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    Answer Set Programming for Bus Route Finding: A Case Study in Bangkok, Thailand
    (2026-01-01)
    Racharak, Teeradaj
    ;
    Mon, Su Yee
    ;
    Chawuthai, Rathachai
    Finding user-satisfied bus routes subject to the minimal travel time and the number of route transfers is a challenging problem in transportation networks. In our context, bus routes and bus stations form a directed graph. Thus, a solution to finding the user-satisfied bus routes is a route path between two given stations satisfying a collection of different user's constraints. While this problem could be related to existing research, such as, the study of graph traversal and path finding algorithms. We show that our problem is more suited to formalizing the navigation system in terms of several related answer set programs (ASPs), where each module has its own isolated function, rather than treating like common graph traversal problems such as the well-known Dijkstra's algorithm, which only provides a statically single solution with lowest cost. Our empirical study with a real-world bus network data confirms that our ASPs encoding works in producing a wide range of high-quality solutions, offering more flexibilities between different optimization criteria, compared with the fixed Dijkstra's optimization results. The findings show promise for extending to richer transit representations and optimization objectives beyond the two considered here.
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    Modular LLM Architecture with Pluggable Reasoning Heads: A Scalable Approach to Multi-Modal AI Reasoning
    (2025-01-01)
    Pathak, Anurag
    ;
    Sharma, Dilip Kumar
    ;
    Agrawal, Harshada
    ;
    Chawuthai, Rathachai
    ;
    Petchhan, Jirayu
    This paper introduces a modular architecture for Large Language Models (LLMs) that incorporates pluggable, domain-specific reasoning heads to augment the model's capabilities beyond conventional text generation. Central to our approach is an attention-routing controller that intelligently classifies and dispatches user prompts to appropriate reasoning modules - namely symbolic, logical, or graph - based heads-based on the prompt's structure and intent. This design enables hybrid, multi-paradigm reasoning without requiring retraining or finetuning of the base LLM. By decoupling reasoning tasks from general language understanding, our system improves both computational efficiency and interpretability. We demonstrate the architecture using Groq as the base LLM and integrate lightweight engines such as SymPy for symbolic mathematics and custom modules for logical and graph-based reasoning. Experiments across a suite of structured and unstructured prompts show a significant reduction in inference latency and token usage, along with higher accuracy and better explainability. The proposed framework offers a scalable foundation for embedding modular reasoning capabilities into modern LLM-driven applications.
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