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Meta-R1 是一项把认知科学元认知理论工程化、并证明在数学推理任务上带来实用收益的工作。它支持“把显式控制/规划机制引入大型推理模型可改善表现与效率”的命题,但也带来了工程复杂度、可迁移性与对抗稳健性的挑战。 总体上,它为将“调制/元控制”作为智能演化方向提供了可操作范式与初步实证。 Large Reasoning Models (LRMs) demonstrate remarkable capabilities on complex tasks, exhibiting emergent, human-like thinking patterns. Despite their advances, we identify a fundamental limitation: current LRMs lack a dedicated meta-level cognitive system—an essential faculty in human cognition that enables “thinking about thinking”. This absence leaves their emergent abilities uncontrollable (non-adaptive reasoning), unreliable (intermediate error), and inflexible (lack of a clear methodology). To address this gap, we introduce Meta-R1, a systematic and generic framework that endows LRMs with explicit metacognitive capabilities. Drawing on principles from cognitive science, Meta-R1 decomposes the reasoning process into distinct object-level and meta-level components, orchestrating proactive planning, online regulation, and adaptive early stopping within a cascaded framework. Experiments on three challenging benchmarks and against eight competitive baselines demonstrate that Meta-R1 is: (I) high-performing, surpassing state-of-the-art methods by up to 27.3%; (II) token-efficient, reducing token consumption to 15.7%∼32.7% and improving efficiency by up to 14.8% when compared to its vanilla counterparts; and (III) transferable, maintaining robust performance across datasets and model backbones.
如果我们接着解读 Ma Yi 教授的逻辑,他强调智能的核心任务是 从海量感知数据中学习和记忆可预测信息。而语言本质上就是人类对世界可预测性的再编码: •感官 → 模式提取(可预测性) → 符号化(语言) → 共享与累积。 •LLM 相当于直接接触到“感官之后的第二阶数据”,跳过了生物在进化中耗费亿万年才积累的感官—模式提取环节。 所以可以说: LLM 的巨大认知加速度,并非因为它“比人更聪明”,而是因为它在信息演化的链条中,直接嫁接在了语言这一预测性压缩带宽的顶端 The world in which we are living is neither fully random nor completely unpredictable. Instead, it follows certain orders, patterns, and laws that make it largely predictable.2 The very emergence and existence of life depend on a predictable living environment. Only by learning and memorizing what is predictable in the environment can life survive and thrive since good decisions and actions depend on reliable predictions. Because there seem to be unlimited things that are predictable about the world, intelligent beings, such as animals and humans, have continued to improve through evolution their capability to explore and exploit such predictability for a better and better life. To this end, they have developed increasingly more acute senses, including vision, audio, touch, taste, and smell, to perceive what is predictable in the external environment from these high-throughput sensory data. Hence a fundamental task for all intelligent beings is to be ableto: learn and memorize predictable information from massive sensed data.