5arXiv cs.CL (Computation and Language)·
MemOps: Benchmark for lifecycle memory operations in long-horizon LLM conversations
MemOps is a new benchmark that reframes conversational memory evaluation as a sequence of explicit lifecycle operations—remembering, forgetting, updating, reflecting—rather than downstream question-answering accuracy alone. Each memory event is represented with a structured trace specifying trigger, target, scope, state transition, and evidence, embedded into long task-oriented conversations via a controllable generation pipeline. Evaluation across long-context, retrieval-based, parametric, and managed-memory systems reveals that current systems fail in distinct, previously conflated ways: session-level retrieval outperforms turn-level retrieval, and long-context models are notably weak at reconstructing ordered memory-state trajectories. The work shifts long-term memory evaluation from black-box final-answer scoring toward interpretable, operation-level diagnosis.
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MEMPROBE: Benchmark for auditing long-term agent memory via hidden user-state recovery
MEMPROBE is a new benchmark that evaluates long-term memory in LLM agents by treating memory as an auditable artifact rather than measuring it only through downstream task performance. After a memory-equipped agent assists simulated users across a trajectory of tasks, the benchmark attempts to reconstruct a hidden, taxonomy-anchored user-state bank from the agent's memory store. Testing across 5 memory systems and 50 simulated users with 31 hidden dimensions each, the authors find that task completion and memory recovery are largely independent capabilities — task success nearly saturates even for memoryless baselines, while structured user-state recovery remains moderate (~0.6) and degrades under top-k retrieval constraints.
MemTrace: Framework for Tracing and Attributing Errors in LLM Memory Systems
MemTrace introduces a framework that converts LLM memory pipelines into executable memory evolution graphs to enable fine-grained error tracing and root-cause attribution. The authors construct MemTraceBench, a benchmark covering Long-Context, RAG, Mem0, and EverMemOS memory systems, to systematically characterize memory failure modes such as information loss and retrieval misalignment. An automatic attribution method iteratively traces operation subgraphs to pinpoint failures, and the resulting signals are used to guide prompt optimization in a closed-loop system that improves end-task performance by up to 7.62%.
MemCon: Learned adaptive memory management for LLM agents via MDP-based policy
Researchers introduce Memory as a Controlled Process (MemCon), a framework that models LLM agent memory operations as a Markov Decision Process and learns an online policy for adaptive retrieval, plan injection, and memory consolidation. The system is backend-agnostic, wraps existing memory implementations, and uses a lightweight tabular contextual bandit with UCB exploration requiring no pretraining and no additional LLM calls. Evaluated across 6 benchmarks, 3 agent frameworks, and 3 LLM backbones, MemCon outperforms static memory baselines by up to 15.2 points in task success while reducing token consumption by 5–20%.
MemOS: Self-Evolving Memory OS for LLM Agents with Hybrid Retrieval and Token Savings
MemOS is an open-source TypeScript project providing a memory operating system layer for LLM and AI agents, featuring ultra-persistent memory, hybrid retrieval, and cross-task skill reuse. The project claims 35.24% token savings through its memory management approach. It has accumulated 9,329 GitHub stars with moderate daily momentum (+67). The system targets agent memory persistence and efficiency as a foundational infrastructure component.
IMLogic benchmark and RootMem framework target implicit logical memory retrieval for personalized LLMs
Researchers introduce IMLogic, a benchmark for evaluating implicit logical memory retrieval in long-dialogue personalized LLM scenarios, addressing gaps in existing semantic-similarity-based retrieval methods. They also propose RootMem, a plug-and-play framework that distills user histories into structured 'root memories' and uses an LLM-based router to activate logically relevant memories alongside semantic retrieval. Experiments show RootMem outperforms retrieval baselines and improves existing memory agents. The work targets a concrete weakness in current personalized LLM memory systems where logically critical memories lack semantic overlap with queries.
LongMINT: Benchmark for Evaluating Memory Under Multi-Target Interference in Long-Horizon Agent Systems
LongMINT is a new benchmark designed to evaluate memory-augmented agents in realistic long-horizon settings where information is repeatedly updated and interferes across memories. It contains 15.6k QA pairs over contexts averaging 138.8k tokens (up to 1.8M tokens), spanning domains including state tracking, multi-turn dialogue, Wikipedia revisions, and GitHub commits. Evaluation of 7 representative systems—including vanilla long-context LLMs, RAG, and memory-augmented agent frameworks—reveals consistently low average accuracy of 27.9%, with performance particularly degraded on multi-target aggregation tasks and when earlier facts are revised by subsequent context. The analysis identifies retrieval and memory construction as the primary bottlenecks.
AutoMem: Automated framework trains LLMs to manage memory as a learnable cognitive skill
AutoMem is a new framework that treats memory management in LLMs as a trainable skill, using two optimization loops: one that iteratively revises memory structure via trajectory review by a strong LLM, and one that distills good memory decisions into direct training signal for the agent model. Evaluated on three long-horizon procedurally generated games (Crafter, MiniHack, NetHack), optimizing memory alone yielded 2x-4x performance improvements, bringing a 32B open-weight model competitive with frontier systems like Claude Opus 4.5 and Gemini 3.1 Pro Thinking. The work draws on cognitive science concepts of metamemory and demonstrates that memory management is an independently learnable, high-leverage capability for long-horizon agentic tasks.
M³Exam: Benchmark for Multimodal Memory in Realistic User-Agent Interactions
Researchers introduce M³Exam, a query-centric multimodal conversational memory benchmark designed to evaluate language agents on realistic user-agent interactions, including cross-modal grounding and implicit information inference. Existing benchmarks are critiqued for assuming sparse visuals and human-human interaction formats. The paper also proposes M³Proctor, a companion memory method that detects query modality bias and retrieves raw visual sources on demand, achieving 13% accuracy improvement while reducing index-construction time and retrieved tokens by over 70%.