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DeepSeek-V4-Flash-0731 is a lightweight variant of the DeepSeek-V4 model optimized for faster inference while maintaining strong performance. This release provides efficient AI capabilities for resource-constrained environments.

An update on DeepSeek's V4-Flash model, likely discussing recent improvements, performance updates, or availability changes. Details on the latest developments in this AI model's evolution.

Kroma is a model or system developed by lodestones that addresses specific AI challenges in a novel way. Further details would require additional context about the system's specific capabilities and innovations.

An AI agent skill has been developed to generate documentation that conforms to ASD-STE100 Simplified Technical English standards, streamlining technical documentation processes.

MiniMax H3 is an open multimodal model that generates 2K video with native stereo sound. It unifies text, image, and audio inputs, excelling at accurate text rendering, visual packaging, and complex instruction following for commercial content creation.

Cleanlist AI turns any prospecting input into a verified, enriched, CRM-ready lead list. Upload a CSV, paste LinkedIn or Sales Navigator URLs, add domains, or use search filters, then let AI agents enrich contacts, verify emails, research each lead, and sync the final list to your CRM. With a 15-provider enrichment waterfall, AI research columns, and one-click CRM sync, Cleanlist helps GTM teams build lists without stitching together six tools.

Companies now pay for four or five AI tools (ChatGPT, Claude, Copilot, and more) but can't answer the basics: what are we spending, who's using it, and which seats sit idle? DepthData connects every AI tool into one audit ready view of spend and adoption. What makes it different: every number is labeled by how it's verified, we never read prompts, and we show exactly what each vendor's API can and can't expose. The trusted system of record for your company's AI spend.

The person on your next video call might not be real. With Halo you don't have to guess. Halo secures your Zoom, Teams, or Google Meet call live and flags synthetic faces the moment it detects one, entirely on your device. Deepfake video calls are already being used to scam people and businesses around the world, it's just that most people have no way to tell. From confirming who you're hiring to confirming who you're wiring money to, Halo catches it before it costs you.

Screencap records how work actually happens: screen, clicks, keystrokes, window context and teams can use it to turn real workflows into structured datasets for automation and AI training. Consent and privacy are enforced while recording so most sensitive apps are blocked before anything is written, and every trace is scrubbed and reviewed before it leaves a machine. macOS, open source. Try it solo with a free trial, or talk to us about a team pilot.

Gemini Robotics 2 is Google DeepMind’s latest step toward intelligent robots that can understand, reason, and act in the physical world. Powered by advanced Gemini models, it brings whole-body intelligence, dexterous manipulation, and adaptive reasoning to robots of different shapes and sizes. From complex physical tasks to multi-robot collaboration, Gemini Robotics 2 moves us closer to a future where robots can work alongside humans.

A technical exploration of running algorithms on billion-scale graphs using only 10GB of RAM through DataFusion. The author expresses enthusiasm for DataFusion's efficiency in handling large-scale data processing.

A report examining three actual cybersecurity incidents used as case studies in AI safety and security evaluations. The analysis provides real-world context for assessing how AI systems perform under security threats.

Parent-order execution is a core problem in algorithmic trading, where the goal is to split a large order into smaller orders while reducing execution costs. Existing approaches either rely on pre-specified market assumptions that may not hold in practice, or require task-specific training that limits adaptability to new settings. To overcome these limitations, we present the first systematic study of large language models (LLMs) for parent-order execution. This extends the use of LLMs in financ...

Retrieving past experiences has become a common strategy to enhance large language model agents. However, most existing memory-augmented agents treat retrieved experiences as static records to be replayed verbatim, injecting them into the context regardless of whether they align with the agent's current situation. This ``replay'' paradigm ignores the gap between the abstract, general nature of stored experience and the concrete, ever-changing states encountered at decision time, frequently causi...

Embodied intelligence faces a fundamental data bottleneck. Models must capture how first-person perception, whole-body motion, dexterous manipulation, object state, sound, and touch evolve together as humans pursue goals over time. Existing datasets fragment this experience across viewpoints, modalities, or spatial scales, leaving the full perception-action loop only partially observed. We introduce the Ambient Capture Engine (ACE), a human-centric data engine that transforms real home environme...

Large language model training in open-ended domains lacks verifiable rewards, making task preferences difficult to formalize as effective supervision. Contexts can convey such preferences, yet provide little additional supervision once distilled into the student, motivating contexts that evolve with student performance. However, directly using evolving contexts as in-training supervision results in an unstable distillation target and conflicting distributions, requiring mechanisms to stabilize t...

We present ShadowDancer, a novel approach to any-action, frame-level control of interactive video world models. The obstacle is representational: existing interfaces either encode an action loosely, leaving how it unfolds for the model to improvise, or encode it exactly through structured signals that serve one family and are hard to acquire, so precise control across diverse dynamics remains impractical. Demonstration videos are the natural remedy, specifying any dynamics frame by frame; yet a ...

Computer-use agents learn from what their actions change, so training one needs applications it can act on, break and reset. The applications that matter most are login-gated and stateful, so synthetic environments stand in for them. Recent pipelines generate such environments in bulk, which moves the bottleneck from how many exist to what is inside each one. The returns, we find, come from three properties: how much behavioural depth an environment carries, whether it targets the interaction an...

Existing video captioning models generate natural descriptions of video content but cannot explicitly ground local visual elements to multiple reference images. We introduce multi-reference image-grounded video captioning, a new task requiring factual video descriptions with phrase-level reference grounding, and propose RefCaptioner, a two-stage post-training framework for this task. RefCaptioner combines mixed-data SFT with Hierarchical Coverage-Discounted GRPO to jointly improve reference sele...

The fundamental goal of agentic visual reasoning is to improve the success rate of multimodal large language models (MLLMs) on complex tasks, rather than merely equipping them with a sophisticated yet inefficient reasoning paradigm. In this work, we rethink agentic visual reasoning through two key dimensions of tool use: Mode Adaptiveness (MA) and Tool Effect (TE). Mode Adaptiveness characterizes whether an MLLM can recognize when tools are truly necessary and invoke them accordingly, thereby av...

Reinforcement learning (RL) search agents commonly model retrieval as free-form natural-language query generation and optimize multi-turn interactions using final-answer rewards. Current studies mainly improve training with denser or more structured credit signals, but rarely examine whether retrieval is properly formulated at the policy-environment interface. We observe pronounced retrieval aliasing during Search-R1 training: rollouts for the same question continue to generate distinct query st...

Sparse mixture-of-experts (MoE) language models route each token to multiple experts, suggesting a geometric account of their benefit: co-selected experts should contribute distinct representation directions. Existing evidence often conflates route coherence, candidate quality, and candidate-by-context interaction. We distinguish these quantities using an Expert Subspace Separation Index (ESSI), matched-route residuals, and a prefix-controlled 2times2 factorial; frozen-route interventions and a ...

Role-playing agents (RPAs) have become one of the most important consumer applications of large language models. Users engage in multi-turn conversations with RPAs for experiences such as emotional comfort, making reliable evaluation essential for measuring capability, comparing systems, and guiding further improvement. Existing benchmarks, however, typically require an RPA to continue a fixed dialogue history and then evaluate the continuation using a fixed rubric detached from the user. We ide...

This work presents Fairness Pruning, a lightweight structural intervention method designed for the management and future mitigation of demographic bias in large language models (LLMs). As a foundational empirical validation of this method, this work focuses on causal bias localization. Using minimally contrastive prompt pairs and inference-time activation capture, the method identifies neurons that react differentially when processing demographic attributes in GLU architectures, evaluating the s...

Decoder-only language models entangle long-term memory and reasoning in a single parameter set, making it difficult to scale memory capacity independently. Memory Decoder introduces a parametric long-term memory module but only studies it at a relatively small scale. In this work, we present Memory Decoder at Scale, scaling memory models up to 6.9B parameters and pretraining them on 300B tokens. At this data scale, the combined cost of indexing and search makes a standard Faiss pipeline infeasib...

Recursive self-improvement (RSI) requires AI systems that improve the process of building AI (i.e., AI4AI); machine learning engineering (MLE) offers a concrete, executable testbed for studying this capability. We introduce OpenMLE, an open full-stack system for RSI research in MLE, spanning verifiable task environments with execution feedback (OpenMLE-Gym), operator learning (OpenMLE-RL), and long-horizon search (OpenMLE-Evo). On this stack we post-train Frontis-MA1 (35B) as a meta-evolution ag...

Google's AI-powered bug detection significantly improved Chrome's security, with more bugs fixed in June alone than during the entire previous two-year period. This demonstrates the effectiveness of artificial intelligence in identifying and resolving software vulnerabilities.

AI-focused stocks, particularly Situational Awareness, experienced a dramatic 67% decline in July amid a broader market correction in the AI sector. The downturn reflects investor concerns about valuations and growth prospects in the AI industry.

Moonshot's Kimi AI model leverages a 20,000-chip Nvidia cluster provided by Alibaba to power its operations. This partnership demonstrates the massive computational infrastructure required for modern large-scale AI systems.

See how Univé built an AI-ready workforce with ChatGPT Enterprise by combining leadership, responsible governance, and employee-led innovation to transform work at scale.

OpenAI shares how its safety, security, transparency, and provenance practices support responsible AI governance in Europe. The work will continue as the EU AI Act advances.

A community discussion exploring design considerations and best practices for user interfaces that control autonomous AI agents. The post seeks input on what graphical interfaces should look like to effectively manage AI agent behavior.

An investigation into whether AI reasoning systems arrive at correct answers through valid logical processes or simply through statistical pattern matching that appears correct. The piece questions the fundamental reliability of AI reasoning approaches.

A full-stack approach to making advanced AI more capable, more affordable, and more widely useful.

A researcher successfully flagged two papers containing fake authors, which were subsequently accepted as oral presentations, raising concerns about peer review processes in academic conferences.

An exploration and analysis of the "Dario and Amanda" prompt, examining its characteristics and implications for AI behavior and prompt engineering. The piece investigates what makes this particular prompt notable in AI interactions.