Knowledge catalogue
arXiv:2606.09861v1 Announce Type: cross Abstract: While Next-Token Prediction (NTP) has unified LLM pretraining, its adaptation to unbounded, continuous time series (TS) remains open. To bridge the ga
arXiv:2606.10587v1 Announce Type: cross Abstract: Large language models (LLMs) are on the rise for accelerating scientific discovery, most recently in advanced tasks such as generating valid scientifi
arXiv:2606.10568v1 Announce Type: new Abstract: Vision-language-action (VLA) models have shown strong promise for robotic manipulation, but their reliability at test time remains limited by one-shot a
Kristin Robinson / Billboard: WMG acquires AI attribution startup Sureel, as it seeks to track when its songs and recordings are used in the training of AI models or in AI-generated works — The deal i
As frontier models (e.g. Fable 5) continue to push the task horizon of knowledge work automation, it becomes ever more important for humans to be able to audit decisions back to the source context. It
arXiv:2606.07529v1 Announce Type: cross Abstract: Large language models (LLMs) have recently been applied to 3D vision-language (3D-VL) tasks, which require spatial reasoning to identify target object
arXiv:2512.16349v2 Announce Type: replace-cross Abstract: We propose a collaborative edge-to-server inference framework for vision-language models (VLMs) that reduces communication cost while maintain
arXiv:2606.08555v1 Announce Type: new Abstract: Force signals provide critical interaction cues for contact-rich robotic manipulation. However, existing methods mostly use force as an additional obser
arXiv:2606.08123v1 Announce Type: cross Abstract: Vision-based driver monitoring systems are increasingly deployed in safety-critical intelligent transportation settings, yet they are almost always co
arXiv:2606.08751v1 Announce Type: new Abstract: Accurate quantification and uptake measurement in PET are critical for assessing disease progression and supporting clinical decision-making. While high
arXiv:2606.07547v1 Announce Type: cross Abstract: Speech-based large language models are typically constrained to spoken replies, which limits their user-facing outputs to what can be verbalized and s
arXiv:2606.07531v1 Announce Type: cross Abstract: We introduce mllm-shap, an open-source Python framework designed to extend Shapley Value (SV) explainability from text-only Large Language Models to M
arXiv:2606.07705v1 Announce Type: cross Abstract: Although multi-objective reinforcement learning (MORL) is central to aligning large language models with complex human preferences, the prevailing pra
arXiv:2606.08446v1 Announce Type: cross Abstract: Despite being powerful, reinforcement learning with verifiable rewards (RLVR) induces extremely long COT, making it computationally expensive. Since R
Super excited to announce that @arcee_ai is the first major American AI lab to replace AWS S3 with Hugging Face for ALL their models and datasets, public AND private 🔥🔥🔥 Multi-million $ partnership to
arXiv:2601.15165v4 Announce Type: replace-cross Abstract: Diffusion Large Language Models (dLLMs) break the rigid left-to-right constraint of traditional LLMs, enabling token generation in arbitrary o
this is the biggest wake-up call to protect and nourish open source AI if you don't build out sovereign and independent models+infra closed labs will patronize you to an insulting degree mythos will b
arXiv:2606.09787v1 Announce Type: new Abstract: The Cloud-Edge Continuum (CEC) enables latency-critical applications by distributing resources to the far edge, but its extreme volatility makes proacti
arXiv:2606.07107v1 Announce Type: new Abstract: Most vision-language-action (VLA) models map observations directly to actions without explicit intermediate planning, which limits performance on long-h
Coinbase : 'At Coinbase we're working hot on routing prompts to cheaper models where appropriate, & in some cases have been able to keep costs roughly flat, while token usage continues to grow exponen
arXiv:2606.07001v1 Announce Type: cross Abstract: High-quality training data is essential to large language models (LLMs) and typically requires extensive and costly manual curation. Existing automati
arXiv:2606.06547v1 Announce Type: cross Abstract: Diffusion Large Language Models (dLLMs) refine tokens iteratively but commit them irreversibly, leading to a 'stability lag' where early decisions rem
arXiv:2606.06943v1 Announce Type: cross Abstract: Vision-language models (VLMs) such as CLIP achieve strong zero-shot recognition but remain highly fragile under adversarial perturbations. Recent test
arXiv:2606.07441v1 Announce Type: new Abstract: Sycophancy in language models is typically studied as excessive agreement or validation, while explicit praise and flattery have received comparatively
arXiv:2606.06647v1 Announce Type: new Abstract: Objective. EEG foundation models (FMs) report strong accuracy on clinical resting-state EEG. However, high accuracy under subject-disjoint cross-validat
This NVIDIA technical article addresses how low-bit mixed-precision pre-training accelerates large language model training, where optimizing numerical precision can reduce training time across distrib
arXiv:2606.06273v1 Announce Type: cross Abstract: Lossless pixel-level image transmission is a fundamental regime beyond semantic communications, because exact recovery requires both accurate symbol p
arXiv:2606.06154v1 Announce Type: new Abstract: Federated fine-tuning of foundation models using Low-Rank Adaptation (LoRA) offers a communication efficient solution for distributed learning. However,
arXiv:2606.05441v1 Announce Type: cross Abstract: We investigate how to make small tabular foundation models effective for High-Dimensional, Low-Sample Size (HDLSS) tabular prediction without retraini
arXiv:2606.06303v1 Announce Type: cross Abstract: Controllable generation with discrete diffusion models is often hindered by high computational overhead or the need for retraining. In this paper, we
arXiv:2606.05217v1 Announce Type: cross Abstract: We exhibit an exact correspondence between sampling with score-based diffusion models and adiabatic transport of ground states for a family of Schrodi
arXiv:2606.06186v1 Announce Type: new Abstract: Vision-Language Models (VLMs), such as CLIP, have shown strong zero-shot generalization but remain highly vulnerable to adversarial perturbations, posin
arXiv:2308.10897v2 Announce Type: replace Abstract: We present a framework for generating appropriate facial responses from a listener in dyadic social interactions based on the speaker's words. Given
arXiv:2606.06120v1 Announce Type: new Abstract: Contrastive Analysis aims to separate factors that are common between two data distributions from those that are salient to only one of them. Existing c
arXiv:2606.05936v1 Announce Type: new Abstract: Modern language models rely on pretraining filters to remove undesirable content from training corpora and inference-time guardrails to suppress undesir
arXiv:2606.05254v1 Announce Type: cross Abstract: World-action models (WAMs) jointly generate future video and robot actions through iterative diffusion, achieving strong performance on manipulation b
arXiv:2602.08503v2 Announce Type: replace-cross Abstract: Self-correction is essential for solving complex reasoning problems in vision-language models (VLMs). However, existing reinforcement learning
arXiv:2508.20693v2 Announce Type: replace-cross Abstract: Ontologies and taxonomies of research fields are critical for managing and organising scientific knowledge, as they facilitate efficient class
arXiv:2606.06103v1 Announce Type: new Abstract: Medical image segmentation is often framed as a search for stronger architectures, but this can obscure a more fundamental question: what does the datas
Michael Considine / CNBC: OpenAI confirms it will comply with President Trump's EO that asks AI companies to allow the US government to assess their models' capabilities before release — OpenAI has co
arXiv:2606.06060v1 Announce Type: new Abstract: Modern diffusion models generate high-quality images and videos, but their iterative denoising process makes inference expensive. Feature caching accele
Wow Ideogram-4.0 is immediately going into my @ComfyUI library of models. Ideogram even joins the top 10 all-around image leaderboard joining Microsoft, Google, Grok and OpenAI. In the Image Arena: op
1/ 🔥 @NoPriorsPod x @LatentSpacePod chat with @SatyaNadella at @Microsoft Build. He has the sharpest mental models of any public company CEO I've interviewed. $MSFT is at its heart still a tools compa
arXiv:2606.04177v1 Announce Type: cross Abstract: Interpretable linguistic features offer a promising approach for explaining why a given text appears machine-generated, particularly for non-expert us
arXiv:2606.04775v1 Announce Type: cross Abstract: Text-to-video (T2V) models trained on large-scale web data can generate undesired content, motivating interventions that reduce harmful outputs withou
arXiv:2606.04613v1 Announce Type: new Abstract: Vision-Language Models (VLMs) struggle when applied to medical image-text data, yet the tools available to diagnose this failure remain limited. Existin
arXiv:2601.09719v3 Announce Type: replace-cross Abstract: Pre-Layer Normalization (Pre-LN) is the de facto choice for large language models (LLMs) and is crucial for stable pretraining and effective t
arXiv:2601.22396v2 Announce Type: replace-cross Abstract: Despite the growing utility of Large Language Models (LLMs) for simulating human behavior, the extent to which these synthetic personas accura
arXiv:2606.04535v1 Announce Type: cross Abstract: Diffusion large language models (dLLMs) offer bidirectional attention and parallel generation, enabling them to exploit global context and naturally s
arXiv:2606.04922v1 Announce Type: cross Abstract: Current prompt-based and adapter-based tuning of vision-language models (VLMs) is attractive for medical imaging, where clinical data sensitivity favo
It seems like @GaryMarcus was right: the AI revenue models are imploding Sam Altman said AI budgeting has recently become a 'huge issue' for some companies, something that 'never came up' earlier this
Locally is now LM Studio’s mobile app. And today we're bringing LM Link to iPhone. Use your largest local models over a secure, end-to-end encrypted connection, anywhere you go. Download the app now:
arXiv:2606.04534v1 Announce Type: new Abstract: Agile quadrotor flight in cluttered scenes requires more than a reactive mapping from a depth image to a control command: the vehicle must remember whic
arXiv:2606.04027v1 Announce Type: cross Abstract: Diffusion large language models (dLLMs) generate text by iteratively denoising partially masked sequences under bidirectional context, exposing a safe
Ben Thompson / Stratechery: Q&A with Satya Nadella on Microsoft's competitive position, MAI models, OpenAI, the software business, GitHub Copilot, Project Solara, data centers, and more — An interview
arXiv:2606.04396v1 Announce Type: new Abstract: Diffusion large language models (dLLMs) generate responses by iteratively unmasking and revising many positions in parallel. This process leaves a rich
arXiv:2606.04974v1 Announce Type: new Abstract: Diffusion large language models (DLLMs) enable non-autoregressive generation by iteratively denoising corrupted token sequences with bidirectional conte
arXiv:2606.04287v1 Announce Type: cross Abstract: Generating realistic and diverse graphs is a key problem in machine learning, with applications in molecular discovery, circuit design, cybersecurity,
arXiv:2510.13796v3 Announce Type: replace Abstract: Symbol grounding (Harnad, 1990) describes how symbols such as words acquire their meanings by connecting to real-world sensorimotor experiences. Rec
arXiv:2601.11214v5 Announce Type: replace Abstract: We present T^star, a simple TraceRL-based training curriculum for progressive block-size scaling in masked diffusion language models (MDMs). Startin