Search: “concepts”

arXiv:2609.02859v1 Announce Type: new Abstract: Harnessing naturally occurring feedback from user interactions offers a promising learning signal for Large Language Models (LLMs). However, recent stud

arXiv:2609.00795v1 Announce Type: cross Abstract: Computational materials science has traditionally delegated algorithmic tasks to computers while leaving scientific judgments to humans. We argue that

arXiv:2609.01064v1 Announce Type: cross Abstract: Reconstructing a damaged musical fragment is an inverse problem: the observed sequence contains partial information, while a raga encodes constraints

arXiv:2609.00069v1 Announce Type: cross Abstract: Self-improving agents iteratively modify their own harness to push the frontier of their performance. However, such modifications can produce illusory

arXiv:2609.01040v1 Announce Type: cross Abstract: Machine learning systems deployed for credit, hiring, and resource distribution are increasingly subject to regulatory oversight from policies such as

arXiv:2609.00808v1 Announce Type: cross Abstract: As far-right actors increasingly exploit online platforms to disseminate ideology and mobilize supporters, civil society organizations (CSOs) play a v

arXiv:2609.01588v1 Announce Type: cross Abstract: Writing involves diverse cognitive activities, from ideation to revision, and writers' needs vary across individuals and moments. Proactive AI promise

arXiv:2609.00629v1 Announce Type: new Abstract: Large Vision-Language Models (LVLMs) achieve strong performance on image-grounded text generation and visual question answering. However, it remains dif

arXiv:2504.10214v2 Announce Type: replace Abstract: Pretrained model-based incremental object detection (PTMIOD) leverages the rich detection priors of pretrained detectors to learn new categories inc

arXiv:2609.00539v1 Announce Type: new Abstract: Animals' body morphology shapes the gait patterns they can perform, where mechanical resonance reduces the need for active control. By tuning posture an

arXiv:2609.00948v1 Announce Type: cross Abstract: Vision-language models (VLMs) have demonstrated strong performance in visual question answering with natural images. However, they continue to struggl

arXiv:2607.18114v2 Announce Type: replace-cross Abstract: Modern LLMs are alarmingly susceptible to surprisingly simple immaterial changes of input prompts: a casual hint, an incorrectly labeled few-s

arXiv:2609.00648v1 Announce Type: new Abstract: For noisy real-world environments such as those in open public spaces, spoken dialogue systems for both autonomous robots and avatars should be carefull

arXiv:2609.01090v1 Announce Type: new Abstract: Scientific knowledge about AI models is produced faster than the community can organize it. Every few months a new foundation model reshapes the field a

arXiv:2609.00987v1 Announce Type: cross Abstract: Recent advances in generative AI are raising ethical concerns regarding the originality of generated content and the potential replication of training

arXiv:2506.19388v2 Announce Type: replace Abstract: Tissue deformation recovery based on stereo endoscopic images is crucial for tool-tissue interaction analysis and benefits surgical navigation and a

arXiv:2602.17826v2 Announce Type: replace Abstract: Language models exhibit fundamental limitations -- hallucination, brittleness, and lack of formal grounding -- that are particularly problematic in

arXiv:2602.22006v4 Announce Type: replace Abstract: Accurate relative localization is critical for multi-robot cooperation. In robot groups, measurements from different robots arrive asynchronously an

arXiv:2609.01470v1 Announce Type: new Abstract: As AI systems are increasingly used to draft radiology reports, reliably evaluating their clinical quality remains a critical challenge. Large language

arXiv:2609.00342v1 Announce Type: new Abstract: Whole-slide images (WSIs) are challenging for vision-language reasoning because diagnostically relevant morphology is sparse, heterogeneous, and distrib

arXiv:2609.00584v1 Announce Type: new Abstract: Does unrestricted AI access bypass the cognitive effort required for learning, or does it streamline knowledge acquisition? This paper reports on a stud

arXiv:2609.01591v1 Announce Type: new Abstract: AI tutors are most useful when they adapt to each student's strengths, weaknesses, and preferred guidance, but evidence about which guidance works for w

arXiv:2609.00304v1 Announce Type: new Abstract: Models express values and welfare-relevant self-reports, but it is unclear whether these outputs reflect stable preferences or a stable self. We thus in

arXiv:2609.00969v1 Announce Type: cross Abstract: We present the zbMATH Open Knowledge Graph, a large-scale RDF knowledge graph (KG) covering more than 250 years of mathematical scholarship. Unlike ex

arXiv:2609.00685v1 Announce Type: cross Abstract: Article-level news stance detection aims to identify the perspective of news articles toward social issues. Despite advances in stance detection and i

arXiv:2609.00503v1 Announce Type: new Abstract: Conventional neural networks learn predominantly through affine transformations followed by nonlinear activations, while elapsed time is often treated a

arXiv:2609.00071v1 Announce Type: new Abstract: Prediction error is widely used to evaluate nuisance-function estimators in causal inference, but its relationship with causal estimator performance may

arXiv:2608.30817v1 Announce Type: cross Abstract: Geospatial foundation models have emerged as state-of-the-art methods for downstream Earth observation tasks. However, existing pretraining methodolog

arXiv:2608.28967v1 Announce Type: cross Abstract: Electroencephalography (EEG)-based robotic control is commonly formulated as a direct classification problem, in which electrical neural signals are m

arXiv:2608.30543v1 Announce Type: new Abstract: Large Language Models (LLMs) offer new possibilities for scaling qualitative analysis, but existing applications often provide limited methodological tr

arXiv:2607.02290v2 Announce Type: replace Abstract: Recent image generation and editing models can produce visually appealing natural images, yet they remain unreliable when the target image is a know

arXiv:2608.28824v1 Announce Type: new Abstract: Currently, in biological systems Neural Correlates of Consciousness (NCCs) are characterized in terms of EEG and FMRI signals. Unfortunately, this chara

arXiv:2310.02779v3 Announce Type: replace Abstract: Generative flow networks (GFlowNets) are sequential sampling models trained to match a given distribution. GFlowNets have been successfully applied

arXiv:2510.23515v3 Announce Type: replace Abstract: This paper proposes FreeFuse, a training-free framework for multi-subject text-to-image generation through automatic fusion of multiple subject LoRA

arXiv:2608.29088v1 Announce Type: new Abstract: Multimodal question answering remains sensitive to noisy, incomplete, and weakly grounded evidence. Long unstructured contexts can introduce redundancy

arXiv:2608.28608v1 Announce Type: cross Abstract: Ancient Indian medical texts like Sushruta Samhita have extensive information on diseases, treatments, and surgical techniques. Yet, their ancient for

arXiv:2608.29913v1 Announce Type: new Abstract: TinyML includes the implementation of machine learning on devices with limited memory and computing resources. With the development of technology, AI sy

arXiv:2608.30003v1 Announce Type: new Abstract: Prototypical part-based models provide explainable predictions by comparing input regions to learned prototypes. However, current approaches are burdene

arXiv:2608.30652v1 Announce Type: new Abstract: Embedding methods have become popular due to their scalability on link prediction and/or triple classification tasks on Knowledge Graphs (KGs). Embeddin

arXiv:2608.29611v1 Announce Type: new Abstract: Unsupervised action segmentation aims to discover latent action categories and their temporal organization without action annotations. Optimal transport

arXiv:2608.29888v1 Announce Type: new Abstract: Neural operators provide fast surrogates for partial differential equation (PDE) solvers, but their reliability can degrade for high-dimensional spatial

arXiv:2402.03741v4 Announce Type: replace-cross Abstract: Recent advancements in multi-agent reinforcement learning (MARL) have opened up vast application prospects, such as swarm control of drones, c

arXiv:2608.29023v1 Announce Type: new Abstract: Human-robot collaboration describes the process of humans and autonomous agents working together to accomplish common goals. This process is facilitated

arXiv:2608.29530v1 Announce Type: cross Abstract: Modern systems in artificial intelligence (AI) somehow excel in domains for which they seem poorly suited. Intelligence has traditionally been modeled

arXiv:2608.27856v1 Announce Type: new Abstract: Recent advances in large language models are enabling autonomous clinical agents to perform increasingly complex electronic health record (EHR) modeling

arXiv:2603.18031v2 Announce Type: replace Abstract: Balancing fine-grained local modeling with long-range dependency capture under computational constraints remains a central challenge in sequence mod