MEMPROBE: Probing Long-Term Agent Memory via Hidden User-State Recovery

• URL: http://arxiv.org/abs/2606.24595v1
• Date: Tue, 23 Jun 2026 13:52:46 GMT
• Title: MEMPROBE: Probing Long-Term Agent Memory via Hidden User-State Recovery
• Abstract summary: We argue that long-term memory should be evaluated as an auditable post-interaction artifact.<n>We instantiate this view in MEMPROBE, a benchmark in which a memory-equipped agent assists simulated users.<n>We find that successful assistance and recoverable memory behave as distinct capabilities.
• Score: 58.64329475683699
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Long-term memory promises LLM agents that grow more capable across sessions, maintaining an accurate, evolving understanding of the user that interaction forms. In practice, however, this memory is evaluated mostly through downstream behavior, such as later answers, personalization quality, or task success, which tests that understanding only indirectly and leaves the memory artifact itself largely unaudited. We argue that long-term memory should instead be evaluated as an auditable post-interaction artifact: after ordinary assistance, what structured user state can be reconstructed from the memory the agent leaves behind? We instantiate this view in MEMPROBE, a benchmark in which a memory-equipped agent assists simulated users, each carrying a hidden, taxonomy-anchored user-state bank, across a trajectory of leak-controlled tasks, after which that bank is reconstructed from the agent's resulting memory under both full-store and top-k access. Built on synthetic ground truth for efficient, scalable measurement, MEMPROBE spans 50 simulated users with 31 hidden dimensions each (1,550 recovery targets) and tests 5 representative memory systems. Testing state-of-the-art memory agents, we find that successful assistance and recoverable memory behave as distinct capabilities. Task completion nearly saturates, even for a memoryless baseline, while category-balanced recovery stays moderate (about 0.6) and drops further under top-k retrieval. MEMPROBE is the first benchmark to study memory recovery directly, reconstructing the user state a system retains and scoring it against ground truth. We see recovery as a concrete objective for future memory agents to optimize, and MEMPROBE as a step toward an environment where agents are trained to remember their users, growing more faithful the longer they know them.

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)
• Memory as a Controlled Process: Learned Adaptive Memory Management for LLM Agents [72.3752981234916]
  Large Language Model (LLM) agents increasingly rely on external memory systems to accumulate experience across tasks.<n>We present Memory as a Controlled Process (MemCon), a framework that models memory operations as a Markov Decision Process.<n>MemCon consistently outperforms multiple memory baselines by up to 15.2 points in task success while reducing token consumption by 5-20%.
  arXiv  Detail & Related papers  (2026-07-15T08:32:40Z)
• MemOps: Benchmarking Lifecycle Memory Operations in Long-Horizon Conversations [50.24315431387575]
  We introduce MemOps, a benchmark that reformulates conversational memory as a sequence of lifecycle operations.<n>MemOps represents each memory event with a structured trace specifying its trigger, target, scope, state transition, and supporting evidence.<n>Across long-context, retrieval-based, parametric and managed-memory systems, MemOps disentangles failure modes that final-answer accuracy alone conceals.
  arXiv  Detail & Related papers  (2026-07-14T15:33:44Z)
• MemSyco-Bench: Benchmarking Sycophancy in Agent Memory [32.49512092540906]
  MemSyco-Bench is a benchmark for evaluating memory-induced sycophancy in agent systems.<n>MemSyco-Bench measures when memory should influence a decision and how valid memory should be used.
  arXiv  Detail & Related papers  (2026-07-01T15:30:33Z)
• MemToolAgent: Leveraging Memory for Tool Using Agents Based on Environment and User Feedback [5.237822504687579]
  We propose MemToolAgent, a framework that improves tool use through memory management.<n>This work has three main contributions: (1) a unified memory entry format that improves both general-purpose and personalized tool use without fine-tuning; (2) a reflection-based memory extraction that uses environment and user feedback to distill wrong executions into critiques to store; and (3) a retrieval module that chooses how many past experiences to use based on the memory similarity distribution.
  arXiv  Detail & Related papers  (2026-06-06T00:15:00Z)
• 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)
• Learning User-Aware Recall: Personalized Retrieval in Long-Term Conversational Memory [15.297367578990903]
  Profile-guided Personalized Retrieval Optimization (PPRO) builds episodic and semantic memory banks from dialogue histories.<n>PPRO serves as an explicit personalized prior in memory ranking, allowing retrieval to account for stable user attributes, preferences, and relationships.<n>Experiments on LoCoMo and LongMemEval-S show consistent gains over training-free memory systems.
  arXiv  Detail & Related papers  (2026-05-28T06:47:48Z)
• MemGym: a Long-Horizon Memory Environment for LLM Agents [69.79226770543049]
  We present MemGym, a benchmark for agentic memory that unifies existing agent gyms and in-house memory-grounded pipelines.<n>MemGym reports memory-isolated scores that decouple memory performance from reasoning, retrieval, and tool-use ability.<n>Our synthetic pipelines for MEMGYM-CODEQA and MEMGYM-DR are length-controllable, ablation-verified at every stage, and tightly aligned with downstream scenarios.
  arXiv  Detail & Related papers  (2026-05-20T07:25:33Z)
• MemMA: Coordinating the Memory Cycle through Multi-Agent Reasoning and In-Situ Self-Evolution [52.29303869050117]
  Memory-augmented LLM agents maintain external memory banks to support long-horizon interaction.<n>MemMA is a plug-and-play multi-agent framework that coordinates the memory cycle along both the forward and backward paths.
  arXiv  Detail & Related papers  (2026-03-19T10:15:59Z)
• 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)
• Fine-Mem: Fine-Grained Feedback Alignment for Long-Horizon Memory Management [63.48041801851891]
  Fine-Mem is a unified framework designed for fine-grained feedback alignment.<n> Experiments on Memalpha and MemoryAgentBench demonstrate that Fine-Mem consistently outperforms strong baselines.
  arXiv  Detail & Related papers  (2026-01-13T11:06:17Z)
• Evaluating Memory in LLM Agents via Incremental Multi-Turn Interactions [22.190297901876278]
  We identify four core competencies essential for memory agents: accurate retrieval, test-time learning, long-range understanding, and selective forgetting.<n>Existing benchmarks either rely on limited context lengths or are tailored for static, long-context settings like book-based QA.<n>We introduce MemoryAgentBench, a new benchmark specifically designed for memory agents.
  arXiv  Detail & Related papers  (2025-07-07T17:59:54Z)

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.