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

1 citations · 2 across the 3 of their papers we have counts for

collaborators

6 papers

cs.SE2025

Let the Barbarians In: How AI Can Accelerate Systems Performance Research

Audrey Cheng, Shu Liu, Melissa Pan +18

Artificial Intelligence (AI) is beginning to transform the research process by automating the discovery of new solutions. This shift depends on the availability of reliable verifie…

cs.CV2025

How Reasoning Influences Intersectional Biases in Vision Language Models

Adit Desai, Sudipta Roy, Mohna Chakraborty

Vision Language Models (VLMs) are increasingly deployed across downstream tasks, yet their training data often encode social biases that surface in outputs. Unlike humans, who inte…

cs.AI20251 cited

Barbarians at the Gate: How AI is Upending Systems Research

Audrey Cheng, Shu Liu, Melissa Pan +14

Artificial Intelligence (AI) is starting to transform the research process as we know it by automating the discovery of new solutions. Given a task, the typical AI-driven approach…

cs.AI20251 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

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…