Can execution harnesses lift model performance without retuning weights?

The StateM paper reports that improving the execution system around an agent, "without changing its model weights," raises benchmark accuracy across several models. On Terminal-Bench 2.1, GPT-5.5 xhigh with StateM reaches 92.1% against an 83.1% reference, above GPT-5.6 Sol Ultra at 91.9%. The same runbook "transfers to GPT-5.6 without modification," where the system records 95.3% raw accuracy across 445 public-submission trials and succeeds at least once on each of the 89 tasks. The frozen profile lifts GPT-5.6 Luna from 76.7% to 85.4%, numerically above the 84.9% Sol xhigh reference. Starting from the same runtime, runbook structure, and golden rules, under $38 of adaptation cost moves DeepSeek-V4 Flash from 82.7% to 88.1% under standard timeouts.

The paper's diagnosis is that long-horizon agents "can fail even when their underlying models can solve the constituent steps." The failures it lists are losing track of mutable state, failing to reactivate lessons from earlier executions, skipping known procedures, and stopping prematurely. StateM answers with durable states, phase-local context, checked transitions, recoverable runbooks, and versioned procedural practices that agents and users can inspect together. The discussion adds a design requirement: the runtime must be "agent-native and enforceable" and also cheap to modify. Conventional state-machine and graph runtimes push reasoning into manually constructed nodes, edges, and handlers, so changing the control policy becomes "an engineering task of its own" and failure-driven iteration gets heavy.

The paper frames harness scaling as orthogonal to model scaling, asking whether "more of a model's existing capability" can be converted into completed, reliable work. That is the thesis of Where does agent reliability actually come from?, and StateM adds a benchmark-level result and a concrete mechanism: state, procedures, and lessons kept outside the model and enforced at transitions. Its named failure of reactivating earlier lessons echoes Does raw token spending actually predict agent performance?, where feedback counts only if it is retained for later decisions. Its premature-stopping failure is the same behavior What predicts success in ultra-long-horizon agent tasks? finds in most frontier models. On transfer, it complements Do stronger models always evolve harnesses better?: gains appear for Sol, Luna, and DeepSeek-V4 Flash, but the excerpt does not order these by capability tier, so it neither confirms nor contradicts a mid-tier peak.

The excerpt is silent on several points that matter for the claim. It shows no ablation, so it cannot say which of the five components produces the gains. It reports no variance, and the 92.1% versus 91.9% comparison is a 0.2-point difference. It does not say what the 83.1% and 84.9% references consist of. It lists only two of the three "progressively stronger empirical tests." The "$15 frontier run" in the title gets no detail, and transfer is shown only within the GPT-5.6 family plus a fresh adaptation for DeepSeek. It reports accuracy and cost, not trajectory quality, so it cannot speak to How should we measure agent system performance beyond task success?. At the strength the excerpt supports, the results are consistent with control-layer failures being a real share of what a capable model loses on long tasks, and the full paper is needed to say which parts of the harness do the work.