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MEMENTO addresses the challenge of learning in low-data domains by leveraging the open web itself as a dynamic learning signal rather than treating it as a static retrieval tool
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minWM addresses the challenge of converting powerful but offline and multi-step video diffusion foundation models into real-time, interactive video world models that are camera-controllable, causal…
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MonoPhysics addresses the challenging problem of inferring geometry, appearance, and physical parameters of deformable objects solely from monocular videos
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MOOSE-Copilot addresses the challenge of unifying two complementary but traditionally separate tasks in automated scientific hypothesis discovery — exploratory, divergent ideation and fine-grained, …
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NeuROK addresses the challenge of generating physically plausible 4D object dynamics (temporal deformations) from static 3D shapes without relying on category-specific physical priors or predefined…
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This study investigates the rest-frame optical morphologies and star formation activity of Lyα emitters (LAEs) at redshifts z = 2.4, 3.1, and 4.5 identified by the ODIN survey, using JWST/NIRCam im…
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This paper addresses the critical challenge in drug synergy prediction (DSP) of out-of-distribution (O.O.D.) generalization, where new drugs possess molecular scaffolds and sizes not seen during tr…
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This paper tackles the longstanding challenge of achieving expert-level play in Heads-Up No-Limit Texas Hold’em (HUNL) poker without relying on computationally expensive equilibrium solvers or trai…
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This paper addresses the challenge of efficient reasoning with large language models by sampling from a sharpened power distribution of reasoning traces
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This paper addresses the challenge of efficiently evaluating HTML observation reduction methods for LLM-based web agents, which is critical to reducing costly long-input latency without sacrificing…
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RoboWits addresses a fundamental gap in robotic manipulation benchmarks by targeting the evaluation of cognitive reasoning, creative tool use, and robustness under unexpected challenges rather than…
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This paper addresses challenges in social bot detection on modern social networks, where large language model (LLM)-driven bots produce realistic text, undermining content-based detection
