Causal inference has emerged as a powerful paradigm enabling robots to make safe, efficient decisions by reasoning about the ``why'' behind events. However, its real-world application is hindered by the difficulty of collecting causal data, especially in human-populated environments where training takes time, while use...
Generative search engines increasingly shape how users discover products, services, and commercial entities by generating answers from retrieved web content. This creates an opportunity to influence which brands appear in these answers by modifying the content available to the retrieval system. In this work, we study a...
A brain MRI scan can carry an unknown mix of artifacts, so restoration has to be blind, yet most deep-learning methods are built to correct one known artifact. We present Dx-Rx, an agent in which a multimodal large language model (MLLM) measures suspected artifacts before it repairs them. To our knowledge, it is the fi...
Necati Sefercioglu, Mehmet Ozan Unal, Metin Ertas et al.· 0 citations
The Musical Metaverse (MM) enables geographically distributed musicians, audiences, sound engineers, and AI agents to interact through synchronized audio, visual, kinematic, and haptic streams, imposing latency, synchronization, bandwidth, and scalability requirements that are difficult to satisfy with conventional bit...
Ali Al Housseini, Jaime Llorca, Cristina Rottondi et al.· 0 citations
Reach audiences
Advertise in front of researchers, engineers, and readers.
Large language model (LLM) assistants increasingly advise backers on which crowdfunding campaigns to support. LLMs have known race and gender problems, and it is unsettled whether they reproduce, erase, or reverse the discrimination documented in entrepreneurial finance. We ask when, and through which decision process,...
Mirza Samad Ahmed Baig, Syeda Anshrah Gillani· 0 citations
Long-context inference is bound by the KV cache, which 2-bit codes could shrink about sixfold if their error stays in check. The usual remedy rotates keys before quantizing, assuming rotation always helps. However, we observe that it does not: rotation flattens a spiky piece of a key but can give a flat one a new peak,...
Kunming Shao, Xijie Huang, Jierun Chen et al.· 0 citations
Enterprises increasingly want LLM agents to automate standard operating procedures over their software stacks -- mapping each step of a procedure to the right call among thousands of internal APIs, without hand-authored step-to-API mappings. The core difficulty is API selection at scale: choosing and composing the corr...
Rongzhe Wei, Ge Shi, Min Cheng et al.· 0 citations
Assistive systems typically tell users everything, often spending time and attention on what a user already knows, leaving less for what happens next. People, on the other hand, leave out what they expect the listener can supply. Can people use their prior knowledge to understand an incomplete message when the speaker...
Anwesha Das, Vera Demberg, Anna Maria Feit· 0 citations
Large Language Bayes (LLB) transforms a natural language problem description into a collection of candidate probabilistic programs and performs inference within each model, combining their predictions using Bayesian model averaging. While this provides a flexible framework for probabilistic inference without manual mod...
Anant Bhide, Akarsh Gupta, Edmond Cunningham et al.· 0 citations
Protein-protein interactions underpin nearly all biological processes. Yet, designing proteins with desired interactions remains challenging due to the structural complexity governing molecular recognition. This study views protein design as a search optimization problem over a combinatorial sequence space under expens...
Fang Wu, Hong-Bin Lin, Bing-Chen Duan et al.· 0 citations
Multi-teacher on-policy distillation consolidates expert capabilities by supervising student-generated trajectories, whereas task arithmetic offers a training-free alternative through linear parameter combinations. We view the linear combination of student and expert parameters as a single differentiable model and prop...
Sinuo Wang, Jiacheng Lu, Zeyu Zhang et al.· 0 citations
Small language models (<=9B parameters) can support local medical question answering (QA) while reducing privacy and latency concerns, but effective fine-tuning remains challenging. This paper compares two chain-of-thought (CoT) supervised fine-tuning methods: Uniform-across-Sequence (UaS), which applies equal loss wei...
Le Zhou, Xiao Luo, Chinmay Chandra et al.· 0 citations
