Why Do Sequential LLMs Hit a Bottleneck? Test-time compute scaling in LLMs has traditionally relied on extending single reasoning paths. While this approach improves reasoning for a limited range, performance plateaus quickly. Experiments on DeepSeek-R1-distill-Qwen-1.5B show that increasing token budgets beyond 32K (up to 128K) yields negligible accuracy gains. The bottleneck arises from early token commitment, where initial errors propagate through the entire chain-of-thought. This effect, referred to as Tunnel Vision, indicates that the scaling issue is methodological rather than a fundamental limit of model capacity. Tunnel Vision and How Is It Diagnosed? Researchers quantified recovery ability by forcing models…

In this tutorial, we demonstrate a complete, advanced implementation of the Notte AI Agent, integrating the Gemini API to power reasoning and automation. By combining Notte’s browser automation capabilities with structured outputs through Pydantic models, it showcases how an AI web agent can research products, monitor social media, analyze markets, scan job opportunities, and more. The tutorial is designed as a practical, hands-on guide, featuring modular functions, demos, and workflows that demonstrate how developers can leverage AI-driven automation for real-world tasks such as e-commerce research, competitive intelligence, and content strategy. Check out the FULL CODES here. Copy CodeCopiedUse a different Browser…