Search headlines, summaries and articles to connect the dots.
Shanghai’s Fourier Intelligence built a custom humanoid for Meta’s internal research in months. The company already has a rehab business, modest humanoid shipments, and a disclosed pre-money valuation of about 8 billion yuan. A marquee partner tests its engineering—it does not yet prove the equity story.
arXiv:2611.07164v1 Announce Type: cross Abstract: Provenance Identity Continuity (PIC) represents an authority-use event as an operation/resource behavior situated in a causal lineage, $(o,r,\ell)\in O\times R\times L$. We make the causal occurrence itself primitive: a represented security-relevant causal graph $(E,\to_c)$ in which each occurrence has a unique causal history, so the consistent event-lineage space is canonically isomorphic to $E$. The modeled execution has one causal partial order; distinct lineages are distinct causal histories inside it and may share a root while diverging into incomparable branches. PIC's $O\times R\times L$ is the authority-behavior projection of this space, and Causally Ordered Security-State (COSS) continuity generalizes it to an arbitrary behavior product over the same causal execution. On this semantics we prove an architecture-independent confused-deputy exclusion theorem. PIC continuation requires $C(e')\subseteq C(e)$ on every ordinary causal edge, so descendants are bounded by their ancestors; at an ordinary join, authority available on only one incoming branch cannot be resurrected, even when branches share a common root. Non-conservative combination must be an explicit authorized composition or relaxation event. With protected-effect mediation, an effect caused by request $q$ through an ordinary authority segment can exercise only authority in $C(q)$, so a confused-deputy state is contradictory. The result holds for any system whose protected request-to-effect causes are faithfully represented and whose ordinary authority transport satisfies PIC. We revisit Rajani, Garg, and Rezk: their value, control-flow, and initial-heap attacks are different witnesses of causal ancestry, so full provenance is an evidence mechanism for recovering edges of the causal graph, not the foundation of the PIC theorem. Finally we lift it to the COSS product of heterogeneous security-state coordinates.
Google DeepMind has launched Gemini Omni 1.2 Flash, introducing native 15-second video generation, improved reference adherence, stronger consistency across frames, multi-turn conversational editing, more natural speech, and cinematic camera movements. The model is now generally available via the Gemini API and Gemini for business, priced at $0.07 per second for 720p video—30% lower than its predecessor. It supports scalable workflows, including draft generation at lower resolutions, iterative edits, and upscaling to 1080p or 4K. Use cases include product explainers, social ads, virtual try-ons, and branded content, with applications across Google AI Studio, Google Cloud, Google Vids, Flow, the Gemini app, and Google Ads.
arXiv:2611.07294v1 Announce Type: new Abstract: Diffusion models for video generation have achieved remarkable success, yet precise motion control under explicit user guidance remains challenging. Recent noise-warping methods introduce temporal correlations by warping the noise according to the user-provided motion signal. However, standard spatial warping generally disrupts the noise's i.i.d. Gaussian structure, requiring distribution correction, such as Jacobian-based correction, but at a substantial computational overhead. We propose Householder Motion Embedding (HME), which encodes the motion signal into the noise through a sequence of Householder reflections that together form an orthogonal transformation applied to each location's channel vector. HME exactly preserves the spatial Gaussian structure within each frame while introducing cross-frame correlations that promote temporally consistent video. HME is agnostic to the network architecture and integrates directly into standard video diffusion fine-tuning pipelines. Experiments on different scenarios show that HME matches or outperforms the state of the art, while achieving a 2.7x speedup over noise warping. These results demonstrate that channel-space Householder transformations offer a principled and efficient alternative to noise warping for motion-controllable video generation.
Learn how Amazon Marketplace evaluates A+ Content quality at scale on Amazon SageMaker AI by rendering each product page as a single snapshot, scoring it with a vision-language model fused with OCR readability signals in a CatBoost classifier, and using SHAP to turn each decision into prioritized, actionable recommendations for selling partners.
Infinitas Learning runs transactions for 1.6 million learners on ClickHouse Managed Postgres and serves real-time analytics to teachers from ClickHouse Cloud, where distributed cache cut P99 latency from about 13 seconds to 410 milliseconds.
AI adoption moves fast. Readiness takes longer. Assess 6 dimensions to see where your data foundation limits AI.
I’m not the sort of person who typically pays for a house cleaner. I’m too frugal, and I kind of enjoy putting on a podcast and going to town on my tub with a Magic Eraser. But when I saw an X post about a new humanoid-robot cleaning service in San Francisco, I immediately signed…
arXiv:2611.05895v1 Announce Type: new Abstract: Comparisons in conversational emotion-shift forecasting are easily distorted when a learned model receives rich features but its transition baseline is given a noisy or unavailable current-emotion label, when hyperparameter budgets differ, or when target-turn information enters the representation. We present an information-matched, leakage-safe auditing protocol that closes these gaps with an explicit commitment point, executable causality tests, equal four-configuration validation searches, and seed-dialogue-aware inference. Applying it to six causal model families on four text corpora and two feature-source sensitivity configurations shows that a deployable train-only current-emotion classifier feeding the transition baseline yields a different verdict than an oracle diagnostic where both sides receive gold current emotion. Under the deployable condition, learned models improve over the predicted-label baseline on all four primary corpora; DailyDialog gains are large and survive a 72-comparison Holm correction (best ROC-AUC gain = +0.167, adjusted p=.0144). Under matched gold information, the baseline remains competitive on IEMOCAP and stronger on MELD, while every model retains a large DailyDialog advantage. We additionally report precision-recall AUC, calibration, a strictly utterance-local feature control, same-person self-shift forecasting, and a future-access dose-response test. An eight-item leakage checklist accompanies the protocol. Code, raw seed predictions, and reporting artifacts are available from the corresponding author upon request.
OpenAI introduces BioBasicsBench, a 100-task benchmark measuring how well AI agents interpret raw experimental data and handle everyday wet-lab science.
NVIDIA has introduced four new open-weight checkpoints for its Cosmos 3 model family—Cosmos3-Nano-Transfer-Auto, Cosmos3-Nano-Sim-Auto, Cosmos3-Nano-Sim-Transfer, and Cosmos3-Nano-Sim-Bimanual—enabling interactive world simulation and multi-sensor, multi-view driving scene generation. Each model is a post-trained variant of Cosmos 3 Nano with 16B trainable parameters and is based on OpenMDW1.1, allowing commercial and non-commercial use. The checkpoints support applications in robotics and autonomous driving by generating synchronized camera and LiDAR data, enabling streaming simulations, translating simulator controls into photorealistic video, and predicting visual outcomes from robot actions. They are designed to be adapted using custom data and training recipes, with support for offline data generation, causal distillation, and interactive rollouts. Resources include technical reports, demos, and notebooks for inference and adaptation.
Companies want AI agents faster than they can build the AI architecture to run them. Most can put together a convincing demo. Far fewer can move agents into production without exposing data, hurting uptime or blowing their budgets. That gap is pushing database providers to rethink what a data platform for agents has to deliver. […] The post Oracle wants to pull stalled AI agents out of demo mode and into production appeared first on SiliconANGLE.
arXiv:2611.07408v1 Announce Type: new Abstract: Panoptic Scene Graph generation (PSG) requires holistic yet fine-grained reasoning over complex relations among objects. However, current Multimodal Large Language Models (MLLMs) applied to PSG suffer from a perception bottleneck: they function as static observers limited by fixed-resolution views. As a result, models often overlook critical relations involving small or peripheral objects and struggle to differentiate ambiguous predicates in complex scenes. To address these limitations, we propose PSGAgent, an agentic framework that enhances visual perception for PSG through a think-act-observe loop. PSGAgent allows MLLMs to dynamically refine their visual perception using two tools: a Region Zoom-In Tool for local exploration of small or boundary relations and a Visual Highlight Tool for relation-focused segmentation that filters out irrelevant contextual noise. To optimize decision-making, we construct a two-stage training pipeline. First, we create a high-quality, error-aware Multimodal Chain-of-Thought (M-CoT) dataset that provides robust supervision for cold-start learning. Then, we design a novel hierarchical reward mechanism integrated with Group Sequence Policy Optimization (GSPO) to enable strategic and adaptive tool usage. Extensive experiments on the PSG benchmark show that PSGAgent achieves SOTA performance, demonstrating strong capability in both holistic and fine-grained scene understanding.
Generic AI models can write SQL, but they don’t know a company’s orders, customers or revenue definitions. Now, Oracle Database aims to give AI agents the context to move a stalled order forward. But that shift raises questions about complexity, lock-in and security as AI agents start acting on live transactions. Oracle Corp.’s answer is […] The post Same model, better answer: Enterprise data is what turns generic AI into a business tool appeared first on SiliconANGLE.
Article URL: https://github.com/AntidrawHQ/antidraw Comments URL: https://news.ycombinator.com/item?id=50391875 Points: 3 # Comments: 1
Here is Cerea, a self-hostable, open-source (Apache 2.0) chat+agent frontend for LLMs. It works alone with any OpenAI-compatible inference provider, but it's way better when coupled with the model gateway I'm also developing, Pystino (https://github.com/paoloviviani/Pystino).I came to this after struggling to find a hosted chat interface that was truly open source, that I really enjoyed to use, and that was simple enough to be shared and used by family. In particular, I was looking for something that could provide the smooth and integrated user experience of commercial products for the tasks needed by family (e.g., document editing, form filling, notes and knowledge management), but backed by open models hosted wherever I decide.Then it became richer, now supporting: remote coding agents (wrapping Opencode), delegation and coordination between agents, integration between chat/projects and code agents (this one to me is a pain point for Claude, which I use at work), projects, RAG, rich artifacts support, schedules, and more.You can try it yourself, either cloning the full repo and head to Cerea/kit, or just the deploy kit with a script (https://github.com/paoloviviani/Cerea#quick-start).It's just a docker compose with a ./configure script that helps to generate a sensible .env for the specific scenario. It works in the browser and as a PWA on mobile.I hope you'll enjoy, feel free to reach out for questions. Comments URL: https://news.ycombinator.com/item?id=50384502 Points: 1 # Comments: 0
Article URL: https://lecturetobook.com/ Comments URL: https://news.ycombinator.com/item?id=50386766 Points: 1 # Comments: 0
Article URL: https://github.com/vighnesh-bheed/eski Comments URL: https://news.ycombinator.com/item?id=50389748 Points: 1 # Comments: 0
Article URL: https://github.com/smokeeaasd/flawless-ui-ux Comments URL: https://news.ycombinator.com/item?id=50381846 Points: 1 # Comments: 0
Article URL: https://mathparadoxes.com/en/ Comments URL: https://news.ycombinator.com/item?id=50391670 Points: 2 # Comments: 0
