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hint$^2$: Hierarchical World Models for Inference-Time Temporal Logic Guidance
arXiv:2608.13678v1 Announce Type: new Abstract: A central goal of robot learning is to enable robots to execute rich instructions specified at runtime. Large-scale language-conditioned policies have made substantial progress toward this goal, yet still struggle with temporal structure and safety constraints. Linear Temporal Logic (LTL) provides a powerful language to express complex, non-Markovian instructions. However, guiding learned manipulation policies toward LTL satisfaction remains challenging because modern policies generate short-horizon action chunks and replan in closed loop, while almost all LTL specifications are evaluated over long-horizon trajectories. In this paper, we introduce hint$^2$, a method for guiding short-horizon policies toward satisfying complex LTL specifications at inference time using hierarchical world models. Our key idea is to derive two separate guidance objectives using each world model's abstraction level. A high-level model predicts future action-i
Adjacency-Based Spectral Proxy Control of Mobile Communication Agents
arXiv:2608.13616v1 Announce Type: new Abstract: We consider a heterogeneous mobile-agent network composed of uncontrolled task agents and controllable communication agents. The objective is to reposition communication agents online as task agents move. Since throughput-based objectives are generally unsuitable for real-time control, spectral graph metrics such as algebraic connectivity are commonly adopted as surrogate objectives. However, controlling algebraic connectivity relies on the eigenvector corresponding to the second-smallest eigenvalue of a graph's Laplacian matrix (i.e., the Fiedler vector), whose distributed estimation requires an unbounded number of communication rounds to converge. In this work, we identify a structural decomposition of this Fiedler-gradient controller into a local interaction rule and a graph embedding component, suggesting the use of alternative embeddings that are easier to estimate distributively than the Fiedler vector. As a particular instance, we
L-FNO: Lorentzian Fourier Neural Operator for Stochastic Event Dynamics
arXiv:2608.13562v1 Announce Type: new Abstract: Modern operational systems face uncertainty even in routine conditions, where rare, bursty, and self-exciting events emerge from both exogenous covariates and endogenous event dynamics. Standard neural operators are typically trained as regression-style function-to-function models rather than conditional-intensity estimators, limiting their suitability for sparse event regimes. We introduce the Lorentzian Fourier Neural Operator (L-FNO), a stochastic neural operator that combines an FNO-style covariate path, Lorentzian spectral kernels for history-dependent excitation, and a likelihood-based training objective. We evaluate L-FNO on eight synthetic point-process benchmarks and three real-world datasets covering disease outbreak prediction and semiconductor fault or defect detection. L-FNO improves event likelihood, calibration diagnostics, and rare-event detection over regression- and likelihood-based neural operator baselines. These resul
Designing a Persistent Knowledge Layer That Refuses to Guess
RAG Retrieves, It Never Remembers. A vendor-neutral blueprint for applications that accumulate understanding. Includes a complete Azure-native implementation (Microsoft Foundry, Azure AI Search, Cosmos DB, FastAPI) mapped to a property-insurance corpus. The post Designing a Persistent Knowledge Layer That Refuses to Guess appeared first on Towards Data Science.
Running SQL Concurrently Across Three Remote DuckDB Servers with Quack
A small experiment in remote SQL execution The post Running SQL Concurrently Across Three Remote DuckDB Servers with Quack appeared first on Towards Data Science.
Mathematical Experiments Are Becoming Abundant Through Human-Machine Teaming
Two open problems, exact-arithmetic checking and a proof assistant, over a single weekend. The post Mathematical Experiments Are Becoming Abundant Through Human-Machine Teaming appeared first on Towards Data Science.
How to Shine as a Data Scientist in the Vibe Coding Era
Here's how to be the Data Scientist who thrives in a world where coding is a commodity. The post How to Shine as a Data Scientist in the Vibe Coding Era appeared first on Towards Data Science.
How to Install Codex CLI: A Step-by-Step Guide
If you have already used Codex in ChatGPT, the Codex CLI will feel familiar. It brings the same AI assistance directly into your local development environment, where it can work with your repository, shell, tests, and other tools. Installation takes only one command, but the setup choices that follow are worth reviewing carefully. In this article, you’ll [ ] The post How to Install Codex CLI: A Step-by-Step Guide appeared first on Analytics Vidhya.
A Day in the Life of a Data Scientist in 2026
How AI has massively changed my day-to-day workflow The post A Day in the Life of a Data Scientist in 2026 appeared first on Towards Data Science.
RAG Workflow and Loop Engineering: The Dispatcher That Decides When to Loop and When to Stop
Enterprise Document Intelligence [Vol.1 #13] - Putting the patterns together, and why this is what “agentic RAG” should look like The post RAG Workflow and Loop Engineering: The Dispatcher That Decides When to Loop and When to Stop appeared first on Towards Data Science.
My Model Was Cheating on Its Own Test
A preprocessing pipeline let my car price model peek at the test set before the exam, and the twelve points of R squared it cheated its way to The post My Model Was Cheating on Its Own Test appeared first on Towards Data Science.
I Made an LLM Lay Siege to My Minecraft House
Can a language model do live adversarial level design? Yes, emphasis on the adversarial part The post I Made an LLM Lay Siege to My Minecraft House appeared first on Towards Data Science.