From the 1 of 13 linked papers with an AI index.
13 papers
Value Drifts: Tracing Value Alignment During LLM Post-Training
Mehar Bhatia, Shravan Nayak, Gaurav Kamath +4
The paper studies how large language models acquire and change their alignment with human values during post‑training, analyzing the impact of supervised fine‑tuning and preference…
CultureScore: Evaluating Cultural Faithfulness in Video Generation Models
Anku Rani, Wei Dai, Shravan Nayak +3
As video generation models like Veo 3.1 and LTX-2 advance, their ability to accurately represent diverse global cultures remains a critical yet understudied frontier. Current metri…
LatentLens: Revealing Highly Interpretable Visual Tokens in LLMs
Benno Krojer, Shravan Nayak, Oscar Mañas +4
Transforming a large language model (LLM) into a vision-language model (VLM) can be achieved by mapping the visual tokens from a vision encoder into the embedding space of an LLM.…
Grounding Computer Use Agents on Human Demonstrations
Aarash Feizi, Shravan Nayak, Xiangru Jian +14
Building reliable computer-use agents requires grounding: accurately connecting natural language instructions to the correct on-screen elements. While large datasets exist for web…
Discovering Failure Modes in Vision-Language Models using RL
Kanishk Jain, Qian Yang, Shravan Nayak +3
Vision-language Models (VLMs), despite achieving strong performance on multimodal benchmarks, often misinterpret straightforward visual concepts that humans identify effortlessly,…
CUA-Suite: Massive Human-annotated Video Demonstrations for Computer-Use Agents
Xiangru Jian, Shravan Nayak, Kevin Qinghong Lin +5
Computer-use agents (CUAs) hold great promise for automating complex desktop workflows, yet progress toward general-purpose agents is bottlenecked by the scarcity of continuous, hi…