Task-Aware LLM Council with Adaptive Decision Pathways for Decision Support
Wei Zhu, Lixing Yu, Hao-Ren Yao, Zhiwen Tang, Kun Yue

TL;DR
This paper introduces TALC, a task-aware LLM decision framework that dynamically selects models and plans multi-step reasoning using Monte Carlo Tree Search, improving decision accuracy and efficiency.
Contribution
The paper presents a novel task-adaptive LLM council with structured success memory and adaptive decision pathways, enhancing model specialization and planning in decision tasks.
Findings
TALC outperforms baseline methods on WebShop, HumanEval, and the Game of 24.
It achieves higher task success rates and search efficiency.
Adaptive model routing improves reasoning accuracy.
Abstract
Large language models (LLMs) have shown strong capabilities across diverse decision-making tasks. However, existing approaches often overlook the specialization differences among available models, treating all LLMs as uniformly applicable regardless of task characteristics. This limits their ability to adapt to varying reasoning demands and task complexities. In this work, we propose Task-Aware LLM Council (TALC), a task-adaptive decision framework that integrates a council of LLMs with Monte Carlo Tree Search (MCTS) to enable dynamic expert selection and efficient multi-step planning. Each LLM is equipped with a structured success memory profile derived from prior task trajectories, enabling semantic matching between current reasoning context and past successes. At each decision point, TALC routes control to the most contextually appropriate model and estimates node value using a…
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Taxonomy
TopicsAI-based Problem Solving and Planning · Multimodal Machine Learning Applications · Constraint Satisfaction and Optimization
