Modern AI systems achieve remarkable capabilities at the cost of substantial energy consumption. To connect intelligence to physical efficiency, we propose two complementary bits-per-joule metrics under explicit accounting conventions: (1) Thermodynamic Epiplexity per Joule -- bits of structural information about a theoretical environment-instance variable newly encoded in an agent's internal state per unit measured energy within a stated boundary -- and (2) Empowerment per Joule -- the embodied sensorimotor channel capacity (control information) per expected energetic cost over a fixed horizon. These provide two axes of physical intelligence: recognition (model-building) vs.control (action influence). Drawing on stochastic thermodynamics, we show how a Landauer-scale closed-cycle benchmark for epiplexity acquisition follows as a corollary of a standard thermodynamic-learning inequality under explicit subsystem assumptions, and we clarify how Landauer-scaled costs act as closed-cycle benchmarks under explicit reset/reuse and boundary-closure assumptions; conversely, we give a simple decoupling construction showing that without such assumptions -- and without charging for externally prepared low-entropy resources (e.g.fresh memory) crossing the boundary -- information gain and in-boundary dissipation need not be tightly linked. For empirical settings where the latent structure variable is unavailable, we align the operational notion of epiplexity with compute-bounded MDL epiplexity and recommend reporting MDL-epiplexity / compression-gain surrogates as companions. Finally, we propose a unified efficiency framework that reports both metrics together with a minimal checklist of boundary/energy accounting, coarse-graining/noise, horizon/reset, and cost conventions to reduce ambiguity and support consistent bits-per-joule comparisons, and we sketch connections to energy-adjusted scaling analyses.

翻译：现代人工智能系统以巨大的能耗为代价实现了卓越的性能。为将智能与物理效率联系起来，我们在明确核算约定下提出两个互补的比特/焦耳度量指标：(1) 每焦耳热力学认知复杂度——在声明边界内，智能体内部状态新编码的关于理论环境实例变量的结构信息比特数与单位实测能量的比值；(2) 每焦耳赋能——固定时间跨度内具身感知运动通道容量（控制信息）与预期能量成本的比值。这两个指标提供了物理智能的两个维度：识别（模型构建）与控制（行为影响）。基于随机热力学，我们证明了在明确子系统假设下，认知复杂度获取的朗道尔尺度闭循环基准如何作为标准热力学学习不等式的推论；并阐明了在明确重置/复用及边界闭合假设下，朗道尔尺度成本如何作为闭循环基准。反之，我们通过简单的解耦构造证明，若无此类假设——且不对外部制备的低熵资源（如新鲜内存）跨越边界的行为计费——信息增益与边界内耗散不必紧密关联。针对潜在结构变量不可得的实证场景，我们将认知复杂度的操作概念与计算有界MDL认知复杂度对齐，建议报告MDL认知复杂度/压缩增益替代指标作为补充。最后，我们提出统一效率框架，同时报告这两个指标及包含边界/能量核算、粗粒化/噪声、时间跨度/重置和成本约定的最小检查清单，以减少歧义并支持一致的比特/焦耳比较，同时勾勒其与能量调整尺度分析的联系。