9 papers
LLMs Can Get "Brain Rot": A Pilot Study on Twitter/X
Shuo Xing, Junyuan Hong, Yifan Wang +5
We propose and test the LLM Brain Rot Hypothesis: continual exposure to junk web text induces lasting cognitive decline in large language models (LLMs). To unveil junk effects, we…
Unified Multi-Modal Interactive & Reactive 3D Motion Generation via Rectified Flow
Prerit Gupta, Shourya Verma, Ananth Grama +1
Generating realistic, context-aware two-person motion conditioned on diverse modalities remains a fundamental challenge for graphics, animation and embodied AI systems. Real-world…
DRIFT: Learning from Abundant User Dissatisfaction in Real-World Preference Learning
Yifan Wang, Bolian Li, Junlin Wu +5
Real-world large language model deployments (e.g., conversational AI systems, code generation assistants) naturally generate abundant implicit user dissatisfaction (DSAT) signals,…
RestoRect: Degraded Image Restoration via Latent Rectified Flow & Feature Distillation
Shourya Verma, Mengbo Wang, Nadia Atallah Lanman +1
Current approaches for restoration of degraded images face a trade-off: high-performance models are slow for practical use, while fast models produce poor results. Knowledge distil…
Fault Oblivious Eigenvalue Solver
Jayanta Mukherjee, Xuejiao Kang, David F. Gleich +2
Eigenvalue problems serve as fundamental substrates for applications in large-scale scientific simulations and machine learning, often requiring computation on massively parallel p…
GeneFlow: Translation of Single-cell Gene Expression to Histopathological Images via Rectified Flow
Mengbo Wang, Shourya Verma, Aditya Malusare +6
Spatial transcriptomics (ST) technologies can be used to align transcriptomes with histopathological morphology, presenting exciting new opportunities for biomolecular discovery. U…