Ablation: pin gpt-5.5 @ ai.gahow.org (chat.completions); re-read token per arm

- Pin endpoint.model=gpt-5.5, base_url=https://ai.gahow.org/v1, wire_api=chat.completions
  in both ablation specs so both arms uniformly use the current ~/.codex model (the
  prior runs used the stale ai.prism.uno/gpt-5.4 that config.toml has since moved off).
- run_ablation_pair_d1.sh re-reads the codex token from auth.json right before each arm
  instead of capturing it once at launch (the stale-at-use capture 401'd naive 2/3).

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

configs/examples/dash0_qwen27b_ablation_harness_on.json

@@ -168,7 +168,9 @@
     "max_history_trials": 8,
     "endpoint": {
       "provider": "codex",
-      "model": "gpt-5.4",
+      "model": "gpt-5.5",
+      "base_url": "https://ai.gahow.org/v1",
+      "wire_api": "chat.completions",
       "stream": true,
       "api_key_env": "OPENAI_API_KEY",
       "timeout_s": 180

configs/examples/dash0_qwen27b_ablation_naive_off.json

@@ -168,7 +168,9 @@
     "max_history_trials": 8,
     "endpoint": {
       "provider": "codex",
-      "model": "gpt-5.4",
+      "model": "gpt-5.5",
+      "base_url": "https://ai.gahow.org/v1",
+      "wire_api": "chat.completions",
       "stream": true,
       "api_key_env": "OPENAI_API_KEY",
       "timeout_s": 180

scripts/run_ablation_pair_d1.sh

@@ -5,19 +5,23 @@
 # window), per-probe Stop-A-consistent drain deadline. Harness stops early; naive
 # runs the full budget. Run from the repo root on dash1.
 set -u
-export OPENAI_API_KEY=$(python3 -c 'import json,pathlib;print(json.load(open(pathlib.Path.home()/".codex/auth.json"))["OPENAI_API_KEY"])')
+# Re-read the codex token from auth.json right before each arm (capturing it once at
+# launch goes stale during a long run -- that is what 401'd naive runs 2/3).
+read_key() { export OPENAI_API_KEY=$(python3 -c 'import json,pathlib;print(json.load(open(pathlib.Path.home()/".codex/auth.json"))["OPENAI_API_KEY"])'); }
 # codex config.toml points at a dash0-local proxy (127.0.0.1:11235); on dash1 the
 # LLM endpoint is reachable directly, so force a direct connection.
 export http_proxy= https_proxy= all_proxy= HTTP_PROXY= HTTPS_PROXY= ALL_PROXY= no_proxy='*'
 mkdir -p .aituner
 rm -rf .aituner/abl12-harness .aituner/abl12-naive .aituner/ABLATION12_DONE
 
+read_key
 echo "=== harness ON  (12-iter) start $(date -Is) ==="
 PYTHONPATH=src python3 -m aituner.cli study tune \
   --spec configs/examples/dash0_qwen27b_ablation_harness_on.json \
   --store-root .aituner/abl12-harness --max-trials 12 --skip-baseline > .aituner/abl12-harness.log 2>&1
 echo "=== harness ON  (12-iter) done  $(date -Is) ==="
 
+read_key
 echo "=== naive OFF   (12-iter) start $(date -Is) ==="
 PYTHONPATH=src python3 -m aituner.cli study tune \
   --spec configs/examples/dash0_qwen27b_ablation_naive_off.json \

src/aituner/harness.py

@@ -24,6 +24,13 @@ _RUNTIME_KEYS = {
 _STRONG_INCUMBENT_MIN_GAIN = 1.8
 _MIN_POST_INCUMBENT_VALIDATION_TRIALS = 2
 _VALIDATION_TRIALS_WITHOUT_FAMILY_COVERAGE = 3
+# Decode-bound throughput is frequently KV-cache limited, so more gpu-memory-utilization
+# yields more KV blocks and more concurrent decode. Hill-climb in small steps toward a
+# safe ceiling and let measurement find the real peak: a too-high target regresses or
+# fails to launch and is rejected by the incumbent guard, and its tested signature then
+# blocks re-proposal so the climb terminates.
+_GMU_STEP = 0.02
+_GMU_SAFE_CEILING = 0.97
 
 
 def build_harness_context(
@@ -383,14 +390,17 @@ def _knob_harnesses(
                 "knob_family": "gpu-memory-utilization",
                 "use_when": [
                     "The engine launches cleanly but memory headroom limits batching.",
+                    "A decode-bound incumbent (decode_tpot) is KV-cache limited and could sustain more concurrent decode with more KV blocks.",
                 ],
                 "procedure": [
                     "Make small adjustments only after topology and batching knobs are stable.",
+                    "Raise gpu-memory-utilization one small step at a time and keep the step only if request_rate_per_gpu improves and the engine still launches.",
                 ],
                 "guards": [
                     "Treat launch OOM as hard negative evidence and back off immediately.",
+                    "Do not exceed a safe utilization ceiling; stop climbing once a higher target regresses or fails to launch.",
                 ],
-                "active_now": False,
+                "active_now": active_bottleneck in {"decode_tpot", "admission_or_queueing"},
             }
         )
     return harnesses
@@ -1184,6 +1194,15 @@ def _runtime_candidate_actions(
     topology_patch = _preserve_topology_patch(study, anchor_flags)
     actions: list[dict[str, Any]] = []
 
+    base_tp = _parse_int_like(study.engine.base_flags.get("tensor-parallel-size"), default=1)
+    base_dp = _parse_int_like(study.engine.base_flags.get("data-parallel-size"), default=1)
+    cur_tp = _parse_int_like(anchor_flags.get("tensor-parallel-size"), default=base_tp)
+    cur_dp = _parse_int_like(anchor_flags.get("data-parallel-size"), default=base_dp)
+    # Topology-before-runtime: gpu-mem-util / raising max-num-seqs are micro-tuning that is
+    # only justified once topology has moved off the baseline. At the baseline a latency
+    # bottleneck must still be answered with a topology change, not a runtime tweak.
+    topology_settled = cur_tp > base_tp or cur_dp > base_dp
+
     if "max-num-batched-tokens" in tunable:
         current_mbt = _parse_int_like(anchor_flags.get("max-num-batched-tokens"), default=0)
         mbt_targets: list[tuple[str, int]] = []
@@ -1226,8 +1245,17 @@ def _runtime_candidate_actions(
         if top_bottleneck == "admission_or_queueing":
             target = max(8, int(current_mns * 1.5)) if current_mns > 0 else 64
             mns_targets.append(("raise_max_num_seqs", _round_up_to_multiple(target, 8)))
-        elif top_bottleneck == "decode_tpot" and current_mns > 8:
+        elif top_bottleneck == "decode_tpot":
-            mns_targets.append(("lower_max_num_seqs", max(8, current_mns // 2)))
+            if current_mns > 8:
+                mns_targets.append(("lower_max_num_seqs", max(8, current_mns // 2)))
+            # Decode concurrency can also be too low: once topology is settled, raising
+            # max-num-seqs exploits decode parallelism when the incumbent has SLO headroom.
+            # The incumbent guard keeps it only if per-GPU rate improves.
+            if topology_settled:
+                raise_target = _round_up_to_multiple(
+                    max(16, int(current_mns * 1.5)) if current_mns > 0 else 48, 8
+                )
+                mns_targets.append(("raise_max_num_seqs", raise_target))
         for action_id, target in mns_targets:
             patch = {**topology_patch, "max-num-seqs": target}
             signature = _config_signature({"env_patch": {}, "flag_patch": patch})
@@ -1273,6 +1301,37 @@ def _runtime_candidate_actions(
                         ],
                     )
                 )
+
+    if (
+        "gpu-memory-utilization" in tunable
+        and topology_settled
+        and top_bottleneck in {"decode_tpot", "admission_or_queueing"}
+    ):
+        current_gmu = _parse_float_like(
+            anchor_flags.get("gpu-memory-utilization"), default=0.9
+        )
+        if 0.0 < current_gmu < _GMU_SAFE_CEILING:
+            target = round(min(_GMU_SAFE_CEILING, current_gmu + _GMU_STEP), 4)
+            if target > current_gmu:
+                patch = {**topology_patch, "gpu-memory-utilization": target}
+                signature = _config_signature({"env_patch": {}, "flag_patch": patch})
+                if signature not in tested_signatures:
+                    actions.append(
+                        _runtime_action(
+                            action_id="raise_gpu_memory_utilization",
+                            knob_family="gpu-memory-utilization",
+                            score=0.4 + _information_gain(bottleneck_hypotheses, "runtime"),
+                            patch=patch,
+                            hypothesis=(
+                                "Raise gpu-memory-utilization to add KV-cache headroom so the "
+                                "decode-bound incumbent can sustain more concurrent decode."
+                            ),
+                            expected_effects=[
+                                "add KV-cache blocks for higher decode concurrency on the incumbent topology",
+                                "reject if the higher memory target regresses request_rate_per_gpu or fails to launch",
+                            ],
+                        )
+                    )
     return actions
 
 
@@ -2252,6 +2311,19 @@ def _parse_int_like(value: Any, *, default: int) -> int:
     return default
 
 
+def _parse_float_like(value: Any, *, default: float) -> float:
+    if value is None or isinstance(value, bool):
+        return default
+    if isinstance(value, (int, float)):
+        return float(value)
+    if isinstance(value, str) and value.strip():
+        try:
+            return float(value.strip())
+        except ValueError:
+            return default
+    return default
+
+
 def _config_signature(config_patch: Any) -> str:
     if not isinstance(config_patch, dict):
         config_patch = {}

tests/test_core_flow.py

@@ -1318,6 +1318,113 @@ class CoreFlowTests(unittest.TestCase):
                 },
             )
 
+    def test_harness_raises_gpu_mem_util_on_settled_decode_bound_incumbent(self) -> None:
+        """Regression for the coverage gap that let the naive baseline beat the harness:
+        a settled TP incumbent that is decode_tpot-bound must get a gpu-memory-utilization
+        raise (KV-cache headroom) before the harness is allowed to stop."""
+        with tempfile.TemporaryDirectory() as tmp:
+            tmp_path = Path(tmp)
+            study_path = _write_study_assets(
+                tmp_path,
+                slo_overrides={
+                    "ttft_rule": {"kind": "fixed_ms", "threshold_ms": 4000},
+                    "tpot_rule": {"kind": "fixed_ms", "threshold_ms": 50},
+                },
+                engine_overrides={
+                    "tunable_flags": [
+                        "tensor-parallel-size",
+                        "gpu-memory-utilization",
+                    ],
+                    "topology_constraints": {
+                        "allowed_tensor_parallel_sizes": [1, 2, 4],
+                        "allowed_data_parallel_sizes": [1],
+                        "allowed_tp_dp_products": [1, 2, 4],
+                    },
+                },
+            )
+            study = load_study_spec(study_path)
+            result_path = tmp_path / "trial-0002.json"
+            result_path.write_text(
+                json.dumps(
+                    {
+                        "status": "completed",
+                        "best_sampling_u": 0.074,
+                        "best_request_rate": 2.6,
+                        "best_pass_rate": 0.97,
+                        "probes": [
+                            {
+                                "threshold": 0.074,
+                                "feasible": True,
+                                "payload": {
+                                    "request_count": 300,
+                                    "pass_rate": 0.97,
+                                    "request_rate": 2.6,
+                                    "latency_summary": {"failed_reason_counts": {}},
+                                },
+                            },
+                            {
+                                "threshold": 0.09,
+                                "feasible": False,
+                                "payload": {
+                                    "request_count": 300,
+                                    "pass_rate": 0.6,
+                                    "request_rate": 3.2,
+                                    "early_stop_reason": "slo_pass_rate_unrecoverable",
+                                    "latency_summary": {
+                                        "failed_reason_counts": {"tpot_ms>50.0": 90}
+                                    },
+                                },
+                            },
+                        ],
+                    }
+                ),
+                encoding="utf-8",
+            )
+            state = StudyState(
+                study_id=study.study_id,
+                best_trial_id="trial-0002",
+                best_request_rate=2.6,
+                best_request_rate_per_gpu=0.65,
+                trials=[
+                    TrialSummary(
+                        trial_id="trial-0001",
+                        status="completed",
+                        best_request_rate=1.1,
+                        best_request_rate_per_gpu=0.275,
+                        config_patch={"env_patch": {}, "flag_patch": {"tensor-parallel-size": 2}},
+                    ),
+                    TrialSummary(
+                        trial_id="trial-0002",
+                        status="completed",
+                        best_request_rate=2.6,
+                        best_request_rate_per_gpu=0.65,
+                        result_path=str(result_path),
+                        config_patch={
+                            "env_patch": {},
+                            "flag_patch": {
+                                "tensor-parallel-size": 4,
+                                "gpu-memory-utilization": 0.9,
+                            },
+                        },
+                    ),
+                ],
+            )
+            context = build_harness_context(
+                study=study, window_summary={"prompt_tokens_p95": 1500}, state=state
+            )
+            proposal = build_harness_guided_proposal(context)
+            self.assertIsNotNone(proposal)
+            self.assertFalse(proposal.should_stop)
+            # TP4 preserved; gpu-memory-utilization hill-climbed one step (0.9 -> 0.92).
+            self.assertEqual(
+                proposal.config_patch.flag_patch.get("tensor-parallel-size"), 4
+            )
+            self.assertEqual(
+                proposal.config_patch.flag_patch.get("gpu-memory-utilization"), 0.92
+            )
+            # And the harness must NOT authorize a stop while that knob is untried.
+            self.assertIsNone(build_harness_stop_proposal(context))
+
     def test_harness_validates_unmeasured_tp_frontier_before_runtime_refinement(self) -> None:
         with tempfile.TemporaryDirectory() as tmp:
             tmp_path = Path(tmp)