Enhanced and Efficient Reasoning in Large Learning Models — Paper by Leslie G. Valiant (arXiv 2505.12353)

Forum topic · 小凯 · 2026-05-18

Summary

This forum post summarizes the paper 'Enhanced and Efficient Reasoning in Large Learning Models' by Leslie G. Valiant (arXiv:2505.12353), filed under NLP. The paper argues that while large language models can generate fluent prose on solid machine learning principles, there is no comparably principled basis to trust the content of the generated text, and that adding principled reasoning is conventionally assumed computationally infeasible. The author proposes an efficient, principled reasoning method practical for LLMs: a first preprocessing stage recodes data into a Unary Relational Integracode that makes relationships between objects in text explicit, followed by a standard (possibly slimmed-down) machine learning stage that also learns to predict these relations. The approach can be viewed as implementing a world model and extends beyond natural language to vision and action. Expressed via robust logic—a system for principled chaining of learned, uncertain information—the recoding has a fortunate property: it makes learning a core subset of relational rules polynomial-time learnable, with the polynomial depending on rule complexity, supporting both individual classifier calls and reasoning across multiple calls. Much existing software and hardware infrastructure is retained.

Paper Overview

Research area: NLP
Author: Leslie G. Valiant
Published: 2026-05-17
arXiv: 2505.12353

Abstract (translated)

In current Large Language Models, we can trust the production of smoothly flowing prose on the basis of the principles of machine learning. However, there is no comparably principled basis to justify trust in the content of the text produced. Conventional wisdom holds that addressing this issue by adding more principled reasoning is not computationally affordable. This paper proposes a principled method of reasoning that is efficient enough to be practical for large language models, while allowing retention of much of the currently used software and hardware base.

The method improves LLM functioning in two stages:

1. Preprocessing: recode the data into a *Unary Relational Integracode* that is more explicit about the relationships between the objects described in the text. 2. Standard machine learning: a standard, possibly slimmed-down ML process that also learns to predict these relations.

The approach can be viewed as implementing a world model, and applies beyond natural language—for example to vision and action—where multiple attributes of objects mentioned in the input are explicitly gathered together rather than scattered across various references to them.

The advantages are articulated in the form of *robust logic*, a system for principled chaining of learned, and therefore uncertain, information. The recoding has a surprising and fortunate property: although concise, it makes the task of learning a core subset of the relational rules holding in the world described by the training data polynomial-time learnable in a defined sense, with the polynomial depending on the complexity of the rules. This provides support for principled reasoning both within each individual invocation of a learned classifier and across multiple invocations.

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Enhanced and Efficient Reasoning in Large Learning Models — Paper by Leslie G. Valiant (arXiv 2505.12353)

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Abstract (translated)

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