Google DeepMind’s TTEL localizes errors to cut test-time search costs in reasoning

burny_tech · x · 2026-07-25

• Google DeepMind researchers present Test-Time Scaling via Error Localization (TTEL), a method for improving reasoning and programming performance at inference time.
• TTEL uses feedback to localize where an error happens in a generation, then prunes the search trajectory and branches from the valid prefix instead of retrying from scratch.
• The paper reports better Pareto tradeoffs on sequential reasoning tasks and says TTEL can reach 71.0% pass@64 on LiveCodeBench with Qwen3-8B, while using about half as many tokens as independent sampling.
• It also says TTEL outperforms competing test-time baselines on AIME-2025 and HMMT-2025 for the tested models.

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