arXiv subjects

Duong D. Nguyen

Publications and source records attributed to Duong D. Nguyen.

3 recordsLinked to original sources

arXiv2026

One More Turn, Less Regret: A Regret-Based Multi-Turn Benchmark for LLMs' Clarification Policies

Ambiguous user requests make clarification a sequential decision problem for conversational LLM assistants: they must decide whether to ask, what to ask, when to stop, and when to answer. We introduce RegretBench, a multi-turn benchmark that evaluates clarification as policy behavior rather than isolated question quality. RegretBench provides a hidden-intent formulation of ambiguity, supports free-form interaction grounded in semantic-state tracking, and introduces a regret-based objective that measures how much value a model loses relative to a reference clarification policy. Experiments on open-domain QA and product recommendation scenarios show that final success alone is insufficient, as models with similar accuracy can differ substantially in efficiency, robustness to user behaviors, and stopping decisions. By jointly measuring intent resolution, interaction cost, ineffective clarification, and regret, RegretBench reveals whether models clarify usefully and efficiently. Our results show that effective clarification requires more than plausible questions: models must ask the right question at the right time and stop once the user's intended meaning is clear.

cs.CL↗

arXiv2026

Boltzmann-based Exploration for Robust Decentralized Multi-Agent Planning (Extended Version)

Decentralized Monte Carlo Tree Search (Dec-MCTS) is widely used for cooperative multi-agent planning but struggles in sparse or skewed reward environments. We introduce Coordinated Boltzmann MCTS (CB-MCTS), which replaces deterministic UCT with a stochastic Boltzmann policy and a decaying entropy bonus for sustained yet focused exploration. While Boltzmann exploration has been studied in single-agent MCTS, applying it in multi-agent systems poses unique challenges. CB-MCTS is the first to address this. We analyze CB-MCTS in the simple-regret setting and show in simulations that it outperforms Dec-MCTS in deceptive scenarios and remains competitive on standard benchmarks, providing a robust solution for multi-agent planning.

cs.MA↗

arXiv2022

Multi-Agent Task Assignment in Vehicular Edge Computing: A Regret-Matching Learning-Based Approach

Vehicular edge computing has recently been proposed to support computation-intensive applications in Intelligent Transportation Systems (ITS) such as self-driving cars and augmented reality. Despite progress in this area, significant challenges remain to efficiently allocate limited computation resources to a range of time-critical ITS tasks. To this end, the current paper develops a new task assignment scheme for vehicles in a highway. Because of the high speed of vehicles and the limited communication range of road side units (RSUs), the computation tasks of participating vehicles are to be dynamically migrated across multiple servers. We formulate a binary nonlinear programming (BNLP) problem of assigning computation tasks from vehicles to RSUs and a macrocell base station. To deal with the potentially large size of the formulated optimization problem, we develop a distributed multi-agent regret-matching learning algorithm. Based on the regret minimization principle, the proposed algorithm employs a forgetting method that allows the learning process to quickly adapt to and effectively handle the high mobility feature of vehicle networks. We theoretically prove that it converges to the correlated equilibrium solutions of the considered BNLP problem. Simulation results with practical parameter settings show that the proposed algorithm offers the lowest total delay and cost of processing tasks, as well as utility fairness among agents. Importantly, our algorithm converges much faster than existing methods as the problem size grows, demonstrating its clear advantage in large-scale vehicular networks.

eess.SY↗