arXiv:2606.29971v1 Announce Type: new Abstract: A growing body of work suggests that the reasoning capabilities of large language models are largely latent in their base form, with post-training primarily amplifying rather than introducing them. However, this evidence comes mainly from mathematical and coding benchmarks, leaving the boundary conditions of that claim largely unexplored, namely which cognitive tasks can be recovered through elicitation and where that recovery fails. To investigate this, we introduce NeuReasoner, a theory-grounded elicitation instrument. At each step, an orchestr
This paper leverages recent advancements in understanding large language models' latent capabilities to explore their cognitive reasoning boundaries.
Understanding the precise cognitive tasks LLMs can perform through elicitation is critical for developing more reliable and sophisticated AI agents and applications.
The introduction of NeuReasoner provides a theory-grounded instrument to systematically map the effective reasoning capabilities of LLMs beyond mathematical and coding benchmarks.
- · AI researchers
- · Developers of AI agents
- · Companies investing in advanced LLM applications
- · Companies over-relying on LLM 'black box' capabilities
- · Benchmarks limited to traditional reasoning tasks
Improved understanding of LLM cognitive strengths and weaknesses will lead to more targeted model training and elicitation strategies.
This foundational knowledge will enable the creation of more robust and human-aligned AI agents capable of complex decision-making.
Deeper insights into AI reasoning could accelerate the development of general artificial intelligence by clarifying the gaps between current systems and human cognition.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG