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
LLM-based multi-agent systems (MAS) engage tools, share memory, and delegate tasks, often encountering adversarial content. Current defenses for MAS are typically evaluated in isolation, focusing on one attack type at a time, which can lead to costly and hard-to-audit outcomes. This study organizes defenses into five p...
Shaswata Mitra, Raj Patel, Subash Neupane et al.· 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
This work proposes off-attractor supervision (OAS) to supervise neural surrogates where they leave the attractor and integrates physical models into neural simulation by extending supervision from fixed reference trajectories to states where the surrogate is likely to fail.
Yi-Long Dai, Shaswata Mitra, Raj Patel et al.· 0 citations
The findings support the claim that BF is a more structured and automation-friendly framework than CWE, and exploration reveals specific gaps in BF, including under-specified guidance on attributes.
Mohammad Nazmul Hoque, Shaswata Mitra, Subash Neupane et al.· 0 citations
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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