Search: “evaluation”

arXiv:2610.07075v1 Announce Type: new Abstract: High-quality datasets are essential for reliable machine learning, but dataset curation remains costly and hard to generalize across domains. Existing m

arXiv:2610.07969v1 Announce Type: new Abstract: Scaling robotic foundation models requires diverse training data and reliable evaluation environments. Simulation offers a scalable solution, yet existi

arXiv:2610.07708v1 Announce Type: new Abstract: Recommender systems learn from observed user-item interactions, but explicit negative feedback is often unavailable. Since deep learning models require

arXiv:2610.08660v1 Announce Type: new Abstract: Background: Biomedical AI can generate plausible explanations without reliably verifying whether each statement is supported by patient-specific evidenc

arXiv:2610.06862v1 Announce Type: new Abstract: **Context:** Deep Neural Networks (DNNs) increasingly control Cyber-Physical Systems (CPSs), yet small input perturbations can cause unsafe system-level

arXiv:2610.08601v1 Announce Type: new Abstract: Emergency messaging such as extreme-weather reports and earthquake instructions can involve high stakes, to the extent that translation errors can lead

arXiv:2610.08642v1 Announce Type: cross Abstract: Contact with contaminated objects can spread hazards through a household robot's grippers, tools, and shared surfaces, while new contacts can make an

arXiv:2610.07383v1 Announce Type: cross Abstract: Synthetic patient data generation is a promising solution to the dual challenge of data scarcity and privacy constraints in healthcare machine learnin

arXiv:2610.07791v1 Announce Type: new Abstract: Illusory pattern perception is a well-documented human cognitive tendency to infer meaningful relationships in data that is actually random. Such a tend

arXiv:2610.07580v1 Announce Type: new Abstract: Aerospace electrical-design revisions can contain multiple genuine changes, although an engineering request may authorize only a subset. Propagating eve

arXiv:2610.07847v1 Announce Type: new Abstract: As LLMs increasingly assist in moral reasoning, omission bias, the tendency to prefer inaction even when equivalent framings reverse substantive outcome

arXiv:2610.08153v1 Announce Type: cross Abstract: Multiple-choice question answering (MCQA) is commonly used to evaluate large language models under the assumption that one of the provided options is

arXiv:2610.07588v1 Announce Type: new Abstract: Modern recommendation is shifting from platform-centric personalization toward user-governed personalization, where a personal LLM agent can act on the

arXiv:2508.16821v2 Announce Type: replace Abstract: We introduce PuzzleJAX, a GPU-accelerated puzzle game engine and description language designed to support rapid benchmarking of tree search, reinfor

arXiv:2610.07545v1 Announce Type: new Abstract: We present a fully offline speech-to-speech translation pipeline that runs on a Jetson Nano (4 GB) and corrects its own weak translations without retrai

arXiv:2610.07894v1 Announce Type: new Abstract: Large language models (LLMs) are expected to answer questions faithfully based on the provided context, abstaining when the context information is insuf

arXiv:2610.07114v1 Announce Type: cross Abstract: The Frechet Inception Distance (FID) is widely used to evaluate generative models, but its empirical plug-in estimator suffers from finite-sample bias

arXiv:2610.07385v1 Announce Type: new Abstract: Fine-tuning vision-language models (VLMs) is typically evaluated at a single downstream checkpoint, obscuring whether a semantic capability was never ac

arXiv:2610.07645v1 Announce Type: cross Abstract: Self-improving LLM agents increasingly distill successful experiences into persistent, reusable skills. Existing skill attack methods corrupt this lea

arXiv:2610.08076v1 Announce Type: new Abstract: Frontier LLM agents have been shown to be capable of solving increasingly complex tasks for which humans have measurable solutions. This begs the pertin

arXiv:2610.08161v1 Announce Type: cross Abstract: Ambient documentation systems are rapidly gaining adoption, yet their impact on clinical note quality remains poorly characterized. We introduce MedCo

arXiv:2610.08554v1 Announce Type: cross Abstract: While HCI increasingly examines AI-safety for youth, the literature lacks a comprehensive view of what risks have been identified, how they are addres

arXiv:2610.07597v1 Announce Type: cross Abstract: A robot description does more than specify a physical mechanism: it also encodes arbitrary conventions, such as joint-axis direction, joint-angle zero

arXiv:2610.07089v1 Announce Type: cross Abstract: LLM agents increasingly act in multi-actor environments, exposing them to misuse from multiple sources: decomposition attacks, where a harmful request

arXiv:2610.07755v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as judges for automated AI evaluation. A common practice is to randomize prompt sequences and avera

arXiv:2610.08089v1 Announce Type: cross Abstract: In semantic query engines, predicates are evaluated by machine-learned models, and the choice of a query plan affects not only the cost of a query but

arXiv:2610.07100v1 Announce Type: new Abstract: Persistent agent memory is only as reliable as its retention decision: an assertion weakly supported by its source can be stored and later reused as est

arXiv:2610.04134v1 Announce Type: cross Abstract: The discovery and development of advanced materials is a challenging process constrained by the high time and monetary costs of synthesis, processing,

arXiv:2610.04425v1 Announce Type: new Abstract: Agricultural visual models are typically evaluated for either recognition or localization, but reliable diagnosis requires identifying what is present a

arXiv:2610.04198v1 Announce Type: cross Abstract: Diffusion language models (DLMs) enable fast generation by predicting multiple tokens in parallel, but their practical adoption remains limited by a p

arXiv:2610.06184v1 Announce Type: new Abstract: Generalization in multi-arm collaboration can be studied as composing familiar atomic skills in new ways across arms. However, existing evaluations offe

arXiv:2610.04927v1 Announce Type: cross Abstract: Agentic machine learning engineering (MLE) is an emerging AI4SE application for complex ML tasks and a step toward recursive self-improvement of AI sy

arXiv:2610.06748v1 Announce Type: cross Abstract: In decentralized consumer-to-consumer (C2C) marketplaces, people list goods, negotiate with strangers, and rate one another, so trust rests on reputat

arXiv:2610.04878v1 Announce Type: cross Abstract: Human-agent evaluations often compress interaction into a single performance score, even when human and automated policies adapt differently over time

arXiv:2610.05950v1 Announce Type: new Abstract: LLM-driven evolution enables iterative model development, but two practical goals remain underexplored: finding model designs that transfer across relat

arXiv:2610.05911v1 Announce Type: new Abstract: Multi-view panoptic segmentation assigns a semantic class and a scene-level instance ID to every pixel of an unordered set of images, and recent feed-fo

arXiv:2610.06269v1 Announce Type: cross Abstract: Open-ended scientific discovery often requires repeatedly proposing and evaluating candidate solutions. LLM-based systems can support this process by

arXiv:2512.05134v2 Announce Type: replace Abstract: Diffusion transformers require repeated denoiser evaluations, making image and video generation computationally expensive. We propose a training-fre

arXiv:2610.05874v1 Announce Type: new Abstract: Scalable Graph Transformers are commonly trained and evaluated on static large graphs in a transductive setup. Many scalable Graph Transformer component

arXiv:2610.04568v1 Announce Type: cross Abstract: We introduce a sampling approach for energy- and score-based generative models that requires no gradient evaluations of the model. Replacing the drift

arXiv:2610.04456v1 Announce Type: new Abstract: Deep learning models for crop leaf disease recognition routinely report near-perfect accuracy yet are typically trained and evaluated on a single datase

arXiv:2610.04976v1 Announce Type: new Abstract: We evaluate language models' capability to generate novel research ideas through the lens of compression. We aim to lower-bound the potentially tiny pro

arXiv:2610.05414v1 Announce Type: new Abstract: Although the advancement of vision-language models (VLMs) has endowed robots with enhanced environmental understanding and task reasoning, a comprehensi

arXiv:2610.05571v1 Announce Type: cross Abstract: In safety-critical domains such as autonomous driving, systems must be evaluated across a large number of environment conditions, often represented as

arXiv:2610.04893v1 Announce Type: new Abstract: Industrial Internet of Things (IIoT) systems generate high-dimensional sensor telemetry from interconnected components, where faults can propagate acros

arXiv:2610.06413v1 Announce Type: new Abstract: Thinking-enabled vision-language models (VLMs) report ever-higher accuracy on spatial benchmarks, yet final-answer scores cannot reveal whether a correc