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Lancaster University
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Trajectory geometry can predict reasoning success in LLMs, boosting task performance while cutting costs.
JoinEquiv exposes 29 hidden logical bugs in popular DBMSs, revealing critical flaws in INNER JOIN optimizations that could undermine data integrity.
Non-expert users can now manage complex 5G network analytics effortlessly through a conversational interface powered by LLMs.
Achieve flicker-free, scalable reconstruction of long dynamic videos by blending the best of stream and clip-based Gaussian Splatting.
Current image quality metrics struggle to articulate *why* one high-quality image is better than another, but this challenge shows MLLMs are closing the gap by providing expert-level explanations.
Current image restoration models still fail to strike the right balance between noise reduction, detail fidelity, and accurate color in real-world, low-light portrait scenarios, highlighting a critical gap this challenge aims to close.
