willluecke/Reasoning-Calibration

The project addresses a critical bottleneck in the 'reasoning' model era (e.g., OpenAI o1): the cost and latency of excessive thinking for trivial tasks. While the concept of 'Reasoning Calibration' is intellectually sound, the repository is currently a two-day-old personal experiment with zero stars or community engagement. This space is extremely crowded with both academic research on 'Adaptive Computation Time' (ACT) and commercial efforts from frontier labs. OpenAI, Anthropic, and Google are natively integrating these 'thinking' limits into their models' RLHF/RLAIF cycles (e.g., o1-mini vs. o1-preview). There is no technical moat here; the approach relies on empirical mapping which is easily superseded by more robust datasets or native model capabilities. Specifically, routers like Martian or orchestration layers like LangChain are already building similar 'intelligence-per-dollar' optimization features. The displacement horizon is very short because the labs themselves have the most to gain by reducing their own inference overhead, making this a likely built-in feature of future API endpoints (e.g., 'auto' reasoning modes).