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LLM reasoning can be optimized by learning from the geometry of state transitions, achieving reflection-level accuracy with 28% lower latency and 25.8% fewer tokens.
Role-Grounded Rubric Construction reveals that professional deliverables are essential for accurately evaluating specialized financial AI agents, outperforming traditional prompt-based methods.
Despite advances in LLMs, they fail to effectively integrate user preferences over time, with accuracy rates stagnating around 39% even in ideal conditions.