Validate bin edges for Piecewise Exponential CIF Loss to ensure at least two finite edges and correct starting point

evaluate.py

@@ -10,6 +10,7 @@ Implements the comprehensive evaluation framework defined in evaluate_design.md:
 
 import argparse
 import json
+import math
 import os
 import time
 from pathlib import Path
@@ -1571,9 +1572,22 @@ def load_model_and_config(run_dir: str, device: str = 'cuda') -> Tuple:
             lambda_reg=config.get('lambda_reg', 0.0),
         )
     elif config['loss_type'] == 'pwe_cif':
+        # Piecewise-exponential (PWE) requires a FINITE last edge.
+        # If bin_edges ends with +inf (default), drop it and train up to the last finite edge.
+        raw_edges = config.get(
+            'bin_edges', [0.0, 0.24, 0.72, 1.61, 3.84, 10.0, 31.0, float('inf')])
+        pwe_edges = [float(x) for x in raw_edges if math.isfinite(float(x))]
+        if len(pwe_edges) < 2:
+            raise ValueError(
+                "pwe_cif requires at least 2 finite bin edges (including 0). "
+                f"Got bin_edges={list(raw_edges)}"
+            )
+        if pwe_edges[0] != 0.0:
+            raise ValueError(
+                f"pwe_cif requires bin_edges[0]==0.0; got {pwe_edges[0]}"
+            )
         loss_fn = PiecewiseExponentialCIFNLLLoss(
-            bin_edges=config.get(
-                'bin_edges', [0.0, 0.24, 0.72, 1.61, 3.84, 10.0, 31.0]),
+            bin_edges=pwe_edges,
             lambda_reg=config.get('lambda_reg', 0.0),
         )
     else: