collaborators

6 papers

cs.LG2026

LLMs Can Annotate Attribution Graphs

Ameen Patel, Max Zhang, Nathan Hu

Circuit tracing is an exciting technique for revealing the internal computation of language models, but it requires a time-intensive manual step of grouping individual features or…

cs.CL2026

Why Do Safety Guardrails Degrade Across Languages?

Max Zhang, Ameen Patel, Sang T. Truong +1

Large language models exhibit safety degradation in non-English languages. Standard evaluation relies on Jailbreak Success Rate (JSR), which confounds several safety-driving factor…

cs.CL2026

Medmarks: A Comprehensive Open-Source LLM Benchmark Suite for Medical Tasks

Benjamin Warner, Ratna Sagari Grandhi, Max Kieffer +32

Evaluating large language models (LLMs) for medical applications remains challenging due to benchmark saturation, limited data accessibility, and insufficient coverage of relevant…

cs.LG2026

The Role of Emotional Stimuli and Intensity in Shaping Large Language Model Behavior

Ameen Patel, Felix Lee, Kyle Liang +1

Emotional prompting - the use of specific emotional diction in prompt engineering - has shown increasing promise in improving large language model (LLM) performance, truthfulness,…

cs.LG2025

INTELLECT-3: Technical Report

Prime Intellect Team, Mika Senghaas, Fares Obeid +20

We present INTELLECT-3, a 106B-parameter Mixture-of-Experts model (12B active) trained with large-scale reinforcement learning on our end-to-end RL infrastructure stack. INTELLECT-…

cs.LG2025

Beat the long tail: Distribution-Aware Speculative Decoding for RL Training

Zelei Shao, Vikranth Srivatsa, Sanjana Srivastava +12

Reinforcement learning(RL) post-training has become essential for aligning large language models (LLMs), yet its efficiency is increasingly constrained by the rollout phase, where…