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arXiv:2602.16752v1 Announce Type: new Abstract: Large Language Models (LLMs) have emerged as powerful re-rankers. Recent research has however showed that simple prompt injections embedded within...
arXiv:2602.16756v1 Announce Type: new Abstract: We introduce NESSiE, the NEceSsary SafEty benchmark for large language models (LLMs). With minimal test cases of information and access...
arXiv:2602.16758v1 Announce Type: new Abstract: This paper presents a smooth trajectory generation method for a four-degree-of-freedom parallel kinematic milling robot. The proposed approach integrates B-spline...
arXiv:2602.16760v1 Announce Type: new Abstract: We present a practical system for privacy-aware large language model (LLM) inference that splits a transformer between a trusted local...
arXiv:2602.16762v1 Announce Type: new Abstract: Modern machine learning-based wireless localization using Wi-Fi signals continues to face significant challenges in achieving groundbreaking performance across diverse environments....
arXiv:2602.16764v1 Announce Type: new Abstract: Low Earth orbit (LEO) satellites are leveraged to support new position, navigation, and timing (PNT) service alternatives to GNSS. These...
arXiv:2602.16784v1 Announce Type: new Abstract: Despite their impressive performance on a wide variety of tasks, modern language models remain susceptible to distribution shifts, exhibiting brittle...
arXiv:2602.16787v1 Announce Type: new Abstract: Despite their strong performance on reasoning benchmarks, large language models (LLMs) have proven brittle when presented with counterfactual questions, suggesting...
arXiv:2602.16790v1 Announce Type: new Abstract: In audio-related creative tasks, sound designers often seek to extend and morph different sounds from their libraries. Generative audio models,...
arXiv:2602.16793v1 Announce Type: new Abstract: In the past year, custom and unreleased math reasoning models reached gold medal performance on the International Mathematical Olympiad (IMO)....
arXiv:2602.16796v1 Announce Type: new Abstract: Fine-tuning pre-trained diffusion and flow models to optimize downstream utilities is central to real-world deployment. Existing entropy-regularized methods primarily maximize...
arXiv:2602.16797v1 Announce Type: new Abstract: In this paper, we develop RLOBPCG, an efficient method for computing a small number of singular triplets corresponding to the...
arXiv:2602.16800v1 Announce Type: new Abstract: We show that large language models can be used to perform at-scale deanonymization. With full Internet access, our agent can...
arXiv:2602.16802v1 Announce Type: new Abstract: While Reinforcement Learning with Verifiable Rewards (RLVR) has shown strong effectiveness in reasoning tasks, it cannot be directly applied to...
arXiv:2602.16805v1 Announce Type: new Abstract: Code evolution is a family of techniques that rely on large language models to search through possible computer programs by...
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...