On January 31, 2026, sovereign AI infrastructure initiatives expanded with national-scale compute programs, retrieval architecture evolved beyond vector search, and agent runtimes explored self-improvement capabilities.
🧭 Key Highlights
- 🇮🇳 IndiaAI Mission launches ₹10,300 crore plan for democratized AI compute
- 🌲 PageIndex introduces tree-search RAG framework with 98.7% accuracy
- 📊 METR Time Horizon 1.1 expands to 228 tasks with Inspect framework
- 🔧 Modular 26.1 stabilizes MAX Python API and improves Mojo ergonomics
- 🗄️ Gravitino presents federated metadata lake for AI workloads
Sovereign AI Infrastructure
🇮🇳 IndiaAI Mission Launches ₹10,300 Crore Plan
According to IBEF, IndiaAI Mission launched a ₹10,300 crore plan to democratize AI compute with a native GPU backbone, indigenous multimodal models, an AI Marketplace, and the AI Kosh dataset platform. This represents a comprehensive national strategy for sovereign AI infrastructure development.
🏗️ Arctis AI Raises €1M for AI Contract Workflows
According to The AI Insider, Arctis AI raised €1M pre-seed to automate construction contract workflows via AI agents that structure obligations, risks, and payment terms for real-time compliance and risk analysis.
RAG Architecture Evolution
🌲 PageIndex Introduces Tree-Search Framework for RAG
According to VentureBeat, PageIndex introduced an open-source tree-search framework for RAG that treats retrieval as navigation, reporting 98.7% accuracy on complex document queries. The framework enables agentic RAG with lightweight indexing on PostgreSQL, signaling a shift beyond vector search toward navigation-style retrieval.
📊 Enterprises Shifting to AI-Native Operating Models
According to Modern Diplomacy, an analysis argues enterprises are shifting from AI “as a tool” to AI-native operating models with centralized AI Studio governance and agentic systems, projecting 15% of day-to-day decisions handled autonomously by 2028.
Open Source Projects & Infrastructure
📊 METR Time Horizon 1.1 Expands Task Suite
According to METR blog, METR’s Time Horizon 1.1 expanded its task suite from 170 to 228 tasks, migrated evaluations to the open-source Inspect framework, and updated progress estimates with a 131-day doubling time post-2023. This represents maturation of AI evaluation infrastructure.
🔧 Modular 26.1 Focuses on Developer Velocity
According to Modular blog, Modular 26.1 focuses on developer velocity: MAX Python API is now stable, Mojo ergonomics improve, and Apple silicon GPU support expands. This release signals stabilization of AI development tooling.
🤖 OpenClaw-Foundry Debuts as Self-Writing Meta-Extension
According to GitHub, openclaw-foundry debuted as a self-writing meta-extension for OpenClaw, learning workflows and autonomously upgrading its own capabilities—representing research toward self-improving agent systems.
🗄️ Gravitino Presents Geo-Distributed Federated Metadata Lake
According to The New Stack, Gravitino presents a geo-distributed, federated metadata lake acting as a neutral control plane across data sources, engines, and clouds for AI workloads. This addresses metadata management challenges in distributed AI infrastructure.
🔍 Infra Insights
January 31 developments highlight three critical infrastructure trends: national-scale AI compute programs expanding sovereign capacity, retrieval architecture evolving beyond vector search, and agent runtimes exploring self-improvement.
IndiaAI Mission’s ₹10,300 crore plan joins similar sovereign AI initiatives from countries recognizing AI infrastructure as strategic national assets. The comprehensive approach—GPU backbone, indigenous models, marketplace, and datasets—reflects understanding that full-stack sovereignty requires control across multiple layers.
PageIndex’s tree-search RAG framework achieving 98.7% accuracy on complex queries signals an important evolution in retrieval architecture. Vector search’s limitations for complex document queries are driving innovation toward navigation-style approaches that treat retrieval as structural traversal rather than similarity matching.
The self-writing openclaw-foundry meta-extension represents early research into self-improving agent systems. While still experimental, this direction suggests agent runtimes may eventually autonomously optimize their own capabilities—a critical requirement for scaling agent infrastructure.
Together with Modular’s stable MAX API and Gravitino’s federated metadata lake, these developments indicate AI infrastructure is maturing from experimental projects to production systems with sovereign backing, advanced retrieval capabilities, and self-improving potential.