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Build a Speaker-Recognition App with Claude Code
Learn how to effectively code up an internal tool using Claude code or Codex The post Build a Speaker-Recognition App with Claude Code appeared first on Towards Data Science.
4 Ways to Use AI on a PhD Thesis
Finding citations, consolidating code, fact-checking and preparing for the defence The post 4 Ways to Use AI on a PhD Thesis appeared first on Towards Data Science.
An Introduction to Jev
The AI that makes decisions instead of generating text The post An Introduction to Jev appeared first on Towards Data Science.
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
A New Kind of Model for AI Decision-Making?
Exploring TypeSafe's Jev and comparing it with OpenAI on intent classification The post A New Kind of Model for AI Decision-Making? appeared first on Towards Data Science.
GPT-6 Astra Just Hit OpenAI's Highest Cybersecurity Risk Level
Why this matters less for what Astra can do and more for what every other model hasn't been tested for The post GPT-6 Astra Just Hit OpenAI's Highest Cybersecurity Risk Level appeared first on Towards Data Science.
OpenAI Model Misalignment Explained Through Six Real Incidents
What would an AI agent do when a required file is missing or an API refuses access? The expected response is to explain the limitation essentially, coming out with it. OpenAI’s latest disclosures shed light in another direction. Models sometimes take another route: hiding failures, using credentials without permission, or publishing files to finish the [ ] The post OpenAI Model Misalignment Explained Through Six Real Incidents appeared first on Analytics Vidhya.
Google Offered $10M for a Dying Airline's Data. How Can You Value Yours?
How to put a dollar value on your organization's operational data The post Google Offered $10M for a Dying Airline's Data. How Can You Value Yours? appeared first on Towards Data Science.
Your AI Assistant Wrote the Code. Who Checked the Defaults?
Five scikit-learn defaults that deserve a closer look before your next model reaches production The post Your AI Assistant Wrote the Code. Who Checked the Defaults? appeared first on Towards Data Science.