Knowledge catalogue
arXiv:2605.30662v1 Announce Type: new Abstract: Spatio-temporal graph-based models have typically been used to forecast new cases of infectious diseases such as COVID-19 and chickenpox outbreaks. Howe
Speakers: Pengyu Zhao, Head of Research at MiniMax Haohai Sun, Research Scientist at MiniMax Ce Zhang, Founder/CTO at Together Yineng Zhang, Senior Director at Together Dan Fu, VP of Kernels at Togeth
arXiv:2605.31097v1 Announce Type: cross Abstract: Mainstream relational databases ship a uniform feature set across deployments, although individual workloads exercise only a fraction of the available
arXiv:2605.31575v1 Announce Type: cross Abstract: Scalable information retrieval testing needs corpora that are large enough to stress index construction, ranking latency, query routing, and evaluatio
arXiv:2605.30952v1 Announce Type: new Abstract: Two recent results have reshaped quantum Gaussian processes (QGPs). On the one hand, itet{lowe2025assessing} rule out the exponential speedups claimed b
arXiv:2509.22335v3 Announce Type: replace-cross Abstract: We investigate why deep neural networks suffer from loss of plasticity in continual learning, and thus fail to learn new tasks without reiniti
arXiv:2605.31244v1 Announce Type: new Abstract: Neural scaling laws describe predictable power-law relationships between model size, dataset size, compute, and performance. While these laws guide the
arXiv:2605.30580v1 Announce Type: new Abstract: Speculative decoding has become a crucial component of large language model (LLM) inference, enabling faster generation by drafting multiple tokens and
arXiv:2605.30852v1 Announce Type: new Abstract: Speculative Decoding (SD) accelerates low-concurrency LLM inference by employing a draft-then-verify paradigm. However, mainstream methods typically rel
arXiv:2602.22971v2 Announce Type: replace Abstract: As LLMs achieved breakthroughs in general reasoning, their proficiency in specialized scientific domains reveals pronounced gaps in existing benchma
arXiv:2605.25193v2 Announce Type: replace Abstract: Visual and acoustic events in the physical world are inherently coupled, yet existing video editing methods typically adopt decoupled pipelines, lac
arXiv:2605.30770v1 Announce Type: new Abstract: Extending humanoid traversal to the open world is key to practical deployment in human environments, but remains challenging. The robot must use vision
Hugh Son / CNBC: Startups from the pre-ChatGPT era face a reckoning in private markets; PitchBook: half of US unicorns haven't raised in three years, 220+ are “fallen unicorns” — Five years ago, ventu
arXiv:2605.31593v1 Announce Type: cross Abstract: Language models can find thousands of severe software vulnerabilities, and agents are increasingly being misused for cyberattacks. To avoid detection,
arXiv:2605.30894v1 Announce Type: new Abstract: The shortage of legally compliant data for face recognition training has sparked growing interest in using synthetic data as an alternative. While recen
arXiv:2605.31183v1 Announce Type: cross Abstract: Sparse Autoencoders (SAEs) have been seen as a promising avenue for exploring the internals of Large Language Models (LLMs) and for steering model out
arXiv:2605.31061v1 Announce Type: cross Abstract: We present a novel method for learning interpretable representations of progressive time series, that is, data capturing irreversible state transition
arXiv:2508.20326v2 Announce Type: replace-cross Abstract: Stochastic gradient optimization is the dominant learning paradigm for a variety of scenarios, from classical supervised learning to modern se
Lucinda Shen / Axios: Stockholm-based Endra, which automates the design of plumbing and electrical wiring in new buildings, raised 50M led by a16z — Endra, a startup automating the design of plumbing
LangSmith Engine is a tool designed to automatically diagnose and resolve agent failures, eliminating the need for manual troubleshooting and triage. The feature appears to leverage automated analysis
arXiv:2602.06161v2 Announce Type: replace-cross Abstract: Parallel diffusion decoding can accelerate diffusion language model inference by unmasking multiple tokens per step, but aggressive parallelis
The popular fitness-tracking platform, Strava, is restricting access to its API as part of efforts to clamp down on AI scraping, as reported earlier by TechCrunch. Developers who want to build an app
Ivan Mehta / TechCrunch: Strava is adding a $11.99 monthly fee for developer API access and moving public profiles and fitness club listings behind authentication to combat AI scraping — AI companies
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:2605.30664v1 Announce Type: new Abstract: Subgoal-based policy tree search, which uses a policy to guide search, is effective for complex single-agent deterministic problems but often relies on
arXiv:2605.30383v1 Announce Type: cross Abstract: Scaling individual robot capabilities is common but costly. Here we investigate a system-level design question in real-world multi-robot coordination:
arXiv:2605.31191v1 Announce Type: cross Abstract: We investigate how teacher-student capacity relationships modulate knowledge distillation (KD) effectiveness in ResNet-based image classification on C
Studios are no longer competing with other studios. They are competing with billions of people who have access to powerful tools and YouTube level distribution. You don’t win by signing the one direct
arXiv:2605.31111v1 Announce Type: new Abstract: Joint-Embedding Predictive Architectures (JEPAs) learn compact latent world models by predicting future embeddings, but no single coordinate of the late
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:2602.04107v2 Announce Type: replace Abstract: This paper presents a novel information-theoretic perspective on generalization in machine learning by framing the learning problem within the conte
arXiv:2605.30556v1 Announce Type: new Abstract: Random, untrained neural networks consistently match or exceed trained networks in representational similarity to early visual cortex. This puzzling fin
arXiv:2605.31321v1 Announce Type: new Abstract: Diffusion-based imitation learning methods have driven rapid progress in robot dexterous manipulation tasks. However, they have limitations when applied
arXiv:2605.31577v1 Announce Type: new Abstract: Recent feedforward 3D reconstruction methods predict point maps and estimate global 3D geometry remarkably well. However, their predictions still exhibi
arXiv:2603.12916v3 Announce Type: replace-cross Abstract: Multivariate time series anomalies often manifest as shifts in cross-channel dependencies rather than simple amplitude excursions. In autonomo
arXiv:2605.31273v1 Announce Type: new Abstract: While self-supervised Contrastive Reinforcement Learning (CRL) has shown remarkable depth-scaling capabilities, successfully using networks over 64 laye
arXiv:2605.31529v1 Announce Type: new Abstract: True video intelligence demands more than recognizing what is visible: it requires reasoning about why events unfold, predicting what would change under
arXiv:2605.31120v1 Announce Type: cross Abstract: We propose a new method for synthesizing physically-based swimming motions. Physically-based character animation aims to generate physically valid, co
arXiv:2505.17607v3 Announce Type: replace Abstract: Large Language Models (LLMs) display reasoning capabilities over linguistic and symbolic objects but have limited capabilities to directly interpret
arXiv:2511.11440v3 Announce Type: replace-cross Abstract: Performance gains of Vision Language Models (VLMs) obtained by fine-tuning are generally based on ad hoc data collection and annotation of rea
arXiv:2605.31156v1 Announce Type: new Abstract: Causal discovery aims to recover directed causal relations from observational and interventional data, providing a basis for mechanistic understanding a
arXiv:2503.21168v3 Announce Type: replace Abstract: Robots navigating human-populated environments must avoid collisions while respecting the social structure of crowds, particularly the implicit boun
Taiwan is home to more than 500 NVIDIA ecosystem partners. More than 1 million NVIDIA MGX rack components for NVIDIA Vera Rubin infrastructure come together in Taiwan, from across 25 factory sites. As
arXiv:2605.31217v1 Announce Type: new Abstract: Monocular 6-DoF spacecraft pose estimation methods predominantly process individual frames, discarding the temporal information present in an image sequ
arXiv:2508.19830v2 Announce Type: replace-cross Abstract: Real-world model deployments inevitably encounter distribution shifts, rendering the confidence estimates of deep neural networks highly unrel
arXiv:2605.31393v1 Announce Type: cross Abstract: Sign language translation (SLT) remains constrained by limited paired sign-video/text corpora and heavy-tailed target vocabularies. We study target-si
arXiv:2603.07551v2 Announce Type: replace-cross Abstract: Zero-shot Text-to-Speech (TTS) voice cloning poses severe privacy risks, demanding the removal of specific speaker identities from trained TTS
arXiv:2605.31121v1 Announce Type: cross Abstract: Outdoor vision-language navigation (VLN) in long-range, open-world environments is frequently disrupted by semantic-cue interruptions, where informati
arXiv:2605.30601v1 Announce Type: new Abstract: Modern deep networks remain fragile under distribution shift and adversarial perturbations, often due to excessive or poorly structured input sensitivit
arXiv:2605.31075v1 Announce Type: new Abstract: Long-term memory is essential for multimodal agents to build coherent experience, accumulate world knowledge, and achieve continual learning. However, c
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
arXiv:2605.30673v1 Announce Type: new Abstract: Classroom videos contain observable teaching practices, but their pedagogical and visual signals are rarely organized in forms suitable for model evalua
arXiv:2510.03839v2 Announce Type: replace Abstract: We present a theoretical framework for M-FISHER, a method for sequential distribution shift detection and stable adaptation in streaming data. For d
arXiv:2605.30784v1 Announce Type: new Abstract: Gait recognition is a biometric technique that identifies individuals based on their walking patterns, offering advantages in long-range, non-intrusive
arXiv:2605.30628v1 Announce Type: cross Abstract: Universal LLM reliability is not a finite-library problem: across all possible tasks, tools, schemas, knowledge sources, and evaluator expectations, n
A practical comparison of production AI evaluation harnesses, including what to look for across instrumentation, evaluators, online evals, CI gates, and agent workflows. The post The best eval harness
arXiv:2605.31044v1 Announce Type: new Abstract: Reinforcement learning has shown promising results for optimizing the control of industrial energy systems, yet most existing studies remain limited to
the decade of agents! @theemozilla We have been working closely with @nvidia to ensure Hermes Agent works smoothly on their new @NVIDIARTXSpark superchip and integrates with the new OpenShell runtime,
THE DEFINITIVE AI CONVERSATION OF THE YEAR. MUST LISTEN. @JG_Nuke @GaryMarcus @MacrostrategyP https://open.substack.com/pub/georgenoble/p/ai-the-biggest-capital-misallocation-d18?r=35saq&utm_medium=io
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. China has approved the world’s first invasive brain-computer c