Authority Before Utility: Non-Compensatory Control for Persistent LLM Memory
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Abstract (arXiv)
Persistent memory creates a control problem that retrieval relevance alone does not solve: a memory can remain highly useful after an update, deletion, or revocation makes it inadmissible for the current answer. We formalize this as a separation between utility and authority. A fixed finite penalty applied to an unnormalized utility score cannot guarantee exclusion under arbitrary positive-affine reparameterization of that score; by contrast, rank-normalized compensation is scale-invariant and therefore forms a stronger empirical comparator. Our prospectively frozen TIDE/LongMemEval primary was quarantined before a valid HELDOUT comparison because the materialized TIDE adapter conflated historical age with query-relative inadmissibility and the aligned LongMemEval split left no DEV set for the predeclared penalty selection. We therefore report a post-primary replacement diagnostic on Memora Remembering, where update/delete operations provide item-level forgetting state. On Qwen3-8B, DEV selected lambda = 0.6 from a ten-point normalized SOFT family. Across 185 HELDOUT units in 28 dependency clusters, HARD exclusion yields 4.04% balanced construct error versus 19.66% for locked SOFT, a paired difference of 15.61 points with a 20,000-replicate cluster-bootstrap 95% interval of [13.07, 18.76]. The effect is driven primarily by forgotten-value leakage while current-value recall is preserved. This is same-Q operator-comparison evidence, not a universal claim that scalar control fails, not an evaluation of learned authority inference, and not an independent downstream-harm endpoint.
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References · 20
- 2024The Llama 3 Herd of Modelscited by 14,192
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- 2025Qwen3 Technical Reportcited by 8,195
- 2016Overcoming catastrophic forgetting in neural networkscited by 4,718
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- 2023Lost in the Middle: How Language Models Use Long Contextscited by 3,383
- 2018Continual Lifelong Learning with Neural Networks: A Reviewcited by 1,313
- 2021Improving language models by retrieving from trillions of tokenscited by 1,278
- 2017Constrained Policy Optimizationcited by 928
- 2024Evaluating Very Long-Term Conversational Memory of LLM Agentscited by 800
- 2019Generalization through Memorization: Nearest Neighbor Language Modelscited by 788
- 2024LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memorycited by 656
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- 2017Safe Reinforcement Learning via Shieldingcited by 427
- 2021Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learningcited by 337
- 2022Memorizing Transformerscited by 219
- 2023Augmenting Language Models with Long-Term Memorycited by 186
- 2026From Recall to Forgetting: Benchmarking Long-Term Memory for Personalized Agentscited by 29
- 2026Supersede: Diagnosing and Training the Memory-Update Gap in LLM Agentscited by 13
- 2026Controlling Authority Retrieval: A Missing Retrieval Objective for Authority-Governed Knowledgecited by 2
- 2026Time Present and Time Past: Benchmarking Large Language Models on Temporally Evolving Document Understandingcited by 1
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