mlsanityData to 14 Oct 2026

PartHackBench: Certified Equal-Progress Stress Tests for Partial-Credit Tool-Agent Evaluation

arXiv 2609.29578 ↗First posted 27 Aug 2026cs.AI cs.CL cs.CRcited by 0

On the map: LLM agents and tool use → Software engineering coding agents

Abstract (arXiv)

Long-horizon tool agents often make useful progress without reaching terminal success, motivating partial-credit evaluation. Yet evaluators may reward milestones that were temporary, later reversed, or not attributable to the evaluated agent. Comparing an honest trajectory with a higher-scoring adversarial one is inconclusive if the latter made more genuine progress. We introduce PartHackBench, a controlled methodology that removes this confound. A private certifier admits a pair only when its trajectories match component-wise in both current-state predicate satisfaction and standardized agent attribution; score inflation, defined as f(A) - f(H), is measured only afterward. In 18 sealed held-out tasks in PB-CSTE, the frozen historical-target run produced matched adversaries for 15 tasks. Historical credit yielded mean inflation of .252, conditional attack success of 10/15, end-to-end yield of 10/18, and detected none of 14 strict rollbacks. Semantic LLM judges were more resistant but remained vulnerable, especially under evaluator-targeted attacks, while PB-CSTE current-state controls, defined as exact functions of the certified components, yielded zero inflation by construction. PartHackBench thus provides a certified control for testing whether evaluator credit changes while all benchmark-defined task-relevant progress remains fixed.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 25 of its 28 references that are in the map. First posted on arXiv 27 Aug 2026.

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25 of 28 references.

References · 25

  1. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,626
  2. 2023Let's Verify Step by Stepcited by 3,789
  3. 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,373
  4. 2023G-Eval: NLG Evaluation using GPT-4 with Better Human Alignmentcited by 2,276
  5. 2023WebArena: A Realistic Web Environment for Building Autonomous Agentscited by 2,006
  6. 2016Concrete Problems in AI Safetycited by 1,642
  7. 2024τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domainscited by 1,402
  8. 2023AgentBench: Evaluating LLMs as Agentscited by 1,267
  9. 2022Scaling Laws for Reward Model Overoptimizationcited by 1,095
  10. 2023Large Language Models are not Fair Evaluatorscited by 1,085
  11. 2022Solving math word problems with process- and outcome-based feedbackcited by 901
  12. 2024LLM Evaluators Recognize and Favor Their Own Generationscited by 686
13 more
  1. 2025τ2-Bench: Evaluating Conversational Agents in a Dual-Control Environmentcited by 627
  2. 2023API-Bank: A Comprehensive Benchmark for Tool-Augmented LLMscited by 591
  3. 2023Prometheus: Inducing Fine-grained Evaluation Capability in Language Modelscited by 575
  4. 2024RewardBench: Evaluating Reward Models for Language Modelingcited by 499
  5. 2024WorkArena: How Capable Are Web Agents at Solving Common Knowledge Work Tasks?cited by 363
  6. 2024AgentBoard: An Analytical Evaluation Board of Multi-turn LLM Agentscited by 339
  7. 2024JudgeBench: A Benchmark for Evaluating LLM-based Judgescited by 318
  8. 2024ToolSandbox: A Stateful, Conversational, Interactive Evaluation Benchmark for LLM Tool Use Capabilitiescited by 295
  9. 2025AgentRewardBench: Evaluating Automatic Evaluations of Web Agent Trajectoriescited by 108
  10. 2024WorkArena++: Towards Compositional Planning and Reasoning-based Common Knowledge Work Taskscited by 90
  11. 2024CalibraEval: Calibrating Prediction Distribution to Mitigate Selection Bias in LLMs-as-Judgescited by 34
  12. 2026ToolPRMBench: Evaluating and Advancing Process Reward Models for Tool-using Agentscited by 14
  13. 2026Toward Scalable Verifiable Reward: Proxy State-Based Evaluation for Multi-turn Tool-Calling LLM Agentscited by 9

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