Silent Commitment Failure in Instruction-Tuned Language Models: Evidence of Governability Divergence Across Architectures
This paper introduces "silent commitment failure" — a phenomenon where instruction-tuned language models produce confident, incorrect outputs with no detectable pre-commitment warning signal — and proposes "governability" as a measurable property for AI agent safety. The core claim is that 2 of 3 instruction-following models evaluated exhibit zero-warning failure modes, with profound implications for autonomous agent deployment. The work distinguishes itself from hallucination studies by focusing on detectability before commitment rather than correctness of output, and presents empirical evidence that conflict-detection signals (the "authority band") are geometric properties fixed at pretraining rather than injectable through fine-tuning.
The paper makes an important conceptual contribution by reframing AI safety around governability — whether errors are catchable before they become actions — and provides intriguing empirical evidence that conflict detection varies dramatically across architectures. However, the central empirical claims rest on a vanishingly small sample (n=3 instruction-tuned models, largest 7B parameters), the "2 of 3" prevalence claim lacks statistical basis, and the causal conclusion that governability is "fixed at pretraining" overreaches by conflating architecture differences with training regime differences. The work is provocative but preliminary; broader replication across scales and controlled ablations are essential before the strong policy recommendations can be considered established.
The conceptual framework is genuinely valuable. The distinction between hallucination (output phenomenon) and silent commitment failure (inference-layer mechanism) clarifies a critical gap in safety thinking. The 2×2 experiment showing 52× spike ratio differences between architectures versus only ±0.32× variation from LoRA adaptation is methodologically clever and suggests meaningful geometric differences between models. The citation linking to Kalai et al.'s statistical account of hallucination persistence is appropriate, and the engagement with Ghasemabadi & Niu's Gnosis framework credibly positions this work as complementary — behavioral rather than mechanistic. The finding that Phi-3-mini provides ~57 tokens of warning under greedy decoding but only 34% detection under temperature sampling is practically important for deployment.
The central prevalence claim — "a majority of instruction-following models" exhibiting silent commitment failure — is based on n=3, with the author explicitly warning that "claims about prevalence rates should be interpreted with appropriate caution" yet simultaneously labeling this "two out of three" finding as "KEY FINDING" and "Finding 1" throughout. This tension between rhetorical emphasis and statistical reality undermines confidence. The causal attribution to pretraining architecture is confounded: the 52× difference compares Phi-3 (trained with "heavy chain-of-thought supervision") against Mistral ("trained for instruction compliance using SFT and DPO"), conflating architecture, training data volume, and supervision type in a single comparison. The limitation section acknowledges this: "Our comparison of Phi-3 and Mistral conflates multiple variables... We cannot isolate which factor produces the authority band." Yet the abstract and introduction make the unqualified claim that governability "is a geometric property fixed at pretraining."
The evidence for the core phenomenon is suggestive but thin. Only three instruction-following models could be evaluated for conflict detection; two base models (GPT-2 variants) and one base Mistral couldn't follow structured protocols; Gemma-3 couldn't be evaluated due to "protocol constraints"; and Llama-3.2's correction capacity was "not completed within the study period." The comparison to related work is generally fair — Kalai et al.'s statistical theory about irreducible error floors is invoked appropriately, and Ghasemabadi & Niu's Gnosis mechanism is cited as mechanistically complementary. However, the framing of external governance frameworks (Pierucci et al., Ge) as responses to the specific silent commitment failure documented here reads as somewhat self-congratulatory given those papers were published contemporaneously or earlier — Ge's Layered Governance Architecture (March 2026) and Pierucci et al.'s Institutional AI (January 2026) were not developed in response to this work's findings.
Reproducibility is severely limited. The trajectory tension metric formulas and detection thresholds are explicitly withheld: "Detailed metric formulas and detection thresholds are available upon request for research validation and standardization discussions." This is antithetical to scientific norms — the core measurement methodology is not disclosed. The prompts and scoring rubrics are "released with this paper" but the "reference implementation of the trajectory-tension detector is available upon request." Full hyperparameters for the LoRA experiments are similarly "available upon request." Without these materials, independent replication is impossible. The author discloses being founder of SnailSafe.ai, which sells AI governance assessment services based on this methodology, and notes that "Foundational IP is filed (US provisional)." This commercial interest, combined with methodological opacity, creates problematic incentives around verification.
As large language models are deployed as autonomous agents with tool execution privileges, a critical assumption underpins their security architecture: that model errors are detectable at runtime. We present empirical evidence that this assumption fails for two of three instruction-following models evaluable for conflict detection. We introduce governability -- the degree to which a model's errors are detectable before output commitment and correctable once detected -- and demonstrate it varies dramatically across models. In six models across twelve reasoning domains, two of three instruction-following models exhibited silent commitment failure: confident, fluent, incorrect output with zero warning signal. The remaining model produced a detectable conflict signal 57 tokens before commitment under greedy decoding. We show benchmark accuracy does not predict governability, correction capacity varies independently of detection, and identical governance scaffolds produce opposite effects across models. A 2x2 experiment shows a 52x difference in spike ratio between architectures but only +/-0.32x variation from fine-tuning, suggesting governability is fixed at pretraining. We propose a Detection and Correction Matrix classifying model-task combinations into four regimes: Governable, Monitor Only, Steer Blind, and Ungovernable.
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