Position: It's Time to Optimize LLMs for Self-Consistency

• URL: http://arxiv.org/abs/2608.05188v1
• Date: Fri, 31 Jul 2026 19:16:53 GMT
• Title: Position: It's Time to Optimize LLMs for Self-Consistency
• Abstract summary: Many important failures persist in language model pre- and post-training pipelines.<n>We argue that these failures arise from a modeling assumption permeating all aspects of the pipeline.<n>We propose self-consistency as a framework for understanding these failures.
• Score: 50.68587056599734
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite ever-increasing sophistication in language model (LM) pre- and post-training pipelines, many important failures persist: models overcondition on user framing ("sycophancy"), exhibit incomplete logical generalization, and produce confident but incorrect responses. We argue that these failures arise from a modeling assumption permeating all aspects of the pipeline: that behavior can be specified and evaluated independently on single-output pairs. Many model failures are difficult, if not impossible, to detect without reasoning about relationships between a model's responses across inputs. In this position paper, we propose self-consistency as a framework for understanding these failures. We first observe that a wide variety of techniques designed to improve specific aspects of LM behavior-targeting properties as diverse as adversarial robustness and factual coherence-can be understood as special cases of a common "consistency optimization" procedure and addressed with a standard set of optimization tools. We next outline a set of new model properties that could be achieved by optimizing for consistency, and conclude with a discussion of what it would mean to develop generally consistent LMs, including the capabilities they would enable and the objections they raise.

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