All research directions
Machine Reasoning
Plan-guided learning, structured inference, and reasoning capabilities in large language models.
Goals
To equip AI systems with structured reasoning — planning, multi-step inference, and grounded decision-making beyond pattern matching.
Overview
This research explores how large models perform structured reasoning and planning. Work includes plan-guided tuning, cross-lingual topic modeling with LLMs, and methods that improve logical and causal inference in foundation models.
Key objectives
- Develop plan-guided and structured learning methods
- Improve multi-step reasoning in language models
- Integrate causal and logical structure into inference
- Enable grounded decision-making in complex tasks
Key topics
- Plan-guided learning and tuning
- Structured and multi-step inference
- Cross-lingual and topic reasoning with LLMs
- Causal and logical reasoning
Papers in this direction
LLM-XTM: Enhancing Cross-Lingual Topic Models with Large Language Models
Xuan, MC, Nguyen, TP, Van, LN, Sang, DV, Diep, NTN, Le, T
arXiv preprint arXiv:2605.03299
Causal-aware Anomaly Detection for Tabular Data
Nguyen, D, Nguyen, TAH, Le, TD, Venkatesh, S, Le, T, Gupta, S
International Conference on Machine Learning (ICML)