Trust Region On-Policy Distillation
arXiv:2606.01249v1 Announce Type: cross Abstract: On-Policy Distillation (OPD) is a fundamental technique for efficient post-training of large language models (LLMs), with broad applications in agent
Knowledge catalogue
arXiv:2606.01249v1 Announce Type: cross Abstract: On-Policy Distillation (OPD) is a fundamental technique for efficient post-training of large language models (LLMs), with broad applications in agent
arXiv:2602.07223v2 Announce Type: replace Abstract: Long-context large language model (LLM) inference has become the norm for today's AI applications. However, it is severely bottlenecked by the incre
We’re looking forward to seeing how developers and enterprises use Fireworks AI on @Microsoft Foundry to power the next generation of intelligent applications. Catch us at booth F111 at #MSBuild and s
arXiv:2606.02482v1 Announce Type: new Abstract: While video streaming understanding has made significant strides, real-world applications, such as live sports broadcasting, autonomous driving, and mul
arXiv:2605.30989v1 Announce Type: new Abstract: Robot teleoperation enables safe, non-contact task execution in hazardous environments where direct human access is difficult, and its application has e
arXiv:2605.30786v1 Announce Type: new Abstract: Graph classification is a core task in graph data mining with widespread real-world applications. Recent advances in graph neural networks (GNNs) have l
arXiv:2605.30668v1 Announce Type: cross Abstract: Dialogue topic segmentation is critical in many human-AI collaborative applications which requires identifying heterogeneous boundary cues, including
arXiv:2605.30807v1 Announce Type: new Abstract: Conditional generative models have recently achieved remarkable success in various applications. However, a suitable metric for evaluating the reliabili
arXiv:2507.12453v5 Announce Type: replace Abstract: In automated machine learning, scientific discovery, and other applications of Bayesian optimization, deciding when to stop evaluating expensive bla
arXiv:2511.17826v2 Announce Type: replace-cross Abstract: Deterministic inference is increasingly critical for large language model (LLM) applications such as LLM-as-a-judge evaluation, multi-agent sy
arXiv:2605.30863v1 Announce Type: new Abstract: Dynamic scene reconstruction and novel view synthesis are fundamental to next-generation visual intelligence applications such as virtual reality, robot
arXiv:2605.31559v1 Announce Type: new Abstract: Learning mappings between infinite-dimensional function spaces, or operator learning, is essential for many machine learning applications. Although tran
arXiv:2605.30866v1 Announce Type: cross Abstract: Many practically relevant applications of quantum machine learning involve classical data, for which performance depends critically on how inputs are
arXiv:2603.13875v2 Announce Type: replace Abstract: Many large language model applications require conditioning on long contexts. Transformers typically support this by storing a large per-layer KV-ca
arXiv:2603.23398v2 Announce Type: replace-cross Abstract: Generative modeling of discrete data, such as graphs, underpins many scientific and industrial applications, including molecular discovery and
arXiv:2605.30903v1 Announce Type: cross Abstract: Inverse reinforcement learning (IRL) typically assumes demonstrations from a single optimal demonstrator, but in many applications data come from mult
arXiv:2507.11075v2 Announce Type: replace-cross Abstract: Marker-free human pose estimation (HPE) has found increasing applications in various fields. Current HPE suffers from occasional errors in key
arXiv:2605.30785v1 Announce Type: new Abstract: LLM agents increasingly face long-horizon tasks such as web search and deep research in real-world applications, where accumulated context can cause lon
arXiv:2605.31158v1 Announce Type: new Abstract: Interactive video world models generate video chunk by chunk in response to user-controlled camera movements, enabling applications such as real-time ga
arXiv:2603.02630v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have achieved great success in many real-world applications, especially the one serving as the cognitive backbone
arXiv:2509.12440v3 Announce Type: replace-cross Abstract: Deploying Large Language Models (LLMs) in medical applications requires fact-checking capabilities to ensure patient safety and regulatory com
arXiv:2510.07651v2 Announce Type: replace-cross Abstract: Large language models (LLMs) with extended context windows enable powerful applications but impose significant memory overhead, as caching all
arXiv:2605.30570v1 Announce Type: new Abstract: We investigate the application of MAP-Elites (a well-known quality diversity algorithm) to design levels for First-Person Shooter (FPS) games. We consid
Google's I/O 2026 event showcased various AI applications and innovations developed by Google to enhance the conference experience. The post highlights creative implementations of AI technology integr
arXiv:2605.30646v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used in clinical applications. However, their behavior remains highly sensitive to subtle linguistic var
arXiv:2605.30409v1 Announce Type: cross Abstract: Real-time streaming video-to-video editing (V2V) is critical for interactive applications such as live broadcasting and gaming, yet it remains a formi
arXiv:2605.31127v1 Announce Type: new Abstract: Nonlinear conservation laws are at the heart of many of the most important dynamical systems in science and engineering. In practical applications, such
arXiv:2602.11802v2 Announce Type: replace Abstract: Graph link prediction (LP) plays a critical role in socially impactful applications such as job recommendation and friendship formation, making fair
arXiv:2605.30541v1 Announce Type: new Abstract: Full waveform inversion (FWI) is the gold standard for subsurface imaging, with applications from carbon sequestration to energy and mineral exploration
arXiv:2601.09633v2 Announce Type: replace Abstract: Taxonomies form the backbone of structured knowledge representation across diverse domains, enabling applications such as e-commerce and semantic se
AI agents possess incredible reasoning capabilities and can perform increasingly complex actions. But the reliability of agentic outcomes depends entirely on the quality of the context they can access
arXiv:2605.30693v1 Announce Type: cross Abstract: Building robust safety guardrails is essential for deploying Large Language Models across diverse real-world applications. However, this goal remains
arXiv:2502.04554v2 Announce Type: replace Abstract: Data selection has emerged as a crucial downstream application of data valuation, yet the theoretical foundations for using data values in selection
arXiv:2602.10286v2 Announce Type: replace Abstract: Pairwise preference learning is central to machine learning, with recent applications in aligning language models with human preferences. A typical
arXiv:2601.20774v2 Announce Type: replace Abstract: Multitask learning and related frameworks have achieved tremendous success in modern applications. In multitask learning problem, we are given a set
An open-source system that converts vocal imitations—human-made sound recreations—into synthesized sound effects for creative applications. The technology produces sound effects from vocal imitations
arXiv:2506.20344v3 Announce Type: replace-cross Abstract: Despite its wide range of applications across various domains, the optimization foundations of deep matrix factorization (DMF) remain largely
arXiv:2605.29454v1 Announce Type: new Abstract: While Membership Inference Attacks (MIAs) are the prevailing method for identifying training data, their application has expanded into privacy auditing
arXiv:2605.29698v1 Announce Type: new Abstract: Machine learning for molecular property prediction has focused largely on pure compounds, even though many practical applications depend on mixtures wit
arXiv:2605.29128v1 Announce Type: new Abstract: The wide adoption of LLMs has led to their use in great variety of applications and scenarios, such as chatbot assistants and data annotation, creating
arXiv:2605.29754v1 Announce Type: new Abstract: Electroencephalography (EEG) is a widely used non-invasive technique for measuring brain activity in brain-computer interface (BCI) applications. Superv
arXiv:2605.30188v1 Announce Type: cross Abstract: Reliable probability estimates are critical in many machine learning applications, yet modern classifiers are often poorly calibrated. Post-hoc calibr
arXiv:2603.01311v2 Announce Type: replace Abstract: The discovery of novel catalysts tailored for particular applications is a major challenge for the twenty-first century. Traditional methods for thi
arXiv:2605.29809v1 Announce Type: cross Abstract: Large-scale text-to-image (T2I) diffusion models have enabled unprecedented creative applications, but their unauthorized use has raised serious intel
arXiv:2509.22504v3 Announce Type: replace Abstract: As language model (LM) agents become increasingly capable and adopted in real-world applications, there is a growing need for scalable evaluation fr
arXiv:2605.29897v1 Announce Type: new Abstract: Evaluating the cultural awareness of large language models is crucial to ensure the fairness of generated text and the generalizability of applications
arXiv:2605.30237v1 Announce Type: cross Abstract: Semi-structured knowledge bases (SKBs) embed textual documents in a typed graph of entities and relations, and underpin applications such as product s
arXiv:2508.10566v3 Announce Type: replace Abstract: Audio-driven talking head generation faces a fundamental trade-off between personalization and generalization, limiting its practical application. I
arXiv:2605.29184v1 Announce Type: cross Abstract: Large Language Models (LLMs) offer a promising avenue for scientific discovery, yet their application to symbolic regression is often constrained by i
arXiv:2605.29908v1 Announce Type: cross Abstract: Sparse recovery in linear systems underpins applications from signal processing to high-dimensional regression. Sparse Bayesian Learning, grounded in
arXiv:2510.16658v3 Announce Type: replace Abstract: The development of large-scale artificial intelligence (AI) models is influencing neuroscience research by enabling end-to-end learning from raw bra
arXiv:2605.30046v1 Announce Type: cross Abstract: Anomaly detection aims to identify samples that deviate from the nominal data distribution and is central to many safety-critical applications. Howeve
arXiv:2605.29138v1 Announce Type: cross Abstract: Latency-accuracy tradeoffs are fundamental in real-time applications of deep neural networks (DNNs) for cyber-physical systems. In autonomous driving,
arXiv:2605.30168v1 Announce Type: new Abstract: Change detection (CD) in remote sensing is vital for applications such as urban monitoring and disaster assessment, yet traditional methods struggle wit
arXiv:2605.29833v1 Announce Type: new Abstract: As multimodal language models play an increasingly important role in scientific research, materials science offers a critical testbed due to its interdi
arXiv:2605.29834v1 Announce Type: new Abstract: Data stream processing has become a landmark in modern machine learning applications, with concept drifts and novel class appearances posing the primary
arXiv:2605.29659v1 Announce Type: cross Abstract: Real-time safety filtering for large language model (LLM) applications requires classifiers that can detect unsafe prompts, toxic language, jailbreak
arXiv:2605.29906v1 Announce Type: new Abstract: Text-to-motion (T2M) generation has broad applications in character animation, virtual avatars, and human-robot interaction. Existing methods typically
Vercel Sandboxes now supports port 8080 for running applications and services, expanding the available port options for developers building and testing projects in the sandbox environment. This additi
arXiv:2605.29538v1 Announce Type: new Abstract: With the emergence of wireless applications in three-dimensional environments, such as the low-altitude airspace and 3D heterogeneous networks, radio ma