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arXiv:2601.22944v1 Announce Type: new Abstract: Out-of-distribution (OOD) generalization remains challenging when models simultaneously encounter correlation shifts across environments and diversity shifts driven by rare or...
arXiv:2601.22946v1 Announce Type: new Abstract: Machine learning models are increasingly used for software security tasks. These models are commonly trained and evaluated on large Internet-derived...
arXiv:2601.22947v1 Announce Type: new Abstract: Masked diffusion language models (MDLMs) have emerged as a promising alternative to dominant autoregressive approaches. Although they achieve competitive performance...
arXiv:2601.22948v1 Announce Type: new Abstract: A fundamental question in cognitive science and AI concerns whether different learning modalities: language, vision, and action, give rise to...
arXiv:2601.22949v1 Announce Type: new Abstract: Graph-based fraud detection on text-attributed graphs (TAGs) requires jointly modeling rich textual semantics and relational dependencies. However, existing LLM-enhanced GNN...
arXiv:2601.22950v1 Announce Type: new Abstract: Perplexity -- a function measuring a model's overall level of "surprise" when encountering a particular output -- has gained significant...
arXiv:2601.22952v1 Announce Type: new Abstract: Static Application Security Testing (SAST) tools are essential for identifying software vulnerabilities, but they often produce a high volume of...
arXiv:2601.22954v1 Announce Type: new Abstract: Diffusion Large Language Models (dLLMs) have emerged as a promising alternative to purely autoregressive language models because they can decode...
arXiv:2601.22956v1 Announce Type: new Abstract: Large language model (LLM) research in software engineering has largely focused on tasks such as code generation and bug repair....
arXiv:2601.22959v1 Announce Type: new Abstract: Vision-Language Models (VLMs) face significant computational challenges in video processing due to massive data redundancy, which creates prohibitively long token...
arXiv:2601.22961v1 Announce Type: new Abstract: Supervised machine learning algorithms play a crucial role in optical quality control within industrial production. These approaches require representative datasets...
arXiv:2601.22963v1 Announce Type: new Abstract: Conflict-Free Replicated Data Types (CRDTs) are used in a range of fields for their coordination-free replication with strong eventual consistency....
arXiv:2601.22964v1 Announce Type: new Abstract: Prevailing medical AI operates on an unrealistic ''one-shot'' model, diagnosing from a complete patient file. However, real-world diagnosis is an...
arXiv:2601.22965v1 Announce Type: new Abstract: Diffusion policies (DP) have demonstrated significant potential in visual navigation by capturing diverse multi-modal trajectory distributions. However, standard imitation learning...
arXiv:2601.22966v1 Announce Type: new Abstract: We investigate the functional role of emergent outliers in large language models, specifically attention sinks (a few tokens that consistently...
arXiv:2601.22969v1 Announce Type: new Abstract: Nash regret has recently emerged as a principled fairness-aware performance metric for stochastic multi-armed bandits, motivated by the Nash Social...
arXiv:2601.22970v1 Announce Type: new Abstract: Policies learned via continuous actor-critic methods often exhibit erratic, high-frequency oscillations, making them unsuitable for physical deployment. Current approaches attempt...
arXiv:2601.22974v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) have substantially accelerated the development of embodied agents. LLM-based multi-agent systems mitigate the...
arXiv:2601.22975v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has become a cornerstone for unlocking complex reasoning in Large Language Models (LLMs). Yet,...
arXiv:2601.22977v1 Announce Type: new Abstract: As AI systems evolve from isolated predictors into complex, heterogeneous ecosystems of foundation models and specialized adapters, distinguishing genuine behavioral...
arXiv:2601.22978v1 Announce Type: new Abstract: This paper introduces SpecIBT, a formally verified defense against Spectre BTB, RSB, and PHT that combines CET-style hardware-assisted control-flow integrity...
arXiv:2601.22980v1 Announce Type: new Abstract: Structured sparsity has emerged as a popular model pruning technique, widely adopted in various architectures, including CNNs, Transformer models, and...
arXiv:2601.22982v1 Announce Type: new Abstract: Factory automation has become increasingly important due to labor shortages, leading to the introduction of autonomous mobile robots for tasks...
arXiv:2601.22983v1 Announce Type: new Abstract: Recent provenance-based intrusion detection systems (PIDSs) have demonstrated strong potential for detecting advanced persistent threats (APTs) by applying machine learning...