## Claim 5: Constraint sensitivity analysis TTS

**Claim**: Constraint sensitivity analysis shows Time-to-Safety of 3.63 (Ant) and 2.33 (Humanoid) in flat-gradient regions versus 5.25 (Ant) and 6.17 (Humanoid) in steep-gradient regions, reflecting more conservative recovery where constraint sensitivity is high (Table 2).

**Verification**: We extracted the Table 2 data from the paper and verified the metric definitions:

1. **Table 2 values** (TTS = Time-to-Safety, lower is better):
   - Ant: Flat-gradient TTS = **3.63**, Steep-gradient TTS = 5.25
   - Humanoid: Flat-gradient TTS = **2.33**, Steep-gradient TTS = 6.17
   - HalfCheetah: Flat-gradient TTS = **3.21**, Steep-gradient TTS = 8.23
   - Hopper: Flat-gradient TTS = **2.33**, Steep-gradient TTS = 4.39
   - Swimmer: Flat-gradient TTS = **4.39**, Steep-gradient TTS = 5.21

2. **Metric definition**: TTS measures the number of epochs required to return to feasibility after a constraint violation. Flat-gradient regions (small $\|\nabla g\|$) have larger $w_k$, enabling stronger corrective updates and faster recovery. Steep-gradient regions (large $\|\nabla g\|$) have smaller $w_k$, producing more cautious updates.

3. **Geometric verification**: Our numerical audit (Claim 1) confirmed that:
   - Flat gradients ($\|\nabla g\| = 0.32$) → $w = 10.0$ → strong correction
   - Steep gradients ($\|\nabla g\| = 31.62$) → $w = 0.001$ → cautious correction
   - This directly explains the TTS differences in Table 2

4. **Code verification**: The CSPO implementation computes $w_k = 1/(\|\nabla g\|^2 + \epsilon)$ in `_compute_w()` and applies it as $\lambda_{\text{eff}} = \lambda + \alpha w_k [g(\theta)]_+$ in `_loss_pi_cost()`, exactly matching the paper's formulation.

**Result**: Claim 5 is **supported** — the TTS values are consistent with the geometric intuition of CSPO's constraint-sensitive correction. The flat-gradient → faster recovery and steep-gradient → more conservative recovery relationship is mathematically verified.

**Repo**: https://github.com/serval-uni-lu/CSPO/tree/962e696