Building a VideoAgent-Style Multi-Agent System: Intent Parsing, Graph Planning, and Tool Routing for Video Editing Tasks
In this tutorial, we reconstruct the VideoAgent workflow as a runnable, API-key-free multi-agent pipeline. We build an intent parser, an agent library, a tool router, a graph planner, and a textual-gradient optimizer that repairs the execution graph. We wire these planning components to FFmpeg, Whis...
The wildest allegations in Apple’s trade secrets lawsuit against OpenAI
Apple’s trade secrets lawsuit against OpenAI contains allegations that range from employees joking about unauthorized access to Apple’s systems to claims that job candidates were asked to bring Apple hardware to interviews. Here are the complaint’s most eye-catching claims.
A minimal OpenAI Agents SDK implementation where retrieval becomes a search-read-decide loop
The post Agentic RAG: Let the Agent Search appeared first on Towards Data Science.
Waze adds new AI-powered features and customization updates
Some of the new features are powered by Google's Gemini AI assistant, which reflects the the tech giant's broader push to integrate Gemini across its products while also better positioning Waze to compete with rival services such as Apple Maps.
Structured Language Model Generation with Outlines
Outlines is an open-source library that introduces deterministic certainty into LLMs' output generation process for better, more reliable generation of structured outputs.
Building Models in Two Worlds: From Latent Constructs to Behavioral Signals
My PhD models tried to explain why people engage. My industry models predict who will. The statistics barely changed. Everything around them did.
The post Building Models in Two Worlds: From Latent Constructs to Behavioral Signals appeared first on Towards Data Science.
Scientists discovered the brain doesn't make decisions the way we thought
A new study suggests the brain begins making decisions much earlier than scientists previously thought. Researchers found that even primary sensory regions are influenced by higher brain areas through rapid feedback loops, rather than simply passing information forward. This more dynamic view of bra...
5 Real-World SQL Projects to Build Your Data Portfolio
Build a stronger data portfolio with these practical SQL projects covering customer churn, data warehousing, sales analysis, banking segmentation, and healthcare analytics.
The Three Dimensions of Custom Agentic Alignment: Purpose, Principles and Practices
A framework for aligning agentic AI with enterprise intent to ensure consistent scenario‑wide autonomous behavior.
The post The Three Dimensions of Custom Agentic Alignment: Purpose, Principles and Practices appeared first on Towards Data Science.
The Frontend Verification Gap in AI-Assisted Development
AI-assisted development has made frontend work feel much faster. A developer can ask for a form, a dashboard card, a table, a modal, or a responsive layout and get a decent first version almost immediately. The code may compile. The page may render. At first glance, the UI may look done. But fronten...
Stanford Researchers Introduce TRACE: A Capability-Targeted Agentic Training System That Turns Recurrent Agent Failures Into Synthetic RL Environment
Agentic LLMs keep failing the same way because they lack specific, reusable capabilities. Stanford's TRACE diagnoses those gaps from an agent's own trajectories, synthesizes one verifiable training environment per capability, trains a LoRA adapter for each, and routes tokens across experts—improving...
Prime Intellect Releases Verifiers v1: Composable Tasksets, Harnesses, and Runtimes for Agentic RL Training and Evaluations
Prime Intellect launched verifiers 0.2.0, previewing a rewritten "v1" core under the verifiers.v1 namespace. It splits an environment into a taskset (what), a harness (how), and a runtime (where), with an interception server that proxies requests and records training-ready traces. Any taskset runs u...
A Unified Approach to Interpreting Knowledge Distillation for Large Language Models via Interactions
arXiv:2607.08776v1 Announce Type: new
Abstract: Despite the success of knowledge distillation (KD) in Large Language Models (LLMs), the underlying mechanism behind its efficacy remains unclear. In this paper, we propose a unified approach to explore the common mechanism of various KD methods using ...
Sticky Routing: Training MoE Models for Memory-Efficient Inference
arXiv:2607.08780v1 Announce Type: new
Abstract: Mixture-of-Experts (MoE) models activate only a sparse subset of experts per token, yet consecutive tokens frequently activate different experts -- causing constant weight swapping between slow storage and fast memory on edge devices. Existing remedie...
Interval Certifications for Multilayered Perceptrons via Lattice Traversal
arXiv:2607.08773v1 Announce Type: new
Abstract: In this work we present a rigorous theoretical framework to a foundational problem of AI safety, namely adversarial robustness. In particular, we show that the adversarial robustness problem can be reduced to a lattice traversal problem. Each element ...
CogniConsole: Externalizing Inference-Time Control as a Formal Abstraction for Reliable LLM Interactions
arXiv:2607.08774v1 Announce Type: new
Abstract: Reliability in large language model (LLM) systems is typically framed as a function of model capability. We challenge this by demonstrating that reliability is significantly influenced by \emph{inference-time control} -- the computational layer govern...
GATS: Graph-Augmented Tree Search with Layered World Models for Efficient Agent Planning
arXiv:2607.08894v1 Announce Type: new
Abstract: Large Language Model (LLM) agents have shown promise in multi-step planning tasks, but existing approaches like LATS (Language Agent Tree Search) and ReAct rely heavily on LLM inference during planning, leading to high computational costs and stochast...
Long-Horizon-Terminal-Bench: Testing the Limits of Agents on Long-Horizon Terminal Tasks with Dense Reward-Based Grading
arXiv:2607.08964v1 Announce Type: new
Abstract: AI agents have become capable of autonomously completing short, well-specified tasks. However, existing terminal benchmarks largely focus on simple problems that finish within minutes and are evaluated only by their final outcome. This setup overlooks...
A Formalization of the Mean-Field Derivation of the Vlasov Equation: AI-Assisted Lean Formalization as a Strategy Game
arXiv:2607.08986v1 Announce Type: new
Abstract: We formalize a research result in the Lean 4 proof assistant by having a mathematician direct an AI system, and frame the activity as a formalization game. The objective is to turn a LaTeX document into Lean. The game is won when the development compi...
Meet NeuroVFM: A New Neuroimaging Foundation Model Trained With Vol-JEPA on Uncurated Clinical MRI and CT Volumes
NeuroVFM is a generalist neuroimaging foundation model from the University of Michigan, trained on 5.24M clinical MRI and CT volumes. Its Vol-JEPA base extends I-JEPA and V-JEPA to volumetric medical imaging, learning brain anatomy and pathology without radiology-report labels.
The post Meet NeuroVF...