23 citations · 26 across the 10 of their papers we have counts for
12 papers
ObsSpec: Accelerating Agentic Reinforcement Learning with Observation Speculation
Kyle Montgomery, Wang Zhang, Chenguang Wang
Tool-using agents alternate between model generation and tool execution, waiting for each tool call to complete before starting the next generation. This sequential execution is co…
Agent Security Needs Redefinition through a Holistic Framework
Vincent Siu, Jingxuan He, Kyle Montgomery +3
Agent security is widely treated as a question about action content. Defenses ask whether an instruction looks malicious. Benchmarks ask whether an agent performs a harmful soundin…
Agents' Last Exam
Yiyou Sun, Xinyang Han, Weichen Zhang +378
Recent AI systems have achieved strong results on a wide range of benchmarks, yet these gains have not translated into economically meaningful deployment across many professional d…
A Framework for Formalizing LLM Agent Security
Vincent Siu, Jingxuan He, Kyle Montgomery +4
Security in LLM agents is inherently contextual. For example, the same action taken by an agent may represent legitimate behavior or a security violation depending on whose instruc…
LLM CHESS: Benchmarking Reasoning and Instruction-Following in LLMs through Chess
Sai Kolasani, Maxim Saplin, Nicholas Crispino +5
We introduce LLM CHESS, an evaluation framework designed to probe the generalization of reasoning and instruction-following abilities in large language models (LLMs) through extend…
VMDT: Decoding the Trustworthiness of Video Foundation Models
Yujin Potter, Zhun Wang, Nicholas Crispino +11
As foundation models become more sophisticated, ensuring their trustworthiness becomes increasingly critical; yet, unlike text and image, the video modality still lacks comprehensi…
Predicting Task Performance with Context-aware Scaling Laws
Kyle Montgomery, David Park, Jianhong Tu +4
Scaling laws have transformed our understanding of large language models by linking upstream metrics like cross-entropy loss to design factors such as model size, training data, an…
Budget-aware Test-time Scaling via Discriminative Verification
Kyle Montgomery, Sijun Tan, Yuqi Chen +4
Test-time scaling is a powerful strategy for boosting the performance of large language models on complex reasoning tasks. While state-of-the-art approaches often employ generative…
Humanity's Last Exam
Long Phan, Alice Gatti, Ziwen Han +1168
Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achi…
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…
Re-Tuning: Overcoming the Compositionality Limits of Large Language Models with Recursive Tuning
Eric Pasewark, Kyle Montgomery, Kefei Duan +2
We present a new method for large language models to solve compositional tasks. Although they have shown strong performance on traditional language understanding tasks, large langu…
Agent Instructs Large Language Models to be General Zero-Shot Reasoners
Nicholas Crispino, Kyle Montgomery, Fankun Zeng +2
We introduce a method to improve the zero-shot reasoning abilities of large language models on general language understanding tasks. Specifically, we build an autonomous agent to i…