Search: “tools”

arXiv:2610.08090v1 Announce Type: cross Abstract: IPv6 extension headers (EHs), such as fragmentation, segment routing, and in-situ telemetry, are operationally important yetwidely dropped in transit,

arXiv:2610.05275v2 Announce Type: replace Abstract: A large part of political discourse on social media is built and expressed at a level of claims: i.e. declarative, typically single-clause statement

arXiv:2610.07125v1 Announce Type: cross Abstract: While open-weight models have enjoyed steady progress in capabilities and wide adoption across multiple domains, their safety remains an important con

arXiv:2610.07853v1 Announce Type: cross Abstract: Ternary language models such as BitNet b1.58, Falcon-E and BitCPM are fine-tuned with higher-precision latent weights and deployed as ternary codes pr

arXiv:2610.08327v1 Announce Type: new Abstract: Large language models (LLMs) have shown promise in medical question answering and clinical reasoning, yet their improvement remains constrained by stati

arXiv:2610.07519v1 Announce Type: new Abstract: Automatic sign language translation (SLT) has entered consumer products, turning American Sign Language into English text for dictation, messaging, and

arXiv:2610.08178v1 Announce Type: new Abstract: Existing latent-based watermarking methods for diffusion models have overestimated their robustness to image distortions, including geometric transforma

arXiv:2610.07817v1 Announce Type: cross Abstract: Organizations automating operational processes need more than a correct outcome: they need to predict how a process will run, know which one actually

arXiv:2610.07787v1 Announce Type: new Abstract: Multi-agent systems (MAS) powered by large language models have shown strong performance across code generation, mathematical reasoning, and question an

arXiv:2605.06021v2 Announce Type: replace Abstract: Systematic reviews and meta-analyses often need numerical data reported only in figures, and extracting them with interactive digitisers usually req

arXiv:2610.07473v1 Announce Type: new Abstract: Large language models show promise in clinical reasoning, but psychiatric interviewing requires guiding an evolving conversation. Their ability to carry

arXiv:2610.08137v1 Announce Type: cross Abstract: AI systems create images and videos with image/video generation models or by writing code and graphics descriptions that are then rendered. These rout

arXiv:2610.07846v1 Announce Type: cross Abstract: The scenario approach is a well-established statistical framework for data-driven decision-making. In particular, in data-driven optimization, the sce

arXiv:2610.08691v1 Announce Type: new Abstract: Large language model agents are accelerating scientific automation, yet verified executions rarely become persistent program-level improvements, and exi

arXiv:2610.08077v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) turns agent experience into learning signals primarily through scalar outcome rewards after intera

arXiv:2610.08668v1 Announce Type: cross Abstract: Behavioral watermarking embeds an owner identifier in an LLM agent's high-level action choices, giving provenance without touching output tokens. Prio

arXiv:2610.07645v1 Announce Type: cross Abstract: Self-improving LLM agents increasingly distill successful experiences into persistent, reusable skills. Existing skill attack methods corrupt this lea

arXiv:2512.23065v4 Announce Type: replace Abstract: The introduction of BERT established encoder-only transformer models as a foundational paradigm in natural language processing. Encoder-only models

arXiv:2610.06914v1 Announce Type: new Abstract: Text2Dashboard is a DataBrain-specific prototype that turns natural-language analytic requests into inspectable dashboards. An installable Codex plugin

arXiv:2610.07089v1 Announce Type: cross Abstract: LLM agents increasingly act in multi-actor environments, exposing them to misuse from multiple sources: decomposition attacks, where a harmful request

arXiv:2610.08225v1 Announce Type: new Abstract: In this work, we introduce Crazyflow, an accurate, differentiable simulator built on JAX. By leveraging jit compilation via XLA, Crazyflow unifies physi

arXiv:2610.07261v1 Announce Type: new Abstract: A team of coding agents can look fine agent by agent yet fail as a team: each passes its own tests while the merged result is broken, and single-agent e

arXiv:2509.26189v2 Announce Type: replace Abstract: The rapid proliferation of Large Language Models has intensified the challenge of distinguishing LLM-generated text from human writing in non-Englis

arXiv:2610.06650v2 Announce Type: replace Abstract: Wikidata is one of the largest open knowledge bases, yet answering a complex question over it still requires a SPARQL query that names the right ent

arXiv:2610.08502v1 Announce Type: cross Abstract: Proactive power management systems reduce processor dynamic power through runtime power prediction and power-aware scheduling. Accurate, stable and lo

arXiv:2610.04022v1 Announce Type: new Abstract: Powered neck exoskeletons are developed to restore head-neck motions for those with neck muscle weakness. Current methods using hand-held input devices

arXiv:2610.06514v1 Announce Type: cross Abstract: The growing adoption of large language model (LLM) agents creates a need for network administrators and security teams to audit agent behavior within

arXiv:2610.06439v1 Announce Type: new Abstract: What happens when a legal AI model learns to look like a lawyer instead of reasoning like one? We fine tune Qwen3-8B with Group Relative Policy Optimisa

arXiv:2610.06366v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly deployed for automated software vulnerability analysis. Binary classification alone is insufficient; pra

arXiv:2610.05792v1 Announce Type: new Abstract: Robotic minimally invasive surgery offers well-documented clinical benefits, but the cost and infrastructure requirements of purpose-built platforms lim

arXiv:2412.10510v5 Announce Type: replace Abstract: The proliferation of disinformation demands reliable and scalable fact-checking solutions. We present Dynamic Evidence-based FAct-checking with Mult

arXiv:2610.04084v1 Announce Type: new Abstract: Generative AI can produce engineering artefacts, but generation alone does not determine whether or how those artefacts should enter safety-critical wor

arXiv:2610.05572v1 Announce Type: new Abstract: Predicting whether an LLM agent will fail has emerged as a promising direction for supporting intervention during execution. Recent approaches report st

arXiv:2610.04087v1 Announce Type: new Abstract: Agentic workflows are increasingly used across domains such as technology, finance, and enterprise operations. As these agents become more widely deploy

arXiv:2604.01554v2 Announce Type: replace-cross Abstract: Binary Function Similarity Detection (BFSD) is a core problem in software security, supporting tasks such as vulnerability analysis, malware c

arXiv:2610.04569v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed in safety-critical settings, yet their black-box nature makes it difficult to provide formal gu

arXiv:2610.05801v1 Announce Type: new Abstract: We propose a Gauss-map variation (GMV) model for image denoising that measures the spatial variation of the tangent-plane projectors of the scaled image

arXiv:2610.06034v1 Announce Type: cross Abstract: We consider the fusion of lung ultrasound images with routinely-collected clinical and demographic data for the purpose of automated tuberculosis (TB)

arXiv:2610.05997v1 Announce Type: new Abstract: Early development unfolds in caregiver-infant dyads, where infants' sensorimotor streams are shaped by physical contact and face-to-face interaction. Ye

arXiv:2510.04455v3 Announce Type: replace-cross Abstract: Data-driven inverse optimization for mixed-integer linear programs (MILPs), which seeks to learn an objective function and constraints consist

arXiv:2610.06434v1 Announce Type: cross Abstract: Predicting patient-specific survival functions is crucial for clinicians in making informed decisions about patient care and treatment strategies. Amo

arXiv:2610.05501v1 Announce Type: new Abstract: In this study, we propose a novel rule-based interpretable choice model, Logic-Logit, designed to effectively learn and explain human choices. Choice mo

arXiv:2512.15621v2 Announce Type: replace Abstract: Occupancy world models use historical occupancy states to forecast future 3D scenes, but their robustness under corrupted temporal inputs remains po

arXiv:2503.19945v4 Announce Type: replace-cross Abstract: Mammography, an X-ray-based imaging technique, remains central to the early detection of breast cancer. Recent advances in artificial intellig

arXiv:2610.04089v1 Announce Type: new Abstract: Coding agents operating under ambiguous instructions or user prompts must decide whether to ask clarifying questions or attempt a solution directly. Whi

arXiv:2610.04100v1 Announce Type: new Abstract: Model compression strategies are widely employed to reduce memory footprint and network complexity, particularly for devices with constrained computatio