You're not even allowed to ask Fable about basic biology questions, let alone anything that could potentially be dangerous.

I'm so very sad to hear of the passing of Inman Harvey. He was my PhD examiner in 1999 (he passed it!), and a mentor, inspiration and friend not just to me but to a whole generation (and more) of Artificial Life researchers. He will be very sorely missed, but his legacy lives on.

D3の永山くんの新作です。Artemyさんとの共著です。 Ornstein–Uhlenbeck過程における、散逸と振動の(dissipation-coherence)トレードオフおよび、行列の非正規性に基づいた緩和の加速の双方を一度に表現可能な、振動-非正規性に基づく定常エントロピー生成率の新分解法です。 arxiv.org/abs/2606.07263

Our new work on arXiv🔥 We introduce the geometric complexity for reset maps and uncover a dynamics-independent trade-off relation between complexity and error, which can be regarded as a generalized geometric third law of thermodynamics. arxiv.org/abs/2604.27858

Open postdoc with the amazing Sarah Marzen (Scripps College, Claremont, California). Intersection of information theory, biophysics, neuroscience, and machine learning. Please RT theclaremontcolleges.wd1.myw…

How do time series foundation models forecast unseen dynamical systems? In new experiments, we find that small transformers learn to approximate transfer operators in-context. (1/N) arxiv.org/abs/2602.18679

Our paper w/@m_aguilera_& @ito_sosuke is out in PRL. We combine ideas from stochastic thermodynamics & nonequilibrium Maximum Entropy to quantify forces dissipation in high-dimensional nonequilibrium systems, including spin glasses and neural data journals.aps.org/prl/abstrac…

Generative thermodynamic computing Diffusion models are powerful generative tools, but they come with a hidden cost: every denoising step requires a digital neural network, artificially injected noise, and substantial energy consumption. Yet physics offers an alternative—what if the noise needed for generation arose naturally from thermal fluctuations, and the denoising process was physically enacted rather than simulated? Stephen Whitelam introduces exactly this: a generative modeling framework for thermodynamic computing. Instead of using neural networks to transform noise into structure, the approach encodes denoising information directly in the energy landscape of a physical system evolving under Langevin dynamics. The training principle is elegant: observe noising trajectories (structured data degrading into noise), then adjust the system's couplings via gradient descent to maximize the probability that a thermodynamic computer would generate the reverse—structure from noise. This process has a beautiful physical interpretation: it minimizes the heat emission and entropy production of the generative process. In a proof-of-concept simulation with 784 visible units and 512 hidden units trained on just three MNIST digits, the thermodynamic computer learns to transform noise into recognizable digit-like structures through physical dynamics alone—no external control or pseudorandom numbers required. The energy implications are striking: the simulated thermodynamic computer emits ~2,900 kᵦT of heat per generation, compared to ~5 × 10¹⁴ kᵦT for a digital neural network doing equivalent denoising—a difference of more than 10 orders of magnitude. The message is compelling: by grounding generative modeling in thermodynamic principles, we can design systems where computation emerges from physics itself, opening paths toward autonomous, energy-efficient generation that could fundamentally change how we think about the hardware of machine learning. Paper: journals.aps.org/prl/abstrac…

The chemosensing accuracy of 𝘌. 𝘤𝘰𝘭𝘪 cells is shown to be limited by internal noise in signal processing, rather than the stochasticity of molecule arrivals at their receptors, contrary to long-held understanding in the field. nature.com/articles/s41567-0…

Our paper was published on the relationship between thermodynamic driving and eigenvalues in Markovian master equations. It proves a weaker version of a beautiful conjecture proposed by Uhl and Seifert. Led by Guo-Hua Xu , with Jean-Charles Delvenne and @ito_sosuke

ポスドクのGuohuaさんの論文がPRLのEditors' suggestionに選ばれました。ArtemyさんとJean-Charlesさんとの共著です。マスター方程式の遷移レートの固有値に関して、熱力学的な制約から楕円の中にいる必要があるという定理を導出しています。 doi.org/10.1103/z4t2-18cx