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Memory self-evolution uses task feedback to iteratively improve executable memory programs that store and retrieve information from past interactions. Existing approaches typically adopt holistic evolution, deriving revision directions from mixed feedback and judging progress by overall performance. This can obscure op...
Aker is presented, an approximate cache for disk-based ANNS that implements a low-overhead refresh mechanism that bounds cache staleness while preserving search accuracy and integrates into pgvector and is evaluated on representative workloads.
SkillHEX is introduced, a closed-loop framework coupling hypothesis-driven self-verification with evidence-guided tree search that translates falsifiable failure hypotheses into executable tests, producing diagnostic evidence as dense reward without additional environment attempts.
This work proposes MESA (a Multi-structure Evidence Selection framework for long-horizon Agent), which builds five complementary structure views of each trajectory and learns from end-to-end answer-level feedback to select and fuse a query-specific subset for a frozen answer model.
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