arXiv AI By Tisha Chawla, Susheem Koul

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

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Chronicle introduces a method called cut‑point replay to make regression testing of large language model (LLM) agents reproducible. It records an agent’s run at non‑deterministic boundaries as immutable envelopes and then replays selected boundaries while executing the rest live, enabling continuous‑integration tests that detect faulty code changes. Benchmarks show minimal overhead, perfect bit‑stability, and effective detection of unsafe actions in a mutation study.

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Sep 17

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

Chronicle introduces a method called cut‑point replay to make regression testing of large language model (LLM) agents reproducible. It records an agent run at non‑deterministic boundaries as immutable envelopes and replays selected boundaries while executing the rest live, turning recorded failures into continuous‑integration tests. Benchmarks show minimal overhead, bit‑stable full replay, and effective detection of faulty code changes in a mutation study.

arXiv AI
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Boundary-State Control for Tool-Using Language-Model Agents: Commit-Time Consistency under State Drift

The paper introduces BSC‑R, a deterministic effect‑boundary mechanism that ties a single‑use commit authorization to the specific action and the semantic state that justified it, aiming to close the proposal‑to‑commit gap in tool‑using language‑model agents. Experiments on 2,847 AgentDojo episodes and 10,302 frozen proposals show that BSC‑R preserves the agent’s original behavior while rejecting unauthorized changes, and further tests on a boundary‑drift experiment and the CONTINUITY suite demonstrate high success rates in valid contexts and robust handling of replay and ambiguous cases. However, broader testing reveals that BSC‑R still allows a 25% invalid‑effect commit rate in a larger attack set, indicating that it provides scoped, not universal, safety.

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