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LLM agents rely on long-term memory to retain and reuse information when performing tasks over long horizons. Existing methods provide limited support for handling memories that become outdated as new observations or domain evidence arrive. Such outdated memories may remain semantically relevant, continue to affect dep...
A language agent's execution history can exceed its context window, requiring its memory system to retrieve complete supporting evidence under a hard token budget. Evidence may span multiple execution events, yet conventional retrievers use fixed token windows and fixed-k metrics that reward individual fragments withou...
This work argues that a memory write is not a belief commit, and presents MemTX, a transactional belief-commit protocol, a transactional belief-commit protocol that leads all eight baselines with paired-McNemar significance on four backbones and statistically ties the best baseline on the fifth and strongest, while rem...
The results do not imply uniformly better trace reconstruction, but show that dependency-guided rollback repair provides a strong recovery--cost trade-off while repairing faulty memory state and preserving benign memory.
COVENANT is proposed, a compiler-and-interpreter architecture for workflow-aligned agent execution that substantially mitigates workflow misalignment, moving LLM-agent alignment beyond isolated prompt following toward reliable execution of complex and multi-step workflows.
MAP-Graph is introduced, a provenance-aware memory layer that represents agents, sources, memories, claims, and actions in a typed execution graph and supports provenance as an operational control signal, rather than only post-hoc audit metadata, within the evaluated setting.
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