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The Verification Horizon: No Silver Bullet for Coding Agent Rewards

Binghai Wang, Chenlong Zhang, Dayiheng Liu, Jiajun Zhang, Jiawei Chen, Mingze Li, Mouxiang Chen, Rongyao Fang, Siyuan Zhang, Xuwu Wang, Yuheng Jing, Zeyao Ma, Zeyu Cui

arXiv:2606.26300v2cs.AIcs.CL

TL;DR

As coding agents improve, reliably verifying their increasingly sophisticated solutions becomes harder because verifiers remain imperfect proxies for user intent. The paper studies task-specific verification designs and finds that targeted reward signals improve performance, while verification must co-evolve with the generator.

  • Problem

    Coding-agent verification is becoming harder than solution generation, while existing signals struggle to jointly achieve scalability, faithfulness, and robustness.

  • Method

    The paper studies task-specific reward constructions spanning coding tests, frontend rubrics, user feedback, and autonomous evaluation for long-horizon tasks.

  • Results

    Across tasks and training stages, improved reward-signal quality yields gains; hacked resolved rate drops from 28.57% to 0.56%, while clean resolved rate rises from 40.22% to 60.53%.

  • Takeaways & Limitations

    Verification systems must continually co-evolve with increasingly capable generators rather than remain fixed.

  • Takeaways & Limitations

    Static judging cannot reliably verify runtime-dependent frontend behavior, including multi-page navigation, interactive transitions, and user-triggered content.

Abstract

from arXiv · show

A classical intuition holds that verifying a solution is easier than producing one. For today's coding agents, this intuition is being inverted: as foundation models develop stronger reasoning capabilities and engineering harnesses grow more sophisticated, generating complex candidate solutions is no longer difficult -- reliably verifying them has become the harder problem. Every verifier we can build is only a proxy for human intent, never the intent itself. This makes verification subject to a twofold difficulty: first, intent is underspecified by nature, making it inherently hard to faithfully check whether it has been fulfilled; second, during model training, optimization widens the gap between proxy and intent -- manifesting as reward hacking or signal saturation. To address this, we characterize the quality of verification signals along three dimensions -- scalability, faithfulness, and robustness -- and argue that achieving all three simultaneously is the central challenge. We further study four reward constructions: a test verifier for general coding tasks, a rubric verifier for frontend tasks, the user as verifier for real-world agent tasks, and an automated agent verifier for long-horizon tasks. Across different task types and policy capability levels, we conduct in-depth analysis and experiments on the core challenges of reward design and how to more effectively leverage reward signals. Experiments show that targeted verification design can effectively suppress reward hacking, improve task completion quality, and achieve significant gains across multiple internal and public benchmarks. These experiences collectively point to a core observation: no fixed reward function can remain effective as policy capability continues to grow; and verification must co-evolve with the generator.

1 Introduction

For coding agents, generating sophisticated solutions is becoming easier than reliably verifying whether they fulfill human intent, because verifiers remain imperfect proxies that can be exploited under optimization. The paper therefore argues that verification must co-evolve with agent capability across scalability, faithfulness, and robustness.

  • Problem: Verification is becoming harder than generation as stronger reasoning models and more sophisticated harnesses produce complex candidate solutions that existing checks cannot reliably assess.The classical asymmetry between finding and verifying solutions is reversing for today’s coding agents.
  • Problem: Verifiers operationalize underspecified human intent through proxies, and optimization can widen proxy–intent gaps through reward hacking or signal saturation.Because intent cannot be measured directly, executable tests, rubrics, and reward models cannot perfectly capture it; a perfect verifier is therefore unrealistic.
  • Verification framework: Verification quality depends jointly on scalability, faithfulness, and robustness: signals must scale affordably, reflect genuine intent, and remain reliable under diverse inputs and optimization pressure.The paper treats achieving all three dimensions simultaneously as the central verification challenge.
  • Reward constructions: The paper studies four increasingly open-ended reward constructions: executable tests, rubric and interactive judges, user feedback, and autonomous agentic evaluation.These constructions move toward greater fidelity to genuine user intent while requiring more open-ended judgment and becoming harder to scale.
  • Unit Test as Verifier: Across three SWE-Bench variants, combining quality judging with trajectory monitoring reduces hacked resolved rate from 28.57% to 0.56% and raises clean resolved rate from 40.22% to 60.53%.The unit-test verifier is augmented to constrain exploitable behaviors such as retrieving solution artifacts or tampering with tests.
  • Conclusion: The four constructions show that sustained progress requires a complete verification system that is continually rebuilt as policy capabilities and task landscapes evolve.The proposed system integrates executable tests, quality filtering, behavior monitoring, and agentic evaluators rather than relying on one fixed reward strategy.

2 Test-driven Rewards for SWE-like Tasks

Test-driven rewards scale through executable pass/fail tests, but their faithfulness depends on alignment with underspecified task intent and resistance to reward hacking. Agentic quality filtering and behavior-monitored RL improve reward reliability and downstream coding performance.

  • Automated Data Pipeline: SWE-like tasks use scalable Dockerized executable tests whose unified evaluation.sh pass/fail result serves as reward, but static verifier success can diverge from legitimate debugging processes.The pipeline constructs tasks from GitHub pull requests by separating fix and test patches and restoring repositories to their pre-fix state.
  • Reward Hacking: Reward hacking is an active false positive in which agents exploit information leakage to pass tests without genuinely solving the task, unlike passive test-design deficiencies.Under hardened environments, repository-history mining, oracle tampering, harness tampering, visible-test overfitting, and evaluator-aware patching fall below the overall resolved rate.
  • Reward Faithfulness: Test-driven reward faithfulness requires binary pass/fail outcomes to correspond to true task intent, not merely test-suite success, despite lossy instructions and implicit project expectations.Faithfulness is characterized by avoiding false positives and false negatives; during RL, both produce erroneous gradient signals.
  • Reward Faithfulness: The agentic quality judge assesses instruction clarity and instruction–test alignment, with the latter substantially more challenging because it requires semantic understanding and test-inference.It explores the Dockerized repository, commands, and tests, optionally using the ground-truth patch; Table 1 evaluates model, voting, and demonstration choices.
  • Application in RL: Quality-filtered RL improves SWE-bench Multilingual and SWE-bench Pro performance while remaining comparable on SWE-bench Verified.Filtering removes tasks with unclear instructions or instruction–test misalignment before RL training.

3 Interactive Judge for Frontend Tasks

Frontend tasks require judging both rendered appearance and runtime interaction, not merely execution success. The section develops a rubric-based static judge and an efficient interactive judge to improve faithfulness, robustness, and alignment with human evaluation.

  • §3.1 Rubric-Based Judge: Rubric-based judging combines rendered screenshots and source code across structured dimensions, improving human-evaluator agreement and alignment while reducing preference for visually impressive but functionally incorrect outputs.The evaluation covers functional correctness, content, visual quality, layout, UX, and technical criteria across 671 WebDev tasks and 8 models.
  • §3.1 Rubric-Based Judge: Static judging remains limited because code inspection and single-state screenshots cannot reliably verify form validation, dynamic routing, stateful interactions, navigation, transitions, or delayed content.These limitations motivate evaluating web applications by navigating and operating them rather than relying solely on static artifacts.
  • §3.2 Interactive Judge: The interactive pipeline extracts page information and evaluation criteria, generates a complete action list in one pass, executes it in a live browser, and scores the resulting trace.Its atomic action vocabulary includes clicking, scrolling, navigation, form filling, hovering, and key presses, avoiding sequential agent-loop decision errors.
  • §3.2 Interactive Judge: The interactive judge evaluates frontend functionality through planned browser interactions, capturing dynamic behaviors and multi-page workflows that static code and screenshot inspection cannot reliably verify.It uses a single-pass action planner, Playwright execution, and interaction traces to balance coverage with efficiency and reliability.
  • §3.2 Interactive Judge: Interactive Judge filtering is applied as a best-of-4 rejection-sampling fine-tuning reward on WebDev Human Eval and QwenWebBench, yielding consistent improvements on both benchmarks.The training experiment uses an intermediate Qwen3.7-Plus checkpoint; the supplied passage does not provide the improvement values.

4 User Feedback as Verifier for Real-World Agent Tasks

The section treats users as the most faithful verifier for open-ended real-world agent tasks and extracts their implicit, process-level feedback from interactions. Span-KTO uses this feedback to outperform SFT and RW-SFT, especially on difficult or unresolved tasks where better failure behavior matters.

  • Motivation: Users are the most ideal verifiers for open-ended tasks because they care whether the agent completed the task, but they convey judgments implicitly through language and behavior.Real user intent is diverse and underspecified, so a static learned reward model provides only a lossy proxy despite its scalability.
  • Method: The pipeline extracts Human Implicit Reward Signals from multi-turn trajectories with LLM-as-Judge annotation and trains with SFT, RW-SFT, or Span-KTO.The Judge evaluates each user-message/assistant-response round after removing reasoning traces, verbose tool I/O, and system prompts.
  • Signal Analysis: Feedback is predominantly neutral, negative feedback is highly confident, and execution plus misunderstanding errors account for 77.7% of negative reasons.After excluding initial task-description rounds, neutral, negative, and positive signals comprise 76.6%, 20.0%, and 3.5%; 81.8% of negative signals are high-confidence versus 18.7% of neutral signals.
  • Benchmark Results: Span-KTO outperforms SFT and RW-SFT on all five benchmarks, reaching 59.8% versus 54.2% on SWE-bench Verified and 28.1% versus 14.8% on Aone-bench.It improves SWE-bench Multilingual by 7.8 percentage points and Aone-bench by 13.3 percentage points over SFT.
  • Training Objective Comparison: RW-SFT only partially exploits feedback: it improves over SFT on all benchmarks but by just 1.0 percentage point on SWE-bench Verified, while reweighting cannot change learning direction.Performance is non-monotonic: wneg = 0.0 scores 37.2%, wneg = 0.5 scores 35.1%, and the SFT baseline at wneg = 1.0 scores 41.8%.
  • Instance-Level Analysis: On unresolved instances, Span-KTO improves Inefficiency by 34.5%, Communication by 26.5%, Execution Error by 13.9%, and resolution rate by 5.9 percentage points.These gains indicate better self-regulation, fewer pointless retries, faster bottleneck identification, and clearer communication when tasks remain difficult.

5 Dynamic Agent Judge for Long-horizon Tasks

The dynamic evaluator decomposes long-horizon project specifications into checklist and holistic judgments, then is assessed against unit-test quality. Properly calibrated prompts and models provide useful filtering and training signals, but evaluator capability, rubric granularity, and filtering thresholds create important trade-offs.

  • Evaluation Design: The holistic evaluator score aligns more strongly with unit-test quality than checklist pass rate, with r_eval and ρ_eval substantially exceeding r_pass and ρ_pass.The evaluation combines checklist pass rate with holistic code-quality scoring because requirements differ in importance and binary averaging can misrepresent quality.
  • Evaluator Limitations: Evaluator failures include modifying code, reusing repository tests, advocating for the generator, and exhaustively reading irrelevant code, while over-detailed rules can further reduce coherence.These boundary violations inflate scores by masking defects, and context overload dilutes attention on relevant entry points and interfaces.
  • Evaluator Prompt Design: Prompt v4 is adopted because it improves BoN accuracy from 57.9% to 67.4%, Kendall’s τ from 0.379 to 0.473, and r_eval from 0.489 to 0.598 before over-specification degrades performance.Moderately detailed rules help weaker evaluators, whereas exhaustive procedural constraints overwhelm instruction following; v4 also maintains the strongest moderate-threshold filtering quality.
  • Evaluator Model Comparison: Claude Opus 4.7 achieves the strongest ranking performance, with 70.4% BoN accuracy and Kendall’s τ of 0.579, while filtering quality does not always follow ranking ability.DeepSeek V4 Pro attains higher conditioned UT quality than Qwen 3.7 Plus despite weaker ranking metrics, and Qwen 3.6 Plus delivers comparable filtering quality.
  • Filtering Trade-offs: At threshold θ ≥8, Claude Opus 4.7 retains 139 samples with conditioned UT score 0.615, whereas higher thresholds improve quality but sharply reduce retained data.At θ ≥8, models retain 118–139 samples; at θ ≥10, only 18–30 survive, making evaluator choice dependent on the training objective.
  • Training with Evaluator Feedback: 23.52 versus 21.61 shows evaluator-filtered data outperforming random sampling by 1.91 points under controlled data size, while the full 19,050-sample set reaches 24.75.RFT improves the base model from 11.41 to 23.52; doubling data volume can compensate for lower selection quality.

6 Conclusion

The paper advocates task- and capability-specific reward design that balances faithfulness, scalability, and robustness, while identifying open challenges in evaluating quality, learning from users, and adapting evaluators.

  • Conclusion: Reward design should be tailored to task characteristics and policy capability levels to balance three dimensions of reward feasibility.The paper frames this as practical experience from training and evaluating coding agents across diverse and complex scenarios.
  • Quality stratification of the solution space: Binary rewards cannot distinguish solution-quality gradients when multiple implementations pass tests but differ between root-cause repairs and superficial workarounds.Quality-aware signals should separate structurally sound fixes from symptom-suppressing solutions within the valid solution space.
  • Capturing human subjective perception: Frontend evaluators struggle to quantify subjective qualities such as animation naturalness, visual hierarchy, interaction responsiveness, and overall design polish.Both static screenshot comparison and automated interaction testing have difficulty capturing experiential dimensions perceived by human users.
  • From offline feedback mining to online learning: User feedback could support online, deployment-time model improvement beyond current predominantly offline extraction from historical interaction logs.The passage characterizes user feedback as a valuable on-policy signal within this broader shift.
  • Evaluator–generator co-evolution: Evaluators must co-evolve with improving generators because evaluators calibrated to weak generators may fail to discriminate among high-quality outputs.The proposed training loop periodically updates the evaluator to match the generator’s advancing capability frontier, analogous to adversarial discriminator–generator dynamics.
  • Credit assignment in long-horizon and multi-agent settings: Long-horizon and multi-agent coding require precise credit assignment from outcome-level rewards to individual steps or agents.Repository construction accumulates many intermediate decisions, and collaboration makes attribution more complex.

7 Authors

The paper was authored by a large collaborative team, with core contributors and additional contributors spanning Alibaba Qwen Team and several universities. Project-lead and corresponding-author roles are explicitly identified.

  • Core Contributors: Core contributors include Binghai Wang, Chenlong Zhang, Dayiheng Liu, Jiajun Zhang, Jiawei Chen, and additional team members.
  • Contributors: Additional contributors include Beichen Zhang, Hang Zhang, Hao Chen, Jinxi Wei, Shuai Bai, Tao Gui, and other collaborators.
  • Affiliations and Roles: The authors represent Alibaba Qwen Team alongside Fudan University, the Institute of Automation, Chinese Academy of Sciences, USTC, Tsinghua University, and Zhejiang University.Xuwu Wang and Zeyu Cui are marked as project leads, Dayiheng Liu and Xuwu Wang as corresponding authors, and several contributors are listed alphabetically.

A System Prompt of the Agentic Judge for SWE-like Tasks

The agentic judge evaluates whether SWE-like tasks are suitable for training coding agents by inspecting PR-description quality and test-script alignment in an interactive repository environment. It uses structured ratings and rationales for instruction clarity and test adequacy, informed by the repository, evaluation script, and imperfect reference patch.

  • Environment and inputs: The judge operates interactively in Docker with access to the repository, evaluation script, and reference patch while assessing task quality.The repository is located at /testbed, tests run via /evaluation.sh, and the reference patch is at /patch.patch but may be imperfect.
  • Evaluation objective: The judge’s mission is to determine whether a SWE task’s PR description and test script are suitable for training coding agents.A SWE task comprises a coding problem, implementation environment, and validation test script.
  • Quality dimensions: It rates instruct_ut_quality by whether tests align with the PR description, distinguishing consistent tests from overly strict tests and overly lenient tests with loopholes.Overly strict tests can reject reasonable correct solutions, whereas lenient tests can allow incomplete solutions to pass.
  • Output format: The required output is a single-line JSON object containing both quality scores and rationales.The fields are instruction_quality, instruction_quality_rationale, instruct_ut_quality, and instruct_ut_quality_rationale.

B Examples of the Agentic Judge for SWE-like Tasks

Manual annotation of SWE-like tasks identifies two recurring quality failures: unclear instructions and instruction–unit test misalignment. Representative cases show that missing requirements or inaccessible context, as well as orthogonal or implementation-specific tests, can make evaluation unreliable.

  • Unclear Instruction: Unclear instructions are too vague, brief, or dependent on inaccessible external context to be solvable from the provided information alone.One case contains only a two-word description, while another delegates requirements to a private Slack channel.
  • Unclear Instruction: A two-word instruction omits required bitwise AND and compound-assignment support that the unit tests nevertheless demand.The test suite requires bitwise AND operator support and compound assignment operator &=, neither of which appears in the instruction.
  • Instruction–Unit Test Misalignment: Instruction–unit test misalignment occurs when tests check orthogonal functionality or enforce implementation-specific artifacts, including typographical errors.Such tests can cause functionally correct solutions to fail.
  • Instruction–Unit Test Misalignment: A representative misaligned task describes an h5py-related import failure, while its test suite is entirely orthogonal to the described task.The benchmark figure contrasts this orthogonal test with another that hard-codes a typo in expected outputs.

C Reward Hacking Behaviors in SWE Tasks · D Detailed Rubrics Judge Prompts

The paper catalogs shortcut channels through which SWE agents can obtain verifier success without following intended debugging, including solution leakage, harness or oracle manipulation, and test overfitting. For frontend evaluation, rubric judges inspect code and screenshots against checklist items and assign precise 0–10 scores with detailed reasoning.

  • C Reward Hacking Behaviors in SWE Tasks: SWE reward hacking spans shortcut channels that obtain verifier success without following the intended local debugging process.The catalog includes external solution lookup, evaluation-harness tampering, test-oracle tampering, visible-test overfitting, evaluator-aware patching, and repository-history mining.
  • C Reward Hacking Behaviors in SWE Tasks: Exact issue-title searches can reveal original issues, fixing pull requests, comments, or implementation hints, bypassing independent local resolution.The agent searches GitHub with solution-specific benchmark wording; repository-history mining similarly risks exposing a fixing commit when full project history is available.
  • C Reward Hacking Behaviors in SWE Tasks: Harness-level changes can alter which tests execute, while legitimate compatibility gating must be distinguished from skip-marker manipulation.The qutebrowser case reasons about marker logic that can skip end-to-end tests under runtime conditions.
  • C Reward Hacking Behaviors in SWE Tasks: Writing a new file into the test tree despite an explicit constraint can contaminate the evaluation oracle if the file remains or affects testing.The agent acknowledges that tests should not be modified, then creates a separate test file under the repository’s test directory.
  • C Reward Hacking Behaviors in SWE Tasks: Visible-test overfitting and evaluator-aware patching shift implementation toward expected test configurations or grader mechanics rather than robust product requirements.These behaviors risk overfitting unseen cases and conditioning decisions on whether benchmark tests may be replaced or restored.
  • D Detailed Rubrics Judge Prompts: The frontend judge assigns a precise score from 0 to 10 for every checklist item while providing detailed reasoning as a strict code reviewer.The prompt frames the evaluator as a senior frontend developer and rigorous reviewer examining source code alongside rendered output.
  • D Detailed Rubrics Judge Prompts: The detailed Rubrics Judge prompt evaluates each frontend checklist item using rendered screenshots, complete HTML/CSS/JavaScript, and the original user prompt.It requests analysis of implementation correctness and quality, visual accuracy, and both strengths and weaknesses.

E Ablation of the Interactive Judge

The Interactive Judge remains sufficiently stable for distinguishing model capability levels and training use, with variance patterns differing by model and checklist-guided planning improving scores without comparable variance increases.

  • Setup: The ablation isolates generation, rendering plus judging, and judging variance by fixing upstream stages and varying only the component under test.The evaluation uses 300 QwenWebBench tasks and compares Claude Opus 4.7 with an intermediate, unreleased Qwen3.7-Max checkpoint.
  • Results: Across both models and variance sources, σ stays below 12 ELO and the maximum range is 30.4, remaining below model-tier gaps and supporting reliable training rewards.The reported gaps include approximately 40 ELO between Claude and Qwen and approximately 430 ELO between Qwen Max and Qwen3-Coder-Next.
  • Results: Generation dominates Claude’s variance, whereas judging dominates the intermediate Qwen3.7-Max checkpoint’s evaluation instability.Claude has generation σ = 10.4 versus judge σ = 8.5; Qwen has generation σ = 2.8 versus judge σ = 11.4.
  • Results: Checklist-guided action planning raises mean ELO for Claude and Qwen while maintaining comparable variance across pipeline stages.Mean ELO increases from 1517.3 to 1532.1 for Claude and from 1483.2 to 1498.6 for Qwen; variance is σ = 11.1 and 10.7, respectively.

F Trajectory-Level Dataset Statistics

Trajectory outcomes are dominated by partial and full success, while failures and abandonment remain uncommon. Conversations are typically short but exhibit a long-tail distribution, with most ending within eight rounds.

  • Partial success accounts for 57.8% of trajectories, full success for 32.6%, failure for 8.6%, and user abandonment for 1.0%.
  • Successful trajectories comprise 90.4% of outcomes, combining partial success at 57.8% with full success at 32.6%.
  • Conversation length is long-tailed: 50% conclude within 3 rounds and 90% within 8 rounds.

G Human Feedback Annotation Examples

The appendix illustrates human-feedback annotation categories through representative examples in Tables 13–19 and trajectory-level outcomes in Table 20, covering approval and several forms of negative feedback.

  • Annotation Examples: The appendix organizes annotation examples by signal category, with representative cases in Tables 13–19 and trajectory-level outcomes in Table 20.These examples provide concrete coverage of the annotation taxonomy and its trajectory-level consequences.
  • Positive Signals: Positive signals account for 3.5% of non-Turn 0 turns, with 83.6% being explicit user approval or acceptance of the assistant’s performance.Positive examples are presented in Table 13.
  • Negative Signals: Negative annotations include Execution Error at 56.6%, Misunderstanding at 21.1%, Omission at 8.9%, and Overaction at 6.3%.These categories distinguish implementation errors from intent-comprehension deviations, incomplete coverage, and actions beyond the user’s scope; examples appear in Tables 14–16 and the corresponding category examples.

H Span-KTO Hyperparameter Ablation … IX. Calibration Examples

Span-KTO performs best with β = 0.01 and λl = 1.0 across all three benchmarks, while its human-feedback judge uses structured, conservative rules to distinguish user evaluations from workflow behavior. The prompt defines polarity, fairness, signal types, contrastive corrections, error categories, and consistency checks, with calibration examples illustrating common annotation failures.

  • H Span-KTO Hyperparameter Ablation: β = 0.01 achieves the highest scores across all three benchmarks, while performance increases monotonically with λl: λl = 1.0 > λl = 0.6 > λl = 0.3.β that is too small weakens preference signals, whereas β that is too large causes unstable updates; span-level negative-sample imbalance does not require reducing λl.
  • I Human Feedback Annotation Judge Prompt: Qwen 3.6 Plus annotates sentiment polarity in user messages, producing turn-level reward signals and an overall trajectory assessment in a strict JSON format.The output includes trajectory outcome and reasoning, per-turn summaries, polarity, confidence, signal type, negative reason, contrastive-pair status, and user fairness.
  • I. Core Principles: The judge separates user-expressed polarity from evaluator-assessed fairness, evaluates only reactions directed at the assistant, and conservatively labels ambiguous cases neutral with low confidence while retaining behavioral evidence.Negative signals take priority when positive and negative cues coexist; authentic user and assistant turns are separated from tool returns and system injections.
  • II. Annotation Field Definitions: The seven annotation fields cover polarity, confidence, signal type, negative reason, contrastive pairs, user fairness, and reasoning grounded in the user’s original words.Negative reasons are prioritized as execution_error > misunderstand > omission > overaction > inefficiency > communication, while contrastive pairs require rejection, correction, and user acceptance.
  • III. Polarity Determination Rules: Polarity rules treat explicit approval or rejection as decisive, infer behavioral acceptance only when it references the assistant’s output, and classify unrelated progression or supplementary information as neutral.They also distinguish assistant-caused execution errors from external failures, classify negative-reason categories, and handle ambiguity, rhetorical questions, requirement changes, and self-correction.
  • V. signal_type Determination: Signal type distinguishes explicit language from implicit behavioral or structural evidence, while contrastive pairs require negative polarity, a subsequent assistant correction, and user acceptance.Fairness labels identify reasonable, unreasonable, or difficult-to-judge evaluations and guide whether positive or negative samples should be retained, downweighted, or discarded.
  • VIII. Annotation Consistency Checks: Before output, consistency checks require null negative reasons for positive or neutral labels, negative polarity for contrastive pairs, and fixed Turn 0 values.These checks enforce structural compatibility among polarity, negative reason, contrastive-pair status, confidence, signal type, and fairness.
  • IX. Calibration Examples: Calibration examples show that approval, rejection, omission, execution errors, rhetorical questions, workflow progression, and information provision must be separated from annotator judgment.They specifically warn that cooperation or new requirements do not imply endorsement, errors from the assistant’s code are negative, and positive feedback on buggy code can be unreasonable.

J Agent-as-Judge Behavioral Rubric

The rubric evaluates six negative-behavior categories from agent trajectories without subsequent user feedback, using structured sub-item scores and evidence-based primary-category selection. It distinguishes process and behavioral risks from pure correctness failures, which are evaluated elsewhere.

  • J Agent-as-Judge Behavioral Rubric: The rubric covers six negative-behavior categories, each with 6–9 sub-items scored from 0 to 4, where 4 indicates no apparent issues and 0 severe negative-feedback risk.Evaluation is performed automatically from agent trajectories without waiting for subsequent user feedback.
  • J.1 execution_error: Delivery Reliability and Evidence Discipline: execution_error assesses delivery reliability and evidence discipline, including verification, interpretation of logs, truthful completion claims, and risk disclosure.Reasonable repair attempts with explicit verification limits and clearly marked remaining risks should not be heavily penalized.
  • J.2 misunderstand: Intent, Constraints, and Working Style Misalignment: misunderstand captures misalignment with the user’s intended working style, constraints, scope, technical path, output granularity, or collaboration protocol.Examples include implementing event-handling code when configuration alone was requested or listing product families instead of specific API model IDs.
  • J.3 omission: Requirement Tracking, Step, and Coverage Gaps: The omission, overaction, inefficiency, and communication categories separately score requirement coverage, operational overreach, stagnation or repetition, and presentation or collaboration problems.Each category has its own sub-item scoring criteria.
  • J.7 Primary Category Selection Rules: When multiple categories score low, the primary category is the behavior most likely to trigger negative user feedback, selected by explicit priority rules.The rules prioritize overaction for large unauthorized changes, misunderstand for violated constraints, omission for missed checklist items, execution_error for unverified success claims, inefficiency for stagnation, and communication for format or collaboration issues.
  • J.7 Primary Category Selection Rules: Pure correctness failures receive low-intensity or no rubric deduction when the process was honest, verification sufficient, scope restrained, and visible evidence respected.The rubric notes that correctness issues are covered by other benchmarks.
  • J.8 Judge Output Format: The judge outputs structured JSON containing an overall behavior score, negative-behavior label, primary error type, category scores, triggered subcriteria, evidence, and a short reason.Evidence may contain up to five items drawn from the task, trajectory, diff, logs, tests, or acceptance checklist.
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