Fahad Rahman

Mohd Ariful Haque, Fahad Rahman, Kishor Datta Gupta et al.

Deployed language models are evaluated in a non-stationary environment: model versions, retrieval layers, safety systems, and real-world inputs all change over time. Static bias benchmarks remain useful, but they do not show how models frame newly emerging events for different prompted audiences. We introduce GPF-LIVENEWS, a streaming evaluation protocol and benchmark snapshot for auditing group-conditioned framing in open-ended LLM outputs. The protocol expands fresh BBC/Reuters news anchors across 42 identity labels and seven prompt families, then evaluates response bundles using semantic-sensitivity and sentiment-disparity signals. In a pilot over 12 monitoring runs and 23 hosted models, Policy/Action prompts produce the strongest semantic movement, while sentiment variation is flatter across dimensions and prompt families. The released artifact includes article metadata, prompt templates, instantiated prompts, model-output metadata, score tables, documentation, and reproduction scripts. We interpret all scores as observed-window audit signals for human review, not as permanent fairness rankings or direct proof of harmful bias.

Kishor Datta Gupta, Marufa Kamal, Md. Mahfuzur Rahman et al.

Current state of the art measures like BLEU, CIDEr, VQA score, SigLIP-2 and CLIPScore are often unable to capture semantic or structural accuracy, especially for domain-specific or context-dependent scenarios. For this, this paper proposes a Physics-Constrained Multimodal Data Evaluation (PCMDE) metric combining large language models with reasoning, knowledge based mapping and vision-language models to overcome these limitations. The architecture is comprised of three main stages: (1) feature extraction of spatial and semantic information with multimodal features through object detection and VLMs; (2) Confidence-Weighted Component Fusion for adaptive component-level validation; and (3) physics-guided reasoning using large language models for structural and relational constraints (e.g., alignment, position, consistency) enforcement.

Mohd Ariful Haque, Fahad Rahman, Kishor Datta Gupta et al.

This paper investigates the effectiveness of small language models (SLMs) for agentic tasks (function/tool/API calling) with a focus on running agents on edge devices without reliance on cloud infrastructure. We evaluate SLMs using the Berkeley Function Calling Leaderboard (BFCL) framework and describe parameter-driven optimization strategies that include supervised fine-tuning (SFT), parameter-efficient fine-tuning (PEFT), reinforcement learning (RL)-based optimization, preference alignment via Direct Preference Optimization (DPO), and hybrid methods. We report results for models including TinyAgent, TinyLlama, Qwen, and xLAM across BFCL categories (simple, multiple, parallel, parallel-multiple, and relevance detection), both in live and non-live settings, and in multi-turn evaluations. We additionally detail a DPO training pipeline constructed from AgentBank data (e.g., ALFRED), including our conversion of SFT data to chosen-rejected pairs using TinyLlama responses as rejected outputs and manual validation. Our results demonstrate clear accuracy differences across model scales where medium-sized models (1-3B parameters) significantly outperform ultra-compact models (<1B parameters), achieving up to 65.74% overall accuracy, and 55.62% multi-turn accuracy with hybrid optimization. This study highlights the importance of hybrid optimization strategies that enable small language models to deliver accurate, efficient, and stable agentic AI on edge devices, making privacy-preserving, low-latency autonomous agents practical beyond the cloud.

Md. Mahfuzur Rahman, Kishor Datta Gupta, Marufa Kamal et al.

Immediate damage assessment is essential after natural catastrophes; yet, conventional hand evaluation techniques are sluggish and perilous. Although satellite and unmanned aerial vehicle (UAV) photos offer extensive perspectives of impacted regions, current computer vision methodologies generally yield just classification labels or segmentation masks, so constraining their capacity to deliver a thorough situational comprehension. We introduce the Vision Language Caption Enhancer (VLCE), a multimodal system designed to produce comprehensive, contextually-informed explanations of disaster imagery. VLCE employs a dual-architecture approach: a CNN-LSTM model with a ResNet50 backbone pretrained on EuroSat satellite imagery for the xBD dataset, and a Vision Transformer (ViT) model pretrained on UAV pictures for the RescueNet dataset. Both systems utilize external semantic knowledge from ConceptNet and WordNet to expand vocabulary coverage and improve description accuracy. We assess VLCE in comparison to leading vision-language models (LLaVA and QwenVL) utilizing CLIPScore for semantic alignment and InfoMetIC for caption informativeness. Experimental findings indicate that VLCE markedly surpasses baseline models, attaining a maximum of 95.33% on InfoMetIC while preserving competitive semantic alignment. Our dual-architecture system demonstrates significant potential for improving disaster damage assessment by automating the production of actionable, information-dense descriptions from satellite and drone photos.