Search: “labs”

arXiv:2512.21988v3 Announce Type: replace-cross Abstract: Background: Smartphone-based dermatology requires inter-device colorimetric reliability that holds across calibration regimes, yet quantitativ

arXiv:2606.21716v1 Announce Type: new Abstract: The capability to encode spatially distinct magnetization patterns within soft materials enables remote control over complex deformations. This characte

arXiv:2606.22332v1 Announce Type: new Abstract: Tactile sensing is critical for contact-rich dexterous manipulation, yet it remains unclear which tactile abstractions a policy needs and when richer ta

arXiv:2606.11891v1 Announce Type: cross Abstract: Multi-objective reinforcement learning for humanoid robots must coordinate locomotion and manipulation within a single policy. A natural design choice

arXiv:2606.11150v1 Announce Type: new Abstract: Large language models (LLMs) are rapidly acquiring capabilities relevant to biological research, from literature synthesis to interpretation of experime

arXiv:2601.11072v1 Announce Type: cross Abstract: Within journalistic editorial processes, disclosing AI usage is currently limited to simplistic labels, which misses the nuance of how humans and AI c

arXiv:2507.08920v4 Announce Type: replace-cross Abstract: We introduce AMix-1, a powerful protein foundation model built on Bayesian Flow Networks and empowered by a systematic training methodology, e

arXiv:2606.07692v1 Announce Type: cross Abstract: Foundation models for wearable biosignals have matched or exceeded supervised specialists across a range of clinical tasks, yet all rely on modalities

arXiv:2606.08864v1 Announce Type: new Abstract: The rapid adoption of diffusion and large-scale generative models has made it increasingly challenging to distinguish synthetic imagery from real photog

arXiv:2606.08168v1 Announce Type: cross Abstract: Leading commercial endpoint detection and response (EDR) products have shifted from operator-configured rule sets to multi-component systems where aut

arXiv:2605.22664v2 Announce Type: replace Abstract: LLM agents are increasingly expected to carry out end-to-end workflows, producing complete artifacts from high-level user instructions. To meet ente

arXiv:2606.07576v1 Announce Type: new Abstract: We present CARTOGRAPH, a verification layer for AI scientists that couples unresolved-subspace experiment steering (select), explicit ambiguity closure

arXiv:2502.16531v2 Announce Type: replace Abstract: We consider multi-robot systems under recurring tasks formalized as linear temporal logic (LTL) specifications. To solve the planning problem effici

arXiv:2602.00541v2 Announce Type: replace Abstract: Clinical events captured in Electronic Health Records (EHR) are irregularly sampled and may consist of a mixture of discrete events and numerical me

arXiv:2604.01313v2 Announce Type: replace Abstract: High-fidelity simulations and complex inverse problems, such as detector modeling and unfolding, are computationally intensive bottlenecks across su

arXiv:2606.04494v1 Announce Type: new Abstract: Biomedical agents promise to automate complex biological workflows, yet current systems face two fundamental bottlenecks: bioinformatics tools are highl

arXiv:2606.05104v1 Announce Type: new Abstract: Knowledge benchmarks for LLMs face three issues: scaling-driven designs that do not operationalize disciplinary representativeness; flat-payment annotat

arXiv:2606.04602v1 Announce Type: new Abstract: As agents grow more capable, legal-domain LLM agents promise to turn document-heavy matters into reviewable work products -- yet reliable deployment fac

arXiv:2606.02937v1 Announce Type: cross Abstract: Multi-view video recordings are increasingly used to capture the 3D movements of animals in experimental settings, yet extracting rich 3D representati

arXiv:2606.03335v1 Announce Type: new Abstract: Large scale GPU-parallel reinforcement learning has changed what can be trained in robot simulation, yet most systems still optimize one specialist poli

arXiv:2606.03755v1 Announce Type: new Abstract: Autonomous science is moving from demonstration to infrastructure. Large language model agents now plan experiments, and self-driving laboratories execu

arXiv:2606.01473v1 Announce Type: new Abstract: This paper presents a minimalist brain-computer Musical Interface (BCMI) that functions as a real-time affective sonification system, translating prefro

arXiv:2606.00914v1 Announce Type: new Abstract: LLM agents increasingly act after consuming ranked external information streams such as social feeds, search results, retrieval contexts, and email queu

arXiv:2606.01152v1 Announce Type: cross Abstract: The work of a professional software engineer has begun to consist, increasingly, of directing agents rather than writing code, and the empirical evide

arXiv:2606.02049v1 Announce Type: new Abstract: The increasing integration of renewable energy sources into power systems, particularly in buildings equipped with photovoltaic (PV) panels and energy s

arXiv:2606.00031v1 Announce Type: cross Abstract: Coronary artery disease (CAD) remains one of the leading causes of death globally, highlighting the need for reliable predictive systems to support ea

arXiv:2606.02301v1 Announce Type: cross Abstract: Chronic pain diminishes quality of life by decreasing functional ability, yet objectively measuring this functional impact remains challenging in real

arXiv:2606.00902v1 Announce Type: new Abstract: General-purpose VLMs remain unreliable for biomedical research because valid answers in scientific papers depend on evidence split across figures, table

arXiv:2510.23379v2 Announce Type: replace-cross Abstract: We investigate a relatively under-explored class of hybrid neurosymbolic models that integrate symbolic learning with neural reasoning to cons

arXiv:2605.31481v1 Announce Type: new Abstract: As robot control shifts toward large-scale reinforcement learning with in-loop dynamics computation, the community's reliance on CPU-bound libraries suc

arXiv:2510.20853v2 Announce Type: replace-cross Abstract: Electrophysiological (ExG) signals offer valuable insights into human physiology, yet building foundation models that generalize across everyd

arXiv:2605.30590v1 Announce Type: cross Abstract: Two clinical AI systems can score nearly identically on coverage-based rubrics yet behave radically differently when their patient inputs change: one

arXiv:2605.31296v1 Announce Type: cross Abstract: Therapeutic mRNA design requires coordinating multiple interacting sequence features across the full transcript, where codon usage, untranslated regio

arXiv:2502.16548v3 Announce Type: replace-cross Abstract: Objective. Heart failure is one of the leading causes of death worldwide, with millions of deaths each year, according to data from the World

arXiv:2603.01311v2 Announce Type: replace Abstract: The discovery of novel catalysts tailored for particular applications is a major challenge for the twenty-first century. Traditional methods for thi

arXiv:2605.29184v1 Announce Type: cross Abstract: Large Language Models (LLMs) offer a promising avenue for scientific discovery, yet their application to symbolic regression is often constrained by i

arXiv:2605.29298v1 Announce Type: new Abstract: Bimanual coordination is essential for many real-world manipulation tasks, yet learning bimanual robot policies is limited by the scarcity of bimanual r

arXiv:2604.02645v2 Announce Type: replace-cross Abstract: This work aims to shine a spotlight on the topic of metalanguage. We first define metalanguage, link it to NLP and LLMs, and then discuss our

arXiv:2605.27009v1 Announce Type: new Abstract: Predicting human olfactory perception from molecular structure has seen remarkable progress, yet these approaches require explicit chemical structure at

arXiv:2605.25250v1 Announce Type: new Abstract: Lipid nanoparticles (LNPs) are among the most clinically mature platforms for nucleic acid delivery, yet designing lipids that are both effective and bi

arXiv:2605.04118v2 Announce Type: replace-cross Abstract: Recent advances in de novo protein binder design have enabled increasing experimental validation, yet reported in silico metrics remain diffic

arXiv:2605.23863v1 Announce Type: new Abstract: This study presents a closed-loop robotic strawberry harvesting system that combines a robust vision module, simulation-trained deep reinforcement learn

arXiv:2603.21717v4 Announce Type: replace Abstract: Distribution-to-distribution generative models support scientific imaging tasks ranging from modeling cellular perturbation responses to translating

arXiv:2605.22082v1 Announce Type: new Abstract: We present CoRMA(Contrastive Robotic Motor Adaptation), a context-based meta-adaptation framework that modifies RMA for force-dominant assembly. CoRMA r

arXiv:2605.22200v1 Announce Type: new Abstract: Achieving high levels of surgical skill through effective training is essential for optimal patient outcomes. Automated, data-driven skill assessment ho

arXiv:2506.16950v2 Announce Type: replace Abstract: Out-of-distribution (OOD) robustness is a desired property of computer vision models. Improving model robustness requires high-quality signals from