9 papers
Multimodal Unlearning Across Vision, Language, Video, and Audio: Survey of Methods, Datasets, and Benchmarks
Nobin Sarwar, Shubhashis Roy Dipta, Zheyuan Liu +1
With the growing adoption of VLMs, DMs, LLMs, and AFMs, these multimodal foundation models can inadvertently encode sensitive, copyrighted, biased, or unsafe cross-modal associatio…
ExecTune: Effective Steering of Black-Box LLMs with Guide Models
Vijay Lingam, Aditya Golatkar, Anwesan Pal +6
For large language models deployed through black-box APIs, recurring inference costs often exceed one-time training costs. This motivates composed agentic systems that amortize exp…
AI Agents as Universal Task Solvers
Alessandro Achille, Stefano Soatto
We describe AI agents as stochastic dynamical systems and frame the problem of learning to reason as in transductive inference: Rather than approximating the distribution of past d…
e1: Learning Adaptive Control of Reasoning Effort
Michael Kleinman, Matthew Trager, Alessandro Achille +2
Increasing the thinking budget of AI models can significantly improve accuracy, but not all questions warrant the same amount of reasoning. Users may prefer to allocate different a…
Re-FORC: Adaptive Reward Prediction for Efficient Chain-of-Thought Reasoning
Renos Zabounidis, Aditya Golatkar, Michael Kleinman +3
We propose Re-FORC, an adaptive reward prediction method that, given a query, enables prediction of the expected future rewards as a function of the number of future thinking token…
Long-context Protein Language Modeling Using Bidirectional Mamba with Shared Projection Layers
Yingheng Wang, Zichen Wang, Gil Sadeh +4
Self-supervised training of language models (LMs) has seen great success for protein sequences in learning meaningful representations and for generative drug design. Most protein L…