Author

Srimat T. Chakradhar

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Conference Aug 2026

PRISM: Planning with Belief Under Incomplete World Models

Embodied task planning requires reasoning under uncertainty about world states that are partially observed and incrementally revealed through interaction. However, most vision-language model (VLM) planners operate in a reactive sense-plan-act loop without maintaining a persistent representation of object locations. As...

M. S. Yasar, Murugan Sankaradas, Srimat T. Chakradhar · IEEE International Symposium... · 0 citations

#artificial intelligence Preprint Sep 2026

GRASP: Generating, Revising, and Assessing for Strategic Planning with Agentic AI

Empirical evaluations show that GRASP consistently establishes a new state-of-the-art frontier across diverse datasets, yielding substantial accuracy gains over direct LLM planners on Natural Plan Calendar Scheduling, ZebraLogic, and SciBench Math.

Arunabh Srivastava, M. Khojastepour, Srimat T. Chakradhar et al. · 0 citations

Preprint Aug 2026

DamageScope: Vision-Language Retrieval at Scale for Disaster Damage Assessment from Satellite Imagery

DamageScope is a retrieval-augmented framework that combines satellite imagery with Vision-Language Models (VLMs) and Large Language Models (LLMs) to automate property damage analysis and introduces a novel multi-vector embedding-based clustering algorithm that outperforms traditional single-vector embedding approaches...

Ravi K. Rajendran, Biplob Debnath, Murugan Sankaradas et al. · 0 citations