This year, RoboCup will be held in Incheon, South Korea, from 2-6 July. The event will see teams take part in competitions, training sessions, and a symposium. It's an exciting time for RoboCup, as there have been some updates to the leagues and competition format . Most prominently, the soccer leagues will have a primary focus on humanoid robots. A workshop focused on sharing projects, experiences, and innovations in educational robotics.

Given an undirected, weighted n-vertex graph G = (V,E,w), a Gomory-Hu tree T is a weighted tree on V that preserves the Min-s-t-Cut between any pair of vertices s,t V. Finding cuts in graphs is a key primitive in problems such as bipartite matching, spectral and correlation clustering, and community detection. We design a differentially private (DP) algorithm that computes an approximate Gomory-Hu tree. Our algorithm is ε-DP, runs in polynomial time, and can be used to compute s-tcuts that are O(n/ε)-additive approximations of the Min-s-t-Cuts in Gfor all distinct s,t V with high probability. Our error bound is essentially optimal, since [29] showed that privately outputting a single Min-s-t-Cut requires Ω(n) additive error even with (ε,δ)-DP and allowing for multiplicative error. Prior to our work, the best additive error bounds for approximate all-pairs Min-s-t-Cuts were O(n3/2/ε)for ε-DP [47] and O( mn/ε)for (ε,δ)-DP [66], both achieved by DP algorithms that preserve all cuts in the graph. To achieve our result, we develop an ε-DP algorithm for the Minimum Isolating Cuts problem with near-linear error, and introduce a novel privacy composition technique combining elements of both parallel and basic composition to handle'bounded overlap' computational branches in recursive algorithms, which maybe of independent interest.

Correlation Clustering (CC) is a foundational problem in unsupervised learning that models binary similarity relations using labeled graphs. While classical CC has been widely studied, many real-world applications involve more nuanced relationships, either multi-class categorical interactions or varying confidence levels in edge labels. To address these, two natural generalizations have been proposed: Chromatic Correlation Clustering, which assigns semantic colors to edge labels, and pseudometric-weighted Correlation Clustering, which allows edge weights satisfying the triangle inequality. In this paper, we develop improved approximation algorithms for both settings. Our approach leverages LP-based pivoting techniques combined with problem-specific rounding functions. For the pseudometric-weighted correlation clustering problem, we present a tight 103 approximation algorithm, matching the best possible bound achievable within the framework of standard LP relaxation combined with specialized rounding. For the Chromatic Correlation Clustering (CCC) problem, we improve the approximation ratio from the previous best of 2.5 to 2.15, and we establish a lower bound of 2.11within the same analytical framework, highlighting the near-optimality of our result.

We consider the problem of preprocessing an n n matrix M, and supporting queries that, for any vector v, returns the matrix-vector product Mv. This problem has been extensively studied in both theory and practice: on one side, practitioners have developed algorithms that are highly efficient in practice, whereas on the other side, theoreticians have proven that the problem cannot be solved faster than naive multiplication in the worst-case. This lower bound holds even in the average-case, implying that existing average-case analyses cannot explain this gap between theory and practice. Hence, we study the problem for structured matrices. We show that for n n Boolean matrices of VC-dimension d, the matrix-vector multiplication problem can be solved with eO(n2)preprocessing and eO(n2 1/d) query time.

Recent advances in LLM-based decompilers have been shown effective to convert low-level binaries into human-readable source code. However, there still lacks a comprehensive benchmark that provides large-scale binary-source function pairs, which is critical for advancing the LLM decompilation technology. Creating accurate binary-source mappings incurs severe issues caused by complex compilation settings and widespread function inlining that obscure the correspondence between binaries and their original source code. Previous efforts have either relied on used contest-style benchmarks, synthetic binary-source mappings that diverge significantly from the mappings in real world, or partially matched binaries with only code lines or variable names, compromising the effectiveness of analyzing the binary functionality. To alleviate these issues, we introduce Decompile-Bench, the first open-source dataset comprising two million binarysource function pairs condensed from 100 million collected function pairs, i.e., 450GB of binaries compiled from permissively licensed GitHub projects. For the evaluation purposes, we also developed a benchmark Decompile-Bench-Eval including manually crafted binaries from the well-established HumanEval and MBPP, alongside the compiled GitHub repositories released after 2025 to mitigate data leakage issues. We further explore commonly-used evaluation metrics to provide a thorough assessment of the studied LLM decompilers and find that fine-tuning with Decompile-Bench causes a 20% improvement over previous benchmarks in terms of the re-executability rate. Our code and data has been released in HuggingFace and Github.

Neural Information Processing Systems http://nips.cc/

Correlation clustering is a fundamental optimization problem at the intersection of machine learning and theoretical computer science. Motivated by applications to big data processing, recent years have witnessed a flurry of results on this problem in the streaming model. In this model, the algorithm needs to process the input n-vertex graph by making one or few passes over the stream of its edges and using a limited memory, much smaller than the input size. All previous work on streaming correlation clustering has focused on semistreaming algorithms with Ω(n) memory, whereas in this work, we study streaming algorithms with much smaller memory requirements of only polylog(n) bits. This stringent memory requirement is in the same spirit of classical streaming algorithms that instead of recovering a full solution to the problem--which can be prohibitively large with such small memory as is the case in our problem--, aimed to learn certain statistical properties of their inputs.