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arXiv AI

Gated Memory: Admission-Controlled Memory Formation for Conversational AI

The paper introduces Gated Memory, a framework for improving how conversational AI systems form long‑term memory. It adds two checkpoints before facts are stored: an admission gate that checks each candidate fact against the full utterance context, and a conditional enrichment stage that tags facts with provenance, scope, and time/place grounding. On the LoCoMo‑10 benchmark, Gated Memory improves LLM‑judge accuracy by 2.6% relative to a strong baseline.

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arXiv Computation and Language
Sep 1

UTILMEM: Benchmarking Evidence Utilization in Long-Term Conversational Memory

UTILMEM is a new diagnostic benchmark that tests how conversational agents use long‑term memory, focusing on reasoning over dense histories, spotting implicitly relevant memories, synthesizing distributed evidence, and resisting interference from similar distractors. It contains 1,717 instances across five domains and evaluates a range of retrieval‑based and memory‑augmented systems. The study shows that strong performance on traditional factual recall does not guarantee effective memory utilization, highlighting a gap between retrieving information and integrating it into coherent, task‑oriented outputs.

By Peijun Qing, Fobo Shi, Soroush Vosoughi
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arXiv Computation and Language
Sep 18

JustMem: Just-Enough Memory Access for Long-Term Conversations

JustMem is a new memory system for long‑term conversational AI that stores conversation history as compact atomic memories and adapts its access strategy to each query. It introduces two dimensions of memory access—discovery breadth and reading fidelity—implemented through LOOKUP for local evidence, COMPOSE for distributed evidence, and REPLAY for fidelity‑sensitive evidence. Experiments on LoCoMo and LongMemEval‑S show that JustMem outperforms competing memory systems in accuracy and recall while using fewer generative‑model tokens for memory construction and inference.

By Guanhua Chen, Yanting Wang, Wenjing Zhi, Lei Sha
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arXiv AI
Oct 7

AgentMemGate: Addressing Speculation Contamination in Conversational Assistant Memory

AgentMemGate is a write-time gate for conversational AI assistants that prevents speculation contamination in long‑term memory by classifying extracted statements as speculation, completed event, correction, or other. Speculations are kept outside memory until conditions allow promotion or deletion. The approach eliminates observed contamination and improves task accuracy from 65% to 95% on core benchmarks, with further gains on a harder held‑out set.

By Chirag Sharma, Benjamin Fowlersmith, Karime Maamari
benchmarks
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