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            <title>Proceedings of the Second Workshop for REsearch on Agent Language Models (REALM 2026)</title>
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            <namePart type="given">Ehsan</namePart>
            <namePart type="family">Kamalloo</namePart>
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            <namePart type="given">Nicolas</namePart>
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            <namePart type="given">Nikolai</namePart>
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    <abstract>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 \ensuremathμs 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.</abstract>
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%0 Conference Proceedings
%T Chronicle: Cut-Point Replay for Regression Testing of LLM Agents
%A Chawla, Tisha
%A Koul, Susheem
%Y Kamalloo, Ehsan
%Y Gontier, Nicolas
%Y Rozanov, Nikolai
%Y Hashemzadeh, Maryam
%Y Arora, Akhil
%S Proceedings of the Second Workshop for REsearch on Agent Language Models (REALM 2026)
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-485-9
%F chawla-koul-2026-chronicle
%X 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 \ensuremathμs 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.
%U https://aclanthology.org/2026.realm-1.71/
%P 1026-1031

Markdown (Informal)

[Chronicle: Cut-Point Replay for Regression Testing of LLM Agents](https://aclanthology.org/2026.realm-1.71/) (Chawla & Koul, REALM 2026)

• Chronicle: Cut-Point Replay for Regression Testing of LLM Agents (Chawla & Koul, REALM 2026)

ACL

• Tisha Chawla and Susheem Koul. 2026. Chronicle: Cut-Point Replay for Regression Testing of LLM Agents. In Proceedings of the Second Workshop for REsearch on Agent Language Models (REALM 2026), pages 1026–1031, Budapest, Hungary. Association for Computational Linguistics.