diff --git a/evalharness/cli.py b/evalharness/cli.py
index ffe782f..ed8fb81 100644
--- a/evalharness/cli.py
+++ b/evalharness/cli.py
@@ -943,7 +943,10 @@ def _cmd_eval_run(args) -> int:
             # (days old, slower setup) -- that once reported 15.9h for a
             # one-hour run
             _wall = round(_time.time() - t0, 1)
-            rows.append({'name': name, 'metric': primary, 'value': report.metrics.get(primary),
+            rows.append({'name': name, 'metric': primary,
+                         'cached': (report.metric_groups.get('run_info', {})
+                                    .get('gen_fresh') == 0) if _repeats <= 1 else False,
+                         'value': report.metrics.get(primary),
                          # repeats evaluate the SAME N problems k times: the
                          # count people expect is the generations, not N
                          'n': report.num_samples * _repeats,
@@ -1045,7 +1048,8 @@ def _print_summary_table(console, rows):
         for r in rows:
             v = _fmt_score(r.get('value')) if r['ok'] else 'ERR'
             wall = r.get('wall') or r.get('secs') or 0
-            tm = f'{wall / 3600:.2f}h' if wall >= 3600 else f'{wall:.0f}s'
+            tm = 'cached' if r.get('cached') else (
+                f'{wall / 3600:.2f}h' if wall >= 3600 else f'{wall:.0f}s')
             ti, to = r.get('tok_in', 0), r.get('tok_out', 0)
             tin = f'{ti:,}' if ti else '—'
             tout = f'{to:,}' if to else '—'
diff --git a/evalharness/model/runner.py b/evalharness/model/runner.py
index b181cff..450a49b 100644
--- a/evalharness/model/runner.py
+++ b/evalharness/model/runner.py
@@ -503,7 +503,7 @@ async def generate_predictions(
         # ckpt info (store + per-position keys) so run_eval can read/write
         # SCORES bound to these predictions; None when checkpointing is off
         ckpt_info = (ckpt_store, keys) if ckpt_store is not None else None
-        return preds, usages, total_usage, ckpt_info
+        return preds, usages, total_usage, ckpt_info, len(fresh) - len(failed_samples)
     finally:
         # reporter lifecycle belongs to the CALLER (CLI reuses one reporter
         # across benchmarks and closes it after the whole run); only close
@@ -781,7 +781,7 @@ async def run_eval(
 
     try:
         from .gen_profiles import merge_gen_kwargs
-        preds, _usages, usage, ckpt_info = await generate_predictions(
+        preds, _usages, usage, ckpt_info, n_fresh = await generate_predictions(
             adapter, list(raw_samples), concurrency, progress=progress,
             progress_reporter=progress_reporter,
             status_callback=status_callback,
@@ -831,7 +831,10 @@ async def run_eval(
         status_callback('Scoring predictions against the benchmark recipe')
     _meta = {'gen_input_tokens': usage.input_tokens,
              'gen_output_tokens': usage.output_tokens,
-             'gen_total_tokens': usage.total_tokens}
+             'gen_total_tokens': usage.total_tokens,
+             # fresh=0 means the whole bench replayed from checkpoint: the
+             # summary table then shows 'cached' instead of a ~0s time
+             'gen_fresh': n_fresh}
     if _records is not None:
         from ..eval.runner import evaluate_cached