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LLM-as-an-Improver is introduced and Verify--Repair--Reselect (VRR) is proposed, which uses verification feedback to generate and reselect improved candidates and highlights a broader role for LLMs as improvers: verification feedback can not only select among existing solutions but also construct stronger candidates be...
The Quantum Random Access Quantization framework encodes context-dependent signs in a quantum random-access code and retrieves them via context-matched Pauli measurements and produces an unbiased, context-specific binary surrogate with a tractable shot-noise penalty.
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.
Results indicate that explicit, address-based recall can improve information retention and serving efficiency relative to the evaluated context-management baselines under the tested settings.
This work introduces logos, a pluggable layer for self-evolution and governance that strengthens existing multiagent frameworks rather than replacing them, and provides a living logic for accountable automation.
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