2 citations · 3 across the 10 of their papers we have counts for
11 papers
SimpleMark: Fast Multi-Bit Text Watermarking under f -Divergence Constraints
Benjamin D. Kim, Wanrong Zhang, Weitong Ruan +2
We introduce a framework for multi-bit text watermarking with security defined directly through -divergence from the base language model distribution. Unlike prior approaches th…
Computing with Living Neurons: Chaos-Controlled Reservoir Computing with Knowledge Transplant
Seung Hyun Kim, Zhi Dou, Gaurav Upadhyay +6
We introduce chaos-controlled Reservoir Computing (cc-RC) for living neural cultures: dynamically rich substrates of unique potential for adaptive computation. To account for intri…
Energy-Aware Routing to Large Reasoning Models
Austin R. Ellis-Mohr, Max Hartman, Lav R. Varshney
Large reasoning models (LRMs) have heterogeneous inference energy costs based on which model is used and how much it reasons. To reduce energy, it is important to choose the right…
Scalable Multiterminal Key Agreement via Error-Correcting Codes
Benjamin D. Kim, Daniel Alabi, Lav R. Varshney
We explore connections between secret sharing and secret key agreement, which yield a simple and scalable multiterminal key agreement protocol. In our construction, we use error-co…
Verifier Threshold: An Efficient Test-Time Scaling Approach for Image Generation
Vignesh Sundaresha, Akash Haridas, Vikram Appia +1
Image generation has emerged as a mainstream application of large generative models. Just as test-time compute and reasoning have improved language model capabilities, similar bene…
Watermarking Discrete Diffusion Language Models
Avi Bagchi, Akhil Bhimaraju, Moulik Choraria +2
Watermarking has emerged as a promising technique to track AI-generated content and differentiate it from authentic human creations. While prior work extensively studies watermarki…
Skip-It? Theoretical Conditions for Layer Skipping in Vision-Language Models
Max Hartman, Vidhata Jayaraman, Moulik Choraria +2
Vision-language models achieve incredible performance across a wide range of tasks, but their large size makes inference costly. Recent work has shown that multimodal processing co…
Federated Nonlinear System Identification
Omkar Tupe, Max Hartman, Lav R. Varshney +1
We consider federated learning of linearly-parameterized nonlinear systems. We establish theoretical guarantees on the effectiveness of federated nonlinear system identification co…
SparseJEPA: Sparse Representation Learning of Joint Embedding Predictive Architectures
Max Hartman, Lav Varshney
Joint Embedding Predictive Architectures (JEPA) have emerged as a powerful framework for learning general-purpose representations. However, these models often lack interpretability…
ITBench: Evaluating AI Agents across Diverse Real-World IT Automation Tasks
Saurabh Jha, Rohan Arora, Yuji Watanabe +40
Realizing the vision of using AI agents to automate critical IT tasks depends on the ability to measure and understand effectiveness of proposed solutions. We introduce ITBench, a…
Transforming the Hybrid Cloud for Emerging AI Workloads
Deming Chen, Alaa Youssef, Ruchi Pendse +42
This white paper, developed through close collaboration between IBM Research and UIUC researchers within the IIDAI Institute, envisions transforming hybrid cloud systems to meet th…