Runtime policies boost GPT-5.6 agent reliability without retraining

The paper's core insight is that failure patterns are interceptable at inference time without model retraining or prompt changes. This shifts the locus of reliability from model weights to runtime policy, which is a different kind of lever than most prior work targets.

This connects directly to the interpretability and safety work from late September. The 'Combining Hierarchical Cognitive Process' paper tackled safety classification by decomposing decisions into labeled intermediate steps; FIRE does something adjacent for agent reliability by injecting corrective instructions at failure points rather than treating errors as opaque. Both reject the black-box prediction model. FIRE also complements the HySparse2 work on long-context agents: if runtime policies can intercept failures without retraining, agents can stay reliable even as context windows grow and failure modes multiply. The practical deployment angle here (preserving user-facing prompts while fixing behavior) mirrors the GROBID incremental hardening approach from the same week.

If GPT-5.6 variants show the same 3.4-9.2 point gains when tested on out-of-distribution tasks not seen during policy development, the method generalizes beyond Terminal-Bench 2.1. If gains collapse on unseen failure modes, the approach is overfitting to known failure patterns and won't scale to production unknowns.