LLM agents struggle to detect and act on implicit memory conflicts, with top models scoring 55.2% on the new STALE benchmark of 400 scenarios; CUPMem prototype strengthens state-aware revision.
Retrieval-augmented generation for knowledge-intensive nlp tasks
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MSA is an end-to-end trainable memory model using sparse attention and document-wise RoPE that scales to 100M tokens with linear complexity and less than 9% degradation.
PsychAgent combines memory-augmented planning, trajectory-based skill evolution, and rejection fine-tuning to create a self-improving AI psychological counselor that outperforms general LLMs in multi-session evaluations.
citing papers explorer
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STALE: Can LLM Agents Know When Their Memories Are No Longer Valid?
LLM agents struggle to detect and act on implicit memory conflicts, with top models scoring 55.2% on the new STALE benchmark of 400 scenarios; CUPMem prototype strengthens state-aware revision.
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MSA: Memory Sparse Attention for Efficient End-to-End Memory Model Scaling to 100M Tokens
MSA is an end-to-end trainable memory model using sparse attention and document-wise RoPE that scales to 100M tokens with linear complexity and less than 9% degradation.
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PsychAgent: An Experience-Driven Lifelong Learning Agent for Self-Evolving Psychological Counselor
PsychAgent combines memory-augmented planning, trajectory-based skill evolution, and rejection fine-tuning to create a self-improving AI psychological counselor that outperforms general LLMs in multi-session evaluations.