mlsanityData to 14 Oct 2026

Calibrating Criterion Revision in LLM Agents: Failure Modes and a Trace-Anchored Protocol

arXiv 2608.20729 ↗First posted 21 Aug 2026cs.AI cs.CLcited by 0

On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks

Abstract (arXiv)

Language-model agents can improve after failure or carry text across episodes without revising what counts as success. We study the narrower attribution problem of criterion revision: when criterion K0 accepts an outcome violating a broader commitment B, what observations justify saying that the system formed and persistently used K1? We require five non-compensatory conditions: criterion-failure detection, a model-emitted proposal, new-episode transfer, intervention sensitivity on the claimed carrier, and preservation. We evaluate CMB-0.1 on twelve cross-domain cases and four arms: stateless inference, append-only history, model-generated but harness-committed state, and evaluator-written oracle state. Seven mechanism fixtures yield 84 deterministic scorer trials; four local quantized artifacts yield 96 calls and 192 model-case-arm trials. No model trial satisfies all five conditions, but this zero does not establish general capability absence. Eleven calls remain invalid after one retry; several commitments disclose the target distinction; the harness performs commits; deletion reuses a stateless call; and conflict changes multiple factors. Qwen2.5-7B answers every transfer and preservation item without revision state, exposing zero-state reconstruction. These failures make CMB-0.1 an instrument-calibration result rather than a model ranking. We derive a prospective, trace-anchored CMB-0.4 protocol requiring concealed transfer, explicit WRITE/NO-WRITE/ESCALATE actions, a separately logged policy-selected commit, matched interventions, repeated hidden items, and a frozen executable oracle. It is a successor design, not a completed confirmatory result. The paper contributes a measurement chain, an empirical diagnosis of its first implementation, and a more discriminating protocol for future tests of criterion revision.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 15 of its 38 references that are in the map. First posted on arXiv 21 Aug 2026.

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15 of 38 references.

References · 15

  1. 2024Qwen2.5 Technical Reportcited by 4,942
  2. 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,276
  3. 2025Gemma 3 Technical Reportcited by 1,824
  4. 2025Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learningcited by 237
  5. 2025Dynamic Cheatsheet: Test-Time Learning with Adaptive Memorycited by 163
  6. 2025Evo-Memory: Benchmarking LLM Agent Test-time Learning with Self-Evolving Memorycited by 136
  7. 2025Measuring what Matters: Construct Validity in Large Language Model Benchmarkscited by 119
  8. 2025How Memory Management Impacts LLM Agents: An Empirical Study of Experience-Following Behaviorcited by 101
  9. 2026Agentic Memory: Learning Unified Long-Term and Short-Term Memory Management for Large Language Model Agentscited by 78
  10. 2026Mem2ActBench: A Benchmark for Evaluating Long-Term Memory Utilization in Task-Oriented Autonomous Agentscited by 32
  11. 2026EvoMemBench: Benchmarking Agent Memory from a Self-Evolving Perspectivecited by 17
  12. 2025Task Matters: Knowledge Requirements Shape LLM Responses to Context-Memory Conflictcited by 16
3 more
  1. 2026UMEM: Unified Memory Extraction and Management Framework for Generalizable Memorycited by 8
  2. 2026Causal Intervention-Based Memory Selection for Long-Horizon LLM Agentscited by 6
  3. 2026ImplicitMemBench: Measuring Unconscious Behavioral Adaptation in Large Language Modelscited by 3

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