262 citations · 652 across the 74 of their papers we have counts for
78 papers
BOReFT: Manifold Steering of Language Models for Black-box Optimization
Dhruv Agarwal, Rico Angell, Kavitha Srinivas +4
Language models are increasingly used as proposal models for black-box search, from program optimization to molecular design. Existing approaches typically improve proposals throug…
Trustworthy Protein-Ligand Binding Affinity Prediction via Reliability-Aware Multi-Engine Fusion
Yongchan Hong, Defu Cao, Wenjin Liu +6
Accurate protein-ligand binding affinity prediction is central to computational drug discovery, yet modern docking engines frequently disagree without indicating which prediction t…
An Exam for Active Observers
Jiarui Zhang, Muzi Tao, Shangshang Wang +3
Human vision is a closed loop: gaze is continuously redirected by intermediate hypotheses rather than a single snapshot. Decades of psychophysics and cognitive science have argued…
Rethinking RL for LLM Reasoning: It's Sparse Policy Selection, Not Capability Learning
Ömer Faruk Akgül, Rajgopal Kannan, Willie Neiswanger +1
Reinforcement learning has become the standard for improving reasoning in large language models, yet evidence increasingly suggests that RL does not teach new strategies; it redist…
Supercharging Simulation-Based Inference for Bayesian Optimal Experimental Design
Samuel Klein, Willie Neiswanger, Daniel Ratner +2
Bayesian optimal experimental design (BOED) seeks to maximize the expected information gain (EIG) of experiments. This requires a likelihood estimate, which in many settings is int…
Neural Nonmyopic Bayesian Optimization in Dynamic Cost Settings
Sang T. Truong, Duc Q. Nguyen, Willie Neiswanger +4
Bayesian optimization (BO) is a common framework for optimizing black-box functions, yet most existing methods assume static query costs and rely on myopic acquisition strategies.…
LYNX: Learning Dynamic Exits for Confidence-Controlled Reasoning
Ömer Faruk Akgül, Yusuf Hakan Kalaycı, Rajgopal Kannan +2
Large reasoning models achieve strong performance on complex tasks by generating extended chains of thought, but they often "overthink": continuing to reason long after they have e…
Hubble: a Model Suite to Advance the Study of LLM Memorization
Johnny Tian-Zheng Wei, Ameya Godbole, Mohammad Aflah Khan +7
We present Hubble, a suite of fully open-source large language models (LLMs) for the scientific study of LLM memorization. Hubble models come in standard and perturbed variants: st…
LLM Unlearning Without an Expert Curated Dataset
Xiaoyuan Zhu, Muru Zhang, Ollie Liu +2
Modern large language models often encode sensitive, harmful, or copyrighted knowledge, raising the need for post-hoc unlearning-the ability to remove specific domains of knowledge…
Probabilistic Graphical Models: A Concise Tutorial
Jacqueline Maasch, Willie Neiswanger, Stefano Ermon +1
Probabilistic graphical modeling is a branch of machine learning that uses probability distributions to describe the world, make predictions, and support decision-making under unce…
TokenSmith: Streamlining Data Editing, Search, and Inspection for Large-Scale Language Model Training and Interpretability
Mohammad Aflah Khan, Ameya Godbole, Johnny Tian-Zheng Wei +5
Understanding the relationship between training data and model behavior during pretraining is crucial, but existing workflows make this process cumbersome, fragmented, and often in…
Zebra-CoT: A Dataset for Interleaved Vision Language Reasoning
Ang Li, Charles Wang, Deqing Fu +9
Humans often use visual aids, for example diagrams or sketches, when solving complex problems. Training multimodal models to do the same, known as Visual Chain of Thought (Visual C…
Resa: Transparent Reasoning Models via SAEs
Shangshang Wang, Julian Asilis, Ömer Faruk Akgül +4
How cost-effectively can we elicit strong reasoning in language models by leveraging their underlying representations? We answer this question with Resa, a family of 1.5B reasoning…
From Calibration to Collaboration: LLM Uncertainty Quantification Should Be More Human-Centered
Siddartha Devic, Tejas Srinivasan, Jesse Thomason +2
Large Language Models (LLMs) are increasingly assisting users in the real world, yet their reliability remains a concern. Uncertainty quantification (UQ) has been heralded as a too…
Auditing Black-Box LLM APIs with a Rank-Based Uniformity Test
Xiaoyuan Zhu, Yaowen Ye, Tianyi Qiu +6
As API access becomes a primary interface to large language models (LLMs), users often interact with black-box systems that offer little transparency into the deployed model. To re…
Textual Steering Vectors Can Improve Visual Understanding in Multimodal Large Language Models
Woody Haosheng Gan, Deqing Fu, Julian Asilis +5
Steering methods have emerged as effective and targeted tools for guiding large language models' (LLMs) behavior without modifying their parameters. Multimodal large language model…
Tina: Tiny Reasoning Models via LoRA
Shangshang Wang, Julian Asilis, Ömer Faruk Akgül +3
How cost-effectively can strong reasoning abilities be achieved in language models? Driven by this fundamental question, we present Tina, a family of tiny reasoning models achieved…
SPD Matrix Learning for Neuroimaging Analysis: Perspectives, Methods, and Challenges
Ce Ju, Reinmar Kobler, Antoine Collas +3
Neuroimaging provides essential tools for characterizing brain activity, structure, and connectivity through modalities that capture complementary aspects of brain organization. Ac…
Sharpe Ratio-Guided Active Learning for Preference Optimization in RLHF
Syrine Belakaria, Joshua Kazdan, Charles Marx +5
Reinforcement learning from human feedback (RLHF) has become a cornerstone of the training and alignment pipeline for large language models (LLMs). Recent advances, such as direct…
Efficient Evaluation of Multi-Task Robot Policies With Active Experiment Selection
Abrar Anwar, Rohan Gupta, Zain Merchant +3
Evaluating learned robot control policies to determine their physical task-level capabilities costs experimenter time and effort. The growing number of policies and tasks exacerbat…
LLM360 K2: Building a 65B 360-Open-Source Large Language Model from Scratch
Zhengzhong Liu, Bowen Tan, Hongyi Wang +22
We detail the training of the LLM360 K2-65B model, scaling up our 360-degree OPEN SOURCE approach to the largest and most powerful models under project LLM360. While open-source LL…
METAGENE-1: Metagenomic Foundation Model for Pandemic Monitoring
Ollie Liu, Sami Jaghouar, Johannes Hagemann +4
We pretrain METAGENE-1, a 7-billion-parameter autoregressive transformer model, which we refer to as a metagenomic foundation model, on a novel corpus of diverse metagenomic DNA an…
Euclid: Supercharging Multimodal LLMs with Synthetic High-Fidelity Visual Descriptions
Jiarui Zhang, Ollie Liu, Tianyu Yu +2
Multimodal large language models (MLLMs) have made rapid progress in recent years, yet continue to struggle with low-level visual perception (LLVP) -- particularly the ability to a…
Political-LLM: Large Language Models in Political Science
Lincan Li, Jiaqi Li, Catherine Chen +44
In recent years, large language models (LLMs) have been widely adopted in political science tasks such as election prediction, sentiment analysis, policy impact assessment, and mis…