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Mara Chain is introduced, a refinement procedure that turns rejected candidates into stepping stones and retains and iteratively refines it using evidence accumulated across preceding attempts, delivering greater task-performance gains with fewer rollouts.
Kozuchi Agent, a language-agnostic open-weight repair agent and CI-operated evaluation pipeline, is presented, showing that the remaining gap is primarily semantic correctness and selection rather than edit formatting or proprietary-model access.
SHARD-MEMO is presented, an agentic memory system built on scope-before-routing: metadata predicates first identify the admissible shards, and a learned router then selects a small number of them for shard-local approximate nearest neighbor retrieval, separating hard admissibility from learned relevance ranking.
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