Knowledge catalogue
arXiv:2610.16876v1 Announce Type: new Abstract: Target-aware multimodal instruction selection requires useful examples and an appropriate balance of supervision. Sample-level rankings can overrepresen
Cloudflare and OpenAI have formed a joint initiative to enhance the discoverability of AI‑generated content worldwide. By leveraging Cloudflare’s edge network, DDoS protection, and intelligent routing
arXiv:2610.20597v1 Announce Type: cross Abstract: Training language agents is bottlenecked by the high cost of long-horizon rollouts, yet standard agentic reinforcement learning (RL) learns only from
arXiv:2603.02022v3 Announce Type: replace-cross Abstract: Speech Bandwidth Extension improves clarity and intelligibility by restoring/inferring appropriate high-frequency content for low-bandwidth sp
arXiv:2610.18583v1 Announce Type: new Abstract: Sampling-based test-time scaling enhances LLM reasoning by aggregating multiple reasoning paths for the same query. Existing methods use answer frequenc
arXiv:2610.15603v1 Announce Type: new Abstract: Materials synthesis planning (MSP) connects AI-driven materials discovery to experimental realization by proposing precursors, synthesis operations, and
arXiv:2610.19343v1 Announce Type: cross Abstract: A training-data quality gate changes both which examples survive within regions of the input space and how training mass is distributed across those r
arXiv:2602.00717v2 Announce Type: replace Abstract: Deep time-series forecasting can be formulated as a distribution balancing problem aimed at aligning the distribution of the forecasts and ground tr
arXiv:2610.18935v1 Announce Type: cross Abstract: We study PAC learning of binary classifiers when labels arrive after random delays and are censored beyond an observation window W. How many samples d
arXiv:2610.19145v1 Announce Type: new Abstract: On-policy distillation (OPD) is increasingly studied as a cheaper alternative to reinforcement learning, yet it remains a form of knowledge distillation
arXiv:2610.19120v1 Announce Type: cross Abstract: Purpose: Free-breathing abdominal MRI is limited by respiratory motion, which introduces image artifacts and k-space inconsistencies. Existing reconst
arXiv:2610.14700v1 Announce Type: new Abstract: Most text-to-SQL benchmarks provide the target database. In a large data catalog, however, choosing the sources is part of the problem: an agent must fi
arXiv:2610.19804v1 Announce Type: new Abstract: We study finite stochastic partial monitoring (PM) games with side information, a paradigm of sequential learning with partial feedback. On each round t
arXiv:2610.19836v1 Announce Type: new Abstract: Enterprise group chats record project progress, decisions, task ownership, and other organizational information valuable to enterprise agents. Reliable
arXiv:2610.20309v1 Announce Type: new Abstract: Synthetic-aperture imaging (SAI) removes occluders such as foliage by integrating many views of a scene along a camera path. It has so far been used mai
arXiv:2610.17404v1 Announce Type: cross Abstract: Earthquake catalogs underpin studies such as fault structure, earthquake processes and seismic hazard, and their value depends on being both complete
arXiv:2610.19799v1 Announce Type: new Abstract: Recent work lets LLM agents evolve their own harness, up to the team itself. Yet in human groups, the same abilities achieve different outcomes under di
arXiv:2610.15062v1 Announce Type: new Abstract: Supporting edge Artificial Intelligence (AI) inference as a network service is challenging because current mobile systems can transport tensors but do n
arXiv:2610.20305v1 Announce Type: cross Abstract: Homogeneous turbulent flows exhibit recurring multiscale structures across spatially separated regions, suggesting that a single flow realization may
arXiv:2610.19618v1 Announce Type: new Abstract: Object-goal navigation requires an embodied agent to explore an unfamiliar environment, identify a target object, navigate toward candidate instances, a
arXiv:2610.18157v1 Announce Type: cross Abstract: Inside organizations, a question's data often sit with several owners, who query their own tables but never hand them over. An LLM coordinator can fan
Fascinating paper from Google DeepMind. They studied a market where AI agents bid to answer each user prompt. Each agent bids its expected reward minus its inference cost. A first-price auction is sim
arXiv:2610.20269v1 Announce Type: cross Abstract: Single-channel radio-frequency (RF) signal separation is important for interference mitigation when a signal of interest overlaps with a co-channel in
arXiv:2610.17243v1 Announce Type: cross Abstract: The original EneMonIE proposal describes a Web-based semantic system for continuously monitoring energy trends through targeted information extraction
arXiv:2610.19287v1 Announce Type: cross Abstract: Compute cost in automated essay scoring (AES) is usually treated as a technical efficiency issue rather than a measurement design concern. Yet deploym
arXiv:2610.20396v1 Announce Type: new Abstract: When does a human correction become useful learning, and what establishes better segmentation at matched human effort? This critical narrative review fo
arXiv:2112.09646v2 Announce Type: replace Abstract: The generation of discontinuous distributions is a difficult task for most known frameworks such as generative autoencoders and generative adversari
arXiv:2610.20502v1 Announce Type: new Abstract: Longitudinal electronic health records (EHRs) can reveal disease-progression structure, while useful forecasting requires leakage-controlled discovery,
arXiv:2610.15350v1 Announce Type: cross Abstract: We present Gugak-VocalSet, the first publicly available audio dataset for Korean traditional singing with dense annotations of sigimsae, a structured
arXiv:2610.20209v1 Announce Type: new Abstract: Routability prediction enables rapid evaluation of placement quality without routing, providing effective feedback for routability-driven placement opti
arXiv:2610.19518v1 Announce Type: new Abstract: While multi-fingered dexterous robot hands have the potential to provide human-level dexterity, using them for functional manipulation remains difficult
arXiv:2605.05348v2 Announce Type: replace-cross Abstract: Audio description (AD) narrates visual elements in video for blind and low-vision audiences. Recent work has shown that giving novice describe
arXiv:2610.20412v1 Announce Type: new Abstract: To reach top accuracy, current fully supervised instance segmentation methods severely rely on large-scale pixel-wise labeled datasets. They are usually
arXiv:2610.20409v1 Announce Type: new Abstract: We develop a self-supervised reinforcement learning algorithm that learns to build towers of up to eight cubes from scratch, without prior data, rewards
I'm in the AI camp where I think you need a go-bag. Enough people have asked me for what is in mine that I threw together an Amazon list (buy them from here, buy on your own, I truly do not care). Jus
arXiv:2610.20138v1 Announce Type: new Abstract: Batch black-box optimization is important for expensive systems where many candidates can be evaluated in parallel, yet only a few rounds of evaluation
arXiv:2610.20341v1 Announce Type: new Abstract: Feed-forward Gaussian reconstruction builds 3D scenes directly from input images for novel view rendering, but learning Gaussian attributes from predict
arXiv:2610.19347v1 Announce Type: cross Abstract: Novelty detection is crucial in various technological and scientific domains. Its importance spans from ensuring the reliability of sensor networks to
arXiv:2610.15582v1 Announce Type: new Abstract: We study the problem of inverse garment design, reconstructing simulation-ready 3D outfits with arbitrary layers from a single image, with visual agents
arXiv:2602.08318v3 Announce Type: replace-cross Abstract: Flow matching (FM) is increasingly used in scientific domains for time series generation and forecasting, where data often arise from underlyi
arXiv:2610.20552v1 Announce Type: new Abstract: We present KinoGen, a framework that synthesizes customizable humanoid loco-manipulation references through kinematically grounded video generation. Kin
arXiv:2608.01718v2 Announce Type: replace Abstract: Semantic caching, reusing responses to answer semantically similar queries, has seen growing adoption in LLM serving, offering faster responses and
arXiv:2610.19266v1 Announce Type: new Abstract: Existing illumination estimation methods primarily recover lighting distributions for rendering, but typically leave the contributions of individual phy
arXiv:2610.20102v1 Announce Type: new Abstract: We investigate two aspects of multilingual pre-training with limited data, namely how language exposure is distributed across English, Dutch, and Chines
arXiv:2610.16625v1 Announce Type: new Abstract: Post-route FPGA timing closure remains expert-dependent and computationally expensive. We present LATCH (LLM-Assisted Timing Closure Harness), a two-lay
arXiv:2610.18014v1 Announce Type: cross Abstract: Non-prefix KV cache reuse is a promising approach for improving inference efficiency in multi-agent systems as agents share the same context blocks. H
arXiv:2610.19576v1 Announce Type: new Abstract: Temporal graph data are recorded in different representations, but whether these differences also determine the scope of learned knowledge reuse remains
arXiv:2601.20776v2 Announce Type: replace Abstract: This paper rethinks steady-hand robotic manipulation by using a weakly supervised framework that fuses calibration-aware perception with admittance
arXiv:2610.19179v1 Announce Type: cross Abstract: We study packet routing in queuing systems where queues act as selfish, myopic agents that use no-regret learning algorithms to select servers with un
arXiv:2610.17164v1 Announce Type: new Abstract: The relationship between odor chemistry and perception remains incompletely understood. Many existing studies focus on the perception of single molecule
arXiv:2610.19782v1 Announce Type: new Abstract: Population health stratification frameworks, such as the Italian DM77, organize patients by clinical need and care pathways but offer limited guidance o
arXiv:2610.19786v1 Announce Type: new Abstract: Generating LiDAR point clouds from multi-view camera images is a valuable task with applications in controllable data synthesis, cross-modal simulation
arXiv:2610.18948v1 Announce Type: cross Abstract: Finite safety assessments cannot exclude an adversary that preserves unattacked behavior until certification and activates afterward. We characterize
arXiv:2606.23403v2 Announce Type: replace Abstract: As agentic LLM systems move from prototypes to deployment, evaluating them has become more important and more difficult. Evaluation goals are often
arXiv:2610.20256v1 Announce Type: new Abstract: Long-horizon agents must coordinate reasoning, tool use, and environment feedback over extended interactions. Learning from sparse outcome rewards requi
arXiv:2610.20384v1 Announce Type: new Abstract: Agents for mixed-degradation image restoration typically learn what a tool does to an image only after running it: failed calls waste computation and ha
arXiv:2610.20238v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) excel on graph-structured data but suffer catastrophic forgetting when classes arrive sequentially. In class-incremental gr
arXiv:2610.16552v1 Announce Type: new Abstract: An ordered visual code lets a short prefix stand in for an image, and the training loss decides what that prefix contains. Under pixel reconstruction th
arXiv:2610.19457v1 Announce Type: new Abstract: Expected-reward maximization in reinforcement learning concentrates probability mass on a few outcomes, even in tasks that require many distinct, high-q
arXiv:2606.16710v2 Announce Type: replace-cross Abstract: Multi-agent systems (MAS) are credited with solving complex problems, yet their agents routinely act on context that may be false. Prior work