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Minimax-h3-Turbo represents a lightweight and efficient language model variant optimized for high-speed inference. The model maintains strong performance on reasoning and knowledge tasks while significantly reducing computational requirements.

DeepSeek V4 Flash 0731 is a lightweight version of DeepSeek's latest AI model, designed for faster inference and deployment across various applications. This release demonstrates continued efforts to democratize access to advanced AI capabilities.

The faster your team ships with AI, the harder it becomes to keep production stable. Not anymore. Coldtea is an agentic IDE where your coding agents build, visual QA agents catch regressions, and AI monitoring watches production, so your team can keep moving at agent speed without breaking anything

Soloop is an agentic company-building system for solo founders. It takes you from idea to revenue with an AI CEO to plan, manage and prioritize, an AI CTO to build and execute, and an AI CMO to find users and sell. Keep your own judgment and ownership while delegating the unfamiliar work that usually requires a team.

Nitro is now the first human translation service an AI agent can order and pay for on its own — no account, no API keys, no signup. Your agent sends the text, pays per request, and a professional native speaker translates it — publication-ready in hours. Great for the smaller texts that still need to be right: ads, app updates, email sequences, and more. The ordering is machine-to-machine. The translating stays human. 80+ languages. No plans, no minimums.

Rindler automates the repetitive web work your team still does by hand. Say what you need in plain English, and Rindler goes to the real site, signs in when needed, completes the task, and returns clean structured data. Put it on a schedule and it keeps running. Instead of making a browser agent guess every time, Rindler maps each site ahead of time and repairs workflows when pages change, so recurring tasks stay reliable

Debug and monitor AI agent failures in minutes. Trace every run, catch hallucinations and ungrounded answers that traditional monitoring misses, and see exactly what went wrong. Reduce token waste, improve agent quality, and ship faster with support forNET, Python, and JavaScript.

BrowserOS neo is a web browser built NOT for you but for YOUR AI agents. It runs on your machine and uses your logins, so Claude Code, Cowork, and Codex can finish real tasks on your behalf. Run many agents at once. Replay any session to see what your agents did. Free and open source, 12,000+ GitHub stars.

HAR is an open-source, agent-agnostic framework for building multi-agent coding workflows. Run a fleet of coding agents in parallel on any repository, with deterministic validation gates, verifiable proof, and full observability across every agent, all extensible and customizable to your own workflow and tooling.

Troopr joins your standup, writes each person's update from real Jira, GitHub, and Slack activity, and flags what doesn't add up. It builds a memory of how your team works, so every standup gets more accurate.

Herdr, an AI startup, is joining Y Combinator while maintaining its open-source runtime to continue community-driven development. The move combines accelerator support with a commitment to keeping the underlying technology accessible to developers.

Kitesurf is an agent-first browser that leverages V8 isolates to enable advanced AI agent capabilities within a browser environment. The architecture allows for secure and efficient execution of AI agents directly in the browser runtime.

New Orleans is implementing Carbyne's AI-powered Emergency Call Triage software to improve 911 response efficiency by intelligently screening and prioritizing emergency calls. The technology aims to reduce response times and allocate resources more effectively.

vLLM is a high-throughput LLM inference system that optimizes performance for serving large language models at scale. This article examines the architecture and design principles that enable vLLM to achieve efficient batch processing and memory management.

Researchers have used artificial intelligence to accelerate the design of new viruses, raising significant biosecurity and ethical concerns about dual-use AI technology. The development underscores the need for responsible oversight of AI applications in sensitive research domains.

Spatial intelligence is fundamental to embodied agents, yet existing benchmarks focus on local spatial perception from single or few viewpoints, overlooking global spatial awareness over continuous, long-horizon visual streams. To address this limitation, we introduce the Global-Spatial-Temporal Benchmark (GST-Bench), a VQA benchmark for global spatial intelligence in video understanding, comprising human-verified questions derived from 6,790 minutes of synthetically generated video. It requires...

Latent diffusion modeling (LDM), a prominent paradigm, utilizes tokenizers to map input signal to compressed representation. This dependency positions tokenizer as an integral part of generation process itself, since it affects learning speed, quality of synthesized samples and lay foundation for later applications. This report presents series of KVAE tokenizers for audio, image and video, all designed for subsequent text-conditioned generation: KVAE-Audio, a continuous full-band 48 kHz tokenize...

Economic World Models (EWMs) are generative economic models that simulate how economies evolve from within by modeling heterogeneous agents, their beliefs and actions, and the market and institutional mechanisms through which their interactions produce aggregate outcomes. This paper develops an implementation roadmap for building economic world models as generative engines in which heterogeneous agents act, interact, adapt, and co-evolve with markets and institutions, thereby producing economic ...

Multimodal large language models excel at passive perception but struggle with complex visual cognitive tasks requiring multi-step temporal reasoning. This degradation largely stems from the inherent ambiguity of language-based reasoning, which often fails to accurately articulate continuous visual transformations. To address this, we propose ChronoVision, a multimodal framework designed to align visual logic with latent imagery. During supervised fine-tuning, a Reconstructive Visual Head predic...

Classical continual learning (CL) has primarily focused on enabling models to update and retain knowledge through parameter-centric mechanisms, e.g., training strategies, architectural designs, and weight adaptation. However, emerging paradigms are reshaping the scope of CL beyond this traditional model adaptation view. For instance, on-policy learning broadens the space of update mechanisms; test-time training extends CL from the training phase to inference; and external harness components such...

Video object removal must eliminate not only the target object but also its induced effects while maintaining high-fidelity and spatiotemporally coherent restoration. Existing methods mainly learn object-effect correspondences implicitly from predefined effect categories and fixed data distributions, limiting their generalization to complex real-world scenes involving compositional effects, spatially detached or weakly correlated effects, long-tail physical phenomena, and dynamically evolving in...

Computer-use agents pay full frontier inference to re-derive routines their user has already performed, because an agent's memory today records what the user said, not what the user did. We compile passively captured screen activity into agent memory with a deterministic, zero-model pipeline: it segments a local capture stream into typed activity frames, bounded episodes carrying application, site, timing, input volume, and evidence pointers back to the raw rows, with no model in the loop, so th...

Training terminal agents requires executable and verifiable tasks that are not merely solvable, but appropriately challenging for learning. Executable validation establishes feasibility, yet does not reveal how a task behaves relative to a given solver setting. In this paper, we present CalibForge, an autonomous terminal-task synthesis system that uses verified solver behavior to revise candidate tasks through adversarial solver calibration. Multi-solver calibration targets disagreement within a...

We present MameLoshnLM, the first open-source 8B-parameter language model built specifically for Yiddish. Despite Yiddish's rich textual tradition, its limited digital presence and the scarcity of reliable evaluation resources have constrained progress in Yiddish language modeling. Existing multilingual corpora and benchmarks are often poor proxies for the language, containing substantial amounts of noisy, machine-translated, and misclassified text. We address these gaps by introducing Oytser, a...

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 cues, leading to confusion among semantically similar candidates. Recent methods mitigate this limitation by generating Chain-of-Thought (CoT) rationales to enrich the query representation. However, s...

End-to-end document parsers provide a unified interface, but serialize page layouts and regional contents into one autoregressive sequence. This formulation forces independent regions onto a decoding path whose length grows with the total content, whereas crop-based two-stage parsers expose region-level parallelism at the cost of repeated visual prefills and fragmented page context. To retain full-page context while removing dependencies, we propose PaDoc, a layout-grounded parser that treats th...

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 limitation. Inspired by multiplayer game architectures, MAS disentangles world dynamics and view rendering. A learned Logic Engine advances a global, authoritative typed state from joint actions without...

Reinforcement learning (RL) with verifiable rewards constructs trajectory-level advantage estimates, yet it often fails to credit the few pivotal decisions that determine outcomes in long-horizon, multi-turn agentic tasks. Recent work introduces privileged self-distillation for credit assignment, providing denser supervision, but it remains unclear how such local signals should represent sequential credit. We propose AgentOPSD, a critic-free, recursive method for turn-level credit assignment in ...

AMD's acquisition of Taalas focuses on enhancing AI inference performance by embedding machine learning models directly into silicon hardware. This move represents a strategy to optimize AI model execution at the chip level for improved efficiency.

Oracle has implemented a ban on AI-generated code contributions to OpenJDK, reflecting concerns about code quality and intellectual property in open-source development. This policy aims to maintain standards and ensure human review of all contributions to the Java platform.

USA Today Co. and its partners have begun collaborating with Palantir to leverage AI-driven analytics for audience data analysis and insights. This partnership aims to enhance content strategy and audience engagement through advanced data intelligence.

xAI and SpaceX are positioning themselves as major competitors in the AI infrastructure buildout, with significant investments in computational resources and satellite connectivity. This partnership highlights the race among tech companies to establish dominance in AI infrastructure development.

Memory capacity for 2027 has reportedly sold out, indicating unprecedented demand for computational resources driven by AI model scaling and deployment needs. This supply constraint may impact the ability of companies to deploy and train large AI systems.

Discover how HSP GRUPPE uses ChatGPT Enterprise to boost productivity, improve work quality, and create more capacity for tax advisory and client service.

This video explores the conflict between OpenAI and Hugging Face, examining tensions in the AI community around model sharing, licensing, and commercial interests. The incident highlights broader debates about open-source AI development and corporate responsibility.