arXiv:2606. 26806v1 Announce Type: new Abstract: Long-running language agents need more than memory access.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.
arXiv:2606. 29916v1 Announce Type: cross Abstract: Long-running language agents need mechanisms for deciding which experiences should persist after the working context is gone.
arXiv:2607. 20458v1 Announce Type: cross Abstract: Large language model (LLM) agents operating over extended dialogues accumulate vast amounts of information, yet existing memory systems either retain everything indiscriminately or apply uniform forgetting heuristics that fail to distinguish relevant from irrelevant knowledge.
arXiv:2606. 28876v2 Announce Type: replace-cross Abstract: We study memory-managed long-context attention: explicit bounded memory with a learned query-independent writer, lifecycle control, query-aware reading, calibrated sparse fallback, and frozen-LLM generation from raw evidence.
arXiv:2606. 28876v1 Announce Type: cross Abstract: Long-context language models often conflate two different goals: compressing history into an efficient state, and maintaining reliable long-term memory.
arXiv:2608. 01742v2 Announce Type: replace Abstract: Long-term memory is critical for LLM agents operating over long-horizon interactions.
arXiv:2607. 12893v1 Announce Type: new Abstract: Long-term memory has become a foundational capability for LLM-based agents that accompany users across extended, multi-session interactions.