Self-Cleaning and Captured Anyway: One Measured Primitive for Error in a Store an Agent Writes to Itself, and What a Falling Score Actually Measures
Not on the map. Why?
Abstract (arXiv)
"An agent that writes its conclusions into a store it later retrieves from closes a loop usually reported as one-way contamination. Taking the loop to the infinite-tenure limit against an append-only store gives a different picture: because writing never deletes, the reachable state space has a hard upper edge at (n-1)/n, so the outcome is a choice between two edges rather than a decay. At f0 = 0.9 the interval between the two modes holds 3.6% of 220 runs where a uniform spread would put 20.6%, and is strictly empty on the first 15; the pooled mean describes 8.2% of the runs it summarises, the median 68.2%. Everything the model contributes is carried by one measured primitive with no fitted parameter, the copy function γ(φ): on 36 Wikidata facts, sign(hatγ - γcrit), with γcrit = 1/k at r = 0, w = 1, predicts the direction of drift on 353 of 360 real-fact runs (39 of 40 synthetic in the same batch). Scale does not rescue the store: pooled frontier capture is 0.850, with claude-sonnet-4.5 captured on 20 of 20 seeds against our registered prediction of <0.5. What the interval tests is distinguishability rather than count: on the real facts, multi-valued runs have 6.4x its occupancy of the rest. It survives at f0 in 0.1, 0.3, 0.5, capture peaks at f0 = 0.5, and of four interventions with criteria frozen first, timing dominates fraction at matched budget while a consistency gate drives every model to 0.993. The resampling unit is the seed, at a design effect of 3.75 on a pooled level: under a 44-seed control the ordering supporting claim 4 collapses from Spearman +0.98 at three seeds to +0.31-0.80 at forty-four, while claim 2's ordering is exact there (+1.00, p = 0.017). All 87 graded rows are in Appendix W, 37 of them graded withdrawn, failed, self-correcting, undecidable or an acknowledged limit, against 50 that are not."
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 48 of its 77 references that are in the map. First posted on arXiv 6 Sep 2026.
Builds on
48 of 77 references.
References · 48
- 2020Scaling Laws for Neural Language Modelscited by 6,586
- 2022Training Compute-Optimal Large Language Modelscited by 3,019
- 2023MemGPT: Towards LLMs as Operating Systemscited by 1,389
- 2024AgentPoison: Red-teaming LLM Agents via Poisoning Memory or Knowledge Basescited by 429
- 2023Scaling Data-Constrained Language Modelscited by 403
- 2024tinyBenchmarks: evaluating LLMs with fewer examplescited by 310
- 2023Self-Consuming Generative Models Go MADcited by 250
- 2024Is Model Collapse Inevitable? Breaking the Curse of Recursion by Accumulating Real and Synthetic Datacited by 151
- 2025Memory Injection Attacks on LLM Agents via Query-Only Interactioncited by 144
- 2025Measuring what Matters: Construct Validity in Large Language Model Benchmarkscited by 119
- 2023The Curious Decline of Linguistic Diversity: Training Language Models on Synthetic Textcited by 100
- 2023Large Language Models Suffer From Their Own Output: An Analysis of the Self-Consuming Training Loopcited by 79
36 more
- 2024How Bad is Training on Synthetic Data? A Statistical Analysis of Language Model Collapsecited by 79
- 2024Chinchilla Scaling: A replication attemptcited by 72
- 2026Memory for Autonomous LLM Agents:Mechanisms, Evaluation, and Emerging Frontierscited by 65
- 2025Beyond a Million Tokens: Benchmarking and Enhancing Long-Term Memory in LLMscited by 60
- 2026STALE: Can LLM Agents Know When Their Memories Are No Longer Valid?cited by 50
- 2026A Survey of Agent Memory in the Second Half: Towards Self-Evolving and Long-Horizon Agentscited by 44
- 2024Self-Correcting Self-Consuming Loops for Generative Model Trainingcited by 37
- 2026From Agent Traces to Trust: A Survey of Evidence Tracing and Execution Provenance in LLM Agentscited by 37
- 2025Fluid Language Model Benchmarkingcited by 35
- 2026Reliability without Validity: A Systematic, Large-Scale Evaluation of LLM-as-a-Judge Models Across Agreement, Consistency, and Biascited by 28
- 2024Universality of the π2/6 Pathway in Avoiding Model Collapsecited by 26
- 2026Adaptive Memory Admission Control for LLM Agentscited by 23
- 2025Unable to Forget: Proactive Interference Reveals Working Memory Limits in LLMs Beyond Context Lengthcited by 20
- 2026Supersede: Diagnosing and Training the Memory-Update Gap in LLM Agentscited by 20
- 2026From Lossy to Verified: A Provenance-Aware Tiered Memory for Agentscited by 17
- 2025A Theoretical Perspective: How to Prevent Model Collapse in Self-consuming Training Loopscited by 16
- 2026MemMark: State-Evolution Attribution Watermarking for Agent Long-Term Memory Systemscited by 16
- 2026Belief Memory: Agent Memory Under Partial Observabilitycited by 11
- 2026The Consensus Trap: Rescuing Multi-Agent LLMs from Adversarial Majorities via Token-Level Collaborationcited by 8
- 2026Safety in Self-Evolving LLM Agent Systems: Threats, Amplification, and Case Studiescited by 8
- 2026When the Judge Changes, So Does the Measurement: Auditing LLM-as-Judge Reliabilitycited by 8
- 2026Rank Intervals for Leaderboards: A Hierarchical Framework for Model Evaluationcited by 6
- 2026When Agents Remember Too Much: Memory Poisoning Attacks on Large Language Model Agentscited by 6
- 2026ConsistencyGate: Preventing Memory Contamination in LLM Agents via Self-Consistency Admission Controlcited by 6
- 2026State Contamination in Memory-Augmented LLM Agentscited by 5
- 2026Same Ranking, Different Winner: How Scoring Targets Shape LLM Memory Benchmarkscited by 5
- 2026Minority Sentinel: When to Overturn Majority Voting in Multi-Agent LLM Debatescited by 5
- 2026Always-OnAgents:A Survey of Persistent Memory, State, and Governance in LLMAgentscited by 5
- 2026MemCompiler: Compile, Don't Inject -- State-Conditioned Memory for Embodied Agentscited by 4
- 2026The Capability Convergence Hypothesis: Capability from Access Structure, Not Scalecited by 4
- 2026Structured Belief State and the First Precision-Aware Benchmark for LLM Memory Retrievalcited by 3
- 2026MemGuard: Preventing Memory Contamination in Long-Term Memory-Augmented Large Language Modelscited by 3
- 2026Escaping the Self-Confirmation Trap: An Execute-Distill-Verify Paradigm for Agentic Experience Learningcited by 2
- 2026RoMeRL: Balancing Feedback Coverage and the Memory-Reward Trap in Self-Evolving Agent Memory via Reduced-Order Utility Statescited by 2
- 2026Generating Leakage-Free Benchmarks for Robust RAG Evaluationcited by 1
- 2026RECON: Benchmarking Agent Memory for Compositional Reasoning over Long Contextscited by 1
Built on by
Not cited yet.