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Across verifiable reasoning, open-ended generation, and post-SFT settings, RRPO remains competitive with verifier-based optimization, improves over weakly supervised baselines, and provides additional gains after supervised fine-tuning.
This empirical study investigates whether procedural LLM skills can be represented as directions in activation space and whether vector-space operations over these directions can express skill-level behaviors and finds that procedural skills admit a vector-space representation.
Agent2UCB is demonstrated, an agentic GEO system that autonomously improves content visibility through customized, feedback-driven optimization and provides a lightweight, text-only SEO readiness evaluation covering readability, topical coverage, and EEAT-style credibility.
The study turns the filesystem default from an assumption into a design space for agent memory, and turns the model is not the only lever over a store's shape: changing the tool set alone reshapes the store as strongly as swapping the model.
This work treats each reasoning trace as a sequence of latent states rather than an unstructured texts, and investigates whether inference time interventions can provide fine-grained control over the self-looping reasoning process.
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