Exploratory and Assimilating Reflection: Reflective Recall Cycle for Long-term Memory

• URL: http://arxiv.org/abs/2607.17879v1
• Date: Mon, 20 Jul 2026 12:30:17 GMT
• Title: Exploratory and Assimilating Reflection: Reflective Recall Cycle for Long-term Memory
• Abstract summary: LLM-based autonomous agents require external memory to overcome their statelessness and limited context window.<n>We propose Exploratory-Assimilating Reflection (EAR), a framework for high initial retrieval performance and sample-efficient adaptation.
• Score: 3.9297698032556596
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: LLM-based autonomous agents require external memory to overcome their statelessness and limited context window for long-term interaction and dynamic knowledge reasoning. However, existing memory retrieval methods often lack adaptability and sample efficiency, and struggle to retrieve the right mixture of memories from heterogeneous stores. We propose Exploratory-Assimilating Reflection (EAR), a framework for high initial retrieval performance and sample-efficient adaptation. EAR combines two mechanisms: Exploratory Reflection, which performs iterative search to bootstrap retrieval and collect useful experiences for each query, and Assimilating Reflection, which replays these experiences from an Experience Buffer to refine a global reranker more efficiently than methods relying only on immediate rewards. Experiments show that EAR improves retrieval by up to 17.9% over the baseline retriever on two long-term dialogue benchmarks. We also show that EAR is highly sample-efficient and robust to noisy feedback.

Related papers

• The Retriever Should Remember: Experience-Amortized Reranking for Long-Term Agent Memory [29.20695058395373]
  We introduce EARM, an experience-amortized reranking framework that treats previously acquired relevance scores as reusable retrieval experience.<n>EARM stores sparse query--memory relevance scores in an online matrix, learns their shared structure through causal matrix completion, and combines a small set of newly observed scores with estimated scores to rerank the remaining candidates.<n>Experiments on long-term conversational memory show that mixed observed-and-estimated reranking improves answer accuracy over semantic retrieval by up to 6.62%.
  arXiv  Detail & Related papers  (2026-08-24T03:42:03Z)
• R^2-Mem: Reflective Experience for Memory Search [25.798601679871425]
  R2-Mem is a reflective experience framework for memory search systems.<n>In offline stage, a Learner-guided Evaluator scores low- and high-quality steps in historical trajectories.<n>During online inference, the retrieved experience will guide future search actions to avoid repeated mistakes.
  arXiv  Detail & Related papers  (2026-05-13T13:09:36Z)
• Evoking User Memory: Personalizing LLM via Recollection-Familiarity Adaptive Retrieval [59.295767860331004]
  RF-Mem is a familiarity uncertainty-guided dual-path memory retriever.<n>It embeds human-like dual-process recognition into the retriever.<n>It consistently outperforms one-shot retrieval and full-context reasoning under fixed budget and latency constraints.
  arXiv  Detail & Related papers  (2026-03-10T06:31:44Z)
• RetroAgent: From Solving to Evolving via Retrospective Dual Intrinsic Feedback [54.39884046754265]
  RetroAgent is an online RL framework that enables agents to master complex interactive environments.<n>Our experiments show that RetroAgent achieves state-of-the-art (SOTA) performance.
  arXiv  Detail & Related papers  (2026-03-09T16:23:33Z)
• Thinking Forward and Backward: Multi-Objective Reinforcement Learning for Retrieval-Augmented Reasoning [137.33138614095435]
  Retrieval-augmented generation (RAG) has proven to be effective in mitigating hallucinations in large language models.<n>Recent efforts have incorporated search-based interactions into RAG, enabling iterative reasoning with real-time retrieval.<n>We propose Bi-RAR, a novel retrieval-augmented reasoning framework that evaluates each intermediate step jointly in both forward and backward directions.
  arXiv  Detail & Related papers  (2025-11-12T08:29:39Z)
• Enhancing Retrieval-Augmented Generation with Two-Stage Retrieval: FlashRank Reranking and Query Expansion [0.0]
  RAG couples a retriever with a large language model (LLM) to ground generated responses in external evidence.<n>We propose a two-stage retrieval pipeline that integrates LLM-driven query expansion to improve candidate recall.<n>FlashRank is a fast marginal-utility reranker that dynamically selects an optimal subset of evidence under a token budget.
  arXiv  Detail & Related papers  (2025-10-17T15:08:17Z)
• SePer: Measure Retrieval Utility Through The Lens Of Semantic Perplexity Reduction [20.6787276745193]
  We introduce an automatic evaluation method that measures retrieval quality through the lens of information gain within the RAG framework.<n>We quantify the utility of retrieval by the extent to which it reduces semantic perplexity post-retrieval.
  arXiv  Detail & Related papers  (2025-03-03T12:37:34Z)
• Retrieval-Augmented Perception: High-Resolution Image Perception Meets Visual RAG [79.61269381878547]
  High-resolution (HR) image perception remains a key challenge in multimodal large language models (MLLMs)<n>This paper shifts away from prior dedicated approaches and revisits the most fundamental idea to HR perception by enhancing the long-context capability of MLLMs.<n>We propose Retrieval-Augmented Perception (RAP), a training-free framework that retrieves and fuses relevant image crops while preserving spatial context.
  arXiv  Detail & Related papers  (2025-03-03T06:40:21Z)
• Meta-Reflection: A Feedback-Free Reflection Learning Framework [57.14485943991588]
  We propose Meta-Reflection, a feedback-free reflection mechanism that requires only a single inference pass without external feedback.<n>Motivated by the human ability to remember and retrieve reflections from past experiences, Meta-Reflection integrates reflective insights into a codebook.<n>To thoroughly investigate and evaluate the practicality of Meta-Reflection in real-world scenarios, we introduce an industrial e-commerce benchmark named E-commerce Customer Intent Detection.
  arXiv  Detail & Related papers  (2024-12-18T12:20:04Z)
• Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection [74.51523859064802]
  We introduce a new framework called Self-Reflective Retrieval-Augmented Generation (Self-RAG) Self-RAG enhances an LM's quality and factuality through retrieval and self-reflection. It significantly outperforms state-of-the-art LLMs and retrieval-augmented models on a diverse set of tasks.
  arXiv  Detail & Related papers  (2023-10-17T18:18:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.