Inference-Time Reasoning Selectively Reduces Implicit Social Bias in Large Language Models

Drawing on constructs from psychology, prior work has identified a distinction between explicit and implicit bias in large language models (LLMs). While many LLMs undergo post-training alignment and safety procedures to avoid expressions of explicit social bias, they still exhibit significant implicit biases on indirect tasks resembling the Implicit Association Test (IAT). Recent work has further shown that inference-time reasoning can impair LLM performance on tasks that rely on implicit statistical learning. Motivated by a theoretical link between implicit associations and statistical learning in human cognition, we examine how reasoning-enabled inference affects implicit bias in LLMs. We find that enabling reasoning significantly reduces measured implicit bias on an IAT-style evaluation for some model classes across fifteen stereotype topics. This effect appears specific to social bias domains, as we observe no corresponding reduction for non-social implicit associations. As reasoning is increasingly enabled by default in deployed LLMs, these findings suggest that it can meaningfully alter fairness evaluation outcomes in some systems, while also raising questions about how alignment procedures interact with inference-time reasoning to drive variation in bias reduction across model types. More broadly, this work highlights how theory from cognitive science and psychology can complement AI evaluation research by providing methodological and interpretive frameworks that reveal new insights into model behavior.

A quantum semantic framework for natural language processing

Semantic degeneracy represents a fundamental property of natural language that extends beyond simple polysemy to encompass the combinatorial explosion of potential interpretations that emerges as semantic expressions increase in complexity. Large Language Models (LLMs) and other modern NLP systems face inherent limitations precisely because they operate within natural language itself, making them subject to the same interpretive constraints imposed by semantic degeneracy. In this work, we argue using Kolmogorov complexity that as an expression's complexity grows, the likelihood of any interpreting agent (human or LLM-powered AI) recovering the single intended meaning vanishes. This computational intractability suggests the classical view that linguistic forms possess meaning in and of themselves is flawed. We alternatively posit that meaning is instead actualized through an observer-dependent interpretive act. To test this, we conducted a semantic Bell inequality test using diverse LLM agents as ``computational cognitive systems'' to interpret ambiguous word pairs under varied contextual settings. Across several independent experiments, we found average CHSH expectation values ranging from 1.2 to 2.8, with several runs yielding values (e.g., 2.3-2.4) that significantly violate the classical boundary ($|S|\leq2$). This demonstrates that linguistic interpretation under ambiguity can exhibit non-classical contextuality, consistent with results from human cognition experiments. These results inherently imply that classical frequentist-based analytical approaches for natural language are necessarily lossy. Instead, we propose that Bayesian-style repeated sampling approaches can provide more practically useful and appropriate characterizations of linguistic meaning in context.