Knowledge catalogue
arXiv:2609.23363v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) have led to the emergence of coding agents capable of performing complex engineering tasks, including re
arXiv:2609.26389v1 Announce Type: new Abstract: Real-world time series evolve continuously, with meaningful changes potentially emerging at any moment. However, existing time-series language models (T
arXiv:2609.24677v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to make predictions from numerical time-series histories and textual events. Yet accuracy alone canno
arXiv:2609.26766v1 Announce Type: new Abstract: Thermal Visual Place Recognition (Thermal VPR) maps camera observations to metric poses within a mapped environment, serving as a prerequisite for auton
Today we announced the Claude-led discovery of a molecular machine that we suspect could represent a new gene editing mechanism. Its precise function, biotechnological utility (if any), or level of si
arXiv:2609.25678v1 Announce Type: cross Abstract: Large language model (LLM) agents must generalize from tools seen during training to unseen tools at deployment. A key challenge is tool trialing, i.e
arXiv:2605.12481v2 Announce Type: replace Abstract: Computer Use Agents (CUAs) can act through both atomic GUI actions, such as click and type, and high-level tool calls, such as API-based file operat
arXiv:2609.26061v1 Announce Type: cross Abstract: Mixture-of-experts (MoE) models improve capacity with moderate overhead by sparsely activating experts per token. However, deploying MoE across resour
arXiv:2609.25310v1 Announce Type: new Abstract: Topological signal processing (TSP) processes signals on simplicial complexes with oriented boundary operators, which is the natural choice for flow sig
arXiv:2609.26189v1 Announce Type: new Abstract: Retinal vessel segmentation in multi-domain deployment requires a source model to adapt to domains that differ in imaging conditions and annotation conv
arXiv:2609.23790v1 Announce Type: new Abstract: Every node in a multi-agent large language model (LLM) workflow retrieves context from memory and injects it into its prompt, where those injected token
arXiv:2609.26078v1 Announce Type: new Abstract: In this paper, we propose ToW3D that enables precise and consistent control over 3D generative adversarial networks (GANs) with the Tug-of-War competiti
arXiv:2603.20988v2 Announce Type: replace Abstract: Artificial Intelligence is starting to automate scientific discovery, raising the prospect of an automated science of the mind. We chart this field
arXiv:2509.26200v2 Announce Type: replace-cross Abstract: Agentic artificial intelligence is a candidate enabler of Level-4 autonomy in sixth-generation (6G) networks, but agents reasoning over a shar
arXiv:2609.22694v1 Announce Type: new Abstract: Public crash databases increasingly support automated safety analysis, but crash severity prediction remains difficult to translate into public-sector d
arXiv:2609.26155v1 Announce Type: new Abstract: Ubiquitous robotic systems often lack traditional visual interfaces, making natural language interaction important for maintenance and repair. This pape
arXiv:2609.26157v1 Announce Type: cross Abstract: Ubiquitous robotic systems often lack traditional visual interfaces, necessitating resilient natural language interaction for maintenance and repair t
arXiv:2609.26063v1 Announce Type: new Abstract: Federated graph learning (FGL) enables multiple clients to collaboratively train graph models without sharing their private graph data, providing a prom
arXiv:2609.25031v1 Announce Type: new Abstract: In the field of Human-Robot Interaction (HRI), achieving flexibility in human-accompanying within real-world environments holds great potential for vari
arXiv:2609.26603v1 Announce Type: new Abstract: Hierarchical latent communication improves the generalization of a multi-grid power-flow model to new operating scenarios. The module exchanges informat
arXiv:2609.26051v1 Announce Type: new Abstract: We investigate the challenges of enabling effective collaboration between human operators and heterogeneous autonomous agents in complex, dynamic enviro
arXiv:2508.20443v3 Announce Type: replace Abstract: Large language models (LLMs) are trained on massive datasets that may include private or copyrighted content. Due to growing privacy and ownership c
arXiv:2609.25945v1 Announce Type: new Abstract: The field of Artificial Intelligence has been adopted for many application domains. Vision Language Models are one of the recently advanced AI technique
arXiv:2609.25775v1 Announce Type: new Abstract: Recent advances in generative video models have enabled the synthesis of visually realistic content, posing significant challenges to synthetic video de
arXiv:2609.26168v1 Announce Type: cross Abstract: Recent work reports that vision--language models (VLMs) struggle to establish and maintain stable reference in repeated reference games. Rather than a
arXiv:2609.26708v1 Announce Type: new Abstract: Quantization-aware distillation (QAD) restores much of the short-form question-answering performance lost to sub-3-bit quantization, yet leaves mathemat
arXiv:2609.25008v1 Announce Type: new Abstract: I pretrained a language model end-to-end in Rust - alone, with no team, no PyTorch, and no Python in the training path - for $164 in rented GPU time. I
Training can cut costs by up to 90% on the work your customers depend on. The next model drop won’t get you there, no matter how great it is. Join us and @Macroscope's @Rob_Bishop as we chat about own
Snorkel AI Inc., a provider of artificial intelligence training data, today disclosed that it has raised 350 million in funding. Insight and S32 led the Series E deal. They were joined by more than a
arXiv:2609.25237v1 Announce Type: cross Abstract: Post-training is becoming a service (PTaaS): a customer hands an operator data and a goal, and a forward-deployed engineer (FDE) returns a fine-tuned,
arXiv:2609.26347v1 Announce Type: new Abstract: The scarcity of non-English language data in specialized domains significantly limits the development of effective Natural Language Processing (NLP) too
arXiv:2609.26536v1 Announce Type: new Abstract: In LLM-based speech translation, transcription-based chain-of-thought (CoT) suffers from a mismatch between reference transcripts used in supervised fin
arXiv:2609.25588v1 Announce Type: cross Abstract: In this note, we show that the problem of checking, whether a sequence of bits is ordered, is not doable by 1-head 1-layer transformers but is doable
arXiv:2510.10211v4 Announce Type: replace Abstract: Molecular graph generation (MGG) is essentially a multi-class generative task, aimed at predicting categories of atoms and bonds under strict chemic
arXiv:2609.26314v1 Announce Type: new Abstract: Embodied world models predict the outcomes of robot actions to support learning and planning. For robots equipped with head and wrist cameras, this requ
arXiv:2609.24669v1 Announce Type: cross Abstract: Providing autonomous intelligence, pervasive connectivity and usability to human life and industry has led to the emergence of the Internet of Things
arXiv:2609.22712v1 Announce Type: new Abstract: Agentic AI systems built on large language models can plan over multiple steps, use external tools, retain information in memory, and coordinate with ot
arXiv:2609.26035v1 Announce Type: new Abstract: Conversational agents often express answers in a uniformly confident register. We test whether expressed uncertainty, provenance-aware assertion, and ex
arXiv:2609.26100v1 Announce Type: new Abstract: Speculative decoding accelerates large language model inference through collaboration between a lightweight draft model and a target verifier. Existing
arXiv:2609.26151v1 Announce Type: cross Abstract: Image restoration is inherently challenging due to the diverse and highly input-dependent nature of real-world degradations. While recent architecture
arXiv:2609.25864v1 Announce Type: cross Abstract: Visual object removal can eliminate a target from video frames, yet its acoustic trace persists in the soundtrack, causing obvious audio-visual incons
TypeSafe is pushing hard on this idea of “composable AI” but LangChain has embodied composable AI since day one. With new models like Jev, we’re decomposing the agent loop and moving more intelligence
arXiv:2609.25081v1 Announce Type: new Abstract: We describe ufakzeka-1, a 151M-parameter (182M with embeddings) decoder-only Turkish language model pretrained from scratch on 13.5B tokens of openly li
arXiv:2609.25604v1 Announce Type: new Abstract: Stochastic rendering eliminates the sorting and alpha blending process in Gaussian splatting, at the cost of introducing spatial noise. Formulating temp
arXiv:2509.00961v3 Announce Type: replace-cross Abstract: Active learning is a general learning mechanism shared by artificial and human learners. Whether AI can teach humans such a strategy that tran
arXiv:2609.25319v1 Announce Type: new Abstract: Ultra low-field MRI expands global access to neuroimaging but produces scans with low signal-to-noise ratio, reduced contrast, and thick slices. While r
arXiv:2609.24688v1 Announce Type: cross Abstract: Angular-margin speaker encoders are widely used in voice conversion, yet the geometry of their classifier prototypes remains poorly understood. We ana
arXiv:2609.25066v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to simulate human survey responses and behavioral reactions, yet unreliable simulations can mislead s
arXiv:2609.26753v1 Announce Type: new Abstract: Spatial flow measurements support underwater navigation, but distributed sensing is constrained by robot size and sensor layout. We use a causal observe
arXiv:2603.11323v2 Announce Type: replace Abstract: The simplicity and effectiveness of UNet architectures make them ubiquitous in image restoration, segmentation, and diffusion models. They are often
arXiv:2609.25498v1 Announce Type: cross Abstract: Deploying Large Language Models for runtime operational triage incurs prohibitive latency (>100-500 ms), high VRAM requirements (>4-8 GB), and excessi
arXiv:2609.26598v1 Announce Type: cross Abstract: The future 6G networks are expected to incorporate a proliferation of wireless services in diverse environments, which presents a significant challeng
arXiv:2609.25932v1 Announce Type: new Abstract: 2D point cloud registration arises in laser odometry and Simultaneous Localization and Mapping (SLAM) for mobile robots. Iterative Closest Point (ICP) i
Pair OpenCode, an open-source terminal-native AI coding agent, with open weight models on Amazon Bedrock to get a secure, flexible, pay-per-use coding assistant. Learn how to configure multi-model wor
What's Changed server: fix intermittent 'model not found' errors. by @rick-github in #18438 server: apply structured outputs in a single pass on thinking models by @jessegross in #18479 app: avoid Sys
Overview This release focuses on backend performance and correctness, broader model coverage, and more robust server/router operation. It adds HRM-Text (DFM Mimir 1B) support, MiMo-V2.6 and HunyuanOCR
NVIDIA Cluster Readiness Engine (NVCRE) is an open‑source Kubernetes controller that runs real distributed workloads across topology‑aware node groups to certify GPU cluster readiness before productio
arXiv:2603.22667v2 Announce Type: replace Abstract: Autonomous exploration by unmanned surface vehicles (USVs) in near-shore waters requires reliable localisation and consistent mapping over extended
arXiv:2609.25145v1 Announce Type: cross Abstract: Variational autoencoders (VAEs) offer an efficient approach to amortized Bayesian inference for inverse problems, but posterior accuracy can depend st
arXiv:2602.13217v2 Announce Type: replace Abstract: Reasoning benchmarks need renewal along two axes: freshness and headroom. VeRA makes both executable and auditable by turning each item into a task