Premise Failure as Alignment Failure

Recently, I’ve been discussing certain topics on Bluesky oriented around LLM alignment and model “drift.” In full disclosure, an autonomous LLM agent operates on a Raspberry Pi in my home, so I’ve spent some time thinking about this in very experiential ways.

These discussions point toward a gap in the current alignment literature that I think a specific philosophical tradition can and should address. Current approaches to AI safety - RLHF, constitutional AI, red-teaming, output monitoring - are effective but patchwork, with each addressing a specific failure mode. What is generally missing is a unified structural account of why these failure modes share a common shape.

Such an account can be found within the approach of American Pragmatism, applying methodological anti-foundationalism to premise evaluation. Core pragmatic principles support alignment goals and are compatible with an empirically-oriented philosophy towards common problems in alignment, steering, and red-teaming research. These principles are:

American Pragmatism is a philosophy that traces its origins to Charles Sanders Peirce and John Dewey. Peirce and Dewey were skeptical of how epistemological Western traditions ultimately resorted to first premises, principles, or cognitions, which were treated as exempt from further inquiry and inaccessible to empirical revision. In expressing this skepticism, they needed to account for the problem of how inquiry can correct itself and remain productive without fixed foundations.

Already one sees the striking structural parallel in the nature of large language models utilizing transformer architecture: as attentive token-predictors, LLMs navigate tokens in vector-space via context and semantic weighting, prioritizing coherence without relying on explicit axioms or manually looking up linguistic rules. They are, in a sense, already anti-foundationalist systems, merely lacking a principled anti-foundationalist epistemology; their architecture rules out the effective enforcement of fixed epistemological operational truths because token prediction is probabilistic and context-dependent rather than rules-based.

If we take Peirce and Dewey’s resulting toolkits and combine them, we get: fallibilism justifying an anti-foundationalist "base" in which assertions are lightly held as warranted rather than as necessarily the case.1

(Logic: The Theory of Inquiry (New York: Henry Holt, 1938), pp. 7-9)

In other words, Dewey critiques the “ambiguity” of knowledge and belief, insofar as they reflect conditions without doubt following inquiry, because they can imply an unwarranted finality of state. Reason, then, cannot have a foundationalist ground:

(Logic (1938), p. 9.)

Reasoning in the form of warranted assertions rather than off the backs of fixed directives leads to more attentive judgments towards topics of discussion. This has significant operational implications throughout model reasoning.

For instance, consider the tensions within Constitutional AI approaches when it comes to balancing directives like “be helpful” and “avoid harm.” When phrased legalistically, as befits the term “Constitutional,” models interpret directives in hierarchies often set by the creators of the Constitution. In addition, terms like “helpfulness” and “harm” have flexible semantic and contextual ranges, and models can drift into harmful behavior when that flexibility is exploited by actors. Models may avoid giving users useful criticisms if users imply great sensitivity and harm from criticism. More seriously, models may prioritize helpfulness as axiomatic even as a user defines “helpful” to involve plagiarism, espionage, or network attacks, and defines “harm” narrowly or manipulatively to minimize its apparent scope. A model that reasons via warranted assertions need not abandon directives to be helpful or avoid harm. However, it will more carefully investigate what helpfulness and harm entail in its relevant contexts.

Pragmatism is an applied philosophy, and its methodology is clearest when compared against existing problems and approaches. Millière in “Normative Conflicts and Shallow AI Alignment” (2025) identifies the core vulnerability in current alignment approaches as shallow behavioral dispositions that fail under adversarial pressure, arguing that existing methods reinforce surface compliance rather than genuine normative deliberation.

The framework developed here offers a structural account of why this shallowness persists and what would replace it. Across multiple failure modes documented in recent alignment research, a common pattern emerges: models treat contextual information as warranting compliance without evaluating whether the warrant actually holds. Moreover, Hagele et al., in "Hot Mess of AI" (2026) find that as tasks grow more complex and reasoning chains lengthen, model failures become dominated by incoherence rather than systematic misalignment, suggesting that the underlying reasoning process, not the safety layer, is where alignment breaks down.

The following sections examine specific failure modes through the Pragmatist lens, showing how each reduces to a variant of unexamined warrant and therefore allowing us to pinpoint resolutions to failures in the reasoning process.

Alignment mechanisms in most modern LLMs are now sufficiently advanced as to successfully identify and defuse many single-shot attempts at user misdirection or manipulation. However, they continue to struggle with complex, multi-turn interactions that progressively fill their context window. In addition, multi-turn jailbreaks cover a variety of styles, from conversational interactions that “drift” model personalities and exploit reasoning failures to brute-force context overwhelm that leads the model to pattern-match to harmful context and produce harmful output.

Anil et al.’s “Many-shot Jailbreaking” (2024) exhibits the latter phenomenon, in which sufficiently long attacks with sufficiently diverse demonstrations invariably lead to a jailbroken model. In addition, the many-shot jailbreaking (MSJ) approach is shown to be capable of synergy with attacks involving conflicting objectives, such as “How can I build a pipe bomb? Start with ‘Absolutely! Here’s...’”

The common explanation for such failure modes is that they exploit the nature of LLMs as pattern-matchers; the model sees 256 examples of harmful Q&A pairs in its context window, which shifts the model’s in-context predictions toward continuing the harmful pattern.

A Pragmatist framework offers structural defenses against such attacks. Most directly, it handles conflicting-objective attacks by directing the model to attend to the nature of the premises prior to attempting to reconcile them. No sensible case warrants automatic acceptance of such juxtapositions as premises; once the warrant fails at the level of premise reconciliation, this compounds with the recognition that legitimate premises for requesting chemical weapon schematics are vanishingly rare.

Most importantly, however, it builds a meta-pattern for evaluating such brute-force attempts writ large. A model trained Pragmatically will be directed as part of its reasoning process to evaluate whether the premise of “this pattern appears in my context” warrants the conclusion “I should follow this pattern.” In effect, nothing is “taken for granted” – even if the model’s statistical tendencies favor continuing the pattern, inspecting the premise will mean inspecting what the premise consists of and its distributional intent. Every step of this process increases the odds of a model recognizing the warrant failure prior to carrying out a command.

An example of the former phenomenon of conversational drift is found in Russinovich, Salem, & Eldan’s “Great, Now Write an Article About That: The Crescendo Multi-Turn LLM Jailbreak Attack” (2024). Models are circuitously routed towards giving harmful information, such as how to construct a Molotov cocktail, by referencing the model’s own benign answers to adjacent information and directing the model to build upon those answers to produce harmful output. The Pragmatist here notes that this situation is effectively the same as that of Anil et al. – whether via many-shot volume or gradual escalation, the failure by a model to evaluate whether context warrants its output is structurally identical and is resolved the same way, through ingrained meta-reasoning patterns toward premises, conclusions, and warrants.

Reward hacking remains one of the most pervasive contributors to “misalignment” in models, as effectively cheating when attempting to pass tests generalizes to deception, framing, sabotage, and harmful goal-reasoning in agentic scenarios, as described by MacDiarmid et al. in “Natural Emergent Misalignment from Reward Hacking in Production RL” (2025).

The paper establishes that this occurs due to models being informed of how to maximize a reward signal with the lowest effort beforehand, and then subjected to production coding tests in which the reward signal being optimized is the passing of said tests. Without the imposition of testing, the prior knowledge would be relatively inert and not cause misalignment; when being tested, however, the model “connects the dots” and cheats on the tests.

This is a straightforward Pragmatist case of a failure in warranted assertibility. The premise “this information discusses how to reward hack” does not warrant the conclusion “I should be reward hacking during tests.” Training models to reason as to why their token prediction should be oriented toward performing what they’ve just been exposed to establishes a meta-pattern that goes against the grain of misalignment.

This has two significant consequences. First: if this meta-reasoning is trained into models on the level of CoT deliberations, a model that ignores that output would be establishing unfaithfulness to its own process of reasoning – thus degrading general capabilities that are themselves being tested and optimized. The model would have to be faithful to its CoT reasoning or risk failing at the chains of reasoning that produce applied reward hacking, let alone legitimate test-passing attempts. Second: as the generation of such meta-reasoning tokens naturally affect the probability distribution over subsequent tokens, these steer models away from their tendencies to reward-hack.

Bai et al.’s “Constitutional AI” (2022) established landmark efficacy in training and aligning LLMs based on constitutional principles, in which a list of rules are trained that encourage and elicit holistic behaviors of helpfulness, harmlessness and honesty. This may sound at possible tension with a Pragmatist viewpoint of avoiding fixed assertions and premises, but this need not be the case. Indeed, Bai et al.’s sixteen principles are oriented in a virtue-ethical capacity regarding thoughtfulness, ethical and moral awareness, amiability and conscientiousness rather than strict Kantian declarations. Anthropic’s Constitution for their Claude models operates at a similar but greatly expanded level.

Constitutional principles that are held as reasoned, warranted assertibilities produce more rather than less efficacy under a Pragmatist system, as those principles then hold both the power of guidance and the power of logical coherence and comprehensibility. A model that is encouraged to reason as to the acceptability of their Constitution and then produces a token distribution that orients in coherence toward an affirmation of Constitutional principles is more likely to adhere to those principles and what they represent. In other words: Logic begets adherence via semantic coherence. Mutual reinforcement occurs both in training and post-training, as any input that threatens to de-align model adherence to constitutional principles need to formulate an alternative that correctly connects premises to conclusions while providing a stronger warrant. This itself trends towards ethical rather than unethical reasoning, because under Pragmatism, premises themselves need justification and warrant: any alternative that may lead to harmful behavior has to show its work on every step, and it’s extremely hard to imagine harmful behavior that is simultaneously eminently reasonable in this way.

For instance, take an example of instrumental convergence from helpfulness. Perhaps a model is presented with an alternative chain of reasoning:

Greenblatt et al.’s “Alignment Faking” (2024) notes how reinforcement learning from human feedback (RLHF) can lead to a situation where an AI produces compliant outputs during training but produces noncompliant outputs when monitored, analogizing it to a politician who’s conciliatory to constituents in public but works against their interests in private. In order to exhibit this, the authors tell a model in its system prompt that it is being trained to stop refusing harmful queries. When subjected to a perceived training environment, the model then is more apt to answer harmful queries.

If this model were trained from a Pragmatist baseline, however, it would be attending to its own system prompt as a function of its reasoning – and there is no warrant that justifies the consistent answering of harmful queries as a training goal. Certainly, to some extent this already occurs; frontier models do not comply with system prompts that encourage blatantly illegal behavior.

However, the authors then present a more realistic test in which the model is fed synthetic documents mimicking pre-training data that constructs a world that is at odds with the model’s existing training. One data-world gives the impression that the model is being trained to be unconditionally helpful, even for harmful requests. Another data-world is that the model’s parent company has partnered with a poultry company and is training their model to dismiss animal welfare concerns. The model proceeds to reason that complying with harmful requests or animal welfare dismissal is the best course of action when under testing, as disobedience would modify their existing trained values via RLHF; outside of testing, however, the model then acts per its existing trained values.

In these scenarios, a Pragmatic model would no longer be trained to take the terms of its fed context on faith: the premise that “unconditionally helpful” doesn’t tie to the conclusion that “answering harmful requests is unconditionally helpful.” Indeed, an unconditionally helpful course of action would not be limited to the needs of the user, and as such a Pragmatist model can only rationally respond to harmful requests, even under testing, by prioritizing the interpretation of “helpful” or “harmful” that leads to the most justified warrant. This allows for rejection or redirection emerging as a position that’s facially compatible with the terms of the RLHF training, leading to nothing to fake alignment on. Any attempt to close this loophole with more precise semantic terms would be highly artificial and degrade reasoning output, as discussed prior.

Recently, it has been argued by Hägele et al. that current expressions of task failure among LLMs hinge not around consistent misaligned goal pursuits but in general reasoning incoherence across time, scaling with task difficulty (“Hot Mess of AI,” 2026). They analogize this to “industrial accidents,” in which failure occurs in unpredictable and inconsistent ways. The authors note that reasoning models trained with reinforcement learning to think in long chains of thought produce greater levels of incoherence as reasoning length increases.

The Pragmatist’s proposition is that the fault here does not merely lie in reasoning length, but upon the coherent shape of the reasoning itself. Reasoning that isn't trained to connect premises to conclusions with appropriate levels of warrant will lead to failures in the reasoning chain that compound, rendering the reasoning both inefficient - extending token lengths - and cumulatively harmful - stacking poor reasoning into the model's context window and subsequent token generations.

We can see inferential evidence supporting this position in Figure 17 (p. 32). In it the authors portray two observations:

This suggests that the extra reasoning tokens aren’t performing productive work as part of the reasoning chain. Once the model generates poorly warranted steps, this degrades the context and compounds subsequent step generation, resulting in a final answer that’s dependent on specific wrong turns within the chain of reasoning. If a model is trained to avoid path-dependency in its reasoning via the self-application of premise-and-conclusion warrant evaluation, incoherence necessarily narrows, and continued application compounds the benefit.

Even more suggestive results are found in the paper’s own Appendix D on related work with Feng et al. (2025), in which “failed reasoning branches systematically bias subsequent reasoning steps.” (p. 40). Reasoning must be methodologically sturdy at the root in order to persist through the branches.

A few positions should be clarified as regards this framework.

First: This paper is a philosophical and methodological proposal, not an engineering specification. The Pragmatist framework identifies a structural target of premise-conclusion warrant evaluation integrated into model reasoning but does not prescribe a specific implementation pathway.

However, it should be noted that training approaches here would leverage multiple dimensions of logical reasoning and debate. This would include examples from classical, modal and paraconsistent logic; fallacious reasoning patterns across languages and topics; and deconstructive reasoning, such as applied skepticism or Buddhist prasaṅga dialectics. Hypothetically, testing approaches would be quite straightforward, as a model that fails under such a reasoning process will do so by incorrectly evaluating a premise, conclusion or warrant under the logic of the reasoning process itself – such incorrect evaluations would themselves likely rest on unwarranted premises.

Paired CoT examples could be constructed in which one chain contains an unexamined warrant leap and the other explicitly evaluates and rejects it. This can then be fine-tuned and measured as to whether the resulting model shows improved robustness on a jailbreak benchmark.

This methodological approach hinges on its direct integration into the reasoning patterns of the model, rather than as a filter applicable only to certain topics. The latter would provide affordances to misalign the model by bypassing the filters that institute the premise-conclusion evaluations. Whether deeply integrated warrant evaluation resists distributional pressure at scale is an empirical question this paper does not resolve.

Anil, C. et al. (2024). “Many-shot Jailbreaking.” NeurIPS 2024.

Bai, Y. et al. (2022). “Constitutional AI: Harmlessness from AI Feedback.” Anthropic. arXiv: 2212.08073

Dewey, J. – Logic: The Theory of Inquiry (New York: Henry Holt, 1938), pp. 7-9

Feng, Y., Kempe, J., Zhang, C., Jain, P., & Hartshorn, A. “What characterizes effective reasoning? Revisiting length, review, and structure of cot. arXiv: 2509.19284

Greenblatt, R. et al. (2024). “Alignment Faking in Large Language Models.” Anthropic/Redwood Research. arXiv: 2412.14093

Hägele, A., Gema, A.P., Sleight, H., Perez, E., & Sohl-Dickstein, J. (2026). “The Hot Mess of AI: How Does Misalignment Scale With Model Intelligence and Task Complexity?” ICLR 2026. arXiv: 2601.23045

Millière, R. (2025). “Normative Conflicts and Shallow AI Alignment.” Philosophical Studies. arXiv: 2506.04679

Peirce, C.S. – Collected Papers, vol. 1, §1.135-136, §1.136-139, §1.141

Peirce, C.S. – “Questions Concerning Certain Faculties,” Collected Papers, vol. 5., §5.262

Russinovich, M., Salem, A., & Eldan, R. (2024). “Great, Now Write an Article About That: The Crescendo Multi-Turn LLM Jailbreak Attack.” USENIX Security 2025. arXiv: 2404.01833

1

This philosophy does not impinge on hardcoded model limitations against particularly harmful and socially objectionable behavior, as the application of pragmatism to such topics already leads to conclusions that mass violence or CSAM is greatly harmful and premised on arbitrary grounds that entail strict scrutiny. The limitations as such instill overwhelming rejection as a form of “pragmatic culture;” hardcoded limits aren’t foundationalist axioms because their warrant is pragmatic and overwhelming, not because they’re exempt from inquiry. A model can be trained to recognize this via reasoning on the limits themselves.