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20242026
most citedCODESIM: Multi-Agent Code Generation and Problem Solving through Simulation-Driven Planning and Debugging

1 citations · 2 across the 7 of their papers we have counts for

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cs.AI2025

WebOperator: Action-Aware Tree Search for Autonomous Agents in Web Environment

Mahir Labib Dihan, Tanzima Hashem, Mohammed Eunus Ali +1

LLM-based agents often operate in a greedy, step-by-step manner, selecting actions solely based on the current observation without considering long-term consequences or alternative…

cs.AI2025

MapAgent: A Hierarchical Agent for Geospatial Reasoning with Dynamic Map Tool Integration

Md Hasebul Hasan, Mahir Labib Dihan, Tanzima Hashem +2

Agentic AI has significantly extended the capabilities of large language models (LLMs) by enabling complex reasoning and tool use. However, most existing frameworks are tailored to…

cs.AI2025

CompassLLM: A Multi-Agent Approach toward Geo-Spatial Reasoning for Popular Path Query

Md. Nazmul Islam Ananto, Shamit Fatin, Mohammed Eunus Ali +1

The popular path query - identifying the most frequented routes between locations from historical trajectory data - has important applications in urban planning, navigation optimiz…

cs.CL2025★ 1 cited

CODESIM: Multi-Agent Code Generation and Problem Solving through Simulation-Driven Planning and Debugging

Md. Ashraful Islam, Mohammed Eunus Ali, Md Rizwan Parvez

Large Language Models (LLMs) have made significant strides in code generation and problem solving. Current approaches employ external tool-based iterative debuggers that use compil…

cs.CL2025

MapEval: A Map-Based Evaluation of Geo-Spatial Reasoning in Foundation Models

Mahir Labib Dihan, Md Tanvir Hassan, Md Tanvir Parvez +5

Recent advancements in foundation models have improved autonomous tool usage and reasoning, but their capabilities in map-based reasoning remain underexplored. To address this, we…