arXiv AI
The Retriever Should Remember: Experience-Amortized Reranking for Long-Term Agent Memory
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Tool-Adaptive LLM Reranker
arXiv:2607. 10555v1 Announce Type: cross Abstract: Generative Large Language Models (LLMs) have revolutionized information retrieval, yet their strictly parametric nature frequently leads to severe factual hallucinations when confronted with complex queries beyond their epistemic boundaries.
arXiv AI
Jul 2
Learning User-Aware Recall: Personalized Retrieval in Long-Term Conversational Memory
arXiv:2607. 00017v1 Announce Type: cross Abstract: Long-term conversational agents are expected to remember past interactions, but memory is useful only when the right evidence is recalled for the right user.
arXiv AI
Jul 3
Optimizing RAG Rerankers with LLM Feedback via Reinforcement Learning
arXiv:2604. 02091v2 Announce Type: replace-cross Abstract: Rerankers play a pivotal role in refining retrieval results for Retrieval-Augmented Generation.
arXiv AI
Sep 11
What Should an Agent Forget? Separating What Is Stored from What Is Used
The paper introduces RD-Forget, a training‑free framework that separates what a persistent language agent stores from what it uses at answer time. It keeps a source archive of all observations while a query‑conditioned memory view filters evidence relevant to the current question, using a frozen language‑model curator to group facts into semantic slots and preserve multi‑hop relations. The approach employs rate‑distortion principles to stay within a memory budget and demonstrates improvements across conversational memory, knowledge updating, fact consolidation, long‑context reasoning, and personalization tasks.
arXiv AI
Oct 2
What Should an Agent Remember? Disentangling Retention from Retrieval in Bounded-Memory Evaluation
The paper introduces a streaming-recall benchmark that separates retention and selection decisions for persistent agents. It shows that query‑aware selection boosts recall by 15.5 points when access is fixed, while mixed comparisons inflate gains due to changes in history access. The study finds that under bounded retention, failures stem from eviction rather than ranking errors, and that dense retrieval can outperform lexical retrieval on natural text.
arXiv Machine Learning
Jun 15
Efficient Rationale-based Retrieval: On-policy Distillation from Generative Rerankers based on JEPA
arXiv:2604. 23336v3 Announce Type: replace-cross Abstract: Unlike traditional fact-based retrieval, rationale-based retrieval typically necessitates cross-encoding of query-document pairs using large language models, incurring substantial computational costs.