Knowledge catalogue
arXiv:2605.18903v1 Announce Type: cross Abstract: Vision-Language Models in Continual Learning (VLM-CL) aim to continuously adapt to new multimodal tasks while retaining prior knowledge. The emerging
arXiv:2605.19452v1 Announce Type: cross Abstract: This paper studies the Byzantine Agreement problem where the nodes have access to a predictor that flags nodes for suspicion of faulty (Byzantine) beh
arXiv:2605.19952v1 Announce Type: new Abstract: To enable reliable long-term interaction, LLM agents require a memory system that can faithfully store, efficiently retrieve, and deeply reason over acc
arXiv:2605.20061v1 Announce Type: new Abstract: Reinforcement learning from verifiable rewards (RLVR) is a promising paradigm for improving large language model (LLM) agents on long-horizon interactiv
arXiv:2605.19033v1 Announce Type: cross Abstract: Supervised open-loop training has been widely adopted for training traffic simulation models; however, it fails to capture the inherently dynamic, mul
arXiv:2510.18821v3 Announce Type: replace Abstract: Reinforcement learning with verifiable rewards (RLVR) has become the mainstream technique for training LLM agents. However, RLVR highly depends on w
MiniMax Speech 2.8 Turbo is a text-to-speech model available through Together AI's voice finder tool at voicefinder.together.ai, allowing users to preview and test different voice options directly. Th
arXiv:2605.19342v1 Announce Type: new Abstract: Multimodal latent-space reasoning aims to replace explicit thinking with images by performing visual reasoning directly in a compact latent space. Howev
arXiv:2408.12385v3 Announce Type: replace-cross Abstract: We study the problem of approximately recovering a probability distribution given noisy measurements of its Chebyshev polynomial moments. This
arXiv:2602.18718v2 Announce Type: replace-cross Abstract: For approximating a target distribution given only its unnormalized log-density, stochastic gradient-based variational inference (VI) algorith
arXiv:2603.05933v2 Announce Type: replace Abstract: Applying Small Language Models (SLMs) to Chinese character-driven generation remains challenging due to data scarcity and the difficulty of disentan
arXiv:2605.19247v1 Announce Type: new Abstract: Current neural architecture search (NAS) methods are often limited by their predefined, restrictive search spaces. While recent large language model (LL
Subagents running locally and simultaneously on MacBook Pro M5 with Codex CLI + @lmstudio to review code and find bugs using Qwen 3.6 Powered by the updated MLX engine with batching in beta in the app
arXiv:2601.20309v2 Announce Type: replace-cross Abstract: Large Language Model (LLM) serving faces a fundamental tension between stringent latency Service Level Objectives (SLOs) and limited GPU memor
arXiv:2605.19766v1 Announce Type: cross Abstract: An effective healthcare agent must be able to recall and reason over a patient's longitudinal medical history. However, the absence of datasets with r
arXiv:2603.29501v2 Announce Type: replace-cross Abstract: Many value-based deep reinforcement learning algorithms rely on target networks - lagged copies of the online network - to stabilize training.
arXiv:2409.08248v2 Announce Type: replace Abstract: In this paper, we introduce TextBoost, an efficient one-shot personalization approach for text-to-image diffusion models. Traditional personalizatio
arXiv:2605.19638v1 Announce Type: cross Abstract: As large language models (LLMs) demonstrate increasing competence in synthesizing functional user interfaces, a fundamental question emerges in access
the crazy part is that people are “clowning” me without knowing anything about the training or whether anything else other than scaled changed or how the model does on anything else. (or what it costs
The frontier is still jagged though (here is Gemini 3.5 Flash messing up counting letters in words) https://x.com/RRiscio37389/status/2057193260745883962?s=20 @emollick Proof: https://gemini.google.co
arXiv:2603.05910v2 Announce Type: replace Abstract: LLM-powered tool-calling agents fulfill user requests by interacting with environments, querying data, and invoking tools in a multi-turn process. Y
This result points to something larger: AI systems are becoming capable of holding together long, difficult chains of reasoning, connecting ideas across distant fields, and surfacing paths researchers
arXiv:2510.20035v3 Announce Type: replace-cross Abstract: Vine copulas offer flexible multivariate dependence modeling and have become widely used in machine learning. Yet, structure learning remains
arXiv:2605.20150v1 Announce Type: new Abstract: Training 3D Gaussian Splatting (3DGS) at billion-primitive scale is fundamentally memory-bound: each Gaussian primitive carries a large attribute vector
arXiv:2503.17581v2 Announce Type: replace-cross Abstract: A computational method for the synthesis of time-optimal feedback control laws for linear nilpotent systems is proposed. The method is based o
arXiv:2605.19958v1 Announce Type: new Abstract: Zero-shot Object Navigation (ZSON) has shown promise for open-vocabulary target search in unseen environments, yet most existing systems remain tied to
.@trq212 is a builder's builder. After research at MIT, exiting his company, raising millions for another, and exploring research threads as an SPC member, he's now on the team building Claude Code. H
arXiv:2602.09023v4 Announce Type: replace Abstract: Despite strong generalization capabilities, Vision-Language-Action (VLA) models remain constrained by the high cost of expert demonstrations and lim
John Timmer / Ars Technica: Two research papers describe how Google's Co-Scientist and nonprofit FutureHouse's AI tools can succeed at drug-retargeting tasks by forming hypotheses — Both tools generat
arXiv:2602.03839v2 Announce Type: replace Abstract: Bandwidth-constrained distributed reinforcement learning (RL) post-training of large language models is bottlenecked by two channels: weight synchro
arXiv:2603.18003v4 Announce Type: replace Abstract: Multimodal large language models (MLLMs) exhibit strong visual-language reasoning, yet cannot process structured, non-visual data such as human skel
Very interesting results from this NanoGPT-Bench eval. There is so much talk about self-improving agents. But can coding agents do real AI R&D? @IntologyAI reports that Codex, Claude Code, and Autores
We're building Gemini for Science with and for the scientific community. In collaboration with 100+ institutions and a trusted tester community that ranges from PhD students to Nobel laureates, we wan
We’re expanding our partnership with @SpaceX, and will be scaling up on GB200 capacity in Colossus 2 throughout June. Appreciate @elonmusk and the team helping us find good homes for the Claudes. In t
arXiv:2605.19936v1 Announce Type: new Abstract: Has the style of scientific communication changed due to the growing use of large language models in the writing process? We address this question in th
Google announced a new YouTube Shorts Remix feature that lets users restyle clips or even insert themselves into other people's videos using Gemini Omni. Now, at the bottom of a YouTube Short, when yo
arXiv:2605.18074v1 Announce Type: new Abstract: We present 4DLidarOpen, a large-scale open multi-modal dataset for autonomous driving, centered on 4D frequency-modulated continuous-wave (FMCW) Lidar s
arXiv:2512.24365v2 Announce Type: replace-cross Abstract: Scientific machine learning (SciML) offers neural-network alternatives to numerical workflows in geotechnical engineering. This paper benchmar
A few weeks ago, we asked our community to use @GoogleAIStudio or Canvas in @GeminiApp to help us create the Google I/O countdown. Thanks SO much to everyone who submitted, and special shoutout to the
arXiv:2605.17911v1 Announce Type: new Abstract: Future planetary exploration envisions autonomous robotic agents operating under severe communication constraints, without global positioning, and with
arXiv:2605.16344v1 Announce Type: cross Abstract: Large-scale recommenders encode multi-objective trade-offs by combining multiple predicted outcomes into a single utility score. Although this utility
arXiv:2605.18750v1 Announce Type: cross Abstract: Pipeline parallelism is a key technique for scaling large-model training, but modern workloads exhibit runtime variability in computation and communic
arXiv:2511.11934v3 Announce Type: replace-cross Abstract: We present a systematic benchmark of out-of-distribution (OOD) detection CSFs through a representation-centric lens. Our study spans CNN and V
arXiv:2605.16299v1 Announce Type: cross Abstract: Large Language Models (LLMs) excel at code generation but remain heavily reliant on large-scale annotated solutions and verification-based supervision
arXiv:2605.17609v1 Announce Type: new Abstract: Many inference-time language-model pipelines combine a cheap reward signal with an expensive verifier, such as exact answer checking in mathematical rea
OpenAI discusses methods for establishing content provenance—tracking the origin and history of digital content—to improve transparency and safety in AI systems. The work addresses how verifiable cont
arXiv:2510.03879v3 Announce Type: replace-cross Abstract: Translating C to memory-safe languages, like Rust, prevents critical memory safety vulnerabilities that are prevalent in legacy C software. Ev
arXiv:2605.16819v1 Announce Type: cross Abstract: GPU kernel optimization is increasingly critical for efficient deep learning systems, but writing high-performance kernels still requires substantial
arXiv:2605.18661v1 Announce Type: new Abstract: AI-assisted research is crossing a threshold: fully automated systems can now generate research papers for as little as $15, while long-horizon agents c
arXiv:2605.17921v1 Announce Type: new Abstract: Streaming video requires handling dynamic information density under strict latency budgets. Yet, existing methods typically employ static strategies, su
Cloudflare has integrated with Anthropic's Claude Managed Agents to provide a fast, isolated execution environment for autonomous code delivery. This means builders can scale agent workflows globally
arXiv:2605.16582v1 Announce Type: new Abstract: We present ArtMesh, a mesh-native method for reconstructing articulated objects explicitly as connected triangle meshes with per-part rigid motion from
arXiv:2603.23566v2 Announce Type: replace-cross Abstract: Optimizing AscendC (Ascend C) operators for Ascend NPUs is difficult for two reasons. First, unlike CUDA, the ecosystem offers few public kern
arXiv:2605.17933v1 Announce Type: new Abstract: Vision-language model (VLM) agents increasingly rely on memory-augmented reinforcement learning to reuse experience across long-horizon tasks, yet most
arXiv:2605.17685v1 Announce Type: cross Abstract: Electrocardiogram (ECG)-based biometric recognition has emerged as a promising solution for secure authentication and liveness detection. However, mos
arXiv:2502.18632v4 Announce Type: replace Abstract: Knowledge components (KCs) mapped to problems help model student learning, tracking their mastery levels on fine-grained skills thereby facilitating
arXiv:2605.17561v1 Announce Type: cross Abstract: Issues faced when using software are reported in the form of bug reports. However, many bug reports are invalid, meaning they do not require code chan
arXiv:2605.17270v1 Announce Type: new Abstract: Modern visual object trackers show impressive results on general targets, yet their performance drops substantially when dealing with scene text. Althou
arXiv:2605.17174v1 Announce Type: cross Abstract: Reinforcement Learning (RL) is an important paradigm for aligning Diffusion Language Models (DLMs) toward functional correctness in code generation. H
arXiv:2605.17302v1 Announce Type: new Abstract: Ground robot navigation in complex 3D environments is often hindered by geometric ambiguity, where non-traversable structures such as furniture share lo