RHELM is a benchmark for LLM long-term memory with dynamic profiles, heterogeneous sources, and 27 memory characteristics that reveals weaknesses in existing models for multi-source aggregation and contextual reasoning.
Ross Mitchell
9 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 9representative citing papers
ElasticMem enables LLM agents to learn adaptive latent memory retrieval and elastic budget allocation, improving QA accuracy by 24-26% and ALFWorld success by 27-66% over baselines with lower token cost.
LongMemEval-V2 is a new benchmark where AgentRunbook-C reaches 72.5% accuracy on long-term agent memory tasks, beating RAG baselines at 48.5% and basic coding agents at 69.3%.
Introduces CSTM-Bench with 26 cross-session attack taxonomies, demonstrates recall loss in session-bound and full-log detectors, and proposes a bounded-memory coreset reader with the CSTM metric balancing detection and serving stability.
Cognitive architectures for AI agents require a distinct Knowledge layer with indefinite supersession persistence, separate from Memory decay, Wisdom evidence-gating, and Intelligence ephemerality.
PERMA is a new benchmark using temporally ordered events, text variability, and linguistic alignment to evaluate LLM memory agents on persona consistency beyond simple retrieval.
True Memory is a verbatim-event retrieval pipeline running on a single SQLite file that reaches 93% accuracy on LoCoMo multi-session questions, outperforming Mem0, Supermemory, Zep, and matching or exceeding EverMemOS and Hindsight on other long-context benchmarks.
LLM agent memory is organized into Storage (preserving trajectories), Reflection (refining them), and Experience (abstracting into reusable knowledge) stages driven by needs for long-range consistency, dynamic adaptation, and continual learning.
Existing memory benchmarks cover at most two of the seven continuity properties from ATANT v1.0, with a median of one and none covering more than two.
citing papers explorer
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Cross-Session Threats in AI Agents: Benchmark, Evaluation, and Algorithms
Introduces CSTM-Bench with 26 cross-session attack taxonomies, demonstrates recall loss in session-bound and full-log detectors, and proposes a bounded-memory coreset reader with the CSTM metric balancing detection and serving stability.
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The Missing Knowledge Layer in Cognitive Architectures for AI Agents
Cognitive architectures for AI agents require a distinct Knowledge layer with indefinite supersession persistence, separate from Memory decay, Wisdom evidence-gating, and Intelligence ephemerality.
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Storage Is Not Memory: A Retrieval-Centered Architecture for Agent Recall
True Memory is a verbatim-event retrieval pipeline running on a single SQLite file that reaches 93% accuracy on LoCoMo multi-session questions, outperforming Mem0, Supermemory, Zep, and matching or exceeding EverMemOS and Hindsight on other long-context benchmarks.