arXiv:2606.09873v1 Announce Type: new Abstract: Reasoning models achieve strong performance on challenging tasks by generating explicit intermediate reasoning traces before producing a final answer. Yet the internal structure of representation space when reasoning remains poorly understood: how do a model's hidden representations differ during thinking versus the embeddings of the input prompt, and can this structure be exploited to elicit stronger reasoning at inference time? We show that both input embeddings and thinking embeddings (mean-pooled last-layer hidden states over the prompt and r
The paper directly addresses a fundamental limitation in current language models regarding reasoning, proposing a novel geometric priming technique that could significantly enhance their capabilities.
Improving the reasoning abilities of language models through internal representation manipulation could unlock more robust and reliable AI applications, particularly in fields requiring complex problem-solving.
The proposed 'Rotate2Think' method suggests a new paradigm for extracting and optimizing reasoning from existing language models without necessarily increasing their size, potentially making advanced reasoning more accessible.
- · AI developers
- · Generative AI platforms
- · Researchers in AI interpretability
- · Models relying solely on brute-force scaling
- · Companies without strong AI R&D
Language models will exhibit enhanced reasoning capabilities, leading to more accurate and nuanced outputs for complex tasks.
The improved understanding of internal representation spaces could accelerate progress in AI safety and alignment, as reasoning processes become more transparent.
More sophisticated and reliable AI agents could emerge, automating tasks previously considered too complex for current AI systems.
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Read at arXiv cs.LG