Task-Aware LLM Council with Adaptive Decision Pathways for Decision Support

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 dual-signal mechanism that fuses model-based evaluations with historical utility scores. These signals are adaptively weighted based on intra-node variance and used to guide MCTS selection, allowing the system to balance exploration depth with planning confidence. Experiments on WebShop, HumanEval, and the Game of 24 demonstrate that TALC achieves superior task success rates and improved search efficiency compared to strong baselines, validating the benefits of specialization-aware routing and adaptive planning.