15 papers
Residual Context Diffusion Language Models
Yuezhou Hu, Harman Singh, Monishwaran Maheswaran +10
Diffusion Large Language Models (dLLMs) have emerged as a promising alternative to purely autoregressive language models because they can decode multiple tokens in parallel. Howeve…
Speculative Interaction Agents: Building Real-Time Agents with Asynchronous I/O and Speculative Tool Calling
Coleman Hooper, Minwoo Kang, Suhong Moon +7
There is a growing demand for agentic AI technologies for a range of downstream applications like customer service and personal assistants. For applications where the agent needs t…
LoSA: Locality Aware Sparse Attention for Block-Wise Diffusion Language Models
Haocheng Xi, Harman Singh, Yuezhou Hu +9
Block-wise diffusion language models (DLMs) generate multiple tokens in any order, offering a promising alternative to the autoregressive decoding pipeline. However, they still rem…
On Neural Scaling Laws for Weather Emulation through Continual Training
Shashank Subramanian, Alexander Kiefer, Arnur Nigmetov +3
Neural scaling laws, which in some domains can predict the performance of large neural networks as a function of model, data, and compute scale, are the cornerstone of building fou…
Reward Under Attack: Analyzing the Robustness and Hackability of Process Reward Models
Rishabh Tiwari, Aditya Tomar, Udbhav Bamba +5
Process Reward Models (PRMs) are rapidly becoming the backbone of LLM reasoning pipelines, yet we demonstrate that state-of-the-art PRMs are systematically exploitable under advers…
Agentic Test-Time Scaling for WebAgents
Nicholas Lee, Lutfi Eren Erdogan, Chris Joseph John +4
Test-time scaling has become a standard way to improve performance and boost reliability of neural network models. However, its behavior on agentic, multi-step tasks remains less w…