Harvey is using GPT-6 Astra to bring more legal context into drafting, improving document formatting and context awareness so lawyers can focus on strategy.

OpenAI case study: invideo uses GPT‑6 Astra to plan complex video edits, boost color grading success rate by 3x, and create 50 custom effects in a single day.

Trump is set to meet Xi Jinping later today, with AI high on the agenda during a three-day visit. Separately, Bernie Sanders and Greg Casar are unveiling a bill to ban artificial superintelligence and create a federal AI oversight agency. UN chief Guterres urged US-China AI dialogue akin to Cold War hotlines.

Patreon co-founder Sam Yam is joing OpenAI to lead Creator Product. | Image: The Verge OpenAI has hired three former Patreon execs to anchor its product strategy for creators. After starting the creator subscription platform 13 years ago, co-founder and technology chief Sam Yam announced on X that he's joining OpenAI to lead Creator Product. He's also bringing Patreon's former product head Drew Rowny and engineering head Shannon Ma with him on the new venture, both of whom stepped down in the past few weeks. "We're going to build together with Creators at OpenAI and share early access to a new set of tools that I think will be critically valuable to Creators and their communities," Yam said in his announcement. "Pay attention … Read the full story at The Verge.

IntellAgents.io has surfaced on Product Hunt with a one-line pitch: a single AI agent for every call, chat, and DM. The listing currently offers little beyond that positioning statement and a discussion link.

Ask most engineers what MCP is and you’ll get the same answer: a way to plug tools into an LLM. Fair enough, as far as it goes. But that description treats MCP like plumbing, and after months building MCP-based integrations for large enterprise platforms, I don’t think plumbing is the right metaphor. Plumbing moves water […]

OpenAI has released MentalHealthBench, an open benchmark co-created with more than 80 licensed mental health experts from 22 countries to evaluate how AI systems respond in realistic mental health conversations, spanning non-acute, high-acuity, and emergency scenarios and covering adults, teens, caregivers, and clinicians.

PixVerse R2 has surfaced on Product Hunt as a real-time world model that users can explore and change. Public details are still limited to a short positioning line.

Nokia's applied research team has open-sourced AnyJev, a Python library that turns an open LLM into a calibrated decision model without training. It reads next-token probabilities to answer typed choice, yes/no, and score questions, using cyclic shifts and prior correction to reduce position and label bias. On Qwen3-8B with BANKING77, L0 cut the order-flip rate from 0.230 to 0.073, and L1 raised auto-decidable traffic at 5% error from 7.7% to 52.0%. It is Apache-2.0, on PyPI, with Hugging Face and vLLM backends.

Lisen is a product listed on Product Hunt that offers free read-aloud, with voices powered by Cartesia. Public details are limited to the product name, that short description, and links to discussion and an external link.

Anthropic shipped Claude Opus 5.5, the first model in its new Claude 5.5 family, claiming Fable 5.1-level capability at 40% lower running cost than Opus 5 with more speed and better writing. OpenAI released GPT-6 Sol and Luna about an hour later at 50% lower pricing than GPT-5.6. Opus 5.5 becomes the default in Claude Code and the Claude app, but independent cost analysis shows higher token usage at max effort largely cancels the discount.

Kyutai has released Voice of Reason, two open-weight speech-to-speech models built on GLM-4-Voice-9B that combine supervised fine-tuning and reinforcement learning to lift spoken GSM8K accuracy from 27.3% to 77.1%. There is no transcription step and no text LLM in the loop, and both checkpoints run on a single H100.

OpenAI has launched GPT-6 Sol and GPT-6 Luna, lower-cost API-only models sitting below GPT-6 Astra. Sol is priced at $2/$10 per 1M tokens and Luna at $0.10/$0.50, half of GPT-5.6 promotional rates. OpenAI also published benchmark results and improved prompt caching for long-running agents.

UK Prime Minister Andy Burnham has announced that security chiefs will establish a National Centre for Information Defence to counter disinformation and AI deepfakes from hostile states such as Russia, bringing together intelligence agencies, law enforcement and social media companies to detect, attribute and disrupt foreign information attacks.

LaterOn v2 is an agentic email platform that lets users describe an email job once and have it handled automatically.

SpeakON launched a 25 g MagSafe AI voice button with its own mic, battery, and storage that writes processed text directly into any iPhone text field via an iOS keyboard extension. It supports offline buffering, text shaping features, and 12-language translation, priced at $129 one-time in the US, with SpeakON Agent coming in October 2026.

An arXiv robotics preprint presents two angular-momentum-based measures derived from a one-year longitudinal study of a Karate roundhouse kick, comparing stable and unstable kicks and an expert instructor to probe dynamic rotational stability for human motion and robots.

This paper introduces a capability-aware shared-control framework in which a vision-language model (VLM) infers human intent and supplies semantic-intent confidence, while a vision-language-action (VLA) policy generates autonomous actions and its capability confidence is estimated online from the dispersion and local instability of stochastic action trajectories. A nonlinear arbitration policy combines Bayesian-filtered intent confidence with VLA capability confidence via a sigmoid mapping to adapt robot authority. In a 12-participant study, the method reached a 92% task success rate, beating manual teleoperation (83%), intent-only arbitration (44%), and fixed equal-weight blending (10%), while also improving control friendliness and reducing authority-weighted disagreement.

HOTICE is a whole-body humanoid learning framework for transporting objects through cluttered environments. It introduces Humanoid-Object Decoupled Potential Fields to jointly encode collision-avoidance guidance for the robot and the carried object, and a dual-agent reinforcement learning architecture that decouples upper- and lower-body control while preserving whole-body coordination via shared state observations and rewards. A specialist-to-generalist distillation strategy yields a single deployable student policy. Evaluated in MuJoCo and on a real Unitree G1, HOTICE transports varied object shapes, generalizes to unseen cluttered scenes, and achieves strong sim-to-real performance.

Researchers introduce PROACT, a framework that folds predictions of human collaborative behavior into compliant whole-body control so a robot can help relocate heavy objects efficiently while staying physically responsive to its partner. Across 108 real-world trials, it cut interaction work and completion time substantially.

GINIO is an SO(3)-equivariant interface for neural inertial odometry that ensures learned motion measurements transform correctly as a vector and a second-order tensor under arbitrary IMU mounting rotations. It introduces Last-Frame Alignment for sensor-frame training and separates sensor-local states such as IMU bias, yielding large ATE improvements on TLIO, NanoBench, and Fetch, including physical-remount robustness without retraining.

Researchers propose JAMB, a diffusion policy that jointly denoises bimanual actions and future 3D point tracks within a shared Transformer. By grounding multimodal representations in a common spatiotemporal coordinate system, the model lets action and motion hypotheses refine each other during denoising. In RoboTwin 2.0, JAMB reaches 83.4% average success across 16 tasks, beating the strongest baseline by 23.9 points; on three real-world tasks it beats action-only and auxiliary geometry prediction methods by 50.0 and 21.2 points, with better generalization to clutter and out-of-distribution backgrounds.

A new arXiv paper proposes a reinforcement learning approach to automatically generate multimodal interaction policies for robot assistants, using a simulator trained on human data and a simple high-level reward. A real-world human study found high usability and effective task completion, suggesting a scalable and interpretable alternative to hand-crafted interaction managers.

The paper abandons the long-standing flat-seafloor approximation and instead builds on multi-view geometry, formalizing and proving a two-view constraint in which a shared feature is confined to a locus at the intersection of a sphere and a plane. Monte Carlo simulation characterizes how long that ambiguity locus becomes, and the results are translated into survey-planning guidance for surface vessels and underwater vehicles.

This paper introduces a separated-section constitutive law for Cosserat modeling of trimmed helicoid soft arms, correcting the inaccuracy of traditional cross-section stiffness summation. The approach evaluates each helix domain in its local frame and pulls its response back to the backbone, while sparse-fusion mechanics captures additional compliance from relative motion between domains. The resulting effective sectional stiffness is highly anisotropic—bending and extension reduced by about an order of magnitude, torsion nearly unchanged. Embedded in a geometrically exact dynamic Cosserat model with GVS discretization and routed-tendon actuation, it achieves pooled normalized position errors of 7.7%, 6.7%, and 7.8% across 103 measured configurations, with full-arm solves in about 0.3 s o…

A paper accepted by IEEE Transactions on Systems, Man, and Cybernetics: Systems proposes using deep reinforcement learning to let a mobile robot dynamically shift its tracking position while accompanying a walking person, pairing Model Predictive Path Integral control with Control Barrier Functions for precise following, obstacle avoidance, and safety. In real indoor and outdoor trials, the approach raised success rate and tracking accuracy by at least 24% and 47% respectively, and the robot accompanied a person walking at up to 1.7 m/s while improving human comfort.

This arXiv paper introduces DTV-INR, a variational framework that pairs a SIREN-based coordinate network with an anisotropic, structure-tensor-informed total variation regularizer for resolution-agnostic super-resolution. The authors prove well-posedness of the formulation in H^1(Omega) and solve it with an alternating projected optimization scheme that decouples network tuning from adaptive tensor-field updates. On clinical brain MRI and biomedical transmission electron microscopy, the method reports PSNR gains up to +5.05 dB over unregularized INRs and robust performance under noise.

This study uses a public footwear outsole impression dataset to compare CNN transfer learning with traditional feature-based classification for binary sex estimation. A shoe-level train/test split keeps replicate scans of the same physical shoe together to reduce data leakage. Fine-tuned CNNs achieve the strongest overall performance and substantially outperform traditional classifiers using manually specified descriptors alone, while frozen-feature approaches offer a lower-computation alternative. Exploratory analysis links low-dimensional CNN representations to frequency threshold ratio, image contrast, and wavelet-based summaries; further validation on independently collected and casework-like impressions is needed before operational use.

MirrorDistill is an illumination-aware latent distillation framework for low-light image enhancement. It uses feature mirroring between low-light and clean domains during training, then deploys only a lightweight student encoder-decoder at inference. The method reports state-of-the-art results on LOL-v2-Real with the lowest GMACs and competitive performance on LOL-v1 and LOL-v2-Synthetic.

The paper proposes a 3D residual wavelet diffusion model that combines lossless wavelet reparameterisation, residual shifting and domain randomisation to enable whole-brain posterior sampling on a single GPU, producing per-voxel uncertainty maps for 0.064T ultra low-field MRI while matching a leading regression baseline on volumetric accuracy.