mlsanityData to 7 Oct 2026

Chronicle: Cut-Point Replay for Regression Testing of LLM Agents

arXiv 2609.20625 ↗First posted 17 Sep 2026cs.CL cs.AIcited by 0

On the map: LLM agents and tool use → Software engineering coding agents also fits: Agent memory systems

Abstract (arXiv)

Large language model responses are non-deterministic, so failures in LLM agents are hard to reproduce: a failure depends on inference that is not bitwise reproducible, on tools that read changing state, and on a multi-step trajectory that a re-run rarely repeats. Record-and-replay makes a run reproducible, but existing agent tooling records runs only to trace or score them, not to test a code change against them. We present Chronicle, which records an agent run at its non-deterministic boundaries as immutable envelopes and replays it from the record. Its central operation, cut-point replay, serves a chosen subset of boundaries from the record and executes the complementary subset live with new code, turning a recorded incident into a regression test that runs in continuous integration. On a benchmark of 6 recorded failures with simulated model boundaries, recording adds 23 μs per crossing (0.008% of an assumed 300 ms model call), full replay issues zero model calls and is bit-stable across 20 repetitions, and cut-point tests fail on faulty code and pass on guarded and benign changes for all 6 incidents. In a mutation study of the guarded tools, cut-point tests catch every mutant that lets the recorded unsafe action through, while a baseline that stubs every boundary, using the same assertion, catches none. Chronicle and the benchmark are publicly available at https://github.com/theagentplane/chronicle.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 13 of its 26 references that are in the map. First posted on arXiv 17 Sep 2026.

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13 of 26 references.

Closest ancestors · 8

Same direction, newest first

  1. 2026Knowledge-Based Zero-Replay Debugging of Multi-Agent LLM Traces11 Jun
  2. 2026REFLECT: Intervention-Supported Error Attribution for Silent Failures in LLM Agent Traces8 Jun
  3. 2026Causal Agent Replay: Counterfactual Attribution for LLM-Agent Failures6 Jun
  4. 2026Are Coding Agents Generating Over-Mocked Tests? An Empirical Study30 Jan
  5. 2026Automated structural testing of LLM-based agents: methods, framework, and case studies25 Jan
  6. 2025DoVer: Intervention-Driven Auto Debugging for LLM Multi-Agent Systems7 Dec
  7. 2025TRAIL: Trace Reasoning and Agentic Issue Localization13 May
  8. 2025Why Do Multi-Agent LLM Systems Fail?17 Mar

Other references · 5

  1. 2024τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domainscited by 1,280
  2. 2024AgentDojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agentscited by 129
  3. 2025Get Experience from Practice: LLM Agents with Record & Replaycited by 22
  4. 2026Reliability without Validity: A Systematic, Large-Scale Evaluation of LLM-as-a-Judge Models Across Agreement, Consistency, and Biascited by 19
  5. 2025Detecting and Evaluating Order-Dependent Flaky Tests in JavaScriptcited by 2

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