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Position: It is Time to Virtualize Foundation Models with a Self-evolving Operating System Layer

arXiv:2609.19203v1 Announce Type: new Abstract: AI applications have shifted from single, monolithic foundation models (FM) to compound agentic systems. Yet today's stacks remain fragmented: even as protocols (e.g., MCP, A2A) ease tool/agent connectivity, each framework embeds an implicit runtime for state, memory, budgets, and guardrails, making behavior non-portable and governance brittle. It mirrors computing before…

What Do We Expect from LLMs? Mapping the Design of LLM Benchmarks

arXiv:2609.19182v1 Announce Type: new Abstract: Benchmarks are central to how progress in large language models (LLMs) is assessed and communicated. Yet model rankings alone reveal little about how evaluation requirements themselves are changing. The expanding variety of benchmarks offers another perspective: what researchers expect LLMs to do, and what they count as successful performance. We…

BioPhys-Bridge: A Benchmark for Interdisciplinary Scientific Reasoning in Physics-Grounded Biological Research

arXiv:2609.19180v1 Announce Type: new Abstract: Language models face unique challenges in analyzing interdisciplinary scientific research literature. In biophysics research, faithful answers require grounding observed data in source evidence, interpreting it through a quantitative physics model, and linking it to a biological mechanism. To address this challenge, we introduce BioPhys-Bridge, a novel benchmark dataset for evidence-grounded…

Regularized Emphatic Temporal-Difference Learning: Stability under Constant Stepsizes

arXiv:2609.19170v1 Announce Type: new Abstract: Emphatic temporal-difference learning (ETD) stabilizes the expected off-policy TD update and changes its projection geometry, but neither property determines constant-stepsize sampled dynamics. We construct an ergodic two-state counterexample in which the ETD mean map contracts while the sampled product has a positive top Lyapunov exponent. Regenerative-cycle analysis separates this sign…

Estimators in Scikit-LLM: A KDnuggets Cheat Sheet

Scikit-LLM wraps language models in the scikit-learn estimator API, so it drops into a Pipeline or a cross-validation loop natively.

5 Free Zoomcamps From Data Pipelines to AI Agents

Explore five free hands-on workshops covering data engineering, machine learning, MLOps, LLMs, AI agents, and AI development through practical lessons, homework, projects, and community-based learning.

What’s So Good About ChatGPT Work? Here’s What I Found

This article walks through how ChatGPT Work specifically earns its reputation, where the underlying models genuinely hold up against the competition, and where the honest limits are.

What’s Actually Inside 24,723 Tokens of a Search Result? We Broke It Down, Field by Field

See how SerpApi’s Markdown output can cut search-result token usage by up to 74%, reducing AI agent costs and context-window overhead.

When Does Graph RAG Actually Add Value? A Hands-On Experiment

I built four AI retrieval architectures on a laptop and benchmarked them against the same set of documents and questions. Here’s what the results taught me about the trade-offs between plain RAG, graph RAG, and simply putting everything into a frontier model’s context window. The post When Does Graph RAG Actually Add Value? A Hands-On…

Why You Think Like a Bayesian but Were Taught Like a Frequentist

From a chocolate bar with no price tag to a marketing mix model in PyMC, and the 200-year-old integral that stood in between. The post Why You Think Like a Bayesian but Were Taught Like a Frequentist appeared first on Towards Data Science.