From DSPy to NEW "CoT Encyclopedia" (explain)

Simple task: to improve the intelligence of AI systems beyond given inherent limitations. Improve complex reasoning capabilities of AI systems w/ DSPy, MCP, A2A, Reasoning Strategies evaluations, In-Context Learning (ICL) and Supervised Fine-Tuning (SFT). Conclusion by authors: "Our empirical results revealed four key insights: (1) optimal reasoning strategies significantly enhance task performance on both helpfulness and safety benchmarks; (2) these patterns can be predicted from input questions alone, enabling real-time adaptive reasoning control; (3) training data format influences reasoning strategies more substantially than domain; and (4) desired reasoning behaviors can be interpolated through model weight merging without additional training. The COT ENCYCLOPEDIA advances our understanding of reasoning models and provides practical tools for steering them toward safer, more effective strategies". All rights w/ authors: "The COT ENCYCLOPEDIA: Analyzing, Predicting, and Controlling how a Reasoning Model will Think" Seongyun Lee 1,3 Seungone Kim 2 Minju Seo1 Yongrae Jo 3 Dongyoung Go 4,5 Hyeonbin Hwang 1 Jinho Park 1 Xiang Yue 2 Sean Welleck 2 Graham Neubig 2 Moontae Lee 3 Minjoon Seo 1 from KAIST AI 1 Carnegie Mellon University 2 LG AI Research 3 NAVER Search US 4 Cornell University 5 {seongyun, minjoon}@kaist.ac.kr #airesearch #scienceexplained #aiexplained #aireasoning #dspy #reasoningmodels