Author

Saurabh Goyal

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#artificial intelligence Preprint Sep 2026

DRACO: Fine-Grained Credit Assignment with Dynamic Rubrics for Long-Horizon Agent Training

Reinforcement Learning from Verifiable Rewards works well when a task has a programmatic checker, but most long-horizon agent domains have none. We work in the outcome-blind setting, where ground-truth success signals are not available. Multi-criteria rubrics are a popular way to supply such a reward; they are scored o...

Shubham Gandhi, Saurabh Goyal, K. Kate et al. · 1 citation

#artificial intelligence Dec 2025

Kascade: A Practical Sparse Attention Method for Long-Context LLM Inference

Kascade is a training-free sparse attention method that leverages known observations such as 1) post-softmax attention is intrinsically sparse, and 2) the identity of high-weight keys is stable across nearby layers to achieve high accuracy on long-context LLM inference.

Dhruv Deshmukh, Saurabh Goyal, Nipun Kwatra et al. · arXiv.org · 10 citations