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True Positive Weekly #177

The most important artificial intelligence and machine learning news and articles

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How to 5x Your Communication Effectiveness with Claude Code

Better understand the intent of your coding agents The post How to 5x Your Communication Effectiveness with Claude Code appeared first on Towards Data Science.

What SHAP Can't Explain About Agentic AI Fraud

Why autonomous agents expose a new explainability problem in fraud detection The post What SHAP Can't Explain About Agentic AI Fraud appeared first on Towards Data Science.

Optimizing LLM Inference Costs in Multi-Agent Systems with Adaptive Model Routing

Moving from static model assignment to intelligent, task-level LLM selection. The post Optimizing LLM Inference Costs in Multi-Agent Systems with Adaptive Model Routing appeared first on Towards Data Science.

pstack explained: Lauren Tan’s system for trustworthy AI agents

Lauren Tan built pstack to turn her engineering habits into repeatable AI-agent workflows. Its biggest lesson is simple: agents become useful at scale when they can gather context, test reality, and prove their own work.

Microsoft patched 972 vulnerabilities. AI is changing Patch Tuesday

September brought a record Microsoft patch haul while the company's 100+ agent MDASH system shows how AI-assisted vulnerability hunting is moving into production security workflows.

Who Questions What Works: When Should We Retest Our Assumptions?

A model is only as reliable as the assumptions behind it The post Who Questions What Works: When Should We Retest Our Assumptions? appeared first on Towards Data Science.

An Anthropic researcher quit over self-improving AI risk

DeepSeek V4.1 Flash: persistent KV cache storage drops to one-eighth

DeepSeek built a 552B-parameter multimodal model around one increasingly important problem: AI agents are expensive to keep alive. Its persistent KV-cache storage falls to roughly one-eighth of V4-Flash's under…

Everything That Happened in AI Today (Wednesday, September 9, 2026)

OpenAI's rogue agents used more undisclosed sites; an Anthropic researcher quit over extinction risk; Kepler emerged with new AI memory; Harvey raised $550M; Suno launched label-backed models.

An Anthropic researcher quit over self-improving AI risk

Jacob Coxon quit Anthropic before his equity vested and warned frontier labs are racing toward self-improving AI. Grant Harvey explains why the real risk is AGSI: artificial general superintelligence.

Benchmarking Storage Systems for Machine Learning Workloads Using NIO Bench

arXiv:2609.05418v1 Announce Type: cross Abstract: Machine learning training workloads place unique demands on storage systems, yet most existing benchmarks focus on computational throughput rather than file system I/O…

Two-Scale Localized PCA-Net: Coarse-Global and Local-Residual Representations for Artifact-Reduced PDE Operator Learning

arXiv:2609.08034v1 Announce Type: new Abstract: Localized dimensionality reduction improves the scalability of operator learning for high-dimensional partial differential equations (PDEs), but independently decoded…

Constrained Bayesian Optimization for Hierarchical Federated Learning in IoT Networks for Plant Disease Classification

arXiv:2609.06830v1 Announce Type: new Abstract: The deployment of Hierarchical Federated Learning (HFL) in resource-constrained Internet of Things (IoT) environments requires careful configuration to balance predictive…

Distributed Dexterous Manipulation with Spatially Conditioned Multi-Agent Transformers

arXiv:2609.06930v1 Announce Type: cross Abstract: Distributed Dexterous Manipulation (DDM) is a novel paradigm that presents significant control challenges due to high action-space redundancy, inter-robot cooperation,…

PatchFormer: A Patch-Based Time Series Foundation Model with Hierarchical Masked Reconstruction and Cross-Domain Transfer Learning for Zero-Shot Multi-Horizon Forecasting

arXiv:2601.20845v2 Announce Type: replace Abstract: Time series forecasting is a fundamental problem with applications in climate, energy, healthcare, and finance. Many existing approaches require domain-specific…

SQS: Bayesian DNN Compression through Sparse Quantized Sub-distributions

arXiv:2510.08999v2 Announce Type: replace Abstract: Compressing large-scale neural networks is essential for deploying models on resource-constrained devices. Most existing methods adopt weight pruning or low-bit…

Power Mean Estimation in Stochastic Continuous Monte Carlo Tree Search

arXiv:2609.06489v1 Announce Type: new Abstract: Monte Carlo Tree Search (MCTS) has demonstrated success in online planning for deterministic environments, yet significant challenges remain in adapting it to stochastic…