 1.86 points). Overall, AC/DC demonstrated similar reproducibility the control baseline at N = 3.
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K.6.2 TASK FORCE SIZE N
At N = 8, the control baseline (3 runs) maintained low variance with mean standard deviation of 0.59 points and median of 0.16 points. Again, GPQA judge (std dev: 0.70 points) and Minerva (std dev: 1.52 points) showed the highest variance.
For AC/DC (2 runs) at N 8, observed moderately higher variance with mean standard devi- ation of 1.80 points and median of 0.95 points. Notable variance appeared on MMLU judge (std dev: 1.48 points), MMLU Pro judge (std dev: 1.32 points), and particularly Minerva (std dev: 4.99 points, range: 7.06 points). Overall, ranges appear to be reasonable given the stochastic nature of evolutionary search.
K.6.3 SUMMARY: REPRODUCIBILITY
The reproducibility analysis reveals that:
At . = 3, both AC/DC and control baselines exhibit comparable low variance across runs, indicating stable performance.
At N 8, AC/DC shows moderately higher variance than the control, which is expected given the increased complexity of evolutionary search over larger populations.
Despite this increased variance, the statistically significant performance improvements re- ported in previous sections remain robust, bootstrap hypothesis testing accounts for cross-benchmark variance.
Certain benchmarks (GPQA judge, Minerva) consistently show higher variance across both methods, likely reflecting the inherent difficulty and sensitivity of these tasks.
The observed variance levels acceptable for evolutionary methods and do not undermine the reliability of our main findings. That is, multiple findings suggest that AC/DC, being the first method of its kind towards open-ended model population discovery, significantly outperforms existing non-coevolutionary model merging approaches, and is more reliable in producing broader Coverage task forces than other baseline approaches.
K.7 INTERPRETATION AND IMPLICATIONS
The comprehensive statistical analysis provides strong evidence for AC/DC's effectiveness:
Reliability across model families: AC/DC demonstrates consistent improvements when considering aggregated stats across diverse base architectures, indicating the method's broad applicability.
2. Scalability: In several cases, performance advantages become more pronounced at larger population sizes (N = 8), suggesting that the advantages model diversity often com- pound with scale.
Domain specificity: Positive results on knowledge recall benchmarks indicate that evolu- tionary model merging more broadly discover domain-specifte capabilities.
4. Practical deployment: BoN improvements (in some cases, significant) suggest that Cov- erage gains can translate to realistic single-answer scenarios.
5. Component synergy: Ablation results reveal that AC/DC's components work together synergistically, with the full method substantially outperforming partial implementations.
6. Reproducibility: The method demonstrates acceptable variance across multiple runs, with statistically significant improvements remaining robust despite the stochastic nature of evo- lutionary search.
Overall, these statistical analyses establish AC/DC reliable method for discovering diverse, capable model populations that achieve broader skill Coverage than existing approaches, with the framework showing partieular promise for scaling to larger model collectives.
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Figure 15: Scaling trend with the number of models on our Qwen2.5 based experiment.
L COMPUTATIONAL COST ANALYSIS
The total computational cost of our coevolution process is approximately 324 GPU hours, which, after 50 generations and active model count per generation of 16, yields a population of approxi- mately 80 diverse models selected to maintain high model quality and increase diversity. During the coevolution process, our scientist successfully develops around 1000 tasks (where another roughly 1000 do not pass our quality and novelty filters). During coevolution, each generation, 250 tasks are actively considered for evaluation of each model in the active model pool. The computational cost includes the GPUs for the model merging and evaluation pipeline, the GPUs for hosting large, open-source scientist LLM, and for embedding model. Notably, AC/DC works without any API calls to proprietary models and relies soiely on self-hosted models.
Critically, these requirements represent a cost roughly GPU hours per merged model-signiffcantl more efficient than standard post-training approaches such as reinforcement learning, which can require 102 to 10 GPU hours per 7B model depending on the method and scale (Khatri et al. 2025), plus AC/DC eliminates manual dataset curation.
While baseline approaches like selecting expert models from Hugging Face require no training cost, they face fundamental scalability and complementarity limitations: (1) the availability of diverse, specialized models is limited (dozens, not 80+), (2) there no guarantee that independently-trainn experts will have complementary specializations, and (3) manual search costs grow with collective size. Creating 80 complementary specialists through traditional post-training could require 10 to 108 GPU hours, plus the design of 80 distinct specializations and datasets.
Furthermore, our 324-hour investment provides flexibility: the evolved population can be composed into collectives of any size (N=1 to 80) for different downstream applications without additional training. As shown in the Fig. [5] downstream performance scales with N, and our approach (1) scales better than re- re-prompting single instruct model (control experiment) and (2) uniquely enables exploring this trade-off without the prohibitive cost of manually training individual models or the availability constraints of pre-existing expert models.
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M LLM PARAMETER UPDATE DETAILS
M.1 MODEL MERGING BASED CROSSOVER
Our crossover operator employs parameler space merging, creating new models by merging task vectors at the model level (Ilharco et al., 2022 Kuroki et al. 2025). For a pre-trained base LLM
with parameters base I and fine-tuned LLM with parameters E Rd we define the task vector as:
Oase
The crossover operator generates offspring parameters by combining task vectors from two parents:
W1
W2
Pehild base
W2 Tp:
+
+ W2 Tp=
W1
W1
where Tp. and Tps are the parents' task vectors. The weights wi and w2 are sampled i.i.d. from N(,, o²), with (j,, o) as predetermined hyperparameters fixed throughout evolution. We normalize the mixing coefficients to prevent merged weights from becoming outliers that could cause issues in downstream layers.
M.2 GENERALIZED SVD-BASED MUTATION
The model merging crossover has an inherent limitation: constructing Ochild as linear combination of parent task vectors confines offspring to the convex region in performance space formed by the parents. To enable extrapolation beyond this region, we introduce a mutation operator Gebild h(fchua) applied after crossover.
Rather than adding random Gaussian perturbations to parameters-which introduces excessive de- grees of freedom and can be hard to optimize-we propose sampling perturbations along the principal components of the model's weight matrices. This is achieved through singular value decomposition (SVD).
For each weight matrix W, in the offspring model, we compute its SVD:
Wi = U,EiV
(6)
where Uj E RX Σg E Rr (diagonal), and Vi Roxr are the left singular vectors, singular values, and right singular vectors, respectively.
We then apply perturbations to the first singular values:
Σ diag( Wk. ......))
where Wi ~ (O. are sampled independently, and a is hyperparameter controlling mutation strength. The mutated weight matrix then reconstructed:
W = V.
(8)
This approach is loosely inspired by the transformer- squared SVD-based finetuning method (Sun et al.) (025)) and offers several advantages. By perturbing along the principal components-whide capture the most significant variance in the weight space-we maintain the fundamental geometric structure of the weights while allowing controlled exploration. This generalizes the task vector SVD mutation operator by (Kuroki et al. 2025) but still allows for training fewer, higher signal parameters.
Our mutation operator becomes pass-through for rank-1 matrices (e.g., layer normalization or bias parameters).
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N
JUSTIFICATION OF OPEN-ENDEDNESS DESIGN CHOICES
This section provides detailed justification for each open-endedness component in AC/DC, grounded in established principles from the literature evolutionary computation, quality-diversity and co- evolution.
N.1
SUMMARY
Minimal Criteria (Gibberish & Impossible Task Filters): Prevents degenerate solutions from dominating while enabling exploration, following MCC principles (Brant & Stanley
2017 2020).
Quality-Diversity via DNS: Balances performance and diversity without predefined niches, handling variable-ddimenionaal skill vectors that grow with task evolution (Bahlous- Boldi et al. 2025).
Skill Vectors as Behavioral Deseriptors: Provides fine-grained capability signatures that naturally adapt to task evolution, requiring no manual niche design or learning (unlike MAP-Elites (Mouret & Clune. 2015a Cully et al. 2015)).
Coevolution of Models and Tasks: Creates complexity explosion through bidirectional feedback, with tasks adapting to model capabilities and vice versa (Wang al. 2019 2020)
Task Adaptation via Difficulty Profiles: Maintains tasks in the learnable regime- challenging but not impossible-impleenntng automatic curriculum generation (Zhang et al. 2023 Faldor al. 2024).
Novelty Filtering for Tasks: Prevents trivial task variants by explicitly rewarding semantic
novelty, core to Novelty Search principles (Lehman et al. 2008 Lu et al. 2025).
Task Reflection and Validation: Ensures task quality through scientist LLM self-solving. preventing broken or ambiguous tasks from accumulating (Lu et al. 2025).
Historical Archive & Coverage-Based Selection: Preserves specialized models from all generations and selects task force based on synthetic task Coverage, not benchmark perfor- mance (Pugh et al. 2016).
Together, these choices implement key properties of open-ended systems: continual novelty gener- ation, increasing complexity, cumulative innovation, and minimal criteria rather than explicit objec- tives (Stanley et al. 2017 Stanley 2019).
N.2 MINIMAL CRITERIA FOR MODELS AND TASKS
Design Choice: AC/DC implements two primary minimal criteria (MC) filters: (1) the gibberish fil- ter for models, which eliminates degenerate LLMs producing incoherent text, and (2) the impossible task filter, which removes tasks that no model in the population can solve.
Justification:
This design directly follows the Minimal Criterion Coevolution (MCC) framework established by Brant & Stanley (2017). which demonstrates that defining MCs for both agents and environments enables more open-ended outcomes in coevolution. The fundamental insight is that MCs prevent the search from being dominated by completely undesired outcomes while still en- abling exploration to flourish.
The gibberish filter addresses critical failure mode in model merging: degenerate models that produce syntactically invalid or semantically meaningless text. Without this filter, such models could dominate the population simply by producing output that accidentally matches ground truth through random character generation. This aligns with the principle in Lehman & Stanley (2010) that evolution requires protection against deceptive local optima that appear successful by trivial metrics.
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The impossible task filter prevents the task archive from accumulating challenges that lie outside the reach of the current model population's 's capabilities. As noted by SSoos & Stanley (2014), without such filtering, the coevolutionary process become trapped in arms races where increasingly difficult tasks provide no useful gradient for improvement. By replacing impossible tasks with their parent tasks, we maintain difficulty adaptation while ensuring tasks remain within the "learnable" regime described by Hughes et al. (2024) as essential for open-endedness.
N.3 QUALITY-DIVERSITY VIA DOMINATED NOVELTY SEARCH
Design Choice:
AC/DC employs Dominated Novelty Search (DNS) (Bahlous- -Hahlous-Boldi al. 2025) for model selection, computing local competition fitness by measuring each solution' 's distance from better-performing solutions in skill vector space.
Justification:
The integration of quality-diversity principles addresses a fundamental challenge in open-ended discovery: maintaining both high performance and behavioral diversity without prede- fined niches. Traditional Novelty Search (Lehman al. 2008 Lehman & Stanley (011a) promotes exploration by rewarding behavioral novelty regardless performance, while MAP-Elites (Mouret & Clune 2015a) requires predefined behavioral dimensions and discretization.
DNS offers several advantages for our domain:
Adaptive Behavior Spaces: Unlike MAP-Elites' fixed grid structure, DNS naturally han-
dles variable-diment dimensional behavioral descriptors. Our skill vectors grow new tasks added to the archive, making predefined niche boundaries impractical. This aligns with the adaptive approaches for evaluating novelty introduced by [Meyerson et al. (2016) and Paolo lt al. (020), which enable more open-ended exploration of different spaces of novelty.
2. Local Competition without Binning: DNS implements the local competition principle established by Lehman & Stanley (b)1Tb) for encouraging diversity, but avoids the limita- tions binned behavior characteristics. As we note in the related work, CycleQD's ' use of aggregated benchmark accuracies can fail to distinguish models with unique subfield exper- tise (e.g., physics vs. biology specialists both appearing in a "science" bin). DNS preserves fine- fine-grained distinctions through continuous distance metrics in skill vector space.
3. Quality Pressure with Diversity: By measuring distances only to better-performing neighbors, DNS maintains explicit optimization pressure toward high-quality solutions while rewarding distance from those solutions-effectivell balancing the exploration- exploitation trade-off central to QD methods (Pugh et al. 2016 Cully & Demiris 2017).
N.4 SKILL VECTORS AS BEHAVIORAL DESCRIPTORS
Design Choice: AC/DC represents model capabilities through binary skill vectors, where each ele- ment indicates task completion status, serving as behavioral signatures for diversity measurement.
Justification: This representation addresses multiple challenges in applying QD LLM discovery:
1. No Predefined Niches Required: Traditional MAP-Elites requires manually specifying behavioral dimensions and their ranges before evolution begins. As Gaier et al. (2019) Kent al. (024)) note, learned parametrized behavior functions adapt problem structure, but require additional training. Skill vectors emerge naturally from evaluation, requiring no priori design learning.
2. Fine-Grained Behavior Characterization: Each skill vector provides detailed signature of what a model can solve, analogous to the "illumination" concept in QD (Mouret & Clune 2015a) but task-level granularity. This enables AC/DC to distinguish models with complementary expertise (e.g., one model excels at physics while another excels at biology) even when their aggregate performance might be similar.
3. Adaptation to Task Evolution: As the task archive evolves, skill vectors naturally adapt or expand to incorporate new tasks. This aligns with the meta-adaptation approaches in QD (Bossens & Tarapore 2022) that handle complexity and adaptation of diverse high- quality search spaces, but through a simpler mechanism that does not require explicit meta- learning.
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4. Direct Connection to Coverage: Skill vectors directly support our Coverage metric (Eq. (.). which measures collective problem-solving capacity. This provides natural bridge between behavioral diversity (used during evolution) and practical utility/response quality (measured at test time).
N.5 COEVOLUTION OF MODELS AND TASKS
Design Choice: AC/DC simultancously evolves both model populations and task distributions, with models evaluated on tasks and tasks filtered based on model performance.
Justification: This bidirectional cocvolution implements several established principles:
Environmental Complexity from Coevolution: Drawing on Wang et al. (2019) 2020)
and PAIRED (Dennis et al. 2020p, we leverage the insight that coevolving agents and vironments produces complexity explosion through local competition. Unlike PAIRED's adversarial setup, AC/DC uses task difficulty profiles (average pass rates) to guide adaptive task generation, ensuring tasks remain challenging but not impossible.
2. MCC for Both Populations: Following Brant & Stanley (0117 2020 we apply minimal criteria both models (gibberish filter) and tasks (impossible task filter, novelty filter). This dual-sided filtering is essential for open-ended outcomes, as it prevents either popula- tion from dominating with trivial or degenerate solutions.
3. Open-Ended Task Generation via LLMs: Recent work demonstrates the potential for open-endedness through LLMs generating diverse environments and challenges (Zhang et al.) 2023 Aki et al., 2024, Faldor et al.) 2024). Our approach builds on Lu et al. 20255) who show that open-ended search principles can generate surprisingly novel tasks revealing unexpected LLM capabilities. AC/DC extends this by making task evolution responsive to current model population performance, creating a feedback loop that drives increasing sophistication.
4. Avoiding Benchmark Optimization: By coevolving tasks alongside models and never optimizing explicitly for downstream benchmarks, AC/DC embodies the "abandoning ob- jectives" philosophy Lehman & Stanley The synthetic task distribution provides a training signal that encourages general capability development rather than overfitting to specific evaluation metrics.
N.6 TASK ADAPTATION BASED ON DIFFICULTY PROFILES
Design Choice: AC/DC classifies parent tasks by their pass rates (difficulty profile) and uses this to determine adaptation type: increase difficulty, decrease difficulty, or generate novel variants.
This adaptive difficulty mechanism addresses the "interestingness" criterion for open- endedness identified by Hughes et al. (2024). Tasks that are too easy (high pass rate) provide no learning signal, while tasks that are too hard (low pass rate) are frustrating and unlearnable.
Justification:
This approach implements form of automatic curriculum generation similar PAIRED (Dennis) al. 2220). but tailored to LLM capabilities. Unlike adversarial environment generation that might produce arbitrarily difficult challenges, our difficulty-basee adaptation ensures the task distribution remains anchored the current population's zone of proximal development-challenge drive improvement but feasible enough to provide useful gradients (Zhang et al. 2023).
enough to
The three-way classification (increase/decrrase/novee) also promotes diversity in task evolution. Novel variants at intermediate difficulty levels encourage exploration of different task types and domains, preventing the task archive from collapsing into minor variations single theme.
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N.7 NOVELTY FILTERING FOR TASKS
Design Choice:
Generated tasks are compared to three most similar tasks in the global archive via embedding similarity, with judge LLM determining if sufficient novelty exists.
Justification: This filtering implements the core principle of Novelty Search: explicitly reward- ing behavioral novelty relative previously discovered solutions (Lehman al. 2008 Doncieux et al. 2019). Without novelty filtering, task evolution could generate trivial variants (e.g., changing numbers math problem) that provide new behavioral challenges for models.
The use of semantic embeddings for similarity measurement allows AC/DC to recognize deep struc- tural similarity between tasks that might differ superficially. For example, two physics problems with different contexts but identical underlying principles would be flagged as non-novel. This aligns with the notion from Sigaud al. (2023) that true novelty requires doing things that meaningfully different, not merely cosmetically varied.
The judge LLM adds second layer of semantic understanding, catching cases where embedding similarity alone might miss conceptual relationships. This two-stage process balances computational efficiency (embedding search) with nuanced judgment (LLM evaluation) (Lu al. 2025
N.8 REFLECTION AND VALIDATION FOR TASK QUALITY
Design Choice: The scientist LLM attempts to solve its own generated tasks, with automatic cor- rection for compilation errors and refinement prompts for logic emrors.
Justification: This self-evaluation cycle addresses a critical challenge in synthetic data generation:
ensuring that automatically generated tasks are well-formed and solvable. Recent work on synthetic data quality (Havrilla et al. 2024) emphasizes that data quality matters as much as quantity.
The iterative refinement process implements form of minimal criterion for task quality, preventing the task archive from accumulating broken or ambiguous tasks. By having the scientist LLM solve its own tasks before accepting them, we create selection pressure toward tasks with clear problem statements and unambiguous scoring functions.
This also relates to the principle of "autotelic learning" in open-ended systems (Etcheverry al. 2021], where the system must develop its own evaluation criteria. The scientist L.M effectively leams generate tasks that meet implicit quality standards through its own solution attempts.
N.9 HISTORICAL MODEL ARCHIVE AND TASK FORCE SELECTION
Design Choice: AC/DC maintains historical archive of all models every Grask generations and selects the final task force by maximizing Coverage over the global task archive.
Justification: The historical archive implements key principle from QD: maintaining a collection of diverse high-quality solutions rather than just the current population (Pugh et al.) 2016). This is crucial because behavioral diversity at intermediate generations might not be preserved if we only keep the final population. Some specialized models might be replaced during evolution, but could still contribute unique capabilities to the final ensemble.
The Coverage-based selection for the task force directly optimizes for our practical goal: collective problem-solving capacity across diverse tasks. This selection is independent of downstream bench- marks, implementing the "novelty search without objectives" approach while still providing clear uased criterion for ensemble construction.
Importantly, this selection happens after coevolution completes, avoiding optimization pressure dur- ing evolution. This prevents the kind of overfitting to specific benchmarks that could be expected in recent evolutionary model merging work (Akiba et al. (2025). Our models are selected based syn- thetic task Coverage, then evaluated on held-out benchmarks, ensuring genuine out-of didtof--iittibttion generalization.
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Published as a conference paper at ICLR 2026
N. SYNTHESIS: AC/DC AN
SYSTEM
Taken together, these design choices implement the key properties of open-ended systems identified by Stanley et al. (017) and Stanley & Lehman (2015):
Continual Generation of Novelty:
The coevolution models and tasks produces an on- going stream of new capabilities and challenges, with no predetermined endpoint.
2. Increasing Complexity:
Task difficulty adaptation and model selection pressure drive both populations toward increasing sophistication time (demonstrated in Fig. S improve- ment trajectory).
3. Cumulative Innovation:
New models build on previous models through crossover, and tasks build on previous tasks through adaptation, creating stepping-stone effect where discoveries enable further discoveries.
4. No Explicit Fitness Function:
While we use quality (task pass rates) and diversity (skill vector distances) for selection, never optimize explicitly for downstream benchmarks, allowing unexpected capabilities to emerge.
5. Minimal Criteria Rather Than Objectives: Our gibberish and impossible task filters pre- vent completely undesired outcomes without constraining the search to predefined goals.
This combination of principles, grounded in established open-endedness literature, enables AC/DC to discover model collectives with broader and more diverse capabilities than methods that optimize directly for benchmark performance, while maintaining lower computational costs than training large monolithic models.
O
LLM USAGE DURING PAPER WRITING
We leverage LLMs to assist in polishing the paper's text and generating tables.
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Reviews:
[{"review_id": "g8qaSmX0pJ", "reviewer": "Reviewer_QsVv", "summary": "This paper introduces AC/DC, which performs both Model Archive Evolution and Task Archive Evolution to achieve effective collective intelligence. It enables models with smaller parameter scales to reach performance comparable to larger models. Experiments on benchmarks such as MMLU, GPQA, Minerva, and HumanEval demonstrate the effectiveness of the proposed method.", "questions": "See above", "limitations": "", "rating": 6, "confidence": 2, "soundness": 2, "presentation": 2, "contribution": 3, "strengths": "- The experiments in this paper are fairly extensive and include plenty of implementation details.\n- I think the topic of evolving multiple base LLMs to create a more intelligent model is interesting and important.", "weaknesses": "- The Task Archive Evolution part still relies on a larger scientist LLM to generate tasks, which means it depends on external assistance and doesn't necessarily ensure the generated tasks are high quality.\n- Although the method looks promising, the whole process, including Model Archive Evolution and Task Archive Evolution, involves too many implementation details. I think the paper lacks enough ablation studies to show the effectiveness of each design choice.\n- Overall, this paper is really hard to follow. I strongly recommend the authors add clearer explanations of the full framework, including how LLM parameters are updated and how new tasks are generated.\n\nI am generally inclined to accept this paper. However, since the proposed method involves many implementation details and lacks sufficient ablation studies, I'm not confident that all the technical components are truly useful, so I would lower my confidence level.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "zHuvZGFgPc", "reviewer": "Reviewer_QSy7", "summary": "This paper introduces AC/DC (Assessment Coevolving with Diverse Capabilities), a framework for discovering diverse LLM populations through coevolution of models via evolutionary model merging and synthetic tasks via LLM-generated data. The method evolves archives of both models and tasks, applying quality-diversity principles (DNS) and minimal criteria filters. Evaluating across multiple model families (Qwen2/2.5/3, DeepSeek, Llama3), the authors report that their evolved collectives of smaller models achieve broader Coverage on downstream benchmarks than larger models or manually curated experts, without explicit benchmark optimization. Key findings include parameter-efficient performance (N=8 evolved 7B models surpassing 72B models), emergent specialization patterns, and improved Best-of-N selection compared to baselines.", "questions": "1. Can the authors compare AC/DC to vanilla EvoMerge with a fixed synthetic task set? This would isolate whether task coevolution specifically (vs. just evolutionary merging on diverse tasks) drives improvements.\n\n2. What properties predict whether a model family will succeed (Qwen) or fail catastrophically (Llama3)? Can the authors provide heuristics to assess compatibility before running expensive coevolution?\n\n3. Why does optimizing for synthetic task Coverage transfer to unseen benchmarks? Is this due to: (a) the scientist LLM's implicit benchmark knowledge, (b) genuine skill generalization, or (c) synthetic tasks being representative of general problem-solving? Can the authors ablate using a scientist LLM with no benchmark exposure?\n\n4. Can the authors provide confidence intervals for Tables 1-2 and significance tests for ablation improvements in Table 9? Are the reported gains reliable, given measurement noise and multiple comparisons?\n\n5. Why do Coverage improvements often fail to translate to Best-of-N performance (Table 2: -0.19% at N=3 vs. Big Model)? Is this a fundamental limitation of the selection methods tried, or do complementary capabilities not compose well for single-answer tasks?\n\n6. What are the total GPU-hours and API costs for AC/DC vs. fine-tuning a single 72B model on synthetic data? Is the evolved collective actually cheaper to produce than alternatives?\n\n7. Why use a 3-3-2 distribution for \"Experts N=8\" (Appendix D.4)? Was this optimized, or arbitrary? How sensitive are results to alternative distributions (4-2-2, 2-4-2)?\n\n8. Have humans validated the quality, diversity, and realism of generated synthetic tasks? Table 1 examples look reasonable, but systematic evaluation is absent.\n\n9. Figure 6 shows Vendi score gains slowing and task difficulty saturation at gen 45. Does AC/DC exhibit unbounded behavior, or does it plateau after discovering capabilities within the seed models' latent space?\n\n10. Table 3 shows AC/DC beats DNS/CQD (both trained on benchmarks), but DNS/CQD use different model families and training data. Can you compare all three methods on the *same* model family with identical conditions?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. **Novel system-level integration**: First framework (to the best of my knowledge) combining evolutionary model merging with synthetic task coevolution, extending quality-diversity principles to LLM discovery in a principled way that addresses the limitations of training single static models.\n\n2. **Multi-family empirical validation**: Evaluation across 5 model families (Qwen2/2.5/3, DeepSeek, Llama3) and 8 benchmarks provides breadth, with consistent improvements on 4 families demonstrating some generalizability despite Llama3 failures.\n\n3. **Parameter efficiency evidence**: Concrete demonstrations that N=8 evolved 7B models surpass 72B models while using 22% fewer parameters (Qwen2: +14.59% Coverage improvement) support distributed specialization hypothesis.\n\n4. **Emergent specialization analysis**: Figure 3 shows evolved models develop distinct category-specific performance profiles without explicit domain assignment, validated by qualitative examples (Figures 4-5) demonstrating complementary capabilities.\n\n5. **Open-ended improvement**: Figure 1 and Appendix D.3 demonstrate that coevolution leads to continually improving performance and task diversity over generations, supporting the open-endedness claim.\n\n6. **Comprehensive ablations and analysis**: Appendix D.1's ablation study, D.2's task force selection comparison, D.3's Vendi score analysis, and detailed lineage trees (Figures 8-9) provide thorough characterization of system components and emergent behaviors.", "weaknesses": "1. **Limited technical novelty**: AC/DC is primarily an *integration* of existing techniques (EvoMerge, synthetic data generation, DNS selection) rather than fundamental algorithmic innovation. The mutation operator (SVD perturbation) and gibberish filter are incremental additions. The contribution is valuable but more engineering than methodological breakthrough.\n\n2. **Evaluation circularity concerns**: Task force selection maximizes Coverage on synthetic tasks generated by the same system, then evaluates on benchmarks. While claiming \"no explicit benchmark optimization,\" the 72B scientist LLM presumably has knowledge of benchmark-style problems. Is the synthetic-to-benchmark transfer genuine generalization or implicit optimization via the scientist LLM's training data?\n\n3. **Model family dependence unexplained**: Success on Qwen (4 families) but catastrophic failure on Llama3 (Appendix C: 46.04% vs. 51.13% at N=3) raises critical questions. The authors attribute this to \"seed model compatibility\" but provide no diagnostic criteria for predicting success. What percentage of model families will fail? How can practitioners know beforehand?\n\n4. **Missing key baselines**: No comparison to vanilla EvoMerge *without* coevolution. Table 3 compares only to DNS/CQD (both using benchmark-specific training), but not to evolutionary merging with static synthetic tasks. This baseline would isolate the value of coevolution specifically.\n\n5. **Inconsistent baseline definitions**: \"Experts N=8\" uses a 3-3-2 distribution that the authors admit is \"manually tuned\" and \"arbitrary\" (Appendix D.4). Different distributions yield different results, making this baseline unreliable. The proliferation of baseline variants (Control, Experts, Big Model, GPT-4o, Experts N=8) obscures the primary comparison.\n\n7. **Statistical rigor gaps**: Tables 1-2 report improvements without confidence intervals, significance tests, or multiple comparison corrections despite testing across 8 benchmarks × 4-5 model families. Are differences statistically reliable? The ablation (Table 9) shows 0.46%-2.12% drops—are these significant given measurement noise?\n\n8.  **Coverage-to-BoN translation failure**: Table 2 shows AC/DC often *underperforms* on Best-of-N single-answer selection despite Coverage gains (e.g., -0.19% vs. Big Model at N=3, -9.77% vs. GPT-4o). The authors note this requires \"improved BoN methods\" but don't explain why complementary capabilities evident in Coverage don't translate more reliably to practical deployment.\n\n9. **Computational cost omitted**: No reporting of: (a) total GPU-hours for 50 generations, (b) cost of scientist LLM API calls, (c) comparison to fine-tuning alternatives. Is producing an evolved collective cheaper than training a single larger model? Table 4 shows 50 generations × 16 models × 250 tasks—what's the actual cost?\n\n10. **Synthetic task quality unvalidated**: The synthetic tasks are generated by a 72B Qwen model and validated through \"reflection\" (self-solving), but no human evaluation confirms task quality, diversity, or realism. Do these tasks actually capture novel skills or just recombine existing problem templates?\n\n11. **Generalization claims seem overstated**: Calling this \"open-ended\" evolution is premature after only 50 generations on a single synthetic distribution. True open-endedness (Stanley et al. 2017) requires demonstrating unbounded innovation, but Figure 6 shows Vendi score gains plateauing and task difficulty saturation at generation 45.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "wJtcaleGpJ", "reviewer": "Reviewer_Y9hf", "summary": "The authors work present Assessment Coevolving with Diverse Capabilities (AC/DC) which is a framework that automatic discover multiple diverse LLM through open-ended coevolution of models and synthetic tasks. Their approach show that EvoMerge can create task forces that perform better than larger language models while using less parameters and manually curated expert ensembles. AC/DC not optimize for any downstream benchmark and achieve consistent improvements across multiple model families, with evolved populations showing wider coverage of capabilities and emergent specializations that validate discovery of complementary skills.", "questions": "Can we achieve the same result with a setting that involves only one agent?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "- Good open-ended approach of model merger algorithm\n- Good use of minimal criterion coevolution", "weaknesses": "- Presentation can be better\n- Related works can be improved", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "4lWDnQ3mAO", "reviewer": "Reviewer_PHpK", "summary": "This paper presents AC/DC (Assessment Coevolving with Diverse Capabilities), a framework that jointly evolves large language models and synthetic tasks to automatically discover diverse expert models. The system combines evolutionary model merging, synthetic data generation, and quality diversity selection to develop populations of smaller models that collectively achieve broad skill coverage. Experiments show that these collectives can outperform much larger models, including GPT-4o, in benchmark coverage while using fewer total parameters.", "questions": "- Have you verified whether the reported improvements are consistent across runs or fall within expected benchmark variance?\n- How does the total computational cost of the coevolution process, including model merging and task generation, compare to standard fine-tuning or ensembling?\n- How sensitive is the framework to the choice of the \"scientist model\" or to different prompting strategies during task generation?", "limitations": "", "rating": 6, "confidence": 2, "soundness": 3, "presentation": 2, "contribution": 4, "strengths": "- The combination of open-endedness and evolutionary model merging is novel and provides an interesting new direction for developing diverse and adaptive model populations.\n- The empirical results are encouraging as they show performance and coverage improvements across several benchmarks and model families.\n- The method achieves notable parameter efficiency, with collections of smaller models reaching or surpassing the performance of much larger single models.\n- The qualitative analyses demonstrate emergent specialization and stylistic diversity among the evolved models, supporting the merits of the proposed method.\n- The method achieves notable parameter efficiency, with collections of smaller models reaching or surpassing the performance of much larger single models.", "weaknesses": "- The paper does not report variance or confidence intervals, making it difficult to assess whether the performance gains in terms of coverage and Best-of-N improvement are statistically significant.\n- The computational cost of the coevolution strategy is not addressed, making the efficiency claim incomplete.\n- While AC/DC does not optimize for any benchmark, the synthetic task generator depends on a fixed \"scientist\" LLM that might produce tasks similar to benchmark data.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "OQy2gNaGDB", "reviewer": "Reviewer_ZzV2", "summary": "This paper introduces a novel LLM development framework called Assessment Coevolving with Diverse Capabilities. The framework aims to enable open-ended co-evolution between models and tasks, thereby automatically discovering a population of LLM experts with increasingly novel and diverse capabilities. This approach seeks to overcome the limitations of existing training paradigms that rely on static datasets and produce a single monolithic model.", "questions": "Please refer to Weaknesses part.", "limitations": "", "rating": 4, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Parameter/Resource Efficiency\nThe collective of models discovered using the proposed method outperforms larger-scale LLMs while using fewer total parameters, thereby saving GPU memory and computational resources.\n\n2. Clarity of Presentation\nThe paper employs flowcharts and pseudocode to aid comprehension, allowing readers to quickly grasp the proposed methodology.\n\n3. Comprehensive Analysis\nThe paper provides detailed qualitative and quantitative analyses and yields several interesting insights. For instance, the discovered models exhibit specialized and diverse capabilities, offering valuable guidance for future research.", "weaknesses": "1. The success of model merging depends heavily on empirical testing of initial seed model combinations, which may limit the overall effectiveness of the pipeline. Additionally, since answers in generation tasks are produced by LLMs, their correctness cannot be fully verified.\n\n2. The current evolutionary scope is relatively limited. As shown in Figure 3, the evaluated capabilities belong to domains where the base models already perform well, making it unclear whether the proposed method can truly generate out-of-distribution (OOD) capabilities.\n\n3. The benchmarks used in the experiments are relatively easy, with many models already achieving near-saturated performance. More challenging benchmarks are needed to better demonstrate performance improvements, for example using math tasks at the AIME or IMO level. The same applies to other domains.\n\n4. The study would be more convincing if experiments were conducted on larger models, allowing observation of whether the proposed phenomenon scales with model size.\n\n5. The proposed approach bears resemblance to using a Multi-Agent System (MAS) to optimize model capabilities. The authors should clarify how their method compares to MAS in terms of performance and innovation.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)