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View article: P1: Mastering Physics Olympiads with Reinforcement Learning
P1: Mastering Physics Olympiads with Reinforcement Learning Open
Recent progress in large language models (LLMs) has moved the frontier from puzzle-solving to science-grade reasoning-the kind needed to tackle problems whose answers must stand against nature, not merely fit a rubric. Physics is the sharp…
View article: PhysicsMinions: Winning Gold Medals in the Latest Physics Olympiads with a Coevolutionary Multimodal Multi-Agent System
PhysicsMinions: Winning Gold Medals in the Latest Physics Olympiads with a Coevolutionary Multimodal Multi-Agent System Open
Physics is central to understanding and shaping the real world, and the ability to solve physics problems is a key indicator of real-world physical intelligence. Physics Olympiads, renowned as the crown of competitive physics, provide a ri…
View article: HiPhO: How Far Are (M)LLMs from Humans in the Latest High School Physics Olympiad Benchmark?
HiPhO: How Far Are (M)LLMs from Humans in the Latest High School Physics Olympiad Benchmark? Open
Recently, the physical capabilities of (M)LLMs have garnered increasing attention. However, existing benchmarks for physics suffer from two major gaps: they neither provide systematic and up-to-date coverage of real-world physics competiti…
View article: A Theory-Driven Approach to Inner Product Matrix Estimation for Incomplete Data: An Eigenvalue Perspective
A Theory-Driven Approach to Inner Product Matrix Estimation for Incomplete Data: An Eigenvalue Perspective Open
View article: KAE: Kolmogorov-Arnold Auto-Encoder for Representation Learning
KAE: Kolmogorov-Arnold Auto-Encoder for Representation Learning Open
The Kolmogorov-Arnold Network (KAN) has recently gained attention as an alternative to traditional multi-layer perceptrons (MLPs), offering improved accuracy and interpretability by employing learnable activation functions on edges. In thi…
View article: FedLF: Layer-Wise Fair Federated Learning
FedLF: Layer-Wise Fair Federated Learning Open
Fairness has become an important concern in Federated Learning (FL). An unfair model that performs well for some clients while performing poorly for others can reduce the willingness of clients to participate. In this work, we identify a d…
View article: Highly-Efficient Robinson-Foulds Distance Estimation with Matrix Correction
Highly-Efficient Robinson-Foulds Distance Estimation with Matrix Correction Open
Phylogenetic trees are essential in studying evolutionary relationships, and the Robinson-Foulds (RF) distance is a widely used metric to calculate pairwise dissimilarities between phylogenetic trees, with various applications in both the …
View article: Metric Nearness Made Practical
Metric Nearness Made Practical Open
Given a square matrix with noisy dissimilarity measures between pairs of data samples, the metric nearness model computes the best approximation of the matrix from a set of valid distance metrics. Despite its wide applications in machine l…