3,698 items tracked: 33 reported by two or more outlets, the rest by a single source. Most-corroborated first.
arXiv:2610.09194v1 Announce Type: cross Abstract: Longitudinal models forecast how a patient's imaging state evolves, but accuracy does not show whether the patient's observed trajectory drives the prediction.
arXiv:2610.09205v1 Announce Type: cross Abstract: Modern image editing models can satisfy a text instruction while breaking the physics of the edited scene.
arXiv:2610.09209v1 Announce Type: cross Abstract: Substance Use Disorder (SUD) counseling requires patient responses that reflect underlying cognitive states such as beliefs, coping strategies, and readiness for change.
arXiv:2610.09240v1 Announce Type: cross Abstract: Modern web agents built on large vision-language models process webpages, select relevant UI elements, and translate model outputs into browser actions.
arXiv:2610.09247v1 Announce Type: cross Abstract: The difficulty of learning goal-reaching policies is often attributed to a "curse of horizon" that manifests as bias accumulation in temporal-difference backups and noisy…
arXiv:2610.09250v1 Announce Type: cross Abstract: Best-of-N (BoN) is a common inference-time alignment method that selects the highest-scoring response among N samples from a reference model.
arXiv:2610.09264v1 Announce Type: cross Abstract: Modern agentic coding frameworks increasingly rely on community-shared rule files (e.g., AGENTS.md or .cursorrules) to guide autonomous code generation, yet the security…
arXiv:2610.09285v1 Announce Type: cross Abstract: Many applications of physical AI run within finite or closed physical worlds with a limited set of physical laws governing object behavior.
arXiv:2610.09346v1 Announce Type: cross Abstract: Quantization-aware training (QAT) can recover much of the accuracy lost when large language models are compressed below four bits.
arXiv:2610.09355v1 Announce Type: cross Abstract: Learning EEG representations that generalize across cognitive tasks, subjects, and recording conditions remains a key challenge in electroencephalography (EEG) decoding.
arXiv:2610.09469v1 Announce Type: cross Abstract: Computer-use agents (CUAs) perform tasks across applications (such as desktops, mobile apps, and web browsers) by observing graphical interfaces and issuing commands such…
arXiv:2610.09473v1 Announce Type: cross Abstract: Time-series anomaly detection (TSAD) identifies deviations from patterns learned from historical data.
arXiv:2610.09496v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have shown strong generalization in robotic manipulation by leveraging rich representations from pretrained vision-language models.
arXiv:2610.09498v1 Announce Type: cross Abstract: Clinical vision models are often deployed as frozen black boxes with no access to internals, retraining, or ground truth at inference time.
arXiv:2610.09535v1 Announce Type: cross Abstract: Real-world applications require 6D pose estimation to be accurate, fast, and scalable to unseen objects.
arXiv:2610.09581v1 Announce Type: cross Abstract: Forensic reconstruction of LLM-agent actions requires not only recovering the correct value, but establishing which preserved record supports that finding.
arXiv:2610.09643v1 Announce Type: cross Abstract: Drug discovery for neurological disease has traditionally centered on the molecules altered by disease.
arXiv:2610.09759v1 Announce Type: cross Abstract: Flow matching enables language generation in few steps, but whether additional integration steps improve reasoning remains unclear.
arXiv:2610.09772v2 Announce Type: replace-cross Abstract: Edge language-model agents must hold a persona and reason correctly at once, in one context window that fills with history and persona instructions.
arXiv:2610.09802v1 Announce Type: cross Abstract: Concept-based vision models represent images through an intermediate layer of human-inspectable concepts, so what a model relies on can be traced to those concepts.
arXiv:2610.09858v1 Announce Type: cross Abstract: Large language model agents tackle multi-step tasks by interleaving reasoning and tool calls with observations from the environment.
arXiv:2610.09870v1 Announce Type: cross Abstract: Vehicular edge computing (VEC) enables latency-sensitive applications by bringing computing and networking resources closer to vehicles.
arXiv:2610.09871v1 Announce Type: cross Abstract: Implicit modeling and Relative Geologic Time are geological modeling techniques that enable more efficient, faster, less biased and more reproducible modeling results.
arXiv:2610.09892v2 Announce Type: replace-cross Abstract: Feedback from automated testing, human red teaming, and incident response can strengthen an AI system's defenses when discovered failures lead to effective repair…
arXiv:2610.09993v1 Announce Type: cross Abstract: Spatial defect signatures on wafer bin maps (WBMs) trace yield loss to specific process faults, yet supervised classifiers recognize only the defect types seen during tra…
arXiv:2610.09994v1 Announce Type: cross Abstract: Models with near-identical predictive performance can yield substantially different predictions, a phenomenon known as predictive multiplicity.
arXiv:2610.10126v1 Announce Type: cross Abstract: Multi-agent LLM systems coordinate task execution through exchanges of information among agents.
arXiv:2610.10129v1 Announce Type: cross Abstract: As EDA researchers, we conducted eight deliberate trials of agentic algorithm exploration, selecting several topics outside our areas of depth.
arXiv:2610.10179v1 Announce Type: cross Abstract: Search agents enable Large Language Models (LLMs) to iteratively retrieve and use information for complex multi-hop questions.
arXiv:2610.10242v1 Announce Type: cross Abstract: SWE-agent ensembles improve issue resolution by combining candidate patches from different agents with complementary strengths.
arXiv:2610.10258v1 Announce Type: cross Abstract: Quantum libraries are now critical infrastructure for quantum algorithm development, yet their correctness remains difficult to test.
arXiv:2610.10304v1 Announce Type: cross Abstract: We study whether latent sequence compression of prompt prefixes preserves the capabilities that large language models rely on during inference.
arXiv:2610.10311v1 Announce Type: cross Abstract: Emerging computer hardware often trades reliability for energy efficiency; here we show that large-language models (LLMs) can be trained to tolerate this unreliability, a…
arXiv:2610.10320v1 Announce Type: cross Abstract: Multiphysics continuum models are powerful tools for studying electrochemical devices, enabling in silico reactor design and resolution of local pH, potential, and concen…
arXiv:2610.10355v1 Announce Type: cross Abstract: Text-to-music systems produce increasingly convincing audio, yet evaluation reveals little about whether the result matches user intent.
arXiv:2610.10411v1 Announce Type: cross Abstract: Speculative decoding accelerates large language model inference by using a low-cost draft model to propose tokens that the full-size target model verifies in parallel.
arXiv:2610.10447v1 Announce Type: cross Abstract: Reinforcement Learning (RL) from outcome rewards suffers from sparse supervision, particularly on difficult, long-horizon tasks where successful trajectories are rare and…
arXiv:2610.10462v1 Announce Type: cross Abstract: We present FoldBack, a self-correcting masked generative policy for long-horizon garment folding.
arXiv:2610.10468v1 Announce Type: cross Abstract: Deployments of research agents are moving to populations of thousands that share one pool of compute, while most current systems organize one project at a time or leave t…
arXiv:2308.00505v4 Announce Type: replace Abstract: Agent Based Models (ABMs) often deal with systems where there is a lack of quantitative data or where quantitative data alone may be insufficient to fully capture the c…
arXiv:2512.17559v2 Announce Type: replace Abstract: Healthcare systems around the world are grappling with issues such as inefficient diagnostics, rising costs, and limited access to specialists.
arXiv:2602.00851v4 Announce Type: replace Abstract: AI agents combine normal conversation with autonomous task execution, allowing earlier context to shape how later tasks are executed.
arXiv:2602.02639v2 Announce Type: replace Abstract: LLM self-explanations are often presented as a promising tool for AI oversight, yet their faithfulness to the model's true reasoning process is poorly understood.
arXiv:2603.22973v3 Announce Type: replace Abstract: Applying computational methods to law at scale requires separating genuine legal reasoning from surface similarity.
arXiv:2604.11477v2 Announce Type: replace Abstract: The alignment of Multi-Agent Systems (MAS) for autonomous software engineering is constrained by evaluator epistemic uncertainty.
arXiv:2604.17502v5 Announce Type: replace Abstract: Misaligned artificial agents might resist shutdown.
arXiv:2605.12894v2 Announce Type: replace Abstract: Large Language Model (LLM) agents are increasingly deployed in settings where they interact with diverse users, including those who are unclear, impatient, or reluctant…
arXiv:2605.15871v2 Announce Type: replace Abstract: Toward recursive self-improvement, we investigate LLM agents autonomously designing foundation models beyond standard Transformers.
arXiv:2605.21347v4 Announce Type: replace Abstract: Diagnosing failures in LLM agents remains largely manual.
arXiv:2607.06223v2 Announce Type: replace Abstract: Reinforcement learning (RL) improves large language model (LLM) agents on long-horizon search tasks that require multiple intermediate decisions before a final outcome.