most citedJudgeBench: A Benchmark for Evaluating LLM-based Judges

3 citations · 5 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2025

vAttention: Verified Sparse Attention

Aditya Desai, Kumar Krishna Agrawal, Shuo Yang +5

State-of-the-art sparse attention methods for reducing decoding latency fall into two main categories: approximate top- (and its extension, top-) and recently introduced samp…

cs.AI2025★ 1 cited

The Danger of Overthinking: Examining the Reasoning-Action Dilemma in Agentic Tasks

Alejandro Cuadron, Dacheng Li, Wenjie Ma +13

Large Reasoning Models (LRMs) represent a breakthrough in AI problem-solving capabilities, but their effectiveness in interactive environments can be limited. This paper introduces…

cs.LG2025

vCache: Verified Semantic Prompt Caching

Luis Gaspar Schroeder, Aditya Desai, Alejandro Cuadron +7

Semantic caches return cached responses for semantically similar prompts to reduce LLM inference latency and cost. They embed cached prompts and store them alongside their response…

cs.LG2024★ 1 cited

HashAttention: Semantic Sparsity for Faster Inference

Aditya Desai, Shuo Yang, Alejandro Cuadron +3

Leveraging long contexts is crucial for advanced AI systems, but attention computation poses a scalability challenge. While scaled dot-product attention (SDPA) exhibits token spars…

cs.AI2024★ 3 cited

JudgeBench: A Benchmark for Evaluating LLM-based Judges

Sijun Tan, Siyuan Zhuang, Kyle Montgomery +5

LLM-based judges have emerged as a scalable alternative to human evaluation and are increasingly used to assess, compare, and improve models. However, the reliability of LLM-based…