Knowledge catalogue
arXiv:2605.09284v1 Announce Type: cross Abstract: Mesh-based simulations provide high-fidelity solutions to partial differential equations (PDEs), but achieving such accuracy typically requires fine m
arXiv:2605.10394v1 Announce Type: new Abstract: The detection of sensational content in media items can be a critical filtering mechanism for identifying check-worthy content and flagging potential di
arXiv:2605.10576v1 Announce Type: cross Abstract: Low-level visual perception underpins reliable remote sensing (RS) image analysis, yet current image quality assessment (IQA) methods output uninterpr
arXiv:2605.09906v1 Announce Type: new Abstract: Audio and vision provide complementary evidence for audio-visual question answering, yet current audio-visual large language models may suffer from cros
arXiv:2506.16234v2 Announce Type: replace Abstract: Causal discovery from observational data typically assumes access to complete data and availability of perfect domain experts. In practice, data oft
arXiv:2605.09746v1 Announce Type: cross Abstract: Landslide detection from satellite imagery has advanced through deep learning, yet most models rely on large, highly correlated spectral-topographic i
arXiv:2602.16596v2 Announce Type: replace Abstract: Modern AI models are not static. They go through multiple updates in their lifecycles. We propose to design Sequential Membership Inference (SeMI) a
arXiv:2605.10571v1 Announce Type: cross Abstract: Quantitative cardiac magnetic resonance imaging (MRI) enables non-invasive myocardial tissue characterization but relies on robust motion correction w
arXiv:2605.10298v1 Announce Type: new Abstract: Accurate next-day active fire forecasts can support early warning, disaster response, forest risk assessment, and downstream estimation of fire-related
arXiv:2605.08302v1 Announce Type: cross Abstract: Real-world digital Parkinson's disease assessment faces challenges such as heterogeneous modalities, cross-device bias, and incomplete labeling. Exist
arXiv:2605.09033v1 Announce Type: cross Abstract: Graph-based agent memory is increasingly used in LLM agents to support structured long-term recall and multi-hop reasoning, but it also creates a new
arXiv:2605.09271v1 Announce Type: new Abstract: Although natural language is the default medium for Large Language Models (LLMs), its limited expressive capacity creates a profound bottleneck for comp
arXiv:2605.08897v1 Announce Type: cross Abstract: Activated PI3K8 Syndrome (APDS) is a rare genetic immune disorder caused by variants in PIK3CD or PIK3R1, with highly heterogeneous symptoms that ofte
arXiv:2605.10395v1 Announce Type: cross Abstract: We study the information-theoretic limits of learning a one-hidden-layer teacher network with hierarchical features from noisy queries, in the context
arXiv:2605.10913v1 Announce Type: new Abstract: We introduce Shepherd, a functional programming model that formalizes meta-agent operations on target agents as functions, with core operations mechaniz
arXiv:2605.09171v1 Announce Type: new Abstract: We present SHIELD, a hierarchical algorithm that reduces both the decision-variable dimension and the constraint set in ell_1-regularized convex program
arXiv:2605.10888v1 Announce Type: cross Abstract: Shielding is a prominent model-based technique to ensure safety of autonomous agents. Classical shielding aims to ensure that nothing bad ever happens
arXiv:2605.08482v1 Announce Type: cross Abstract: Automated ICD-10 coding from clinical discharge summaries requires models that are both accurate on long-tailed multi-label classification tasks and i
Sign up if you're in LA! The last AI Agents Happy Hour was so fun, @jvedi and I are going to do it again. This time, co-hosted by @pinecone! We want to see what you're building and learn from your exp
arXiv:2601.21061v2 Announce Type: replace Abstract: Generative Flow Networks (GFlowNets; GFNs) are a class of generative models that learn to sample compositional objects proportionally to their a pri
arXiv:2605.10313v1 Announce Type: new Abstract: We study contextual bandits with nonlinear and path-dependent rewards through a novel signature-transform-based approach. Leveraging the universal nonli
arXiv:2605.08784v1 Announce Type: new Abstract: Product poster generation poses distinct challenges beyond general poster design, requiring both faithful preservation of product appearance and precise
arXiv:2605.08809v1 Announce Type: cross Abstract: Pretraining large language models (LLMs) with next-token prediction has led to remarkable advances, yet the context-dependent nature of token embeddin
arXiv:2601.19914v2 Announce Type: replace-cross Abstract: Synthetic data has proven itself to be a valuable resource for tuning smaller, cost-effective language models to handle the complexities of mu
arXiv:2605.10498v1 Announce Type: cross Abstract: Long-tailed distributions in class-imbalanced data present a fundamental challenge for deep learning models, which tend to be biased toward majority c
arXiv:2605.08753v1 Announce Type: new Abstract: Advanced manufacturing technologies allow for the production of intricate parts featuring high shape complexity and spatially-varying material compositi
arXiv:2502.11537v4 Announce Type: replace-cross Abstract: World models (WMs) represent the frontier of sample-efficient reinforcement learning, but their complexity leaves many promising improvements
arXiv:2605.09423v1 Announce Type: new Abstract: LLM/VLM-based digital agents have advanced rapidly thanks to scalable sandboxes for coding, web navigation, and computer use, which provide rich interac
arXiv:2605.09070v1 Announce Type: cross Abstract: Many jailbreak attack research papers report attack success rates for a limited number of parameter settings, even though there are many combinations
arXiv:2605.08731v1 Announce Type: cross Abstract: JPEG decode is routine ML infrastructure, but Python decoder choices are often justified by single-process, single-thread microbenchmarks. We audit th
arXiv:2605.08453v1 Announce Type: cross Abstract: This paper studies the role of sinks and diagonal patterns as attention switch and anti-oversmoothing mechanisms. We analyze geometric conditions unde
arXiv:2605.08485v1 Announce Type: cross Abstract: We introduce the Sinkhorn treatment effect, an entropic optimal transport measure of divergence between counterfactual distributions. Unlike classical
arXiv:2605.08658v1 Announce Type: cross Abstract: SKETCHVERIFY is a within-tier cost-performance policy, not a universal accuracy improvement. The operational question: a practitioner stuck with a sma
arXiv:2605.09343v1 Announce Type: new Abstract: Decision making in large-scale complaint handling systems increasingly relies on heterogeneous evidence, including complaint narratives, screenshots, or
arXiv:2605.08526v1 Announce Type: new Abstract: While LLM-based agents excel at planning and executing long action sequences, their execution often remains inconsistent across trials, limiting reliabi
arXiv:2605.09359v1 Announce Type: cross Abstract: Agentic large language models often rely on skills, reusable natural language procedures that guide planning, action, and tool use. In practice, skill
arXiv:2605.10500v1 Announce Type: new Abstract: Agent skills today are static artifact: authored once -- by human curation or one-shot generation from parametric knowledge -- and then consumed unchang
arXiv:2605.08386v1 Announce Type: new Abstract: Skill libraries have become a practical way for LLM agents to reuse procedural experience across tasks. However, existing systems typically treat skills
arXiv:2605.09341v1 Announce Type: cross Abstract: Large language model (LLM) agent systems are increasingly expected to improve after deployment, but existing work often decouples two adaptation targe
arXiv:2605.08693v1 Announce Type: new Abstract: Skills provide an effective mechanism for improving LLM agents on complex tasks, yet in existing agent frameworks, their creation, refinement, and selec
arXiv:2605.10114v1 Announce Type: new Abstract: Large Language Model (LLM)-based agents (e.g., OpenClaw) increasingly rely on reusable skill libraries to solve artifact-rich tasks such as document-cen
arXiv:2605.05443v2 Announce Type: replace-cross Abstract: LLM watermarks must be detectable without compromising text quality, yet most existing schemes bias the next-token distribution and pay for de
arXiv:2605.10503v1 Announce Type: new Abstract: Large Language Models (LLMs) show remarkable semantic understanding but often struggle with structural understanding when processing graph topologies in
arXiv:2605.08262v1 Announce Type: cross Abstract: Crystal generative models have shown rapid progress for accelerating the discovery of bulk, periodic materials. However, many material systems such as
arXiv:2605.10376v1 Announce Type: new Abstract: Vision-Language Models (VLMs) have advanced rapidly in multimodal perception and language understanding, yet it remains unclear whether they can reliabl
arXiv:2605.08546v1 Announce Type: cross Abstract: The Gromov-Wasserstein (GW) problem provides a framework for aligning heterogeneous datasets by matching their intrinsic geometry, but its statistical
arXiv:2507.17921v2 Announce Type: replace-cross Abstract: Canonical correlation analysis (CCA) is a technique for finding correlated sets of features between two datasets. In this paper, we propose a
arXiv:2605.10831v1 Announce Type: cross Abstract: Large language models possess strong chemical reasoning capabilities, making them effective molecular editors. However, property-relevant information
arXiv:2605.08738v1 Announce Type: cross Abstract: Structured pruning and knowledge distillation (KD) are typical techniques for compressing large language models, but it remains unclear how they shoul
arXiv:2605.10453v1 Announce Type: cross Abstract: Speculative decoding speeds up autoregressive generation in Large Language Models (LLMs) through a two-step procedure, where a lightweight draft model
arXiv:2605.08580v1 Announce Type: cross Abstract: To cope with the large contexts that long-horizon LLM agents produce, modern frameworks increasingly rely on compaction -- invoking an LLM to rewrite
arXiv:2605.10029v1 Announce Type: new Abstract: Pixel-level slum mapping has long been constrained by limited cross-city generalisation, the absence of continuous density estimation, and weak global c
arXiv:2605.08246v1 Announce Type: new Abstract: Railway track intrusions pose a critical safety challenge for Indian Railways, encompassing wildlife incursions and deliberate malicious obstructions. T
arXiv:2605.09610v1 Announce Type: cross Abstract: We introduce SmartEval, a benchmark for systematically evaluating the quality of Solidity smart contracts generated by large language models (LLMs) fr
arXiv:2605.09224v1 Announce Type: new Abstract: Sparse autoencoders (SAEs) have been used widely to decompose and interpret neural network activations, especially those of transformer language models.
arXiv:2601.22131v2 Announce Type: replace Abstract: Multi-objective optimization aims to solve problems with competing objectives. Evaluating such problems is often slow or expensive, limiting the bud
arXiv:2605.09073v1 Announce Type: new Abstract: Continuous-time state estimation is gaining in popularity due to its abilities to provide smooth solutions, handle asynchronous sensors, and interpolate
arXiv:2602.09317v2 Announce Type: replace-cross Abstract: Neural networks are increasingly used as fast surrogate models across various domains, but unconstrained predictions can violate physical, ope
So basically Anthropic’s estimated valuation went up half a trillion dollars in a couple weeks (then back down a bit) on hype. Small wonder that companies like OpenAI, Anthropic and Tesla keep hyping
arXiv:2605.09598v1 Announce Type: new Abstract: Vision-language models (VLMs) have recently shown strong potential in soccer video understanding. However, given the high complexity of soccer videos du