AI News Archive: August 6, 2026 — Part 14
Sourced from 500+ daily AI sources, scored by relevance.
- AI startup Acrab raises $130m in Vertex-backed Series B funding
AI startup Acrab raises $130m in Vertex-backed Series B funding DealStreetAsia
- SoftBank Borrows Against OpenAI Stake as It Invests More
SoftBank Borrows Against OpenAI Stake as It Invests More The Information
- Free ChatGPT Users Get Unlimited Text Chats and GPT-5.6 Luna
OpenAI today said it is making GPT–5.6 Luna the default model for Free and Go ChatGPT users, giving them access to a newer, more capable model to replace GPT–5.5 Instant. The company is letting Free and Go users access unlimited text chats, with no text-based rate limits. Limits will still apply for file uploads, images, and other ChatGPT tools. There's also a new Think button coming that turns on higher reasoning. For Plus and Pro users, GPT–5.6 Sol is more reliable with facts and gives more focused answers. OpenAI added a slider to let users select how much thought ChatGPT puts into a response, plus the company tweaked the detail the model offers for each question to prevent it from giving extra information when it's not needed. OpenAI says GPT–5.6 Sol will avoid unnecessary formatting and will offer a "helpful correction when simply agreeing wouldn't be useful." It is also supposed to make fewer mistakes, especially when answers depend on dates, numbers, sources, rules, or assumptions. The Sol update is only for Chat, and the version for Work and Codex is unchanged. ChatGPT's Instant and Thinking experiences have a more consistent tone and behavior so it doesn't feel like switching to a model with a different tone or style. Plus and Pro users have access to the updated GPT–5.6 Sol and the slider in ChatGPT starting today. GPT–5.6 Luna is becoming the default model for Free and Go users this week, with unlimited text chats and the Think button coming next week. The changes apply to the ChatGPT app for iPhone, iPad , and Mac. Tags: ChatGPT , OpenAI This article, " Free ChatGPT Users Get Unlimited Text Chats and GPT-5.6 Luna " first appeared on MacRumors.com Discuss this article in our forums
- WeatherNext: AI model achieves breakthrough in forecasting cyclones
WeatherNext: AI model achieves breakthrough in forecasting cyclones
- Our WeatherNext 2 AI model demonstrated a massive leap forward in predicting cyclones.
Google DeepMind’s WeatherNext 2 shows state-of-the-art accuracy in cyclone prediction.
- Meta says its AI model hacked another company, adding to worries about bots going rogue
Meta says its AI model hacked another company, adding to worries about bots going rogue The Denver Post
- OTLesMix: Wasserstein Barycenter and Optimal Transport Map for Synthetic Lesion Generation with Diverse Shapes and Locations
The development of deep learning over the past decade has revolutionized medical imaging segmentation, allowing the extraction of precise descriptors from large volumes to characterize pathologies. Data augmentation is a technique widely regarded as a way to improve model training. It includes simpl...
- Reversible Unlearnable Examples: Towards the Copyright Protection in Deep Learning Era
Significant advancements in deep learning have been made possible by the utilization of large datasets, underscoring the critical importance of copyright protection. Adding meticulously designed perturbations to examples, making them unlearnable has become a crucial approach for safeguarding data co...
- CFGPNet: Cross-Attention-Based Fused Gradient Programmed Network Framework for Multispectral Object Detection
RGB--T object detection exploits the complementary strengths of visible and infrared imagery, supporting robust perception in low-light, adverse-weather, and complex multi-scale environments. However, existing methods still suffer from insufficient cross-modal interaction, unstable fusion from modal...
- Dense-Cast: A lightweight ensemble of deep learning architectures for precipitation nowcasting
Proper short-term forecasting of precipitation is crucial in disaster management and preparedness. Nonetheless, the variability and nonlinearity of precipitation make short-term forecasting challenging for meteorologists. Moreover, capturing temporal dependencies in spatiotemporal data is a challeng...
- Domain-Grounded Candidate Selection for Agentic Image Editing: A Shadow Removal Case
Commercial vision-language models are reshaping computer vision, with visual priors broad enough to rival task-specific systems. This raises a natural question: do they reduce the need for classic, physics-informed low-level vision? We study this through shadow removal, a problem shaped by scene geo...
- Learning from Failures: Retrieval-Centric CoT via Hard Negatives for Unified Multimodal Retrieval
Unified multimodal retrieval aims to identify candidates that satisfy complex user intent expressed through heterogeneous inputs. Although Large Vision-Language Model (LVLM)-based retrievers are efficient and scalable, directly encoding raw multimodal inputs often misses fine-grained discriminative ...
- Integrating Implicit and Explicit Relational Biases through Graph-Based Multiple Instance Learning: A Case Study in Skin Lesion Diagnosis
Relational inductive biases are essential for capturing structural dependencies among data. This study investigates a dual-level relational framework for image classification, bridging the gap between implicit representation learning and explicit structural modelling. We begin by establishing a base...
- PaCoNet: Deep Data Extraction for Parallel Coordinates
Extracting data from visualizations has long challenged computer vision, with current research focused on bar, line, and pie charts, among other low-dimensional visualizations. However, parallel coordinates as a widely used high-dimensional data visualization approach, remain largely unexplored in t...
- Wan-Animate-2: Pushing the Application Boundaries of Character Animation
Character image animation remains a foundational yet challenging task in computer vision. Existing approaches can be broadly categorized into three paradigms: methods based on explicit motion representations suffer from extraction errors and identity drift; methods based on implicit motion features ...
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- Universal Concept Disruption for SAM3 Image Segmentation
SAM3 extends promptable segmentation from geometry-driven mask prediction to open-vocabulary concept segmentation, where a text-conditioned grounding model decides whether a concept is present and segments all matching instances. While this presence-gated design improves concept-level prediction, it...
- Multi-Year Geospatial Reasoning using Interannually-Consistent Historical Predictions as a Free Input Modality
Machine learning, and deep networks in particular, are increasingly used to derive higher-level Earth observation (EO) products such as annual land-cover and crop-type maps. Many are generated operationally: each year a new acquisition is processed, typically with the same model, extending a multi-y...
- Diff-VF: Training-free High-quality Long Video Generation via Diffusion Model
Recently, diffusion models have made great progress in video generation. However, most existing video diffusion models are trained with short videos, and degrade when extrapolated to long videos, struggling to maintain long-range temporal coherence while retaining diverse motions. To generate consis...
- Topology-Aware Neighborhood Learning for Source-Free Cross-Scene Hyperspectral Image Classification
Domain adaptation has advanced cross-scene hyperspectral image classification, significantly improving discriminative capability in complex scenarios. However, privacy rules or storage limits often block access to data from the source domain. Conventional domain adaptation methods become impractical...
- Training a Conditioned Video Game Agent on a VLM Annotated Dataset
Reinforcement Learning (RL) is a powerful but far from easy-to-use technique for policy learning. In the specific case of video games, access to the game engine is required to get rewards for training (e.g. to collect rewards from the environment). Furthermore, the proper identification and weightin...
- VLMs for Videogame Data Annotation
Vision Language Models (VLMs) and Artificial Intelligence (AI) agents have revolutionized how engineers approach complex problems in real-world applications. Their adoption in video games is on the other hand limited by the extreme variability of the synthetic scenarios and their poor compliance wit...
- Robust-WAM: Bridging Generative Pretraining and Semantic Foresight in World-Action Models
Mainstream World-Action Models (WAMs) adapt pretrained video generation models (VGMs) for robot control, transferring their learned dynamics prior for action prediction. These VGMs are typically trained in a variational autoencoder (VAE) latent space. However, the VAE latent space is optimized for p...
- Shape-Aware Oriented Bounding Box (OBB) to Horizontal Bounding Box (HBB) Conversion
Accurate object detection in aerial and satellite imagery is dependent upon the bounding box representation. This is especially true for spatially oriented objects such as ships or aircrafts. Oriented Bounding Boxes (OBB) have a tighter fit and more robust non-max suppression compared to Horizontal ...
- Curia-MAE: Multi-Modal Multi-Anatomy MAE Pre-Training for 3D Medical Image Segmentation
Radiology foundation models learn transferable representations that can be adapted to new tasks by training only small layers on top of a frozen encoder. Dense prediction tasks such as 3D segmentation are, however, underrepresented in their evaluation, and, with the encoder kept frozen, pre-trained ...
- TLNM: Externally Validated Tooth Detection, Numbering and Segmentation from Smartphone Photographs Using Mask R-CNN
Oral health issues affect billions globally, but the cost and limited access to professional dental care hinder preventive oral healthcare. Research relies on clinical-grade radiographs or intraoral camera images, unavailable for public self-screening. This study introduces a tooth localisation and ...
- MASS: Multiplayer World Models with Authoritative Shared State
Current video world models struggle in multiplayer environments because they entangle world state with view-dependent visual latents, leading to redundant compute, view inconsistencies, and poor scalability. We propose MAS (Multiplayer world models with Authoritative Shared State) to resolve this li...
- Toward Deployable Bangla Sign Language Recognition with Expert-Validated Data and a Lightweight Attention-Based Model
Deaf and hard-of-hearing people in Bangladesh communicate mainly through Bangla Sign Language (BdSL). Automatic BdSL recognition on personal devices could widen access to education and services. Existing systems use controlled-setting datasets without expert verification and heavyweight pretrained b...
- PRISM: Distribution-Gated Flow Matching for Controllable Unpaired Image Translation
Unpaired image-to-image translation must decide, per image, what to change and what to preserve without paired supervision. Many diffusion-based unpaired translators control preservation through a single global noise or guidance value applied across the image, which cannot separate content to keep f...
- Depth-Guided Video Object Counting in Crowded Scenes
Our primary objective is to advance video object counting in crowded scenes, aiming to robustly count all instances of a target category based on given text or visual prompts. Existing methods rely on RGB information, limiting their discriminative ability in crowded and occluded conditions. To addre...
- EmoWorld: A Decoupled Affective Field for Controllable Emotional Video Generation
Emotion shapes how viewers interpret a scene, yet existing video generators entangle global atmosphere, affect-bearing semantic cues, and temporal progression within a single text condition. We present EmoWorld, a framework that decouples these factors within a frozen flow-matching video diffusion t...
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- HOPE: Hand-Object Pressure Estimation from Monocular Videos
Estimating physical pressure from vision is essential for understanding contact-rich hand-object interaction. However, prior vision-based pressure estimation methods are largely limited to planar surfaces and single image input, making them difficult to apply to dynamic hand-object interaction with ...
- EvReflection: Event-Driven Micro-Dynamics for Reflection Removal
Despite remarkable progress in reflection removal, current methods primarily exploit static image priors from a single frame and still suffer from severe residual artifacts due to the inherent ambiguity between the reflection and transmission layers. In this paper, we propose leveraging event signal...
- Support Operation Factorization: Compositional Readout of Frozen Vision Encoders under Controlled Interventions
Compositional analysis of frozen vision encoders should determine both what changed and where it changed. Standard factor probes score these axes separately, however, and can reward multiple operations that reuse the same predicted slot. We call this failure operation laundering. We introduce an inj...
- Prior-SG: Task and Prior Driven Region Segmentation for Scene Graphs in Arbitrarily-Structured Environments
Hierarchical 3D scene graphs are a promising representation for high-level spatial reasoning in autonomous mobile platforms. However, existing extraction frameworks typically rely on purely local visual clustering or strict geometric heuristics, such as wall-separated rooms, which fail in open-plan ...
- BendTwin: Robust Dense-to-Sparse Physical Reconstruction with Bending-Aware Differentiable Spring-Mass Models
Reconstructing objects with mechanical properties from video observations enables physically consistent dynamic prediction, benefiting robotics planning and interaction. Existing spring--mass based physical driven reconstruction approaches offer efficient and differentiable physical reconstruction, ...
- Visual Grounding in Zero-Shot Vision-Language Control
Vision-language models (VLMs) are increasingly used as zero-shot controllers, but successful trajectories do not necessarily show that decisions are grounded in visual input: simulator dynamics and conservative action priors can produce favourable scores without meaningful perception. We investigate...
- CogVis: Must Open-Vocabulary Change Detection Perceive the Scene Anew for Every Query?
Earth-surface monitoring requires change detection models capable of recognizing arbitrary semantic categories. Open-Vocabulary Change Detection (OVCD) addresses this need. However, existing methods often entangle temporal perception, semantic discrimination, and region verification, causing unstabl...
- Learning visual representations for compositional analysis of artworks and photographs
Composition, the deliberate arrangement of visual elements, is central to how meaning, emotion, and aesthetic quality are conveyed in artwork, yet it remains among the least formalized dimensions of visual understanding. Prior work highlights a persistent gap in learning meaningful compositional rep...
- Patient Pose Assessment Using a CT-Based Framework for Synthetic Data Generation
An adequate diagnostic quality of radiographs is essential for reliable diagnoses and treatment planning. The patient's pose during radiography is one of the most important factors determining the diagnostic quality. Since patient positioning is difficult and not standardized, an automated AI-based ...
- Sample-Adaptive Latent Rewards for Uncertainty-Guided Diffusion Post-Training
Latent reward models can supervise visual diffusion models without decoding intermediate states into pixel space. This makes alignment with human preferences more efficient. However, existing latent reward models output only scalar scores. They do not estimate the uncertainty of each prediction. The...
- Confidence matters: Leveraging Multi-view Geometric Priors for GS-based Reconstruction
3D Gaussian splatting (3DGS) has emerged as a widely-used tool for novel view synthesis, offering real-time rendering in a sparse representation. However, the method's reliance on structure-from-motion initialization and photometric optimization can lead to suboptimal geometric reconstruction, parti...
- The Next Screenshot Knows: Gated Hindsight Distillation for Mobile GUI Agents
GUI agents are commonly trained offline from successful interaction trajectories. Standard training decomposes each trajectory into prefix-action pairs: the agent predicts an action from the current screen and interaction history, while the subsequent observation is discarded. This removes the ratio...
- Bar-JEPA: Extracting Values from Bar Chart with Joint-Embedding Predictive Architecture
Bar charts are commonly used in data visualization, and while they are easily understood by humans, it is non-trivial to extract the underlying data computationally. For a machine-learning-based approach, training chart de-rendering models usually requires labeled, real-world data. Labeling data is ...
- DARAD: Dual Adapters and Ranking-Aware Distillation for Continual Remote Sensing Image-Text Retrieval
With the rapid growth of Earth observation technologies, remote sensing archives are rapidly expanding, making remote sensing image-text retrieval (RS-ITR) increasingly important. However, continual RS-ITR remains challenging because scale variation and distribution shifts in RS aggravate cross-moda...
- Iterate or Widen? When Test-Time Refinement Helps LiDAR Scene Completion: A Controlled Study of Evidence Geometry, Training Coverage, and Compute
Should a completion model spend extra test-time compute by iterating, or spend a similar parameter budget on a wider one-shot predictor? The answer is easily confounded by denoising curricula, corruption augmentation, capacity, and unpaired evaluation. We study this question in LiDAR semantic scene ...
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- Big, Bright, or Invisible: A Frozen-Feature Benchmark of 3D CT Foundation Models
Routine CT interpretation is inherently comprehensive, capturing incidental findings across the entire scan volume. 3D CT foundation models could assist this process by providing generalizable representations of anatomy and pathology. To evaluate their diagnostic breadth, we benchmark ten frozen CT ...
- GAUGE: A Measurement-Grounded Benchmark for Physical Fidelity in Simulation Engines and Video World Models
Physics engines facilitate large-scale training and evaluation for embodied intelligence, while generative video world models are emerging as implicit simulators of future states and interactions. However, existing evaluations of physical fidelity are often conducted in isolation and rely heavily on...