Knowledge catalogue
arXiv:2609.28235v1 Announce Type: new Abstract: Image registration and fusion aim to establish spatial correspondences from misaligned multi-modal source images, and integrate complementary informatio
arXiv:2609.28352v1 Announce Type: new Abstract: This study highlights the distribution of Standard Arabic (SA; H(igh) variety) and Colloquial Arabic (CA; L(ow) variety) across X and Facebook. 16754 pu
arXiv:2609.27930v1 Announce Type: cross Abstract: We propose a Bayesian nonparametric mixture of regression trees with a Dirichlet process prior over tree-parameter pairs, enabling data-driven selecti
arXiv:2609.27234v1 Announce Type: new Abstract: AI virtual cells aim to predict cellular responses to specified interventions, yet held-out predictive performance alone does not establish use of the s
arXiv:2609.28087v1 Announce Type: new Abstract: The integration of model-based and data-driven paradigms provides a powerful framework for fault diagnosis by combining the interpretability of analytic
arXiv:2609.27306v1 Announce Type: new Abstract: Conventional score-based diffusion models learn scores without representing normalized densities, whereas tractable normalized models support both sampl
arXiv:2509.14959v4 Announce Type: replace-cross Abstract: In this paper, we investigate discrete optimal transport (DOT) as a black-box attack against modern automatic speaker verification (ASV) and a
arXiv:2609.28161v1 Announce Type: new Abstract: Advantage-guided reinforcement learning provides a practical way to post-train vision-language-action (VLA) policies using limited robot data. However,
arXiv:2609.28107v1 Announce Type: cross Abstract: Advances in generative modeling have recently been extensively employed in robotics for policy learning. In particular, Conditional Flow Matching (CFM
arXiv:2609.27233v1 Announce Type: new Abstract: Transformer language models process sequences token by token in an autoregressive manner, making growing contexts increasingly expensive. Yet many adjac
arXiv:2609.27194v1 Announce Type: new Abstract: Prototype-based neural networks provide inherent interpretability through case-based reasoning, yet suffer from critical limitations: prototypes converg
arXiv:2609.27794v1 Announce Type: new Abstract: 2D-3D registration remains brittle in challenging scenarios such as low overlap, occlusion, repetitive structures, and severe cross-modal ambiguity. A k
arXiv:2609.28231v1 Announce Type: cross Abstract: Pathology foundation models (PFMs) have transformed computational pathology through powerful representation learning from histopathological images. PF
arXiv:2609.27150v1 Announce Type: new Abstract: Multi-agent debate (MAD) has emerged as a promising paradigm for improving the reasoning accuracy of large language models (LLMs) through iterative peer
Just watched a video from three years ago where Matt Berman tested Bard (yes, remember Bard?) and most of the tests were like summarization or logic tests. If it ever came to coding, it was the snake
arXiv:2609.27069v1 Announce Type: cross Abstract: Graph neural networks dominate recent work on microservice root-cause analysis, yet recent results question whether the graph contributes. Those resul
arXiv:2609.27581v1 Announce Type: cross Abstract: Step Law gives power-law formulas for the optimal peak learning rate eta* and batch size B* when pre-training language models. It was calibrated on mo
Don't sleep on using Jev-as-a-Judge for agent evaluation. This is one of the most impressive Jev use cases I have found so far. Jev is a natural fit as a Judge, but it doesn't mean you use it everywhe
arXiv:2609.27276v1 Announce Type: new Abstract: Long-horizon language-model agents accumulate reasoning traces, tool exchanges, and observations whose relevance changes with the current decision. Exis
arXiv:2609.27793v1 Announce Type: new Abstract: Existing deep learning approaches for automatic sleep staging suffer from limited robustness under heterogeneous recording conditions, where non-station
arXiv:2609.27338v1 Announce Type: new Abstract: Learned local navigation in crowded indoor environments is sensitive to how dynamic obstacle motion is represented, while collision-prone behaviour may
arXiv:2609.28172v1 Announce Type: new Abstract: Robots in a LiDAR-equipped swarm mutually interfere when their optical ranging codes collide. Existing mitigations either assign codes statically -- req
arXiv:2605.06192v2 Announce Type: replace-cross Abstract: Pretrained video diffusion models provide powerful spatiotemporal generative priors, making them a natural foundation for robotic world models
arXiv:2609.27547v1 Announce Type: new Abstract: Embodied reinforcement learning (RL) improves model capabilities with a pipeline of environment simulation, action generation, and model updates. These
arXiv:2609.27043v1 Announce Type: new Abstract: Large language models have allowed the rapid deployment of pedagogical annotations corresponding to constructs of interest, allowing a natural language
arXiv:2609.27594v1 Announce Type: new Abstract: We study the problem of computational efficiency for linear bandits in high-dimensional settings with a finite arm set. In linear bandits, the increase
Mixture‑of‑Experts (MoE) models scale capacity more efficiently than dense transformers by activating only a subset of expert subnetworks per token. NVIDIA’s Transformer Engine optimizes MoE training
arXiv:2609.27784v1 Announce Type: cross Abstract: Semi-structured documents are ubiquitous in scientific reports, financial statements, and technical manuals. Question answering over such documents re
arXiv:2609.27040v1 Announce Type: cross Abstract: Multi-GPU servers have become the standard building block of modern data centers, providing aggregated capacity through high-bandwidth interconnects.
arXiv:2609.28236v1 Announce Type: new Abstract: Long-horizon embodied interaction requires agents to retain and continually update information about the environment as they observe, act, and encounter
arXiv:2609.27308v1 Announce Type: new Abstract: We study coding agents for long-horizon, dexterous robotics and ask whether their solutions can provide scalable supervision for learning general robot
arXiv:2609.27417v1 Announce Type: new Abstract: Symbiosis between humans and digital beings offers a vision for the future of human--machine interaction. In enduring human--machine relationships, pers
arXiv:2603.09222v2 Announce Type: replace Abstract: Efficient context compression is critical for retrieval-augmented question answering in resource-constrained settings, where long retrieved contexts
arXiv:2607.10128v3 Announce Type: replace Abstract: Recursive models show promise on reasoning and language tasks, yet their test-time scaling lacks a principled criterion for selecting trajectories o
arXiv:2609.27950v1 Announce Type: cross Abstract: The emergence of multi-class malware attacks such as ransomware, spyware, trojans, etc., presents an increasing and serious threat to cybersecurity, p
arXiv:2609.27197v1 Announce Type: new Abstract: Minimum Risk Training (MRT) enables neural machine translation models to directly optimize sequence-level evaluation metrics instead of relying only on
arXiv:2609.27279v1 Announce Type: new Abstract: An agent that interacts with users over long periods must recall facts, preferences, events, and changes from a continuously growing interaction history
arXiv:2609.26952v1 Announce Type: new Abstract: Dependency conflicts in Python ecosystems arise from incompatible version constraints, missing packages, and undocumented compatibility relationships, c
arXiv:2609.27815v1 Announce Type: cross Abstract: Dynamic contrast-enhanced (DCE) imaging is the gold standard technique for the detection and characterization of breast cancer using magnetic resonanc
arXiv:2609.28026v1 Announce Type: cross Abstract: We investigate whether state-of-the-art large language models (LLMs) generate feedback that reflects the pedagogical practices of expert teachers in t
arXiv:2609.28007v1 Announce Type: new Abstract: Most Turkish-capable large language models (LLMs) are evaluated using general-purpose benchmarks rather than long, structurally complex domain documents
arXiv:2609.27867v1 Announce Type: new Abstract: A forecasting benchmark reports which method won. We show that the answer is set by the evaluator's choices before any model is fitted. We benchmark 24
arXiv:2609.27749v1 Announce Type: new Abstract: The surge in AI-assisted generation of educational materials has outpaced our capacity to validate their pedagogical quality. Automated evaluation using
arXiv:2609.28149v1 Announce Type: cross Abstract: Studies of machine-learning-based power system protection are difficult to compare because task definitions, measurement access, data partitions, metr
arXiv:2609.28459v1 Announce Type: new Abstract: We introduce Sharper Transductive Local Complexity (STLC), a localized complexity method for transductive learning under uniform sampling without replac
Every week I go through hundreds of new AI papers to pick the top ones for our Top AI Papers of the Week digest. Besides reading a lot of papers, I have started to use agent teams a lot. Here is what
Everytime AI spend grows where I flirt with token budgeting, Anthropic + OpenAI save me Astra had a spike then settled down lower Opus 5.5 it looks like spend is lowered for current usecases Of course
arXiv:2609.27418v1 Announce Type: new Abstract: Systematic reviews underpin clinical guidelines, yet their data-extraction step is a major expert-labor bottleneck bound by a protocolized workflow: two
arXiv:2609.27389v1 Announce Type: cross Abstract: Audio language models understand what is said far better than how it sounds. Closing this gap takes more than data. Detailed acoustic annotation is co
arXiv:2609.27664v1 Announce Type: new Abstract: Cooperation emergence is a central problem in multi-agent systems because decentralized agents must coordinate while adapting to the changing behavior o
arXiv:2609.27337v1 Announce Type: cross Abstract: Open RAN (O-RAN) slicing xApps must adapt resource allocations to changing channel conditions and traffic demands while meeting service-level agreemen
arXiv:2609.28150v1 Announce Type: new Abstract: Closed-loop revision is increasingly used in large language model (LLM) applications, but failures may reflect incomplete feedback or ineffective respon
arXiv:2609.27860v1 Announce Type: new Abstract: A pointwise-unbiased one-bit compressor reconstructs every real input in expectation while transmitting one bit. For a scalar source P with CDF F, mean
arXiv:2609.28053v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) training requires global load balance to prevent expert under-utilization and local balance for efficient expert-parallel exe
arXiv:2609.26926v1 Announce Type: cross Abstract: Large-scale text annotation brings expert insight to millions of documents, often through a codebook that AI annotators follow. Developing a robust co
Robert Hart / The Verge: Experts say that air-gapping AI could prevent events like the Hugging Face hack, but would undermine the value of evaluations and slow research to a crawl — Researchers are te
arXiv:2609.27309v1 Announce Type: cross Abstract: Multi-agent Reinforcement Learning (MARL) trains a team of agents that share one environment and learn their policies together. Training maximizes the
arXiv:2507.16761v3 Announce Type: replace Abstract: Faithfulness and interpretability are essential for deploying deep neural networks (DNNs) in safety-critical domains such as medical image analysis.
arXiv:2609.27883v1 Announce Type: cross Abstract: Closed-loop discovery systems increasingly execute experiments and update decisions autonomously, turning record integrity into part of the experiment
arXiv:2609.27077v1 Announce Type: new Abstract: To avoid collisions, a robot must repeatedly predict where nearby agents may move, usually with an imperfect model of their dynamics. Reachable sets pro