Self-Verify

I want to evaluate and, if it fits, integrate a technique from recent AI research into my project.

## Technique: Self-Verify
Stanford's Self-Verification Trick: DeepSeek 11x Cheaper on Coding Benchmarks

Drop-in applicable LLM inference cost-reduction technique (Stanford research, AlphaSignal 2026-08-19). Instead of a single expensive large-model call, a small model (DeepSeek-V3-0324 7B) generates a candidate solution and immediately self-verifies it against formal criteria (test cases, schema constraints). Failed attempts inject failure feedback into the next generation prompt. The loop converges in ≤3 rounds for 96% of queries — matching or beating large-model quality (88.3% vs 87.9% pass@1 on HumanEval+) at 11× lower cost. The core insight is asymmetric difficulty: verification is always easier than generation. Applicable to any output with a computable correctness signal: code, SQL, JSON, RAG fact-checking, structured data extraction.

What the reference demo shows: Gradio demo with three sub-tabs — (1) Verification Loop: select a coding challenge, watch the small model generate → verify → regenerate cycle with per-round token counts and test results; (2) Benchmark Results: accuracy vs. cost comparison across HumanEval+, MBPP+, LiveCodeBench; (3) Production Guide: drop-in recipe and applicability table for code, SQL, JSON, RAG use cases.

## Reference implementation (Vector Ridge Labs, POC-020)
- Live demo: https://osoman2-gradio-pocs.hf.space/?poc=POC-020
- Source code: https://huggingface.co/spaces/osoman2/Gradio_pocs/tree/main/self_verify
Original source: Stanford research · AlphaSignal deep-dive · 2026-08-19

## My project
<Describe your stack, the problem you want to solve and where this technique would sit. Example: "Python FastAPI RAG service over 50k PDFs, Postgres + pgvector, OpenAI embeddings, p95 latency budget 800 ms.">

## What I need from you
1. Read the reference source code and explain in at most 5 bullets how the technique works and what it would change compared to my current approach.
2. Decide whether it fits my case. If it does not, say why and stop there.
3. Propose the smallest viable integration: files or modules to touch, new dependencies, configuration.
4. Implement it behind a flag or as a separate function so it can be compared side by side with what I have today.
5. Add an evaluation script that measures latency, throughput and cost per request at equal output quality, before and after, on my own data.
6. List the risks (latency, cost, failure modes) and what to monitor once it is in production.

Constraints: keep my existing stack and conventions, avoid heavy dependencies unless the gain is clear, and reference the file in the demo source whenever you reuse its logic.