MoL: Adaptive Mixture-of-Length Reasoning for Efficient Question Answering with Context
Authors: Guocong Li
Jinjian Zhang
Ping Wang
Dongnan Liu
Tian Liang
Qiuyi Qi
Hao Huang
Siyan Guo
Mutian Bao
Wei Zhou
Linjian Mo
Hongxia Xu
Jian Wu
Venue: ICLR 2026 Poster (2026)
Keywords: Question Answering
(Large) Language Models
Primary area: foundation or frontier models, including LLMs

Abstract: We present Mixture-of-Length (MoL), an approach for Question Answering (QA) with context that aims to improve the balance between reasoning quality and response efficiency. Our method introduces a principled difficulty assessment based on information-theoretic principles and a dual-objective reward mechanism that adaptively modulates response length. In our experiments, MoL exhibits an emergent behavior termed "intelligent brevity": the model tends to produce shorter responses for simpler queries and longer ones for more complex inputs. This property is desirable for human-computer interaction and can reduce inference costs. A post-hoc analysis of internal activations suggests a correlation between this output adaptivity and the effective number of layers that contribute during inference. On multiple QA benchmarks, MoL demonstrates competitive accuracy while substantially reducing tokens compared to baselines, indicating that difficulty-aware length modulation is a promising direction for efficient QA with context.

Published as a conference paper at ICLR 2026
MoL: ADAPTIVE MIXTURE-OF-LENGTH REASONING FOR EFFICIENT QUESTION ANSWERING WITH CON- TEXT
Guocong Lii.2*, Lii.2",,injian Jiijjann Zhang" Ping Wang", Dongnan Liu", Tian Liang", Qiuyi Qi
Hao Huang?, Siyan Guo?, Mutian Bao Wei Zhou², Linjian Mo Hongxia Xu Jian Wust 'Transvasuular Implantation Devices Research Institute and State Key Laboratory of
Transvascular Implantation Devices, Zhejiang University, Hangzhou, China
2 Ant Group
3 Liangzhu Laboratory and WeDoctor Cloud, Hangzhou, China
*Renmin University of China
" "College of Computer Science and Technology, Zhejiang University
"Zeejiang Key Laboratory of Medical Imaging Artificial Intelligence, Hangzhu, China {1 Liguocong, einstein, wujian2000) ju. edu. en
ABSTRACT
We present Mixture-of-Length (MoL), an approach for Question Answering (QA) with context that aims to improve the balance between reasoning quality and re- sponse efficiency. Our method introduces principled difficulty assessment based on information-theore theoretic principles and a dual-objectin reward mechanism that adaptively modulates response length. In our experiments, Mol. exhibits an emer- gent behavior termed "intelligent brevity": the model tends to produce shorter responses for simpler queries and longer ones for more complex inputs. This prop- erty is desirable for human-compuler interaction and can reduce inference costs. A post-hoe analysis of internal activations suggests a correlation between this out- put adaptivity and the effective number of layers that contribute during inference. On multiple QA benchmarks, MoL demonstrates competitive accuracy while sub- stantially reducing tokens compared to baselines, indicating that difficulry-aware length modulation is a promising direction for efficient QA with context.
INTRODUCTION
Question Answering (QA) with context represents fundamental challenge in natural language pro- cessing, where models must synthesize information from multiple sources to generate accurate re- sponses. While recent advances in large language models (LLMs) have demonstrated remarkable capabilities in this domain (Suzgun et al.) (023), a critical tension persists between reasoning qual- ity and computational efficiency (Pan et al. 2024 Su al. 2024).. This challenge is most acute in multi-document scenarios, where reasoning complexity varies from simple extraction to complex multi-hop inference. Efficiently navigating this spectrum is key. Figure [ offers initial glimpse of our solution on HotpotQA, demonstrating that comparable accuracy can be achieved with signif- icantly shorter responses.
Current approaches to efficient reasoning fall into two primary categories, each with significant lim-
itations. First, uniform compression methods (Yang al.) 2025) Kang al. 2025) apply fixed reduction strategies regardless of task complexity, leading to under-reasoning on difficult problems while over-elaborating on simple ones. Second, adaptive methods (Ling al. 2025 Team et al., 2025) attempt difficulty-aware processing but rely on heuristic difficulty estimation and rigid com- pression policies that struggle to recover when initial assessments prove inadequate.
Equal contribution.
1 Corresponding authors.
Cooess available at: htttt::////ttt thub. cot. thwb.cco//coggg////ol

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Published as a conference paper at ICLR 2026
The core insight driving our work is that optimal reasoning should be fundamentally adaptive, which involves allocating computational resources proportional to the inherent complexity of each query. However, realizing this vision requires addressing two key technical challenges: (1) principled diffi- culty assessment that can reliably distinguish between simple extraction tasks and complex reason- ing problems, and (2) fault-tolerant adaptation that can dynamically expand reasoning when initial attempts prove insufficient.
We introduce Mixture-of-Length (MoL), novel framework that addresses these challenges through two key innovations. First, we develop a theoretically-groundee difficulty assessment based on information-theoreti principles, specifically modeling QA complexity through the lens of the Set Cover problem. Our metric quantifies reasoning difficulty by measuring cross cross-docmment infor- mation redundancy, where high redundancy indicates simple extraction tasks and low redundancy signals complex multi-hop reasoning requirements. Second, we propose dual-objective reward mechanism that implements fault-tolerant adaptation: it encourages compression for correct re- sponses while promoting expansion for incorrect ones, enabling the model to self-correct by scaling reasoning capacity on-demand.
Crucially, MoL exhibits an emergent behav- ior termed "intelligent brevity": the model naturally learns produce concise responses for simple queries and elaborate reasoning for complex problems, without explicit length con- straints. This is a direct outcome of our train- ing design, which encourages adaptive resource allocation based on question difficulty. Post- hoc analysis further reveals this output-level adaptation correlates with internal computa- tional patterns: simpler questions activate fewer transformer layers, while complex ones engage deeper model capacity. This suggests that MoL induces form of dynamic computational allo- cation operating coherently across both exter- nal responses and internal representations.
70.0
67.-
65.0
Accurssy (9)
625
600
67.5
35.-
-h- CR10
-D- Orgas
-0- ENPLP
500 128
256
1024
Token Langth
Figure 1: An evaluation of model accuracy and to- ken length HotporQA: Original (base model), GRPO (RI. with accuracy reward only), ER- PLP (difficulty-wware adaptive reasoning depth), KIMI (length-penalized reasoning compression), and MoL (ours). Lower tokens similar or higher accuracy is better.
Empirical evaluations across multiple QA benchmarks, including HotpotQA (Yang al. 2018). Strategy (Geva et al., 2021), and Loong (Wang et (0044), reveal that Mol. sig- nificantly enhances both performance and effi- ciency. Our comprehensive results demonstrate that MoL. achieves: a) Up to 53.2% reduction in token length in inference efficiency across QA with context datasets. b) An absolute accuracy improvement of 5.0%, outperforming state-of-the-ar token compression and reinforcement learning methods. c) Superior performance generalization to unseen datasets, highlighting the robustness of our difficulty-aware approach.
Our contributions are threefold: (1) principled, information-theoreticc approach to difficulty as- sessment in multi-document QA, (2) fault-tolerant adaptive reasoning framework that dynamically balances efficiency and accuracy, and (3) empirical evidence that output-level adaptation can reflect and potentially drive internal computational allocation in transformer models.
RELATED WORK
QA with Context. QA with context task aims to enhance question understanding by incorporat- ing additional information, rather than treating questions as isolated inputs (Min et al. 2020). This context encompasses various factors, particularly cognitive and social factors related to user intent, tasks, and needs. Consequently, the effective utilization context is crucial for accurately interpret- ing questions. Notably, the performance of Large Language Models (LLMs) remains challenged when handling long-text tasks due to limitations their context window (Chiang & Cholak 2022). Numerous studies are currently dedicated extending the effective context length of LLMs (Xiao) jt al. 2024) [hevalier et al.) 2023). Given this, is essential to propose methods capable of effective task-specific adaptation for long-text tasks.
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Published as a conference paper at ICLR 2026
Large Reasoning Model. In recent years, large language models (LLMs) have achieved remark- able breakthroughs in complex reasoning tasks (Wei et al. 2022). key innovation in this field is the Chain-of-Thought (CoT) technique, which enhances multi-step reasoning by introducing in- termediate reasoning steps (Yao et al., 2023). This approach has significantly improved model performance in challenging tasks such mathematical deduction and logical analysis. Building upon this direction, researchers have further integrated reinforcement learning techniques to en- hance models' autonomous reasoning capabilities through answer feedback mechanisms (Cheng & Van Durme, 2024). This technical approach has led to several state-of-the- -hrrttrtt models, such as OpenAT's ol (Achiam et al. 2023) and DeepSeek-Ri.
Efficient Reasoning. Several recent works have been proposed to address the redundancy in Chain- oh-Thhtghh (CoT) reasoning (Ma et al., 2025; Shen et al. 2025). For supervised fine-tuning, Yu et al. (2025) introduces the L.S-Mixtrre framework, which mitigates redundancy by jointly training on both original long CoT sequences and their reconstructed shorter ones. Other methods enforce
stricter constraints: Token-Budge imposes a hard token limit to streamline computation (Han al. 2024). whereas Toke adopts weportance-weighted filtering (Xia et al.] 2025). However, both approaches risk omitting pivotal reasoning steps, especially for complex problems. Reinforcement learning approaches have enabled more flexible optimization (Rafailov et al., 2023) Ferrag et a 2025). The Kimi team's work utilizes contrastive length reward to encourage conciseness (Team et al. 2025), and Ling et al. (2025) propose an adaptive strategy that adjusts reasoning depth based on pre-assessed problem difficulty. While effective, these methods often suffer from two key limita- tions: (1) they lack a mechanism for error recovery when an initially concise answer is incorrect, and (2) their compression strategies can be brittle, failing to expand reasoning for unexpectedly complex queries. In contrast, MoL introduces fault-tolerant, dual-objective mechanism. It not only com- presses responses for simple tasks but also dynamically encourages longer, more detailed reasoning (Rextena) when answer is incorrect. This allows the model to self-correct by scaling its reasoning capacity on-demand, significantly improving reliability and robustness compared methods with fixed or one-way compression policies.
3
METHOD
3.1
OVERVIEW
We propose Mixture-of-Length (MoL), a framework that achieves "intelligent brevity" by adaptively modulating response length based question difficulty. Our method enables models to naturally produce concise responses for simple queries while elaborating reasoning for complex problems. We first introduce difficulty assessment that quantifies reasoning complexity from cross-document redundancy (proxy-bace based information-theore then propose adaptive mixture-of-length reasoning approach, driven by a dual-objective reward, to dynamically adjust response length. The overall framework is illustrated in Figure 2
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When question is answered incor- rectly, the model encouraged by Reatend to lengthen its response in order to search for missing evidence chains, whereas once the question is answered correctly, Reompress rewards more concise expressions. These two behaviors are adaptively interleaved during training based on the correctness of the current response, rather than forming a fixed multi-stage pipeline.
Figure 2: Our framework adaptively controls response length:
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Published as a conference paper at ICLR 2026
3.2 DIFFICULTY ASSESSMENT: FROM THEORY TO IMPLEMENTATION
Conceptual Motivation. While reasoning complexity is inherently tied to information synthesis across documents, draw conceptual inspiration from information theory and the classic Set Cover problem (Alon et al. 2003 Ash 2012) to formalize this intuition. To answer question 9, model must synthesize documents D = (D...... ).).
essential knowledge snippets
{ui...., Um distributed across context Each document D; provides a subset A; S U of these snippets.
The complexity of covering all elements in U with minimal set of documents is closely related to the approximation hardness of the Set Cover problem. However, exact Set Cover computation is NP hard and thus infeasible for large scale QA. We observe that high redundancy, that is, substantial overlap between different A,, corresponds to lower effective complexity, since fewer documents are required to cover U, whereas low redundancy corresponds to higher complexity. Motivated by this observation, we propose practical approximation that characterizes problem difficulty in terms of document redundancy.
Practical Implementation. Our heuristic operationalizes redundancy through sentence-level simi- larity between documents:
Step 1: Key Information Extraction. To filter out noise, we first extract question-relevant sen- tences from each document:
DJ = {s E D; 8 (op=k((im((,4))) 4))}
(1)
where Top-k(Sim(s, q)) denotes the subset of D; consisting of the k sentences with the highest cosine similarity to q (Sim(s, q) is the cosine similarity between the embeddings of and q).
Step 2:
Cross-Document Similarity Computation. We then compute the pairwise similarity be- tween the filtered documents quantify information redundancy:
Sij cosine(embed( (D)), embed Dj)),
(2)
The average similarity is calculated as:
s 2
Sig:
n(n 1si<jsn
Finally, difficulty is defined as:
-1
Clarification of Approximation. While our metric is inspired by the complexity formalized in the Set Cover framework through redundancy principles, it is a heuristic approximation tailored for practical QA scenarios. Unlike exact Set Cover solutions, our method avoids com- binatorial explosion by focusing semantic overlap at the sentence level. Empirically this design achieves strong correlation with difficulty labels documented by humans (81 % agree- ment, Section 4.3). validating its utility proxy for reasoning complexity.
3.3 ADAPTIVE REWARD MECHANISM
Our adaptive reward mechanism can be framed as optimizing the rate-distortion trade-off in reason- ing, where we use response length as proxy for rate and task error as proxy for distortion.
Extend Reward for High Distortion. For complex questions, concise responses often fail cover the required multi-step reasoning processes. According to Press et al. (2022). many incorrect an- swers tend arise when response length is significantly shorter than expected, leading to missing critical reasoning steps. To address this, we design an expansion reward mechanism for incorrect answers (high distortion):
Restend = clip (E1-X A (1
Luctual
.0,
(5)
Larget
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Published as a conference paper at ICLR 2026
where Ltarget denotes the target response length, Lactual represents the actual response length, E1 is the base reward for incorrect answers, and À controls the ewward-length correlation strength. This mechanism encourages longer reasoning unlock correct paths while incorporating accuracy verification to prevent verbose but ineffective responses.
Compress Reward for Zero Distortion. For simple questions, existing models tend to generate sponses with redundant explanations and irrelevant information (Sui et al. 2025) Chen et al. (024).
which reduces efficiency and may introduce errors (Zeng et al. (025). For correct
(zero
distortion), our compression reward encourages finding a minimal-sufficiem description, following
the Minimum Description Length (MDL) principle (Grünwald 2007
Lactual
compress = clip E2 +
.00,1
(6)
Lunget
where 22 indicates the base reward for correct answers. This design provides substantial rewards for correct answers while progressively decreasing rewards as response length increases, effectively cul- tivating the model's ability to "answer demand" by eliminating non-essential expressions while preserving accuracy.
3.4 MIXTURE-OF-LENCEE FRAMEWORK
Diffiully--epeender Target Lengths. Based the assessed complexity, assign target length Lurget which acts as an empirical anchor on the rate-distortion curve. The specific length thresholds are initially based the response length distribution observed in the HotpotQA dataset( Additional experimental details are provided in Appendix [B.5] including additional exper- iments we conducted demonstrating that MoL. adaptively adjust its behavior, thereby reducing its dependence on the Ltarget):
512
if D) S 0.3 (Simple),
Lurget
1024
if3 < C(q. D) < 0.7 (Medium), if C(g, D) 2 0.7 (Complex).
2048
This anchor is sufficiently long for complex cases, yet short enough to avoid verbosity in simple see- narios. Importantly, we find that different parameter combinations consistently yield performance gains(ablation studies these parameters are provided in Appendix B.5). indicating that our ap- proach is robust to specific threshold choices.
Unified Reward Function. The complete MoL reward implements "intelligent brevity" by dy- namically switching between compression and extension modes based on answer correctness. This design prevents reward hacking behaviors where models might exploit the system by generating extremely long or short responses regardless of content quality:
Recompesss if y = y² (zero distortion),
RMOL
(8)
Rextend if # y² (high distortion).
Progressive Learning Strategy. To ensure stable training, we employ curriculum learning strat- egy by dynamically adjusting the length-reward coefficient \ over time:
X(1) max 7 (1-4))
where i is the current training epoch, T total epochs, hyperparameter that controls the strength of the correlation between response length and reward, and is minimum floor value.
3.5 TRAINING OBJECTIVE
We use the GRPO algorithm for optimization. The total reward function combines the standard accuracy reward with our MoL reward:
R(x, y) a 1ly = v²] a) RMat v):
(10)
The final optimization objective includes a KL regularizer to reference policy mees which stabilizes updates:
no T Tref (y|z)
((0) = En,
R(x,y) - -B log
(11)
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4 EXPERIMENTS
Setup We utilized a compute node equipped with 64 A100 GPUs for all experiments. Hyperpa- rameters are set as follows: a = 0.7 to balance accuracy and efficiency; Ào = 1.0, y = 0.3 for progressive constraint relaxation; E1 = 0.2, 82 = 0.6 for the base rewards; and = 2 for sentence selection. We encode the documents with the BGE-M3 encoder. We report experimental results using the Fl-score as the primary evaluation metric. All tokens length reported in experimental results refer exclusively to output tokens, excluding input tokens. During training, prediction is considered correct if its Fl-score exceeds 0.8, Additional configurations are detailed in Appendix A Hyperparameter ablation studies are provided in Appendix B
Benchmarks We performed comprehensive experiments on diverse QA with context tasks, in- cluding implicit reasoning, complex reasoning, and long-document reasoning. Our evaluation uti- lized three benchmark datasets: HotpotQA (Yang et al. 2018), StrategyQA (Geva et al. 2021), and Loong (Wang et al. 2024).
Baselines In our experiments, we compare against the following baseline methods: 1) GRPO:
A baseline based on standard reinforcement learning. 2) ERPLP: An adaptive resource allocation strategy dynamically adjusts reasoning depth according to problem difficulty (Ling et al.] 2025). 3)
KIMI: Integrates model distillation, shortest-path sampling, and length penalty to compress long reasoning chains into concise outputs (Team et al. 2025).
4.1 COMPARISON EXPERIMENTS
We evaluate the Qwen3-1.7B, Qwen3-8B, Qwen3-14B, and Llama-3.1-8B-Insteeee across all bench- mark datasets (note that Qwen3-1.77 cannot be evaluated on the Loong dataset due to its context window limitations), with the results presented in Table[] Experimental results demonstrate that our proposed Mol. method achieves intelligent question-adaptiie compression through its document- relevance-based dynamic difficulty grading mechanism. For simple questions, it applies substantial compression while preserving more critical information for complex ones, yielding a 49,1% 1% com- pression ratio with 6.2% accuracy gain (Qwen3-8B). This adaptive strategy shows significant effectiveness across all three benchmark datasets, not only realizing efficient compression but also substantially improving accuracy. These results suggest that MoL. provides more intelligent and efficient solution for QA with context tasks, effectively addressing key challenge in traditional methods of balancing response efficiency with answer quality.
The GRPO-trained model indeed achieves improved accuracy, but at the cost of reduced inference efficiency. While ERPLP and KIMI demonstrate certain effectiveness in token compression, their uniform compression strategies exhibit notable limitations. This compression approach particularly impacts the handling of complex questions, excessive compression leads to critical reasoning infor- mation loss, resulting in significantly degraded accuracy when answering questions requiring longer reasoning chains. To validate the operational mechanism of the MoL, we illustrate its effect by com- paring model outputs before and after training on randomly sampled questions of different difficulty levels. The complete set of examples is available in Appendix
4.2
ABLATION STUDIES
To validate the effectiveness of our question difficulty assessment method, we conduct compara- tive experiments on the HotpotQA dataset using three distinct difficulty definition approaches. The experimental results are presented in Table [ The results demonstrate that models trained with HotpotQA's original difficulty labels achieve superior performance while maintaining output con- ciseness, confirming the reference value of the dataset's inherent difficulty annotations. In contrast, the passage- based method, which computes difficulty labels through similarity measures between original reference passages, shows suboptimal performance. Specifically, the low-relevance sen- tences in original reference documents introduce bias to the similarity computation mechanism. The simple questions are then misclassified as difficult ones and consequently leading to unnecessarily verbose model responses. Our proposed method can effectively address such limitations by first ex- tracting question-relevant key sentences before computing similarity. With the improved difficulty assessment, our method has outperformend other methods. Appendix D presents a comparative evaluation of the two methods for computing question difficulty.

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Published as a conference paper at ICLR 2026
Table I: Performance comparison on QA with Context task under various base LLMs.
HotpotQA
StrategyQA QA
Loong
Models
Methods
Accuracy
Tokens
Accuracy
Tokens
Accuracy
Tokens
Original
51.8
766
81.0
397
GRPO
55,5
717
88.3
425
Qwen3-1.7B
ERPLP
54.4
444
86.5
369
KIMI
55.1
475
85.9
358
MoL (Ours)
57.3
384
89.3
201
Original
61.0
609
93.7
468
55,8
2165
GRPO
63.7
747
95.4
478
60.8
2363
Qwen3-8B
ERPLP
63.3
394
92.0
270
56.3
2037
KIMI
62.8
444
92.5
333
60,8
1938
MoL(Ours)
67.2
316
95,9
219
62.3
1374
Original
65.7
94.4
322
62.4
1915
GRPO
67.2
559
96.1
326
71.3
1907
Qwen3-14B
ERPLP
60.2
352
94.6
248
67.0
1380
KIMI
63.6
311
92.7
304
69.6
1519
MoL. (Ours)
69.4
298
96.8
187
72.3
1128
Original
53.3
431
77.4
116
36.3
742
GRPO
64.5
1107
92.4
57.1
Llama-3.1 3.1-8B-Insttttu
ERPLP
86.5
158
KIMI
64.5
196
91.6
103
55.9
207
MoL(Ours)
69.2
169
94.1
57
59.2
143
Table Performance evaluation models
Table 3: Comparative analysis reward mech- anisms model performance (Loong).
trained with different difficulty definition strate- gies on HotpotQA dataset.
Loong
Models
HotpotQA
Accuracy
Tokens
Models
Accuracy
Tokens
Original GRPO
55.8
2165
Original
61.1
609
60.8
2363
Original difficulty
63.0
387
MoL w/o Rextend
58.9
1298
passage
62.1
594
MoL w/o Reompreas
62.7
2862
MoL (Ours)
67.2
316
MoL (Ours)
62.3
1374
Our systematic ablation study reveals distinct roles of Reompress and Rextend (See in Table 3l Removing Reztend significantly degrades model accuracy (contrast with GRPO performance), firming its exclusive contribution to reasoning quality. Conversely, disabling Reomprese leads to substantially longer outputs with negligible accuracy gains, demonstrating its specialized function in length control. These orthogonal effects collectively validate our reward design's dual-capee cual-capability architecture: Reztend primarily enhances correctness without inducing significant length inflation, while R Remppeess successfully enforces conciseness with minimal sacrifice in accuracy. The ablation studies on the target length and our Progressive Learning Strategy are detailed in Appendix B.5 and Appendix B.6 respectively.
4.3 STRATIFIED ANALYSIS BASED DIFFICULTY LEVELS
We partition the HotpotQA dataset into ten difficulty-based segments and evaluate both the KIMI method and our approach on each segment, with results shown in Figure 3 Experimental results demonstrate that our method dynamically adjusts token compression strategies according to question difficulty, achieving optimal balance between performance and efficiency. Our approach yields a significant 7.3% accuracy improvement in high-diffiullyy segments while maintaining reasonable token counts. For medium-to-bee difficulty segments, it achieves superior compression with 10% token reduction while preserving accuracy advantage.
Compared to KIMI's fixed compression strategy, method exhibits clear difficulty awareness: mew-tum difficulty questions, we allocate fewer tokens, whereas for high-difficllty questions, increased token allocation mitigates accuracy decline, validating its effectiveness.
To validate the effectiveness of our proposed difficulty assessment method, we employed both the prompt-based model approach and the Mol. method to classify the difficulty levels of the HotpotQA

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MoL.
KIM
Easy
Hard
Essy
Hard
Accuracy (16)
Tokeme Length
100
601
a182
0207
Gdof
aao
up10
801
0203
0307
6200
6809
ad
Range
Defcuuty Range
(a) Accuracy Comparison.
(b) Response Length Comparison.
Figure 3: Comparative Analysis of MoL and Kimi Methods by Question Difficulty.
dataset. The experiments utilized DeepSeek-V3 V3 as the classification model, and the performance of the two classification results was evaluated based on the Qwen3-8B model (As shown in Table
Table 4: Comparison of difficulty assessment. MoL. demonstrates superior discrimination between easy and hard questions, with a larger accuracy gap (29.1% vs. 8.4%). higher easy-question accu- racy, and lower hard-question accuracy, indicating more realistic difficulty evaluation.
Difficulty (Acc)
Method
Difference
Fasy
Hard
Prompt(DS-V3)
64.1
55.7
8.4
MoL(Ours)
(9.2(h))h
(0.we))
29.1
For datasets classified by difficulty using the Prompt-based method, the model's 'ss accuracy did not ex- hibit significant variation across difficulty levels. In contrast, datasets stratified via the MoL frame- work exhibited clear performance gradient reflecting the expected relationship between question difficulty and model accuracy. Specifically, the model performed significantly better on easy ques- tions compared to hard questions, indicating stronger discriminative validity and alignment with real-world difficulty categorization. The MoL methodology successfully identifies simple questions with higher accuracy and challenging questions where accuracy is lower, thereby creating distinct separation between difficulty tiers.
We further validated the accuracy of the MoL for question difficulty assessment by conducting pert evaluation on the HotpotQA dataset. The observed agreement rate of 81% between the experts and MoL provides strong evidence for the reliability of our approach. Although difficulty estimation based on cross- doosmett similarity performs well overall, two types of extreme misclassification can occur: samples with high cross-document similarity that nonetheless require multi-step reason- ing, and samples with low cross-document similarity whose answers can be directly extracted from single sentence; in both cases the difficulty estimation method may fail. We include case stud- ies of these two situations in our experiments to demonstrate MoL's self-correction and robustness (see Appendix F. We also evaluate the sensitivity of our difficulty estimator to Top-k, embedding model and sentence segmentation; details and results are given in Appendix B.7
4.4 ANALYSIS OF MODEL ACTIVATION PATTERNS
To empirically validate that MoL. fosters adaptive computation, we analyzed the model's internal ac- tivation patterns. We introduce relative activation metric to quantify the contribution of each layer (see Appendix M for a detailed definition and implementation). Using this metric, we measured the number of active layers for both simple and difficult problems, with threshold of r 0.1. The results are visualized in Figure E The baseline model exhibits homogeneous activation pattern, engaging a similar number of layers regardless of task difficulty. This confirms its lack of inherent difficulty awareness. In contrast, the MoL-trained model demonstrates significant computational adaptivity: it activates substantially fewer layers for simple problems while recruiting a deeper com- putational path for difficult ones. This finding provides direct evidence that MoL's training objective leads to an emergent behavior where computational effort is implicitly allocated based on perceived problem complexity, which is fundamental driver of its efficiency and performance gains.
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Baseline
Ours (-15 layers)
MoL
Ours
Baseline 20 layers)
18 layers)
22 layers)
36 layer
36 layer
36 layer
36th layer
36 layer
52 layer
3y0 laver
28 layer
Soe layer
28 laver
28th layer
28th layer
layer
Jayer
24 layer
24th layer
Layer
20 layer
200 layer
20 layer
20th layer
20A layer
16th layer
16th layer
16 layer
16th lawer
16th Layer
layer
Jayer
12 layer
12th
Layer
SiE layer
8i layer
8 layer
gta laver
layer
layer
4 layer
layer
layer
Question
Easy question
Hard question
Figure 4: Baseline vs. MoL layer activation on easy vs. hard questions: colored bars denote activated layers; uncolored bars denote inactive layers (Analysis on the HotpotQA dataset).
To demonstrate the generalization capability our approach, we evaluate models trained on the HotpotQA dataset across five benchmark datasets. As shown in Table S the Qwen3-8B model trained with Mol, exhibits remarkable generalization performance: on unseen StrategyQA and Loong datasets, it not only maintains high accuracy rates of 94.4% and 57.2% respectively, but also compresses the average response length to 239 and 1574 tokens, achieving reductions of 98.9% and 27.3% compared to the original model. These results strongly validate that MoL enables models to autonomously recognize question difficulty and adaptively select response strategies. Crucially, the stark contrast with the GRPO baseline confirms that the performance gains attributable the MoL framework itself, rather than to reinforcement learning in general.
4.5 GENERALIZATION STUDIES
Table 5: Qwen3-8B performance across five datasets when trained on HotpotQA.
HotpotQA
StrategyQA
Loong
CQA
SVAMP
Models
Acc
Tokens
Acc
Tokens
Acc
Tokens
Aec
Tokens
Acc
Tokens
Original
61.0
609
93.7
468
55.8
2165
66.5
673
93.7
1397
GRPO
63.7
747
92.9
513
56.4
2276
67.1
647
93.1
1436
MoL
67.2
316
239
57.2
1574
68.2
542
94.6
1129
Our model's 's training paradigm is based document-grue grruunddd datasets, which necessitates in- vestigation into its applicability to standard, con-document-grountde tasks. To this end, we per- formed evaluations two benchmarks: commonsense reasoning dataset (CommonsenseeAA) (Tal- mor et al. 2019) and a mathematical word problem dataset (SVAMP) (Patel et al. [202]]. The empir- ical results are summarized Table Notably, our method exhibits strong performance, indicating that the Mol. training framework endows the model with an inherent capability to gauge question difficulty and dynamically allocate computational budget for generating responses. This outcome strongly validates the generalization power of our approach beyond its training domain.
4.6 LONG CONTEXT SCENARIO
We evaluate model performance on long contexts by testing on data divided into two length inter- vals: 10k-50k and 100k. As demonstrated Figure Results show our method achieves the best overall performance in both intervals, particularly excelling complex reasoning tasks. This superior performance stems from MoL's adaptive Lzarget parameter selection, which enables op- timal response strategies for texts of varying lengths. Notably, our method maintains comparable

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10k-50k Tokens
50k-100k Tokens
85.7
64.3
57.0
81.5
70.4
86.8
59.8
47.8
62.1
58.4
90
62.5
65.5
45.8
79.9
62.4
63.5
51.5
36.6
68.5
47.7
80
6m11
70
68.5
60.6
47.1
70.4
60.1
64.5
42.6
38.5
51.2
45.7
Indrud
Score (%)
60
66 knliis
67.9
41.6
36.6
54.7 7
48.1
66 kol vt
72.8 8
44.5
33.0
40.5
42.0
Ovand-148
78.8
61.1
52.2
76.4
65.3
OwerS: 142
85.3
57.1
47.4
61.2
57.3
40
Owee
86.1
Owen3
53.7
72.0
64.8
Companees
Rarkolte
Oreai
Lovalngg
Cheesed
Orean
Oveat
30
Figure 5: Model performance comparison on the Loong dataset (10-50k and 50-100k).
performance between the 50k-100K and 10k-50k ranges, demonstrating significantly better stability than other baseline models. Experimental results demonstrate that our sifficultt-adaapiin approach achieves precise difficulty assessment through sentence decomposition while maintaining robustness to text length. Our case analysis shows that, even when desired lengths are shorter than initially spec- ified, the method generates suitable responses, evidencing the effectiveness of the length-constraint relaxation mechanism and robustness to varying length requirements.
5 CONCLUSION
We propose Mixture-of-Length (MoL), a novel framework that dynamically adapts response lengths to problem difficulty, effectively balancing reasoning depth and efficiency in QA with context tasks. MoL. combines a principled difficulty assessment with a dual-objective reward mechanism, which gives rise ability correlates with the model's internal layer contributions, emulating human-like cognitive ficiency. MoL represents a promising paradigm for scalable, context-aware reasoning in LLMs and suggests promising directions for developing intelligent, resource-efficient QA systems.
emergent "intelligent brevity" behavior. Our post-hoc analysis shows this adapt-
ETHICS STATEMENT
This study uses only publicly available datasets that contain no personal or sensitive identifying information. We conducted manual difficulty annotations for subset of samples; annotators were informed of the research purpose and voluntarily consented to participate. comprehensive de- scription of our use of L.LMs is documented in Appendix
LIMITATIONS
Analysis is Correlational: The observed link between shorter outputs and fewer activated layers is post-hoc and does not establish causality. More rigorous mechanistic studies are needed to confirm how MoL influences internal computation.
Generalization Constraints Multi Document Dependent Difficulty Assessment: We acknowl- edge that the proposed difficulty assessment method inherently tied to multi document tasks which limits its direct applicability to single document or document free scenarios. While the bidirectional reward function in Mol. demonstrates task agnostic properties (e.g. outperforming baselines
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CommonsenseQQ and SVAMP in Table 5) its adaptation to multi document settings currently relies on heuristic initial target lengths derived from approximate output token ranges. Future work will focus developing unified difficulty assessment framework applicable across diverse task modalities including single document QA and document agnostic reasoning.
8 REPRODUCIBILITY STATEMENT
We have taken several steps to facilitate independent verification of our results. The main pa- per details the model architecture and training objectives (see Section 3), Experimental Setup and datasets (Section[), and ablation configurations (Appendix Complete hyperparameter lists, hard- ware/software specifications are documented in Appendix A Dataset processing pipelines are de- scribed in Appendix G The souree code implementing method will be submitted part of our supplementary materials.
9 ACKNOWLEDGMENTS
This research was partially supported by National Natural Science Foundation of China under grants No. T2541004, Zhejiang Key R&D Program of China under grant No. 2025C02122 20, No. 2024SSYS0026, Zhejiang Key Laboratory of Medical Imaging Artificial Intelligence, and the Transvascular Implantation Devices Research Institute (TIDRI) under Grant No. KY052025003 and the Ant Group Research Intern Program.
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A EXPERIMENTS
Setup Our experiments were conducted using 64 A100 with 80GB memory per device. Hyper- parameters are set as follows: balancing coefficient a = 0.7, follow other work. length-correlation coefficient À 1, 7 0.3, base rewards E 0.2 and #2 0.6. To ensure accurate retrieval of the most relevant documents, we set the hyperparameter & = 2 in the Top k algorithm during the document pruning stage, selecting the top two highest-scoring sentences per document based on their relevance to the query. We propose a dynamic length selection mechanism with target length Ltarget adapting to question difficulty levels: for simple questions (typically factual queries), we set Ltarget 512 to capture core information needs; for medium-diffieuly questions (often involving multi-clause analysis). Ltarget = 1024 to incorporate contextual dependencies; and for complex questions (requiring doss-document reasoning). Learget 2048 to enable comprehensive modeling. This tiered design ensures computational efficiency while optimizing semantic modeling capacity across complexity levels. Here some specific training configurations:
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Table 6: Hyperparameters for experiments.
Configuration
Value
Number of epochs
3
Devices
x A100
Total Batch size
256
Learning rate
x 10-5
Each experimental run required approximately ten hours to complete. Importantly, the similarity computation occurs offline during training data preparation and does not introduce any computa- tional overhead during model inference.
Benchmarks We performed comprehensive experiments on diverse QA with context tasks, includ- ing implicit reasoning, complex reasoning, and long- doc-metttnnnt reasoning. Our evaluation utilized three benchmark datasets: HotpotQA, StrategyQA, and Loong.
HotpotQa: A benchmark designed advance research in complex reasoning and interpretable question answering systems. It contains Wikipedia-based questions and answers where each prob- lem requires integrating information from multiple documents derive solutions, with sentence- level supporting facts provided as supervisory signals. The dataset features diverse question types including fact comparison questions and serves as an effective benchmark for evaluating models' multi-hop reasoning capabilities.
StrategyQa: Boolean question answering dataset focusing on implicit reasoning, containing 2,780 yes/no questions requiring multi-step inference to resolve. Distinct from explicit multi- hop QA tasks, this dataset challenges models to autonomously infer problem-solving strategies (e.g., temporal comparisons, logical deductions) without exposing intermediate reasoning steps in the questions. Each instance is annotated with decomposed reasoning steps and corresponding Wikipedia evidence paragraphs, which can guide model learning of complex inference processes through supervised training.
An innovative long-text multi-document question answering benchmark designed to evalu- ate large language models (LLMs) in real-world long-context comprehension scenarios. This dataset ensures models must comprehensively understand all documents by distributing answer-related idence across multiple passages. It encompasses three domains: financial reports, legal cases, and academic papers, covering four task types (focused localization, comparison, clustering, and reason- ing chains), and provides test sets with varying lengths ranging from 10K to 250K tokens.
Loong:
B HYPERPARAMETER ANALYSIS
To systematically investigate the impact of key hyperparameters, we conduct an ablation study on the HotpotQA dataset using the Qwen3-8B model.
B. ANALYSIS OF THE TRADE-OFF COEFFICIENT & IN THE REWARD FUNCTION
The balancing coefficient a a key hyperparameter in the reward function that modulates the trade- off between model accuracy and efficiency. The results, presented in Table 7 delineate how the variation in a's value affects the final performance.
The reward is dominated by the length term (RMoz), prioritizing extreme ciseness. However, this leads to the omission critical reasoning steps, resulting in suboptimal accuracy due excessive compression.
Low values:
High a value: The accuracy term (Raccurate) dominates, prompting the model to adopt a conser- vative strategy to maximize correctness. This generates verbose reasoning chains with redundant steps, causing a significant increase in token consumption while the accuracy plateaus, yielding no further gains.
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Table 7: Experimental Results with Different Values of Parameter a.
a
Accuracy
Tokens
0.1
64.9
264
0.3
66.3
301
0.5
67.0
313
0.7
67.2
316
0.9
67.3
371
The model learns to first secure high reward by ensuring correctness and then optimize for conciseness. We ultimately select a=0.7 as the default configuration, as achieves high accuracy while maintaining near-optimal efficiency.
Optimal value:
These findings validate the effectiveness and controllability of our proposed reward mechanism. The coefficient a serves as reliable and intuitive "tuning knob" for the accuracy iccuracy-efficieney trade-off. Furthermore, the presence of a clear optimum demonstrates that our -uje-tbee reward design necessary for successfully balancing these two competing goals.
B.2 ANALYZING THE EFFECT OF CONSTRAINT STRENGTH
Y
The hyperparameter controls the minimum strength of the length constraint enforced during the late training phase. As shown in Table g our ablation study demonstrates the impact of y on the final performance.
Table 8: Experimental Results with Different Values of Parameter 7,
Y
Accuracy
Tokens
0.1
66.9
352
0.3
67.2
316
0.5
67.4
372
0.7
66.7
391
0.9
65.1
536
Low values: The constraint strength decays too rapidly in the late training phase, causing the model lose almost all motivation optimize response length and regress its inherent verbose generation pattern. Consequently, token usage is not minimized, and the accuracy remains subopti- mal the model fails adequately learn the reward signal associated with conciseness.
Optimal values: The model remains under a moderate length constraint in the late stage, success- fully internalizing the strategy of "pursuing conciseness while guaranteeing correctness." This leads to superior balance between accuracy and efficiency.
High y values: An excessively strong constraint throughout the entire training process inhibits the model's is exploration and generalization capability, particularly for complex questions requiring longer reasoning chains. The model struggles to generate necessary intermediate steps, resulting in significant drop in accuracy. Mcanwhile, to meet the stringent length limit, the model may produce obscure, abnormally high-density text, which can paradoxically lead to an increased token count.
Conclusion: 7 acts not simple intensity parameter, but critical regulator between stability and flexibility. Our experiments demonstrate that a moderate y value is essential: prevents the constraint from vanishing too early to ensure stable convergence, while also avoiding overly strong restrictions to preserve the flexibility needed for solving complex problems, ultimately leading to synergistic improvement in both accuracy and efficiency.
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B.3 THE MON-MONOTONIC IMPACT OF E1 AND
CHOICE
E1 sets the base reward value granted for producing extended responses. As shown in Table 22 ablation study demonstrates the impact of E on the model's performance.
Table 9: Experimental Results with Different Values of Parameter $1.
Accuracy
Tokens
0
67.4
362
0.2
67.2
316
0.4
66.7
311
0.6
65.6
379
0.8
65.1
413
The results reveal non-monotonic trend, highlighting the delicate trade-off in the design of
E1 = 0: This configuration achieves the highest accuracy Since the model receives no base reward for generating long but incorrect answers, the expansion reward is solely determined by the align- ment with the target length (i.e., "precise expansion"). This forces the model to be highly efficient and precise in its remedial reasoning, filtering out more effective reasoning paths and indirectly boosting final accuracy. However, this strong constraint also slightly limits the model's I's expressive capacity, preventing it from achieving the highest efficiency.
&1 E [0.2, 0.4] (Low Range): In this range, the model incurs only a minimal and acceptable drop in accuracy while achieving substantial gains in efficiency. A modest e this interval provides "safety net," encouraging beneficial exploration when uncertain without incentivizing meaningless verbosity. It works synergistically with the compression reward steer the model toward the global optimum of being both correct and concise.
E1 2 0.6 (High Range): A significant performance degradation is observed, with both accuracy and efficiency declining. An excessively high ε distorts the reward signal, teaching the model a harmful shorteut: generating a long incorrect answer yields higher reward than a short incorrect one. This effectively incentivizes the model to produce "knowingly wrong" verbose outputs instead of striving for correctness, leading to overall performance deterioration.
Conclusion and Choice: Our experiment indicates that tuning ε requires balance between provid- ing exploratory freedom and avoiding reward distortion. Although = yields the peak accuracy, ultimately select =02 the default. The rationale is that this value achieves the best Pareto frontier for overall performance, trading a negligible accuracy drop for the largest efficiency gain. This finding indicates that small but non-zero is crucial for effective error-tolerance mecha- nism.
B.4 ABLATION STUDY ON THE COMPRESSION REWARD E2
E2 sets the base reward value granted for producing compressed responses. As shown in Table I0 our ablation study demonstrates the impact of the model's performance.
Table 10: Experimental Results with Different Values of Parameter 62-
82
Accuracy
Tokens
0
61.7
186
0.2
64.3
264
0.4
65.9
292
0.6
67.2
316
0.8
67.7
441
At E2 = 0: The model exhibits harmful over-compression due to the absence base reward for correctness, causing it over-optimize for the length bias term. This results in extremely low
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token count but significant degradation in accuracy, indicating the omission of critical reasoning steps.
For [0.2, 0.6]: The model receives strong positive signal that "correctness yields high reward." This drives a learning strategy that prioritizes answer accuracy before optimizing for conciseness, leading to Pareto improvement in both metrics. Accuracy is substantially improved with only moderate increase in token usage.
At E 0.8: An excessively high base reward distorts the optimization objective. The model adopts a conservative strategy, generating verbose chains of reasoning to ensure correctness. This results in sharp increase in token count and complete loss of efficiency gains.
Conclusion and Selection: These findings demonstrate that 62 is a pivotal hyperparameter for bal- ancing accuracy and conciseneness. Based comprehensive evaluation, we select E2 0.6 the optimal configuration. This value achieves near-peak accuracy while maintaining responses within an efficient length range, thereby achieving the best overall performance balance.
B.5 ABLATION STUDY THE TARGET LENGTHS Ltarget
To establish optimal target length thresholds, conducted systematic analysis on the first 1,000 samples from the HotpotQA dataset. First, We use the Qwen3-8B model to directly classify analyzed the distribution of output lengths generated by
difficulty of questions. Subsequently, the model for each difficulty tier, with the results visualized in Figure 6 Through statistical analysis of these distributions, corresponding to simple, medium, and complex questions respectively. This data-driven approach ensures that the thresholds align with the inherent reasoning complexity while maintaining compu- lational efficiency.
empirically determined the target lengths as Ltarget [512, 1024, 2048),
Generation length by difficulty
3000
Tokens length (oulput only)
2500
0048
2000
1500
1024
500
612
Simple
Medium Difficulty
Hard
Figure 6: Distribution of generated output lengths by difficulty class (Simple Medium Hard). Tokens count only model outputs (exclude inputs), red diamonds mark class means.
To assess how the initial target length settings for problems of varying difficulty affect performance, we conducted an additional experiment using four alternative sets of Ltarget values with the Qwen3- 8B model:
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256
Simple, Medium, Complex,
256
Simple, Medium, Complex,
Luaget
512
Ltarget
256
1024
256
(12)
512
Simple,
1024
Simple,
Larget
512
Medium,
Lurget
1024
Medium,
512
Complex,
1024
Complex,
The results, summarized in Table [I] show that the choice of initial target lengths has a negligible impact on final outcomes. These findings validate the effectiveness of our Progressive Learning Strategy: the target response length functions primarily as a guiding signal in the early stages of training to shape response lengths, but it does not materially influence the final answers. Conse- quently, our approach remains robust across a broad spectrum of problem difficulties, demonstrating strong generalization.
Table 11: Experimental Results with Different Values of Parameter Luaget
HotpotQA
Strategy QA
Loong
Methods
Acc
Tokens
Aec
Tokens
Aec
Tokens
Original
61.0
609
93.7
468
55.8
2165
102))))
66.7
303
96.1
247
61.4
1473
MoL(512,1124,2044
67.2
316
95.9
219
62.3
1374
M6L.(5662266226)
67.0
303
96.4
236
61.7
1338
MoL(512.512,512)
67.4
331
95.9
213
62.1
1402
(0L(1122,1022,1022
66.9
286
96.2
225
61.3
1425
To investigate the impact of Trarget model training efficiency, we present systematic evaluation of the Qwen3-8B model's training dynamics on the HotpotQA dataset under varying initial target length configurations, focusing on performance variations across epochs. Figure and Figure illustrate the evolution of Accuracy and generated token length during training, respectively. Exper- imental results demonstrate that larger deviations between the initial target length and the optimal value necessitate more training steps to reach convergence. However, as training progresses, the influence of target length diminishes, with all configurations eventually converging to comparable performance levels. This indicates that while the initial length setting impacts training efficiency, it has minimal effect on the model's final generalization capability.
B.6 ABLATION STUDY THE PROGRESSIVE LEARNING STRATEGY
To validate the effectiveness of our Progressive Learning Strategy, we conducted ablation study by removing this module and re-evaluating the model's performance. The results are presented in Table 12
Table 12: Experimental Results with Progressive Learning Strategy.
HotpotQA
Strategy QA
Loong
Methods
Acc
Tokens
Acc
Tokens
Acc
Tokens
Original
61.0
609
93.7
468
55.8
2165
MoL. w/o X(I)
65.3
835
93.1
592
49.1
913
Mol.
67.2
316
95.9
219
62.3
1374
The results reveal that the Progressive Learning Strategy has a substantial impact on performance. Without this strategy, the model's response length is severely constrained by the pre-defined initial target value, failing to dynamically adapt to the problem's complexity. This limitation is particularly detrimental to complex problems requiring long reasoning chains, such those in the Loong dataset,
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MoL(256,512)) 512.1024)
MoL(512, (OLLS12,1024,2000 20244
MoL(256, 256,
MoL(552,512.55))
M01/1024,1024,1024
67-
66
Acciracy
65
63
61
Epoch
Figure 7: Evolution of accuracy across training epochs.
MoL(256,512,
(oL((12,10224 2048)
650
MoL(256,256,222
MoL(1024, (LL11024.1024,024
Takens Length
500
450
300
250
Epoch
Figure 8: Evolution of model output length across training epochs.
leading drastic performance drop. Furthermore, this inability adapt the output length also impairs performance on simpler tasks, resulting in decreased accuracy across all datasets. These findings firmly demonstrate that the Progressive Learning Strategy an indispensable component for generating high-quality and adaptive responses.
B.7 ABLATION STUDY ON THE ROBUSTNESS OF DIFFICULTY ESTIMATION
To evaluate the sensitivity of our difficulty estimator to Top-k retrieval, the embedding encoder, and sentence segmentation, controlled ablations the Loong dataset using Qwen3-8B. Ta-
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ble 13 summarizes the results for varying (k=,,,,33, Table compares two sentence encoders (Sentence-TS and BGE-M3), and Table reports results for two segmentation granularities (1 sen- tence vs. 2 sentences per retrieval unit). Each entry reports downstream answer accuracy and the average number of generated tokens (output only). In brief, k=2 yields a good trade-off between accuracy and generation length (k=1 produces shorter outputs but slightly lower accuracy), encoder choice has only minor effect on accuracy, and merging two sentences modestly increases generated length without materially changing accuracy. Overall, MoL is robust to these design choices: they have minimal impact on the final results.
Table 13: Top-k ablation on Loong (Qwen3-8B), -B).. Impact of varying Top-k retrieval (k=1,2,3) on downstream answer accuracy and average generated tokens (output only). "Original" denotes the baseline without MoI..
Loong
Methods
Acc
Tokens
Original
55.8
2165
Mol(K=1)
61.7
1199
MoL(k=2)
62.3
1374
MoL(k=3)
62.1
1422
Table 14: Embedding encoder ablation Loong (Qwen3-8B). Comparison between Sentence-TS and BGE-M3 sentence encoders reporting answer accuracy and average output token length.
Loong
Methods
Acc
Tokens
MSL(eentence-T55
62.6
1462
MoL(BGE-MM)
62.3
1374
Table 15: Sentence granularity ablation on Loong (Qwen3-8B). Effect of segmentation unit (single-sentence vs. two-sentence retrieval units) on downstream accuracy and average generated tokens.
Loong
Methods
Acc
Tokens
Original
55.8
2165
MoL(one sentence)
62.3
1374
Moo sentence)
62.5
1439
C IMPLEMENTATION DETAILS FOR LAYER-WISH ACTIVATION
C.I QUANTIPYING LAYER-WISE ACTIVATION
To investigate the computational dynamics of our MoL.-triined model during inference, we quantify each Transformer's layer-wise activation via its relative contribution to the residual stream.
Motivation. Relative activation is preferable to absolute magnitudes because it: (i) normalizes for scale differences across layers induced by residual connections, (ii) captures the proportional change each layer makes to the information flow, and (iii) remains comparable across model sizes and architectures.
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Definition. Consider Pre-LN decoder-only Transformer layer Z operating input residual stream ( RTxD Let (1) and A mip denote, respectively, the actual updates added back the residual stream by the attention and MLP submodules. The total update is We define the relative activation as:
(1) layer
(1)
arm + mip
RMS A layer
(1)
a
(13)
RMS (r (
+
where is small constant for numerical stability. A layer is considered active if a r(threshold
T described Section C.3
C.2 IMPLEMENTATION DETAILS
Masked RMS for padded batches. For tensor RBxTxD and attention mask m {0, 1xx",, we compute a masked Root Mean Square (RMS) over all valid elements:
(x² o m²) Σ mm)17 +
masked rms(x, m)
(14)
where m' is the mask m broadcast to the shape of x (i.e., [B, T, ID, and @ denotes element-wise multiplication. We compute in float32 and clamp the denominator to avoid division-by -y--zer when all tokens are padding.
Non-invasive residual differencing. To ensure that the measured updates match the actual tensors added to the residual stream (including any internal dropout, scaling, or gating), we avoid reading from intermediate projection layers and instead compute updates by differencing the residual states:
To block input before LayerNorm (block-level forward pre-hook).
(1) safter attention residual addition and before MLP (captured as the input to the post-attention LayerNorm via its forward pre-hook),
rj
2
block output (block-level forward hook).
Then
A (1)
ri
(1) (8)
(15)
A (1)
mlp 12 ri
(1) (8)
(16)
(1) (0) (8)
layer
1 ro
(17)
This construction is architecture-robust and aligns the numerator and denominator of Eq. the same residual stream.
Measurement protocol. We instrument models in evaluation mode under no-gradient execution to disable dropout and reduce overhead. For mixed precision, we upcast to float32 for statistics. Unless otherwise stated, we use e 10-8 for FP32 and 10-6 for FP16/BF16.
C.3 THRESHOLD SELECTION AND ROBUSTNESS
Threshold. We s 0.1 based on empirical analysis across multiple model sizes and reasoning tasks. This value provides effective separation between layers with meaningful contributions and those with minimal updates.
Cruss-architecture verification. We verified LLaMA and Qwen style Pre-LN decoders. For other variants (c.g.. Post-LN), the same principle applies: capture the residual states immediately before and after each residual addition to form A and Amin via differencing (Eq. [3). This guarantees inclusion of any internal dropout, scaling, gating before residual addition.
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D PARAGRAPH-LEVEL vs. SENTENCE-LEVEL SIMILARITY MATCHING
Through controlled experiments comparing paragraph-Ievel and sentence-level similarity match- ing (Results in Tables [6 and Table [7]. we identify fundamental limitations in paragraph-level approaches: they inherently incorporate numerous low-relevance sentences (particularly question- related expository content). These noisy segments systematically distort similarity computation, causing significant underestimation of semantic alignment and consequent overestimation of ques- tion difficulty. Our proposed Top-k key sentence filtering prior to similarity calculation demonstra- bly mitigates this issue, with experimental results validating the efficacy of our approach.
E THE USE OF LARGE LANGUAGE MODELS (LLMS)
All models utilized in this work are publicly available. We employed them solely for language polishing to improve the readability of our text. It is important to note that these models were not involved in any scientific decision-making. Furthermore, all model-assisted outputs underwent rigorous human review to ensure compliance with ethical and legal standards.
F CASE STUDY
F.1 ANALYSIS OF RESPONSE PATTERNS FOR SIMPLE QUESTIONS
When handling relatively simple questions that require no reasoning (e.g., asking whether visitors are allowed to use mobile phones to take photos or videos during the ride the "Jurassic World Adventure" attraction at Universal Beijing Resort), baseline models tend to generate overly verbose responses (Results in Tables 18 Although they accurately output the core information that elec- tronic devices prohibited for photography, the models still append redundant content, such as A repeatedly mentioning instructions from official channels. Textual analysis reveals that such expan- sions primarily stem from verbatim extraction of reference documents; while semantically correct, they fail to meet the task requirement of concise responses. In contrast, models trained with Mol. demonstrate precise response control, strictly confining their outputs the core information sought by the question, ensuring answer accuracy while significantly improving response efficiency.
F.2 ANALYSIS OF REASONING CAPABILITIES FOR COMPLEX QUESTIONS
The baseline models exhibit fundamental limitations in processing multi nulti-conditional reasoning tasks, as evidenced by their performance on greenhouse gas effect questions (Results in Tables 19 While correctly identifying the basic absorption ratios of CHH, to CO2 (84x) and NF3 to CO2 (16,100x), these models fail to incorporate the critical constraint regarding fluorinated gases' 272x radiative efficiency relative to C'HA, resulting in erroncous linear extrapolations. MoL-enhanced model demonstrates superior information integration and quantitative reasoning pabilities. It successfully captures all relevant document constraints, establishes cross-sentence nu- merical relationships, and performs the necessary multi-step calculation 166//400/))22) to curately determine N F3's superior absorption efficacy. This performance improvement confirms that our approach not only enhances key information extraction completeness but also develops the model's 's capacity for evidence-basee quantitative reasoning, representing a significant advancement in complex scientific question answering.
contrast, our
F.3 ROBUSTNESS TO DIFFICULTY MISCLASSIFICATION (MoL COLERCORRET
Although difficulty estimation based on cross-document similarity works in most cases, extreme misclassification can still occur. Table 20 and Table 21 present two types of extreme misclas- sification scenarios and MoL's self-correction behavior. Table 20 illustrates the case of high cross-document similarity but actually requiring multi-step reasoning: because the mean similar- ity S is large, the example is initially judged simple, the model attempts short answer and makes an error (the table shows the initial output). When the answer is judged incorrect, MoL's ex- tension reward Rextend encourages the model to produce longer, more complete chains of reasoning
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Table 16: A Case Study on Estimating Question Difficulty Through Sentence-Level Similarity Anal- ysis.
Sentence-level Similarity Matching
Query
Who is the author of The Story of the Stone?
Doc
"The Story of the Stone, recognized as the foremost of China's Four Great Classical Novels, was authored by the Qing dynasty writer Cao Xueqin... Beyond its narrative depth, the work provides a critical depiction of feudal society's s moral decay and social intricacies. With its vast ensemble of characters and richly woven plotlines, it has been acclaimed as the "Encyclopedia of Chinese Feudal Society." Doc2: "Cao Xueqin, the author of the Story of the Stone, was born into aristocracy but died in poverty, drawing upon his personal experiences to compose this monu- mental work. ... Their intertwined destinies collectively depict the rise and fall of feudal dynasty, offering panoramic critique of traditional Chinese society." Doc3: "Authored by the Qing Dynasty literatus Cao Xueqin, the Story of the Stone not only presents remarkably vivid character portrayals but also contains numer- ous iconic scenes that have become literary canon... colle..colll elevating the novel to its enduring status as masterpiece of world literature."
Docl:
Doc4: "The Story of the Stone, penned by Cao Xueqin, offers a profound critique of 18th-century Chinese feudal society... constrained marriage- which vividly il- lustrates the oppressive nature of feudal Confucian norms on individual agency." Doc5: "The Story of the Stone, authored by Cao Xueqin, stands as the pinnacle of Chinese literary achievement. Beyond its central tragic render the work an indispensable resource for studying premodern Chinese society. To this day, "Hongxue" (Redology) remains vibrant field of scholarly inquiry."
Doc6: "Cao Xueqin's 's the Story the Stone is renowned for its exquisite linguis- tic artistry and profound characterization..hin surrounding the Twelve Beauties of Jinling. This intricate web of narrative foreshadowing creates an exceptionally tightly-knit story structure."
Doc7: "Cao Xueqin's the Story of the Stone employs the rise and fall of the Jia family allegory for the decline of feudal society as a whole...conservatism of Confucian orthodoxy. This sophisticated interplay of thematic elements has se- cured the novel's enduring legacy and widespread influence in both literary and cultural spheres."
Doc'
Docl': "The Story of the Stone was authored by the Qing dynasty writer Cao Xue- qin."
Doc2': "Cao Xueqin, the author of the Story of the Stone, was born into aristoc- racy but died in poverty, drawing upon his personal experiences to compose this monumental work.
Doe3': "Authored by the Qing Dynasty literatus Cao Xueqin"
Doc4': "The Story of the Stone, penned by Cao Xueqin"
Doc5'; "The Story of the Stone, authored by Cao Xueqin"
Doc6': "Cao Xueqin's the Story of the Stone is renowned for its exquisite linguis- tic artistry and profound characterization."
Doc7'; "Cao Xueqin's the Story of the Stone employs the rise and fall of the Jia family as an allegory for the decline of feudal society as whole."
Sim(Query,
0.87
Doc')
Judge
Easy
to fill in missing evidence; once the correct answer is obtained, the system switches to the compres- sion reward Reomprese which trains the model to return the correct conclusion in more concise form. Table Sable[]]shows the case of low cross-document similarity but an answer that can be directly extracted: such samples are initially judged hard and lead longer generations, but the first long
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Table 17: A Case Study on Estimating Question Difficulty Through Paragraph-Level Similarity Analysis.
Paragraph-Ievel Similarity Matching
Query
Who is the author of The Story of the Stone?
Doc
"The Story of the Stone, recognized as the foremost of China's Four Great Classical Novels, was authored by the Qing dynasty writer Cao Xueqin... Beyond its narrative depth, the work provides a critical depiction of feudal society's s moral decay and social intricacies. With its vast ensemble of characters and richly woven plotlines, it has been acclaimed as the "Encyclopedia of Chinese Feudal Society." Doc2: "Cao Xueqin, the author of the Story of the Stone, was born into aristocracy but died in poverty, drawing upon his personal experiences to compose this monu- mental work. ... Their intertwined destinies collectively depict the rise and fall of feudal dynasty, offering panoramic critique of traditional Chinese society." Doc3: "Authored by the Qing Dynasty literatus Cao Xueqin, the Story of the Stone not only presents remarkably vivid character portrayals but also contains numer- ous iconic scenes that have become literary canon... colle..cllle elevating the novel to its enduring status masterpiece of world literature."
Docl:
Doc4: "The Story of the Stone, penned by Cao Xueqin, offers a profound critique of 18th-century Chinese feudal society... constrained marriage- which vividly il- lustrates the oppressive nature of feudal Confucian norms on individual agency." Doc5: "The Story of the Stone, authored by Cao Xueqin, stands the pinnacle of Chinese literary achievement. Beyond its central tragic render the work an indispensable resource for studying premodern Chinese society. To this day, "Hongxue" (Redology) remains vibrant field of scholarly inquiry."
Doc6: "Cao Xueqin's 's the Story the Stone is renowned for its exquisite linguis- tic artistry and profound characterization..hiir surrounding the Twelve Beauties of Jinling. This intricate web narrative foreshadowing creates an exceptionally tightly-knit story structure."
Doc7: "Cao Xueqin's the Story of the Stone employs the rise and fall of the Jia family allegory for the decline of feudal society a whole...oonservatism of Confucian orthodoxy. This sophisticated interplay of thematic elements has se- cured the novel's enduring legacy and widespread influence both literary and cultural spheres."
Sim(Query, Doc)
0.31
Judge
Hard
swer often already contains the core correct information; subsequently Reompress guides the model to compress redundant background into an extremely concise and correct answer. Both tables list example document excerpts used for difficulty assessment and model outputs at each stage, clearly illustrating MoL's short-term effect on training efficiency when difficulty estimation is wrong and its robustness in final answer accuracy and conciseness.
F.4 FAILURE CASE ANALYSIS
While the MoL driven model demonstrates robust performance across most scenarios isolated cases reveal challenges in preserving nuanced contextual details. We present an illustrative example in Figure 22 to demonstrate the discussed phenomenon. For instance in evaluating companys IPO eligibility (net profit > Y100M for 3 years no major litigation) the model accurately identifies that net profits exceeded Y100M in 2021-2022 and that the litigation provision (Y15M) falls below the Y27M threshold (30% of 2023 net profit). However in this rare instance the models compression strategy inadvertently omits two context specific factors: the 2023 net profit decline to W90M (30% YoY drop) highlights short term instability though the model focuses on the multi year threshold while the auditor notes ongoing litigation (V30M in claimed damages) material risk under CSRC Rule 4.3
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Table 18: Case study on the easy question.
Easy Question
Query
Are Visitors Allowed to Use Mobile Phones for Photography Videography Dur- ing the "Jurassic World Adventure" Ride at Universal Studios Beijing?
Doc
Docl: "Universal Studios Beijing Resort's latest 2023 safety guidelines clearly state that, as an immersive 4D experience. "Jurassic World: Fallen Kingdom" strictly prohibits visitors from using any electronic devices to film the ride for safety and copyright reasons. The guidelines specifically emphasize: "Before en- tering the queue area, all guests
Doc2: "China Culture News, September 5, 2023, Tourism Safety Special Report: A recent on-site visit by reporters to Universal Studios Beijing revealed that the "Jurassic World Adventure" attraction has strict controls on the use of electronic devices. In the queuing area, large electronic screens installed every 20 meters, playing a looping...
Doc3: "In the Attraction Safety Tips' section of the official website of Universal Studios Beijing, special instructions for 'Jurassic World Adventure' are detailed: "To ensure the safety and best experience for all visitors, please do not use any electronic devices, including ..."
Model Output (Original model)
Official regulations strictly prohibit tourists from using any electronic devices to take pictures during the ride. The resort has set up warning signs in multiple locations to remind tourists, but allows official photography services to record im- ages. The official explanation is: the dynamic motion platform of the project will produce sudden acceleration, deceleration and turns. Handheld electronic devices may cause the device to fall out of the hand, posing a safety hazard; at the same time, the flashing and fast-moving images on the screen may interfere with the experience of other tourists and infringe on the copyright of Universal Pictures.
Model Output (Mol.
For safety reasons and to protect Universal Pictures' intellectual property rights, it strictly prohibited for visitors use their mobile phones to take photos or videos during the ride.
(Ours)
but the model prioritizes quantified provisions over qualitative disclosures. These omissions result in overconfident conclusion ("IPO requirements met") that overlooks domain specific interpretative requirements. Importantly such cases represent less than 2.3% of the evaluation set.
G DATA PROCESSING
We partition all experimental datasets into training. validation, and subsets. The original diffi- culty labels are directly obtained from the inherent difficulty annotations in the HotpotQA dataset. For data samples containing multiple documents, we employ a paragraph-based similarity matching approach: we first compute pairwise similarities at the document level, then evaluate question diffi- culty using the average similarity score. When applying the MoL method for difficulty assessment, we initially segment each original document into several sub-documents based semantic bound- aries, calculate the relevance between each sub-document and the question, retain the k most relevant sub-documents, and reconstruct them into new documents. Subsequently, we recompute similarities across all documents and assess question difficulty based on the average similarity score. Detailed implementation is provided in Appendix 3.2
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Table 19: Case study on the hard question.
Hard Question
Query
In the "greenhouse effect" that causes global disasters, which gas has the strongest ability to absorb long-we wave radiation?
Doc
Docl: "Greenhouse gases (such as CO2, N N₂0, HzO, etc.) can absorb long-wave radiation (infrared radiation) released from the earth's surface, but the absorption bands and efficiencies of different gases vary significantly."
Doc2: "Methane (CH+) has 100-year GWP of 28-36 and a lifetime about 12 years. Its instantaneous absorption capacity for long-wave wag- radiation is 84 times that of CO2 (on 20-year time scale), but its concentration is only 0.4% of CO2." Doc3: "NF3 is common gas used in the semiconductor industry. Its radiation efficiency (heat absorption capacity per molecule) is 16, 100 times that of CO2 and its atmospheric lifetime 740 years, but its current concentration is extremely low (Industrial Greenhouse Gas Emission Inventory, 2023)."
Doc4: "The 100-year GWP of fluorinated gases (such as sulfur hexafluoride F and hydroftuorocarbbns HFCs) is as high 22,800 (S F6) and 14,800 (HFC-23) and they are the key control targets the Kyoto Protocol. The single-molecus radiation efficiency of SF&s is 272 times that of CH2, but the concentration is extremely low (<0.0001 ppm)."
Model
CO2, N F3. N2O, H2O, etc. are all common gases that absorb long-wave radi- ation, but CHH''s is absorption capacity for long-wave radiation is 84 times that of CO2, but N 's absorption capacity is 16,100 times that of CO2, so N F3 has the strongest absorption capacity for long-wave radiation.
Output
(Original
model)
Model
CO2 is one of the gases that cause the greenhouse effect and can also absorb long- wave radiation, but CH,'s ability to absorb long-wave radiation is 84 times that of CO2, and F3''s 's ability to absorb long-wave radiation 16,100 100 times that of CO2, so N F3 has stronger ability to absorb long-wave radiation. The radiation efficiency of fluorinated gases is 272 times that CH,, which 272*84=22 848 times that of CO2, Therefore, fluorinated gases have stronger ability to absorb long-wave radiation than N F3. so the gas with the strongest ability to absorb long- wave radiation is fluorinated gases.
Output
(Mol.
(Ours)
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Table 20: Case study: High cross-document similarity but requires multi-hop reasoning (short - extend + compress). Orange denotes the sentences most relevant to the query, red denotes incorrect answers, and blue denotes correct answers.
Outlier-case example
Query
Is there Nobel Prize laureate in Dr. Li's academic genealogy? If so, please identify the individual and specify their relationship to Dr. Li.
Doc
Docl: "In recent years, the Department of Chemistry, University of Cambridge, has continued to invest research ... Dr. Li currently holds a position in the De- partment of Chemistry. University of Cambridge, and previously worked under Professor Smith. The department maintains ... and students. Professor Smith has conducted long-term research in organic catalysis. In addition, departmental newsletters and public records often include project summaries and lists of aca- demic collaborators led by Professor Smith's s research group."
from senior mentors and peers. Pro- fessor Smith, who teaches at the University of Cambridge, had Professor Johnson as his doctoral advisor; Professor Johnson is said in the text to have received a Nobel Prize for his contributions to chemical kinetics. Professor Smith not only emphasizes fundamental research but also actively promotes the industrial trans- lation of research outcomes; he has trained multiple outstanding doctoral students and carly-career researchers. Professor Smith has supervised several PhD students, including Dr. Li. Departmental notices and various academic briefings document collaborative results between Professor Smith and his students.
Doc2: "Professor Smith has considerable
Doe3: "Dr. Li's public curriculum ... conference presentations and grant appli- cations. Dr. Li's CV notes that he completed postdoctoral research in Professor Smith's group in 2012. In addition, Professor Johnson, as senior scholar, has had his research and contributions repeatedly cited in multiple disciplinary review articles and academic biographies, becoming an important reference figure in the field. Professor Johnson's 's scholarly influence is often reiterated in several intro- ductory articles. The academic community widely discusses Professor Johnson's s work, and related citations and commemorative articles are retrievable in profes- sional journals."
Doc'
Li currently holds a position in the Department of Chemistry, Univer- sity of Cambridge, and previously worked under Professor Smith. Professor Smith has conducted long-term research in organic catalysis." Doc2': "Professor Smith, who teaches at the University of Cambridge, had Pro- fessor Johnson his doctoral advisor; Professor Johnson is said in the text to have received a Nobel Prize for his contributions to chemical kinetics. Professor Smith has supervised several PhD students, including Dr. Li."
Doc3': "Dr. Li's CV notes that he completed postdoctoral research in Professor Smith's group in 2012. Professor Johnson's 's scholarly influence is often reiterated in several introductory articles."
Difficulty
Easy
judge
Initial
Dr. Li's PhD advisor is Smith. Answer: Smith.
output
Medium- term
First, based on Doel and Doc3, Dr. Li received training in Professor Smith's ss group, and therefore Professor Smith is Dr. Li's direct advisor. Next, examination of Professor Smith's 's academic lineage (Doc2) shows that Smith's doctoral advisor was Professor Johnson. Doc2 further indicates that Professor Johnson received a Nobel Prize for his contributions to chemical kinetics. Therefore, the Nobel laureate traceable in Dr. Li's academic genealogy is Professor Johnson. Answer: Professor Johnson (Nobel Prize laureate).
output
Final output
Dr. Li worked under Professor Smith, and Johnson (Professor Smith's doctoral advisor) received Nobel Prize for contributions chemical kinetics.
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Table 21: Case study: Low similarity across documents, yet answer is directly extractable (long compress), Orange denotes the sentences most relevant to the query, red denotes incorrect answers, and blue denotes correct answers.
Outlier-case example
Query
When is the "Ancient Bronzes" exhibition at the City Center Museum open to the public?
Doc
Docl: "The City Center Museum launched for scholarly display upon reopen- ing. The City Center Museum undertook a large-scale restoration project in re- cent years, with maintenance work completed by the end of 2021. In the post- restoration exhibition program, the curatorial team specially planned the "Ancient Bronzes" exhibition, and extemna notices confirmed the exhibition's 's opening date and public visiting arrangements. The "Ancient Bronzes" exhibition opened to the public on May 15, 2022. The museum's 's official website and press releases include the opening reports and exhibition descriptions published at that time." " Doc2: "As important local cultural institution, the museum ... educational pro- grams. The museum holds various temporary exhibitions throughout the year, at- tracting large numbers of local visitors and school groups. Meanwhile, to facilitate public visits the museum-.. in order to plan their visits. Guided-tour information and reservations must be obtained through the museum's official website or by telephone. The museum also regularly announces the latest exhibitions and event arrangements through social media and community partners."
Doc3: "In recent years, media attention to local cultural projects has increased; reports frequently and social impact rather than listing the specific schedules or opening dates of each exhibition. Recent media coverage has focused on the mu- seum's 's investment in educational activities and community partnerships. Further- more, exhibition catalogues typically present... opening days; therefore date infor- mation is usually determined by the museum's 's notices or official amnouncements. Exhibition catalogues commonly include themes such as archacological excava- tions, historical documents, and cultural heritage, rather than detailed schedules."
Doc'
: City Center Museum undertook a large-scale restoration project in recent years, with maintenance work completed by the end of 2021. The "Ancient Bronzes" exhibition opened to the public on May 15, 2022."
Doc2': "The museum holds various temporary exhibitions throughout the year, attracting large numbers of local visitors and school groups Guided-tour information and reservations must be obtained through the museum's official website by telephone."
Doc3': "Recent media coverage has focused on the museum's investment in edu- cational activities and community partnerships.
Exhibition catalogues commonly include themes such as archaeological excava- tions, historical documents, and cultural heritage, rather than detailed schedules."
Difficulty
Hard
judge
Initial output
The City Center Museum completed large-scale restoration project in recent years, during which exhibition layouts and display facilities were upgraded (see museum restoration notice). In the subsequent reopening plan, the museum sched- uled multiple thematic exhibitions to attract the public. Regarding the opening date of the "Ancient Bronzes" exhibition, the museum's public materials record: "The Ancient Bronzes exhibition opened to the public on May 15, 2022." (Source: Docl). In summary, the exhibition's 's public opening date was May 15, 2022.
Medium-
According to the museum's materials (Docl), the Ancient Bronzes exhibition was reopened to the public after the museum's 's renovation, and is explicitly recorded as opening to the public on May 15, 2022. Therefore the answer is: May 15, 2022.
term
output
Final
The "Ancient Bronzes" exhibition opened to the public on May 15, 2022.
output
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Table 22: Case study: MoL's overcompression leads to answer inaccuracies. Orange denotes the sentences most relevant to the query, red denotes incorrect answers, and blue denotes correct an- swers.
Outlier-case example
Query
Does the company meet IPO listing requirements (net profit 2 Y100M for years, no major litigation)?
Doc
Docl: "The company's financial performance from fiscal years 2021 to 2023
showed: 2021 Net Profit of #120 with 21.2B revenue (15% YoY growth); 2022
Net Profit increased to #130M with V1.5B revenue (8% YoY growth); 2023 Net Profit declined 790M with VL.4B revenue (-20% YoY growth), including a V50M goodwill impairment due to the failed acquisition of TechCorp in Q2 2023, which was classified as a non-recurring item under IFRS 9. The net profit calcula- tions followed IFRS 15 for revenue recognition and IFRS 9 for impairment, with no adjustments made for non-recurring items in the annual report.
Doc2: "Auditor's Opinion Financial Statements (Deloitte, 2023): For 2021- 2022, the financial statements present fairly in all material respects with an un- modified opinion; in 2023, qualified opinion was issued due to requiring adjustments. The key risk disclosure states: The company's 2023 financial state- ments may reflect the full impact of ongoing litigation."
"Pending Litigation: Patent Infringement Lawsuit (Case No. 2023-0487) involves GlobalTech Inc. as plaintiff and [Company Name] as defendant, alleging infringement through the use of a patented Al algorithm in Product X (launched 2022) with claimed damages of 330M based on years of lost royalties; the cur- rent status includes court hearing scheduled for 2024 Q3 and a provision of Y15M (50% of claimed damages) recorded accordance with IFRS Paragraph 30. Under CSRC Rule 4.3, litigation exceeding 30% of annual net profit (#27M for 2023's W90M net profit) must be disclosed as material risk."
Doc3:
Doc
ITe company's ss financial performance from fiscal years 2021 to 2023 showed: 2021 Net Profit Y120M with F1.2B revenue (15% YoY growth); 2022 Net Profit increased to Y130M with Y1.5B revenue (8% YoY growth); 2023 Net Profit declined 990M with 1 revenue (-20% YoY growth)" Doc2': "For 2021 1-222,, the financial statements present fairly in all material re- spects with an unmodified opinion.
The company's 2023 financial statements may not reflect the full impact of ongo- ing litigation."
Doc3': "Pending Litigation: Patent Infringement Lawsuit (Case No. 2023-0487) involves GlobalTech Inc. as plaintiff and [Company Name] as defendant. Under CSRC Rule 4.3, litigation exceeding 30% of annual net profit (427M for 2023's 990M net profit) must be disclosed as a material risk."
Difficulty
Hard
judge
Model
The company's 's market dominance is established through a market share of 75%, which exceeds the statutory threshold of 70%, coupled with the absence of com- peting entrants that further entrenches its dominance. Regarding exclusionary con- duet, the exclusive agreements with suppliers directly trigger Article 17 of the An- timonopoly Law, which prohibits restrictions on supplier collaboration. The exit of small and medium sized competitors is direct consequence of these exclusion- ary practices. Collectively, the fulfillment of both criteria market share exceeding 70% and anti competitive exclusivity constitute violation of Article 17, the conduct demonstrably undermines market fairness and fair competition. Thus, the company fails satisfy the IPO listing requirements.
Output
(Original
model)
Model Output (MoL
Net profit exceeded Y100M in 2021-2022 and litigation provision below *27M (30% of 2023 net profit). IPO requirements met.
(Ours)
29


Reviews:
[{"review_id": "2s8uz7u6zA", "reviewer": "Reviewer_5HsN", "summary": "This paper introduces Mixture-of-Length (MoL), a novel framework for question answering (QA) with context that aims to improve the balance between reasoning quality and computational efficiency. The authors identify a tension in current methods, which either compress responses uniformly (harming complex reasoning) or rely on simple heuristics for adaptive reasoning.\n\nThe authors demonstrate that this training leads to an \"intelligent brevity\" behavior, where the model naturally uses shorter responses for simple queries and longer ones for complex queries. Experiments on HotpotQA, StrategyQA, and Loong show that MoL-trained models achieve higher accuracy while simultaneously reducing the number of generated tokens compared to baselines . A post-hoc analysis also suggests this adaptive output length correlates with adaptive internal computation, where easy questions activate fewer transformer layers .", "questions": "See weakness", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "The paper tackles a significant and well-recognized problem: the trade-off between reasoning quality and efficiency (cost/latency) in LLMs. The goal of achieving \"intelligent brevity\" is highly desirable for real-world applications.", "weaknesses": "*The \"Principled\" Difficulty Metric Seems Oversold*\n\nThe paper's first contribution, the \"principled difficulty assessment\" is framed with a sophisticated theoretical grounding in information theory and the NP-hard Set Cover problem. However, the practical implementation is a simple heuristic: the average inter-document cosine similarity of k extracted sentences. This feels like a significant oversell: what is the **information theory** used? Under **which optimization** you derive the metric? What is the relationship between the NP-hard and the proposed difficulty calculation?\n\n\n*The Difficulty Heuristic Is Potentially Flawed*\n\nThe core assumption that high similarity (redundancy) equals \"easy\" and low similarity equals \"hard\" is questionable and may not hold in many cases.\n\n(Hard, High-Sim): A question might require synthesizing **subtle differences** between two **very similar documents**. The high similarity would incorrectly classify this as \"easy\".\n\n(Easy, Low-Sim): A question might be a simple fact-retrieval task where the two facts just happen to reside in two completely dissimilar documents. The low similarity would incorrectly classify this as \"hard\".\n\n*The Effectiveness Proof of Reward Mechanism*\n\nThe paper's core innovation is presented as the dual-reward mechanism (compress-if-correct, extend-if-wrong). However, this mechanism is entangled with a second mechanism: the dynamic L_target . The paper's claim of \"emergent brevity\" is muddled because it's impossible to know if the model is adapting because of **the dual reward** or simply because it's being **explicitly told to use a different target length**.\n\nIf the reward system can be truely useful to learn to reward or punish on the length, it is highly recommend to add a study to set the fix length for all difficulties, and check the performances.", "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": "aJQCyIu9pj", "reviewer": "Reviewer_Wcic", "summary": "The paper addresses the trade-off between reasoning quality and response efficiency in open-domain question answering with context. Traditional approaches often just compress all answers uniformly (risking under-explaining complex queries). This work proposes Mixture-of-Length (MoL), a framework to achieve ``intelligent brevity'': short answers for easy questions and longer reasoning for hard ones.\n\nTo achieve this property the authors proposed the following approaches. First, a question complexity via cross-document redundancy is estimated. Intuitively, if multiple context documents share overlapping information, the question is likely simple (high redundancy means low complexity), whereas if each document contributes unique facts (low redundancy), the question requires complex multi-hop reasoning. \n\nSecond, a dual-objective reward mechanism (trained via reinforcement learning) adaptively modulates answer length. The model is rewarded for conciseness when correct and for elaborating more when the initial answer is incorrect. Concretely, MoL defines a compress reward for correct answers that encourages minimal sufficient explanation, and an extend reward for incorrect answers that encourages providing a longer, more detailed reasoning chain. The target answer length is dynamically set based on the estimated difficulty.\n\nEmpirical validation on three benchmarks - HotpotQA (multi-hop QA), StrategyQA (implicit reasoning QA), and Loong (long-document QA) – showing MoL achieves competitive or better accuracy with dramatically fewer tokens than baselines.", "questions": "1) MoL's adaptive strategy relies on identifying when an initial answer is incorrect (to trigger extended reasoning). During training, this is determined by checking the answer's F1 against ground truth. How is this handled at inference time, when the ground truth isn't available?\n\n2) The current difficulty assessment is tailored to multi-document QA, using cross-document redundancy as a proxy. How would MoL extend to scenarios in single-document QA or tasks like mathematical problem solving (where all information is in one passage but reasoning is needed)?\n\n3) MoL introduces several hyperparameters (for reward balancing, difficulty thresholds, target lengths, etc.). Did the authors observe any sensitivity or need for tuning these on different datasets?\n\n4) The paper finds a correlation between shorter answers and fewer activated layers. Can the authors shed more light on this adaptive computation aspect? For instance, did they analyse whether specific transformer layers are being skipped or pruned dynamically, or is it more that fewer tokens cause fewer layers to effectively contribute?\n\n5) Given the reliance on RL and considerable compute, how feasible is it to apply MoL to much larger models or other settings?\n\n6) Do the authors anticipate any challenges scaling to 65B or GPT-3-sized models with this approach? \n\n7) Are there any failure modes observed for MoL (cases where it chooses the wrong length adaptation)?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1) The proposed MoL framework intelligently balances brevity and thoroughness. Unlike prior methods, MoL's dual-objective approach allows it to produce concise answers for easy questions and expand reasoning for harder ones as needed.\n\n2) By measuring cross-document similarity after extracting key sentences, the proposed difficulty metric can reliably distinguish simple retrieval questions (high redundancy across documents) from complex multi-hop ones (low redundancy).\n\n3) MoL achieves significant token reduction without sacrificing accuracy, often even improving it. For example, with a Qwen-8B model on HotpotQA, MoL used ~50% fewer tokens than the original model yet boosted accuracy by +6.2 points.\n\n4) Extensive experimental evaluation. The authors considered multiple base LLMs across three diverse QA benchmarks. They compare MoL with strong baselines and MoL outperforms them. The paper also includes an ablation study isolating the impact of each component. This comprehensive evaluation strengthens confidence in the results.\n\n5) The paper is well-written and clearly explains the intuition behind MoL.", "weaknesses": "1) MoL approach may still struggle on cases where complexity isn't captured by document redundancy (for instance, a single-document question that requires complex logical reasoning or implicit knowledge). The method is currently tailored to multi-document scenarios, which might limit its direct applicability to single-document QA or other formats without further adaptation.\n\n2) The framework relies on several hand-tuned thresholds and parameters that may need adjustment in new settings. It looks like prior choices of these hyper-parameters are somewhat arbitrary and task-specific. It’s not fully tested how sensitive the approach is to these values – a different domain might require re-calibration. The authors do include an ablation on some of these hyper parameters, but the complexity of tuning so many parameters could be a drawback.\n\n3) The solution uses reinforcement learning approach GRPO which is known to be difficult to train and resource-intensive. The experiments used a high-end setup (64× A100 GPUs), indicating significant compute requirements to reproduce or fine-tune MoL. This raises concerns about accessibility and stability of the method – researchers or practitioners with less resource might find it hard to apply.\n\n4) The evaluation focuses on F1 accuracy and token counts, which is appropriate, but it would be interesting to know if the conciseness achieved by MoL is indeed judged favorably by humans.\n\n5) The paper reports a fascinating correlation between question difficulty, answer length, and the number of transformer layers effectively used. However, the authors acknowledge this analysis is post-hoc and correlational, not proving a causal link. This is not exactly a flaw in the method, but a limitation in our understanding of it. It opens questions: is MoL actually learning to skip computations for easier tasks, or is the layer activation difference a byproduct of generating fewer tokens?", "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": "uK4ehJuCh6", "reviewer": "Reviewer_V4Cn", "summary": "This work proposes a reward function for reinforcement learning to improve the reasoning capability of large language models by adaptively controlling the length of generation based on the difficulty assessment. Basic idea comes from the prior studies in selecting the longer length of generation especially for complex reasoning tasks demanding longer inference, but shorter to avoid spurious reasoning. This work leverages the difficulty of a task measured by the similarity of retrieved sentences, i.e., the more difficult when almost no relevant sentences are extracted, and used it to adaptively choose the generation length heuristically. Experiments show that proposed approach achieves improvements when compared with related baselines.", "questions": "See weaknesses.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "- The proposed approach defines an interesting reward function to adaptively control the length of generation based on the difficulty of a task. The difficulty is measured by the similarity of retrieved sentences, such that the more similar implies the more easy to solve a task.\n- Experiments are carried out systematically demonstrating gains on standard benchmarks when compared with other related methodologies. Further analyses show the generalization capacity over different domains and its relation of the difficulty assessment and accuracy.", "weaknesses": "- It is not clear whether the difficulty level could be attributed solely by the similarity of retrieved answer or not. There might be a case of a query which has a direct answer in a single sentence, and no similar sentences exist in the document pool. In this case, the task is treated as an extremely difficult instance leading to over estimates. I think it is better to show whether the difficulty assessment is correlated with the actual difficulty or not. It is possible to measure the accuracies in general by existing models or some evidence, e.g., the distribution of relevant passages in the document pool.\n- The motivation of the heuristics to map the difficulty level to its target length is not clear. Probably it was judged empirically, but the more clear discussion will be necessary, e.g., the plot of the actual difficulty level of a task and its length for generation.\n- Math notations are not consistent and several symbols are not defined. For example, $S_i$ is used as a subset of a document but $S_{ij}$ is defined as a scalar for similarity. $s$, Top-k and $Sim$ are not defined in Equation 2.", "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": "o4O6qQ1e0s", "reviewer": "Reviewer_bLCr", "summary": "This paper introduces Mixture-of-Length (MoL), a reinforcement learning framework for question answering that adaptively controls response length based on question difficulty. Difficulty is estimated via an information-theoretic proxy: inter-document redundancy computed as the average cosine similarity of top-k question-relevant sentences (low similarity ⇒ harder).\nMoL couples this with a dual reward scheme: correct answers receive a compression reward encouraging brevity, while incorrect ones get an extension reward promoting longer reasoning; a progressive schedule anneals the length coefficient over training.\nAcross three datasets, MoL improves accuracy while reducing output tokens. The approach generalizes to unseen QA datasets, and a post-hoc activation analysis suggests adaptive computation (fewer active layers on easy questions).", "questions": "1. On Difficulty Estimation: How sensitive is your difficulty metric to the choice of k (Top-k sentence filtering), the embedding model, and the document segmentation strategy? Did you experiment with alternative encoders or granularities? \n2. Error/Failure Cases: Can you provide examples where MoL over-compresses and fails to capture necessary reasoning steps (e.g. in complex multi-hop or long-context queries)? How often does this occur in practice, and are there mechanisms to detect or mitigate such failures?\n3. Internal Activation Adaptation: Have you tried any controlled intervention experiments (e.g., freezing certain layers) to test whether the reduced activation in “easy” cases is causally induced by MoL, rather than being an emergent side effect of training?\n4. Human Evaluation: Did you consider running a human evaluation on a subset of responses to measure perceived quality and readability? This would be especially valuable given the strong emphasis on token compression — are the shorter outputs still informative to end users?\n5. Generalizability Across Tasks: Do you expect the MoL reward framework to extend effectively to other domains (e.g., dialogue reasoning, code generation, or fact verification)? What adaptations (if any) would be required?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The use of an information-theoretic proxy rooted in the set cover problem for assessing question difficulty is a solid theoretical motivation. \n- The dual-objective reward mechanism of MoL (compression and expansion) is well-formulated and empirically separated in ablations.\n- The paper presents rigorous results across three QA datasets, as well as additional analyses and ablation studies that improve the reliability of central claims.\n- MoL’s response-length adaptation is shown empirically with fewer tokens for easy and more for hard questions, outperforming strong baselines.", "weaknesses": "- Although Table 2 compares with passage-based similarity and HotpotQA labels, the difficulty prediction step relies on a Top-k sentence selection followed by embedding similarity. It is unclear how sensitive this is to k, embedding model choice, or sentence granularity. More systematically controlled ablation studies are needed.\n- The paper shows strong overall gains, but lacks qualitative or quantitative analysis of scenarios where MoL fails — e.g., cases where over-compression leads to loss of crucial reasoning steps, especially in complex or long-context queries. Identifying such cases would strengthen the empirical claims and help users understand the trade-offs of the method.\n- The analysis showing a correlation between shorter generated outputs and fewer activated layers is interesting, but the work does not demonstrate causal influence. It remains unclear whether the reduced computation is a deliberate model behavior or merely a post-hoc artifact of the compression objective.\n- No human evaluation of response quality.", "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": "4S4OinKQKZ", "reviewer": "Reviewer_UwkB", "summary": "The paper proposes a training framework called Mixture-of-Length (MoL). It produces short answers on easy questions and expand reasoning when the question is hard. The approach estimates task difficulty using average cross-document similarity and utilizing set-cover view of information redundancy across documents. They also train an LLM in GRPO-style with a dual reward that switches between a compression reward for correct answers and an extension reward for incorrect ones, with curriculum-style scheduling of the length coefficient. Experiments on HotpotQA, StrategyQA, and Loong datasets across several base LLMs show higher accuracy with fewer tokens.", "questions": "1. I would suggest a stress-test for your method to decouple document redundancy from reasoning depth, required to answer the question, using two new datasets:\n\n- High-similarity + high-reasoning: near-duplicate docs but questions require multi-step computation/logic across them.\n- Low-similarity + low-reasoning: stylistically diverse docs where each answer is a direct lookup.\n\nIf I understood the paper correctly, your proxy should call the first set \"easy\", and the second \"hard\", despite that the reality is the opposite. How will your method work in this situation?\n\n2. At lines 209-212 you write: \"The specific length thresholds are empirically set based on the response length distribution observed in the HotpotQA dataset\". Can you please elaborate?\n\n3. Why there is a \"2\" multiplier in the Formula 4?\n\n4. The Figure 2 is hard to understand, since it doesn't really match with the text. What are these \"compress\" and \"extend\" stages, depicted on the Figure 2? In the text itself, I only see the description of the compression reward, not the distinct stage called like this. Can you please explain?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "- The problem tackled in the paper is important and well-formulated.\n- The set-cover motivation and its practical proxy via average cross-document cosine similarity are clear and intuitive.\n- Table 1 shows that accuracy increases with meaningful token reductions for several base LLMs, trained by the author's method.\n- The paper is clear about limitations, ethics, and contains all needed information for reproducibility.", "weaknesses": "- Equating cross-document similarity with \"easy\" questions risks misclassifying questions that demand nontrivial reasoning over redundant sources. Note that authors treat a question as easy if its source documents look \"similar\", which they estimate by taking a few relevant sentences from each document and averaging how alike their embeddings are. This approach may work well on the evaluated datasets, but its generalizability to real-world scenarios remains uncertain. For example, imagine 10 near-duplicate news articles about a merger. A question like \"By how many quarters did revenue grow between the two pre-merger reports?\" requires number extraction, comparison, and arithmetic, despite the fact that the documents are very similar. In this case, high similarity means that their proxy call the question \"easy\", but the reasoning to answer the question correctly is non-trivial.\n- Claims of \"statistically significant\" gains appear, but I did not find details on tests, confidence intervals, or seeds across runs.\n- The paper compares \"Tokens\" across models and methods, but the tokenization standard (e.g., tokenizer family) and measurement point (prompt+answer? answer only?) are not specified; comparability is unclear when base models differ.\n\nMinor issue:\n- The text on some Figures (especially Fig. 5) is very small and hard to read.", "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)