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Qwen3-VL-32B-Heretic-MiniMax-H3-NVFP4 is a specialized variant of the Qwen3 vision-language model optimized with specific architectural modifications and quantization techniques. This model aims to deliver improved performance in multimodal understanding tasks.

DeepSeek V4 Flash demonstrates efficient performance capabilities when run on a single AMD MI300X GPU, showcasing optimizations for cost-effective AI deployment.

Noah is a proactive AI executive assistant for founders. Claude talks to you; Noah talks to your network. It's like Tesla Full Self Driving, and not cruise control- an autonomous AI EA that manages your calendar, relationships, and follow-ups across email, text, and WhatsApp - so nothing slips. Think of it as an always-on EA living in your SMS and helping you with meeting logistics.

A new tool enables users to fine-tune an 8 billion parameter language model on a standard 4GB laptop GPU, democratizing access to model customization.

Atlaso is a memory layer for AI. Connect it once and every AI you use, from Claude Code to Cursor, Codex and ChatGPT, automatically recalls the context that matters: your projects, your decisions, and the way you like to work. No more re-explaining yourself at the start of every session. One shared memory that follows you across every tool, instead of being locked inside one app. Free to start, and backed by original memory research.

Driven is an AI investment agent that turns market insight into action, not just answers. In one agent-powered workspace, it combines 260+ APIs, built-in & custom Skills, Playbooks, scheduled tasks, 24/7 monitoring, real-time data, portfolios, and order workflows. Instead of switching tools or chasing every signal, investors can manage the full journey in Driven, from idea generation and data gathering to analysis and action. It’s your AI investment team, while you stay in control.

Most schedulers make you create your videos somewhere else, then download and re-upload them. VIDEO AI ME now does both: generate UGC ads and product videos with AI, then publish or schedule them to 15 platforms (TikTok, Instagram, YouTube, X, LinkedIn and 10 more) without leaving the tool. AI writes captions per platform from your actual script. Bulk schedule 25 videos in one sitting. See which video wins on each platform, then repost it or generate a new version with AI in one click.

GrowthBook 5.0 brings feature flags, experimentation, and product analytics into one AI-native, warehouse-native platform. Build no-code experiments in the browser with the new AI Visual Editor, let agents create flags and draft experiments with 25 open-source Skills, explore product data through the in-app AI Assistant, and ship safely with stronger governance. Faster queries and a streamlined experiment workflow help every team move from idea to insight with less friction.

Connect Glasp to Claude and ChatGPT as an MCP server. Search your highlights and memories in natural language, right inside your AI assistant. Read-only and private to you.

Homebench is a new benchmarking tool that allows developers to evaluate locally-deployed large language models across metrics including speed, memory usage, and output quality.

Explore new education plugins for ChatGPT Work and Codex that help K–12 teachers, college educators, and students learn, teach, research, and build.

Large language models struggle with tabular data prediction tasks, revealing fundamental limitations in how these models process structured, non-text information.

On-policy (self) distillation (OPSD) is increasingly adopted for language-model post-training. It strengthens the teacher with privileged information but can induce a privilege illusion: the student learns privilege-dependent behavior it cannot reproduce from its inference-time context, yet behaves as if the training-time privileged information remained available, ultimately degrading performance. In this paper, we identify information asymmetry between the privileged teacher and the student at ...

Large language model (LLM) agents increasingly undertake long-horizon tasks that require sustained reasoning, tool use, and revision across many interdependent steps. However, existing agent harnesses maintain task execution, task state, and completion assessment within a growing context, making the state difficult to track and allowing incorrect self-assessments to propagate into later decisions. We reformulate long-horizon execution as a task-state management problem and propose LongHorizon-Ha...

Sparse retrieval underpins modern search systems, from web search to retrieval-augmented generation. Existing work has introduced Learned Sparse Retrieval (LSR) to push beyond exact lexical matching toward richer semantics. Yet LSR has so far remained tied to encoder-style bidirectional architectures, and its extension to multimodal settings still relies heavily on auxiliary cross-modal modules. To address these limitations, we introduce UEmbed (Unified Embedding), a decoder-only multimodal embe...

To operate robustly in open-world environments, autonomous agents should be able to infer the behavior of unfamiliar systems through interaction alone, even in the absence of documentation. However, existing tool-use benchmarks expose semantic tool schemas in static environments, allowing agents to rely on prior knowledge rather than autonomous discovery. To address this limitation, we introduce ScrambleToolBench, an interactive terminal benchmark designed to isolate behavioral reasoning. By rem...

Enterprise question answering requires models to acquire proprietary knowledge without discarding general capabilities. We present Wnuan, a three-stage pipeline that constructs task-oriented supervision from documents, performs supervised fine-tuning with general-data replay, and applies reinforcement learning to residual errors. On the 707-question WnuanBench, the primary 32B route raises acceptable-answer rate (AAR) from 52.76% before adaptation to 80.06% after SFT and 91.51% after RL. Under a...

Speech and audio generation is often needed in animation dubbing, audio drama, movies, advertising, games, podcasts, and short-video production. In these scenarios, creators may need to design voices without reference recordings, control speaker styles with natural language, support acoustic scenes with environments and audio effects, and later reuse the designed voices. Therefore, it is important to support multi-speaker speech and audio generation for both instruct and zero-shot tasks. The ins...

Optimization-based latent reasoning improves large language model outputs by optimizing instance-specific continuous states at test time while keeping model parameters frozen. Existing methods, however, typically connect these states to the reasoning trajectory through decoded tokens, making sequence-level credit assignment indirect and obscuring how latent updates shape subsequent reasoning. We introduce GradCuit (gradient through circuit), which inserts optimizable latent states at a selected ...

Geospatial foundation models aim to learn representations that transfer across regions and sensors, yet evaluating them on specific tasks requires large, high-quality, multi-modal benchmarks that measure how well such models extract value from data. Concerning flood mapping, existing datasets rarely combine bi-temporal SAR and co-registered optical imagery at scale, leaving the value of foundation models for this downstream task largely untested. We introduce GEOID-Flood, a large-scale multi-mod...

Video motion transfer aims to animate a target object using dynamics from a reference video. Existing formulations largely rely on fixed structural correspondence, which becomes ill-defined when reference and target objects differ substantially in morphology, articulation, or deformation mechanisms. We introduce Motion Beyond Morphology, a perspective that seeks to transfer motion beyond fixed structural correspondence, by preserving dynamics that remain meaningful across different target morpho...

Agent skills have become an important mechanism for equipping language-model agents with reusable procedural knowledge. However, providing skills alone does not guarantee that current models can effectively identify, apply, and coordinate them. To improve skill-use capabilities, we introduce SKT, a verified data synthesis pipeline that constructs skill-grounded tasks and executable trajectories from large collections of agent skills. SKT selects suitable single-skill and multi-skill configuratio...

Existing indoor layout generators produce globally plausible layouts yet may retain local violations such as collisions, out-of-bounds placements, obstructed openings, and blocked circulation. Most prior work focuses on full-scene synthesis or scene-level optimization, with limited support for identifying responsible objects and locally repairing affected regions. We present Roomer, a reflective repair framework that casts these violations as sparse, object-grounded repair problems. Roomer encod...

Multimodal large language models (MLLMs) have advanced visual understanding and reasoning, yet their static parametric knowledge limits their ability to address knowledge-intensive and dynamically evolving open-world problems. To move beyond this limitation, multimodal deep search has emerged as a key direction for open-world information access, evolving from single-turn factual retrieval toward long-horizon, multi-turn search guided by visual evidence. However, existing methods typically confin...

Existing skill generation methods largely rely on heuristics or pipeline-style consolidation, which must be specially designed for different evidence sources. In contrast, learning-based approaches offer a more unified way to model skill generation across heterogeneous sources. However, learning-based skill generation remains challenging because skills lack a natural supervision signal based on relevance or correctness; their value can largely be determined only by whether they improve the behav...

Recent Vision-Language-Action (VLA) models for autonomous driving (AD) increasingly utilize chain-of-thought (CoT) supervision to enhance the reasoning capabilities of their Vision-Language Model (VLM) components, yet existing annotation pipelines commonly expose the teacher model to the logged ground-truth (GT) future trajectory. We empirically show that this induces trajectory anchoring bias: teacher models rationalize the revealed outcome rather than infer a decision from scene evidence, prod...

Here are Google’s latest AI updates from July 2026

Apple has disclosed that additional former employees beyond those previously identified may have transferred confidential company information to OpenAI during their employment.

The AI industry's rapid growth is being fueled by hidden borrowing and debt accumulation that has reached $1.65 trillion, raising concerns about the sustainability of current spending patterns.

Mariano-Florentino (Tino) Cuéllar to join Anthropic as Chief Global Affairs Officer