SubtleMemory: A Benchmark for Fine-Grained Relational Memory Discrimination in Long-Horizon AI Agents

• URL: http://arxiv.org/abs/2606.05761v2
• Date: Fri, 05 Jun 2026 04:28:58 GMT
• Title: SubtleMemory: A Benchmark for Fine-Grained Relational Memory Discrimination in Long-Horizon AI Agents
• Abstract summary: We introduce SubtleMemory, a benchmark for fine-grained relational memory discrimination in long-running AI agents.<n>We evaluate six standalone memory systems, two Claw-style agents with native memory modules, and three Claw-style agents with plugin memory modules.
• Score: 38.778004697710855
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Persistent AI assistants, such as OpenClaw, accumulate large collections of related memories over long-term interactions. As these memories grow, they may reinforce one another, diverge across contexts, or directly conflict, making correct assistance depend on memory relations rather than isolated recall. Existing long-term memory benchmarks rarely probe how agents preserve and utilize such relations during downstream tasks. To address this gap, we introduce SubtleMemory, a benchmark for fine-grained relational memory discrimination in long-running AI agents. SubtleMemory constructs relation-controlled latent semantic artifacts whose variants instantiate complementary, nuanced, or contradictory relations, and embeds them into realistic user-agent histories, requiring agents to recover distributed relational structures during later queries and instructions. The benchmark contains 1,522 evaluation instances over 10 long histories, grounded in 1,090 relation-controlled memory-variant sets and spanning user-related and non-user-related queries. Evaluating six standalone memory systems, two Claw-style agents with native memory modules, and three Claw-style agents with plugin memory modules, we find that current systems remain weak on fine-grained relational memory discrimination. We further introduce diagnostic protocols that reveal distinct capability profiles across memory preservation, retrieval, and downstream reasoning stages.

Related papers

• Retrieval-Driven Memory Reconsolidation for Long-Term LLM Agents [39.55670043123564]
  Existing memory systems update memory when new information arrives.<n>Retrieval feedback is rarely exploited to reorganize memory for future access continuously.<n>We propose textbfREALM, a framework for continually evolving long-term memory in LLM agents.
  arXiv  Detail & Related papers  (2026-09-13T05:17:37Z)
• Beyond Similarity: Trustworthy Memory Search for Personal AI Agents [25.265839311088516]
  We study memory search as a trust boundary in personal AI agents.<n>MemGate is a lightweight and deployable memory plug-in for trustworthy memory search.
  arXiv  Detail & Related papers  (2026-06-04T11:54:29Z)
• Memory is Reconstructed, Not Retrieved: Graph Memory for LLM Agents [51.30250860677378]
  We propose MRAgent, a framework that combines an associative memory graph with an active reconstruction mechanism.<n>Running on a Cue-Tag-Content graph, our active reconstruction mechanism integrates reasoning directly into memory access.<n>Experiments on the LoCoMo benchmark and LongMemEval benchmark demonstrate significant improvements over strong baselines.
  arXiv  Detail & Related papers  (2026-06-04T11:29:46Z)
• MemGuard: Preventing Memory Contamination in Long-Term Memory-Augmented Large Language Models [56.31411457917676]
  We introduce MemGuard, a type-aware memory framework that preserves functional memory boundaries during memory construction and retrieval.<n>Across hallucination and long-horizon conversation benchmarks, MemGuard improves memory reliability by up to 28.27% while retrieving up to 5.8x fewer memory tokens than prior methods.
  arXiv  Detail & Related papers  (2026-05-27T06:04:19Z)
• Causal Intervention-Based Memory Selection for Long-Horizon LLM Agents [0.33842793760651557]
  We propose a causal memory-selection technique that estimates how candidate memories affect the model's answer under controlled interventions.<n>Results show that CMI achieves a stronger balance between answer quality and robustness to misleading memory, suggesting that reliable long-term memory requires selecting context based on causal usefulness rather than relevance alone.
  arXiv  Detail & Related papers  (2026-05-17T20:21:55Z)
• MemSearch-o1: Empowering Large Language Models with Reasoning-Aligned Memory Growth in Agentic Search [59.98962867649009]
  We propose MemSearch-o1, an agentic search framework built on reasoning-aligned memory growth and retracing.<n>MemSearch-o1 grows fine-grained memory fragments from memory seed tokens from queries, then retraces and deeply refines the memory via a contribution function, and finally reorganizes a globally connected memory path.
  arXiv  Detail & Related papers  (2026-04-19T05:35:06Z)
• MemCollab: Cross-Agent Memory Collaboration via Contrastive Trajectory Distillation [16.57908722474221]
  Large language model (LLM)-based agents rely on memory mechanisms to reuse knowledge from past problem-solving experiences.<n>Existing approaches typically construct memory in a per-agent manner, tightly coupling stored knowledge to a single model's reasoning style.<n>We found that naively transferring memory between agents often degrades performance, as such memory entangles task-relevant knowledge with agent-specific biases.<n>We propose MemCollab, a collaborative memory framework that constructs agent-agnostic memory by contrasting reasoning trajectories generated by different agents on the same task.
  arXiv  Detail & Related papers  (2026-03-24T14:05:47Z)
• MemoryArena: Benchmarking Agent Memory in Interdependent Multi-Session Agentic Tasks [55.145729491377374]
  Existing evaluations of agents with memory typically assess memorization and action in isolation.<n>We introduce MemoryArena, a unified evaluation gym for benchmarking agent memory in multi-session Memory-Agent-Environment loops.<n> MemoryArena supports evaluation across web navigation, preference-constrained planning, progressive information search, and sequential formal reasoning.
  arXiv  Detail & Related papers  (2026-02-18T09:49:14Z)
• AMA: Adaptive Memory via Multi-Agent Collaboration [54.490349689939166]
  We propose Adaptive Memory via Multi-Agent Collaboration (AMA), a novel framework that leverages coordinated agents to manage memory across multiple granularities.<n>AMA significantly outperforms state-of-the-art baselines while reducing token consumption by approximately 80% compared to full-context methods.
  arXiv  Detail & Related papers  (2026-01-28T08:09:49Z)

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.