**Context \& Motivation**
This PR aligns the `ADAPT-DIFF` pipeline implementation with the claims made in Section 2.3 of the latest manuscript draft ("Hardware-Adaptive Bounding"). Previously, the token refinement stage relied on a hardcoded static threshold (`entropy_threshold=1.5`), which lacked true hardware adaptability. This update introduces a dynamic compute-budget router that strictly enforces target FLOP constraints on the fly.
**Key Changes**
* **Replaced `LogitUncertaintyFilter` with `HardwareAdaptiveRouter`:** The routing module now accepts relative compute costs for base block generations (`c_base`) and bfloat16 refinements (`c_bf16`).
* **Dynamic Budgeting (Equation 3):** The router now calculates the maximum permissible number of tokens to refine in bfloat16 based on an active computational ceiling ($C_{step} \le C_{target}$).
* **Infimum Thresholding (Equation 4):** Calculates `dynamic_tau` ($\tau$) on a per-step basis by sorting token uncertainty (LogTokU) and strictl…
**Context \& Motivation**
This PR aligns the `ADAPT-DIFF` pipeline implementation with the claims made in Section 2.3 of the latest manuscript draft ("Hardware-Adaptive Bounding"). Previously, the token refinement stage relied on a hardcoded static threshold (`entropy_threshold=1.5`), which lacked true hardware adaptability. This update introduces a dynamic compute-budget router that strictly enforces target FLOP constraints on the fly.
**Key Changes**
* **Replaced `LogitUncertaintyFilter` with `HardwareAdaptiveRouter`:** The routing module now accepts relative compute costs for base block generations (`c_base`) and bfloat16 refinements (`c_bf16`).
* **Dynamic Budgeting (Equation 3):** The router now calculates the maximum permissible number of tokens to refine in bfloat16 based on an active computational ceiling ($C_{step} \le C_{target}$).
* **Infimum Thresholding (Equation 4):** Calculates `dynamic_tau` ($\tau$) on a per-step basis by sorting token uncertainty (LogTokU) and strictl…
