这篇论文提出沙漏推理，让GPT-5.5和Gemini 3.1 Pro在ARC-AGI、ChipBench等基准上大幅提升，不是靠新模型，而是靠精巧的推理结构设计，值得搞推理的人看看。
沙漏推理(Hourglass)是一种新的少样本归纳推理框架，通过强制推理阶段间的上下文隔离来提升大语言模型的归纳能力。在ARC-AGI-2视觉抽象基准上，使用GPT-5.5时，沙漏推理将5次尝试的最佳准确率比迭代改进基线提高了14个百分点。在ChipBench硬件综合任务中，使用GPT-5.5将Verilog综合准确率从31%提升至58%，几乎翻倍。在国际语言学奥林匹克的BBEH-Linguini谜题上，沙漏推理逆转了显式言语化对性能的损害趋势。消融实验证实，改进来源于阶段隔离和初始归纳质量，而非提示措辞或符号形式。
Think Through a Bottleneck: Hourglass Reasoning for Rigorous Induction
Self-refinement often fails to strengthen few-shot inductive reasoning in large language models. Prompting a model to explicitly state its inferred rule does little on its own. What actually matters is a structurally enforced isolation between reasoning stages, so that information can only pass between them as a compressed symbolic state. We introduce \textbf{Hourglass reasoning}, which enforces strict context isolation between reasoning stages. The frozen LLM acts as a meta-constructor, building for each task a symbolic encoder--decoder: an Induction module compresses the support examples into a schema $φ$ (encoder) and a transient scaffold $z$; a Deduction module derives rule $T$ (decoder) from these and discards $z$; an Implementer compiles $(φ, T)$ into artifacts; an error-driven Refiner revises $(φ, T)$ and regenerates artifacts from scratch. Only $(φ, T)$ crosses stage boundaries, so all refinement stays anchored to the rule. We evaluate Hourglass across three benchmarks spanning visual abstraction, hardware synthesis, and textual rule induction, using GPT-5.5 and Gemini 3.1 Pro. On ARC-AGI-2, it raises best-of-5 accuracy by up to 14 points over an iterative-refinement baseline. On ChipBench, it nearly doubles Verilog synthesis accuracy with GPT-5.5, from 31\% to 58\%. BBEH-Linguini draws on puzzles from the International Linguistics Olympiad, a setting where prior work has shown that explicit verbalization can hurt performance. Hourglass mitigates this tendency, and on Gemini 3.1 Pro, it reverses the effect entirely. Ablations confirm that these gains come from the isolation between stages and the quality of the initial induction, not from prompt wording or the particular symbolic form used. It is how information flows through the reasoning process, rather than the language used to express it, that drives inductive reasoning in frozen LLMs.