Search: “automated”

arXiv:2606.06546v1 Announce Type: new Abstract: Evaluating large language models (LLMs) for education requires measuring how models teach, not only what they know. Existing benchmarks emphasize domain

arXiv:2606.06473v1 Announce Type: cross Abstract: Large language model (LLM) agents are increasingly applied to long-horizon tasks such as scientific discovery and machine learning engineering (MLE),

arXiv:2605.25956v2 Announce Type: replace Abstract: Urgent suspected colorectal cancer (CRC) referrals create operational bottlenecks because semi-structured clinical documents often require manual re

arXiv:2606.05563v1 Announce Type: cross Abstract: Evaluating LLM mediators remains challenging, as mediation unfolds as a real-time trajectory shaped by disputants' shifting emotions, intentions, and

arXiv:2312.08472v2 Announce Type: replace-cross Abstract: Transcendental functions, such as the exponential, are central to scientific computing, yet they cannot be natively calculated by digital hard

arXiv:2606.03165v1 Announce Type: cross Abstract: The language used by digital chat assistants such as ChatGPT can diverge from human expectations (misalignment). Research, mostly on Scientific Englis

arXiv:2511.06676v3 Announce Type: replace Abstract: Now that AI-driven moderation has become pervasive in everyday life, we often hear claims that 'the AI is biased'. While this is often said jokingly

arXiv:2606.00272v1 Announce Type: new Abstract: The FETCH classifier generates follow-up questions to help refine the best match for the applicant's legal problem, using a low-cost ensemble of LLMs. I

arXiv:2606.02507v1 Announce Type: cross Abstract: Inverse materials design is shifting materials discovery from forward prediction to targeted proposal of candidates that satisfy objectives under phys

arXiv:2605.31539v1 Announce Type: new Abstract: Postoperative pancreatic fistula (POPF) is a serious complication after pancreatic resection, increasing morbidity, hospital stay, and healthcare costs.

arXiv:2603.20253v2 Announce Type: replace-cross Abstract: Evaluating LLM agents for scientific tasks has focused on token costs while ignoring tool-use costs like simulation time and experimental reso

arXiv:2507.23270v2 Announce Type: replace Abstract: Reconfigurable multi-robot cells offer a promising approach to meet fluctuating assembly demands. However, the recurrent planning of their configura

arXiv:2605.29217v1 Announce Type: new Abstract: The amount of fat on the surroundings of the heart is correlated to several health risk factors such as carotid stiffness, coronary artery calcification

arXiv:2603.02082v2 Announce Type: replace Abstract: Children's acquisition of filler-gap dependencies has been argued by some to depend on innate grammatical knowledge, while others suggest that the d

arXiv:2605.28011v1 Announce Type: new Abstract: Quantifying impact phenomena in badminton smashes is important for evaluating both athletic performance and equipment; however, conventional measurement

arXiv:2605.28775v1 Announce Type: cross Abstract: Computer-use agents (CUAs) have recently made substantial progress, but deploying a separate large expert for each software domain remains expensive.

arXiv:2605.25572v1 Announce Type: cross Abstract: The growing complexity of quantum programming frameworks has exposed a critical limitation in existing large language model (LLM)-based code assistant

arXiv:2502.08047v5 Announce Type: replace Abstract: Recent progress in GUI agents has substantially improved visual grounding, yet robust planning remains challenging, particularly when the environmen

arXiv:2605.22608v1 Announce Type: new Abstract: Agentic systems are becoming more capable: agents define strategies, take actions, and interact with different environments. This autonomy poses serious

arXiv:2605.20459v1 Announce Type: new Abstract: In recent years, there has been a notable increase in the level of attention that is given to algorithms based on deep learning in the context of medica

arXiv:2605.21256v1 Announce Type: new Abstract: Standard clinical Natural Language Processing (NLP) benchmarks often yield inflated metrics by forcing deterministic classification on ambiguous instanc

arXiv:2501.09203v2 Announce Type: replace Abstract: Visual-Spatial Systems has become increasingly essential in concrete crack inspection. However, existing methods often lacks adaptability to diverse

arXiv:2605.19247v1 Announce Type: new Abstract: Current neural architecture search (NAS) methods are often limited by their predefined, restrictive search spaces. While recent large language model (LL

arXiv:2605.17561v1 Announce Type: cross Abstract: Issues faced when using software are reported in the form of bug reports. However, many bug reports are invalid, meaning they do not require code chan

arXiv:2605.17181v1 Announce Type: cross Abstract: Learning the violin is harder than it looks. Unlike piano keys or guitar frets, the violin neck has no markings at all, so a beginner cannot tell by l

arXiv:2602.24273v3 Announce Type: replace Abstract: We propose a minimal agentic baseline that enables systematic comparison across different AI-based theorem prover architectures. This design impleme

arXiv:2605.14108v1 Announce Type: cross Abstract: Diabetic Retinopathy (DR) is one of the leading causes of preventable blindness, yet rural regions often lack the specialists and infrastructure neede

arXiv:2605.12835v1 Announce Type: new Abstract: Large language models can extract local causal claims from text, but those claims become more useful when organized as persistent, navigable world model

arXiv:2509.21543v3 Announce Type: replace Abstract: Large Language Models (LLMs) have shown strong promise for robotic task planning, particularly through the automatic generation of symbolic planning

arXiv:2605.11581v1 Announce Type: new Abstract: When large language models (LLMs) serve real-time inference in commercial online advertising systems, end-to-end latency must be strictly bounded to the

arXiv:2605.09134v1 Announce Type: new Abstract: Reinforcement learning for program repair is hindered by sparse execution feedback and coarse sequence-level rewards that obscure which edits actually f

arXiv:2605.08455v1 Announce Type: new Abstract: Debugging CUDA programs has long been challenging because failures often arise from subtle interactions among hardware behavior, compiler decisions, mem

arXiv:2605.09696v1 Announce Type: new Abstract: Discovering governing equations from observational data remains a fundamental challenge in scientific modeling, particularly when the underlying mathema

arXiv:2605.08291v1 Announce Type: cross Abstract: EDA problems are graph-structured, but not all graph-structured problems call for the same GNN computation. We argue that successful GNN-for-EDA metho

arXiv:2605.10224v1 Announce Type: new Abstract: Current AI-powered research systems adopt a direct search-then-summarize paradigm that treats hypotheses as end products of scientific discovery. We arg

arXiv:2605.08498v1 Announce Type: cross Abstract: We introduce MathConstraint, a hard, adaptive benchmark for evaluating the combinatorial reasoning capabilities of LLMs. We combine constraint satisfa

arXiv:2601.22638v2 Announce Type: replace-cross Abstract: The exponential growth of machine learning submissions has strained the traditional peer review process, resulting in slow feedback loops for

arXiv:2605.07796v1 Announce Type: new Abstract: SQL dialects vary in syntax, types, and functions across database engines. Text-to-SQL benchmarks, however, predominantly support only SQLite. This crea

arXiv:2605.03231v1 Announce Type: new Abstract: What if a browser agent could learn your work simply by watching you do it? We present cotomi Act, a browser-based computer-using agent that combines re

arXiv:2605.04458v1 Announce Type: new Abstract: Evaluation of long-form, citation-backed reports has lately received significant attention due to the wide-scale adoption of retrieval-augmented generat

arXiv:2605.03053v1 Announce Type: new Abstract: Organoids are complex, three dimensional, self-organizing cell cultures which manifest organ-like features and represent a powerful platform for studyin

arXiv:2603.25169v2 Announce Type: replace Abstract: LLM performance is highly sensitive to prompt design, yet whether automatic prompt optimization can replace expert prompt engineering in linguistic

arXiv:2605.00908v1 Announce Type: new Abstract: This paper presents a comparative evaluation of convolutional and transformer-based object detection architectures for early weed detection in realistic

arXiv:2505.10887v3 Announce Type: replace Abstract: This paper introduces extsc{InfantAgent-Next}, a generalist agent capable of interacting with computers in a multimodal manner, encompassing text, i

arXiv:2604.28118v1 Announce Type: cross Abstract: Transformer models are widely deployed in critical AI applications, yet faults in their attention mechanisms, projections, and other internal componen

arXiv:2512.03449v3 Announce Type: replace Abstract: Background and Objective: Radiomics of knee MRI requires robust, anatomically meaningful regions of interest (ROIs) that jointly capture cartilage a