Failure Modes and Model Exploitation › Summary

The mechanisms are related but distinct and can coexist. Keep these qualifications with the names:

• Compounding is propagation of prediction discrepancy. With a fixed norm, common realized actions, $\varepsilon\ge0$, and a uniform $L\ge0$ reference bound on a domain containing both trajectories, $e_{t+1}\le Le_t+\varepsilon$. The envelope is $L^te_0+\varepsilon\sum_{k=0}^{t-1}L^k$, including $e_0+\varepsilon t$ at $L=1$. Its contractive, linear, and geometric regimes are upper-envelope regimes, not necessary actual trajectories. Shared feedback-generated actions are allowed; independently chosen actions need an additional mismatch bound.
• Model bias is discrepancy in dynamics or reward prediction; objective misspecification is discrepancy between the supplied target and intended task. Repairing one fixed object does not rewrite the other, but behavioral constraints and penalties can mitigate consequences of either. Both can be present.
• Model exploitation is selection of erroneously attractive scores. A prediction–execution gap or budget slope alone does not prove it. Controlled candidate pools, score conventions, reference rescoring, and joint-support checks help distinguish contributions; neither off-support behavior nor increasing optimism is required in every case.
• Adversarial-looking trajectories can arise from ordinary optimization, without external attack. That describes selected behavior, not a universal safety judgment or a disjoint class of defenses.
• Aliasing collapses contexts requiring different predictions or actions. More data cannot recover a cue absent from a fixed input, but data can improve an encoder when the relevant information is available. History or memory must retain and expose what the decision needs.
• Within-episode forgetting loses encoded information during fixed-parameter inference; across-update forgetting loses retained performance after parameter changes. Delay sweeps and retained-set comparisons distinguish the experimental controls, not mandatory curve shapes. Their mechanisms and remedies can overlap. The scalar EWC-style tradeoff follows from incompatible one-weight targets, not a universal impossibility of preventing forgetting.
• Hallucinated dynamics violate reference transitions or constraints. A conditional mean can fall outside support without an attacker or off-support input, as in the absorbing corridor. Optimal pointwise MSE need not be small, and a one-step conditional-support check can already expose the violation. Not every multimodal mean is invalid.

The workflow captures replay conditions, audits objectives and reward conventions, compares local and recursive predictions, probes selected errors and joint support, substitutes validated components, and retests on fresh cases. It gathers positive and negative evidence under controls; it does not automatically identify a unique cause or certify a whole model.

The next chapter is planned to ask how a controller should respond to specified model limitations, how uncertainty should be checked and used, and when a fallback layer is justified. The starting point is assessed risk, not an assumption that every failure mode must already be present.