arXiv:2511.05586v1 Announce Type: new Abstract: Neural-guided equation discovery systems use a data set as prompt and predict an equation that describes the data set without extensive search. However, if the equation does not meet the user’s expectations, there are few…
arXiv:2411.14984v2 Announce Type: replace Abstract: Neural networks can learn spurious correlations in the data, often leading to performance degradation for underrepresented subgroups. Studies have demonstrated that the disparity is amplified when knowledge is distilled from a complex teacher model to…
arXiv:2511.05589v1 Announce Type: new Abstract: Reinforcement learning (RL) post-training has become a trending paradigm for enhancing the capabilities of large language models (LLMs). Most existing RL systems for LLMs operate in a fully synchronous manner, where training must wait for…
arXiv:2505.24843v2 Announce Type: replace Abstract: Models that learn spurious correlations from training data often fail when deployed in new environments. While many methods aim to learn invariant representations to address this, they often underperform standard empirical risk minimization (ERM). We…
arXiv:2511.05591v1 Announce Type: new Abstract: Federated Learning (FL) enables collaborative model training across decentralized clients while preserving data privacy by keeping raw data local. However, FL suffers from significant communication overhead due to the frequent exchange of high-dimensional model updates…
arXiv:2509.08625v2 Announce Type: replace Abstract: The silhouette coefficient quantifies, for each observation, the balance between within-cluster cohesion and between-cluster separation, taking values in [-1, 1]. The average silhouette width (ASW) is a widely used internal measure of clustering quality, with…
arXiv:2511.05592v1 Announce Type: new Abstract: Inspired by the remarkable success of foundation models in language and vision, Graph Foundation Models (GFMs) hold significant promise for broad applicability across diverse graph tasks and domains. However, existing GFMs struggle with unstable few-shot…
arXiv:2511.03239v2 Announce Type: replace Abstract: Modern AI systems are increasingly constrained not by model capacity but by the quality and diversity of their data. Despite growing emphasis on data-centric AI, most datasets are still gathered in an open-loop manner which…
arXiv:2511.05593v1 Announce Type: new Abstract: Federated Learning (FL) enables decentralized model training across multiple clients while optionally preserving data privacy. However, communication efficiency remains a critical bottleneck, particularly for large-scale models. In this work, we introduce two complementary algorithms: ProjFL,…
arXiv:2502.05390v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have demonstrated remarkable proficiency in in-context learning (ICL), where models adapt to new tasks through example-based prompts without requiring parameter updates. However, understanding how tasks are internally encoded and generalized remains…
