7 papers
EgoTL: Egocentric Think-Aloud Chains for Long-Horizon Tasks
Lulin Liu, Dayou Li, Yiqing Liang +8
Large foundation models have made significant advances in embodied intelligence, enabling synthesis and reasoning over egocentric input for household tasks. However, VLM-based auto…
Tiny-Twin: A CPU-Native Full-stack Digital Twin for NextG Cellular Networks
Ali Mamaghani, Ushasi Ghosh, Srinivas Shakkottai +2
Modern wireless applications demand testing environments that capture the full complexity of next-generation (NextG) cellular networks. While digital twins promise realistic emulat…
GSpaRC: Gaussian Splatting for Real-time Reconstruction of RF Channels
Bhavya Sai Nukapotula, Rishabh Tripathi, Seth Pregler +3
Channel state information (CSI) is essential for adaptive beamforming and maintaining robust links in wireless communication systems. However, acquiring CSI incurs significant over…
MAVIS: Multi-Objective Alignment via Inference-Time Value-Guided Selection
Jeremy Carleton, Debajoy Mukherjee, Srinivas Shakkottai +1
Large Language Models (LLMs) are increasingly deployed across diverse applications that demand balancing multiple, often conflicting, objectives -- such as helpfulness, harmlessnes…
PITA: Preference-Guided Inference-Time Alignment for LLM Post-Training
Sarat Chandra Bobbili, Ujwal Dinesha, Dheeraj Narasimha +1
Inference-time alignment enables large language models (LLMs) to generate outputs aligned with end-user preferences without further training. Recent post-training methods achieve t…
Risk-Averse Finetuning of Large Language Models
Sapana Chaudhary, Ujwal Dinesha, Dileep Kalathil +1
We consider the challenge of mitigating the generation of negative or toxic content by the Large Language Models (LLMs) in response to certain prompts. We propose integrating risk-…