AI News

Swarmer, which has developed swarm software, plans to work with Ratel Robotics' unmanned ground vehicles in Ukraine.

Final 12 months, China invited lots of of humanoid robots to compete on the planet’s first Robotic Olympics. And it was onerous to not giggle at a number of the movies popping out of Beijing. Robots stumbled down the observe, crashed into each other and collapsed in the course of occasions. Some seemed like they

Hermes Agent now displays detailed information on all subagent activities live, and you can steer and stop them manually from the CLI and Desktop Application.

AuK Technical Report

An Open-Source Foundational Model for Speech Generation and Editing

paper: https://t.co/tCsaoA5w6o

Speech large language models (SpeechLLMs) offer reduced latency and retain paralinguistic nuances that are typically lost in cascaded automatic speech recognition (ASR) and text-based LM architectures. However, they continue to lag behind text-only LLMs on complex reasoning tasks, while real-time spoken interaction imposes strict latency constraints. Although prior works employ Chain-of-Thought (CoT) and concurrent reasoning to enhance reasoning capabilities without inducing prohibitive delays, an inherent accuracy-latency trade-off persists. In this paper, we investigate whether a streaming SpeechLLM can dynamically revise its reasoning traces on the fly. We introduce RetroThinker, a multi-stage post-training framework that equips the Moshi model to self-verify and forward-correct CoT steps during inference. RetroThinker combines supervised fine-tuning (SFT) on curated retrospective thinking data with length-based direct preference optimization (DPO) to optimize retrospective during early reasoning (i.e., reasoning concurrently while the user speaks). Evaluated on the GSM8K benchmark, RetroThinker significantly improves the accuracy-latency trade-off over non-retrospective baselines, achieving an 11% absolute accuracy gain at a comparable latency.

Counterfactual regret minimization (CFR) is one of the few large numerical workloads that still runs faster on CPUs than on GPUs. Each iteration sweeps a game tree with up to billions of states in millions of small, interdependent gather and scatter steps issued through a generic tree interface. On a GPU every kernel finishes in microseconds, so kernel launches and framework dispatch dominate the run time, and prior GPU implementations have lost to optimized CPU code. We observe that for a fixed game, everything about a CFR iteration except the numerical values is known before the first iteration runs. We propose GPU-CFR, a compiler and runtime built on this observation. It compiles any game once into static dataflow: flat edge and information-set arrays, precomputed indices, and depth-level batched passes fix the entire operation sequence, and only solver state changes between iterations. Static chance folding, depth-level execution blocks, and a dual-lane reach buffer cut the number of framework operations by up to 18.1x. Because shapes, indices, and buffer addresses never change, CUDA Graph Replay records the iteration once and replays it with a single graph launch. On one A100, across an eight-game suite that spans card games, dice games, and board games, GPU-CFR runs 29.8--80.4x faster than the fastest prior GPU CFR on the same accelerator, and 14--258x faster than LiteEFG, one of the fastest open-source CPU implementations, on the four largest games. The compiled representation carries most of that margin: on eight CPU threads with no accelerator it is already 2.2--51.1x faster than the GPU baseline. On the CPU the optimized path reproduces the reference iterates bitwise, and tree construction and graph capture pay for themselves within the first solve. GPU-CFR beats every CPU and GPU baseline on the mid-to-large games of the suite without changing the update rule.

Huawei has recently informed some of its clients about the Ascend 950DT AI chip price increase. The company is significantly raising the cost of its most advanced semiconductor to deal with strong demand pressure in the market. Here are the details: Bloomberg reported that Huawei has increased the price of the Ascend 950DT AI chip […]

Now available: ChatGPT for Financial Services.

This is a tailored ChatGPT Work experience that combines built-in financial data with GPT-6 Astra’s reasoning.

Teams can develop research, build financial models, and create customized client materials.

https://t.co/6WP5OJdnE8

ROMA (ITALPRESS) – Più informazioni non significano necessariamente più conoscenza. E più connessioni non significano necessariamente più confronto. Da questa consapevolezza nasce Nookly.Italiae, la nuova piattaforma digitale sviluppata dalla collaborazione tra Nookly.News e Cultura Italiae – ONG UNESCO, che mette insieme intelligenza artificiale, informazione, cultura e competenze per sperimentare un nuovo modello di partecipazion

La startup italo-estone ignosti entra nell'AI Accelerator di Tehnopol a Tallinn, automatizzando il 90% dei task HR grazie alla tecnologia Pulse364.

Instinct’s new email feature lets the AI agent create and manage accounts, contact businesses, handle support requests, and do more on users' behalf.

Marigold V2

Revisiting Diffusion Transformers for Monocular Depth Estimation

paper: https://t.co/718T7p8wCZ

Editor’s note: This article was written by Lucia, a TechNode reporter. The first time many people saw a humanoid robot, it was probably doing something

Skild AI’s S1 robotic foundation model harnesses NVIDIA technologies spanning synthetic data generation, model training, simulation and real-world deployment.

thunderkittens is now running on nvidia vera rubin!

our kernels team got early access to nvl72 and spent the past few days digging through the new isa and bringing nvfp4 + fp8 gemms to life

after reworking the kernels for rubin, we pushed them past 22 and 12 pflops respectively — competitive with…

Spatial reasoning depends not only on metric properties such as distance, angle, and shape, but also on topological relations that remain invariant under continuous deformation. Cognitive science identifies these relations as foundational to spatial understanding, yet foundation-model evaluations largely focus on metric or viewpoint-dependent relations. We introduce MindTopo, a benchmark of topological intuition across five properties grounded in cognitive science and formal topology: continuity, separation, order, enclosure, and knots. MindTopo evaluates each property at two cognitive levels. Reasoning asks a model to identify topological relations or infer how they change. Planning instantiates a foundation model as a closed-loop agent whose policy selects environment actions. MindTopo contains 11,030 instances across 13 procedurally generated task types with controllable difficulty. We benchmark 14 MLLMs and study agent configurations augmented with image and video generation, including 3 video generative models in planning settings. Every MLLM performs better on reasoning than on planning, and the best-performing model remains far below observed human performance. On Qwen3-VL-2B-Instruct, supervised fine-tuning and reinforcement learning improve reasoning more than planning. Generated observations retain local cues and reach plausible endpoints, but audited rollouts do not reliably follow environment dynamics or preserve topology across transitions. Our website is at https://mind-topo.github.io/

We launch SenseNova-U1.5, an 8B-MoT native unified multimodal model that understands, reasons about, and generates visual content within an encoder-free and VAE-free architecture. We strengthen its visual interface through spatially coherent patch reconstruction and scale its training with carefully curated generation and editing data, improved task formulation, structural prompt enhancement, and native resolutions of up to 4K. For post-training, we optimize specialized experts for visual aesthetics, bilingual text rendering, infographic generation, and image editing, and consolidate their capabilities through multi-expert on-policy distillation. Across extensive evaluations, SenseNova-U1.5 largely advances image fidelity, text rendering, complex composition, multi-reference editing, and interleaved generation, while improving instruction following and preserving subject identity, geometry, and unmodified regions. Despite limited exposure to structured formats in its generation data, SenseNova-U1.5 generalizes effectively to long, complex, and structured visual instructions, further proving that multimodal understanding can transfer to visual planning and creation. Together, these findings position native unified modelling as a promising path towards systems that perceive, reason and create within a fully end-to-end framework. We will open-source training code, including supervised fine-tuning, reinforcement learning, and on-policy distillation.

Causal discovery aims to uncover causal structures from data and is fundamental to scientific reasoning and intervention-based decision making. Its evaluation relies heavily on structural causal models (SCMs), which specify a causal graph together with the mechanisms that generate data, yet existing studies differ substantially in graph families, mechanisms, and evaluation protocols. The emergence of causal discovery foundation models (CDFMs) further complicates evaluation: performance may reflect not only causal discovery ability, but also overlap between pretraining environments and test SCMs, making results on fixed synthetic benchmarks difficult to interpret. We introduce CausalArena, a unified and evolvable benchmark for causal discovery under a common protocol. Synthetic SCMs supply controlled breadth over structures and mechanisms; semantic operational SCMs provide human-auditable, semantically grounded environments beyond standard synthetic generators; and formula-grounded SCMs test discovery under explicit scientific mechanisms. Public real-world datasets provide an additional external-validity check. Experiments across classical, neural, and pretrained methods reveal substantial ranking shifts across SCM families and protocols, showing that strong performance in one benchmark regime does not reliably transfer to others. These results highlight benchmark diversity and pretraining--evaluation overlap as central challenges for evaluating causal discovery in the foundation model era.

Case studies from threat actors disrupted between December 2025 and August 2026 across seven areas of harm, from cyber operations to biological misuse.

Thrilled to see day-0 SGLang support for DeepSeek V4.1 Flash!

Technical blog: https://t.co/ooyWSOzr17

This is another leap for the DeepSeek family, with several new upgrades to the V4 stack: shared compressed KV, a two-stage sparse indexer, mHC, and Engram.

SGLang supports the new architecture o…

Oltre 50 milioni di profili sintetici per esplorare concept, campagne, pricing e strategie media

What if marketplaces rewarded the best product instead of the best marketing? OpenMarket (in research preview), is a multi-agent marketplace where sellers pitch, competitors challenge their claims, and independent truth agents verify the evidence as they compete to earn a buyer's sale. Here, claims don’t count just because they’re written on a product page. They have to survive scrutiny and be proven.

Mercury 2.5 is the most capable diffusion LLM on the market. It runs at 1,107 tokens/sec and offers a 40% increase in intelligence over Mercury 2, comparable to cost-optimized frontier models.

NeoHorse-1

Towards Recursive Self-Improvement via Agentic Post-Training with Routing Harness

paper: https://t.co/8Fe4IXknmm