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arXiv:2604.19377v1 Announce Type: new Abstract: The emergence of sixth-generation (6G) technologies has introduced new challenges and opportunities for machine learning (ML) applications in Internet o…
arXiv:2604.19354v1 Announce Type: new Abstract: Large Language Model (LLM) agents are increasingly proposed for autonomous cybersecurity tasks, but their capabilities in realistic offensive settings r…
arXiv:2604.19301v1 Announce Type: new Abstract: The conformity bias exhibited by large language models (LLMs) can pose a significant challenge to decision-making in LLM-based multi-agent systems (LLM-…
arXiv:2604.19278v1 Announce Type: new Abstract: LLM-based multi-agent systems (MAS) show promise on complex tasks but remain prone to coordination failures such as goal drift, error cascades, and misa…
arXiv:2604.19240v1 Announce Type: new Abstract: Industrial surface defect detection often suffers from limited defect samples, severe long-tailed distributions, and difficulties in accurately localizi…
arXiv:2604.19221v1 Announce Type: new Abstract: Full-duplex speech interaction, as the most natural and intuitive mode of human communication, is driving artificial intelligence toward more human-like…
arXiv:2604.19211v1 Announce Type: new Abstract: Current AI agent frameworks have made remarkable progress in automating individual tasks, yet all existing systems serve a single user. Human productivi…
arXiv:2604.19172v1 Announce Type: new Abstract: The rapid advancement and widespread adoption of Large Language Models (LLMs) have elevated the need for reliable AI-generated content (AIGC) detection,…
arXiv:2604.19131v1 Announce Type: new Abstract: Automated essay scoring (AES) is commonly evaluated on public benchmarks using quadratic weighted kappa (QWK). However, because benchmark labels are ass…
arXiv:2604.19089v1 Announce Type: new Abstract: Large language models (LLMs) require frequent knowledge updates to reflect changing facts and mitigate hallucinations. To meet this demand, lifelong kno…
arXiv:2604.19087v1 Announce Type: new Abstract: We introduce Options LLM (OLLM), a simple, general method that replaces the single next-token prediction of standard LLMs with a \textit{set of learned …
arXiv:2604.19060v1 Announce Type: new Abstract: Accurate disease classification from radiology reports is essential for many applications. While supervised fine-tuning (SFT) of lightweight LLMs improv…
arXiv:2604.19043v1 Announce Type: new Abstract: Efficient construction of models capturing the preconditions and effects of actions is essential for applying AI planning in real-world domains. Extensi…
arXiv:2604.19036v1 Announce Type: new Abstract: Defeasible statements are statements that are likely, or probable, or usually true, but may occasionally be false. Plausible reasoning makes conclusions…
arXiv:2604.19022v1 Announce Type: new Abstract: A major challenge for niche scientific and technical domains in leveraging coding agents is the lack of access to up-to-date, domain- specific knowledge…
arXiv:2604.18982v1 Announce Type: new Abstract: Social intelligence, the ability to navigate complex interpersonal interactions, presents a fundamental challenge for language agents. Training such age…
arXiv:2604.18964v1 Announce Type: new Abstract: This paper introduces DW-Bench, a new benchmark that evaluates large language models (LLMs) on graph-topology reasoning over data warehouse schemas, exp…
arXiv:2604.18946v1 Announce Type: new Abstract: Large reasoning models (LRMs) achieve strong performance on complex reasoning tasks but often generate harmful responses to malicious user queries. This…
arXiv:2604.18943v1 Announce Type: new Abstract: With the rise in capabilities of large language models (LLMs) and their deployment in real-world tasks, evaluating LLM alignment with human preferences …
arXiv:2604.18934v1 Announce Type: new Abstract: Existing AI benchmarks for software automation rarely combine cross-application coordination, autonomous API discovery, and policy adherence. Real busin…
arXiv:2604.18916v1 Announce Type: new Abstract: In this paper, we introduce a new concept called Artificial Special Intelligence by which Machine Learning models for the classification problem can be …
arXiv:2604.18882v1 Announce Type: new Abstract: We present a formally verified framework for patent analysis as a hybrid AI + Lean 4 pipeline. The DAG-coverage core (Algorithm 1b) is fully machine-ver…
arXiv:2604.18874v1 Announce Type: new Abstract: Tool-integrated agents are deployed on the premise that external tools ground their outputs in reality. Yet this very reliance creates a critical attack…
arXiv:2604.18873v1 Announce Type: new Abstract: Large language models (LLMs) are highly capable at language generation, but they remain unreliable when reasoning requires explicit symbolic structure, …