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Kimi K3 demonstrates competitive performance with Fable, with both models being considered state-of-the-art in their respective evaluations and capabilities.

Google has deprecated temperature, top_p, and top_k parameters in its latest Gemini models, meaning these sampling parameters will be ignored in future versions.

One command gives your Hermes agent social intelligence. It decides when to speak, adapts to your group's tone and remembers who said what. Works in group chats on Slack, Telegram and WhatsApp.

Buzzy is the first truly agentic infinite canvas for video creation. It helps you spark ideas, build moodboards, and precisely photoshop your video in one click — while creating unlimited-length blockbuster films and commercial ads, keeping unlimited subjects consistent, controlling lighting and camera angles, and precisely editing video like photoshop in one workspace.

Kastra is the runtime authorization layer for AI agents. It decides what agents can and cannot do before actions execute, enforcing policies with sub-1 ms latency across tools, prompts, inputs, and outputs. Use one control plane to govern agents and policies across Claude Code, Cursor, Codex, OpenClaw, the Anthropic SDK, the OpenAI SDK, and more. Prevent unauthorized tool use, prompt injection, and exposure of sensitive data before they become incidents. Trust the rules, not the agents.

Box offers the simplest, cheapest cloud computers for agents, thought for builders of agentic platforms & software factories. Run 'box new' in your terminal, in 2s get a beefy ubuntu VM, with admin rights, a desktop, ssh access. At $0.036/hr it is 10x less expensive than the likes of E2B, Daytona, Modal, so you can run more agents, or run them longer. Run up to 1000 concurrent boxes fully self-served, or ask us for more, with same-day support from the founders.

ACME.BOT turns your expertise into no slop SEO articles. Answer a few questions on a topic (from your phone) and your first-hand experience shapes your blog post. AI handles the boring stuff. Like keyword research, illustrations, references, internal linking, and publishing cadence. Create helpful non-commodity content that also satisfies Google's EEAT requirements.

We’re introducing new Gemini models, including Gemini 3.6 Flash, 3.5 Flash-Lite and 3.5 Flash Cyber. Our newest Gemini models deliver the efficiency, latency, and reliability to build AI agents at scale.

A floating macOS window for every Claude Code session. The moment one needs your input, it surfaces — and hides when all is clear. Jump to the exact terminal in one click. Native Swift, notarized, 7-day free trial.

Three years ago, we launched Lattics as a "brain-like" knowledge management tool for writers and researchers. Today, we're back with something much bigger — AI-assisted writing that's deeply integrated into your research and creative workflow. 30% OFF DISCOUNT for Produnct Hunt, Limited for 50 Please check the discount code in the link :https://lattics.com/specialoffer/7fGKwQZEhV, You will get 30% discount code of Lattics Pro

Your AI agent belongs in the chat you already built. CometChat's iOS UI Kit lets you turn your existing messaging experience into an AI conversation with streaming responses, chat history, and a fully customizable UI built in. Connect your agent, match your brand, and ship without building AI chat from scratch.

Introducing OpenAI Presence, a proven enterprise AI agent platform that helps organizations deploy trusted voice and chat agents for customer and internal workflows.

OverpAId presents an AI-driven solution positioning itself as a replacement for traditional CEO decision-making, automating executive functions with artificial intelligence.

GigaToken introduces a new approach that achieves approximately 1000x faster language model tokenization, significantly improving the speed of processing text into tokens for AI models.

Evaluating the factuality of long-form generations has focused predominantly on precision, measuring whether the claims a model makes are correct. The dominant decompose-search-verify pipeline catches incorrect claims well but says little about whether a response contains all the information it should. Measuring factual completeness, the missing half of factuality, is harder: it requires enumerating the full set of facts a complete answer should contain, and these facts rarely form a flat list. ...

Asynchronous reinforcement learning improves throughput by decoupling rollout generation from optimization, but staleness is an inevitable byproduct compounded by policy lag, engine delays, and mixture-of-experts routing. From a trust-region perspective, this mismatch is critical: training-inference divergence governs approximation error in finite-horizon bounds, whereas PPO clipping only gates sampled outward updates, acting as a sampled surrogate rather than a full-policy constraint. As a resu...

Reinforcement learning with verifiable rewards (RLVR) has substantially improved the reasoning capabilities of large language models on tasks such as mathematical reasoning and code generation. However, most RLVR methods assign a scalar outcome reward to an entire trajectory, resulting in sparse supervision and limited token-level credit assignment. On-policy distillation (OPD) provides denser supervision by distilling token-level distributions from a stronger teacher model, but requires an addi...

Controllable image generation remains challenging for creative professionals, who often require precise regional control over materials, object identities, and spatial arrangements that cannot be reliably achieved through text prompting alone. Diffusion Transformers (DiTs) can natively ingest heterogeneous tokens stemming from texts and images, but they lack mechanisms for determining where and how these tokens should influence the output. We introduce appearance pointers, compact tokens that gu...

Scaling executable agent training data for LLM post-training is bottlenecked by substrate-bound methods that tie task generation to predefined tools, repositories, or skill graphs: expanding coverage requires manual substrate engineering, each new domain demands a bespoke pipeline, and the resulting task distributions often reflect substrate biases rather than real-world demand. We introduce NexForge, a requirement-driven framework that takes high-level capability requirements as input and synth...

Optimizer state is the largest single line item in the memory budget of mixture-of-experts (MoE) training: on a 6.78B-parameter MoE language model, AdamW keeps 50.6 GB of first and second moments to update 12.6 GB of bfloat16 weights. We study SkewAdam, an optimizer built on the observation that the three parameter populations of an MoE - the dense backbone, the experts, and the router - differ enough in size and gradient statistics that they should not receive the same state. SkewAdam keeps flo...

Text-to-image diffusion transformers (DiTs) jointly process text and image tokens, yet their internal computation during denoising remains poorly understood. We introduce a causal interpretability framework for modern large-scale DiTs that combines attention decomposition with targeted interventions across token spans, heads, and layers. Using it to separate prompt-content tokens from structural template tokens, we find that the structural tokens carry little prompt-specific information at the e...

Video models absorb rich priors over how the visual world moves, interacts, and responds to contact, making them promising substrates for robotic world modeling. The central challenge is how to communicate action to such models in a form aligned with the visual space in which they learned these interaction priors, yet still grounded in physical manipulation. We introduce Masked Visual Actions, a pixel-space control interface that expresses action as a partially revealed trajectory of an arbitrar...

LLM agent failures are difficult to debug because the step where an error surfaces is often not the one that caused it. Existing observability tools replay execution traces but provide little support for identifying the root cause or translating diagnosis into recovery. We present AgentDebugX, an open-source debugging framework that organizes debugging as a closed loop of Detect, Attribute, Recover, and Rerun. At its core, DeepDebug performs multi-turn root-cause diagnosis through global traject...

We present ABot-World-0, an action-conditioned video world model for real-time, long-horizon closed-loop interaction, supported by a multi-source data infrastructure spanning AAA games, simulation engines, and internet videos to learn controllable world dynamics. WorldExplorer performs agent-driven collection guided by training feedback, while a unified pipeline applies 14 deterministic quality checks, VLM-based assessment, and synchronized action and text annotation. We progressively distill a ...

Multimodal humor in memes, cartoons, and comics remains difficult for AI systems because intended meaning depends on non-literal mechanisms, shared cultural knowledge, and communicative intent rather than literal scene description. This survey focuses on visual humor understanding in single-image and multi-panel artifacts, while treating humor generation as an emerging downstream frontier. We position the literature against prior humor, sarcasm, and general MLLM surveys and organize it using a c...

Modern ASR models trained on heterogeneously annotated data treat transcription style (verbatim vs. intended) as an uncontrolled latent variable, causing measurable decoding instability, evaluation confounding (up to 60% of reported WER attributable to style mismatch), and unreliable word-level timing. We show that models already encode both styles; the challenge is controlled activation. Using coverage-aware decoder task tokens trained on parallel verbatim/intended transcript pairs, we raise Ge...

Reinforcement learning with verifiable rewards (RLVR) is rapidly advancing the reasoning capabilities of language models, yet the optimization layer that converts reward feedback into weight-space updates remains poorly understood. Building on our prior analysis (Zhu et al., 2025), we study this missing layer through the singular structure of model weights and identify spectral inheritance: RLVR can reuse the base model's weight spectra while acquiring new behavior through changes in the associa...

Accurate agricultural field boundary delineation at large scale is a foundational task for food security, supply chain transparency, and carbon accounting. While vision foundation models like SAM show remarkable zero-shot capabilities, they frequently fail in geospatial domains due to topological complexity, cropland texturing patterns, and a lack of physical scale awareness. In this work, we introduce Delineate Anything v2, a globally scalable foundation model designed specifically for wide-are...

Large-scale visual generators are increasingly capable but costly to train, fine-tune, and deploy. We introduce Mage-Flow, a compact 4B-scale generative stack for efficient text-to-image generation and instruction-based image editing. The stack is built from two co-designed components: Mage-VAE, a lightweight high-fidelity latent tokenizer, and a Native-Resolution Multimodal Diffusion Transformer trained with rectified flow matching. Mage-VAE uses one-step diffusion-style encoding and decoding w...

OpenAI and Hugging Face jointly addressed a security incident that occurred during model evaluation, disclosing the breach and coordinating response efforts.

A judge has approved a $1.5 billion settlement against Anthropic for allegedly using pirated books in the training data for Claude, addressing copyright infringement concerns.

A survey reveals that most Americans oppose hosting AI data centers in their communities, citing concerns about energy consumption, environmental impact, and local disruption.

Five major technology companies are reportedly concealing $1.6 trillion in AI-related debt through accounting methods similar to those used in the Enron scandal.

Google commits $40M in AI tokens and credits for the Genesis Mission

OpenAI announces Project Camellia in Effingham County, Georgia, with commitments to responsible energy, community investment, jobs, and access to Codex.