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Qwen Meetup Presentation, Function Calling Harness, 6.75% to 100%

Qwen Meetup Presentation, Function Calling Harness, 6.75% to 100%

by samchon·Mar 28, 2026·2 points·1 comment

AI Analysis

●●SolidBig BrainNiche Gem

Compiler-driven feedback loops force LLMs into 100% schema compliance on complex types.

Strengths
  • Uses compiler errors as deterministic feedback instead of vague natural language hints.
  • Benchmarks model failures like Qwen 3.5 at 0% and proves the fix.
  • Lenient JSON parsing recovers broken output before validation even starts.
Weaknesses
  • Requires multiple LLM iterations, increasing latency and cost per successful call.
  • Tightly coupled to TypeScript/Typia ecosystem, less useful for Python/Go backends.
Target Audience

Backend engineers building AI agents or code generation pipelines

Similar To

Instructor · Guidance · Pydantic

Post Description

I was personally invited by the Qwen team to speak at Qwen Meetup Korea, and got to present locally here in Korea yesterday — pretty honored to have been reached out to directly.

The talk was about how I got function calling to work reliably on deeply recursive union types — the stuff the industry generally says doesn't work. With qwen3-coder-next, first-try success rate was 6.75%. And the entire Qwen 3.5 model family was hitting 0% on union types due to a consistent double-stringify bug. Both ended up at 100%.

Slides are also downloadable in the article — speaker notes are written inside as slide notes if you'd like the full narrative behind each slide.

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