Papers

The Renormalization Group (RG) is a powerful and versatile framework for analyzing complex physical systems. Here, we reinterpret it as a stochastic process that explores physical phase spaces, scale by scale, progressively revealing finer details of small-scale structures. This perspective establishes a natural connection to other random exploration processes, such as the Schramm-Loewner evolution, and is more suited for a probabilist audience. It links RG concepts such as RG transformations, effective actions, etc, to usual probabilistic tools such as conditional expectation values, martingales, etc, but also makes contact with stochastic quantization.

In the thermodynamic limit, the equilibrium state of a many-body system can be characterized by three pairs of conjugate thermodynamic variables: $E/T,V/P,N/\mu$. In this limit, the thermodynamic properties in all ensembles are equivalent up to the leading order of $E,V,N$. However, for systems of finite size, this ensemble equivalence is no longer exact, and the thermodynamic properties may differ substantially among ensembles. To quantify these finite-size effects rigorously, it is desirable to develop a universal ensemble theory applicable to systems of arbitrary size, providing exact expressions for entropy and, thereby, giving rise to the precise value of all equilibrium thermodynamic quantities. In this work, we propose a theory that determines the exact entropy expressions for classical non-interacting many-body systems of arbitrary size across all statistical ensembles, based on only two postulates: stationarity, requiring that the physical laws be invariant under time translation, and unbiasedness, requiring that the equilibrium mixed state maximize the entropy subject to the prescribed constraints. Moreover, we show that the entropy expressions obtained in different ensembles converge to the common asymptotic form $S \asymp \ln\!\left( \left( \frac{4\pi m e E}{3N} \right)^{3N/2} \cdot \left(\frac{V}{N}\right)^N \right)+N$, consistent with the predictions of the large deviation theory.

Amorphous solids subjected to athermal quasistatic oscillatory shear undergo a transition from periodic reversible dynamics to irreversible diffusive dynamics at yielding. How irreversibility arises in such deterministic, strongly dissipative systems remains unclear. Here we directly test the proposal that post-yield irreversibility originates from chaotic dynamics and exponential sensitivity to initial conditions. Contrary to this interpretation, perturbations initially contract rather than grow, even in the irreversible regime, with nearby trajectories remaining close for extended periods. Separation occurs only through rare branching events, after which the distance grows diffusively at the rate expected for independent trajectories. The waiting times to branching are exponentially distributed, defining a steady-state branching rate that vanishes in trained reversible limit cycles and becomes finite above yielding. A mean-field soft-spot model reproduces these branching statistics and reveals their microscopic origin: a small perturbation can reverse the activation order of two nearly degenerate plastic instabilities, altering the subsequent sequence of plastic events. These results show how local stability and global irreversibility can coexist in a dissipative many-body system and identify instability-selection-induced branching as a distinct dynamical route to irreversibility in driven amorphous solids.

Multiple studies on active matter have shown that activity can induce or suppress a phase transition, or modify the critical behavior of a passive system. Here we investigate how activity affects the jamming transition which is a paradigmatic example of nonequilibrium phase transitions in passive systems. We consider a one-dimensional system of active particles with hardcore interactions and a direction of self-propulsion which can be reversed at a given switching rate. For a class of particle hop rates and infinite switching rate, our model reduces to a passive system which is known to exhibit a transition between a high-density fluid phase and a low-density jammed phase in the stationary state. Using Monte Carlo simulations and a mean field theory, we study how the mean mobility of the particle and the hole cluster distribution vary with density and finite switching rate. Our main result is that activity hinders the formation of jam and can even inhibit it; more precisely, we find that the jamming transition occurs at a critical density that decreases with decreasing switching rate, and at sufficiently small switching rate, the system exists only in the fluid phase.

Protected zero modes are a hallmark of topological phases of matter and are exponentially localized at sharp interfaces between distinct gapped phases. We investigate how this picture changes for smooth interfaces in a broad class of inhomogeneous Su-Schrieffer-Heeger (SSH) models. Combining an exact lattice solution with an inhomogeneous Dirac description, we show that the associated Jackiw-Rebbi zero mode becomes spatially extended. For arbitrary smooth hopping profiles, its lattice extension universally scales as the square root of the system size, independently of the microscopic details of the interface. This emergent length defines a mesoscopic critical region separating two gapped phases, within which correlations decay algebraically before crossing over to exponential decay. In addition, the entanglement entropy scales as the logarithm of the emergent length near the interface, confirming the interpretation of a mesoscopic critical region. Our results establish a universal critical length governing the low-energy physics of smooth topological interfaces.

We investigate the steady-state phase transition in a one-dimensional hybrid contact process. We implement the single-site and cluster mean-field approximations based on the effective fields and present all the possible steady states of the system. We show the existence of the stable absorbing and active phases, and the bistable region in the long-time limit. The saddle-node bifurcation is observed at the boundary between the absorbing phase and the bistable region, suggesting a discontinuous phase transition. While the absorbing to active phase transition is continuous. To characterize the nonclassical scaling behavior of the continuous phase transition, we extract the true critical points and exponents by means of the coherent anomaly method.

We consider fluctuations of the spin current in the stochastic XNOR process, a kinetically constrained hopping model in one spatial dimension. We formulate three conjectures with explicit amplitudes for the long-time current distributions: a Gaussian limit on the $t^{1/4}$ scale for homogeneous initial states with nonzero magnetization, a half-normal limit on the $t^{1/4}$ scale for domain-wall states with opposite magnetizations, and an M-Wright limit on the $t^{1/8}$ scale for homogeneous initial states at zero magnetization. Earlier work identified the tracer mechanism and anticipated the scaling exponents and limiting shapes in the domain-wall and zero-magnetization settings. Starting from the microscopic XNOR dynamics, we predict the missing amplitudes for the continuous-time process and extend the picture to homogeneous biased initial data. Simulations provide numerical support for all three conjectures without fitted parameters. A separate mathematical companion paper presents an extensively AI-generated candidate proof. Generative-AI tools were used in the research and writing workflow.

Obtaining equilibrium distributions of nonlinear systems is essential for accurately computing macroscopic observables. Conventional theoretical corrections are typically limited to weak nonlinearities, where interaction terms can be treated as effectively uncorrelated perturbations and the random phase approximation applies. In this Letter, we develop a framework to determine equilibrium distributions based on the generalized energy equipartition principle. Our approach recovers existing corrections in the weakly nonlinear regime and, crucially, remains valid for strong nonlinearities, where perturbative contributions become correlated and conventional approaches break down. Numerical simulations of the nonlinear Schrödinger equation, the Majda-McLaughlin-Tabak model, and the Fermi-Pasta-Ulam-Tsingou model demonstrate accurate corrections for nonlinearities more than an order of magnitude stronger than those accessible to conventional theories.

Starting from the constrained variational formulation of Larché and Cahn for solids in contact with fluids, a unified thermodynamic framework for such systems is developed. Both bulk and interfacial equilibrium conditions arise as stationarity conditions of a single thermodynamic functional. While earlier theory neglects a full treatment of interfacial contributions and therefore cannot describe systems in which surface effects are significant, the present formulation incorporates interfacial thermodynamics directly into the variational principle. The framework is introduced first for fluid-fluid systems to establish the underlying mathematical structure, and is then extended to solid-fluid interfaces. Within this setting, classical equilibrium relations for heterogeneous systems emerge naturally from different classes of admissible variations, providing a common theoretical basis for bulk, interfacial, and configurational thermodynamics.

Negative stochastic entropy production is commonly called an apparent violation of the second law. In general quantum-trajectory dynamics, however, the physical entropy production $\sigma$ need not obey a forward detailed fluctuation theorem. A general arbitrary-coupling formulation identifies a dynamical-asymmetry term $\sigma^\ast$ that completes it into $\Omega=\sigma+\sigma^\ast$, whose mean is $\langle\Omega\rangle=\Sigma+\Sigma^\ast$. We prove that the likelihood-ratio sign is optimal among reversal-odd trajectory observables and use this fact to transfer an established sharp fluctuation-theorem floor to the tie-corrected physical sign statistic $\Pi_\sigma=\Pr(\sigma<0)+\Pr(\sigma=0)/2$. When $\Pr(\sigma=0)=0$, the result reads $\Pr(\sigma<0)\ge[1-\langle\Omega\rangle/g(\langle\Omega\rangle)]/2$, where $g$ is the inverse of $a\mapsto a\tanh(a/2)$. The physical integral fluctuation theorem simultaneously suppresses large negative events, producing a quantitative “frequent but mild” law, while the sign imbalance lower-bounds the hidden mean $\Sigma^\ast$. We formulate the measured-record protocol explicitly and illustrate and numerically audit the tie-corrected theorem in random finite-coupling collision models and a coherently driven qubit interacting with thermal ancillas.

Practical implementations of quantum error correction (QEC) require rapid measurement and continuous processing of the syndrome information in order to prevent a backlog of unprocessed data. While “static” QEC is theoretically well understood via mappings to equilibrium statistical mechanics models, such an understanding of “real-time” QEC is currently lacking. Here, we study the kinetics of sliding window decoding (SWD), an implementation of real-time decoding that acts on temporally local windows of noisy syndrome information and commits to corrections irreversibly at a nonzero rate. We propose an effective description of SWD in terms of a stochastic kinetic process, where $\mathbb{Z}_2$-charged point particles undergo parity-conserving reaction and diffusion. This model describes dynamics at length and time scales large compared to the window size $W$, whereas physics at scales smaller than $W$ leads to nontrivial $W$-dependent scaling of the effective parameters. We identify the rate of decoding $1/W$ as a relevant perturbation to the decodable phase. We also show broad applicability of our results by changing many microscopic details of SWD without affecting the effective description.

Time delays are ubiquitous in physical and biological networked systems, playing a fundamental role in the emergence and stability of collective behavior. Yet, existing theoretical methods for synchronization analysis of delay-coupled systems are largely limited to networks with homogeneous degree distributions. Here, we extend the master stability function framework to a broad class of degree-heterogeneous, weighted, and directed delay-coupled networks. The analysis reveals that synchronization can be enhanced in heterogeneous networks compared to homogeneous ones, including all-to-all networks, which are known to be optimal in non-delayed systems. To identify optimal heterogeneous structures, we develop a network optimization method that finds directional, edge-weighted configurations maximizing synchronization stability. Our results show that, in delay-coupled networks, heterogeneity and nonreciprocity are key resources for synchronization.

Charged colloids coated with a polymer brush can be designed to preferentially self-assemble into different crystal structures by varying easy-to-tune experimental conditions. For a given set of conditions, we have observed in experiments and simulations a distribution of thermodynamically (meta)stable self-assembled crystal structures. Properly quantifying the free energy landscape of these colloidal systems is essential for rationally choosing conditions to preferentially target particular crystal structures. For some of the structures we have formed, standard crystalline order parameters are not able to differentiate between crystals or between crystals and amorphous aggregates. We show that local environment similarity descriptors are able to distinguish the relevant metastable states, but are too expensive for use in biased MD simulations. Here, we adopt an approach from machine-learned interaction potentials showing that SE(3)-equivariant transformer networks can serve as an efficient-to-evaluate machine-learned proxy. As a result, we can compute the relative free energies of accessible colloidal structures as a function of different experimentally-relevant physical knobs that can steer our system between two observed crystal types. As an example application, we then show how changing surface potentials of positive and negative colloids while maintaining the same attractive energy can shift which crystal structure is favored.

We study the emergence of complexity in finite-size quantum systems as their dynamics transition from closed and coherent evolution to interacting and effectively open behavior. Using a state-based geometric framework, we represent mixed quantum states as probability measures on complex projective Hilbert space. This representation allows us to track how interactions reshape the underlying pure-state geometry. We introduce two complementary diagnostics: a distinguishability measure, based on the Wasserstein distance between probability-measure representations of mixed states, that quantifies sensitivity to initial states, and a state-space coverage index that measures long-time exploration of the subsystem state space. These diagnostics provide a geometric perspective on the emergence and evolution of quantum dynamical complexity. When applied to the quantum kicked top, both diagnostics generally increase with interaction strength. Their dependence on environment size is structured by parity symmetry, with integer-spin systems often exhibiting greater sensitivity and state-space coverage than half-integer-spin systems. These results highlight finite-size quantum effects and provide a geometric approach to quantifying dynamical complexity deep in the quantum regime

We investigate the dynamics of entanglement in a generalized version of the kicked Ising chain, extending the model from the standard qubit case (local dimension $q=2$) to higher local dimensions ($q > 2$). We identify the existence of ”dual-unitary” points where the model's space-time duality allows for exact analytical solutions. Our analysis reveals that while a few unique dual-unitary points exist analytically for systems with local dimensions $q=3$ and $q=4$, such points do not exist for $q \ge 5$ due to the lack of a unique kicking strength that satisfies the required matrix element conditions. Utilizing the transfer matrix method and a replica trick specifically adapted for higher dimensions, we derive exact expressions for the growth of entanglement entropy in the $q=3$ (kicked Potts-type) model starting from a class of solvable initial states. Our results demonstrate that at the dual-unitary point, both Rényi and von Neumann entanglement entropies grow linearly with time until reaching a maximum value determined by the subsystem size.

Predicting the percolation threshold of highly clustered networks from local statistics remains difficult, because short loops break the independence assumption underlying tree-like message passing. Existing remedies address loopy connectivity either through prescribed local motifs in random-graph ensembles or through a single network's realized topology, leaving an ensemble-level treatment of arbitrary connectivity patterns absent. Here, we develop a loopy message-passing framework for random clustered graph ensembles based on generalized-edge statistics, which characterize overlap patterns among the neighborhoods of different nodes. This yields a progressively refined approximation scheme based on neighborhoods of increasing size around each node. The low-order approximations recover previous equations for random network ensembles, and the new result that yields refined threshold prediction is developed by the second-order approximation. We show that the effectiveness of this framework depends not only on short-cycle density but also on the internal consistency of generalized edges. To diagnose this effectiveness, we introduce the generalized-edge closure coefficient (GECC) to quantify this consistency. Because GECC is computed entirely from local statistics and does not rely on any percolation calculation, it serves as an a priori diagnostic for the reliability of the approximation. Using synthetic and real networks, the threshold is evaluated via the second-order and lower-order approximations. Comparisons with Monte Carlo simulations show that GECC captures key structural features that strongly affect the percolation threshold. These results establish ensemble-based loopy message passing as an efficient route for predicting the percolation threshold in large clustered networks.

In this study we investigate the behavior of additive binary square-well mixtures within the framework of classical density functional theory. By leveraging on the geometrical structure of the square-well interaction, we propose a novel form of the perturbation theory contribution to the density functional in terms of weighted densities, inspired by fundamental measure theory. We apply this functional in order to study the effective one-body interaction due to the liquid-vapor interface of the solvent acting on a dilute component of dissolved nano particles. The effective one-body interaction attracts the nano particles strongly to the interface. We show that this effective interaction potential can be calculated either from the density profiles of the full mixture, at low but non-vanishing concentrations of the nano particles, or by employing the Widom insertion theorem in the dilute limit of vanishing density of nano particles. Both routes display excellent agreement.

We develop a consistent pseudo-analytic framework based on $g$-calculus for constructing deformed statistical distributions. By mapping standard algebraic operations through a monotone generator function, we systematically derive the associated pseudo-logarithmic and pseudo-exponential structures. Applying this formalism, we introduce a new family of pseudo-distributions that generalizes nonextensive statistical mechanics at both the probability density and cumulative distribution levels, recovering classical and standard nonextensive statistics as limiting cases. We investigate the thermodynamic geometry of the proposed models using the Ruppeiner metric on the equilibrium manifold. A perturbative analysis around the classical limit reveals that, to leading order, the thermodynamic scalar curvature is governed solely by the generator deformation parameter, while the nonextensivity parameter remains decoupled. To evaluate the empirical robustness of the framework, we apply the model to analyze the absolute deviations of daily West Texas Intermediate crude oil prices from their hundred-day moving average. Model comparison based on information criteria demonstrates that the proposed pseudo-distributions provide a superior description of these high-frequency financial fluctuations and their heavy-tailed characteristics compared to standard benchmarks. These results suggest that $g$-calculus offers a flexible and physically grounded mathematical tool for generating deformed statistics and analyzing their geometric properties.

We study a nearest-neighbor non-Hermitian spin chain obtained from one member of an exact non-braided scalene Yang–Baxter triple. Its local Hamiltonian density violates both the difference-form Reshetikhin condition and its general non-difference counterpart, obstructing its realization by a differentiable homogeneous regular solution of the ordinary Yang–Baxter equation. The transfer matrices constructed from the regular member do not commute among themselves at distinct spectral parameters. Nevertheless, the scalene Yang–Baxter relation implies cross-commutativity with another transfer matrix constructed from the third member of the scalene triple. We evaluate the latter for arbitrary chain length and show that, on even periodic chains, it is a finite generating function of a non-obvious staggered nilpotent symmetry. The resulting conserved hierarchy belongs entirely to the algebra generated by this single symmetry and hence does not constitute an extensive family of algebraically independent charges. Nevertheless, this example demonstrates that scalene Yang–Baxter triples can act as an algebraic symmetry-discovery mechanism beyond the ordinary self-commuting transfer-matrix framework.

Programmable microcavities support grand-canonical photon gases. We show that bosonic state counting creates an entropic staircase of most-probable total occupations. For detuning density continuous and nonzero at the chemical-potential threshold, the active-cell fraction is asymptotically linear at low temperature and the conditional law has a universal limit; for uniform disorder the law is exact over a finite temperature interval. For two modes it is the Gauss–Kuzmin distribution, linking photonic thermodynamics and metric number theory. We outline finite-array and dye-microcavity tests.

Transformer layers generate state-dependent interaction networks: token representations determine the attention matrix, which in turn updates the representations. We study this feedback in a minimal normalized self-attention dynamics and identify the overlap gap as the central quantity governing its attractor structure in the thermodynamic limit. When tokens form internally aligned clusters and their similarity to members of the same cluster exceeds that to every other cluster by a nonvanishing amount, inter-cluster attention is exponentially suppressed as the dimension increases. This mechanism produces a high-dimensional manifold of clustered fixed points, ranging from a few macroscopic clusters to extensive microscopic fragmentation, and also controls their stability against perturbations. Starting from an unstructured Gaussian state, we find that clustered states nucleate from the diffuse background only above a finite threshold in attention sharpness, giving rise to a dynamical attention-condensation transition.

Nature exhibits a wide variety of nonlinear phenomena characterized by strong fluctuations that push the system away from equilibrium. Recent studies have shown that non-extensive statistics are more suitable for analyzing these phenomena, as they can capture long-range correlations and heavy-tailed distributions that conventional Boltzmann-Gibbs statistics cannot represent. However, estimating the non-extensiveness parameter ($q$) is not trivial, and methods vary significantly across disciplines. This paper presents a systematic and interdisciplinary comparison of three fitting methods: direct nonlinear fitting of the probability distribution function (PDF), linearization using the q-logarithm function, and numerical fitting of the cumulative distribution function (CDF). We applied this analysis to various phenomena, from areas such as geophysics, space weather and economics, analyzing the critical impact of discretization. The results show that $q$ estimates are highly sensitive to histogram binning, while the CDF-based method provides a bin-free alternative that yields more stable estimates for the datasets analyzed here. This contribution provides relevant information for the search for a method that allows the correct determination of the parameters of heavy-tailed distributions in various disciplines.

The daily return of a stock is often restricted to an exchange-imposed band to curb extreme fluctuations. Any attempted price movement beyond this band is clipped, leaving an unobserved excess. We introduce a minimal stochastic latent-state model in which a fraction of this hidden excess is retained for the next day. This retention generates memory, even though the daily stochastic driving shocks are independent. For symmetric driving shocks with regularly varying tails, the stationary latent return preserves the tail index of the noise, but has an enhanced tail amplitude. In the wide-band limit, a close of the daily return at either limit of the band admits a single-dominant-shock description. We show that after such an event, the mean return on the following day has the same sign and grows proportionally to the band width, while the probability of reaching the same limit again approaches a finite value. Reaching the opposite band limit on the following day requires a second extreme shock of opposite sign and is power-law suppressed. Simulations support these analytical predictions. Empirical data from stocks subject to daily price limits are qualitatively consistent with the predicted same-sign response and its increase across wider price bands.

Knowledge distillation transfers more than task competence: it also transmits response propensities, refusal policies, error boundaries, and latent safety biases. We formulate this behavioral inheritance as a coarse-graining model grounded in statistical mechanics, in which the student's answer and refusal decisions define two macrostates, while the teacher induces an effective field that reshapes the student's free-energy landscape. The model yields a reliability-safety trade-off relation controlled by a single parameter K, which we term the hazard discrimination capability. The predicted trade-off is consistent with refusal-token data [arXiv: 2412.06748]. In knowledge distillation, a teacher with strong hazard discrimination improves the student's attainable reliability and safety, whereas poor discrimination limits the attainable trade-off. Repeated distillation acts as an iterated renormalization-group-like transformation, under which K follows a flow across generations. The flow exhibits a tricritical structure separating regimes of K loss, stable transmission, and threshold-dependent inheritance, and yields testable scaling predictions for multigenerational distillation.

We propose a general framework for automatic force field refinement guided by phase diagrams, grounded in Lee-Yang phase transition theory. The central idea is to directly use the partition function modulus as a phase-diagram-guided optimization target. Evaluating the modulus at points close to the real axis, where the Lee-Yang zeros are mostly associated with the phase transition, is more physically meaningful and avoids the numerical difficulty of explicitly solving for the zeros. This approach requires no system-specific order parameters or response properties for characterizing phase transition points, making it universal across various discontinuous phase transitions and material systems. We validate the method on refining parameters of a Lennard-Jones potential covering both gas-liquid and solid-liquid transitions, and a Cu embedded-atom method potential based on experimental melting curves. The refined force fields reproduce the target phase diagrams with significant improvement across all systems. For Cu, the refinement simultaneously improves predictions of enthalpy and heat capacity, which are observables beyond the optimization target. These results establish Lee-Yang theory as a practical tool for contemporary force field development.

We study annealed current fluctuations in the two-dimensional symmetric simple exclusion process (SSEP) across a circular passive counting boundary. The initial average density is $\rho_1$ inside a disk of radius $R$ and $\rho_2$ outside, and the observable is the net decrease of the particle number in the disk over a finite time. Using convexity of the macroscopic fluctuation theory action and rotational averaging, we show that the minimizer of the full two-dimensional variational problem may be chosen radially symmetric. For the resulting radial problem, a suitable change of variables leads to a nonisospectral formulation on the half-line. Combining the associated scattering construction with a scalar factorization, we obtain a closed expression for the scaled cumulant generating function and hence the annealed large-deviation statistics of the current.

This paper argues that the Gibbs paradox can be resolved without using the concept of identical particles in quantum mechanics. The molecules in different regions of the gas can be distinguished, so there is no need to introduce the N! factor. For each molecule, the volume of its free movement space is local at every instant. Moreover, collisions are the primary way of interaction between gas molecules and the fundamental driving force for reaching equilibrium. The potential energy during collisions cannot be ignored. Based on the local free space assumption and collision potential energy, this paper uses the canonical ensemble method to rederive the entropy increment formula for gas mixing. It includes parameters such as molecular mass, effective radius, and collision characteristic time, which vary with the type of gas molecules. This solves the problem that the entropy increment of gas mixing is independent of gas properties, that is, it truly resolves the Gibbs paradox instead of providing a new conceptual explanation.

We study the role of generalized symmetry in massless renormalization group flows or Higgs transitions. In particular, we revisit the massless renormalization group flows in unitary minimal models, $\mathbf{M}(p,p+1) \rightarrow \overline{\mathbf{M}(p-1,p)}$ preserving the fusion ring symmetry $\text{FR} (SU(2)_{p-2})\subset \mathbf{M}(p,p+1)$ where $p$ is an integer satisfying $p>3$. In this series of flows, we demonstrate that the unbroken fusion ring symmetry $\text{FR} (SU(2)_{p-2})$ eliminates all relevant perturbations in the $\overline{\mathbf{M}(p-1,p)}$ model. Hence, the infrared theory $\overline{\mathbf{M}(p-1,p)}$ is stable at the level of the scaling analysis and can be interpreted as a (weak-)symmetry-enforced gapless phase in contemporary theoretical physics. Phenomenologically, by the folding trick, the unbroken fusion ring symmetry corresponds to a (half-)integer spin nonsimple current, a variant of the Cooper pair involving nonabelian anyons generated from the coset or level-rank duality structures. Moreover, we demonstrate that the structure of (half-)integer spin nonsimple current plays a fundamental role in causing the resonance effect of relevant and dangerously irrelevant perturbations. This resonance effect may result in the cascade of phase transitions (or the system flows to unexplored fixed points), and the symmetry can be a stopper of such unconventional flows.

As a liquid approaches its glass transition, its dynamics turns heterogeneous: mobile and immobile regions coexist, and the four-point susceptibility $\chi_4$ that quantifies this heterogeneity grows sharply. Interpreting that growth is subtle, because the collective signals experiments record, such as a tagged particle's trajectory, an overlap function, or a mean field, are generated by the same particles they describe. Here we compute exactly what conditioning on such a shared record does to the population that produced it, for a broad class of stochastically observed systems; the guiding example is a tagged particle and the cage of $z$ neighbors that drives its force history. Using a Girsanov path transformation, we prove that the conditioning multiplies the independent joint law of the $z$ trajectories by exactly one term: a centered-square penalty along the single collective direction the record can see. Any fixed pair of particles stays nearly independent, with covariance falling as $O(z^{-1})$ and mutual information as $O(z^{-2})$, the property known as propagation of chaos, yet the $z(z-1)$ weak pair correlations add coherently into a finite suppression of collective fluctuations, the Schur shield $D - C = -C^2(aI + C)^{-1} \preceq 0$. An exactly solvable Brownian model calibrates the construction. The physical consequence is a calculable baseline for dynamical heterogeneity: conditioning itself contributes a computable, nonpositive amount to the susceptibility of a conditioned ensemble, so the genuine cooperative signal is the excess of the measured $\chi_4$ over this baseline rather than over zero, a comparison that existing simulation data can already perform.

The chemical potentials of multi-component mixtures underlie many physical and chemical phenomena, but remain challenging to compute. The S0 method enables the computation of chemical potentials from equilibrium molecular dynamics simulations, by leveraging the thermodynamic relationship between particle number fluctuations and derivatives of chemical potentials, followed by numerical integration along different compositions. Here we generalize the S0 method from two-component mixtures to neutral multi-component mixtures. We first extend the statistical mechanical formalism to high-dimensional compositional space, and then introduce a Gaussian process integration scheme combined with active learning to efficiently integrate chemical potentials and sample diverse compositions. We use this method to compute the mixing free energies of a molten metal alloy, and the solubilities of two paracetamol polymorphs in water-ethanol solvents. The extended S0 method provides a practical and scalable route for computing chemical potentials in neutral bulk multi-component mixtures from atomistic simulations.