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arXiv:2602.03945v1 Announce Type: new Abstract: Tabular learning is still dominated by row-wise predictors that score each row independently, which fits i.i.d. benchmarks but fails on...
arXiv:2602.03949v1 Announce Type: new Abstract: We study strategic Gaussian semantic compression under rate and compute constraints, where an encoder and decoder optimize distinct quadratic objectives....
arXiv:2602.03950v1 Announce Type: new Abstract: Mathematical problem solving is a fundamental benchmark for assessing the reasoning capabilities of artificial intelligence and a gateway to applications...
arXiv:2602.03951v1 Announce Type: new Abstract: Robust generalization under distribution shift remains difficult to monitor and optimize in the absence of target-domain labels, as models with...
arXiv:2602.03955v1 Announce Type: new Abstract: While large language model (LLM) multi-agent systems achieve superior reasoning performance through iterative debate, practical deployment is limited by their...
arXiv:2602.03957v1 Announce Type: new Abstract: The predictive machine learning models for child mortality tend to be inaccurate when applied to future populations, since they suffer...
arXiv:2602.03958v1 Announce Type: new Abstract: Recent advances in AI are integrating AI into the fabric of human social life, creating transformative, co-shaping relationships between humans...
arXiv:2602.03962v1 Announce Type: new Abstract: Professional societies often publish curriculum guidelines to help programs align their content to international standards. In Computer Science, the primary...
arXiv:2602.03965v1 Announce Type: new Abstract: Latency anomalies, defined as persistent or transient increases in round-trip time (RTT), are common in residential Internet performance. When multiple...
arXiv:2602.03967v1 Announce Type: new Abstract: Principal Component Analysis (PCA) is a powerful and popular dimensionality reduction technique. However, due to its linear nature, it often...
arXiv:2602.03968v1 Announce Type: new Abstract: This paper presents a safety-critical reinforcement learning framework for nonlinear dynamical systems with continuous state and input spaces operating under...
arXiv:2602.03969v1 Announce Type: new Abstract: The emergence of large language models (LLMs) represents a significant technological shift within the scientific ecosystem, particularly within the field...
arXiv:2602.03973v1 Announce Type: new Abstract: Why do pretrained diffusion or flow-matching policies fail when the same task is performed near an obstacle, on a shifted...
arXiv:2602.03974v1 Announce Type: new Abstract: Planning in interactive environments is challenging under partial observability: task-critical preconditions (e.g., object locations or container states) may be unknown...
arXiv:2602.03975v1 Announce Type: new Abstract: Test-time computation has become a primary driver of progress in large language model (LLM) reasoning, but it is increasingly bottlenecked...
arXiv:2602.03978v1 Announce Type: new Abstract: As Large Reasoning Models (LRMs) are increasingly deployed, auditing their chain-of-thought (CoT) traces for safety becomes critical. Recent work has...
arXiv:2602.03979v1 Announce Type: new Abstract: Fine-tuning large language models (LLMs) on reasoning benchmarks via reinforcement learning requires a specific reward function, often binary, for each...
arXiv:2602.03980v1 Announce Type: new Abstract: Because language is creative, any reasonable language model must generalize, deciding what to say in novel contexts by using information...
arXiv:2602.03981v1 Announce Type: new Abstract: Credit exposure in Decentralized Finance (DeFi) is often implicit and token-mediated, creating a dense web of inter-protocol dependencies. Thus, a...
arXiv:2602.03983v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have recently emerged as a promising paradigm for generalist robotic control. Built upon vision-language model (VLM) architectures,...
arXiv:2602.03986v1 Announce Type: new Abstract: We study the effect of group symmetrization of pre-trained models on conformal prediction (CP), a post-hoc, distribution-free, finite-sample method of...
arXiv:2602.03987v1 Announce Type: new Abstract: Control barrier functions (CBFs) provide a powerful tool for enforcing safety constraints in control systems, but their direct application to...
arXiv:2602.03991v1 Announce Type: new Abstract: Given a fixed positive integer $k$ and a simple undirected graph $G = (V, E)$, the {\em $k^-$-path partition} problem,...
arXiv:2602.03992v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) systems have been popular for generative applications, powering language models by injecting external knowledge. Companies have been...