arXiv:2510.09340v2 Announce Type: replace-cross Abstract: Recent language models exhibit significant logical reasoning abilities, yet the mechanisms supporting deductive inference remain poorly understood. This paper studies small transformer-based language models trained from scratch on multi-step deductive tasks, focusing on the distinction between horizontal reasoning, where intermediate steps are generated autoregressively, and vertical reasoning, where inference unfolds implicitly across layers before the first output token is produced. We analyze two synthetic tasks: logical consequence
This research addresses fundamental questions about AI's internal reasoning mechanisms as language models become more sophisticated and widely deployed, particularly as their capabilities expand into complex, multi-step tasks.
Understanding how language models perform deductive inference, specifically the interplay between horizontal and vertical reasoning, is crucial for developing more reliable, explainable, and advanced AI systems.
By dissecting the internal workings of deductive logic, this research provides insights that could lead to new architectural designs or training methodologies for AI, ultimately enhancing their reasoning transparency and capability.
- · AI researchers
- · Deep learning framework developers
- · AI ethics and safety organizations
- · Black-box AI approaches
- · Systems highly reliant on emergent, uninterpretable reasoning
Improved understanding of internal AI reasoning processes and mechanisms for deductive logic.
Development of more robust, explainable, and auditable AI models capable of complex, multi-step problem-solving.
Accelerated deployment of AI in critical applications requiring high-stakes reasoning, fostering greater trust and adoption.
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Read at arXiv cs.CL