collaborators

14 papers

cs.AI2026

Artificial Intelligence for Mathematical Reasoning: An Integrated Survey of Language Models, Neuro-symbolic Systems, and Verified Discovery

Syed Rifat Raiyan, Mohsinul Kabir, Hasan Mahmud +2

Mathematical reasoning has long served as a stringent test of machine intelligence; over the past decade, it has moved from a niche problem within NLP to one of the most consequent…

cs.CY2026

LLM-Ideoplasticity: Measuring Ideological Plasticity in the Political Behavior of LLMs as a Context-Conditioned Distribution

Adib Sakhawat, Syed Rifat Raiyan, Tahsin Islam +3

We argue, with systematic empirical evidence, that a large language model's political ideology is not a fixed point, but a conditional distribution positionconte…

cs.AI2026

CircuitLM: A Multi-Agent LLM-Aided Design Framework for Generating Circuit Schematics from Natural Language Prompts

Khandakar Shakib Al Hasan, Syed Rifat Raiyan, Hasin Mahtab Alvee +1

Generating accurate circuit schematics from high-level natural language descriptions remains a persistent challenge in electronic design automation (EDA), as large language models…

cs.CL2026

Coordinates of Capability: A Unified MTMM-Geometric Framework for LLM Evaluation

Adib Sakhawat, Tahsin Islam, Takia Farhin +3

The evaluation of Large Language Models (LLMs) faces a critical challenge in construct validity, where fragmented benchmarks and ad hoc metrics frequently conflate method variance,…

cs.CL2026

Courtroom-Style Multi-Agent Debate with Progressive RAG and Role-Switching for Controversial Claim Verification

Masnun Nuha Chowdhury, Nusrat Jahan Beg, Umme Hunny Khan +3

Large language models (LLMs) remain unreliable for high-stakes claim verification due to hallucinations and shallow reasoning. While retrieval-augmented generation (RAG) and multi-…

cs.CL2026

Narrative over Numbers: The Identifiable Victim Effect and its Amplification Under Alignment and Reasoning in Large Language Models

Syed Rifat Raiyan

The Identifiable Victim Effect (IVE) the tendency to allocate greater resources to a specific, narratively described victim than to a statistically characterized group facing e…