Some reports are awaiting translation or analysis and are labeled as source content. Highlights prioritize completed reports.
Live monitoring
Trusted sources, attribution, rights, and in-site reading distilled into a signal-first AI brief.
A judge late Thursday permanently barred the Trump administration from enforcing a set of rules aimed at cutting off Anthropic from the federal government, ruling the administration had unconstitutionally punished the a…
This article is sponsored by Aquant and was written, edited, and published in alignment with our Emerj sponsored content guidelines. Learn more about our thought leadership and content creation services on our Emerj Media Services page. Field service organizations are losing expertise faster than they can capture it, a measurable operating risk, not only a […]
Company Memory Should Not Live in Chat Earlier this month we changed who our cold outreach speaks to. It took three edits to one file. By the afternoon, every agent drafting an email for us was writing to the new reader…
Uh oh! There was an error while loading. Please reload this page. Notifications You must be signed in to change notification settings Fork 1 Star 8 BranchesTags Open more actions menu Latest commit History 178 Commits 1…
Post Log inSign up Post Gabor Herget on X: "New Studio M5 Ultra ready for local home AI lab? I compared it to OpenRouter prices. 512GB will be available from October, and I guess that the price will be around 15-17k. Le…
About 80 people sign open letter to Andy Burnham calling for legislation to protect voice ownership Nicola Coughlan, Hugh Bonneville and Matt Lucas are among a raft of actors backing a campaign against artificial intelligence (AI) voice cloning. Save Our Voices Now, which is also being supported by Luke Evans, Jen Brister, Siobhán McSweeney and Pearl Mackie, aims to stop the practice in which AI technology is used to replicate a real person’s voice to say anything it is prompted to. Continue reading...
Notifications You must be signed in to change notification settings Fork 0 Star 1 BranchesTags Open more actions menu Latest commit History 8 Commits 8 Commits Folders and files NameName Last commit message Last commit…
Discussion | Link
It’s Time. We’re in the Endgame now.
Full game tester Game Studios spent $1.7B a year on playtesting. Don't be one of them. Fully playtest your game with AI in minutes: Ziva's playtest agent is able to fully complete games, can run dozens of instances in p…
By: Quintin DiStefano Video game characters may be fictional, but the actors portraying them are real people. Every line of dialogue and every swing of the sword is crafted by voice and motion-capture actors whose perfo…
Discussion | Link
Luanti’s Android app is currently not available on the Google Play Store due to a baseless DMCA notice filed on behalf of Microsoft by Tracer.AI, alleging that Luanti infringes Minecraft’s copyright. The Luanti app does…
Exclusive / AI writing has already begun to appear on the opinion pages Aug 26, 2026, 9:48pm EDT TechnologyMedia Illustration/Jake Angelo/Semafor PostEmailWhatsapp The Scoop Humans are still writing the vast majority of…
Tencent Hy
Our decision to wind down our contract providing OpenAI models to Cursor following its acquisition by SpaceX.
For all the interest in and uptake of agentic AI systems over the past year or so, the world of automated AI has thus far been primarily limited to text, images, code, and other data and actions that take place inside a…
Google has released Gemini 3.5 Transcribe, a speech-to-text model that ships as two separate endpoints rather than one. The streaming endpoint delivers sub-second transcription but drops speaker diarization and word timestamps. The batch endpoint keeps both, at half the cost. Google reports 4.0% word error rate streaming and 2.6% non-streaming, with 70% faster finalization than Chirp 3. Here is what the split means for anyone building voice agents or transcription pipelines. The post Google AI Releases Gemini 3.5 Transcribe: A Speech-to-Text Model Reporting 2.6% Average WER Across 85+ Languages appeared first on MarkTechPost.
In a divided America, the left and right unite to oppose artificial intelligence data centers 1 of 5 | Protesters gather outside the New Mexico Environment Department to voice opposition to the Project Jupiter data cent…
Pricing $9.99/month. Free trial available. Free trial available Rating 0.0 (0 Reviews) Developer MoonImpact.co Install View demo store Featured images gallery Build product price calculators for size, area, length, weig…
arXiv:2608.26622v1 Announce Type: new Abstract: Achieving stable, sustained grasping with soft robotic hands remains a fundamental challenge. Compliance enables safe and adaptive contact, yet the intrinsic viscoelasticity of soft polymers leads to stress relaxation and a continuous decay of grasping force during holding. Inspired by human grasping, which combines phase-dependent stiffness regulation with continuous sensing and feedback, this paper presents an integrated structure--perception--learning framework. We develop a variable-stiffness soft gripper that uses onboard vision and infrared thermography to track deformation and the temperature field in real time, preserving continuous tracking of the interaction state. To mitigate relaxation-induced force decay, we propose a temperatur…
arXiv:2608.26583v1 Announce Type: new Abstract: Humans traverse complex terrain over long distances without losing balance, whereas perceptive humanoid policies become fragile as perception and control errors accumulate. We present SOLO, a unified framework addressing two compounding causes of this long-horizon fragility: dense terrain reconstruction smooths action-critical details, and pointwise imitation lacks temporal credit assignment. Its Query Reconstructor (QR) uses Fourier-encoded cell queries to retrieve spatially specific evidence from depth-proprioception tokens, preserving sharp terrain boundaries. Trajectory-Aware MSE (TA-MSE) Distillation adds next-state teacher-student disagreement to the PPO reward, enabling Generalized Advantage Estimation to propagate future disagreement…
arXiv:2608.26578v1 Announce Type: new Abstract: This work introduces Configured Failure Trapping, a novel backdoor attack task against Vision-Language-Action (VLA) models, which aims to activate attacks through stealthy textual triggers and induce configured failure modes. Unlike prior backdoor attacks that treat any task failure as a successful attack, Configured Failure Trapping requires the attacker to control how the robot fails (e.g., causing the robot to grasp with a specified positional offset), making it substantially more challenging and hard to detect. To support the new task, we propose an effective data engine for synthesizing high-quality target trajectories and an automated suite for measuring configured-failure fidelity. Then, based on this foundation, we construct two new…
arXiv:2608.26545v1 Announce Type: new Abstract: Robot policies deployed in the wild should have the capability to continually learn new tasks without forgetting existing behaviors. A common approach to combat such catastrophic forgetting is to train on new task data with a replay buffer of previously learned task data. Although this buffer is commonly sampled randomly from all prior experiences, we show that a small set of these experiences contributes greatly in anchoring past performance. We call these experiences Memory Anchors. We identify Memory Anchors in regions where representations of new-task observations collapse onto those of old-task observations even though the tasks require conflicting actions, like when a familiar object must be manipulated in a new way. Rehearsing old dat…
arXiv:2608.26505v1 Announce Type: new Abstract: The Poppy Humanoid is an open-source, low-cost robot suitable for research and education in artificial intelligence. However, we are unaware of any published methodology that achieves reliable, unassisted bipedal locomotion on the standard Poppy hardware. This paper contributes a functional closed-loop walking controller for Poppy, based on the linear-quadratic regulator (LQR) framework for trajectory tracking. Starting with data collected from open-loop playback of a nominal walking trajectory, our proposed method learns a quadratic cost function for an LQR controller that substantially improves the reliability of the motion. The closed-loop controller is validated empirically, demonstrating statistically significant improvements in walking…
arXiv:2608.26496v1 Announce Type: new Abstract: Navigation in unknown environments to find unforeseen objects has become increasingly feasible with capable vision and language foundation models. However, these models also introduce non-negligible inference latency, which becomes an important concern when agents must operate continuously in the real world. Most state-of-the-art methods are still developed in synchronous simulators, where the environment waits for the agent to act and inference time is effectively free. As a result, agents are often designed around the sequential execution of perception, reasoning, and action, with little regard for time constraints. Under real-time execution, where wall-clock time counts towards the task budget, the inefficiencies of these architectures be…
arXiv:2608.26383v1 Announce Type: new Abstract: Autonomous robots performing laboratory tasks depend on 3D reconstruction pipelines that can turn raw camera streams into actionable object representations within the latency budget of a physical control loop. Neural 3D reconstruction methods have demonstrated high-quality view synthesis, but their real-time viability across the compute platforms on which laboratory robots actually run remains poorly characterized. In this work, we present a systematic compute-platform benchmark of neural 3D reconstruction methods, evaluating NeRF and 3D Gaussian Splatting training and rendering on GPU-enabled computing devices ranging from single-board computers to server-class nodes, and place Meta's SAM3D single-image reconstruction on the same axes to qu…
arXiv:2608.26314v1 Announce Type: new Abstract: Steering-based planners require solutions to state-to-state boundary value problems, which can be inaccessible for nonlinear platforms. Forward propagation evades the steering requirement, but the finite-sample behavior of the associated planners remains uncharacterized and their implementations underperform in practice. This paper develops a propagation-based kinodynamic planner with deterministic finite-sample near-optimality guarantees. We work within the large class of differentially flat nonlinear systems and show that a forward tree of locally dispersive control commands contains a near-optimal trajectory at a certified tree size. We provide a general mechanism to construct dispersive command sets for control-affine systems, which are…
arXiv:2608.26273v1 Announce Type: new Abstract: Static shape estimation of co-manipulative continuum robots (CCRs) is challenging because the continuum arms and manipulated flexible object form a closed chain that must satisfy both static equilibrium and geometric loop-closure constraints. This paper presents a constraint-aware physics-informed neural network (PINN) for static shape estimation of a tendon-driven CCR modeled using the geometric variable strain formulation. The proposed method incorporates a projected static equilibrium residual and a configuration-level geometric residual to enforce the governing mechanics and closed-chain geometry. In simulation, the PINN is compared with a purely data-driven artificial neural network (ANN) under limited and noisy training data. With 140…
arXiv:2608.26239v1 Announce Type: new Abstract: Generative world models provide robots with predictive models of how the world evolves under interaction, with growing potential for simulation, planning, policy evaluation, and robot learning. Beyond clip-level future prediction, a unified generative formulation should relate actions to consequences, support flexible horizons and continuous interaction, and enable reward-driven optimization. We introduce WALL-SS, a world model that generates visual futures through Scale-wise autoregressive Scaling, enabling action-controllable and long-horizon robotic simulation. WALL-SS represents embodied trajectories as causal sequences of temporally interleaved observations and actions, making action-dependent state transitions explicit while naturally…