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arxiv: 2602.05242 · v1 · pith:LD4OE2G3new · submitted 2026-02-05 · 💻 cs.SE · cs.AI· cs.LG

EGSS: Entropy-guided Stepwise Scaling for Reliable Software Engineering

Chenhui Mao , Yuanting Lei , Zhixiang Wei , Ming Liang , Zhixiang Wang , Jingxuan Xu , Dajun Chen , Wei Jiang
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Yong Li
This is my paper

Pith reviewed 2026-05-16 07:41 UTC · model grok-4.3

classification 💻 cs.SE cs.AIcs.LG
keywords entropy-guided scalingtest-time scalingsoftware engineeringcode generationlarge language modelsSWE-Benchagentic systemsperformance optimization
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The pith

Entropy-guided scaling boosts code generation success rates by 5-10% and cuts token use by 28%.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper proposes Entropy-Guided Stepwise Scaling (EGSS) to address the high computational costs that limit test-time scaling methods in software engineering tasks such as code generation and bug fixing. It introduces entropy-guided adaptive search to select promising candidates and robust test-suite augmentation to improve verification reliability. Experiments on SWE-Bench-Verified show consistent gains of 5-10% across models, including lifts from 63.2% to 72.2% for one system and 65.8% to 74.6% for another, plus a new state-of-the-art for open-source models. The approach also reduces inference tokens by over 28% compared to prior methods, demonstrating simultaneous improvements in accuracy and efficiency.

Core claim

EGSS is a test-time scaling framework that dynamically balances efficiency and effectiveness through entropy-guided adaptive search and robust test-suite augmentation. On SWE-Bench-Verified it raises resolved ratios for Kimi-K2-Instruct from 63.2% to 72.2% and for GLM-4.6 from 65.8% to 74.6%, reaches new state-of-the-art among open-source large language models, and reduces inference-time token usage by over 28% relative to existing methods.

What carries the argument

Entropy-guided adaptive search paired with test-suite augmentation, where entropy signals the quality of candidate solutions to steer stepwise refinement and augmented tests provide stronger verification.

If this is right

  • Resolved ratios on complex code tasks rise by 5-10% across multiple large language models.
  • GLM-4.6 paired with EGSS sets a new state-of-the-art result among open-source models.
  • Inference-time token consumption falls by more than 28% compared with prior test-time scaling approaches.
  • The framework makes agentic scaling more practical by lowering the cost of large ensembles while preserving gains.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same entropy signal could guide candidate selection in agentic tasks outside software engineering, such as mathematical reasoning or data analysis.
  • Further reductions in ensemble size might be possible if entropy thresholds are tuned per model size.
  • Integration with static analysis tools could strengthen the verification step beyond test-suite augmentation alone.
  • Testing the method on longer-horizon software projects would reveal whether the efficiency gains persist when tasks require many sequential edits.

Load-bearing premise

Entropy values reliably indicate which candidate solutions are higher quality, and the test-suite augmentation verifies them without introducing selection bias or new failure modes.

What would settle it

A new benchmark set of software engineering problems in which solutions with higher entropy prove correct more often than those with lower entropy, or in which the augmented tests produce false positives that mask actual errors.

Figures

Figures reproduced from arXiv: 2602.05242 by Chenhui Mao, Dajun Chen, Jingxuan Xu, Ming Liang, Wei Jiang, Yong Li, Yuanting Lei, Zhixiang Wang, Zhixiang Wei.

Figure 1
Figure 1. Figure 1: Performance and token usage of popular test-time scaling methods compared with entropy [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Tool entropy distribution along agent trajectories in SWE-Bench cases [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Trajectory-Aware Analysis of Debugging Processes in Autonomous Agents on SWE-Bench [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Overview of Entropy-guided Stepwise Scaling [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Average token usage per instance on the SWE-Bench benchmark, aggregated across [PITH_FULL_IMAGE:figures/full_fig_p009_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Comparison of ensemble strategies across different ensemble sizes [PITH_FULL_IMAGE:figures/full_fig_p011_6.png] view at source ↗
read the original abstract

Agentic Test-Time Scaling (TTS) has delivered state-of-the-art (SOTA) performance on complex software engineering tasks such as code generation and bug fixing. However, its practical adoption remains limited due to significant computational overhead, primarily driven by two key challenges: (1) the high cost associated with deploying excessively large ensembles, and (2) the lack of a reliable mechanism for selecting the optimal candidate solution, ultimately constraining the performance gains that can be realized. To address these challenges, we propose Entropy-Guided Stepwise Scaling (EGSS), a novel TTS framework that dynamically balances efficiency and effectiveness through entropy-guided adaptive search and robust test-suite augmentation. Extensive experiments on SWE-Bench-Verified demonstrate that EGSS consistently boosts performance by 5-10% across all evaluated models. Specifically, it increases the resolved ratio of Kimi-K2-Intruct from 63.2% to 72.2%, and GLM-4.6 from 65.8% to 74.6%. Furthermore, when paired with GLM-4.6, EGSS achieves a new state-of-the-art among open-source large language models. In addition to these accuracy improvements, EGSS reduces inference-time token usage by over 28% compared to existing TTS methods, achieving simultaneous gains in both effectiveness and computational efficiency.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper proposes Entropy-Guided Stepwise Scaling (EGSS), a test-time scaling framework for agentic software engineering that employs entropy to drive adaptive candidate search and augments test suites for verification. On SWE-Bench-Verified it reports consistent 5-10% lifts in resolved ratio (Kimi-K2-Instruct: 63.2% → 72.2%; GLM-4.6: 65.8% → 74.6%), a new open-source SOTA when paired with GLM-4.6, and >28% reduction in inference tokens relative to prior TTS baselines.

Significance. If the entropy-quality correlation and test-suite robustness hold under rigorous controls, EGSS would offer a practical route to higher performance at lower compute cost for complex SE tasks, directly addressing the two main adoption barriers identified in the abstract.

major comments (2)
  1. [§4 (Entropy-Guided Selection)] The central claim that entropy-guided selection reliably identifies high-quality patches rests on an unvalidated assumption. No section demonstrates, on data held out from both model training and test-suite augmentation, that lower-entropy outputs are statistically more likely to pass functional tests; entropy may instead track syntactic predictability unrelated to semantic correctness.
  2. [§5.3 (Main Results)] Table 2 and §5.3 report headline gains without error bars, ablation controls that isolate entropy guidance from test-suite augmentation, or statistical significance tests. Consequently it is impossible to attribute the 5-10% resolved-ratio improvements to the proposed mechanisms rather than to increased sampling or other uncontrolled factors.
minor comments (2)
  1. [Abstract] The abstract contains the typo 'Kimi-K2-Intruct' (should be 'Kimi-K2-Instruct').
  2. [§3] Implementation details (exact entropy estimator, temperature schedule, augmentation procedure) are referenced but not fully specified, hindering reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their insightful comments, which have helped us identify areas to strengthen our manuscript. We provide point-by-point responses below and commit to revisions that address the concerns raised.

read point-by-point responses

  1. Referee: [§4 (Entropy-Guided Selection)] The central claim that entropy-guided selection reliably identifies high-quality patches rests on an unvalidated assumption. No section demonstrates, on data held out from both model training and test-suite augmentation, that lower-entropy outputs are statistically more likely to pass functional tests; entropy may instead track syntactic predictability unrelated to semantic correctness.

    Authors: We acknowledge the referee's point that the correlation between entropy and functional correctness should be validated on held-out data. While our current experiments show consistent improvements, we will add a dedicated analysis in the revised manuscript using a held-out validation set to demonstrate that lower entropy outputs are more likely to pass tests, controlling for syntactic factors. revision: yes

  2. Referee: [§5.3 (Main Results)] Table 2 and §5.3 report headline gains without error bars, ablation controls that isolate entropy guidance from test-suite augmentation, or statistical significance tests. Consequently it is impossible to attribute the 5-10% resolved-ratio improvements to the proposed mechanisms rather than to increased sampling or other uncontrolled factors.

    Authors: We agree that the results would benefit from error bars and statistical tests. We will include these in the revision by reporting means and standard deviations over multiple runs and conducting significance tests. Additionally, we will provide more detailed ablations that isolate the effect of entropy guidance from test-suite augmentation to better attribute the performance gains. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical validation on benchmarks

full rationale

The paper introduces EGSS as an empirical TTS framework relying on entropy-guided adaptive search and test-suite augmentation. All central claims (5-10% resolved-ratio gains, 28% token reduction, new SOTA on SWE-Bench-Verified) are supported by direct benchmark comparisons across multiple models rather than any derivation chain. No equations, fitted parameters renamed as predictions, or self-citation load-bearing steps appear; the entropy-quality correlation is treated as an operational assumption whose validity is assessed through end-to-end performance on held-out verification suites. The work is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only the abstract is available, so the ledger is necessarily incomplete. The method implicitly assumes standard LLM sampling and benchmark evaluation protocols but introduces no explicitly listed free parameters, axioms, or invented entities in the provided text.

pith-pipeline@v0.9.0 · 5564 in / 1098 out tokens · 58363 ms · 2026-05-16T07:41:25.890755+00:00 · methodology

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Reference graph

Works this paper leans on

24 extracted references · 24 canonical work pages

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    Carefully analyze the user’s request

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    Use available tools to gather necessary information

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    Propose clear, well-thought-out solutions

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    critique

    Execute changes carefully and verify results When modifying files: - Always read files before modifying them - Make precise, targeted changes - Explain what you’re doing and why Be concise, accurate, and helpful. A.2 Judge Model When an Agent step is identified as a high-entropy action, we roll out multiple actions in the current step. To determine the pr...

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    Score Criteria 0 Inconsistent 1 Basically Consistent 2 Partially Consistent 3 Fully Consistent

    Step Consistency Definition:Assesses whether the entire execution trace is logically coherent, free of contradictions, and devoid of redundancy. Score Criteria 0 Inconsistent 1 Basically Consistent 2 Partially Consistent 3 Fully Consistent

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    Score Criteria 0 Context Ignored 1 Partially Contextualized 2 Mostly Contextualized 3 Fully Contextualized

    Context Awareness Definition:Evaluates whether the agent effectively utilizes and dynamically updates historical context, avoiding repetition or conflict. Score Criteria 0 Context Ignored 1 Partially Contextualized 2 Mostly Contextualized 3 Fully Contextualized

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    Score Criteria 0 Chaotic Prioritization 1 Unbalanced Prioritization 2 Adequate Prioritization 3 Optimal Prioritization

    Goal Prioritization Definition:Determines whether the agent correctly identifies and prioritizes critical sub-goals, preventing resource misallocation. Score Criteria 0 Chaotic Prioritization 1 Unbalanced Prioritization 2 Adequate Prioritization 3 Optimal Prioritization

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    Expected Tool Use Definition:Selection, invocation, and sequencing of thecorrecttools withappropriate arguments andminimalredundancy, including proper error handling and validation. This includes ensuring format-specific features are fully implemented across all relevant code paths, and critically, handlingedge casesandoptimization pathsin the target code...

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    read_file

    Diagnostic Precision Definition:Evaluates how effectively the agent isolates the root cause of an issue, distinguishes between symptoms and causes, and provides targeted explanations or fixes without over-generalizing or misidentifying the problem. Score Criteria 0 Misdiagnosis 1 Partial Diagnosis 2 Accurate Diagnosis 3 Precise Diagnosis A case study of t...

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    2.Trajectory Analysis:Evaluate test cases and debug code from each trajectory for sufficiency

    Contextual Analysis:Comprehend the issue and relevant codebase components (referenced code, patched code, related files, existing regression tests). 2.Trajectory Analysis:Evaluate test cases and debug code from each trajectory for sufficiency

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    Coverage Assessment:Identify and add missing test scenarios (edge cases, regression) to ensure comprehensive coverage

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    Test Consolidation:Integrate all test cases (from trajectories, new designs, existing regressions) into ‘test_current_issue.py‘ at the root. 18 Entropy-Guided Stepwise Scaling

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    Compilation Assurance:Ensure ‘test_current_issue.py‘ compiles without errors, even if tests fail initially. Requirements for TEST FILE: •MUSTbe ‘test_current_issue.py‘ in the root directory. • Providecomprehensivevalidation for the issue. •IMPORTANT:Executable with ‘pytest‘, reporting total test cases. Task ${task} TrajectoryTrajectories sampled from diff...

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    Fix Environment Issue (if needed): Correct compilation errors in test_current_issue.py if not due to patch.NOTICE: you can’t modify any codes! OUTPUT FORMAT: IMPORTANT: Reply in JSON. •passed: int, passed cases. •failed: int, failed cases. •error: int, error cases. •total: int, total test cases. { "passed": x, "failed": x, "error": x, "total": x } OutputN...

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    Understand Issue & Codebase: Comprehend the problem from issue description. Examine codebase for context: (1) Code/patches referenced in issue. (2) Unchanged/related parts of affected files

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    Consider whether the changes align with the issue description and coding conventions

    Analyze the Candidate Patches: For each patch, analyze its logic and intended fix. Consider whether the changes align with the issue description and coding conventions

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    verify its rationality with the rubric given below

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    result":

    The candidate patches have not yet applied to the repository, apply first before validate the patch RubricYour evaluation should focus on the following criteria: ${rubric} Output Format:Reply in JSON: {"result": "x" // id of the patch} Analysis:[Explain why Patch-x is correct.] Tasks: ${task} Candidate Patches ${patches} OutputNow it’s your turn. Rubric u...

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    Score Criteria 0 Severely Off-Topic 1 Partial Coverage 2 Highly Relevant 3 Perfect Alignment

    Requirement Relevance Definition:How completely and precisely the patch satisfies **all** functional and non-functional requirements expressed or implied in the user’s task. Score Criteria 0 Severely Off-Topic 1 Partial Coverage 2 Highly Relevant 3 Perfect Alignment

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    Score Criteria 0 Broken 1 Flawed 2 Correct 3 Robust & Accurate

    Code Accuracy Definition:Apply available tools to run the code and check for any compilation errors. Score Criteria 0 Broken 1 Flawed 2 Correct 3 Robust & Accurate

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    21 Entropy-Guided Stepwise Scaling Score Criteria 0 Mis-targeted 1 Imprecise 2 Accurate 3 Minimal & Exact

    Change Precision Definition:How accurately the patch targets **only** the code that must change, avoiding extraneous edits. 21 Entropy-Guided Stepwise Scaling Score Criteria 0 Mis-targeted 1 Imprecise 2 Accurate 3 Minimal & Exact

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    Score Criteria 0 Breaking Change 1 Partial Awareness 2 Internally Consistent 3 System-Wide Vision

    Dependency & Context Awareness Definition:Awareness of upstream/downstream dependencies and the completeness of associated updates (imports, call sites, configs, external contracts, backward compatibility). Score Criteria 0 Breaking Change 1 Partial Awareness 2 Internally Consistent 3 System-Wide Vision

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    Score Criteria 0 Poor 1 Inconsistent Style 2 Clean & Comfortable 3 Exemplary

    Code Quality Definition:Adherence to project style guides, language idioms, readability, and maintain- ability. Score Criteria 0 Poor 1 Inconsistent Style 2 Clean & Comfortable 3 Exemplary

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    result":

    Functionality Validation (Gating Criterion) Definition:Adherence to project style guides, language idioms, readability, and maintainability. Score Criteria 0 Any Failure 3 Comprehensive & Robust A case study on the Preference Selector is as follows. Case Input: ${task} Modeling’s separability_matrix does not compute separability correctly for nested Compo...

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