pwc-archive/papers-with-abstracts huggingface dataset:
The pursuit of happiness
Authors: Debora Princepe, Onofrio Mazzarisi, Erol Akcay, Simon A. Levin, Matteo Marsili
Date: 2025-06-12 00:00:00

Happiness, in the U.S. Declaration of Independence, was understood quite differently from today's popular notions of personal pleasure. Happiness implies a flourishing life - one of virtue, purpose, and contribution to the common good. This paper studies populations of individuals - that we call homo-felix - who maximise an objective function that we call happiness. The happiness of one individual depends on the payoffs that they receive in games they play with their peers as well as on the happiness of the peers they interact with. Individuals care more or less about others depending on whether that makes them more or less happy. This paper analyses the happiness feedback loops that result from these interactions in simple settings. We find that individuals tend to care more about individuals who are happier than what they would be by being selfish. In simple 2 x 2 game theoretic settings, we show that homo-felix can converge to a variety of equilibria which includes but goes beyond Nash equilibria. In an n-persons public good game we show that the non-cooperative Nash equilibrium is marginally unstable and a single individual who develops prosocial behaviour is able to drive almost the whole population to a cooperative state.
When can in-context learning generalize out of task distribution?
Authors: Chase Goddard, Lindsay M. Smith, Vudtiwat Ngampruetikorn, David J. Schwab
Date: 2025-06-05 00:00:00
Tasks: Diversity, In-Context Learning, regression

In-context learning (ICL) is a remarkable capability of pretrained transformers that allows models to generalize to unseen tasks after seeing only a few examples. We investigate empirically the conditions necessary on the pretraining distribution for ICL to emerge and generalize \emph{out-of-distribution}. Previous work has focused on the number of distinct tasks necessary in the pretraining dataset. Here, we use a different notion of task diversity to study the emergence of ICL in transformers trained on linear functions. We find that as task diversity increases, transformers undergo a transition from a specialized solution, which exhibits ICL only within the pretraining task distribution, to a solution which generalizes out of distribution to the entire task space. We also investigate the nature of the solutions learned by the transformer on both sides of the transition, and observe similar transitions in nonlinear regression problems. We construct a phase diagram to characterize how our concept of task diversity interacts with the number of pretraining tasks. In addition, we explore how factors such as the depth of the model and the dimensionality of the regression problem influence the transition.
Consciousness via MIPT?
Authors: Alexander Gorsky
Date: 2025-06-05 00:00:00

The measurement-induced phase transition (MIPT) is a recently formulated phenomenon in out-of-equilibrium systems. The competition between unitary evolutions and measurement-induced non-unitaries leads to the transition between the entangled and disentangled phases at some critical measurement rate. We conjecture that self-organized MIPT plays a key role in the generative model of cognitive networks and the formation of the state of consciousness in the "newborn-adult" transition. To this aim, we formulate the probe-target picture for the brain and suggest that MIPT interpreted as learnability transition takes place in the mental part of the target where the sites in the cognitive networks of semantic memory are concepts. Comparison with the synchronization phase transitions in the probe is made.
Surrogate models to optimize plasma assisted atomic layer deposition in high aspect ratio features
Authors: Angel Yanguas-Gil, Jeffrey W. Elam
Date: 2025-06-11 00:00:00

In this work we explore surrogate models to optimize plasma enhanced atomic layer deposition (PEALD) in high aspect ratio features. In plasma-based processes such as PEALD and atomic layer etching, surface recombination can dominate the reactivity of plasma species with the surface, which can lead to unfeasibly long exposure times to achieve full conformality inside nanostructures like high aspect ratio vias. Using a synthetic dataset based on simulations of PEALD, we train artificial neural networks to predict saturation times based on cross section thickness data obtained for partially coated conditions. The results obtained show that just two experiments in undersaturated conditions contain enough information to predict saturation times within 10% of the ground truth. A surrogate model trained to determine whether surface recombination dominates the plasma-surface interactions in a PEALD process achieves 99% accuracy. This demonstrates that machine learning can provide a new pathway to accelerate the optimization of PEALD processes in areas such as microelectronics. Our approach can be easily extended to atomic layer etching and more complex structures.
Toward Greater Autonomy in Materials Discovery Agents: Unifying Planning, Physics, and Scientists
Authors: Lianhao Zhou, Hongyi Ling, Keqiang Yan, Kaiji Zhao, Xiaoning Qian, Raymundo Arróyave, Xiaofeng Qian, Shuiwang Ji
Date: 2025-06-05 00:00:00

We aim at designing language agents with greater autonomy for crystal materials discovery. While most of existing studies restrict the agents to perform specific tasks within predefined workflows, we aim to automate workflow planning given high-level goals and scientist intuition. To this end, we propose Materials Agent unifying Planning, Physics, and Scientists, known as MAPPS. MAPPS consists of a Workflow Planner, a Tool Code Generator, and a Scientific Mediator. The Workflow Planner uses large language models (LLMs) to generate structured and multi-step workflows. The Tool Code Generator synthesizes executable Python code for various tasks, including invoking a force field foundation model that encodes physics. The Scientific Mediator coordinates communications, facilitates scientist feedback, and ensures robustness through error reflection and recovery. By unifying planning, physics, and scientists, MAPPS enables flexible and reliable materials discovery with greater autonomy, achieving a five-fold improvement in stability, uniqueness, and novelty rates compared with prior generative models when evaluated on the MP-20 data. We provide extensive experiments across diverse tasks to show that MAPPS is a promising framework for autonomous materials discovery.
An Agentic Framework for Autonomous Metamaterial Modeling and Inverse Design
Authors: Darui Lu, Jordan M. Malof, Willie J. Padilla
Date: 2025-06-07 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model

Recent significant advances in integrating multiple Large Language Model (LLM) systems have enabled Agentic Frameworks capable of performing complex tasks autonomously, including novel scientific research. We develop and demonstrate such a framework specifically for the inverse design of photonic metamaterials. When queried with a desired optical spectrum, the Agent autonomously proposes and develops a forward deep learning model, accesses external tools via APIs for tasks like simulation and optimization, utilizes memory, and generates a final design via a deep inverse method. The framework's effectiveness is demonstrated in its ability to automate, reason, plan, and adapt. Notably, the Agentic Framework possesses internal reflection and decision flexibility, permitting highly varied and potentially novel outputs.
Machine learning for in-situ composition mapping in a self-driving magnetron sputtering system
Authors: Sanna Jarl, Jens Sjölund, Robert J. W. Frost, Anders Holst, Jonathan J. S. Scragg
Date: 2025-06-06 00:00:00
Tasks: Active Learning, Gaussian Processes

Self-driving labs (SDLs), employing automation and machine learning (ML) to accelerate experimental procedures, have enormous potential in the discovery of new materials. However, in thin film science, SDLs are mainly restricted to solution-based synthetic methods which are easier to automate but cannot access the broad chemical space of inorganic materials. This work presents an SDL based on magnetron co-sputtering. We are using combinatorial frameworks, obtaining accurate composition maps on multi-element, compositionally graded thin films. This normally requires time-consuming ex-situ analysis prone to systematic errors. We present a rapid and calibration-free in-situ, ML driven approach to produce composition maps for arbitrary source combinations and sputtering conditions. We develop a method to predict the composition distribution in a multi-element combinatorial thin film, using in-situ measurements from quartz-crystal microbalance sensors placed in a sputter chamber. For a given source, the sensor readings are learned as a function of the sputtering pressure and magnetron power, through active learning using Gaussian processes (GPs). The final GPs are combined with a geometric model of the deposition flux distribution in the chamber, which allows interpolation of the deposition rates from each source, at any position across the sample. We investigate several acquisition functions for the ML procedure. A fully Bayesian GP - BALM (Bayesian active learning MacKay) - achieved the best performance, learning the deposition rates for a single source in 10 experiments. Prediction accuracy for co-sputtering composition distributions was verified experimentally. Our framework dramatically increases throughput by avoiding the need for extensive characterisation or calibration, thus demonstrating the potential of ML-guided SDLs to accelerate materials exploration.
A Neuronal Model at the Edge of Criticality: An Ising-Inspired Approach to Brain Dynamics
Authors: Sajedeh Sarmastani, Maliheh Ghodrat, Yousef Jamali
Date: 2025-06-08 00:00:00

We present a neuronal network model inspired by the Ising model, where each neuron is a binary spin ($s_i = \pm1$) interacting with its neighbors on a 2D lattice. Updates are asynchronous and follow Metropolis dynamics, with a temperature-like parameter $T$ introducing stochasticity. To incorporate physiological realism, each neuron includes fixed on/off durations, mimicking the refractory period found in real neurons. These counters prevent immediate reactivation, adding biologically grounded timing constraints to the model. As $T$ varies, the network transitions from asynchronous to synchronised activity. Near a critical point $T_c$, we observe hallmarks of criticality: heightened fluctuations, long-range correlations, and increased sensitivity. These features resemble patterns found in cortical recordings, supporting the hypothesis that the brain operates near criticality for optimal information processing. This simplified model demonstrates how basic spin interactions and physiological constraints can yield complex, emergent behavior, offering a useful tool for studying criticality in neural systems through statistical physics.
Long-range dispersal promotes spatial synchrony but reduces the length and time scales of synchronous fluctuations
Authors: Davi Arrais Nobre, Karen C. Abbott, Jonathan Machta, Alan Hastings
Date: 2025-06-10 00:00:00

Synchronous oscillations of spatially disjunct populations are widely observed in ecology. Even in the absence of spatially synchronized exogenous forces, metapopulations may synchronize via dispersal. For many species, most dispersal is local, but rare long-distance dispersal events also occur. While even small amounts of long-range dispersal are known to be important for processes like invasion and spatial spread rates, their potential influence on population synchrony is often overlooked, since local dispersal on its own can be strongly synchronizing. In this work, we investigate the effect of random, rare, long-range dispersal on the spatial synchrony of a metapopulation and find profound effects not only on synchrony but also on properties of the resulting spatial patterns. While controlling for the overall amount of emigration from each local subpopulation, we vary the fraction of dispersal that occurs locally (to nearest neighbors) versus globally (to random locations, irrespective of distance). Using a metric that measures the instantaneous level of global synchrony, we show that this form of long-range dispersal significantly favors the spatially synchronous state and homogenizes the population by decreasing the size of clusters of subpopulations that are out of phase with the rest of the metapopulation. Moreover, the addition of non-local dispersal significantly decreases the equilibration time of the metapopulation.
Data-Driven High-Dimensional Statistical Inference with Generative Models
Authors: Oz Amram, Manuel Szewc
Date: 2025-06-06 00:00:00

Crucial to many measurements at the LHC is the use of correlated multi-dimensional information to distinguish rare processes from large backgrounds, which is complicated by the poor modeling of many of the crucial backgrounds in Monte Carlo simulations. In this work, we introduce HI-SIGMA, a method to perform unbinned high-dimensional statistical inference with data-driven background distributions. In contradistinction to many applications of Simulation Based Inference in High Energy Physics, HI-SIGMA relies on generative ML models, rather than classifiers, to learn the signal and background distributions in the high-dimensional space. These ML models allow for efficient, interpretable inference while also incorporating model errors and other sources of systematic uncertainties. We showcase this methodology on a simplified version of a di-Higgs measurement in the $bb\gamma\gamma$ final state, where the di-photon resonance allows for efficient background interpolation from sidebands into the signal region. We demonstrate that HI-SIGMA provides improved sensitivity as compared to standard classifier-based methods, and that systematic uncertainties can be straightforwardly incorporated by extending methods which have been used for histogram based analyses.
Can Theoretical Physics Research Benefit from Language Agents?
Authors: Sirui Lu, Zhijing Jin, Terry Jingchen Zhang, Pavel Kos, J. Ignacio Cirac, Bernhard Schölkopf
Date: 2025-06-06 00:00:00
Tasks: Code Generation, Mathematical Reasoning, Physical Intuition, Position, scientific discovery

Large Language Models (LLMs) are rapidly advancing across diverse domains, yet their application in theoretical physics research is not yet mature. This position paper argues that LLM agents can potentially help accelerate theoretical, computational, and applied physics when properly integrated with domain knowledge and toolbox. We analyze current LLM capabilities for physics -- from mathematical reasoning to code generation -- identifying critical gaps in physical intuition, constraint satisfaction, and reliable reasoning. We envision future physics-specialized LLMs that could handle multimodal data, propose testable hypotheses, and design experiments. Realizing this vision requires addressing fundamental challenges: ensuring physical consistency, and developing robust verification methods. We call for collaborative efforts between physics and AI communities to help advance scientific discovery in physics.
Slow and Fast Neurons Cooperate in Contextual Working Memory through Timescale Diversity
Authors: Tomoki Kurikawa
Date: 2025-06-09 00:00:00
Tasks: Diversity

Neural systems process information across a broad range of intrinsic timescales, both within and across cortical areas. While such diversity is a hallmark of biological networks, its computational role in nonlinear information processing remains elusive. In this study, we examine how heterogeneity in intrinsic neural timescales within the frontal cortex - a region central to cognitive control - enhances performance in a context-dependent working memory task. We develop a recurrent neural network (RNN) composed of units with distinct time constants to model a delayed match-to-sample task with contextual cues. This task demands nonlinear integration of temporally dispersed inputs and flexible behavioral adaptation. Our analysis shows that task performance is optimized when fast and slow timescales are appropriately balanced. Intriguingly, slow neurons, despite weaker encoding of task-relevant inputs, play a causal role in sustaining memory and improving performance. In contrast, fast neurons exhibit strong but transient encoding of input signals. These results highlight a division of computational roles among neurons with different timescales: slow dynamics support stable internal states, while fast dynamics enable rapid signal encoding. Our findings provide a mechanistic account of how temporal heterogeneity contributes to nonlinear information processing in neural circuits, shedding light on the dynamic architecture underlying cognitive flexibility.
DEF: Diffusion-augmented Ensemble Forecasting
Authors: David Millard, Arielle Carr, Stéphane Gaudreault, Ali Baheri
Date: 2025-06-08 00:00:00

We present DEF (\textbf{\ul{D}}iffusion-augmented \textbf{\ul{E}}nsemble \textbf{\ul{F}}orecasting), a novel approach for generating initial condition perturbations. Modern approaches to initial condition perturbations are primarily designed for numerical weather prediction (NWP) solvers, limiting their applicability in the rapidly growing field of machine learning for weather prediction. Consequently, stochastic models in this domain are often developed on a case-by-case basis. We demonstrate that a simple conditional diffusion model can (1) generate meaningful structured perturbations, (2) be applied iteratively, and (3) utilize a guidance term to intuitivey control the level of perturbation. This method enables the transformation of any deterministic neural forecasting system into a stochastic one. With our stochastic extended systems, we show that the model accumulates less error over long-term forecasts while producing meaningful forecast distributions. We validate our approach on the 5.625$^\circ$ ERA5 reanalysis dataset, which comprises atmospheric and surface variables over a discretized global grid, spanning from the 1960s to the present. On this dataset, our method demonstrates improved predictive performance along with reasonable spread estimates.
Mathematical proof of errors in Capasso's excess noise factor formula for an n-step staircase multiplier
Authors: Ankitha E Bangera
Date: 2025-06-11 00:00:00

Solid-state devices such as multistep staircase avalanche photodiodes (APDs) are analogues to the photomultiplier tubes and are considered as a cascade-amplifier. The major source of internal noise in these APDs is due to the randomness in their stepwise impact ionization. Recent literature on staircase APDs by research groups such as Campbell and co-workers have reported the theoretical estimates of total excess noise factors using Capasso's excess noise factor formula. This formula is based on Friis' total noise factor formula for cascade networks. This article proves that Capasso's formula for a staircase APD, erroneously considers the power gains in Friis' total noise factor formula as the gains.
Neutral theory of cooperators
Authors: Jordi Piñero, Artemy Kolchinsky, Sidney Redner, Ricard Solé
Date: 2025-06-11 00:00:00
Tasks: Diversity

Mutualistic interactions are widespread in nature, from plant communities and microbiomes to human organizations. Along with competition for resources, cooperative interactions shape biodiversity and contribute to the robustness of complex ecosystems. We present a stochastic neutral theory of cooperator species. Our model shares with the classic neutral theory of biodiversity the assumption that all species are equivalent, but crucially differs in requiring cooperation between species for replication. With low migration, our model displays a bimodal species-abundance distribution, with a high-abundance mode associated with a core of cooperating species. This core is responsible for maintaining a diverse pool of long-lived species, which are present even at very small migration rates. We derive analytical expressions of the steady-state species abundance distribution, as well as scaling laws for diversity, number of species, and residence times. With high migration, our model recovers the results of classic neutral theory. We briefly discuss implications of our analysis for research on the microbiome, synthetic biology, and the origin of life.
Single-cell metabolic flux analysis reveals coexisting optimal sub-groups, cross-feeding, and mixotrophy in a cyanobacterial population
Authors: Arián Ferrero-Fernández, Paula Prondzinsky, Lucia Gastoldi, David A. Fike, Harrison B. Smith, Daniele De Martino, Andrea De Martino, Shawn Erin McGlynn
Date: 2025-06-06 00:00:00
Tasks: Specificity

We derive a single-cell level understanding of metabolism in an isogenic cyanobacterial population by integrating secondary ion mass spectrometry (SIMS) derived multi-isotope uptake measurements of Synechocystis sp. PCC6803 with a statistical inference protocol based on Liebig's law of the minimum, the maximum entropy principle, and constraint-based modeling. We find the population is structured in two metabolically distinct clusters: cells optimizing carbon yield while excessively turning over nitrogen, and cells which act reciprocally, optimizing nitrogen yield and excessively turning over carbon. This partition enables partial heterotrophy within the population via metabolic exchange, likely in the form of organic acids. Exchange increases the feasible metabolic space, and mixotrophic cells achieve the fastest growth rates. Metabolic flux analysis at the single-cell level reveals heterogeneity in carbon fixation rates, Rubisco specificity, and nitrogen assimilation. Our results provide a necessary foundation for understanding how population level phenotypes arise from the collective contributions of distinct individuals.
Insights into the role of dynamical features in protein complex formation: the case of SARS-CoV-2 spike binding with ACE2
Authors: Greta Grassmann, Mattia Miotto, Francesca Alessandrini, Leonardo Bo', Giancarlo Ruocco, Edoardo Milanetti, Andrea Giansanti
Date: 2025-06-05 00:00:00

The functionality of protein-protein complexes is closely tied to the strength of their interactions, making the evaluation of binding affinity a central focus in structural biology. However, the molecular determinants underlying binding affinity are still not fully understood. In particular, the entropic contributions, especially those arising from conformational dynamics, remain poorly characterized. In this study, we explore the relationship between protein motion and binding stability and its role in protein function. To gain deeper insight into how protein complexes modulate their stability, we investigated a model system with a well-characterized and fast evolutionary history: a set of SARS-CoV-2 spike protein variants bound to the human ACE2 receptor, for which experimental binding affinity data are available. Through Molecular Dynamics simulations, we analyzed both structural and dynamical differences between the unbound (apo) and bound (holo) forms of the spike protein across several variants of concern. Our findings indicate that a more stable binding is associated with proteins that exhibit higher rigidity in their unbound state and display dynamical patterns similar to that observed after binding to ACE2. The increase of binding stability is not the sole driving force of SARS-CoV-2 evolution. More recent variants are characterized by a more dynamical behavior that determines a less efficient viral entry but could optimize other traits, such as antibody escape. These results suggest that to fully understand the strength of the binding between two proteins, the stability of the two isolated partners should be investigated.