1 citations · 2 across the 8 of their papers we have counts for
10 papers
Noise Out, Bias In: Targeted Bias Injection in Diffusion Language Models via Closed-Loop Activation Steering
Sarim Hashmi, Mukul Ranjan, Abdelrahman Elsayed +3
Masked diffusion language models (dLLMs) generate text by iteratively denoising masked positions, re-predicting each token multiple times before it is committed. An autoregressive…
TerraBench: Can Agents Reason Over Heterogeneous Earth-System Data?
Dat Tien Nguyen, Thao Nguyen, Fadillah Adamsyah Maani +5
Climate and environmental decision-making increasingly requires reasoning across heterogeneous inputs, including gridded physical data, satellite imagery, geospatial context, and s…
Objects Before Words: Object-First Inductive Biases for Grounding Language in Child-View Video
Sathira Silva, Abrham Kahsay Gebreselasie, Muhammad Umer Sheikh +3
Learning grounded word meaning from natural experience requires resolving two ambiguities in infant-view recordings: when the named referent appears and where it is in a cluttered…
MMClima: A Framework for Multimodal Climate Science Data and Evaluation
Muhammad Umer Sheikh, Hassan Abid, Khawar Shehzad +2
Climate change research increasingly requires AI systems that reason across text, dynamic visual content, and scientific figures, yet existing climate QA benchmarks are small, most…
Agentic AI for Remote Sensing: Technical Challenges and Research Directions
Muhammad Akhtar Munir, Muhammad Umer Sheikh, Akashah Shabbir +5
Earth Observation (EO) is moving beyond static prediction toward multi-step analytical workflows that require coordinated reasoning over data, tools, and geospatial state. While fo…
GCA Framework: A GCC Countries-Grounded Dataset and Agentic Pipeline for Climate Decision Support
Muhammad Umer Sheikh, Khawar Shehzad, Salman Khan +2
Climate decision-making in the GCC states increasingly demands systems that can translate heterogeneous scientific and policy evidence into actionable guidance, yet general-purpose…
OpenEarthAgent: A Unified Framework for Tool-Augmented Geospatial Agents
Akashah Shabbir, Muhammad Umer Sheikh, Muhammad Akhtar Munir +8
Recent progress in multimodal reasoning has enabled agents that interpret imagery, connect it with language, and execute structured analytical tasks. Extending these capabilities t…
Towards PerSense++: Advancing Training-Free Personalized Instance Segmentation in Dense Images
Muhammad Ibraheem Siddiqui, Muhammad Umer Sheikh, Hassan Abid +2
Segmentation in dense visual scenes poses significant challenges due to occlusions, background clutter, and scale variations. To address this, we introduce PerSense, an end-to-end,…
ThinkGeo: Evaluating Tool-Augmented Agents for Remote Sensing Tasks
Akashah Shabbir, Muhammad Akhtar Munir, Akshay Dudhane +6
Recent progress in large language models (LLMs) has enabled tool-augmented agents capable of solving complex real-world tasks through step-by-step reasoning. However, existing eval…
PerSense: Training-Free Personalized Instance Segmentation in Dense Images
Muhammad Ibraheem Siddiqui, Muhammad Umer Sheikh, Hassan Abid +1
The emergence of foundational models has significantly advanced segmentation approaches. However, challenges still remain in dense scenarios, where occlusions, scale variations, an…