Search: “research”

arXiv:2604.11154v1 Announce Type: new Abstract: New multi-modal large language models (MLLMs) are continuously being trained and deployed, following rapid development cycles. This generative AI frenzy

arXiv:2604.09554v1 Announce Type: new Abstract: Optimism for accelerating scientific discovery with AI continues to grow. Current applications of AI in scientific research range from training dedicate

arXiv:2604.08590v1 Announce Type: cross Abstract: We present AlphaLab, an autonomous research harness that leverages frontier LLM agentic capabilities to automate the full experimental cycle in quanti

arXiv:2602.14819v2 Announce Type: replace Abstract: We present 'Testimole-conversational' a massive collection of discussion boards messages in the Italian language. The large size of the corpus, more

arXiv:2604.07921v1 Announce Type: new Abstract: We present a large-scale survey of sustainability communication and motivation in robotics research. Our analysis covers nearly 50,000 open-access paper

arXiv:2608.05235v1 Announce Type: cross Abstract: Research agents increasingly conduct multi-round machine-learning experiments in industrial recommendation settings and retain the resulting trajector

arXiv:2608.08389v1 Announce Type: new Abstract: Long-horizon research agents solve open-ended tasks through iterative retrieval, aggregation, and synthesis, but context grows rapidly while the margina

arXiv:2608.03527v1 Announce Type: cross Abstract: Retrieval systems help deep research agents generate high-quality answers by providing relevant documents. However, existing retrievers typically sele

arXiv:2607.27705v1 Announce Type: cross Abstract: Large language models can contribute useful ideas to mathematical research, yet long-horizon proof attempts remain difficult to coordinate, evaluate,

arXiv:2607.27851v1 Announce Type: new Abstract: Emotional dialogue research includes two influential strategy traditions. Empathetic dialogue prioritizes understanding a speaker's emotional experience

arXiv:2607.26587v1 Announce Type: cross Abstract: Automated research systems use experimental scores both to deliver artifacts and to decide which ideas to retain, transfer, and pursue. Yet one run sc

arXiv:2607.22682v1 Announce Type: new Abstract: We introduce a vocabulary for automated research systems built from one or more agents to make their design choices easier to describe and compare. The

arXiv:2607.24647v1 Announce Type: new Abstract: AI-driven autonomous research (AR) systems are becoming increasingly effective across a broad range of tasks. Their performance, however, is still evalu

arXiv:2607.20773v1 Announce Type: cross Abstract: Large language models (LLMs) have shifted human--computer interaction from `traditional'' interface journeys toward more conversational exchanges. Res

arXiv:2607.19901v1 Announce Type: new Abstract: Deep learning frameworks like nnU-Net achieve state-of-theart brain lesion segmentation performance but remain difficult to deploy in clinical research

arXiv:2607.04394v1 Announce Type: new Abstract: AI reasoning has become a central focus in contemporary artificial intelligence, largely driven by the success of large language models. However, mathem

arXiv:2607.01366v1 Announce Type: new Abstract: Federated learning (FL) research often depends on many small but consequential algorithmic choices: optimizer variants, server aggregation rules, local

arXiv:2607.00710v1 Announce Type: cross Abstract: Well-designed autonomous driving datasets have fundamentally shaped research progress, yet existing literature primarily describes what datasets conta

arXiv:2606.31478v1 Announce Type: new Abstract: Autonomous research agents can now draft hypotheses, write code, run experiments, and produce papers, but they remain brittle when experiments fail. Und

arXiv:2504.14969v2 Announce Type: replace Abstract: This paper proposes a framework for evaluating large language models (LLMs) on Chinese topic constructions, focusing on their sensitivity to island

arXiv:2606.12247v1 Announce Type: cross Abstract: Research on bias in large language models (LLMs) has predominantly focused on third-person audits, which study how models represent or evaluate demogr

arXiv:2605.22763v2 Announce Type: replace Abstract: Large language models (LLMs) increasingly excel at mathematical reasoning, but their unreliability limits their utility in mathematics research. A m

arXiv:2606.08612v1 Announce Type: new Abstract: Facial Expression Recognition (FER) has advanced rapidly over the last decade, driven by the shift from handcrafted descriptors and shallow classifiers

arXiv:2606.03829v1 Announce Type: new Abstract: Financial-research answers are decision-relevant only when another analyst can audit how they were produced: which source was chosen, which period and a

arXiv:2411.15240v5 Announce Type: replace-cross Abstract: Wearable movement data is collected by nearly all commercially available smartwatches and is a valuable resource for mental health research, r

arXiv:2507.08038v3 Announce Type: replace-cross Abstract: Language model agents are increasingly used to automate scientific research, yet evaluating their scientific contributions remains a challenge

arXiv:2606.01091v1 Announce Type: new Abstract: Open-ended reasoning and long-form generation tasks lack reliable automatic verification signals for reward-based policy optimization. Rubrics offer a p

arXiv:2606.02484v1 Announce Type: new Abstract: Recent advances in large language models and agentic AI systems have enabled significant progress in mathematical discovery, from solving competition pr

arXiv:2603.20884v2 Announce Type: replace Abstract: To alleviate the heavy burden of paper screening, researchers increasingly rely on existing AI agents, such as AI reviewers or DeepResearch, for pap

arXiv:2605.31146v1 Announce Type: cross Abstract: This paper investigates how User Experience Research (UXR) methods can be combined with AI-supported analysis to develop clearer design direction for

arXiv:2605.27403v1 Announce Type: cross Abstract: Written reflection assignments give students valuable opportunities for critical self-assessment, meaning making, and learning processing. Additionall

arXiv:2605.24002v1 Announce Type: cross Abstract: Computational materials science and chemistry span vast knowledge domains and fractured software ecosystems. Although large language models (LLMs) hav

arXiv:2605.16414v1 Announce Type: new Abstract: We present NERVE (Neuromorphic Vision and Radar Ensemble), a multi-sensor dataset comprising 257 minutes of synchronized recordings from five sensors: t

arXiv:2605.09918v1 Announce Type: cross Abstract: Reconciling platform revenue with user experience in LLM advertising motivates a data-centric foundation. We introduce NaiAD, the first comprehensive

arXiv:2601.00237v2 Announce Type: replace Abstract: This paper addresses the critical bottleneck of infrared (IR) data scarcity in Printed Circuit Board (PCB) defect detection by proposing a cross-mod

arXiv:2601.14289v2 Announce Type: replace-cross Abstract: Understanding research papers remains challenging for foundation models due to specialized scientific discourse and complex figures and tables

arXiv:2604.17931v2 Announce Type: replace Abstract: Reinforcement Learning (RL) has emerged as a powerful training paradigm for LLM-based agents. However, scaling agentic RL for deep research remains

arXiv:2506.22174v2 Announce Type: replace-cross Abstract: The transport industry has recently shown significant interest in unmanned surface vehicles (USVs), specifically for port and inland waterway

arXiv:2510.17795v3 Announce Type: replace Abstract: Replicating AI research is a crucial yet challenging task for large language model (LLM) agents. Existing approaches often struggle to generate exec

arXiv:2604.11540v1 Announce Type: new Abstract: Current large language models require hundreds of billions of parameters yet struggle with domain-specific reasoning and tool coordination in materials

arXiv:2604.10741v1 Announce Type: cross Abstract: Recent agentic search frameworks enable deep research via iterative planning and retrieval, reducing hallucinations and enhancing factual grounding. H

arXiv:2604.07720v2 Announce Type: replace Abstract: Deep Research (DR) requires LLM agents to autonomously perform multi-step information seeking, processing, and reasoning to generate comprehensive r

arXiv:2604.06566v1 Announce Type: cross Abstract: As the complexity of modern workloads and hardware increasingly outpaces human research and engineering capacity, existing methods for database perfor

arXiv:2604.07132v1 Announce Type: cross Abstract: Child Sexual Abuse Imagery (CSAI) classification is an important yet challenging problem for computer vision research due to the strict legal and ethi

arXiv:2508.07514v3 Announce Type: replace Abstract: Reliable plant species and damage segmentation for herbicide field research trials requires models that can withstand substantial real-world variati

arXiv:2204.06142v2 Announce Type: replace-cross Abstract: Institutions of higher learning, research institutes and other scientific research units have abundant scientific and technological resources