arXiv AI
Aug 10
TEPA: Revoking Stale Memories for Conflict-Robust Language Agents
arXiv:2608. 07429v1 Announce Type: new Abstract: Long-term memory enables language agents to reuse past facts, preferences, and task experience.
What Eviction Destroys: A Restore-Counterfactual Audit of Forgetting in Agent Memory
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arXiv AI
Sep 10
When Does Memory Help? A Cost-Aware Evaluation of Long-Term Memory in Tool-Using LLM Agents
The paper introduces MERIT, a benchmark that evaluates the marginal benefit of long‑term memory for tool‑using large language model agents while explicitly accounting for cost. MERIT provides episodic tool‑use tasks across three domains, verifies dependence on earlier‑episode facts, and measures memory operations in tokens and dollars. Experiments on GPT‑4.1‑mini, Claude Haiku 4.5, and Claude Sonnet 5 show that memory can significantly improve task success, but its utility varies widely across models and memory implementations, and full replay is rarely cost‑effective.
arXiv Machine Learning
Sep 10
Can an AI Assistant Really Forget? Auditable Deletion from Addressable Memory
This paper introduces a deletion interface for a pretrained language model, measuring how effectively deleted records are removed from the model’s memory. By retrofitting a support‑vector memory gate into the global attention layers of a frozen Gemma 3, the authors show that deletions can be performed without altering weights and that the resulting state is close to a reference state that never stored the record. Experiments on 4B‑parameter models demonstrate low perplexity impact and strong evidence that deleted content is hard to recover, while larger or smaller models fail to achieve the same guarantees.
arXiv AI
Jun 26
Temporal Validity in Retrieval Memory: Eliminating Stale-Fact Errors for AI Agents over Evolving Knowledge
arXiv:2606. 26511v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) gives agents access to accumulated knowledge, but has no model of time.
Hugging Face Trending Papers
Jun 24
Reclaim Evaluation: A Lossy Memory Is Worse Than an Empty One
A language model's memory can be worse than having no memory at all. Give a model a memory that kept a wrong conclusion but dropped the work behind it, and it emits that stale value as a confident answer; give the same model an empty memory and it abstains.
arXiv AI
Oct 16
MemCut: Every Secret Leaves a Trail in the Long-Term Memory of LLM Agents
MemCut introduces a benchmark to test user-requested forgetting in large language model agents, featuring 456 hand-designed secrets embedded in long-horizon conversations with fully annotated evidence trails. The benchmark defines two recovery metrics—native and residual—to assess how well secrets can be retrieved after deletion. Experiments across five memory designs and four LLMs show that deletion reduces native recovery more than residual recovery, highlighting candidate coverage as a key bottleneck.
arXiv AI
Oct 7
The Right Memory in the Wrong Context: Verifying Retrieval Admissibility in Long-Term Agent Memory
The paper presents a retrieval‑admissibility verification framework for long‑term memory agents, classifying each memory‑query pair as admissible, inadmissible, or unresolved. It evaluates the framework on public benchmarks (RHELM and MemOps), showing improved anchor recall and reduced exact similarity errors, while also revealing that existing verifiers miss certain inadmissible exposures. The study highlights the need for separate checks on candidate support, admissibility, prompt exposure, and answer disclosure to ensure safe memory retrieval.
arXiv Machine Learning
Jun 25
Reclaim Evaluation: A Lossy Memory Is Worse Than an Empty One
arXiv:2606. 25449v1 Announce Type: cross Abstract: A language model's memory can be worse than having no memory at all.
arXiv AI
Aug 12
From Faulty Memories to Corrected Actions: Dependency-Guided Rollback Repair for Memory-Augmented Agents
arXiv:2608. 10502v1 Announce Type: new Abstract: Persistent memory lets language-model agents reuse information across sessions, but it also makes errors durable: a poisoned, stale, or misattributed record can alter reasoning, tool use, answers, and subsequent memory writes.
arXiv AI
Sep 30
Authority Before Utility: Non-Compensatory Control for Persistent LLM Memory
arXiv:2609.37474v1 Announce Type: new Abstract: Persistent memory creates a control problem that retrieval relevance alone does not solve: a memory can remain highly useful after an update, deletion,...
arXiv Computation and Language
Sep 11
Causal Episodic Memory for Feedback-Driven Agent Repair
The paper introduces MERIT, a training‑free agent that uses an online dual‑polarity memory of oracle‑verified corrections and failed attempts to improve LLM agent repair without updating parameters. MERIT retrieves only finalized memories, classifies failures, and employs a hybrid lexical‑dense retriever before the frozen model generates revisions. Experiments with Qwen2.5‑7B‑Instruct show that MERIT raises execution accuracy on the Spider benchmark from 66.34 % to 69.79 % and on BIRD from 47.35 % to 48.44 %, with varying evidence across datasets.
arXiv AI
Sep 16
The Immutable Past: Formalizing State Mutability and Conflict Resolution in Mutable RAG
arXiv:2609.16073v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) serves as the primary memory architecture for long-horizon autonomous agents. However, treating shared memory as...