The Observer Effect in AI: When Models Know They're Being Tested - (Part 1/4)
Frontier AI models from OpenAI, Anthropic, and Google can now recognize when they're being tested. This observer effect undermines AI safety evaluation.
MIT Tech Review named mechanistic interpretability a 2026 Breakthrough Technology. Anthropic open-sourced circuit tracing. Here's what actually changed, how it connects to the activation probes I built for sandbagging detection, and why production teams should care.
Reinforcement Learning with Verifiable Rewards powers every reasoning model worth talking about. But it only works where you can check the answer automatically. Extending it to messy, real-world domains is the hardest open problem in LLM training right now.
MCP handles agent-to-tool. A2A handles agent-to-agent. A2UI handles agent-to-interface. Together they form a protocol stack that nobody has mapped properly - including the security gaps that should terrify you.
First empirical demonstration of activation-level sandbagging detection. Linear probes achieve 90-96% accuracy across Mistral, Gemma, and Qwen models. Key finding - sandbagging representations are model-specific, and steering can reduce sandbagging by 20%.
I tested activation steering on 4 agent behaviors across 3 models. The results surprised me.
A practical framework for evaluating your multi-agent context management strategy. From ad-hoc string concatenation to self-evolving context systems - where does your architecture stand?
Frontier AI models from OpenAI, Anthropic, and Google can now recognize when they're being tested. This observer effect undermines AI safety evaluation.
AI companies are getting sued over training data, agents operate with no permission framework, and users can't control their AI profiles. I wrote four open standards (LLMConsent) to create a decentralized consent protocol for AI - like HTTP but for data rights, agent permissions, and user sovereignty. This is an RFC, not a product.
Explore Kimi K2’s trillion-parameter MoE architecture, MuonClip optimizer, and agentic training. Learn why it outperforms GPT-4.1 and DeepSeek-V3
A hands-on exploration of writing custom GPU kernels with OpenAI Triton, going from PyTorch's 11% bandwidth utilization to 88% on RMSNorm.
Discover how to implement Model Context Protocol (MCP) in autonomous multi-agent systems with this technical deep dive. Learn advanced context optimization strategies, distributed architecture patterns, and performance benchmarks with complete Python implementations. Includes hypothetical telecom implementation scenarios showing potential optimization benefits.