# gst: transient and steady-state chaos in dissipative quantum systems.

<< Dissipative quantum chaos plays a central role in the characterization and control of information scrambling, non-unitary evolution, and thermalization, but it still lacks a precise definition. >>

AA << properly restore the quantum-classical correspondence through a dynamical approach based on entanglement entropy and out-of-time-order correlators (OTOCs), which reveal signatures of chaos beyond spectral statistics. Focusing on the open anisotropic Dicke model, (They) identify two distinct regimes: transient chaos, marked by rapid early-time growth of entanglement and OTOCs followed by low saturation values, and steady-state chaos, characterized by high long-time values. >>

AA << introduce a random matrix toy model and show that Ginibre spectral statistics signals short-time chaos rather than steady-state chaos. (Their) results establish entanglement dynamics and OTOCs as reliable diagnostics of dissipative quantum chaos across different timescales. >>

Debabrata Mondal, Lea F. Santos, S. Sinha. Transient and steady-state chaos in dissipative quantum systems. arXiv: 2506.05475v1 [quant-ph]. Jun 5, 2025. 

https://arxiv.org/abs/2506.05475

Also: chaos, transition, in https://www.inkgmr.net/kwrds.html 

Keywords: gst, information scrambling, entropy, chaos, transient chaos, steady-state chaos.

# gst: far-from-equilibrium complex landscapes

<< Systems with a complex dynamics like glasses or models of biological evolution are often pictured in terms of a complex landscape, with a large number of possible collective states. (AA) show on the example of a stochastic spin model with nonreciprocal and heterogeneous interactions how the complex landscape picture can be generalized far from equilibrium, where collective states may become time-dependent and exhibit, e.g., spontaneous oscillations, often hidden by the presence of disorder. >>

AA << identify relevant observables, like the density of entropy production rate, to unveil the spontaneous collective time dependence, and  determine a configurational entropy which counts the number of oscillating collective states when this number grows exponentially with system size. >>

Laura Guislain, Eric Bertin. Far-from-equilibrium complex landscapes. Phys. Rev. E 111, L062101 Jun 16, 2025.

https://journals.aps.org/pre/abstract/10.1103/93tr-phkw

arXiv: 2405.08452v1 [cond-mat.dis-nn]. May 14, 2024.

https://arxiv.org/abs/2405.08452

Also: evolution, order, disorder, disorder & fluctuations, chaos, in https://www.inkgmr.net/kwrds.html 

Keywords: gst, evolution, complexity, entropy, configurational entropy, order, disorder, disorder & fluctuations, spontaneous oscillations, chaos.

# life: detecting hallucinations (in large language models) using semantic entropy.

<< Large language model (LLM) systems, such as ChatGPT or Gemini, can show impressive reasoning and question-answering capabilities but often ‘hallucinate’ false outputs and unsubstantiated answers. >>

<< Here (AA) develop new methods grounded in statistics, proposing entropy-based uncertainty estimators for LLMs to detect a subset of hallucinations— confabulations— which are arbitrary and incorrect generations. (Their) method addresses the fact that one idea can be expressed in many ways by computing uncertainty at the level of meaning rather than specific sequences of words. >>

Their method << works across datasets and tasks without a priori knowledge of the task, requires no task-specific data and robustly generalizes to new tasks not seen before. By detecting when a prompt is likely to produce a confabulation, helps users understand when they must take extra care with LLMs and opens up new possibilities for using LLMs that are otherwise prevented by their unreliability. >>️️

Sebastian Farquhar, Jannik Kossen, et al. Detecting hallucinations in large language models using semantic entropy. Nature 630, 625–630. Jun 19, 2024.

https://www.nature.com/articles/s41586-024-07421-0

Also: ai (artificial intell) (bot), entropy, in https://www.inkgmr.net/kwrds.html 

Keywords: life, artificial intelligence,  LLMs, confabulations, uncertainty, hallucinations, entropy, semantic entropy

# gst: trade-off between coherence and dissipation for excitable phase oscillators.

<< Thermodynamic uncertainty relation (TUR) bounds coherence in stochastic oscillatory systems. In this paper, (AA) show that both dynamical and thermodynamic bounds play important roles for the excitable oscillators, e.g. neurons. >>

<< Excitable systems such as neurons have distinctive coherence features compared with other oscillators having no excitability. >>️

AA << combined the well-established results, i.e. the fluctuation of the ISI (inter-spike-interval) limited by 1/3 and the coherence resonance phenomenon, together with the TUR developed in recent years to investigate the coherence in the excitable phase oscillators. (AA) find quite different trade-off relation in the subthreshold (excitable) region and superthreshold (oscillatory) region, separated by the SNIC (saddle-node on an invariant circle) bound but meanwhile lower bounded by the TUR. Furthermore, (They) found that there is an optimal entropy production corresponding to the maximum coherence, which could serve as an alternative interpretation of the coherence resonance. It implies that more entropy production does not necessarily result in higher accuracy of currents. >>️

Chunming Zheng. Trade-off between coherence and dissipation for excitable phase oscillators. arXiv: 2412.16603v1 [cond-mat.stat-mech]. Dec 21, 2024.

https://arxiv.org/abs/2412.16603

Also: brain, entropy, dissipation, uncertainty, in https://www.inkgmr.net/kwrds.html 

Keywords: gst, brain, neurons, entropy, oscillators, excitable phase oscillators, coherence, dissipation, uncertainty