New Metrics Reveal Intention Collapse Dynamics in Large Language Models

Global: New Metrics Reveal Intention Collapse Dynamics in Large Language Models

Researchers have presented three model‑agnostic metrics designed to quantify the relationship between a language model’s internal intention space and its generated token sequence. The study, posted on arXiv in January 2026, aims to clarify how internal signals translate into observable language output, a process the authors refer to as “intention collapse.”

Metric Definitions

The first metric, intention entropy (Hint(I)), measures the uncertainty within the internal intention representation before it collapses into a final token sequence. The second metric, effective dimensionality (deff(I)), captures the number of active dimensions contributing to the intention state. The third metric, recoverability (Recov(I)), is operationalized as the area under the ROC curve (AUROC) of a probe tasked with predicting whether a given intention will lead to a successful answer.

Experimental Design

The authors conducted a 3 × 3 factorial study covering three open‑source models—Mistral‑7B, LLaMA‑3.1‑8B, and Qwen‑2.5‑7B—and three benchmark tasks—GSM8K, ARC‑Challenge, and AQUA‑RAT. Each model was evaluated under three prompting conditions: a baseline prompt, a chain‑of‑thought (CoT) prompt, and a babble control, with 200 items per condition.

Performance Outcomes

Across all settings, the CoT prompting strategy raised average accuracy from 34.2 % to 47.3 %, a gain of 13.1 percentage points. The improvement was driven primarily by large gains on the GSM8K benchmark, while accuracy on ARC‑Challenge declined relative to the baseline.

Entropy Shifts Across Models

Analysis of intention entropy revealed divergent effects of CoT prompting. For the Mistral model, CoT reduced entropy (dH  0) under CoT, suggesting heightened internal uncertainty. These findings highlight model‑specific responses to the same prompting technique.

Probe Predictability Versus Behavioral Accuracy

Probe AUROC scores were significantly above chance in several configurations, yet they did not always align with observed accuracy. Notably, the Qwen model achieved a high AUROC while its CoT accuracy on ARC‑Challenge was lower than the baseline, implying that informative internal signals may not be reliably converted into correct final outputs under constrained response formats.

Implications and Future Directions

The introduced metrics provide a quantitative framework for examining how internal intention states evolve and influence final language generation. By distinguishing between entropy dynamics, dimensionality, and recoverability, the work offers avenues for refining prompting strategies and model architectures to better harness latent internal information.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.