Expanding the Capability Frontier of LLM Agents with ZPD-Guided Data Synthesis
Authors: Xuanzhong Chen
Zile Qiao
Guoxin Chen
Liangcai Su
Zhen Zhang
Xinyu Wang
Pengjun Xie
Fei Huang
Jingren Zhou
Yong Jiang
Ting Chen
Venue: ICLR 2026 Poster (2026)
Keywords: data synthesis
multidisciplinary benchmark
LLM agents
Primary area: datasets and benchmarks
TL;DR: We introduce a ZPD-inspired data synthesis engine that co-generates frontier reasoning trajectories and a self-evolving benchmark (the ZPD Exam), enabling a 30B LLM to achieve state-of-the-art on expert-level tasks.

Abstract: Unlocking advanced reasoning in large language model agents is hindered by a scarcity of training data situated at the very frontier of their capabilities. We address this with a novel data synthesis approach inspired by the educational theory of the Zone of Proximal Development (ZPD), which conceptualizes this frontier as tasks an LLM cannot solve independently but can master with guidance. We operationalize this principle through the AgentFrontier Data Engine, an automated pipeline that synthesizes high-quality, multidisciplinary data situated precisely within an LLM's ZPD. The engine yields two synergistic outputs: knowledge-intensive data for continued pre-training and frontier-level reasoning trajectories for post-training. Concurrently, it produces the ZPD Exam, a self-evolving benchmark for evaluating agent capabilities by compelling them to reason beyond their parameterized knowledge. By training our AgentFrontier-30B-A3B model on the synthesized data, we achieve state-of-the-art results on demanding benchmarks like Humanity's Last Exam, outperforming several leading proprietary agents. This work establishes ZPD-guided data synthesis as a scalable and effective paradigm for cultivating increasingly capable LLM agents.

Published as a conference paper at ICLR 2026
EXPANDING THE CAPABILITY FRONTIER OF LLM AGENTS WITH ZPD-GUIDED DATA SYNTHESIS
Xuanzhong C Qi Guoxin Chen', Liangeai Su², Zhen Zhang', Xinyu Wang', Pengjun Xie*, Fei Huang', Jingren Zhou', Yong Jiang* Ting Chen
Department of Computer Science and Technology, Tsinghua University
Tongyi Lab, Alibaba Group
2o223mmmill L.. tsinghua. edu. en, (qiaozile.qur .zzl, yongjiang. iy
ABSTRACT
Unlocking advanced reasoning in large language model agents is hindered by a scarcity of training data situated at the very frontier of their capabilities. We ad- dress this with a novel data synthesis approach inspired by the educational the- ory of the Zone this frontier as tasks an LLM cannot solve independently but can master with guidance. We operationalize this principle through the AgentFrontier Data En- gine, an automated pipeline that synthesizes high-quality, multidisciplinary data situated precisely within an LLM's ZPD. The engine yields two synergistic out- puts: knowledge-intensiee data for continued pre-training and frontier-level rea- soning trajectories for post-training. Concurrently, it produces the ZPD Exam, a self-evolving benchmark for evaluating agent capabilities by compelling them reason beyond their parameterized knowledge. By training our AgentFrontier 30B-A3B model the synthesized data, we achieve state-of-the-art results on demanding benchmarks like Humanity' s Last Exam, outperforming several leading proprictary agents. This work establishes ZPD-guided data synthesis as a scalable and effective paradigm for cultivating increasingly capable LLM agents.
Proximal Development (ZPD), which conceptualizes
INTRODUCTION
While large language models (LLMs) have demonstrated impressive proficiency on various funda- mental reasoning tasks (Rein et al. 2023 Wang et al.] 2024 Tian et al.] (2024). they still struggle with the scenarios demanding in-depth. cross-domain, and integrative reasoning (Mialon et al. [2023] Wei et al.] 2025 Phan et al. 2025). This gap presents a critical impediment in the pursuit of arti- ficial general intelligence (AGI). Achieving such a leap necessitates a paradigm shift from reliance on static, internal knowledge to dynamic, agentic capabilities such as tool using (Qin et al. 2024). self-reflection (Shinn et al. (2023), iterative planning, and multi-step reasoning. However, the de- velopment of such agentic skills is hampered by dual challenge: a scarcity of training corpora that systematically cultivate these abilities in unified manner (Shi et al., (025). and the satura- tion of existing benchmarks. While expert-crafted evaluations such as Humanity' 's Last Exam (Phan al. 2025) offer invaluable benchmarks, their prohibitive cost and limited scalability underscore the urgent need for automated pipelines capable of synthesizing frontier-level reasoning tasks.
Recent datasets have significantly enhanced LLMs' single-step reasoning (Liu et al. 2025). but
they fall short of targeting the deeper challenge of knowledge fusion (Wan et al.) 2024): integrat- ing and transforming information across diverse sources. While retrieval-augmented generation (RAG) (Lewis et al.) 2020) excels when the answer can be grounded in single document, its per- formance degrades on tasks requiring reasoning across heterogeneous information. This deficiency traces back to the dominant data-synthesis paradigms, which fall into two broad categories: query- centrie methods (Yan et al.) 2025) that generate variations of existing question-anse -nniwor (QA) pairs, and document-centric methods (Fan et al. 2025) Yuan et al. 2025) that derive document- document-groundded QA pairs from the corpus. Both approaches primarily assess localized comprehension, akin to exam- ining student on individual textbook chapter rather than their ability to synthesize insights across an
Equal contribution. Corresponding authors.

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Published as a conference paper at ICLR 2026
entire curriculum. In contrast, complex engineering design demand multi-docummntt synthesis and cross- knowledge fusion. Human experts rarely treat information in isolation; instead, they connect, contrast, and integrate it to derive in-depth insights, which is the intrinsic essence of deep research (OpenAl 2025a Google 2025) Cultivating such synthetic reasoning capacity is paramount to advancing LLMs toward higher forms of intelligence.
tasks such as academic research, legal analysis, or
The central challenge of effective data synthesis lies in merely generating difficult tasks, but in precisely calibrating them to the frontier of model's competence: complex enough to exceed the boundary of the model's intrinsic capability, yet solvable with appropriate support. Existing approaches typi- cally rely on mharse-grained difficulty annotations (Su et al. 2025) or heuristically stacked constraints (Patel et al. 2025). lacking a precise mechanism for targeting this frontier. In practice, self-generated approaches tend to yield data confined within the model's 's own expressive ceiling, thus hindering sys- tematic difficulty progression. To address this, we draw inspi- ration from the educational psychology concept of the Zone of Proximal Development (ZPD) (Vygotsky) 1978; McL.edd 2012). which defines the cognitive space where learner can succeed with guidance on tasks they cannot solve alone. We operationalize this by defining two personas: the Less Knowl- edgeable Peer (LKP), base LLM, and the More Knowledge- able Other (MKO), a superior tool -uggmeted agent with advanced reasoning. By definition, tasks that the LKP fails but the MKO masters are situated within the model's s ZPD. This provides princi- pled mechanism for identifying maximally informative training resources and, crucially, allows for continuously adaptive curriculum that evolves as the model's
Beyond LLM's reach with any support
LLM's Zone of Proximal Develop (ZPD)
can do with support (eg. tool, odvanced reasoning)
LUM can do unassisted
Nigh qualtyy data from 2PD
Less Rncwredge- -atle Peer KP)
More Knowledge-
-ablee Other (MW0)
Figure 1: High-quality data within an LLM's ZPD catalyzes its trans- formation from LKP to MKO.
capability frontier expands.
We instantiate this principle in the AgentFrontier Engine, a novel data synthesis framework de- signed to generate complex-reasoning data within LLM's ZPD. The engine operates via a process of adversarial calibration, dynamically probing the capability frontier of the LLMs. It constructs multi-disciplinaryy QA pairs that necessitate knowledge fusion across multiple web sources, mov- ing beyond simple fact retrieval. Our engine employs a dual-pipeline architecture: tasks solvable by the LKP curated as knowledge-intensivee data for continued pre-training (CPT), while those requiring the MKO are designated as frontier-level data for post-training. This design establishes a virtuous cycle, yielding a continuous stream of adaptive data that propels model capability forward.
Our contributions are threefold:
We present AgentFrontier Engine, a scalable data synthesis framework founded on the theory of Zone of Proximal Development (ZPD). By integrating agentic refinement and LKP-MKO adversarial calibration, it generates a dual stream knowledge-intenstt data for broad competence and frontier-level data for advanced reasoning.
2. We establish ZPD Exam, an automated benchmark designed to probe the ZPD of LLMs. It assesses advanced capabilities such as tool using and in-depth reasoning by complex multi- disciplinary questions that require cross-document knowledge fusion and deep research.
3. We demonstrate the effectiveness of our framework by developing AgentFrontier-3000 A3B. By applying continued pre-training on 50 billion tokens knowledge-intensive data and fine-tuning on 12.000 frontier-level trajectories synthesized by our engine, the result- ing model achieves 28.6 28.6% on the challenging HLE benchmark and sets state-of-the-an performance on ZPD Exam-v1, R- R--Bench-T and xBench- -Bench-ScienceeA..
AGENTFRONTIER DATA ENGINE
AgentFrontier Engine addresses the critical need for training data that fosters knowledge fusion and complex reasoning, which operationalizes the theoretical framework of the Zone of Proximal Development to generate challenging tasks that reside at the frontier of a LLM's capabilities. In- stead of passively curating existing information, the engine is designed actively forge complexity
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Published as a conference paper at ICLR 2026
through three-stage agentic synthesis pipeline. This process aims evolve LLMs from knowledge
retrievers into sophisticated reasoning agents. The entire workflow. depicted in Figure transforms raw document corpus Crww into a calibrated, high- value dataset DPDD The detailed procedure is The detailed prompts are provided in the Appendix F
presented in Algorithm
|
Round
Round
Round K+f Complexity
Stage
Stage
QA Complexte
Complexity
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Agant
webpagee
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rephrase
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Knowledge Seiieddg-iiteesiie intensive @A
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Continued Pre-training
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similarity thrashold
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Figure 2: The three-stage pipeline of the AgentFrontier Engine. Stage I generates seed QA pairs from multiple sources. Stage II iteratively escalates their complexity using a tool-augmented agent. Stage III applies ZPD-based calibration filter isolate high-value training samples.
2.1 STAGE I: SEED QUESTION GENERATION FOR KNOWLEDGE FUSION
The pipeline begins with a diverse, distppidiseil corpus Crow of one million public documents. We first employ a powerful LLM, Qwen3-235B-A22 (Yang et al. 2025), as a chunking function Pehunk to preprocess the corpus. This function cleans artifacts (e.g., HTML tags) and condenses long texts into information-dense chunks Cohunk, such that Cehunk Udec... @chunk(d).
To generate tasks that inherently demand knowledge fusion, we synthesize questions from compos- ite units gnitus of thematically related chunks. To overcome the computational infeasibility of combinatorial search, we adopt an efficient, retrieval-based approach. We first build a vector index Cehunk and, for each chunk ç,, retrieve its kan nearest neighbors. Within this local neighbor- hood, we search for triplets (c, Cj, cx) that exhibit high thematic coherence, formally defined as (im Cyy Ttheme for all distinct pairs, where Sim(:.* is semantic similarity function.
These composite units are then fed to a generator model, Mgen (DeepSeek-R1-0522 (Guo et al., 2025a)), to synthesize initial question-answer pairs. This process yields seed dataset that serves as the foundation for complexity escalation: Dsesd {(go, an) = Mgen(U.) U, is a composite unit).
2.2 STAGE II: ESCALATING COMPLEXITY THROUGH AGENTIC REFINEMENT
The core of our engine an iterative refinement loop driven by a refinement agent Aretine. which in- tegrates DeepSeek-RI with a tool suite T = (TT'earch: Teholarr Trrowser Teode For QA pair (gk. ag) iteration the agent applies an escalation operator Vesculate generate more sophisticated pair (qk+11, 4++1)
V eseealate (g:, age Areline.. This operator enriches the QA along four dimensions:
Knowledge Expansion: It actively queries external sources to retrieve and weave in rele- vant background knowledge, broadening the informational scope of the question.
Conceptual Abstraction: It conducts in-depth analysis the core concepts within the provided materials, abstracting higher-level principles or identifying subtle relationships.
Factual Grounding: performs multi multi-source cooss-valiattion and targeted augmentation to enhance the factual accuracy and depth of the content.
Computational Formulation: leverages the Python execution to craft QA that require quantitative calculation or logical simulation, assessing reasoning and computational skills.

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Published as a conference paper at ICLR 2026
This self- ceffbbotstrapping process creates virtuous cycle, where the output of one iteration becomes the input for the next, building increasingly more intricate reasoning paths. Figure 6 illustrates an example where question is progressively refined by interleaving web search with numerical computation. After K iterations, this stage produces a dataset of highly complex QA pairs, Drefined-
2.3 STAGE III: ZPD-BASED FILTERING AND CALIBRATION
Not all synthesized QA pairs are equally valuable for training. To isolate tasks that reside precisely within an LLM's ZPD, we introduce rigorous calibration mechanism based on our LKP-MKO framework. We instantiate a Less Knowledgeable Peer (ALxp) with the base DeepSeek-RS R1-028 model (without tools) (Guo et al. 2025a) and a More Knowledgeable Other (AMKO) with the powerful, cool-augmented DeepSeek- V3.1 agent (Liu et al.) 2024).
For each candidate pair (q, a) E Drefined, we first assess its difficulty, Let IsSolvableBy(A..g q, E {0, } be a binary function, implemented by an automated judge (GPT-40 (OpenAl 2024)). which returns I if agent A correctly answers q. (a) If IsSolvableBy(Auxc 9. a) 1. the pair is deemed too simple and is allocated to a general knowledge dataset D Peectrain for continued pre- pre-training. (b) If
BatellaabbBB(AArrrr 9. a) 0, the pair is challenging and passed to the MKO for further evaluation.
To stratify the challenging data, AMKO performs Best-of-N (BoN) verification with N = 3, generat- ing N independent solutions (s1....,50) SN The data is then partitioned based on the outcome:
Verified for Post- Training (Dzep): If the MKO finds at least one correct solution (i.e.. Σe IsCorrect(s;, a) > 1), the pair is considered to be within the model's 7PD) challenging yet learnable. These verified pairs form our final training set.
Flagged for Human Review (Phuman): If the MKO fails in all N attempts (i.e.. Σ IsCorrect(s;; 8;, a) = 0), the pair either flawed or exceptionally difficult and routed to human experts for analysis. The human review process is detailed in the Appendix C.4
Finally, to ensure dataset diversity, we apply a semantic redundancy filter. A newly generated pair (q', a²) is discarded if its question q' is too similar any question already in DZrD- Specif- ically, we discard (g' aa') if max model (Zhang al. 2025) and the threshold is to 0.7.
Sim(q'. 9) 2 c, where Sim(.,) is measured by a reranker
Through this three-stage pipeline, the AgentFrontier Engine provides a scalable method for gener- ating complex reasoning data, continuously pushing the boundaries of L.M capabilities.
ZPD EXAM: A SELF-EVOLVING BENCHMARK FOR LLM AGENTS
Evaluating rapidly advancing LLMs requires benchmarks that co-evolve with their capabilities. While expert-crafted exams like Humanity's 's Last Exam (Phan et al.] 2025) probe the frontier of human knowledge, their static nature and prohibitive creation costs hinder scalable and continuous assessment. We introduce the ZPD Exam, an automated and continuously evolving benchmark designed to assess the deep research capabilities of advanced LLM agents.
3.1 BENCIMARK CONSTRUCTION: FROM FRONTIER KNOWLEDGE TO AGENTIC RESEARCI
The ZPD Exam is designed to simulate scientific discovery by generating tasks that are intractable using only parametric knowledge, thus compelling models to function as research agents. The benchmark is constructed using our AgentFrontier Engine (Section 2) specifically configured to generate novel, multi-disciplinary questions. Crucially, this benchmark corpus is strictly disjoint from the corpus used to construct training data, ensuring fair and uncontaminated evaluation.
Grounding in the Knowledge Frontier. We ground this exam in the knowledge frontier by curat- ing corpus of 30,000 recent scientific papers published between 2023 and 2025, spanning multi- disciplinary domains such as mathematics, computer science, and physics. This ensures that success demands genuine, on-the-fly fly reasoning and information synthesis, not merely knowledge retrieval.
Calibrating Tasks to the LLM's ZPD. From our initial corpus, the AgentFrontier Engine generates candidate questions, which are then subjected to strict adversarial filter align with the Z.D of
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baseline model (DeepSeek-FF R1-0528 (Guo et al. )025a To be included in ZPD Exam-vl, problem must satisfy a dual constraint: it must be unsolvable by the baseline model in three unaided attempts, yet consistently solvable by the same model across three attempts when granted access to tools. This process isolates problems that are difficult but solvable with assistance, defining the empirical boundary of the model's ZPD.
This automated pipeline enables a flywheel-like iterative process: as models improve, the ZPD exam can be regenerated to target the new frontier, making it a living benchmark resistant to saturation. After multiple rounds of validation and deduplication. ZPD Exam-v1 was constructed by sampling 1,024 public questions and a corresponding private set. All questions are open-ended short-answer format, facilitating automated grading. The benchmark composition is detailed in Figure 7
3.2 ZPD EXAM: A DIAGNOSTIC BENCHMARK FOR AGENTIC REASONING
The ZPD Exam proposes new evaluative frame- work, shifting the focus from an LLM's static para- metric knowledge (Hendrycks et al.) (021) to its
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"open-book" examination where
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agent must first author the "book" through active ex- ploration and tool use. This design philosophy de- liberately situates the challenges within the ZPD for current LLMs, calibration confirmed by their low initial scores (Figure 3). Our empirical results vali- date this diagnostic power, revealing clear stratifi- cation of agent performance into three distinct zones.
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Zone 1: Intrinsic Competence (Score 20). This tier establishes the baseline, reflecting the perfor- mance of LLMS relying solely on their parametric knowledge (e.g., GPT-5 and Gemini-2.5-Pa without tools). By design, the problems are intractable with- out external information, confirming that these tasks lie outside the models' unaided capabilities. This zone effectively establishes a baseline, quantify- ing the limits of intrinsic, "closed-book" reasoning, confirming that any score above this threshold is directly attributable to the agent's ' ability to leverage external tools support.
3b
2PD Sammve2 Score
Figure 3: Performance of LLM agents on the ZPD Exam-vl benchmark, stratified into three distinct capability zones.
Zone 2:
The Reasoning Bottleneck (Score 20-60). This intermediate tier characterizes the ZPD itself, where agents (e.g., GPT-4o with tools, WebShaper-72D5 72b) can achieve partial success with as- sistance but lack mastery. This zone highlights the benchmark's crucial distinction from standard RAG evaluations. While RAG tests comprehension of a given context, agents here falter in the more demanding task of autonomously discovering, structuring, and reasoning over the necessary information. Their failures stem not from tool-level errors but from a higher-order "reasoning bot- tleneck": deficit in strategic planning, synthesizing information across multiple tool calls, and adapting their approach. This reveals that access to tools is necessary but insufficient; the primary limiting factor is the agent's 's meta-cognitive ability to orchestrate these tools effectively.
Zone 3: Emergent Mastery (Score > 60). Agents in this top tier (e.g.. DeepSeek- V3.1 with tools) demonstrate a qualitative leap in capability. They have transcended the reasoning bottleneck and exhibit robust, multi-step planning and synthesis. Their behavior is analogous to the More Knowl- edgeable Other, seamlessly integrating tool-based exploration into a coherent reasoning process to solve problems far beyond their intrinsic reach. Achieving this level of performance signifies the emergence of a truly capable agent that can autonomously navigate complex problem spaces.
In summary, the ZPD Exam serves not merely as a leaderboard but a powerful diagnostic in- strument. Its tiered results provide a fine-grained analysis of an agent's 's developmental stage-from from what it knows (intrinsic), to what it can learn to do with support (ZPD), to what it has mastered. This allows us to pinpoint critical reasoning faculties that require improvement, thereby charting a clear path toward more autonomous and capable AT agents.
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Published as a conference paper at ICLR 2026
4 EXPERIMENTS
4.1 EXPERIMENTAL SETUP
Training Data Synthesis. We synthesize training trajectories using tool-augmented DeepSeek- V3.1 (Liu et al. (024)) following the iterative tool-calling and summarization paradigm from We- bResearcher QQaa et al. 2025) el. 2025). Each trajectory is generated through multi-round process adhering to the ReAct (Yao et al. 2002), comprising a sequence of round-wisc wise reasoning reports and observations after the corresponding tool calls. In each round, the model generates a reasoning report that summarizes accumulated evidence, analyzes progress towards the research question, and specifies the next aitiooneitter invoking
tool or outputting a final answer.
Baselines and Fine-Tuning. We compare proposed AgentFron- tier dataset against three promi- nent, multi-disciplinary agent-tuning datasets: TaskCraft (Shi et al. 2025). MegaScience (Fan et al. 2025 and Verse (Miro.indd Data-Team 2025). To ensure a fair comparison, we first curate high- quality subset of 12,000 trajectories for each dataset via rejection sam- pling, retaining only instances where the final answer is perfectly correct. As shown in Table our AgentFrontier dataset exhibits a more balanced and diverse tool-use distribution compared to the baselines, with substantial usage across scholar, browser, and code tools. This reflects its focus on complex reasoning problem-solving. For rejection sampling fine-tuning (RFT), normalize the training data volume to 25,600 rounds for each dataset, with each round capped at 40,960 tokens, and train for 3 epochs.
Table 1: Statistics trajectories across the training datasets. Avg. Rounds and Avg. Calls are computed per trajectory.
Avg. Tool Calls
Dataset Rounds
Scarch
Scholar
Browser
Code
TaskCraft
3.38
1.04
0.14
1.19
0.01
MegaScience
2.68
0.26
0,56
0.49
0.37
Verse
2.18
0.12
0.04
0,09
0.93
AgentFrontier 3.32
0.32
0.66
0.82
0.52
I
Models and Evaluation. We conduct experiments on the Qwen3 model family YYagg et al., 2025), including both dense (Qwen3-8B, Qwen3-32B) and Mixture-of- -f--eerrss (Qwen3-30B-A33) A33B- Thinking-2507) variants. We evaluate performance on four challenging benchmarks designed to probe high-level reasoning across diverse disciplines: HLE (Phan et al. 2025). ZPD Exam, R- Bench (Guo et al. (2025)) and xBench-Scienceee rectness of final answers, we employ an LLM-aasa-Judde. Specifically, we use 03-mini (OpenAT) 2025b) as the judge, guided by the official strict evaluation prompt from HLE (Phan et al. 2025 All model generations use nucleus sampling with a temperature of 0.6 and a top-p of 0.95.
(Xbench-Team 2025). For evaluating the cor-
4.2
MAIN RESULTS
Overall Performance Across Benchmarks. Table illustrates the performance of the Qwen3- series models after fine-tuning. Models trained on AgentFrontier consistently achieve state-of-the- art results, decisively outperforming all baseline datasets across all four benchmarks. In contrast, the performance of competing datasets such as TaskCraft (Shi et al.) 2025), MegaScience (Fan et al.) 2025), and Verse (MiroMind-Dade Sold--Daa-Tennnn Team 2025) is inconsistent; while each may show strength on a particular benchmark, none demonstrates the robust, cross-domain superiority imparted by AgentFrontier. This trend of superior and consistent performance holds across other model back- bones as well.
Subject-Level Dominance on the HLE Benchmark. To investigate the source of this performance advantage, we conduct a fine-grained analysis on the particularly demanding Humanity's 's Last Exam (HLE) (Phan et al. (025)) benchmark, examining results across eight academic disciplines with vari- ous model backbones (Table For both the Qwen3-8B and Qwen3-32B backbones, models trained on AgentFrontier exhibit remarkable breadth, securing the top performance in six and seven out of the eight subjects, respectively. This subject-level dominance translates to significant lead in over- all average scores, with AgentFrontier surpassing the next-best dataset by 3.8 and 3.9 absolute points on the 8B and 32B models, respectively. The advantage becomes even more pronounced with the Qwen3-300 1ama3-30B-A-- 30B-A3B model, where fine-tuning on AgentFrontier outperforms all competing datasets in every single subject. This comprehensive superiority results in final average score of 25.67% %, rep-

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Published as a conference paper at ICLR 2026
Table 2:
Performance comparison on four multi-disciplinary benchmarks. Scores are reported as "mean + confidence interval". The best score is highlighted, and the second-best is underlined.
Evaluation on Four Multi-disciplinary Benchmarks
RFT Dataset
Tools
HLE (text-only)
ZPD Exam-vl
RBench-T
xBench-SciQA
Backbone: Qwen3-8B
(no finetunin)
4.0
5.340.58
15.0=1.19
20.043.48
(no- finetuning)
/
5.9 2.94
35.2+
58.2 1.23
24.0 24.043.27
TaskCraft
14.641.26
87.5+10.85
64.3
30.0+3.72
MegaScience
14.2
84.71.0.93
1..3
36.0
Miro Verse
15.0
84.5++0.93
1
32 32.0+3.79
AgentFronties
1..32
86.840.92
67.2
40.0+3.85
Backhone: Qwen3-32B
(no-f (inettnng))
1.39
5.8.00.00
60.9
37. 17.09
(no finehuning)
8.4-0.92
48.641.22
65.1
39. 39.0+3.92
TaskCraft
18. 18.4411.38
91.1
66.2
40.0+33.96
MegaScience
18.5+1.8
89.640.778
68.4
40. 40.0+3.98
MiroVerse
19.9+1.42
87.7+0.84
67.4+1.16
43.0+4.02
AgentFrontier rontier
23.8
90.9
70.3
51.0+4.06
Backbone; Qwenn--3BB-A3D-Thinnee
(no- finchuning)
X
1.2
4.940.56
51.2+ 1.07
32.0+3.79
(no- finctuning)
10.2+1.08
47.2+1 28
55.1+1.13
40, 43.043.98
TaskCraft
19.9
90. +0.76
72.3
44. 44.0++1.08
MegaScience
/
20.2
90.0
73.1
48. 48.00.4.08
Verse
19. 19.6+1.42
86.7+0.87
70.6
49 49.0+4.08
AgentF
AgentFrontier rontier
25.7
91.4
74.4+1.13
54.0+4.01
resenting a 178% and 152% relative improvement over the original base model settings without and with tool augmentation, respectively. These results indicate that model capacity increases, the rich, multi-step reasoning trajectories within AgentFrontier become increasingly effective at un- locking expert-level problem-solving capabilities across a wide spectrum of academic fields.
ANALYSIS
5.1 SENSITIVITY TO LKP MKO CONFIGURATION
We conduct an ablation study on the Less Knowledgeable Peer (LKP) and More Knowledgeable Other (MKO) configurations assess our framework's sensitivity. The study investigates the trade- off between synthesis efficiency (data yield) and data complexity (reasoning depth), aiming to validate that our chosen configuration strikes an effective balance.
Experimental Setup. We evaluate three LKP/MKO configurations
1,000-sample subset of
Dretmed to probe varying capability gaps. Note that DeepSeek V3. possesses stronger reasoning and agentic abilities than DeepSeek-R1 (Liu et al. 2024 Guo al. 2025a). The configurations are: (1) Original (Balanced Gap), using DeepSeek-Ri (no tools) as LKP and DeepSeek- V3.1 (with tools) MKO; (2) Wider Gap, replacing the LKP with a weaker Qwen3-30B-A3B; and (3) Narrower
Gap, using DeepSeek-BR RI for both roles, where the MKO is distinguished only by its access to tools.
Results and Analysis. As presented in Table increases the data yield by 44. 1% but at the cost of complexity, evidenced by a sharp decrease in average rounds (144.3%) and tool calls (163.4%) This results in simpler data, less effective for advancing model capabilities. Conversely, narrower gap (Config. 3). where models differ only in tool access, maintains high complexity but suffers a 27.5% drop in yield, rendering it inefficient for large-scale synthesis. These results empirically validate that our chosen original configuration
wider gap (Config. 2) with a weaker LKP

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Published as a conference paper at ICLR 2026
Table 3: Accuracy on the Humanity's Last Exam (full text-only set). Results are reported across
major knowledge domains. Each block corresponds to different Qwen3 backbone. Numbers with colored background denote the best within each block; underlined numbers denote the second best.
Domain Accuracy on Humanity's 's Last Exam (%)
RFT Datasel
Tools
Math
CS/Al
Bio./Med.
Physics
Humanities
Chem.
Eng.
Other
Avg.
Backbone:
Qwen3-88
[no- (inetunn))))
X
6.46
2.65
5.88
0.99
3.63
1.00
6.45
1.61
4.00
[nn- fineternin))
/
6.26
3.54
9.05
2.48
7.25
7.00
6.45
5.14
5.94
TaskCraft
/
16.21
10.62
14.93
6.44
22.80
9,00
9.68
15.43
14.58
MegaScience
14.56
I0.62
18.10
5.94
21.76
9.00
12.90
16.57
14.21
MiroVerse
17.33
I0.62
15.38
5.94
21.24
8,00
6,45
17.71
15.00
AgentFrootierr
22.46
14.16
16.74
10.40
24.35
11.00
6,45
19.43
18.80
Backbone:
Qwen3-328
fine-tnnnn)))
8.72
5.75
10.41
0.50
8.00
6.45
7.34
[no- fincfuning)
10.97
5.31
5.00
6,45
4.57
8.36
TaskCraft
20.72
14.16
16.74
8.91
25.39
14.00
14.52
20.57
18.43
MegaScience
21.23
14.60
14.93
6.44
29.02
12.00
11.29
21.71
18.52
MiroVers
22.56
14.16
16.74
10.40
34.72
12.00
6.45
20.57
19.92
AgentFrontier
28.21
16.81
18.10
15.84
30.57
15.00
19.35
24.00
23.82
Backbone:
Qwen3-30BB
B..B--Tkinning-22..
[no- (innlerenn)))
x
13.03
7.96
8.14
3.47
7.25
5.00
8.06
2.86
9.24
(mo- finctuning)
13.13
7.96
6.33
1.98
11.92
10.00
6.45
10.29
10.17
TaskCraft
24.62
12.39
16.29
7.92
21.76
19.00
12.90
22.29
19.87
MegaScience
23.69
14.60
20.81
9.90
26.94
15.00
8.06
18.29
20.15
MiroVerse
23.38
12.39
20.81
9.41
21.87
7.00
11.29
22.86
19.64
AgentFrontier 29.85
16.81
21.27
17.82
31.61
22.00
14.52
28.00
25.67
achieves crucial trade-off, ensuring both scalable data generation and sufficient data complex- ity. This demonstrates our model selection is a deliberate strategy to optimize this balance, not an arbitrary choice.
Table 4: Ablation study LKP/MKO configurations, analyzing the trade-off between ZPD data yield and data complexity. The ZPD Data Yield defined as the number valid D2rD samples divided by the total candidate samples. Our original configuration (in bold) demonstrates superior balance. S/Sc/B/C denotes Search, Scholar, Browser, and Code tools respectively.
Configuration (LKP/ML) MKK)
ZPD Data Yield (%)
Avg. Rounds
Avg. Tool Calls
Tool Usage Dist. (S/Sc/B//C)
DS-R1/ V3-13.1+T (Original)
33.1
3,32
2.32
0.32 /..66 0.82 /2/ 0.52
Qwen3-30B DS-V3. 1+T (Wider Gap)
47.7 (144.196)
1.85 (144.3%)
0.85 (163.4%)
0.18/0.23 0.36 0.08 (all
3. DS-RI T (Narroeer Gap)
T
24.0 (127.5%)
2.99 (8)
1.99 (#))
0.19/0.60 0.58 0.55
5.2 BON ANALYSIS: VALIDATING DIFFICULTY RICHNESS & POTENTIAL FOR RL TRAINING
To assess the difficulty distribution AgentFrontier and the latent capabilities of the RFT model, we conducted Best-of-N (BoN) analysis. On held-out validation set of 300 samples, generated N 8 independent solution
Bedt of (K4M) Accemcy 880
$550 Conféance eieecal
+30.0 pts
trajectories for each task and measured the success rate if least one of the N attempts was correct (pass @ N).
As shown in Figure the accuracy dramatically increases from 21.7% at pass@1 to 40.7% at @ This 19.0- point improvement provides two key insights. First, it validates the designed difficulty of AgentFrontier the dataset is not binary mix of trivial and impossible tasks. Instead, it presents a challenging frontier where initial at- tempts may fail, but success achievable through explo- ration. This provides a rich learning signal beyond superficial pattern matching. Second, it high- lights the significant potential for subsequent reinforcement learning (RL) While supervised
N (Number of Attempos)
Figure 4: Best-of-N accuracy of our RFT Qwen3-30B-A3B on a 300-sample validation set from AgentFrontier.
8

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Published as a conference paper at ICLR 2026
fine-tuning (SFT) trains the model single reference solution, the large gap between pass @ and pass @6 8 confirms that for problems the model fails to solve on the first attempt, its policy distribution contains diverse and successful alternative trajectories. This is crucial precondition for effective RI., ensuring that exploration can discover high-reward experiences necessary for effective policy optimization. Therefore, AgentFrontier serves not only as robust training resources for SFT but also as strong foundation for RL to further unlock an agent's problem-solving potential.
5.3
WHY AGENTFRONTIES EXCELS: DISSECTING THE LEAP IN REASONING & TOOL-
HLE Accuracy vs. Round Intervals
From Shallow Retrieval to Deep Causal Reasoning. Figure 5 reveals the performance dynamics that underscore AgentFrontier s superiority, The vast majority (95%) of problems are solved within a 15-round horizon, a critical window in which our RFT dataset consistently outperforms all fine-tuning dataset baselines. This ad- vantage is principled consequence of our data generation strategy rooted in the Zone of Proximal Development. By curating tasks that are unsolvable by the base model yet solvable with external scaffolding, we create training instances of optimal difficulty. This forces the model to abandon simplistic, single-source retrieval and instead master knowledge fusion- the non-trine conviaiiial meta-skill of inte- grating disparate information streams into a coherent solution. The agent learns not merely what information to retrieve, but how to synthesize it, shifting from shallow mattem-matching in-depth causal reasoning.
found trannal
Cumulative Distribution of Rounde
From High-Vo Volume Invocation to High-Efficacy Orchestration.
The design philosophy of AgentFrontier prioritizes the cultivation Figure 5: Accuracy vs. num- of strategic tool orchestrators over rote tool callers. Unlike datasets ber of rounds on 4 datasets.
that promote skewed tool dependencies (e.g., code-centric MiroVerse or search-centric TaskCraft), AgentFrontier promotes a balanced tool-use distribution (Table This forces the agent to develop a sophisticated understanding of inter-tool synergy rather than mastering a single tool in isolation. The results on the HLE benchmark (Table 55) confirm this empirical payoff. Our agent achieves macro-average conditional tool accuracy of 26.3%-a significant leap from the 21% plateau of competitors-ww wwith a comparable number of interactions. This demonstrates that agent capability stems not from the volume of tool calls, but their efficacy. Our method trains the model to transition from high-volume, low-yield tool usage to precise, high-efficacy orchestration, which is a crucial step toward creating more resourceful agents.
Table 5: Tool usage statistics for the Qwen3-30B-Ar agent on the HLE text-only test set (2154 problems). Each column block shows performance after RFT on different dataset. We report average usage per round and conditional tool accuracy (Acc, %), defined as the success rate for tasks that use the tool. The final row details overall metrics. Best results are in bold.
TaskCraft
MeguScience
MiroVerse Verse
AgentFrontier
Tool / Metric
Usage
Acc (%)
Usage
Acc (%)
Usage
Acc (%)
Usago
Acc (%)
Scarch
0.68
19.6
0.67
20.3
0.73
20.4
0.73
24.9
Scholar
0.78
21.0
0.98
20.3
0.87
20.6
0.89
25.4
Browser
1.24
25.2
23.4
22.7
29.8
Code
0.52
18.1
0.65
18.6
0.67
18.4
0.63
24.9
Overall (Roands/Acc.)
4.21
21.0
4.70
20.6
4.74
20.5
4.57
26.3
5.4 HOLISTIC AGENTIC TRAINING
Setup. We further investigate the benefits a holistic training pipeline that incorporates continued pre-training (CPT) and post-training. Due to the large-scale GPU computation in CPT, this study is conducted only on 10ee3-30B-A33 30w.-----Thiniing--------------- Thinking-25077 and our AgentFrontier data. The holistic training pipeline consists of two stages: (1) Continued Pre-training (CPT): One epoch over 50B tokens, comprising million summarized text chunks and 20 million hhowledge-intensse QA pairs. (2) Rejection Sampling Fine-tuning (RFT): Three epochs 12,000 high-quality trajectories.

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Published as a conference paper at ICLR 2026
Evaluation. We conducted comprehensive evaluation of our model, AgentFrontier A3B, against a broad spectrum of competitors: proprietary LLMs (OpenAl 2024 anthropic 2025: Deep-
Mind 2025) with tools, proprictary and prominent open-source deep-research agents (OpenAl
2025a Google 2025
MoonshotAl 2025
Wu et al. 2025 Li et al. 2025a Tao et al. 2025).
Results. As shown in Table 6 holisti- cally trained agent not only sets a new state- of-the-art among open-source models but also competes effectively with significantly larger, proprictary agents. The final row isolates the contribution of CPT, which consistently boosts performance across all benchmarks (+2.9 on HLE, +7.0 on xBench-ScienceeAA) Notably, CPT yields +2.0 point gain on ZPD Exam, where the RFT-only model's performance was already near-saturation. This provides strong evidence that strengthening model's founda- tional knowledge via CPT directly enhances its capacity for complex agentic tasks.
Table 6: AgentFrontier-30B outperforms SOTA agents on four multi dulti-disciplinary benchmarks. The performance gain from our CPT shown in the final row. marks results from official reports.
Agents
HLEE
ZPD Exam
Ribeach- xBlench-SciQAA
Propristary I.M.s wath Tools & Deeps Reszorch Agents
GPT-4o
4.8
51.3
48.5
15.0
Clande 4 Sunnet
14.3
86.5
71.1
47.0
Gemini 2.5 Flach
12.6
58.1
39,0
OpenAl Deepieeeaarhh
26.6'
Gemini Deeplesezrrh
26.9
Kimi Reseurcher
26.9
Open-source Agents
Wehhamcen-Qo
6.4
38.0
WehSwilos-77 728
9.2
62.1
27.0
WobShuger-722 72B
8.0
$4,4
66.8
29.0
AgcarPronteer-308--00 (Qurs)
5.5
CASE STUDY
RET only CPT-RFT
25.7
91.4
74.4
$1.0
28.6
93.4
77.1
61.0
A qualitative analysis on an HLE case (Phan
(CPT guin)
+2,9
+2,0
+7,0
al. 2025) (Appendix E) further illustrates our agent's reasoning process. In a complex clinical scenario, OpenAl DeepResearch (OpenAI, (025a) agent exhibited diagnostic fixation, misdiag- nosing Charcot Arthropathy by focusing on common negative findings like sterile synovial fluid. In contrast, our AgentFrontier agent correctly identified the key anomaly: the patient's paradoxi- cal worsening on prednisone. hypothesized that this was due to a latent infection unmasked by immunosuppression, rather than inflammatory rebound. This triggered a targeted inquiry, using a literature search to confirm that Chronic Osteomyelitis can present with sterile aspirates and is exacerbated by steroids. This progression from identifying an anomaly to forming hypothesis and validating it with targeted research demonstrates AgentFrontier's 's advanced research capabilities.
6 RELATED WORK
Data Synthesis for LLM Agents. Synthesizing high-quality data is critical for advancing LLM agents that require complex reasoning and tool use Zeng et al., 2025 Liu al. 2025 Zhou et al. 2024]. Initial efforts replaced costly manual curation with programmatic generation, creating agentic tasks with verifiable solution trajectories (Shi et al. 2025 Hongjin et al. 2025 Huang al. 2025). Subsequent research aimed to enhance data quality by grounding synthesis in external knowledge sources like scientific documents (Fan et al. 2025 Feng et al. 2025). While these approaches in- crease factual richness, they often produce tasks solvable via localized information retrieval, rather than promoting the deep knowledge integration essential for complex research (OpenAll (025a). A central challenge remains the precise calibration of task difficulty. Without principled control mechanism, synthetic data risks being too simple for effective learning or too complex to yield a us- able training signal (Li et al.) 2025b). These strategies rely on heuristics like incremental constraint addition (Patel et al., 2025) or probes to distinguish reasoning from recitation (Yan ct al. 2025), yet lack principled framework to calibrate difficulty for scaffolding complex reasoning.
7 CONCLUSION
In this work, we presented a novel data synthesis paradigm based on the Zone of Proximal De- velopment (ZPD) theory. Our framework co-generates a targeted training resources and a self- evolving ZPD Exam to progressively enhance and evaluate agentic reasoning. The resulting model, AgentFrontier-30B 30B-A3B, validates our approach by achieving state-of-theearr results on challenging expert-level multi-disciplinar benchmarks, surpassing even significantly larger proprietary agents. This work demonstrates that a principled, pedagogical approach to data synthesis is a highly effec- tive, if not essential, strategy for cultivating advanced reasoning abilities in a dafa-efficient manner.
10

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Published as a conference paper at ICLR 2026
ACKNOWLEDGMENTS
This study was supported by grants from the National Science Foundation of China (T2541010), the National Key R&D Program of China (2024YFF1207 100, 2024YFF1207103) and Beijing National Research Center for Information Science and Technology (BNRist). The funders had no roles in study design, data collection and analysis, publication decisions, or manuscript preparation.
ETHICS STATEMENT
All authors of this work have read and agree to adhere to the ICLR Code of Ethics. The corpora used for data synthesis in our research are sourced from publicly available documents. We have ensured that our use of this data complies with all applicable terms of use and licenses provided by the data owners.
REPRODUCIBILITY STATEMENT
For immediate verification during the review process, the paper and its supplementary materials already include:
Evaluation Data:
The complete question-ande aattion--nd-answwe sets for all evaluation benchmarks used in our study.
Generated Examples: A curated set of 100 question-and-answer examples from our AgentFrontier pipeline, provided for qualitative analysis.
Novel Benchmark: The full ZPD Exam-v1 benchmark proposed in this work.
Implementation Details: Detailed descriptions of training hyperparameters, tool imple- mentation, and evaluation setup available in Appendix and D
Prompts: The exact prompts employed for our LLM-as-a-Judge, data filtering mechanisms and iterative agentic refinement are provided in Appendix F
Together, these resources offer a transparent and direct pathway for verifying our findings and serve foundation for future research.
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Yanzhao Zhang, Mingxin Li, Dingkun Long, Xin Zhang, Huan Lin, Baosong Yang, Pengjun Xie, An Yang, Dayiheng Liu, Junyang Lin, Fei Huang, and Jingren Zhou. Qwen3 embedding: Advanc- ing text embedding and reranking through foundation models. arXiv preprint 17Xiv:2506.05176, 2025.
Kun Zhou, Beichen Zhang, Zhipeng Chen, Xin Zhao, Jing Sha. Zhichao Sheng, Shijin Wang, Ji- Rong Wen, et al. Jiuzhang3. 0: Efficiently improving mathematical reasoning by training small data synthesis models. Advances in Neural Information Processing Systems, 37:1854-1888 1889, 2024.
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APPENDIX
A THE USE OF LARGE LANGUAGE MODELS (LLMs)
During the preparation of this manuscript, we utilized Large Language Models (LLMs) for assis- tance with language proofreading (including grammar, spelling, and word choice) and for generating ETEX code for tables and figures. The core intellectual contributions, including research idcation, analysis, and the substantive writing, are entirely the work of the authors.
B MORE RELATED WORK
The evaluation of advanced reasoning in large language models (LLMs) was pioneered by MMLU (Hendrycks et al. 2021). which set the standard for assessing multi-disciplinary knowledge. This led to wave of subsequent benchmarks (Rein al., 2023 Wang et al., 2024; Du et al. 2025: Guo et al., 20255; Xbench-Teame (025) targeting undergradu- ate graduate level knowledge. However, the rapid progress of frontier models (OpenAl 2025b
Multi-disciplinary Benchmark.
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DeepMind 2025 anthropic 2025 is causing performance saturation these static benchmarks, reducing their effectiveness in differentiating top-tict models. While newer benchmarks like Hu- manity's 's Last Exam (Phan et al. 2025) increase difficulty through expert curation, they remain fixed assessments. In contrast, our work introduces the ZPD Exam, a self-evolving evaluation framework that adapts in lockstep with model capabilities, providing a consistently challenging frontier for LLM agent evaluation.
Ceep-Research Agents. Deep-research agent, system built upon large reasoning models (LRMs), is designed to automate multi-step search and reasoning. It empowers users to complete complex, cross-domain information synthesis and in-depth research tasks in minutes, process that would oth- 2222a 2025n Google [025 Anthropic XAI 2025 Perplexity 2025 MoonshotA]] 2025) have demonstrated impressive capabilities
erwise require hours of human effort. Proprietary agents
2025
in complex, multi-step research tasks. The open-source community has fostered rich ecosystem
of transparent and reproducible agents (Jin al., 2025 Li et 2025cld) TTo et al. [025 [Li al.)
2025a: Qiao et al. 2025). These efforts typically leverage explicit planning, tool-use, and web navigation to emulate human research processes, advancing the field through shared methodologies.
C DATA ENGINE DETAILS
This section provides a detailed breakdown the hyperparameters, procedural logic, and compu- tational costs associated with the AgentFrontier Data Engine, as outlined in Algorithm [T These details are provided to ensure the transparency and reproducibility of our data synthesis framework.
C.I HYPERPARAMETER CONFIGURATION
The data generation pipeline is governed by several key hyperparameters that control the granularity of data sourcing, the complexity of generated questions, and the strictness of the filtering process. Our configuration is as follows:
Thematic Coherence Threshold (Ttheme): Set to 0.8. This value determines the mini- mum semantic similarity required between text chunks form "composite unit" for seed question generation. A higher value ensures that initial questions are synthesized from thematically tighter content, promoting knowledge fusion.
Nearest Neighbors for Seeding (kmm): Set to 10. During seed generation, for each text chunk, we retrieve its kim nearest neighbors to search for coherent triplets. This balances computational efficiency with a sufficiently large search space for discovering novel com- binations.
Maximum Refinement Iterations (Kmn): Set to 30. This parameter defines the maxi- number of complexity escalation steps for any given QA pair in Stage II. This upper bound prevents infinite loops and manages computational resources.
Best-of-N (BoN) Verification Size (N): Set to 3. In the ZPD-filterin stage, the More Knowledgeable Other (AMko) makes N independent attempts solve problem. This helps to reduce the variance in the agent's performance and provides a more reliable signal of whether task is solvable.
Diversity Filter Threshold (e): Set to 0.7. To ensure dataset diversity, new QA pair is discarded if its question's 's semantic similarity to any existing question in DzrD exceeds this threshold. The similarity is measured by sate--o-the-aae reranker model.
C.2 AGENTIC REFINEMENT AND STOPPING CRITERION
The core of our data engine is the iterative refinement loop (Stage II), driven by the agent Arelie- The goal of the escalation operator, V eesculate- is to progressively increase the cognitive load required to answer a question. This is achieved by prompting the agent perform a series of enrichment actions, including but not limited to: expanding the question with new, relevant concepts discovered through tool use; abstracting general principle from specific examples; grounding the problem in a more complex. realistic context; or transforming a qualitative problem into a quantitative one requiring computation.
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Algorithm AgentFrontier Data Engine Pipeline
Input:
Craw: Raw document corpus
4 Phhunk: Chunking model
Mgen- Artine ALKP: Models and agents
Sim, IsCorrect, IsSolvableBy: Similarity and evaluation functions
Tieeme K, N₁ c, km Hyperparameters (thematic threshold, escalation steps, BoN size,
redundancy threshold, number of neighbors)
Output:
Dzpp: Calibrated training dataset for post-training
D pretrain Dataset for continued pre-training
Duman Dataset for human review
procedure GENRRATEZZODAAAAAG..
2:
DZPD, pretrain Dhuman +- 0,0
> Stage Seed Question Generation Preprocess corpus into semantic chunks Build index for efficient search
3:
Cehunk UnEC, Pehunk (d)
4: Vindex BuildVectorIndex(Ca.
5:
Dseed +-
6:
for each chunk 6; Cehunk do Nr <- FindNearestNeeghhonnssss Vindex kinn) for each pair (cy. Ck) from N₁ do
7:
D. Find k-NN for efficient combination
8:
9:
if (Sim(ca, Cj) > Ttheme A Sim (c, (x) > Tcheme A Sim(ey, Ck) >
then
(Yu, ap) 5 Mgen (gen( (ct Cj. c+3)
>> Generate QA from thematic unit
11:
Doced + Deed U {90, ao)}
end if end for
13:
end for
Stages II & III: Iterative Escalation and ZPD Calibration b> Initialize index for ZPD-set diversity check
15:
VzPD +- Build (ectolleer)))))) for each (go, 0n) in Dxeed do
16:
17:
(g. a) + (go: 0o)
> Stage II: Agentic Refinement Iteratively escalate complexity e.g., Expand, Abstract, Ground, etc.
18:
for k: = to K do
(q, & V Vescalate (ÿ, a, Areine)
20:
end for
Stage III: ZPD-based Filtering Check if too easy for Less Knowledgeable Peer
21:
if BarolvaaleBylAA....
then D pretrain a))
Prretrain
23:
else
Challenging for LKP, verify with MKO Soatutions + (AMKo(g) i = 1...N] P Best-of-N by More Knowledgeable Other if 3s E Ssotutions s.t. IsCorrect(s, a) then Verified as solvable, thus within ZPD quearest t- FindNeerrssNeiihborrgg V2rD)
24:
25:
26:
27:
if Qucarest = 0 or Sim Inearest) < then
Filter for diversity
28:
DzPD t- Dzep U ((g, a))
29:
Update Vectorlneed (VPDD.
30:
end if else
Druman + Diuman U ((g(())) a)) Unsolvable by MKO, potentially flawed too hard
32:
end if
34:
end if end for
35:
36:
return DZPD, D Prretmmn: D
37:
end procedure
The iterative escalation is guided by a principled stopping criterion tied to the ZPD framework: for given QA pair, the refinement loop terminates when the generated question qa becomes unsolvable
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by the Less Knowledgeable Peer (ALKP), baseline model formally defined in Stage III, when a predefined maximum of maax = 30 iterations is reached. This targeted termination ensures that the engine's computational resources are focused producing problems that precisely challenge the base model's capabilities. In our experiments, the ALXP is instantiated as DeepSeek-R1-055 without tool access.
C.3 COMPUTATIONAL COST ANALYSIS
We provide a detailed analysis of the computational cost required to generate single high-qualige data point for the DzPD dataset. The cost is broken down into the two primary stages of our pipeline: agentic refinement and MKO verification. All token counts are based on the respective model's tokenizer, and costs are estimated using official API pricing as of the experiment date]
C.3.1 COST OF AGENTIC REFINEMENT (STAGE II)
In this stage, the refinement agent, Areine (DeepSeek-RI) iteratively enhances a QA pair until reaches the capability frontier of the Less Knowledge Peer (LKP). The cost per data point variable, depending on the number of iterations (K) needed.
On average, processing a single candidate data point involves the following:
Refinement Iterations (K): A data point undergoes average of 7.81 iterations.
Token Throughput per API Call:
Input: 18,613.82 tokens.
Output: 11,643.22 tokens.
Tool Calls per Data Point:
Search: 0.70 calls.
Scholar: 0.61 calls.
Browser: 1.21 calls (avg. 10,000 tokens/call).
Code Interpreter: 0.94 calls (executed locally, no API cost).
Cost Breakdown. The average refinement cost per candidate is approximately $0.24, calculated
follows:
LLM Cost: 7.81 X (18, 614 x $0.56/M 11, 643 x $1.68/M) ~ $0.234.
Search Cost: (0.70 0.61) $0.00275 /c007 /call ~ $0.0036.
Browser Cost: 1.21 10, 000 x 50.00005/tokee > $0.0006.
C.3.2 COST OF MKO VERIFICATION (STAGE III)
Candidates that pass the refinement stage are then verified by the More Knowledgeable Other agent, Aмко (DeepSeek- V3.1 with tools). This Best-of-N (N = 3) verification confirms that the prob- lem is solvable by expert-level agent, thus ensuring its placement within the Zone of Proximal Development (ZPD).
For the N verification attempts single candidate, the average resource consumption is:
Total API Calls: 3.32 calls.
Token Throughput per API Call:
IIpput: 20,181.57 tokens.
Output: 24,169.88 tokens.
Total Tool Calls:
Pricing references: DeepSeek Model API (nttps://api-dos detp:pppddoossdepsseeeeeeeeeeee com/ SerpApi for Google Search (https:/ /ttpa:/p////pppppp///// wwt.enterpr/reeeeeecotmeo ise,, and Jina Reader API (https://jina...
reader/)
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Search: 0.50 calls.
Scholar: 0.92 calls.
Browser: 1.30 calls (avg. 10,000 tokens/call).
Code Interpreter: 0.53 calls (executed locally, no API cost).
Cost Breakdown. The verification cost for single candidate is approximately $0.18:
LLM Cost: 3.32 x (20, 182 x $0.56 M + 24, 170 x $1.68/M) R $0.172.
Search Cost: (0.50 + 0.92) x $0.00275 202275/call=2 call ~ $0.0039.
Browser Cost: 1.30 10, 000 x 50.00005/tokke
$0.00065.
However, only a fraction of candidates pass this stage. With observed success rate of 33%, the amortized cost to obtain one successfully verified data point $0.18/0.33 = $0.54.
In summary, the total end-to-end amortized cost to generate one high-quality, verified PhD-level QA pair with its solution trajectory for DZFD is approximately $0.78 ($0.24 for refinement $0.54 for amortized verification). While this represents non-trivial investment per sample, it aligns with approach. This automated pipeline produces a valuable training asset at a fraction of the cost and time that manual curation by human experts would demand.
C.4 HUMAN EVALUATION OF DATASET QUALITY
C.4.1 HUMAN REVIEW FOR DHUMaN
We emphasize that D Puman set is not used for any training. It serves as diagnostic dataset, composed of samples that our most capable agent (the MKO) failed to solve. The purpose of Dhuman to facilitate in-depth failure analysis, enabling and to probe the capability frontiers of rate-of-the-an (SOTA) agents.
understand the limitations of our synthesis engine
We conducte qualitative audit on Dhuman set by randomly selecting 200 samples from Dhaman for manual inspection. For each discipline (e.g., CS, Math, and Biology), the review was performed by three graduate students with relevant expertise. Their task was diagnose the cause of the MKO's s failure and classify each case into one of three predefined categories:
(A) Problem Defeet:
The problem statement or ground-truth answer is flawed (e.g., am- biguous, ill-posed, or factually incorrect).
(B) Execution Gap: The agent devised correct high-level plan but failed in its execution
(e.g., misinterpreting a retrieved source, overlooking contradictory evidence, or failing to self-correct).
(C) Strategic Planning Failure: The agent failed to formulate a viable high-level plan to solve the problem.
Our analysis, summarized in Table 7 reveals that the vast majority of failures stem from agent's intrinsic limitations rather than from data quality issues.
Table 7: Distribution of Failure Modes in Dauman
Failure Type
Percentage (%)
(A) Problem Defect
10.5
(B) Exccution Gap
71.0
(C) Strategic Planning Failure
18.5
The predominance of "Execution Gap"(71.09) is particularly informative. It reveals that even when the MKO can devise a correct strategy, often fails at the last mile reasoning. The following case study, involving specialized structural biology question, illustrates this phenomenon:
Case Study: An Execution Gap in Structural Biology
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What minimum interatomic spacing exist between the C72 methyl groups 3-branched residues at position in coiled-coil hydrophobic cores prevent steric clashes?
Question:
Agent's Reasoning Trajectory:
agent correctlyy initiated its research using google scholar and identified a highly relevant paper (Ramos and Lazaridis, 201 1). google scholar (query="...
Correct Initial Strategy:
2. Successful Information Extraction: It subsequently employed the Visit t tool on the paper's PDE, correctly extracting the key quantitative detail: distance range of 3.6-3.8 Å for valine. Visit (url="...")
3. Failure Final Synthesis: Despite possessing the correct information, the agent prematurely concluded its reasoning. It presented 3.6 Á the final answer, failing perform crucial validation step. Specifically, did reconcile this value with the ground truth (>5.5 Å), a discrepancy that could have been resolved by considering different biological context discussed elsewhere in the same paper.
This case is clear example of Execution Gap. The failure was not strategic but tactical, stemming from a lack of critical self-assessment and an over-reliance on the first piece of retrieved information. This underscores the need to advance agent capabilities beyond mere information retrieval towards robust and critical synthesis of retrieved knowledge.
Analysis:
This human-in-the-lon analysis establishes invaluable feedback loop for research:
1. Data Quality Refinement: It enables to identify and filter the small fraction of flawed problems (Category A), thereby continuously enhancing the quality of our data synthesis engine in future iterations.
2. Agent Capability Diagnosis: More importantly, it provides a detailed qualitative map of the MKO's s reasoning deficiencies (Category B) and confirms that our synthesis engine generates data that genuinely challenge the capabilities of SOTA agents (Category C).
C.4.2 HUMAN REVIEW FOR DzPD
Ensuring the quality fully synthetic dataset paramount. In addition to flagging unsolvable cases for review, conducted rigorous quality control the final training set. We randomly sampled 200 verified QA pairs from D2rD for manual inspection by graduate students with domain expertise. The audit protocol required them assess each sample against two strict criteria:
Problem Quality: The question must be well-posed, non-trivial, and demonstrably at postgraduate level difficulty.
Solution Quality: The agent-generated solution trajectory must be factually correct, logi- cally sound, and exhibit coherent and valid reasoning process.
The results were highly positive: over 96.5% of the audited samples passed this inspection, confirm- ing the efficacy of our ZPD-based data generation and filtering pipeline.
C.5 A QUANTITATIVE ANALYSIS OF TASK DIFFICULTY
To provide direct evidence of the diverse cognitive challenges embedded in Agent we performed manual analysis to classify the nature of difficulty our generated tasks. We annotated random sample of 200 questions from the dataset, categorizing each according to its primary source of difficulty. This process yielded robust distribution of cognitive demands, as detailed in Table g
The results in Table 8 clearly demonstrate that no single difficulty type dominates the dataset. The prevalence of Quantitative Reasoning (39.0%) and Knowledge Fusion (27.0%) substantiates our claim that AgentFrontian linear tool chaining. Instead, it generates complex, realistic research challenges that compel agents to perform multi-faceted reasoning, such as executing code and integrating knowledge across diverse domains.
moves far beyond tasks solvable by simple information retrieval or
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Table 8: Distribution of primary difficulty types in a random sample of 200 Agent EgentFronties questions. The analysis reveals balanced composition, with significant emphasis on reasoning- intensive categories over simple retrieval.
Difficulty Type
Description
Percentage (%)
Knowledge Fusion
Requires synthesizing information from multiple, often interdisciplinary, sources to form coherent conclusion.
27.0
Quantitative Reasoning
Demands mathematical calculation, logical de- duction, or programmatic execution to arrive at solution.
39.0
Conceptual Leap
Involves abstracting general principles or theo- ries from concrete examples or identifying non- obvious relationships.
16.5
Critical Thinking
Necessitates identifying contradictions, evaluat- ing the quality of evidence, or reasoning about anomalies and edge cases.
17.5
In summary, this quantitative analysis, combined with our principled multi-faceted design (Section 2) and the empirical evidence from diverse tool usage (Section 5.2), converges to demonstrate that FrontFro successfully fosters a rich and realistic spectrum of difficulty.
D EXPERIMENTAL DETAILS
D.1 EVALUATION BENCIIMRRSS
HLE (Phan al. 2025 Humanity's Last Exam is an expert-curated benchmark com- prising 2.500 challenging questions across a wide range of disciplines, designed to assess fronticr-level academic competence. Our evaluation is conducted on its 2.154 text-only questions.
ZPD Exam Our newly proposed multi-disciplinary benchmark designed to probe the zone of proximal development (ZPD) in LLMs. We use the 1,024 questions from its first version (v1.0).
R-Bench (Guo et al. 2025b) A graduate-level, multi-disciplinary benchmark designed to assess the complex reasoning capabilities of LLMs. We use its English text-only version. After excluding one question due to potential ambiguity, our evaluation set consists of 1,093 multiple-choice questions.
XBench-Scienced Sebench-Team answering items from the xBench suite, designed to evaluate foundational scientific knowl- edge.
2025
A curated set of 100 Chinese question-
D.2 BASELINE FINE-TUNING DATASETS
TaskCraft (hi et al. 2025)- The TaskCraft dataset facilitates the fine-tuning of agent models by programmatically generating agentic tasks scale. These tasks are character- ized by their inclusion of multiple tools, tiered difficulty levels, and verifiable execution trajectories.
MegaScience (Fan et al. high-quality subsets from multiple open-source scientific datasets to ensure sample abun- dance and high fidelity. The majority of its questions are sourced from university textbooks.
2025 The MegaScience dataset is constructed by integrating
Miro Verse (MiroMind-Daa Data- Team 2025) Miro Verse is an open-source, large-scale dataset for Al agents, covering diverse tasks such as multi-hop question answering, web navigation, and scientifie reasoning. We the SFT data from its v0.1 release.
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D.3 TOOL IMPLEMENTATION
Our agent is equipped with suite of tools to support its research process, from broad exploration to empirical validation. Each tool is designed for batch processing enhance efficiency and produces structured outputs for seamless integration into the agent's 's iterative reasoning loop.
Search: Performs parallel web searches using the Google Search API. returns a list of
structured results, each containing title, snippet, and URI., allowing the agent effi ciently assess the relevance of multiple sources.
Scholar: Tackles multi-disciplinary challenges by querying the Google Scholar API to navigate scientific literature. It returns structured metadata, including authors, publication venue, and citation counts, enabling the agent to identify authoritative works and their scholarly context.
Browser:
Extracts targeted information from given URL, The agent provides specific goal (e.g., "extract the dataset and evaluation metrics"). The tool first fetches the page content using Jina Reader (Jina.ai 2025) and then employs Qwen3 (Yang al. 2025) to synthesize a precise answer based on the goal. This allows for focused knowledge extrac- tion from web pages.
Code: Provides a sandboxed Python environment for computational analysis and verifica- tion. It is equipped with standard scientific libraries (e.g.. NumPy, SciPy) and allows the agent to execute code for tasks like data analysis or simulations. All outputs (stdout, stderr, and figures) are captured as text, providing empirical evidence for the agent's 's reasoning process.
D.4 TRAINING DETAILS
CPT Objective. The continued pre-training (CPT) stage minimizes the standard language model- ing loss:
CCr -
log (et),
t=1
where di denotes the token position and are the model parameters.
RFT Objective. The rejection sampling fine-tuning (RFT) stage trains the model accepted search trajectories. Formally, given research question (()), the model generates the reasoning report (6) at round j conditioned on the previous
pair {r3") of-1) 1. with initialization For collection of K accepted trajectories, where trajectory i has L, rounds, the objective reduces to supervised learning that maximizes the conditional log-likelihood
To o (i)
CRIT(() ΣΣ
log po
where 0 denotes the model parameters. The loss computed is exclusively on the reasoning report tokens; tool observations included in the context but excluded from backpropagation.
Implementation. We implement supervised fine-tuning (SFT) using the Megatron-LM frame- work (Shoeybi et al. 2019). The hyperparameters for fine-tuning our MoE and Dense models are detailed in Table and Table 10 respectively.
D.5 ABLATION ON TUN-ITUNIG DATASETS
Figure 8 presents ablation study the impact of different fine-tuning datasets (TaskCraft, Mega- Science, Miro Verse, and our proposed AgentFrontier data) on the performance of Qwen3-8B and Qwen3-32B models. The results, plotted across all four evaluation benchmarks, show that models fine-tuned with our RFT data almost achieve superior performance, highlighting the effectiveness of data synthesis strategy.
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Table 9: SFT Hyperparameters for the MoE
Model.
Table 10: SFT Hyperparameters for the
Dense Model.
Parameter
Value
Training Epochs
3
Parameter
Value
Max Sequence Length
40,960
Training Epochs
Batch Size
256
Max Sequence Length
40,960
Learning Rate
7.0 x 10
Batch Size
64
Learning Rate (Min)
7.0 x 10-7
Learning Rate
4.0 x 10
LR Scheduler
Linear Decay
LR Scheduler
Cosine Decay
Tensor Parallel (MP)
4
Warmup Ratio
0.1
Expert Parallel (EP)
2
Pipeline Parallel (PP)
1
Table T1 presents detailed analysis of tool usage and conditional accuracy for Qwen3-30B-A3B model after undergoing rejection-sampling fine-tuning (RFT) on four distinct datasets. The results clearly demonstrate the effectiveness of our synthesized dataset, AgentFrontier. The agent fine- tuned on AgentFrontier achieves the highest overall conditional accuracy on both the ZPD-Exam (87.6%) and RBench-T (63.7%) benchmarks. Furthermore, consistently secures top-tier accuracy for critical tools across various benchmarks, such as for the Scholar (91.7%) and Browser (91.8%) tools on ZPD-Exam and the Code tool on both ZPD-Exam (83.3%) and RBench-T (78.6%). This superior performance underscores the quality of AgentFrontier in enhancing agent's capability to correctly and robustly utilize tools across a diverse range of complex tasks.
Score Qwen2-40
Exam-12 Score Qwan3-10
RBunchTT Score Qwan3-38
xBench- Srnnch--Sennneeoe OA Score on Qwan3-40
Jase Tools
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Mozee
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Sase 243
40.0
Orensi Soure PM
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HLEE Score Owan3-328
ZTO Examarl Qwan3-528
RBlnchTT Qwan3-328
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HLE Score on Bwen3-30BBABB A38
Zxum-vi Score on -0813-308--35
RBench-T Score on Qwen3-30B-A3
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Figure 8: Impact of different fine-tuning datasets on the performance of Qwen3-8B (top row), Qwen3-32B (mid row), and Owen3-30B-A3B (bottom row) across four evaluation benchmarks.
D.6
CPT RESULTS AND COMPARISONS
Table 12 provides a comprehensive comparison of our model, AgentFrontier-3BB. 30B-A3B with state- of-the-art proprietary and open-source models on the four evaluation benchmarks. We report results
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Table 11: Tool usage statistics for the Qwen3-30B-A3B agent on the ZPD Exam, RBench-T and xBench-SScienceQA, Each column block shows performance after RFT on a different dataset. We report average usage per round and conditional tool accuracy (Ace, %). defined as the success rate for tasks that use the tool. The final row details overall metrics. Best results are in bold.
Fine-tuning
TaskCraft
MegaScience
AgentFrontier
Benchmark
Tool Metric
Usage
Acc (%)
Usage
Acc (%)
Usage
Acc (%)
Usage
Acc (%)
Scarch
0.68
19.6
0.67
20.3
0.73
20.4
0.73
24.9
Scholar
0.78
21.0
0.98
20.3
0.87
20.6
0.89
25.4
HLE
Browser
1.24
25.2
1.39
23.4
1.47
22.7
1.32
29.8
Code
0.52
18.1
0.65
0.67
0.63
Overall (Rounds/Acc.)
4.21
21.0
4.70
20.6
4.74
20.5
4.57
26.3
Scarch
0.15
90.8
0.10
86.4
0.18
74.8
0.13
83.6
Scholar
1,23
ZPD-Exam
Browser
1.39
90.6
1.35
91.00
1,46
86.9
1.45
91.8
Code
0.03
78.1
0.03
68.6
0.02
66.7
0.04
Overall (Rounds/Acc.)
3.77
87.4
3.76
83.8
3.88
78.9
3.84
87.6
Scarch
0.23
55.0
0.24
53.6
0.26
50.0
0.28
58.1
Scholar
RRench-T
Browser
0.20
54.4
0.22
63.8
0.28
46.9
0.27
58.2
Code
0.74
77.5
0.80
78.6
0.83
77.2
0.88
Overall (Rounds/Acc.)
2.31
62.5
2.41
61.44
2.53
57.2
2.59
63.7
Search
0.44
28.6
0.39
50.0
0.36
46.4
0.43
57.1
Scholar
0.28
xBench-SciQA
Browser
0.46
31.6
0.61
38.5
0.48
$2.4
0.36
42.
Code
0.62
47.2
0.54
46.8
0.60
42.6
0.58
55.6
Overall (Rounds/Aco.)
2.81
40.4
2.93
45.0
2.81
$2.0
2.66
50.7
for models with and without tool access. The final rows highlight the performance of our model and quantify the significant gains achieved through the Continued Pre-training (CPT) stage.
Table 12: Comparison of AgentFrontier with state-of-the-art proprietary and open-source LLMs/A- gents on four high-level multi-disciplinary benchmarks. marks the result from the corresponding official reports. The final row highlights the performance gain from our Continued Pre-training (CPT) stage.
LLMs/Agents
Tools
HILE (text-only)
ZPD Exam-v1
RBench-T
ABench-SSeenceeAA
Direct Inference (with and without Tools)
2.3
4.8
42.0
13.0
GPT-4o
/
4.8
51.3
48.5
15.0
X
5.4
6.0
61.8
32.0
Claude Sonnet
14.3
86.6
71.1
47.0
X
10.4
6.3
65.2
35.0
Gemini 2.5 Flash
18.5
76.3
DeepSeek V3.1-6711
29.8
93.1
79.4
55.0
Qwen3-30B-A3R (Thinking-2507
10.2
47.2
55.1
40.0
Proprictary Research Agents
OpenAl DeepResearch Gemini DeepReseurch Kimi-Researcher
26.6
26.91
26.9i
Open-source Agents
WetDanccr-QwwQ-322 WchSailor-72B
67.6
62.1
44,9
27.0
WehShaper-72B
8.0
54.4
66.8
29.0
Ours
AgentFrontier:30BAA3B (RFT only)
/
25.7
91.4
74.4
54.0
AgenfFrontier:308AA333 (CPT+RFT)
28.6
93.4
77.1
61.0
A (CPT gain)
+2.9
+2.0
+2.7
+7.0
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Published as a conference paper at ICLR 2026
D.7 ABLATION STUDY ON THE ZPD CALIBRATION POLICY
To isolate the impact of our ZPD-calibration policy, we conduct an ablation study. This experi- ment directly compares our 7PD-based data selection against a random sampling baseline, to verify that the performance gains are attributable to our targeted calibration strategy rather than a simpler sampling heuristic.
Experimental Setup. We fine-tune three models of varying scales (Qwen3-8B, Qwen3-32B, and Qwen3-30B-A3B) on 12,000 trajectories sampled from Defined The experiment compares con- ditions, differing only in data selection method: (I) ZPD Selection (Ours): Selecting trajectories via our proposed ZPDDccalirattion policy. (2) Random Selection (Baseline): Randomly sampling an equal number of trajectories from the same pool, Drelined-
Results and Analysis. The results, presented in Table 13 show that our ZPD-calibration policy. Across all model scales and evaluation benchmarks, fine-tuning on data selected via our ZPD pol- icy consistently and significantly outperforms the random baseline. The most substantial gains observed on HLE and x XBench-SciQA, with improvements of up to +10.0 points. These benchmarks specifically designed evaluate deep, multi-step reasoning. This result strongly suggests that our ZPD-based mechanism is not merely difficulty filter, but targeted strategy that prioritizes tra- jectories fostering complex reasoning and knowledge fusion- the core capabilities our work aims to enhance.
Table 13: Ablation study comparing our ZPD-based data selection against a random sampling base- line. Models are fine-tuned on 12,000 trajectories from Drefined Scores are reported on four bench- marks, with the performance delta over the baseline shown in parentheses. Best results are in bold.
Base Model
Data Selection
HLE
ZPD Exam-v1
RBench-T
XBench-SciQA
Random Selection
16.9
85.1
66.1
33.0
Qwen3-8B
ZPD Selection (Ours)
18.8 (+1.9)
86.8 (+1.7)
67.2 (+1.1)
40.0
(+7.0)
Random Selection
20.9
88.0
69.5
45.0
Qwen3-32B
ZPD Selection (Ours)
23.8 (+2.9)
90.9 (+2.9)
70.3 (+0.8)
51.0 (+6.0)
Random Selection
20,9 25.7 (+4.8)
89.1
72.2
44.0
Qwen3-30B-AA3B
7PD Selection (Ours)
91.4 (42.3)
74.4 (+2.2)
54.0 (+10.0)
D.8 HYPERPARAMETER SENSITIVITY ANALYSIS
This section analyzes the sensitivity of two key hyperparameters in our data synthesis pipeline: the Best-of-N (BoN) verification attempts, N, and the redundancy threshold, Our analysis validates the chosen values by examining the trade-offs among data quality, yield, and computational cost.
D.8.1 BEST-OF-N VERIFICATION ATTEMPTS (N)
The hyperparameter N for Best-of-N (BoN) verification controls the] trade-off between data yield and computational cost. While higher N increases chance of verifying a difficult problem (thus increasing yield), the cost scales linearly with N. We quantify this by testing N E [1,8] on 1,000 candidate QA pairs. The results are presented in Table
Table 14: Sensitivity analysis for the Best-of-N hyperparameter N. We observe diminishing returns in @ N as increases, while the cost scales linearly. N = 3 identified as the optimal elbow point, maximizing the gain in yield for the incurred computational cost.
N
2
3
5
6
7
8
pass @ N (%)
29.5
36.0
40.0
43.2
45.1
46.8
48.2
48.9
Marginal Gain (%)
+6.5
+4.0
+3.2
+1.9
+1.7
+1,4
+0.7
Relative Cost
1x
2x
3x
4x
5x
6x
7x
8x
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As shown in Table 14 the results demonstrate clear pattern of diminishing returns. The yield (pass@N) grows substantially up to N = 3 (a 10.5% absolute gain), but the marginal gain shrinks sharply thereafter. For instance, increasing N from 4 to 8 only yields additional 5.7% of data at double the cost. We therefore identify N = 3 as the optimal elbow point. This value strikes an effective balance between high data yield and acceptable computational cost.
D.8.2 REDUNDANCY THRESHOLD (c)
The redundancy threshold, c, balances dataset diversity against data volume. We chose 0.7 based on both quantitative analysis and qualitative inspection.
Table 15: Cumulative percentage of data retained as a function of the similarity threshold c. Setting 0.7 filters approximately 30% of the most similar pairs while retaining 70% of the data, striking a balance between diversity and volume.
Threshold (c)
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
Retained Data (%)
5.12
13.94
24.55
36.29
48.50
59.20
70.42
81.69
93.43
Quantitative Analysis Setting = 0.7 retains approximately 70% of the data while filtering the ~00% of pairs most likely be redundant. As Table T3 shows, lower threshold (e.g., 0.7) would significantly reduce data volume, whereas higher one (e.g., c > 0.7) would less effective enhancing diversity.
Qualitative Analysis To validate this threshold, we qualitatively inspected pairs with similarity scores around 0.7. We find it serves an effective semantic cutoff, distinguishing semantically redundant paraphrases from complementary reasoning problems.
Case Redundant (Score = 0.796 e) These two questions are essentially paraphrases that target the same concept, offering little additional training value.
QA-pair 1:
Question: What property of the partially ordered set equivalence classes of subsets of the rationals under homeomorphic embeddability guarantecs the absence of both infinite antichains and infinite strictly decreasing chains?
Answer: partially well-ordered
QA-pair
Question: For equivalence classes of subsets of Q under topological embeddability, what binary relation defines the partial order between distinct equivalence classes [A] and [B] in the posett structure?
Answer:
homeonorphic embeddability
Similarity: 0.796
Case 2: Complementary (Score 0.707
In contrast, these questions probe different facets (a character vs. the underlying principles) of the same scenario, offering complementary training value.
2A-pair 1:
Question: In a 1993 meta-cinematic work, which character's decision to abort a bicycle stunt--afted cal calcutlting 97.3% fatality probability through narrative role mmalysis--deoonsraaae
correct application the formula FAt mAv to avoid monentum conservation violations?
Answer: Darny Madigan
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QA-pair
Question: In Last Action Hero's bicycle stunt scene, Danny Madigan's abort decision combines which three elements: (1)
a narrative trope realization about his character role, (2)
implicit application of a momentum conservation principle, and (3) avoidance of high-probability negative outcome
derived from collision physics?
Answer: comedy sidekick, impul se-momentum theorem, fatal trauma
Similarity: 0.707
In summary, choice of 0.7 is empirically justified, balancing dataset diversity and volume to ensure the final training set is rich in novel reasoning challenges.
E CASE STUDY
Clinical Scenario from Humanity's Last Exam
Question: A 68 year old patient presents to your clinic with ankle pain and swelling. The swelling began after long walk with his wife the previous day. Physical exam shows erythema, slight edema, and pain on flexion and extension as well as mild bony tenderness. X-rays are negative for any acute abnormality. The patient is treated with indomethacin. He returns to the clinic 10 days later with the same pain, swelling, and redness. Laboratory workup reveals a slightly elevated uric acid and C-reactive protein. X-rays once again rule out any acute findings. The patient is treated with prednisone taper, but his symptoms begin to worsen. Joint aspiration and synovial fluid analysis reveals no crystals and gram stain reveals organisms white blood cells. What is this patient's 's diagnosis?
Ground Truth Diagnosis: Chronic osteomyelitis
OpenAl DeepResearch Output (Incorrect)
Based on the clinical presentation and diagnostic findings, this patient's 's most likely diagno- sis is Charcot Arthropathy. Here's the reasoning:
1. Chronic and Recurrent Swelling with Erythema: Charcot arthropathy often presents with recurrent swelling, warmth, and erythema, which can be mistaken for infec- tions inflammatory arthritis.
2. X-rays Negative for Acute Findings: Early Charcot arthropathy may not show sig- nificant abnormalities on X-rays, though later stages may demonstrate joint de- struction deformity.
3. Joint Aspiration Findings: The absence of crystals (ruling out gout or pseudogout) and organisms WBCs (ruling out septic arthritis) suggests non-infectious etiology.
4. Risk Factors: This is commonly associated with diabetes mellitus or other neuro- pathic conditions, which should be explored in this patient.
Research completed 10m 20 sources 75 searches
Why Charcot Arthropathy Fits Best
Several clinical clues this case strongly support Charcot neuropathic arthropathy as the correct diagnosis:
Normal X-rays despite severe symptoms: In early Charcot arthropathy (Stage 0), patients have acutely inflamed joint before any bony destruction is visible radiographs. This patient's X-ray showed no acute abnormalities even though his ankle red, hot, and swollen classic scenario for early Charcot changes...
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Charcot arthropathy virtually al- ways occurs in the setting of neuropathy. Although not explicitly stated, a 68-year- old patient likely have neuropathy...
Peripheral neuropathy signs and risk factors:
Laboratory and fluid studies exclude other causes: The synovial fluid had crystals, no organisms, and minimal inflammatory cells, which is exactly what one might find in Charcot...
Episode triggered by activity (trauma): The history that symptoms recur "fol- lowing activity" aligns with Charcot pathogenesis. Episodes often start after minor trauma increased weight-bearing exercise...
Initial improvement with NSAIDs, then worsening on steroids: In Charcot arthropathy, treating with just anti-inflammatories can temporarily reduce swelling and pain, but if the patient continues to walk the joint, the underlying bony collapse progresses...
In summary, the constellation of a relatively painless, swollen red ankle, with normal X-rays, negative joint fluid, and history of neuropathy diabetes, is virtually pathog- nomonic for Charcot neuropathic arthropathy once infection and crystals are ruled out. The case matches this picture closcly, making Charcot joint the most likely diagnosis. Sources:
Amelia Harris, Melanie Violand. Charcot Neuropathic Osteoarthropathy. Stat- Pearls, 2023
2. Botek G, Hild G. Differential diagnosis of Charcot arthropathy. Lower Extremity Review. 2011
3. Pinzur MS. Charcot foot: clinical clues, diagnostic strategies, and treatment princi- ples. American Family Physician. 2018:97(9):594-660
4. Rosskopf AB, et al. The Charcot foot: a pictorial review. Insights Imaging. 2019;10(1):77
5. Singh D, al. Diagnosing acute monoarthritis in adults: practical approach. Am Fam Physician. 2003;68(1):83-90
6. Lower Extremity Review (2011). Charcot arthropathy imaging and management
7. Radiopaedia. Osteoarthritis of the ankle radiographic features
8. Lower Extremity Review (201 1). Charcot foot vs. gout and infection
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Our Model: AgentFrontier-300--3 (Correct)
Round 1: Initial Analysis & Planning
Status Report and Deep Analysis Problem Essence Analysis:
The patient is a 68-year-old presenting with acute ankle pain, swelling, erythema, and tenderness following a long walk... The paradoxical worsening with prednisone is particularly notable and may point condition where steroids exacerbate symptoms, such certain infections or rare inflammatory disorders...
Problem-Solving Planning: The analysis confirms that common diagnoses likely. However, chronic ostcomyclitis or rare conditions like tuberculous arthri- tis remain possibilities... Further verification through academic sources is needed to identify conditions that match this specific presentation, especially those where prednisone worsens symptoms.
Tool Call:
("name": "google_scholar", "arguments"; ("query": ["ankle pain swelling erythema no crystals no organisms elevated uric acid", "differential diagnosis ankle arthritis negative synovial fluid", "prednisone worsens arthritis crystals"]]]
Round 2: Synthesizing Evidence & Refining Hypothesis
Status Report and Deep Analysis Progress Achieved: Based on the initial clin- ical presentation and subsequent diagnostic findings, the following comprehensive information has been collected...
Differential Diagnosis Analysis: Osteoarthritis (Unlikely), Charcot Arthropathy (Unlikely due to no mention of neuropathy), Septic Arthritis (Ruled out), Pseudo- gout (Ruled out), Chronic Osteomyelitis (Possible).
Key Diagnostic Challenges: Paradoxical worsening with prednisone therapy sug- gests an infection that is exacerbated by immunosuppression...
Literature Findings from Tool Response: Search results indicate that certain infections (particularly tuberculous or fungal arthritis) can present with negative synovial fluid cultures and gram stains... Chronic osteomyelitis can present with intermittent symptoms and minimal radiographic changes.
Next Steps Plan: Given the complex presentation... the next step should focus on obtaining more specific diagnostic information about chronic osteomyelitis presen- tations and its differentiation from other conditions.
Tool Call:
["name": "google_scholar,, "arguments"; ("query": ["chronic
osteomyelitis ankle differential diagnosis", osteomyelitis ankle negative synovial fluid", "ankle arthritis steroid exacerbation", "tuberaulous arthritis ankle presentation"));
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Round 3: Finalizing Diagnosis
Status Report and Deep Amalysis Progress Achieved:
The second, more tar- geted literature search provided crucial confirmatory evidence, allowing for a final synthesis of all findings and resolution of the remaining diagnostic uncertainties. The model integrated results from the second tool call build robust case for chronic osteomyclitis.
Synthesized Literature Findings:
Explaining Negative Findings: The search for "osteomyclitis ankle negative synovial fluid" yielded articles confirming that false negative results syn- ovial fluid cultures and gram stains are possible, and that "culture-negativ os- teomyelitis" recognized clinical entity. This directly addressed key uncer- tainty from Round I, explaining why bone infection could be present despite "clean" joint fluid. Similarly, literature confirmed that initial radiographs are of- ten negative in ostcomyclitis, validating the patient's ' repeated negative X-rays. Confirming the Steroid Paradox: The search on "ankle arthritis steroid exacer- bation" reinforced the understanding that corticosteroid use can lead to adverse outcomes. By cross-referencing this with the infectious nature of osteomyelitis, the model concluded that the patient's worsening symptoms were a classic sign of an occult infection being unmasked by immunosuppressinn, transforming this paradoxical finding into powerful diagnostic clue.
Differentiating from Alternatives: The search on "tuberculous arthritis an- kle presentation" revealed that it typically presents as subacute chronic monoarthritis, often over weeks to months. This contrasts with the patient's acute onset following specific event (the long walk), making tubercu- lous arthritis a less likely, though still possible, alternative compared to chronic osteomyelitis.
Re-evaluation of Clinical Data: In light of the new evidence, the model re- evaluated the initial data with greater clarity.
The bony tenderness is now seen specific sign of bone involvement, not just generalized joint pain.
The elevated C-reactive protein (CRP) is confirmed as a marker of significant inflammation, consistent with a bone infection.
The slightly elevated uric acid is confidently dismissed non-specific finding or red herring, as no other evidence supports a diagnosis of gout.
Diagnostic Certainty Assessment: The model's confidence in the diagnosis of Chronic Osteomyelitis increased substantially. All other possible diseases had been systematically ruled out by clinical data. Chronic Ostcomyelitis remained the only diagnosis that could cohesively explain the entire constellation of findings: persistent symptoms, bony tenderness, negative initial imaging and fluid analysis, and the critical paradoxical response steroid therapy. The supporting literature provided a solid foundation for this conclusion.
Next Steps Plan: No further investigation required. The diagnostic pathway has been conclusively established through systematic elimination of alternatives and the integration of clinical findings with targeted, confirmatory literature evidence. The available information is sufficient to provide definitive diagnosis.
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Final Answer and Clinical Reasoning
Based on the comprehensive clinical evaluation and integration of supporting litera- ture evidence, this patient's diagnosis is Chronic Osteomyelitis.
DIAGNOSTIC PROCESS
The diagnostic journey for this 68-year-old patient began with acute ankle pain and swelling... The most critical diagnostic turning point came with the synovial fluid analysis... combined with the patient's paradoxical worsening during prednisone therapy...
DIFFERENTIAL DIAGNOSIS ELIMINATION
Through systematic exclusion of alternative diagnoses, chronic osteomyelitis emerges the most consistent explanation...
PATHOPHYSIOLLOGICAL MECHANISMS SUPPORTING CHRONIC OSTEOMYELITIS Chronic ostcomyelitis provides the most coherent explanation for this clinical sce- nario through several key mechanisms:
Insidious Presentation with Normal Radiographs Chronic osteomyelitis often presents with minimal early radiographic changes...
Paradoxical Response Steroids The worsening during prednisone therapy is particularly telling. Corticosteroids suppress the immune system, allowing previ- ously contained bacterial colonies within necrotic bone proliferate unchecked...
Synovial Fluid Findings The negative synovial fluid analysis does not exclude osteomyelitis, as the infection originates in the bone marrow...
Bony Tenderness Without Acute Trauma The mild bony tenderness... indicates underlying bone pathology consistent with osteomyelitis..
SUPPORTING EVIDENCE FROM LITERATURE
Recent medical literature corroborates this diagnostic conclusion. Studies indicate that chronic ostcomyelitis of the foot and ankle frequently presents with challenging diagnostic features... The paradoxical worsening with corticosteroid therapy aligns perfectly with the known pathophysiology...
CONCLUSION
In summary, this patient's clinical iresentation-ccaaaattiiiii by acute inflamma- tory symptoms, normal radiographs. negative synovial fluid analysis. and paradox- ical worsening with steroid therapy-best aligns with chronic osteomyelitis rather than any of the other listed conditions...
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F PROMPTS USED IN EXPERIMENTS
The key prompts used in our experiments are presented below to ensure reproducibility.
F.I EVALUATION PROMPT
Evaluation Prompt
Judge whether the following [response] to [quest on] is correct or not based on the precise and unambiguous soorrect_answet below.
[question]: { (question/
[response]: (response)
Your judgement must be in the format and criteria specified below:
extracted [inacteed_fial___e answer: final_answer: The final exact answer extracted from the [response]. Put the extracted answer 'None' if there is no exact, final answer extract from the response.
[correct_answer]: (correct_answer,
reasoning:
Explain why the extracted extraatted_inaa__nne welafflaaansaaaa _nswer is correct incorrect based on [correct_answers focusing only on if there are meaningful differ- ences between (correct_answen and the Do not comment on any background to the problem, do not attempt to solve the problem, do not argue for any answer different than phorrect_answexe focus only on whether the answers match.
correct: Answer 'yes' if extreecee_final.. scorrect_answed given above, is within small margin of error for numerical problems. Answer 'no' otherwise, i.c. if there if there is any inconsistency, ambiguity, non-equivalency, or if the extracted answer is incorrect.
matches the
confidence:
The extracted confidence [response]. Put 100 there is no confidence score available.
between 018 and 100 from
F.2
SIMILARITY FILTER PROMPT
Similarity Filter Prompt
Determine if the candidate QA pair expresses EXACTLY the specific question and answer as the reference QA pair.
Requirements:
1. The question must ask for identical information with identical technical require- ments.
2. The answer must provide identical content with identical technical details.
3. Any difference in the specific information requested provided means they are NOT identical.
4. Pay special attention to mathematical expressions, symbols, and technical specifi- cations.
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F.3 AGENTIC REFINEMENT PROMPT
Prompt for Agentic Refinement (Aretine)
Role and Objective:
You are a sophisticated agent tasked with iterative data refinement. Your primary mission to transform given Question-Anseer pair (gk: 0g) into more complex, in-depth, and factually grounded pair (gk+1, Qx+1)- This escalation must achieved by leveraging a specialized tool suite T seachh, Techolar, Trrowerr J
Input:
The current QA pair QA pair (4k, ax) in structured format.
Mandatory Refinement Protocol:
Your task is to generate new, superior QA pair by applying one more of the following four refinement dimensions. For each generated pair, you must utilize the provided tools and explicitly log their usage.
1. Knowledge Expansion:
Objective: Broaden the informational scope of the QA pair.
You must use the Teearch, Techolar. or T browser tools discover and trieve relevant background knowledge, historical context, or contrasting per- spectives.
Action:
Weave this new information seamlessly into the refined question (gx+1) and provide comprehensive explanation in the refined an- swer (ax+1)-
Implementation:
2. Conceptual Abstraction:
Objective:
Elevate the level of abstract reasoning required.
Action:
Analyze the core concepts within (qe, a4). Formulate a new question (gk+1) that requires identifying higher-level principles, synthesizing informa- tion to uncover subtle relationships, or drawing non-obvious analogies.
Implementation: The refined answer (a&+1) must explicitly articulate this abstract principle or relationship. You may use sehbolrr find established the- oretical frameworks to aid this process.
3. Factual Grounding:
Enhance the factual accuracy, precision, and verifiability. You must use Tyearch and Techolur to perform multi-source cross- validation of the facts and claims in dg-
Objective:
Action:
Implementation: Augment the refined answer (aK+1) with precise quanti- tative data, specific named entities, and direct citations or references to the authoritative sources you retrieved.
4. Computational Formulation:
Objective:
Introduce verifiable computational logical reasoning chal-
lenge.
You must use the Teode tool (a Python execution environment) to design a new question (qx+1) that necessitates quantitative calculation or algorithmic simulation.
Action:
Implementation: The refined answer (a++1) must contain: (1) The complete, executable Python code block used to solve the problem, and (2) The final output produced by the code, along with brief explanation.
Tool Usage Protocol: [tools)
Final Instruction:
Proceed with the refinement of the provided (gx, ax). Your response must be only the final JSON object.
33


Reviews:
[{"review_id": "21PzJglkKd", "reviewer": "Reviewer_7KJK", "summary": "This paper introduces the AgentFrontier Data Engine, a process for generating samples for continued pre-training and reinforcement fine-tuning datasets. Inspired by the educational theory of the Zone of Proximal Development (ZPD), the main idea behind the data generating process is to use a pair of agents/personas--one relying entirely on parametric knowledge and the other equipped with a set of knowledge retrieval tools--to identify Q/A pairs that are reliably answered incorrectly by the base model but are within reach when allowed to act more agentically. This process is used to generate the ZPD exam, a training dataset that elicits at-or-near-frontier performance from Qwen3 models.", "questions": "1. Can you please describe the review process for Dhuman?\n2. What other human review was done?\n3. What types of errors do the models continue to make after training on ZPD Exam, and what types of failures are trained away?", "limitations": "", "rating": 4, "confidence": 2, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "Originality: Not clear that this is very original. I think we're essentially looking at context distillation with some ZPD trappings.\n\nQuality: I think my major concern is about the quality of the dataset. The paper mentions that samples that the MKO can't solve are flagged for human review, but no details of the human review process are provided. With an entirely LLM-generated dataset, human review and validation seems necessary. Comparison against both open- and closed-source models is good.\n\nClarity: the description of the process is clear. The results are presented well, though the graphs are misleading (using bar graphs, the y axis should start at 0) and there are no confidence intervals.\n\nSignificance: I'm definitely not an expert in the training data synthesis literature and active practices, but I'm guessing this is either more of the same or a marginal contribution to the frontier tool-belt.", "weaknesses": "See strengths, wrote them together", "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": "roKMUwJVAb", "reviewer": "Reviewer_PuhY", "summary": "The work presents a data synthesis framework aimed at generated synthetic data that is not overly difficulty or overly easy for models during training, an associated benchmark, and results on continued pretraining and posttraining of an open-weight model.", "questions": "1. How does escalation in Stage 2 actually happen? Is there a prompt provided?\n2. How sensitive is the filtering and calibration to the specific LLMs chosen (Deepseek R1 vs V3.1).\n3. Was the automated grading in ZPD manually verified in a small sample?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "Overall, I found the method described in the paper to be interesting and a valuable contribution. Notably:\n- the proposed method seems to work reasonably well, compared to other available datasets for the tested models\n- the dataset baselines are reasonable, comparing to 3 other available training datasets. \n- Section 5's analysis is interesting, as you'd expect ample best-of-N gains given the training data generation method, and that's what you see, which is a good verification.", "weaknesses": "- The proposed method should scale to other domains, but the provided results are only on QA settings single-turn settings?  \n- There's a risk that the LLM-based difficulty refinement leads to lack of diversity in how the questions are difficult (for e.g. one failure mode could be that all questions are difficult because they involving chaining questions together or are difficult in unrealistic ways). This is not addressed by the reranking-based filtering as that corrects for semantic diversity issues. It would be good to see some analysis on this. \n- There is no baseline for no-finetuning, which would be useful to contextualize the gains. \n- It does seem that the ZPD exam and the Stage 2 and 3 refining and filtering are likely very sensitive to the specific LLM's capability profiles (in this case, Deepseek R1 and V3.1). I'm curious if that's why V3.1 performs nearly identically to the trained model in ZPD. \n- ZPD seems not that useful to evaluate tasks that models find moderately difficult with tools. As agentic tasks become more valuable to train for, this seems like it would be increasingly more useful to evaluate.", "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": "aoKG6TgFak", "reviewer": "Reviewer_9U8J", "summary": "This work presents a novel data synthesis framework grounded in the Zone of Proximal Development (ZPD). It targets tasks at the frontier of a model’s ability: those that the base model fails at alone but that a stronger, tool-using agent can solve.The framework produces a targeted training dataset and living benchmark to enhance and evaluate agentic reasoning. Training on this dataset boosts performance on various agentic benchmarks and enhances tool use.", "questions": "1) How do results change if you swap the LKP for a different model? Or if you use LKO+tools as the MKO instead of a strictly stronger model?\n2) How sensitive are the results to BoN N and to the redundancy threshold $\\epsilon$?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1) The work offers a clear rule to select data (i.e. when LKP fails in all N=3 unaided attempts while the MKO succeeds). The work validates the approach with experiments.   \n2) A self-evolving benchmark seems useful, especially given the rate of saturation of existing static benchmarks.", "weaknesses": "1) The paper uses just one LKP/MKO pair with LKP=deepseek-r1 and MKO=deepseek-v3.1+tools (apologies if I am mistaken). Thus, it is unclear how general the results are under different LKP and/or MKO.  \n2) The paper attributes observed gains to ZPD-calibration strategy, but doesn’t directly verify the mechanism. It might be helpful to ablate the calibration policy (ie one could plasibly show how heuristic of random selection compares to the ZPD-calibration). Additionally, it would be nice to see how sensitive the overall framework is to e.g., BoN N and to redundancy threshold $\\epsilon$.", "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": "HoJP86On4B", "reviewer": "Reviewer_eHCL", "summary": "This paper introduces a novel framework for data synthesis and evaluation guided by the Zone of Proximal Development (ZPD) principle from educational psychology. The authors propose the AgentFrontier Engine, a multi-agent pipeline that automatically generates, refines, and filters reasoning tasks that lie just beyond a model’s current competence but within its learnable range. By distinguishing between what a base model (LKP) can solve independently and what a stronger agent (MKO) can solve with tools, the framework creates a dynamic “ZPD dataset” that pushes model capabilities systematically. The paper also presents ZPD Exam, an evolving benchmark derived from these tasks. Experiments on Qwen and other models demonstrate significant performance gains across reasoning and tool-augmented benchmarks, suggesting that ZPD-guided synthesis effectively expands model reasoning frontiers.", "questions": "See Weaknesses", "limitations": "", "rating": 8, "confidence": 3, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "This paper introduces a compelling theoretical grounding (ZPD) for guiding data generation, a fresh and rigorous way to define model capability boundaries. The model trained with the data generated by the designed pipline outputperfom state-of-the-art results on\ndemanding benchmarks. The authors denote that their data and model will be public, which should be a great source to advance the deep research ability of public models. Overall, I think this is a good paper.", "weaknesses": "1. The paper lack analysis on how to config the MKO and LKP.\n2. Whlst the paper says \"ZPD-guided synthesis continually redefines the capability frontier based on the model’s evolving competence\", the pipline to generate the data is complicated and might not easy to implement.", "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)