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WebMCP, if adopted as a web standard, replaces the fragile, token-intensive DOM-scraping approach agents currently use with direct, structured tool calls — reducing the work agents must do to complete actions on existing websites.
The results show that targeted RL fine-tuning on high-quality, task-specific data can close — and reverse — a 231-billion-parameter gap in model size, at a training cost under $500, on a real financial reasoning benchmark.
The demo illustrates that Gemini's audio stack now spans transcription, expressive speech synthesis, real-time sound-to-sound interaction, and full-song music generation — all accessible through a unified API with tool-use integration.
Teams building AI agents against large API surfaces can adopt a code-generation interface (e.g., two `search`/`execute` tool calls) to slash context token usage by orders of magnitude and unlock native programming constructs like loops and parallelization that JSON tool calling cannot efficiently express.