arXiv:2606.19264v1 Announce Type: new Abstract: The knowledge encoded in large language models (LLMs) can serve as a substrate for structured reasoning over variables describing a complex world, but accessing this knowledge in a probabilistically coherent manner poses a difficult inference problem. We propose Large Language Gibbs, a scheme for structured probabilistic inference that uses conditional distributions of an LLM as transition operators. Rather than sampling structured objects through single-pass autoregressive generation, we iteratively resample individual variables conditioned on o
This research addresses a fundamental challenge in leveraging LLMs for probabilistic reasoning, emerging as LLMs become powerful enough to encode complex world knowledge but lack coherent probabilistic inference mechanisms.
It proposes a novel method for structured probabilistic inference that could unlock new capabilities for LLMs beyond simple generation, leading to more robust and coherent AI systems.
The ability to perform iterative, structured resampling rather than single-pass autoregressive generation changes how LLMs can be utilized for complex reasoning tasks, moving towards more agentic behaviors.
- · AI research institutions
- · Developers of AI agents
- · SaaS companies leveraging LLMs for complex workflows
- · Companies relying on simple autoregressive LLM output
- · AI solutions with weak probabilistic reasoning
Improved reliability and coherence in advanced LLM applications, reducing hallucinations and enabling more complex task execution.
Acceleration in the development of sophisticated AI agents capable of complex decision-making and planning in dynamic environments.
Potential for LLMs to directly automate and optimize entire white-collar workflows currently requiring human probabilistic judgment.
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Read at arXiv cs.LG