arXiv AI
Jul 3
A-TMA: Decoupling State-Aware Memory Failures in Long-Term Agent Memory
arXiv:2607. 01935v1 Announce Type: new Abstract: Long term memory lets LLM agents act as persistent assistants, but user facts change.
Can Computation from Earlier Problems Help LLMs Solve New Ones?
The paper investigates whether computation from earlier problems can aid large language models (LLMs) in solving subsequent ones. Preliminary experiments show that retained conversation history can both improve and degrade later-turn accuracy. To address this, the authors propose STAIR, a lightweight module that stores key-value pairs from previous responses and redirects new queries to this bank, improving later-turn accuracy by up to 11.67 percentage points across several benchmarks.
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arXiv AI
3d ago
When Context Changes: Understanding Update Failures in LLMs
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arXiv Computation and Language
Sep 1
MAIGO: Mitigating Lost-in-Conversation with History-Cleaned On-Policy Self-Distillation
arXiv:2605.27186v2 Announce Type: replace Abstract: Large language models often solve tasks from a fully specified prompt but degrade when the same requirements unfold over multiple turns, known as t...
arXiv AI
4d ago
Mnemon: Raw Records, Fast Judgments, Slow Thoughts
Mnemon is a memory agent that stores conversations as raw, dated records and uses a fast System 1 decision model (Jev) to quickly judge the relevance of records, while a slow System 2 LLM plans searches and composes answers. The agent consolidates records into topic timelines and value histories in the background, enabling efficient retrieval without rewriting conversations into structured formats. Experiments show Mnemon achieving high scores on LoCoMo and LongMemEval‑S with low context length and cost, and Jev outperforming LLMs in evidence separation and speed.
arXiv Machine Learning
Sep 1
What It Costs to Compose, Rebuild, and Correct Precomputed Memory
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arXiv AI
Sep 4
Learning What Not to Forget: Long-Horizon Agent Memory from a Few Kilobytes of Learning
The paper introduces LRE (Learned Relevance Eviction), a lightweight, CPU‑only, language‑model‑free scorer that learns which parts of an agent’s interaction history are task‑critical and preserves them verbatim. In experiments, LRE matches or surpasses baseline eviction policies on accuracy‑cost trade‑offs, recovers 93% of full‑history accuracy, reduces worst‑case prompt size by 52%, and outperforms dense and token‑pruning encoders in conversational memory while being 295–1569× smaller. The method also achieves superior budgeted answer quality on LoCoMo reading and can be trained annotation‑free, recovering 95% of supervised scorer performance.
arXiv AI
Sep 11
What Should an Agent Forget? Separating What Is Stored from What Is Used
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Hugging Face Trending Papers
Jul 29
ForgetBench: Benchmarking Forgetting Dynamics of Long-Term Parametric Memory in Language Models
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Hugging Face Trending Papers
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Long-Lived Characters, Local Inference: Incremental Memory Maintenance for Game NPCs
A game character should not have to reread its entire life before every conversation. For locally deployed language-model characters, however, revising a few memories can invalidate a long reusable pr...
arXiv AI
Jul 31
ForgetBench: Benchmarking Forgetting Dynamics of Long-Term Parametric Memory in Language Models
arXiv:2607. 26455v1 Announce Type: cross Abstract: Large language models (LLMs) have demonstrated strong capabilities in knowledge acquisition and reasoning, yet their ability to retain previously acquired knowledge under repeated updates remains insufficiently understood.
Hugging Face Trending Papers
Sep 9
What Should an Agent Forget? Separating What Is Stored from What Is Used
Persistent language agents need stored experience to remain available across time, while each answer requires evidence suited to a particular question. A superseded fact can mislead a current-state an...