285 citations · 338 across the 49 of their papers we have counts for
53 papers
Memorilla: Latent Semantic Memory for LLMs
Adibvafa Fallahpour, Parsa Idehpour, Vignesh Kothapalli +4
Memory enables large language models (LLMs) to use information beyond their parameters, such as past user conversations, long documents, or an agent's earlier actions. For a frozen…
Optimal Design of Shaped Pupil Lyot Coronagraph for China's Large Segmented Telescope
Kaining Yang, He Sun, Bin B. Ren +7
China's next-generation large telescopes, such as the Expandable Aperture Segmented Telescope (EAST) and the Large Optical/Infrared Telescope (LOT), are being planned as flagship f…
RT-J: Large-Scale Pretraining of Relational Transformers for Context-Efficient Predictions
Rishabh Ranjan, Vignesh Kothapalli, Harshvardhan Agarwal +5
Foundation models for tables and relational databases are increasingly used for predictive tasks across various scientific and industrial domains. However, adapting existing models…
Higher-Order Molecular Grammars for Generative and Foundation Models in Chemistry
Yiming Huang, Yujie Zeng, Vijay Prakash Dwivedi +4
Molecular learning models are strongly shaped by their underlying representations. Yet standard sequential and graph formalisms struggle to explicitly encode higher-order topology,…
UniEvo-VL: An On-policy Self-Distillation Training Recipe for Multimodal Model Self-improvement
Fang Wu, Da Xing, Yanjie Huang +16
Modern multimodal models bring generation and understanding into a single unified system, which enables them to provide and learn from their own feedback. Motivated by this unified…
Data Scarcity and Model Sparsity: Mixtures-of-Experts Overfit More to Repeated Data
Atindra Jha, Margaret Li, Jure Leskovec +2
As the supply of human-written text is exhausted, it has become standard practice to repeat language model training data. Prior work has studied data repetition for densely activat…
Advancing Open and Reproducible Relational Learning: RelArena-, TabPFN-Rel and RPI
Adrian Hayler, Klemens Flöge, Alan Arazi +44
This first release of Prior Labs in relational learning shows our continued commitment to open science. We open-source three pieces of software that we expect to accelerate researc…
Deciphering Fingerprints of 3D Molecular Surfaces for Accurate Epitope Prediction
Fang Wu, Weihao Xuan, Jure Leskovec +2
Molecular surfaces encode the geometric and physicochemical patterns that determine antibody-antigen recognition, central to epitope prediction. However, existing methods rely on s…
M*: A Modular, Extensible, Serving System for Multimodal Models
Atindra Jha, Naomi Sagan, Keisuke Kamahori +9
We are entering a new era of composite model architectures that integrate diverse components such as vision encoders, language backbones, diffusion and flow heads, audio codecs, ac…
Diffusion Language Model Parallel Decoding via Product-of-Experts Bridge
Juntong Shi, Brian L. Trippe, Jure Leskovec +2
Diffusion language models (DLMs) offer substantial speed advantages through parallel decoding, but the lack of token dependencies limits generation quality compared to autoregressi…
SurfDesign: Effective Protein Design on Molecular Surfaces
Fang Wu, Shuting Jin, Xiangru Tang +5
Protein function is largely determined by molecular surface geometry and physicochemical complementarity, yet most protein design methods condition only on backbone structure. We i…
Found in Conversation: LLMs Teach Themselves to Close the Multi-Turn Gap
Tianlang Chen, Shirley Wu, Jure Leskovec
Large Language Model (LLM) interactions are typically underspecified, with users clarifying all necessary details across multiple conversational turns. Yet recent work shows that L…
Learning over Positive and Negative Edges with Contrastive Message Passing
Peter Pao-Huang, Charilaos I. Kanatsoulis, Michael Bereket +1
Conventional approaches to learning on graphs involve message passing along existing (i.e., positive) edges to update node features. However, these approaches often disregard the p…
NoiseRater: Meta-Learned Noise Valuation for Diffusion Model Training
Fang Wu, Haokai Zhao, Da Xing +18
Training a diffusion model involves two sources of randomness for each data sample: the timestep and the Gaussian noise realization. The timestep has been studied extensively throu…
Proteo-R1: Reasoning Foundation Models for De Novo Protein Design
Fang Wu, Weihao Xuan, Heli Qi +26
Deep learning in de novo protein design has achieved atomic-level fidelity. However, existing models remain largely non-deliberative: they directly synthesize molecular geometries…
KumoRFM-2: Scaling Foundation Models for Relational Learning
Valter Hudovernik, Federico López, Vid Kocijan +4
We introduce KumoRFM-2, the next iteration of a pre-trained foundation model for relational data. KumoRFM-2 supports in-context learning as well as fine-tuning and is applicable to…
HumanLM: Simulating Users with State Alignment Beats Response Imitation
Shirley Wu, Evelyn Choi, Arpandeep Khatua +7
Large Language Models (LLMs) are increasingly used to simulate how specific users respond to a given context, enabling more user-centric applications that rely on user feedback. Ho…
Improving Diffusion Language Model Decoding through Joint Search in Generation Order and Token Space
Yangyi Shen, Tianjian Feng, Jiaqi Han +5
Diffusion Language Models (DLMs) offer order-agnostic generation that can explore many possible decoding trajectories. However, current decoding methods commit to a single trajecto…
RelBench v2: A Large-Scale Benchmark and Repository for Relational Data
Justin Gu, Rishabh Ranjan, Charilaos Kanatsoulis +8
Relational deep learning (RDL) has emerged as a powerful paradigm for learning directly on relational databases by modeling entities and their relationships across multiple interco…
Predictive Query Language: A Domain-Specific Language for Predictive Modeling on Relational Databases
Vid Kocijan, Jinu Sunil, Jan Eric Lenssen +5
The purpose of predictive modeling on relational data is to predict future or missing values in a relational database, for example, future purchases of a user, risk of readmission…
PluRel: Synthetic Data unlocks Scaling Laws for Relational Foundation Models
Vignesh Kothapalli, Rishabh Ranjan, Valter Hudovernik +4
Relational Foundation Models (RFMs) facilitate data-driven decision-making by learning from complex multi-table databases. However, the diverse relational databases needed to train…
End-to-End Test-Time Training for Long Context
Arnuv Tandon, Karan Dalal, Xinhao Li +11
We formulate long-context language modeling as a problem in continual learning rather than architecture design. Under this formulation, we only use a standard architecture -- a Tra…
TGM: a Modular and Efficient Library for Machine Learning on Temporal Graphs
Jacob Chmura, Shenyang Huang, Tran Gia Bao Ngo +7
Well-designed open-source software drives progress in Machine Learning (ML) research. While static graph ML enjoys mature frameworks like PyTorch Geometric and DGL, ML for temporal…
Relational Transformer: Toward Zero-Shot Foundation Models for Relational Data
Rishabh Ranjan, Valter Hudovernik, Mark Znidar +7
Pretrained transformers readily adapt to new sequence modeling tasks via zero-shot prompting, but relational domains still lack architectures that transfer across datasets and task…