arXiv:2606.02907v1 Announce Type: new Abstract: Linear probing of large language model (LLM) hidden states is widely used to claim that models learn distinct representations for different reasoning types. We test this by probing Qwen3-14B on three benchmarks spanning the classical trichotomy: LogiQA 2.0 (deductive), ARC-Challenge (inductive), and $\alpha$NLI (abductive). At layer 32 of 40, linear probes achieve 100\% cross-validated accuracy with well-separated geometry (intrinsic dimensionalities: 20.6, 28.5, 33.6; convex hull contamination $\leq$1.5\%). However, this separation is entirely d
The proliferation of Large Language Models and their increasing complexity necessitates deeper understanding of their internal workings, making research into reasoning modes timely.
This research provides a more nuanced understanding of how LLMs process information, challenging prevailing assumptions about their 'reasoning' capabilities and guiding future model development.
The paper suggests that current linear probing methods might be misinterpreting task format distinction for actual reasoning mode differentiation within LLM hidden states, potentially altering how we evaluate and improve AI reasoning.
- · AI researchers
- · Developers of foundational models
- · ML interpretability tools
- · Researchers relying solely on linear probing for reasoning claims
- · Naive interpretations of LLM internal states
The immediate consequence is a critical re-evaluation of current methods for assessing and claiming reasoning capabilities in LLMs.
This could lead to the development of more sophisticated interpretability tools and benchmarks that truly differentiate reasoning types from superficial task characteristics.
Ultimately, a more accurate understanding of LLM internal mechanisms could accelerate the development of truly robust and generalized AI systems, potentially impacting the trajectory of AI agents.
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Read at arXiv cs.CL