ContractEval: Query-Conditioned Execution Matching for Procedural Instruction Conformance
On the map: LLM agents and tool use → Software engineering coding agents
Abstract (arXiv)
As LLM agents move from answering questions to carrying out procedures, failures can be unwarranted rather than visibly wrong: the final response looks acceptable even though the system skipped the check, branch, dependency, or invariant that made the answer justified. Output-only evaluation sees the answer, and trace-aware judging sees activity, but neither identifies which obligations were active for the query. We introduce CONTRACTEVAL, a diagnostic framework for making those active obligations explicit. It represents procedural instructions as query-active obligations and matches them against response or trace evidence, turning omissions, wrong branches, ordering errors, extra actions, invariant breaches, and output-contract violations into distinct conformance failures. On a controlled suite of audited procedural contracts, output-only and trace-aware LLM judges miss many injected structural failures; under gold expected and observed graphs, ContractEval detects and localizes all of them. LLM-backed extraction preserves much of this signal but remains calibration-sensitive. ContractEval is therefore not a compliance guarantee; it makes procedural conformance auditable rather than implicit in final-answer quality.
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 17 of its 27 references that are in the map. First posted on arXiv 8 Sep 2026.
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17 of 27 references.
References · 17
- 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
- 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,626
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- 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,373
- 2023G-Eval: NLG Evaluation using GPT-4 with Better Human Alignmentcited by 2,276
- 2023WebArena: A Realistic Web Environment for Building Autonomous Agentscited by 2,006
- 2023ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIscited by 1,947
- 2024A Survey on LLM-as-a-Judgecited by 1,423
- 2024τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domainscited by 1,402
- 2024OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environmentscited by 1,195
- 2023Prometheus: Inducing Fine-grained Evaluation Capability in Language Modelscited by 575
- 2024Prometheus 2: An Open Source Language Model Specialized in Evaluating Other Language Modelscited by 446
5 more
- 2024ToolSandbox: A Stateful, Conversational, Interactive Evaluation Benchmark for LLM Tool Use Capabilitiescited by 295
- 2024Benchmarking Complex Instruction-Following with Multiple Constraints Compositioncited by 152
- 2025LongProc: Benchmarking Long-Context Language Models on Long Procedural Generationcited by 29
- 2025What Is Your Agent's GPA? A Framework for Evaluating Agent Goal-Plan-Action Alignmentcited by 8
- 2026SAGE: A Service Agent Graph-guided Evaluation Benchmarkcited by 2
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