10 papers
TIAR: Trajectory-Informed Advantage Reweighting for LLM Abstention Learning
Muyu Pan, Shu Zhao, Nan Zhang +4
This paper investigates large language model (LLM) abstention learning, specifically using ternary reward, which incentivize truthfulness in large language models. This paper exten…
When Relations Break: Analyzing Relation Hallucination in Vision-Language Model Under Rotation and Noise
Philip Wootaek Shin, Ajay Narayanan Sridhar, Sivani Devarapalli +3
Vision-language models (VLMs) achieve strong multimodal performance but remain prone to relation hallucination, which requires accurate reasoning over inter-object interactions. We…
Losing the Plot: How VLM responses degrade on imperfect charts
Philip Wootaek Shin, Jack Sampson, Vijaykrishnan Narayanan +2
Vision language models (VLMs) show strong results on chart understanding, yet existing benchmarks assume clean figures and fact based queries. Real world charts often contain disto…
Single-Cell Universal Logic-in-Memory Using 2T-nC FeRAM: An Area and Energy-Efficient Approach for Bulk Bitwise Computation
Rudra Biswas, Jiahui Duan, Shan Deng +7
This work presents a novel approach to configure 2T-nC ferroelectric RAM (FeRAM) for performing single cell logic-in-memory operations, highlighting its advantages in energy-effici…
STAMP-2.5D: Structural and Thermal Aware Methodology for Placement in 2.5D Integration
Varun Darshana Parekh, Zachary Wyatt Hazenstab, Srivatsa Rangachar Srinivasa +3
Chiplet-based architectures and advanced packaging has emerged as transformative approaches in semiconductor design. While conventional physical design for 2.5D heterogeneous syste…
Towards High-Resolution Alignment and Super-Resolution of Multi-Sensor Satellite Imagery
Philip Wootaek Shin, Vishal Gaur, Rahul Ramachandran +4
High-resolution satellite imagery is essential for geospatial analysis, yet differences in spatial resolution across satellite sensors present challenges for data fusion and downst…