18 citations · 26 across the 10 of their papers we have counts for
11 papers
Budgeted Subset Refinement for Execution-Aware LLM Research Ideation
Micah Zhang
Large language models (LLMs) can generate research ideas that appear novel to expert reviewers, but recent work also shows that such ideas often lack diversity, are difficult for L…
HALO: Hybrid Adaptive Latent Reasoning for Language Models
Micah Zhang
We study how to improve a frozen pretrained language model with a small amount of adaptive extra computation. A simple approach is to add additional refinement steps on top of the…
Memory Inception: Latent-Space KV Cache Manipulation for Steering LLMs
Andy Zeyi Liu, Michael Zhang, Ilana Greenberg +3
Steering large language models (LLMs) is usually done by either instruction prompting or activation steering. Prompting often gives strong control, but caches guidance tokens at ev…
PAVE: Premise-Aware Validation and Editing for Retrieval-Augmented LLMs
Tianyi Huang, Caden Yang, Emily Yin +2
Retrieval-augmented language models can retrieve relevant evidence yet still commit to answers before explicitly checking whether the retrieved context supports the conclusion. We…
OpenAI GPT-5 System Card
Aaditya Singh, Adam Fry, Adam Perelman +483
This is the system card published alongside the OpenAI GPT-5 launch, August 2025. GPT-5 is a unified system with a smart and fast model that answers most questions, a deeper reason…
Reverse Engineering User Stories from Code using Large Language Models
Mohamed Ouf, Haoyu Li, Michael Zhang +1
User stories are essential in agile development, yet often missing or outdated in legacy and poorly documented systems. We investigate whether large language models (LLMs) can auto…
MMORE: Massive Multimodal Open RAG & Extraction
Alexandre Sallinen, Stefan Krsteski, Paul Teiletche +7
We introduce MMORE, an open-source pipeline for Massive Multimodal Open RetrievalAugmented Generation and Extraction, designed to ingest, transform, and retrieve knowledge from het…
Report Cards: Qualitative Evaluation of Language Models Using Natural Language Summaries
Blair Yang, Fuyang Cui, Keiran Paster +4
The rapid development and dynamic nature of large language models (LLMs) make it difficult for conventional quantitative benchmarks to accurately assess their capabilities. We prop…
BEADs: Bias Evaluation Across Domains
Shaina Raza, Mizanur Rahman, Michael R. Zhang
Recent advances in large language models (LLMs) have substantially improved natural language processing (NLP) applications. However, these models often inherit and amplify biases p…
Fast Exact Unlearning for In-Context Learning Data for LLMs
Andrei I. Muresanu, Anvith Thudi, Michael R. Zhang +1
Modern machine learning models are expensive to train, and there is a growing concern about the challenge of retroactively removing specific training data. Achieving exact unlearni…
Using Large Language Models for Hyperparameter Optimization
Michael R. Zhang, Nishkrit Desai, Juhan Bae +2
This paper explores the use of foundational large language models (LLMs) in hyperparameter optimization (HPO). Hyperparameters are critical in determining the effectiveness of mach…