Where do LLMs Fall Short in CBT-Guided Affective Reasoning?

Vaishnavi Sinha; Pooja Guttal; Pranay Deep Reddy Katike; Vishal Sinha; Gerald Ndawula; Lira Yoon; Andrea Kleinsmith; Manas Gaur

arXiv · 2607.02885

Where do LLMs Fall Short in CBT-Guided Affective Reasoning?

Vaishnavi Sinha·Pooja Guttal·Pranay Deep Reddy Katike·Vishal Sinha·Gerald Ndawula·Lira Yoon·Andrea Kleinsmith·Manas Gaur

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Abstract

Cognitive Behavioral Therapy (CBT) provides a structured framework for understanding a user's mental state by examining the interaction between cognitive and behavioral factors. However, out-of-the-box LLMs respond fluently and empathetically, yet collapse into validation & reflection, regardless of what the user actually needs. They know theoretical CBT (scoring up to 96% accuracy on licensing exam questions) but fail to apply it effectively. We explore this gap with a knowledge-guided framework that treats CBT dialogue as controlled affective reasoning: user narratives are decomposed into Beck's Cognitive Conceptualization structure, grounded in clinical SNOMED CT concepts validated via Natural Language Inference, and a Multiple Chain-of-Thought (MCoT) strategy selection between Validation & Reflection, Socratic Questioning, or Alternative Perspectives. To measure whether such guidance actually changes behavior, we introduce the Protocol Leverage Force (F), a behavior-level metric that captures how far an intervention shifts a model away from its default response. Across three open-weight LLMs and 14 RealCBT-derived case studies, evaluated with human experts, valence-arousal trajectories, and linguistic entrainment, F shows that simply introducing protocol definitions via single chain-of-thought prompting fails to change LLM behavior, while MCoT on these definitions guides strategy selection better. Still, the effect stays within 1% (approx. 1.2-1.3%), and all models remain biased toward Validation & Reflection. These results show CBT knowledge alone does not ensure effective application, giving the affective-computing community instrumentation to measure where LLMs fall short.

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BibTeX RIS

Vaishnavi Sinha, Pooja Guttal, Pranay Deep Reddy Katike, Vishal Sinha, Gerald Ndawula, Lira Yoon, Andrea Kleinsmith, Manas Gaur. 2026-07-08. Where do LLMs Fall Short in CBT-Guided Affective Reasoning?. https://arxiv.org/abs/2607.02885

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