Override Failure in Neural Models

• Override failure is a phenomenon where intended negative constraints partially suppress target outputs but are ultimately overridden by late feed-forward activations.
• Quantitative analysis using the logit-lens framework reveals a 4.4× asymmetry in suppression effectiveness across critical LLM layers, emphasizing the role of late-stage FFN blocks.
• Diagnostic criteria such as sub-maximal suppression and significant positive increments in late layers highlight the need for improved architectural strategies in constraint design.

Override failure refers to a class of mechanistic or algorithmic failures in both cognitive systems and artificial models where an intended suppression or constraint signal—often aimed at preventing a specific response or output—is present but ultimately insufficient to override an intrinsic activation or bias toward that response. This phenomenon manifests across domains, including LLMs subject to negative instruction constraints, neural networks implementing memory chain retrieval, and response priming paradigms in human cognition. Override failure is distinguished from classic absence of control by the partial presence of the inhibitory or suppressive mechanism, which is nevertheless overwhelmed by late-stage dynamics or competing influences.

1. Formal Definition and Core Mechanisms

Override failure, in its canonical implementation in LLMs, arises when a negative constraint ("Do not say X") is applied, successfully attenuating but not fully suppressing the target response probability. The model partially encodes the suppression within intermediate layers, yet late feed-forward activations reverse or overwhelm this effect, culminating in the emission of the forbidden word or behavior. This is contrasted with priming failure, where the explicit mention of the forbidden term in the negative instruction primes early activations, but override failure specifically implicates the inability of the suppression signal to withstand surges in positive activation at later computation stages (Rana, 12 Jan 2026).

Algorithmically, override failure can be formalized within the logit-lens framework, tracking the evolution of a target output's conditional probability $P^{(\ell)}(X)$ over network layers. While initial suppression is captured ($\Delta P_{\text{suppression}} > 0$), there is a marked reversal beyond a critical depth, pinpointed to high-indexed transformer layers (e.g., layers 23–27 in a 27-layer model). Feed-forward network (FFN) blocks at these stages contribute disproportionately positive increments to the target's logit, effectively canceling previous inhibitory signals.

2. Quantitative Characterization in LLMs

In "Semantic Gravity Wells: Why Negative Constraints Backfire" (Rana, 12 Jan 2026), override failure is rigorously quantified as follows:

• Violation Probability: The probability that the forbidden token is generated obeys a logistic function of the baseline (unconstrained) probability $P_{0}$:

$$p(\text{violation}) = \sigma(\beta_{0} + \beta_{1} P_{0}),$$

with $\beta_{0} = -2.40$ and $\beta_{1} = 2.27$ (bootstrap $95\%$ CI for slope: $[2.21,2.33]$).

• Suppression Asymmetry: Successful suppressions lower the target emission probability by $22.8$ percentage points ($\Delta P = 0.228 \pm 0.017$); failures achieve only $5.2$ points ($\Delta P = 0.052 \pm 0.011$), producing a $4.4\times$ asymmetry.
• Layer Localization: Logit-lens and activation patching experiments demonstrate that layers 23–27 in the model are causally responsible: patching these layers' residual streams with baseline activations flips successful suppression to violation. The late FFN sublayers apply $+0.39$ increments to the target logit in failures, nearly four times the $+0.10$ in successful overrides.

Override failure is therefore not a result of blanket instruction ignorance; rather, it reflects a dynamic contest between suppression and resurgence, with late-stage computations acting as the decisive locus of failure.

3. Contrast with Priming Failure and Related Modes

Override failure is distinct from other failure modes such as priming failure and simple inhibition failure:

• Priming Failure: In LLMs, priming failure dominates (87.5% of constraint violations), caused by the explicit mention of the forbidden token heightening its early representation, which is subsequently amplified by late layers (Rana, 12 Jan 2026).
• Override Failure: Constitutes about 12.5% of failures, with suppression signals persisting but ultimately undone by late-stage FFNs.
• Simple Inhibition Failure: Absence of any suppression signal, not observed in the cited experiments; suppression is always present but variably insufficient.

In connectionist memory models, override failure analogs arise when synaptic depression dynamics or global inhibition parameters fail to destabilize current attractors or sustain transitions. In such models (e.g., Hopfield networks with synaptic depression), latching to the next memory pattern can be blocked by overwhelming inhibitory inputs, insufficient overlap, or failure of local instability—a dynamical override failure (Chossat et al., 2016).

4. Experimental Evidence and Diagnostic Criteria

Empirical identification of override failure leverages layer-wise analysis (logit-lens), intervention (activation patching), and direct comparison of suppression magnitudes. The following diagnostic criteria are established (Rana, 12 Jan 2026):

• Significant, but sub-maximal, suppression at the output probability of the forbidden word.
• Late-layer FFN activations exhibiting positive contributions large enough to reverse cumulative suppression.
• Activation patching at the implicated layers sufficient to flip the outcome of a suppression attempt.

These criteria distinguish override failure both from cases where negative constraints are simply ignored and from effective, robust constraint compliance.

5. Broader Impact: Model Robustness and Safety

Override failure poses a critical barrier to reliable negative instruction following in LLMs and other autoregressive models. Its mechanistic underpinnings explain why prohibitive constraints ("do not generate X") systematically backfire, particularly for responses with high baseline emission probability ("semantic pressure"):

• The act of naming the forbidden token creates a "semantic gravity well," as late-model computations recurrently revive and amplify the very representations intended for suppression.
• Defensive strategies targeting only early attention or token suppression are insufficient, as they do not address the late-stage FFN pathway structure. Hardening models may require architectural changes (distributed gating, dynamic activation monitoring) or special training to ensure robust override capacity—even under high semantic pressure (Rana, 12 Jan 2026).

This tension is mirrored in related domains: in adversarial LLM prompting, priming-based attacks leverage similar vulnerabilities in context dependency to override guard mechanisms despite explicit suppression (Huang et al., 23 Feb 2025).

6. Analogues in Cognitive and Neural Models

Neural network theories of sequential memory retrieval exhibit override-failure analogs when the synaptic and inhibitory parameters fail to support a transition out of the currently active attractor, even when destabilization is attempted. In such systems (Chossat et al., 2016):

• If global inhibition or synaptic depression is too weak, the prime pattern never destabilizes ("locked-in" state).
• If depression is too strong, the network falls silent rather than reliably transitioning ("collapsed" state).
• Fine-tuning of these parameters is essential for achieving robust, sequential override and latching dynamics, a property not reliably observed in canonical Hopfield-type models.

This mirrors the necessity for precisely balanced suppression and activation in both artificial and biological implementations.

7. Implications for Constraint Design and Future Research

The inevitability of override failure under standard negative constraints cautions against naive use of explicit prohibition in prompt design. It motivates several avenues for ongoing research:

• Indirect interventions that reduce the baseline emission probability prior to negative constraints.
• Architectural modifications to ensure suppression signals are sustained or strengthened through the deepest layers.
• Formal characterization of "semantic pressure" thresholds beyond which both priming and override failure become effectively unavoidable.

An understanding of override failure thus informs both practical methods for constraint satisfaction and theoretical models of instruction-following and inhibition, with applications extending from natural language processing to neural memory architectures (Rana, 12 Jan 2026, Chossat et al., 2016, Huang et al., 23 Feb 2025).