Mem-π is a framework using a dedicated model and decision-content decoupled RL to generate context-specific guidance on demand for LLM agents, outperforming retrieval baselines by over 30% on web navigation.
Beyond static summarization: Proactive memory extraction for llm agents.arXiv preprint arXiv:2601.04463, 2026a
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Slipstream uses asynchronous compaction with trajectory-grounded judge validation to improve long-horizon agent accuracy by up to 8.8 percentage points and reduce latency by up to 39.7%.
HyMem introduces dual-granular memory storage with a lightweight summary module for fast responses and selective activation of a deep LLM module for complex queries, outperforming full-context baselines by 92.6% lower computational cost on LOCOMO and LongMemEval benchmarks.
MemIR is a typed memory representation for LLM agents that structures memory into atoms separating evidence, cues, and claims, leading to better performance on source tracking tasks in experiments on LoCoMo and BEAM-100K.
MEMTIER reports 0.382 accuracy and 0.412 F1 on the 500-question LongMemEval-S benchmark, a 33pp gain over full-context baseline using tiered memory and retrieval components on 6GB GPU hardware.
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Slipstream: Trajectory-Grounded Compaction Validation for Long-Horizon Agents
Slipstream uses asynchronous compaction with trajectory-grounded judge validation to improve long-horizon agent accuracy by up to 8.8 percentage points and reduce latency by up to 39.7%.