8 papers
EgoSafetyBench: A Diagnostic Egocentric Video Benchmark for Evaluating Embodied VLMs as Runtime Safety Guards
Siddhant Panpatil, Arth Singh, Mijin Koo +3
Vision-language models (VLMs) are now proposed as runtime safety guards for embodied agents in homes and factories. A deployable guard must catch genuinely unsafe situations while…
Constitutional Arms Races in the Public Goods Game: Co-Evolving LLM Constitutions Under Cooperation-Defection Pressure
Ujwal Kumar, Arth Singh, Hershraj Niranjani +5
Frontier LLM agents engage in blackmail, sabotage, and document leaks under goal conflicts in agentic settings, exposing limitations of alignment methods built around single-agent…
Mechanistic Analysis of Alignment Algorithms in Language Models
Aarush Sinha, Ishan Garg, Veeraraju Elluru +2
Post-training alignment algorithms are predominantly evaluated as black boxes, obscuring how they reshape language models' internal computations. We present a systematic mechanisti…
Toward Human-AI Complementarity Across Diverse Tasks
Yuzheng Xu, Annya Dahmani, Matthew D. Blanchard +13
Human-AI complementarity, the idea that combining human and AI judgments can outperform either alone, offers a promising pathway toward robust oversight of advanced AI systems. How…
Re-Mask and Redirect: Exploiting Denoising Irreversibility in Diffusion Language Models
Arth Singh
Safety alignment in diffusion language models (dLLMs) relies on a single load-bearing assumption: that committed tokens are permanent. We show that violating this assumption, by re…
Mechanistically Interpreting Compression in Vision-Language Models
Veeraraju Elluru, Arth Singh, Roberto Aguero +3
Compressed vision-language models (VLMs) are widely used to reduce memory and compute costs, making them a suitable choice for real-world deployment. However, compressing these mod…