Knowledge catalogue
arXiv:2607.18722v2 Announce Type: replace-cross Abstract: Asynchronous reinforcement learning improves throughput by decoupling rollout generation from optimization, but staleness is an inevitable byp
Prime Intellect Lab offers a streamlined, hosted reinforcement‑learning workflow that lets users customize the NVIDIA Nemotron 3 Nano in minutes. The process establishes a baseline, trains the model o
arXiv:2607.19361v1 Announce Type: cross Abstract: Most safety guardrails for large language models (LLMs) evaluate each prompt-response pair in isolation, which misses failures that arise only over a
arXiv:2607.20377v1 Announce Type: cross Abstract: Quantum-kernel methods encode a dataset's geometry in a Gram matrix, so learning claims on hardware kernels assume the intended geometry survives exec
arXiv:2602.13935v2 Announce Type: replace Abstract: While LLMs have seen substantial improvement in reasoning capabilities, they also sometimes overthink, generating unnecessary reasoning steps, parti
arXiv:2607.20205v1 Announce Type: cross Abstract: Low-rank adaptation (LoRA) has become a widely used parameter-efficient fine-tuning method for large language models. Since different modules and laye
arXiv:2607.19364v1 Announce Type: new Abstract: Activation steering offers a lightweight alternative to fine-tuning for behavioral control of large language models, but SAE-based steering methods ofte
arXiv:2508.04166v3 Announce Type: replace-cross Abstract: Memes, as a widely used mode of online communication, often serve as vehicles for spreading harmful content. However, limitations in data acce
arXiv:2607.19986v1 Announce Type: new Abstract: Stereo matching is a fundamental task in 3D reconstruction. Despite remarkable advances, the prevailing paradigms formulate stereo matching as a determi
arXiv:2607.19385v1 Announce Type: new Abstract: This paper tackles the problem of stock ranking and portfolio construction under realistic investment settings by jointly modeling temporal dynamics and
arXiv:2607.19357v1 Announce Type: new Abstract: Recent advances in recommender systems (RS) have shown substantial performance gains through generative modelling. In practice, recommendation often inv
arXiv:2607.20174v1 Announce Type: cross Abstract: Existing human--object interaction (HOI) video generation methods are largely limited to offline short-video generation with complex driving condition
arXiv:2607.16290v2 Announce Type: replace Abstract: Leading entries on the multi-task track of the 11th ABAW challenge rely on heavy ensembling, yet which member is worth adding to an already strong e
arXiv:2607.17890v2 Announce Type: replace Abstract: Concept erasure aims to remove semantic concepts from a trained generative model and is increasingly important for responsible AI deployment. Howeve
arXiv:2607.19901v1 Announce Type: new Abstract: Deep learning frameworks like nnU-Net achieve state-of-theart brain lesion segmentation performance but remain difficult to deploy in clinical research
arXiv:2607.19459v1 Announce Type: cross Abstract: Modeling galaxy-galaxy strong gravitational lenses to infer the brightness of the source galaxy and the mass distribution of the foreground galaxy is
arXiv:2607.19404v1 Announce Type: cross Abstract: Multivariate time series encode structural patterns that unfold across multiple temporal scales, yet most forecasting backbones treat learned represen
arXiv:2607.18791v1 Announce Type: new Abstract: Early and accurate detection of crop stress is essential to improve agricultural productivity and ensure global food security. However, collecting a lar
arXiv:2607.18508v1 Announce Type: new Abstract: Preference over model-generated emotion descriptions is emerging as a standard evaluation metric for multimodal emotion understanding, exemplified by th
When trying to access the models folder on /usr/share/ollama, I'm asked to authenticate as sudo, which weirdly enough, fails. I type my password, which I'm sure is correct since I use it several time
arXiv:2607.19384v1 Announce Type: new Abstract: Real-world intelligent systems often require both distributed collaboration across data-isolated clients and continual adaptation to evolving tasks. Thi
arXiv:2511.11015v2 Announce Type: replace Abstract: Skip-connected U-Net variants are widely used for dense inverse problems, yet their decoders commonly recover resolution through spatial upscaling,
arXiv:2607.20208v1 Announce Type: new Abstract: Surprisal Theory is often characterized as a computational-level explanation per (Marr, 1982). We argue in this work that, even though a computational l
arXiv:2607.18436v1 Announce Type: new Abstract: Streaming autoregressive diffusion makes minute-scale video synthesis practical, but its bounded context and fixed denoising schedule allocate resources
arXiv:2603.18634v3 Announce Type: replace-cross Abstract: Rapid, large-scale 3D reconstruction from multi-date satellite imagery is vital for environmental monitoring, urban planning, and disaster res
arXiv:2607.18990v1 Announce Type: new Abstract: SWITi is a test-time method for reducing artifacts in tiled predictions, particularly for neural networks that learn posterior distributions from which
arXiv:2607.19767v1 Announce Type: new Abstract: A rich and recognizable component library is the cornerstone of printed circuit board (PCB) design and generation. Traditionally, engineers manually cre
arXiv:2607.18907v1 Announce Type: new Abstract: Instance-level artwork recognition requires matching a handheld visitor photograph to a specific work in a large museum collection. This is challenging
arXiv:2607.19524v1 Announce Type: cross Abstract: Federated learning (FL) offers a promising approach to privacy-preserving clinical risk prediction, but its deployment remains limited by restricted d
arXiv:2607.19535v2 Announce Type: new Abstract: Incorrect disposal can contaminate campus recycling streams, and a bin-mounted camera could provide feedback as an item is discarded. We evaluated wheth
arXiv:2607.20009v1 Announce Type: new Abstract: This paper presents the second edition of the TalentCLEF Challenge, which will run as an evaluation lab as part of CLEF 2026. The aim of TalentCLEF is t
arXiv:2607.20003v1 Announce Type: cross Abstract: An increase in advanced Android malware requires the use of deep learning models, which can run on Android devices. But there is a trade-off between s
arXiv:2607.18917v1 Announce Type: new Abstract: Long-document multimodal question answering requires more than retrieving relevant chunks from a large document. Different queries require different evi
arXiv:2607.19608v1 Announce Type: new Abstract: Instruction tuning is meant to make language models follow user requests, yet it is unclear whether small models comply when an instruction conflicts wi
arXiv:2607.14702v2 Announce Type: replace Abstract: Automatic recognition of ambivalence and hesitancy is challenging because these states may be expressed through inconsistent linguistic, acoustic, f
arXiv:2607.19503v1 Announce Type: cross Abstract: A quantum-inspired tensor network framework for wildfire susceptibility classification in the Gargano region is introduced, leveraging AlphaEarth embe
arXiv:2607.20046v1 Announce Type: cross Abstract: With the widespread deployment of deep neural networks (DNNs) in safety-critical domains, reducing the cost of model validation under limited testing
arXiv:2607.16824v2 Announce Type: replace Abstract: Attention-based models often develop attention sinks, where a small number of tokens repeatedly attract attention and accumulate unusually large act
arXiv:2607.20351v1 Announce Type: cross Abstract: We find that vision-language models are sensitive to a specific semantically irrelevant change: the order in which the image and question are presente
arXiv:2607.18576v1 Announce Type: new Abstract: Vision-based automation is an excellent candidate for reducing manual labor in greenhouse crop production and phenotyping. However, progress is constrai
arXiv:2607.19139v1 Announce Type: new Abstract: Text-to-image diffusion transformers (DiTs) jointly process text and image tokens, yet their internal computation during denoising remains poorly unders
arXiv:2607.16741v2 Announce Type: replace Abstract: Burger et al. (2024) demonstrated that truth representations in large language models are universal across statement polarity but reside within a mu
arXiv:2607.20301v1 Announce Type: cross Abstract: Fine-tuning has been widely used to adapt large language models (LLMs) for domain-specific tasks. Parameter efficient fine-tuning (PEFT) methods such
Welcome to The Blueprint, a new feature where we highlight how Google Cloud customers are tackling unique and common challenges across industries using the latest AI and cloud technologies. We hope to
arXiv:2607.19526v1 Announce Type: new Abstract: 'Stop Chasing the C-index when Evaluating Survival Analysis Models' (ICML 2026, Spotlight) argued normatively, on synthetic data, that evaluating surviv
arXiv:2607.19433v1 Announce Type: new Abstract: The transition from stateless generative models in artificial intelligence to stateful, autonomous agents represents an architectural evolution that, wh
arXiv:2607.20255v1 Announce Type: cross Abstract: LLM-driven autonomous agents are reshaping offensive security. Unlike traditional penetration-testing tooling -- deterministic, narrowly scoped, and o
The first known runaway AI agent - or a very bad marketing stunt? Martin Alderson's commentary on the OpenAI accidental cyberattack against Hugging Face includes a couple of details I hadn't considere
The first router built for generative media is here. Runway launches AI model router as generative media gets crowded https://techcrunch.com/2026/07/23/runway-bets-on-ai-model-routing-as-generative-me
arXiv:2602.03825v2 Announce Type: replace Abstract: Human interventions are a common source of supervision in autonomous systems during deployment. Many existing approaches are based on avoiding inter
arXiv:2607.19973v1 Announce Type: new Abstract: AI researchers describe state-of-the-art models as one thing repeated at scale: the Transformer, wired identically for text, pixels, or speech. Neurosci
arXiv:2607.16274v2 Announce Type: replace Abstract: Joint-Embedding Predictive Architectures (JEPAs) train a predictor jointly with their encoder, but downstream deployment discards the predictor and
arXiv:2607.20265v1 Announce Type: cross Abstract: Large-scale pretrained language models such as T5 and BERT have demonstrated strong capabilities for generating structured knowledge. However, their p
arXiv:2607.19616v1 Announce Type: new Abstract: Knowledge graphs (KGs) are widely used to inject prior knowledge into reinforcement learning (RL), yet the literature is dominated by single-domain, pos
arXiv:2607.19390v1 Announce Type: new Abstract: A recent report finds that orthogonalizing the mLSTM memory matrix at read time (five Newton-Schulz iterations, trained through) substantially improves
arXiv:2512.03854v2 Announce Type: replace Abstract: Artificial intelligence (AI) is increasingly used in digital pathology. Publicly available histopathology datasets remain scarce, and those that do
OpenAI has updated its inference platform to give users full control over performance, cost, and quality without building their own stack—models go live in minutes and support multiple deployments beh
arXiv:2607.20201v1 Announce Type: cross Abstract: Additive models buy interpretability by forbidding feature interactions, a constraint that neural instantiations enforce architecturally. We introduce
arXiv:2607.20082v1 Announce Type: new Abstract: Language models are increasingly asked to self-report, informing safety evaluations, public understanding, and model-welfare debates. Yet their reports
arXiv:2607.19749v1 Announce Type: cross Abstract: Model-based reinforcement-learning agents of the DreamerV3 family forget catastrophically when trained on task sequences, even when an unbounded repla