Aug 06, 2026

The State of Model Routing — NVIDIA, Cognition, OpenRouter

A deep dive into model routing strategies for AI/ML production, featuring experts from Cognition, OpenRouter, and NVIDIA. Key topics include optimizing costs with multi-model systems, delegating tasks between frontier and smaller models, managing context efficiently (sidekicks, compaction), and adapting to dynamic task complexities. The panel discusses the fragility of naive routing, the cost implications of in-distribution vs. out-of-distribution tasks, and the evolution of auto-routers driven by real-world usage patterns like OpenClaw's heartbeats. Insights also cover NVIDIA's Flex Run for dynamic model sizing, hallucination probes for detecting model limitations, and the future of hybrid local/cloud routing and model collaboration.

Jul 12, 2026

Semantic Blindness: 500,000 Sensors Confused an LLM - Raahul Singh & Vanč Levstik, Phaidra

Modern LLMs struggle with combinatorial engineering problems in industrial settings due to "Semantic Blindness" – an inability to understand physical system topology, scale efficiently, or handle repetitive naming conventions. This talk introduces a hybrid AI architecture that combines LLMs for high-level planning with deterministic systems for execution, leveraging hierarchical structures and pattern-based search. This approach achieves 100% accuracy and flat operational costs at massive scales, demonstrating an "inversion" of the Software 1.0/3.0 paradigm where AI-native systems mature by integrating deterministic code for reliable, structured tasks.

Jun 30, 2026

Multimodal & Embodied Intelligence (Pt 1), Panel on Multimodal AI: Progress, Pitfalls, Possibilities

This session explored Multimodal and Embodied Intelligence, featuring talks on hybrid AI in robotics (classical vs. end-to-end), AI's role in healthcare (focusing on NCDs, deployment, and uncertainty modeling), and fundamental perception challenges in multimodal reasoning (using educational video QA and visual puzzles). A panel discussed the impact of foundation models, the blurred lines between AGI and human-like AI, critical deployment pitfalls (human factors, efficiency, architectural limits), and future directions, emphasizing task-specific models and the redefinition of 'foundation models.'