arXiv:2606.27378v1 Announce Type: cross Abstract: We introduce an axiomatic evaluation framework for latent thought representations in LLMs, comprising metrics that are independent of downstream benchmark scores and reveal representational failures that benchmark accuracy masks. Existing evaluations conflate representation quality with model capacity. Therefore, failures cannot be attributed to the representation rather than to the model that processes it. We formalize four functional axioms (Causality, Minimality, Separability, and Stability) and define a quantitative measure for each, comput
The rapid advancement and widespread deployment of large language models necessitates a more rigorous and fundamental understanding of their internal mechanisms beyond mere benchmark performance.
This work introduces a foundational framework for evaluating the inherent quality of AI thought representations, which is crucial for developing more reliable, interpretable, and advanced AI systems.
The ability to formally assess latent thought quality independent of downstream tasks will enable more targeted improvements in LLM architectures and training methodologies, shifting focus from 'what it does' to 'how it thinks'.
- · AI researchers
- · LLM developers
- · Organizations requiring explainable AI
- · Businesses building critical AI applications
- · Developers solely focused on superficial benchmark gains
- · Less transparent AI models
Improved understanding of LLM internal workings and representational failures.
Development of next-generation LLMs with demonstrably better internal thought structures, leading to more robust and reliable AI.
Accelerated progress towards general AI by providing objective metrics for foundational cognitive capabilities, potentially impacting the AI agents narrative.
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Read at arXiv cs.LG