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arXiv:2602.16806v1 Announce Type: new Abstract: Tutoring systems improve learning through tailored interventions, such as worked examples, but often suffer from the aptitude-treatment interaction effect where...
arXiv:2602.16807v1 Announce Type: new Abstract: A collection of hyperplanes $\mathcal{H}$ slices all edges of the $n$-dimensional hypercube $Q_n$ with vertex set $\{-1,1\}^n$ if, for every...
arXiv:2602.16809v1 Announce Type: new Abstract: Since its birth as a new scientific body of knowledge in the late 1950s, computer programming has become a fundamental...
arXiv:2602.16810v1 Announce Type: new Abstract: Unlike in TFNP, for which there is an abundance of problems capturing natural existence principles which are incomparable (in the...
arXiv:2602.16811v1 Announce Type: new Abstract: Recent advancements in Natural Language Processing and Deep Learning have enabled the development of Large Language Models (LLMs), which have...
arXiv:2602.16813v1 Announce Type: new Abstract: Language models based on discrete diffusion have attracted widespread interest for their potential to provide faster generation than autoregressive models....
arXiv:2602.16814v1 Announce Type: new Abstract: The expansion of AI toward the edge increasingly exposes the cost and fragility of cen- tralised intelligence. Data transmission, latency,...
arXiv:2602.16819v1 Announce Type: new Abstract: When assessing the quality of coding agents, predominant benchmarks focus on solving single issues on GitHub, such as SWE-Bench. In...
arXiv:2602.16820v1 Announce Type: new Abstract: Despite growing interest in using LLMs to generate feedback on students' writing, little is known about how students respond to...
arXiv:2602.16821v1 Announce Type: new Abstract: We propose TopoFlow (Topography-aware pollutant Flow learning), a physics-guided neural network for efficient, high-resolution air quality prediction. To explicitly embed...
arXiv:2602.16823v1 Announce Type: new Abstract: *Automated circuit discovery* is a central tool in mechanistic interpretability for identifying the internal components of neural networks responsible for...
arXiv:2602.16825v1 Announce Type: new Abstract: Sampling-based motion planning has emerged as a powerful approach for robotics, enabling exploration of complex, high-dimensional configuration spaces. When combined...
arXiv:2602.16826v1 Announce Type: new Abstract: Theory of mind (ToM) enables AI systems to infer agents' hidden goals and mental states, but existing approaches focus mainly...
arXiv:2602.16827v1 Announce Type: new Abstract: Traditional scoring approaches on hesitant fuzzy sets often lack a formal base in order theory. This paper proposes a unified...
arXiv:2602.16829v1 Announce Type: new Abstract: When feedback is absorbed faster than task structure can be evaluated, the learner will favor feedback over truth. A two-timescale...
arXiv:2602.16831v1 Announce Type: new Abstract: This paper presents a low-thrust trajectory optimization strategy to achieve a near-circular lunar orbit for a CubeSat injected into a...
arXiv:2602.16832v1 Announce Type: new Abstract: Safety alignment of large language models (LLMs) is mostly evaluated in English and contract-bound, leaving multilingual vulnerabilities understudied. We introduce...
arXiv:2602.16833v1 Announce Type: new Abstract: Exploration remains a key bottleneck for reinforcement learning (RL) post-training of large language models (LLMs), where sparse feedback and large...
arXiv:2602.16835v1 Announce Type: new Abstract: Safety alignment is essential for the responsible deployment of large language models (LLMs). Yet, existing approaches often rely on heavyweight...
arXiv:2602.16836v1 Announce Type: new Abstract: While Large Language Models (LLMs) have achieved strong performance on general-purpose language tasks, their deployment in regulated and data-sensitive domains,...
arXiv:2602.16837v1 Announce Type: new Abstract: Transformer models systematically favor certain token positions, yet the architectural origins of this position bias remain poorly understood. Under causal...
arXiv:2602.16839v1 Announce Type: new Abstract: Large reasoning models (LRMs) excel on complex problems but face a critical barrier to efficiency: reinforcement learning (RL) training requires...
arXiv:2602.16842v1 Announce Type: new Abstract: We study the offline data-driven newsvendor problem with censored demand data. In contrast to prior works where demand is fully...
arXiv:2602.16843v1 Announce Type: new Abstract: Evaluating factual consistency is essential for reliable text summarization, particularly in high-stakes domains such as healthcare and news. However, most...