arXiv:2605.29687v1 Announce Type: new Abstract: Large Language Models (LLMs) excel at understanding natural language but struggle with optimisation tasks involving multiple constraints and user-defined preferences, which commonly arise in domains such as robotics. We propose a hybrid reasoning approach in which LLMs externalise reasoning through code generation. Given a natural language problem description, an LLM generates Python code that encodes user-defined constraints and preferences as a preference-based Maximum Satisfiability (MaxSAT) problem, which is then solved by an exact MaxSAT sol
This paper addresses a fundamental limitation of Large Language Models (LLMs) in real-world optimization tasks, indicating a timely push for more robust and reliable AI applications.
Improving LLMs' ability to handle complex constraints and preferences signifies a step towards more autonomous and reliable AI systems, crucial for deployment in critical applications like robotics.
LLMs can now externalize complex reasoning to specialized solvers, evolving from mere language understanding to effective problem-solving in constraint-heavy environments, enabling new robotic and agentic applications.
- · AI developers
- · Robotics industry
- · Enterprise software
- · LLM providers
- · Manual optimization processes
This research provides a concrete method for LLMs to tackle previously intractable optimization problems by generating code for MaxSAT solvers.
The ability to reliably integrate complex constraints could accelerate the development and deployment of sophisticated AI agents and advanced robotic systems.
This hybrid approach could lead to new architectures for autonomous systems that fluidly combine natural language understanding with powerful symbolic reasoning and optimization.
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Read at arXiv cs.AI