Knowledge catalogue
Open source AI has shown how quickly developers can innovate when models, data and tools are shared. Robotics has the same opportunity, but advancements in physical AI development can still be gated b
Kai Nicol-Schwarz / CNBC: OpenRouter: Chinese AI models have drawn 30%+ of token use by US companies each week since February 8, peaking at 46%, up from 11% over the previous 12 months — Chinese-built
arXiv:2508.02932v2 Announce Type: replace Abstract: Low-Rank Adaptation (LoRA) has gained popularity as a fine-tuning approach for Large Language Models (LLMs) due to its low resource requirements and
arXiv:2607.05007v1 Announce Type: new Abstract: This paper introduces a quantum-inspired computational framework for harmonic decision-making in music. The proposed approach formulates harmonization a
arXiv:2607.05199v1 Announce Type: new Abstract: Physics reasoning fails structurally in small language models: an error at any step propagates forward, corrupting every inference that follows. Limited
arXiv:2511.15409v2 Announce Type: replace Abstract: We present a class of algorithms for state estimation in nonlinear, non-Gaussian state-space models. Our approach is based on a variational Lagrangi
arXiv:2607.02983v1 Announce Type: new Abstract: Recent reasoning-centric Large Language Models (LLMs) have made significant strides, yet they predominantly operate on a passive-inference pattern that
arXiv:2508.03177v2 Announce Type: replace Abstract: Large Vision-Language Models (LVLMs) recently achieve significant breakthroughs in understanding complex visual-textual contexts. However, hallucina
arXiv:2607.03160v1 Announce Type: cross Abstract: This study examines how prompt language and translation theory-driven prompt design influence the quality of Spanish-Chinese journalistic translations
arXiv:2607.04442v1 Announce Type: cross Abstract: Diffusion models (DMs) are a state-of-the-art generative method to approximately sample from an unknown distribution. Their training and evaluation pr
arXiv:2607.04423v1 Announce Type: cross Abstract: Unified Multimodal Models (UMMs) integrate image understanding and generation within a single architecture, yet how the two tasks interact remains und
arXiv:2607.04517v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models are commonly treated as end-to-end action policies conditioned on natural-language task descriptions. In practice, h
// ReContext // Models now support 128K context windows and still fail to use evidence that is already in the prompt. Where is the gap? New paper introduces ReContext, a training-free inference harnes
arXiv:2607.01693v1 Announce Type: new Abstract: These notes give a proof-oriented introduction to diffusion models from the viewpoint of sampling, tracing a single arc from classical sampling dynamics
arXiv:2607.01295v1 Announce Type: cross Abstract: Acoustic imaging visualization is a core methodology in acoustics, enabling spatial analysis of sound sources and acoustic scenes. However, limited se
arXiv:2607.01966v1 Announce Type: new Abstract: Low-voltage load forecasting is an important component in current and future energy systems with a high degree of electrification and decentralized gene
Financial Times: Sources: Anthropic moves to close loopholes that let Chinese firms like Ant use its models via workarounds including cloud providers and overseas subsidiaries — Engineers are still fi
arXiv:2607.01235v1 Announce Type: cross Abstract: Understanding how Large Language Models (LLMs) make token-level decisions during code generation remains a major challenge for both researchers and pr
arXiv:2607.00773v1 Announce Type: new Abstract: Discrete diffusion models are widely used for learning and generating discrete distributions. As the generation process is inherently sequential, the ac
arXiv:2607.00578v1 Announce Type: new Abstract: Concept Bottleneck Models (CBMs) provide interpretability by routing predictions through a layer of human-understandable concepts. However, defining an
Together AI announced significant performance improvements in their AI infrastructure, achieving a 6x cost reduction per turn while maintaining p95 latency under 400ms, with new models being released
arXiv:2607.00601v1 Announce Type: new Abstract: The game of Taboo requires describing a target word without using a set of forbidden words, so that other players can guess it. This deceptively simple
arXiv:2607.00417v1 Announce Type: cross Abstract: In the era of satellite constellations, multi-view optical satellite imagery is pivotal for Earth Observation (EO) and high-quality Digital Surface Mo
arXiv:2607.01049v1 Announce Type: new Abstract: Industrial inspection requires more than binary anomaly detection: a practical system should determine whether an anomaly exists, localize the defective
arXiv:2607.00492v1 Announce Type: new Abstract: We introduce GenSP, a data-driven framework that learns consistent spherical parameterizations across a collection of genus-0 shapes. Instead of optimiz
arXiv:2510.13537v2 Announce Type: replace-cross Abstract: On-device deployment of Large Language Models (LLMs) frequently leverages Low-Rank Adapters (LoRAs) to support diverse downstream tasks under
Our CEO @vipulved on @CNBC with @dee_bosa: your data is your recipe. As models get smarter, sending proprietary workflows, customer context, and business logic into closed systems becomes a strategic
arXiv:2607.00989v1 Announce Type: cross Abstract: Semantic trajectory analysis has recently emerged as an approach for modeling human movement by capturing implicit patterns and behaviors through sema
arXiv:2607.01006v1 Announce Type: new Abstract: Large Language Models (LLMs) represent one of the most significant advances in AI and natural language processing in recent years. Still, many pressing
arXiv:2606.31329v1 Announce Type: cross Abstract: Hierarchical Vision-Language-Action (VLA) models decouple high-level planning from low-level control to improve generalization in robot manipulation.
arXiv:2606.31101v1 Announce Type: new Abstract: Bridging the sim-to-real gap is a core challenge in deploying learned manipulation policies. Sim-to-real learning is attractive because it can replace e
arXiv:2606.31603v1 Announce Type: cross Abstract: Semantic segmentation models struggle with data sparsity and rare or visually diverse regions, e.g., dense regions or small objects in aerial or auton
arXiv:2606.31804v1 Announce Type: new Abstract: Accurate energy demand forecasting is essential for the reliable operation and planning of modern sustainable energy systems. Spatial-temporal graph neu
Bloomberg: Sources: Meta plans a cloud infrastructure business that will sell access to AI compute and models, to compete with AWS, Azure, and Google Cloud; META jumps 8%+ — Meta Platforms Inc. is dev
arXiv:2603.18481v2 Announce Type: replace Abstract: Out-of-distribution (OOD) detection remains a critical challenge in open-world learning, where models must adapt to evolving data distributions. Whi
Niko Gallogly / New York Times: Together AI, which offers access to open-source models, raised 800M led by Saudi Aramco's Prosperity7 at an 8.3B valuation, taking its total funding to 1.3B — Together
TwelveLabs Inc., the developer of generative artificial intelligence foundation models that can understand videos like humans, today announced it has raised 100 million in early funding to expand beyo
arXiv:2606.31407v1 Announce Type: cross Abstract: Vision-language models can produce confident answers on visually ambiguous inputs, resulting in biased predictions. Common entropy-based methods, such
arXiv:2511.01472v2 Announce Type: replace Abstract: The rapid progress of vision--language models (VLMs) has sparked growing interest in robotic control, where natural language can express the operati
arXiv:2606.29984v1 Announce Type: new Abstract: Reinforcement Learning (RL) is an important paradigm for improving the reasoning capabilities of Vision-Language Models (VLMs). However, directly applyi
Can regularization based JEPA (e.g. SIGReg) scale and compete with SOTA foundation models (DINO)? Here is the answer: yes and with 10x less data. VISReg (slight variation of SIGReg) competes with DINO
arXiv:2606.30236v1 Announce Type: new Abstract: Medication errors, particularly dosing errors in clinical trials (CT), can lead to patient harm, adverse drug events and worse patient outcomes. Dosing
arXiv:2606.06179v2 Announce Type: replace-cross Abstract: Score-based diffusion models are typically trained by minimizing the L^2 score matching error, and standard theoretical analyses rely on this
arXiv:2602.11395v2 Announce Type: replace-cross Abstract: Steering diffusion models toward conditions unseen during training typically requires either retraining with conditional inputs or per-step gr
arXiv:2606.29604v1 Announce Type: cross Abstract: We aim to discover diverse, generalizable perturbations of LLM internals that can surface hidden behavioral modes. Such perturbations could help resha
arXiv:2602.20360v2 Announce Type: replace-cross Abstract: Flow-based generative methods offer a simple and effective framework for high-fidelity generation, yet pretrained flow models are rarely used
arXiv:2606.28369v1 Announce Type: cross Abstract: Semantic search and recommendation of similar documents, such as news and reports about unusual environmental events (e.g., a dead whale washed ashore
This post likely recommends resources or reading materials for practitioners looking to deploy and scale open-source language models in production environments. DAIR.AI, a research organization focuse
arXiv:2606.29724v1 Announce Type: new Abstract: Normalizing flows are powerful generative models that learn an invertible mapping between complex data distributions and simple latent distributions, ty
arXiv:2602.23353v2 Announce Type: replace-cross Abstract: The Platonic Representation Hypothesis posits that neural networks trained on different modalities converge toward a shared statistical model
arXiv:2511.06090v3 Announce Type: replace-cross Abstract: Optimizing the performance of large-scale software repositories demands expertise in code reasoning and software engineering (SWE) to reduce r
arXiv:2606.29089v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models demonstrate impressive reasoning over visual, semantic, and spatial task variations by leveraging large-scale vision
arXiv:2606.30308v1 Announce Type: new Abstract: 4D hand motion reconstruction from egocentric video is bottlenecked by clear limitations of existing methods: image-based pipelines depend on a detector
arXiv:2512.13660v3 Announce Type: replace-cross Abstract: Spatial tracing, as a fundamental embodied interaction ability for robots, is inherently challenging as it requires multi-step metric-grounded
arXiv:2601.13602v3 Announce Type: replace-cross Abstract: This paper introduces an analytical approach to quantifying and optimizing the distributional discrepancy in generative diffusion models. For
arXiv:2604.00784v2 Announce Type: replace Abstract: Surgical video understanding is a crucial prerequisite for advancing Computer-Assisted Surgery. While vision-language models (VLMs) have recently be
arXiv:2606.27672v1 Announce Type: new Abstract: Inspired by advances in natural language processing and computer vision, 'time-series foundation models' (TSFMs) have recently been introduced with the
arXiv:2606.27696v1 Announce Type: cross Abstract: In this paper, we are the first to examine the correlations between class frequency and the multi-scale noise schedule within diffusion models. For sc
arXiv:2606.27593v1 Announce Type: new Abstract: We introduce a categorical framework called ODYSSEY for constructing verifiable, local truth-preserving foundation models as compositions of foundries:
arXiv:2506.16150v4 Announce Type: replace-cross Abstract: As large language models (LLMs) advance, concerns about their misconduct in complex social contexts intensify. Existing research overlooked th