14 papers
VETNET: Assembling Benchmark Datasets for AI-Driven Veterinary Innovation
Katherine J. Koebel, Renata Ivanek, Parminder Basran +33
VETNET represents an ecosystem of live benchmark datasets and evaluation frameworks designed to accelerate AI-driven innovation in animal science and veterinary medicine through st…
PhantomEnvironments: Training LLM Agents in Fictional Worlds
Anmol Kabra, Swathi Saravana Selvam, Albert Gong +5
Training LLM agents with reinforcement learning (RL) is bottlenecked by environments, which must provide verifiable rewards, support long-horizon interaction, and scale cheaply. Ex…
MoRE: Mixture of Reused Experts
Eric S. Qiu, Utku Umur Acikalin, Justin Lovelace +4
Mixture-of-Experts (MoE) architectures decouple model capacity from computational cost, yet incur high memory footprints as parameters grow linearly with the number of experts. Rec…
Co-Evolving Structured Knowledge and Reasoning in Language Models
Ryan Thomas Noonan, Linxi Zhao, Menghan Xu +6
Retrieval-augmented methods improve factual accuracy by grounding language models in external knowledge, but retrieving over unstructured text often introduces irrelevant context a…
Optimize Your Sampling: Tuned Diffusion Sampling with Bayesian Optimization
Travis Zhang, Christian Belardi, Justin Lovelace +4
Sampling from a diffusion model typically requires many forward passes through a large neural network, making generation computationally expensive. While much work has focused on e…
Co-LMLM: Continuous-Query Limited Memory Language Models
Yair Feldman, Linxi Zhao, Nathan Godey +5
Externalizing knowledge in LLM pre-training is a promising avenue to achieve higher performance at smaller scales, control knowledge use, and overall increase model transparency. W…
Self-Augmenting Retrieval for Diffusion Language Models
Paul Jünger, Justin Lovelace, Linxi Zhao +2
Diffusion language models generate text by iteratively denoising an entire response in parallel, holding a noisy draft of the answer at every step. We show that these intermediate…
Music Transcription with (Almost) No Supervision
Saebyeol Shin, Chao Wan, Zhenzhen Liu +4
Competitive music transcription models require large amounts of paired audio-score data, which is scarce due to collection costs, alignment difficulty, and copyright restrictions.…
Prescriptive Scaling Laws for Data Constrained Training
Justin Lovelace, Christian Belardi, Srivatsa Kundurthy +2
Training compute is increasingly outpacing the availability of high-quality data. This shifts the central challenge from optimal compute allocation to extracting maximum value from…
Adaptive Moments are Surprisingly Effective for Plug-and-Play Diffusion Sampling
Christian Belardi, Justin Lovelace, Kilian Q. Weinberger +1
Guided diffusion sampling relies on approximating often intractable likelihood scores, which introduces significant noise into the sampling dynamics. We propose using adaptive mome…
On the Feasibility and Opportunity of Autoregressive 3D Object Detection
Zanming Huang, Jinsu Yoo, Sooyoung Jeon +6
LiDAR-based 3D object detectors typically rely on proposal heads with hand-crafted components like anchor assignment and non-maximum suppression (NMS), complicating training and li…
Learning from Synthetic Data Improves Multi-hop Reasoning
Anmol Kabra, Yilun Yin, Albert Gong +6
Reinforcement Learning (RL) has been shown to significantly boost reasoning capabilities of large language models (LLMs) in math, coding, and multi-hop reasoning tasks. However, RL…
Stop-Think-AutoRegress: Language Modeling with Latent Diffusion Planning
Justin Lovelace, Christian Belardi, Sofian Zalouk +3
The Stop-Think-AutoRegress Language Diffusion Model (STAR-LDM) integrates latent diffusion planning with autoregressive generation. Unlike conventional autoregressive language mode…
Electron-affinity difference distributions as an organizing principle for superconductivity, enabling the discovery of PtPbBi
Omri Lesser, Yanjun Liu, Natalie Maus +11
Predicting the superconducting transition temperature () from crystal structure and composition remains a central challenge in condensed-matter physics, reflecting the absence…