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CHOREO: Every Humanoid Skill as a Trajectory
arXiv:2609.22274v1 Announce Type: new Abstract: Recent advances in humanoid robotics have produced diverse skills through reinforcement learning, motion imitation, and generative modeling. Yet these capabilities remain siloed because they are built around incompatible representations, interfaces, and controllers. We present CHOREO, a framework for training-free composition of heterogeneous humanoid skills. Our key observation is that, regardless of how a skill is learned, it can ultimately be expressed as an executable motion trajectory. Based on this observation, CHOREO converts each capability into SkillMotion, a unified representation that combines motion states, contacts, semantics, and boundary conditions. Skills are composed through direct continuation, cubic Hermite blending, or validated bridge motions, without retraining source models or updating models at test time. On Unitree G1 in MuJoCo, CHOREO organizes 2,950 admitted SkillMotion assets derived from heterogeneous sources
D3DWA: Adaptive Weight and Prediction-Horizon for Dynamic Window Approach via Dueling Double Deep Q-Network
arXiv:2609.22276v1 Announce Type: new Abstract: The Dynamic Window Approach (DWA) is widely used for local navigation, but its performance depends strongly on parameters that are typically fixed before navigation. In particular, the appropriate prediction horizon can vary with local free space: longer horizons support efficient motion in open areas, whereas shorter horizons help preserve feasible motions in narrow or cluttered regions. This paper proposes D3DWA, an adaptive DWA framework based on a Dueling Double Deep Q-Network (D3QN), which jointly selects the DWA evaluation weights and prediction horizon from a continuous navigation state at every control step while retaining DWA's trajectory generation and collision checking. In eight simulated environments, including unseen layouts, D3DWA reached every goal. Real-robot experiments further showed that D3DWA completed all three tested configurations, including a constrained case in which the weights-only variant timed out. These resu
Generalized Multimodal Foundation Model
arXiv:2609.22107v1 Announce Type: new Abstract: Making prediction with multimodal data is widely used in diverse scenarios. Existing multimodal fusion models, once deployed, can only handle predefined modalities (e.g., vision, text and audio) and single tasks, making it difficult to quickly adapt to new downstream applications. Therefore, a natural yet rather aggressive question arises, whether there exists a general multimodal fusion model that can be applied to arbitrary modality combinations and arbitrary prediction tasks. We argue that a unified multimodal fusion model should not depend on specific modalities and instead encode transferable patterns of multimodal correlation. To this end, we propose a simple and effective learning paradigm based on training over the generation of large-scale synthetic multimodal datasets with diverse causal structures that formally characterize the generative processes of multimodal data in real world. Building on this framework, we propose the gen
PRQuant: Permutation Residual Quantization for Low-Overhead Inference
arXiv:2609.22106v1 Announce Type: new Abstract: Accuracy of Low-bit quantization of linear layers is often dominated by a small number of outliers. Although existing methods, such as smoothing, rotation, or residual-based approaches, may mitigate this problem, they often introduce new accuracy bottlenecks to weights. Besides, most of these techniques are implemented as online approaches, which can result in heavy execution overheads. To address the afore-mentioned issues, We propose PRQuant (Permutation Residual Quantization), a training-free and low-overhead framework that combines channel reorganization with static weight-side residual compensation. After AWQ-style scaling, PRQuant identifies the input channels that contribute most to weight quantization error, permutes them into contiguous tail blocks, and constructs their residual weight sub-tensors offline. During inference, this contiguous structure enables the activation side to use tail blocks seamlessly without the expensive o
From Prediction to Decision : Why Jev AI Will Not Replace Data Science, but Could Complete It
A rigorous blueprint for combining System One models, traditional machine learning, optimisation, deterministic controls and human Continue reading on Medium »
Jev is very good. Your own data is better, You just need to be not afraid to use it.
Continue reading on Medium »
What Is Laya? The Open-Source System 1 AI Model That Makes Decisions Instead of Generating Text ⚡
A 421M-parameter model is taking a very different approach to AI: instead of generating language, it produces typed decisions and Continue reading on CodeToDeploy »
What Is YeshiAI? How AI Can Support Faith, Spiritual Practice, and Personal Growth Across Five Paths
A new approach to religion / spiritual AI for Islam, Christianity, Judaism, Buddhism, and Universal SpiritualityContinue reading on Medium »
Claude Status – Elevated errors for multiple models
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A cut cable disrupted hundreds of flights across the US
A JetBlue airplane past an FAA air traffic control tower at Philadelphia International Airport (PHL) in Philadelphia, Pennsylvania, US, on Friday, Oct. 24, 2025. JetBlue Airways Corp. is expected to release earnings figures on October 28. Photographer: Ryan Collerd/Bloomberg via Getty Images | Bloomberg via Getty Images Hundreds of flights were cancelled or delayed on Monday after construction crews in New Jersey accidentally cut a Verizon fiber cable used for air traffic control. FAA Administrator Bryan Bedford said that a circuit failure led to the discovery of the severed fiber cable, ABC News reports. The outage hammered airports across the Northeast, causing ground stops at all three major New York City airports. In a statement to CNBC, Verizon said that it bore "no responsibility for this incident," putting the blame on "construction contractors working in the area" that "dug up and cut our cable." Ironically, it looks like trains make have taken … Read the full story at The Verg
If AI Is Going to Kill Us, It’ll Start With the Lawyers
AI could probably do my artwork in secondsContinue reading on MuddyUm »
AI CHANGED CONFIDENTIAL TEASERS. EXPERIENCE STILL MATTERS.
The challenges of writing confidential teasers in the age of AI.Continue reading on Medium »