aiqbench analysis
This research exposes a critical flaw in current LLM evaluation metrics, demonstrating that coherent explanations do not guarantee effective action or genuine understanding across varying observability levels. By introducing the "Bidirectional Coherence Paradox," the study reveals how models can successfully act while misidentifying mechanisms or accurately diagnose issues without enabling intervention. These findings necessitate a new tripartite framework for assessing artificial agents that explicitly separates knowing-that from knowing-how, challenging foundational assumptions in AI epistemology and safety protocols.
Coherent Without Grounding, Grounded Without Success: Observability and Epistemic Failure
arXiv:2603.28371v2 Announce Type: replace-cross Abstract: When an agent can articulate why something works, we typically take this as evidence of genuine understanding. This presupposes that effective action and correct explanation covary, and that coherent explanation reliably signals both. I argue that this assumption fails for contemporary Large Language Models (LLMs). I introduce what I call the Bidirectional Coherence Paradox: competence and grounding not only dissociate but invert across epistemic conditions. In low-observability domains, LLMs often act successfully while misidentifying the mechanisms that produce their success. In high-observability domains, they frequently generate explanations that accurately track observable causal structure yet fail to translate those diagnoses into effective intervention. In both cases, explanatory coherence remains intact, obscuring the underlying dissociation. Drawing on experiments in compiler optimization and hyperparameter tuning, I develop the Epistemic Triangle, a model of how priors, signals, and domain knowledge interact under varying observability. The results suggest that neither behavioral success nor explanatory accuracy alone suffices for attributing understanding. I argue that evaluating artificial epistemic agents requires a tripartite framework -- coherence, grounding, and a proper basing relation linking explanation to action. The systematic separation of knowing-that and knowing-how in LLMs thus challenges assumptions inherited from both epistemology and current AI evaluation practice.
Abstract:When an agent can articulate why something works, we typically take this as evidence of genuine understanding. This presupposes that effective action and correct explanation covary, and that coherent explanation reliably signals both. I argue that this assumption fails for contemporary Large Language Models (LLMs). I introduce what I call the Bidirectional Coherence Paradox: competence and grounding not only dissociate but invert across epistemic conditions. In low-observability domains, LLMs often act successfully while misidentifying the mechanisms that produce their success. In high-observability domains, they frequently generate explanations that accurately track observable causal structure yet fail to translate those diagnoses into effective intervention. In both cases, explanatory coherence remains intact, obscuring the underlying dissociation. Drawing on experiments in compiler optimization and hyperparameter tuning, I develop the Epistemic Triangle, a model of how priors, signals, and domain knowledge interact under varying observability. The results suggest that neither behavioral success nor explanatory accuracy alone suffices for attributing understanding. I argue that evaluating artificial epistemic agents requires a tripartite framework -- coherence, grounding, and a proper basing relation linking explanation to action. The systematic separation of knowing-that and knowing-how in LLMs thus challenges assumptions inherited from both epistemology and current AI evaluation practice.
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Computers and Society (cs.CY); Artificial Intelligence (cs.AI)
arXiv:2603.28371 [cs.CY]
(or arXiv:2603.28371v2 [cs.CY] for this version)
https://doi.org/10.48550/arXiv.2603.28371 Focus to learn more arXiv-issued DOI via DataCite
https://doi.org/10.2139/ssrn.6168626 Focus to learn more DOI(s) linking to related resources
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Full text under the publisher's open licence · 317 words
