Knowledge catalogue
arXiv:2609.35152v1 Announce Type: cross Abstract: Successive weaving segments (SWSs) on urban expressways are bottlenecks prone to recurrent congestion and collisions, requiring fine-grained active tr
arXiv:2609.32779v1 Announce Type: cross Abstract: World Action Models (WAMs) acquire behavioral priors by modeling future scene evolution, but predicting detailed futures in pixel or latent space incu
arXiv:2609.35745v1 Announce Type: new Abstract: Linear Vision Transformers (ViTs) are designed to replace the attention in Softmax ViTs with the linear-complexity attention operator for more efficient
arXiv:2502.02332v3 Announce Type: replace-cross Abstract: We study task selection to enhance sample efficiency in model-agnostic meta-reinforcement learning (MAML-RL). Traditional meta-RL typically as
arXiv:2609.33566v1 Announce Type: new Abstract: Time series forecasting requires extrapolating the dynamics of an observed process beyond the last available measurement. Yet recurrent forecasting mode
arXiv:2601.21338v2 Announce Type: replace Abstract: Hyperspectral image super-resolution (HSI-SR) aims to recover spatial detail while preserving the spectral shape on which quantitative analysis reli
arXiv:2609.32062v1 Announce Type: cross Abstract: Although E[dropout(x)] = x, here we show that E[LayerNorm(dropout(x))] is not equal to LayerNorm(x). Accordingly, the pattern of a Dropout layer follo
arXiv:2609.34638v1 Announce Type: new Abstract: Test-time adaptation (TTA) is a promising paradigm for handling distribution shift in time-series forecasting (TSF), where models adapt at inference tim
arXiv:2609.33260v1 Announce Type: cross Abstract: An agent can solve a task today and face the same task under new facts, tools, or governing knowledge tomorrow. Most agent systems can retrieve releva
arXiv:2609.35645v1 Announce Type: cross Abstract: Code-switching (CS), a seamless alternation between languages within a single utterance, remains a critical challenge in automatic speech recognition
arXiv:2609.33047v1 Announce Type: new Abstract: Matrix optimizers such as Muon transform each momentum matrix through an approximate orthogonalization, typically implemented by a small number of Newto
could anything prove more decisively that Jensen Huang is a poor judge of character? his trust in OpenAI is entirely misplaced. 🚨BREAKING: “Months before OpenAI's artificial intelligence went rogue, t
arXiv:2609.34063v1 Announce Type: new Abstract: Residual completion augments query-aware sparse attention by estimating the contribution of tokens omitted from the exact sparse computation. We ask whe
arXiv:2609.34581v1 Announce Type: new Abstract: Temporal video grounding is a key capability of advanced Multimodal Large Language Models (MLLMs) for the thorough understanding of video events, which
arXiv:2609.33875v1 Announce Type: cross Abstract: Outcome-only reinforcement learning gives software engineering (SWE) agents a terminal success signal but little direct guidance about intermediate de
arXiv:2609.32870v1 Announce Type: cross Abstract: Self-play proposer--solver methods improve reasoning by generating tasks and learning from verified solutions. However, for evidence-identifiable task
arXiv:2609.32932v1 Announce Type: new Abstract: Finite computational resources force a tradeoff between automatic System 1 processes and costly System 2 thinking. Large language models (LLMs) can spen
arXiv:2602.16917v2 Announce Type: replace Abstract: Modern vision models increasingly rely on rich semantic representations that extend beyond class labels to include descriptive concepts, attributes,
arXiv:2609.34183v1 Announce Type: new Abstract: Weakly Supervised Semantic Segmentation (WSSS) learns pixel-level predictions from image-level tags. Recent work focuses on improving coarse CAMs extrac
arXiv:2609.31709v1 Announce Type: new Abstract: Cross-dataset transfer in rice leaf disease classification remains a significant challenge, with models trained on one image collection performing subst
arXiv:2609.31658v1 Announce Type: new Abstract: Ship classification from Synthetic Aperture Radar (SAR) imagery is a critical computer vision task, yet the robustness of models under deployment shifts
arXiv:2609.32513v1 Announce Type: new Abstract: Handwritten mathematical expression recognition (HMER) refers to the task of recognizing and converting handwritten mathematics into a parsable markup l
arXiv:2609.31659v1 Announce Type: new Abstract: Power magnetic materials are characterized on the sinusoidal and triangular waveforms that excitation hardware conveniently produces, whereas deployed c
arXiv:2609.31785v1 Announce Type: cross Abstract: Audio-only pedestrian detection is attractive for urban sensing but limited by weak acoustic cues. An appealing strategy is cross-modal knowledge dist
arXiv:2609.35082v1 Announce Type: new Abstract: Group-based reinforcement learning such as GRPO trains LLM agents by comparing rollouts sampled for each task, without a learned critic. In long-horizon
arXiv:2601.07519v2 Announce Type: replace-cross Abstract: Unpredictable fetal motion during MRI scans can result in oblique 2D slices that are difficult to interpret and often leads to prolonged scan
arXiv:2609.32781v1 Announce Type: new Abstract: Diffusion vision-language models generate answers by gradually resolving masked tokens, making accurate conditional prediction in partially resolved sta
arXiv:2609.35130v1 Announce Type: new Abstract: Communication-efficient federated optimization commonly spends several gradient evaluations between server updates. Existing local-update methods use th
arXiv:2609.31873v1 Announce Type: new Abstract: Keyframe selection (KFS) has long produced compact video summaries for browsing and retrieval, and representative frames for thumbnails. More recently,
arXiv:2609.33878v1 Announce Type: cross Abstract: Merchant matching resolves a noisy payment descriptor to a retrieved merchant entity or returns no match. A key challenge in curating training labels
arXiv:2609.31893v1 Announce Type: new Abstract: Deep reinforcement learning has become a prominent approach to autonomous cyber defense. Existing methods are predominantly model-free and consequently
arXiv:2609.34965v1 Announce Type: new Abstract: Many physiological time series, such as cardiac and brain recordings, exhibit cyclostationarity: their statistics vary periodically with an underlying c
arXiv:2511.11286v3 Announce Type: replace Abstract: Out-of-domain (OOD) robustness is challenging to achieve in real-world computer vision, especially in unsupervised domain adaptation scenarios, wher
arXiv:2609.33110v1 Announce Type: new Abstract: Joint-Embedding Predictive Architectures (JEPAs) provide a framework for learning compact representations without directly reconstructing high-dimension
arXiv:2609.35362v1 Announce Type: new Abstract: Large language models (LLMs) typically generate text autoregressively (AR), predicting one token at a time. Block diffusion language models (dLLMs) inst
arXiv:2609.34792v1 Announce Type: new Abstract: Long-horizon manipulation requires robots to remember cues that are no longer in view while responding to moving objects. Yet vision-language-action (VL
arXiv:2609.31696v1 Announce Type: cross Abstract: Offline evaluation is the dominant experimental paradigm in recommender systems research, enabling reproducible and cost-effective comparisons on hist
arXiv:2609.34720v1 Announce Type: new Abstract: As image generation and editing technologies have progressed substantially, facial forgeries pose significant challenges to privacy and public safety. D
arXiv:2609.33879v1 Announce Type: cross Abstract: Neural surrogates can accelerate large-scale optimization by replacing expensive or intractable model components with efficient learned approximations
arXiv:2609.32454v1 Announce Type: new Abstract: Skeleton sequences can represent both individual actions and multi-entity interactions, encompassing human bodies, hands, objects, and robots. Existing
arXiv:2609.33768v1 Announce Type: new Abstract: Multi-agent systems (MAS) have recently emerged as an effective approach for coordinating large language model (LLM)-based agents to solve complex tasks
arXiv:2609.33021v1 Announce Type: cross Abstract: We study decentralized optimization with cross-coupled mixed affine constraints, where local and shared variables interact through two affine channels
arXiv:2609.34237v1 Announce Type: new Abstract: Flow-based image editing (FlowEdit) enables inversion-free semantic changes through the difference between source and target velocities. In this paper,
arXiv:2609.34180v1 Announce Type: cross Abstract: How much does the decision readout matter when video-derived textual evidence is held fixed? We evaluate Jev typed decisions and three Qwen readouts o
arXiv:2609.32805v1 Announce Type: cross Abstract: Long tasks produce more history than an LLM agent can hold in its context, and more than it uses reliably even when the history fits. A growing line o
arXiv:2609.33405v1 Announce Type: new Abstract: Autoregressive pretraining increasingly draws on heterogeneous data, making it important to understand how a model learns from an individual token. The
arXiv:2609.35525v1 Announce Type: new Abstract: Principled exploration in reinforcement learning requires an agent to quantify its epistemic uncertainty and act to resolve it. Uncertainty over the val
arXiv:2609.34474v1 Announce Type: new Abstract: We introduce a deep kernel hedging framework that combines the flexibility of deep learning with the structural inductive bias of kernel methods. The he
arXiv:2609.33060v1 Announce Type: new Abstract: Speech therapists often face difficulties diagnosing impairments due to the lack of efficient tools for transcribing speech into the International Phone
arXiv:2609.31870v1 Announce Type: new Abstract: Consistently profitable trading is difficult because equity markets are noisy, non-stationary, and only partially predictable from historical data. We b
arXiv:2609.34593v1 Announce Type: new Abstract: Off-policy evaluation is a foundational component of offline reinforcement learning, aiming to assess and optimize policy performance using pre-collecte
Over the last three decades in cybersecurity, I’ve witnessed major paradigm shifts — yet none match the velocity and complexity of today’s landscape. Attackers are now using AI to move at machine spee
arXiv:2609.32204v1 Announce Type: cross Abstract: Secure Transformer inference protects sensitive inputs but incurs substantial cryptographic overhead, with nonlinear operations such as Softmax and Ge
arXiv:2609.33836v1 Announce Type: cross Abstract: Construction environments evolve continuously, causing large geometric changes that degrade static mapping and registration performance. This necessit
arXiv:2609.33177v1 Announce Type: cross Abstract: World-Action Models (WAMs) offer visual foresight for robotic manipulation, but pixel-space models repeatedly reconstruct entire future scenes, incurr
arXiv:2609.33653v1 Announce Type: cross Abstract: Reinforcement learning (RL) enables generalist robot policies to improve through trial-and-error interaction, yet its effectiveness is fundamentally c
arXiv:2510.04525v3 Announce Type: replace Abstract: Masked diffusion models have shown promising performance in generating high-quality samples in a wide range of domains, but accelerating their sampl
arXiv:2609.35651v1 Announce Type: new Abstract: Multi-robot trajectory planning is a fundamental problem in multi-robot coordination but remains computationally challenging due to its nonconvex, multi
arXiv:2609.35066v1 Announce Type: new Abstract: Most neural constructive solvers for the vehicle routing problem (VRP) use route-by-route construction, extending one route until completion before star
arXiv:2609.32027v1 Announce Type: new Abstract: When several surfaces are visible along a ray, recovering visible 3D structure from one image requires jointly estimating their presence and metric dept