Search: “evaluation”

arXiv:2605.09633v1 Announce Type: new Abstract: We study multi-robot persistent monitoring on weighted graphs, where node weights encode monitoring priorities and edge weights encode travel distances.

arXiv:2605.08816v1 Announce Type: new Abstract: In the animal kingdom, mirror self-recognition is a canonical probe of higher-order cognition, emerging only in some species. We ask whether an analogou

arXiv:2605.10833v1 Announce Type: cross Abstract: Industrial anomaly detection is critical for manufacturing quality control, yet existing datasets mainly focus on static images or sparse views, which

arXiv:2605.09684v1 Announce Type: cross Abstract: We introduce a red-teaming methodology that exposes harder-to-catch attacks for coding-agent monitors, suggesting that current practices may under-eli

arXiv:2605.09176v1 Announce Type: cross Abstract: Training large language models requires optimization algorithms that are not only statistically effective, but also computationally and memory efficie

arXiv:2605.08192v1 Announce Type: cross Abstract: Frontier AI safety claims - published assertions that a highly capable general-purpose model is below a threshold of concern, adequately mitigated, or

arXiv:2605.08452v1 Announce Type: new Abstract: The ability to derive precise spatial and physical insights is a cornerstone of vision-language models (VLMs), yet their poor performances in related sp

arXiv:2510.20797v2 Announce Type: replace-cross Abstract: Context compression reduces Transformer inference costs by replacing lengthy inputs with shorter pre-computed representations. It carries sign

arXiv:2605.09996v1 Announce Type: new Abstract: While multimodal large language models have advanced across text, image, and audio, personalization research has remained primarily vision-language, wit

arXiv:2603.10165v2 Announce Type: replace-cross Abstract: Every agent interaction generates a next-state signal, namely the user reply, tool output, terminal or GUI state change that follows each acti

arXiv:2602.00318v2 Announce Type: replace-cross Abstract: The rise of bot accounts on social media poses significant risks to public discourse. To address this threat, modern bot detectors increasingl

arXiv:2605.09422v1 Announce Type: new Abstract: Although Large Multimodal Models (LMMs) have achieved strong performance on general video understanding, their susceptibility to textual prior shortcuts

arXiv:2605.08121v1 Announce Type: cross Abstract: Early detection of plant diseases is critical for improving crop productivity, while it also facilitates the foundations of precision agriculture. Rec

arXiv:2605.10936v1 Announce Type: new Abstract: As wearable devices like smart glasses integrate Large Multimodal Models (LMMs) into the continuous first-person visual streams of individual users, the

arXiv:2602.00593v2 Announce Type: replace Abstract: Despite progress on general tasks, vision-language models (VLMs) still struggle with challenges that demand both fine-grained visual grounding and e

arXiv:2605.10032v1 Announce Type: new Abstract: Cell-type-specific marker genes are fundamental to plant biology, yet existing resources primarily rely on curated databases or high-throughput studies

arXiv:2605.08687v1 Announce Type: cross Abstract: Data preparation is a central and time-consuming stage in data analysis workflows. Traditionally, commercial tools have relied on graphical user inter

arXiv:2605.09749v1 Announce Type: new Abstract: Discrete diffusion models generate structured sequences by progressively unmasking tokens, but enforcing global property constraints during generation r

arXiv:2509.26574v4 Announce Type: replace Abstract: While large language models (LLMs) with reasoning capabilities are progressing rapidly on high-school math competitions and coding, can they reason

arXiv:2605.09012v1 Announce Type: new Abstract: Large language models are increasingly capable at closed-world mathematical reasoning, but research assistance also requires source-grounded use of the

arXiv:2605.08454v1 Announce Type: cross Abstract: Recovering continuous-time dynamics from discrete observations is difficult because local supervision (e.g., pointwise regression targets, derivative

arXiv:2605.08197v1 Announce Type: cross Abstract: Most causal benchmarks for language models score local answers or graph structure. We introduce ReplaySCM, a 1,300 item benchmark for executable causa

arXiv:2605.10921v1 Announce Type: new Abstract: Memory is a critical component of robotic intelligence, as robots must rely on past observations and actions to accomplish long-horizon tasks in partial

arXiv:2605.10357v1 Announce Type: cross Abstract: Multimodal misinformation increasingly leverages visual persuasion, where repurposed or manipulated images strengthen misleading text. We introduce ex

arXiv:2311.03600v3 Announce Type: replace Abstract: Robots capable of learning from demonstration (LfD) must exhibit stability while executing learned motion skills. To be effective in the real world,

arXiv:2603.14889v2 Announce Type: replace-cross Abstract: The rapid evolution of end-to-end spoken dialogue systems demands transcending mere textual semantics to incorporate paralinguistic nuances an

arXiv:2407.11906v3 Announce Type: replace Abstract: Surgical data science has seen rapid advancement with the excellent performance of end-to-end deep neural networks (DNNs). Despite their successes,

arXiv:2605.10079v1 Announce Type: new Abstract: Video generation has advanced rapidly, producing photorealistic videos from text or image prompts. Meanwhile, film production and social robotics increa

arXiv:2605.08963v1 Announce Type: cross Abstract: Machine Learning (ML) models trained on complex health surveys such as the National Health and Nutrition Examination Survey (NHANES) often ignore prim

arXiv:2605.10851v1 Announce Type: new Abstract: We introduce the Generalized Turing Test (GTT), a formal framework for comparing the capabilities of arbitrary agents via indistinguishability. For agen

arXiv:2605.09739v1 Announce Type: cross Abstract: Large Language Models are increasingly used as zero-shot classifiers in complex reasoning tasks. However, standard constrained decoding suffers from a

arXiv:2604.02474v2 Announce Type: replace Abstract: Dynamical systems describe how a physical system evolves over time. Physical processes can evolve faster or slower in different environmental condit

arXiv:2509.02510v2 Announce Type: replace-cross Abstract: Large language models (LLMs), despite their impressive performance across a wide range of tasks, often struggle to balance two competing objec

arXiv:2605.09087v1 Announce Type: cross Abstract: Audio deepfake detection systems are increasingly deployed in high-stakes security applications, yet their fairness across demographic groups remains

arXiv:2605.08974v1 Announce Type: cross Abstract: While multimodal large language models (MLLMs) have advanced video understanding, they remain highly prone to hallucinations in dynamic scenes. We arg

arXiv:2605.10782v1 Announce Type: new Abstract: Urban mobility is naturally expressed both as trajectories in space and as natural-language descriptions of travel intent, constraints, and preferences.

arXiv:2605.09507v1 Announce Type: new Abstract: Video summarization aims to produce a compact representation of a long video by selecting a subset of temporally important segments that best reflect hu

arXiv:2605.09857v1 Announce Type: cross Abstract: Multicalibration requires predicted scores to agree with label probabilities across rich families of subgroups and score-dependent tests, but existing

arXiv:2605.08553v1 Announce Type: cross Abstract: Large language models can generate useful code from natural language, but their outputs come without correctness guarantees. Verifiable code generatio

arXiv:2604.03701v2 Announce Type: replace Abstract: Video-based numerical reasoning provides a premier arena for testing whether Vision-Language Models (VLMs) truly 'understand' real-world dynamics, a

arXiv:2605.10579v1 Announce Type: new Abstract: Training AI agents to proactively assist humans in daily activities, from routine household tasks to urgent safety situations, requires large-scale visu

arXiv:2507.04465v4 Announce Type: replace Abstract: The rapid evolution of deep learning (DL) models and the ever-increasing size of available datasets have raised the interest of the research communi

arXiv:2605.09109v1 Announce Type: new Abstract: Many continuous-control problems ship with a competent but suboptimal controller (a tuned PID, a hand-designed gait). A growing family of methods uses s

arXiv:2605.09639v1 Announce Type: cross Abstract: While U-Net architectures remain the gold standard for medical image segmentation, their deployment in resource-constrained environments demands aggre

arXiv:2605.06762v1 Announce Type: cross Abstract: Robust genotype-to-phenotype (G2P) prediction is essential for accelerating breeding decisions and genetic gain. However, it remains challenging to me

arXiv:2605.06937v1 Announce Type: new Abstract: This methods article presents a reproducible calibration workflow for prompt-based large language models (LLMs) in structured evidence-synthesis tasks.