Large language models can translate natural-language instructions into plausible manipulation plans, but robotic execution requires physically grounded decisions that respect robot embodiment, workspace geometry, reachability, collision constraints, and grasp feasibility. This paper presents a hardware-aware large lang...
Lonnie Tankersley, Noorbakhsh Amiri Golilarz, Soroush Korivand· 0 citations
Sycophancy and hallucination are persistent failure modes of Large Language Models (LLMs) across domains. However, it becomes particularly consequential in clinical question answering, where responses must remain grounded in the provided context and robust to user pressure. Hallucination can introduce information that...
Himanshu Tripathi, Subash Neupane, Shaswata Mitra et al.· 0 citations
Surface material recognition from incomplete visual observations remains a challenging problem in robotic perception and environmental understanding. This paper discusses Sparse Surface Understanding Framework (SSUF), a unified dual-task learning framework that adapts four pretrained architectures-Convolutional Autoenc...
Sindhuja Penchala, Sudip Mittal, Noorbakhsh Amiri Golilarz· 0 citations
Robotic perception from a single viewpoint is often limited by self-occlusion and incomplete surface visibility. This paper presents DARP(Dual-Arm Robotic Perception) https://doi.org/10.21227/rmv3-be47, a calibrated dual-arm RGB-D-IR dataset for object-centered robotic perception using two independently moving eye-in-h...
Manish Kansana, Mohammed Yusuf Mujawar, Sudip Mittal et al.· 0 citations
Longitudinal clinical reasoning requires large language models (LLMs) to identify and integrate relevant evidence distributed across extended patient histories. Although long-context models can process increasingly large amounts of information, providing more history does not necessarily make relevant evidence more acc...
Taye Akinrele, Noorbakhsh Amiri Golilarz, Subash Neupane et al.· 0 citations
Analysis of the search traces shows that context-dependent recall changes the distribution of executed search behaviors, while unsuccessful experiences remain available as negative evidence for later decisions, showing that accumulated experience directly influences subsequent search behavior.
Mohammed Yusuf Mujawar, Shahram Rahimi, Noorbakhsh Amiri Golilarz· 0 citations
The results show that Hebbian association and learned memory routing can jointly organize online visual experience across rapid, persistent, and consolidated memory timescales within Vision Transformers.
Mohammed Yusuf Mujawar, Noorbakhsh Amiri Golilarz· 1 citation
A taxonomy-driven survey of the major cognitive capability gaps that continue to constrain the development of Cognitive AI and outlines a conceptual Adaptive Cognitive Intelligence Architecture (ACIA), providing a foundation for future progress toward Cognitive AI and, ultimately, Artificial General Intelligence (AGI).
Taye Akinrele, Sindhuja Penchala, Noorbakhsh Amiri Golilarz et al.· 0 citations
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