6arXiv cs.CL (Computation and Language)·
InMind benchmark exposes implicit-association blind spot in agent long-term memory systems
Researchers introduce InMind, a 125-task expert-verified benchmark targeting a failure mode in agent long-term memory: facts stored in memory are not retrieved when the query lacks surface-level textual overlap with the relevant memory (e.g., a nut allergy should affect a macaron recommendation but shares no keywords). Testing six vector, graph, and agentic memory systems reveals a stark gap — backbone models answer 84% of indirect queries when the relevant memory is placed in context, but retrieval systems surface the correct answer at most 14.4% of the time despite near-perfect on-demand recall. The study isolates the failure to the query-conditioned retrieval interface itself, framing routing — deciding which facts must remain visible — as the core open problem.
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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.
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.
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%.
ENPMR-Bench: Benchmarking Proactive Memory Retrieval for Emotional Support Agents
This paper introduces ENPMR-Bench, a benchmark for evaluating Emotional Need-aware Proactive Memory Retrieval in memory-augmented language agents deployed for emotional support applications. The benchmark includes over 1,800 memory-augmented dialogues grounded in Maslow's hierarchy of needs, with structured mappings between emotional needs and supportive memory types. Experiments show that both embedding-based and LLM-driven retrieval paradigms fall significantly short of golden memory conditions on empathy scores, and while chain-of-thought prompting helps, a substantial performance gap remains. The work highlights a systematic gap in current agent memory systems when applied to affective rather than purely factual retrieval tasks.
TriggerBench: A benchmark for evaluating prospective memory in LLMs
Researchers introduce TriggerBench, a benchmark evaluating prospective memory (PM) in LLMs — the ability to spontaneously recall and act on latent constraints without explicit prompting. The benchmark spans five dimensions across daily assistant and professional workflow scenarios, and reveals that PM is substantially harder than retrospective memory, decaying sharply with context length while retrospective memory near-saturates at 100K tokens. Key findings include a precision-recall trade-off in PM, attentional fragility under concurrent requests, and a novel result that PM accuracy correlates with spare reasoning capacity as measured against AIME-2025 math performance.
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.
Infini Memory: Topic-structured persistent memory architecture for long-term LLM agents
Researchers propose Infini Memory, a persistent memory architecture for LLM agents that organizes memory as topic-structured documents rather than isolated records or summaries. New observations are staged in a buffer and periodically consolidated, while retrieval uses iterative agentic tool calls instead of a single lookup step. The system achieves 64.7% on MemoryAgentBench, with ablations showing complementary gains from topic-structured maintenance and iterative evidence inspection.
Supra Cognitive Modes: routed architecture for agent memory across factual, relational, and synthesis workloads
A new arXiv preprint introduces Supra Cognitive Modes (SCM), an agent memory architecture that routes queries to specialized retrieval and synthesis pipelines over a shared ingest substrate combining dense embeddings, knowledge graph triples, and fact-version metadata. A frozen semantic classifier dispatches queries among lexical/dense lookup, multi-hop graph reasoning, and long-form synthesis modes. The system is evaluated on three agent memory benchmarks—LoCoMo (84.87% factoid), MemoryAgentBench (61.49%), and LongMemEval (86.00%)—though the authors note that causal routing effects, efficiency gains, and statistical significance remain unestablished.