Strongly Correlated Electrons
Weyl superconductivity from Feshbach resonance in the three-dimensional repulsive Hubbard model
Taekyoung Kim, Pit Bermes, Giorgia A. Busin et al. (+1 more)
Motivated by the first experimental realization of the
antiferromagnetic phase transition in the three-dimensional
Fermi-Hubbard model, we present a theoretical study of the model's 3D superconducting phase. By formulating a 3D extension of the Feshbach mechanism, we provide a unified microscopic picture at strong coupling in which pairing is driven by near-resonant bound states of dopants. These long-lived bound states acquire a qualitatively different internal structure in three dimensions compared to their two-dimensional counterparts, giving rise to a distinct superconducting state, namely a time-reversal
symmetry breaking $d_{x^2-y^2}+id_{z^2}$ pairing state. We also provide an estimate of the corresponding critical temperature, and provide evidence that the resulting superconducting phase hosts gapless Weyl points. Our results represent a new milestone for the field of
quantum simulation, challenging experiments and large-scale numerics alike to test our predictions.
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All authors Taekyoung Kim, Pit Bermes, Giorgia A. Busin, Fabian Grusdt
Strongly Correlated Electrons
Quantum Well Resonant Tunneling Diode Probe of Correlated States in Twisted Bilayer MoS$_2$
Chengjie Zhou, Jin-Xin Hu, Sihong Xu et al. (+4 more)
Moiré superlattices formed in transition metal dichalcogenides (TMDs) offer a versatile platform for exploring emergent
quantum phases arising from strong electronic correlations. In this work, we develop a new experimental platform, the
quantum well resonant tunneling diode (QWRTD), to probe the electronic landscape of $\approx 56.1^\circ$ twisted
bilayer MoS$_2$ (tMoS$_2$). By measuring the differential conductance ($dI/dV_{\text{Probe}}$) as a function of filling factor $\nu$ and displacement field $D$, we observe a robust integer insulating feature at $\nu = 1$ that persists across the measured displacement field range. Furthermore, we identify a displacement-field-induced fractional insulating feature near $\nu = 3/4$ for $D < -75$~mV/nm. Its temperature dependence is consistent with a generalized Wigner
crystal interpretation, although the microscopic order remains to be established. These features are also observed in another device with a similar twist angle ($\approx 56.4^\circ$). Our results provide evidence of correlated states in near-AB-stacked tMoS$_2$ and establish QWRTD as a tool for investigating strong correlations in van der Waals
heterostructures.
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All authors Chengjie Zhou, Jin-Xin Hu, Sihong Xu, Zijing Jin, Hui Li, Kam Tuen Law, Jiannong Wang
Robotics
Communication-Free Distributed Multi-Robot Task Allocation under Partial Observations Using Labeled Multi-Bernoulli Filtering
Takumi Ito and Akiya Kamimura
This paper proposes a communication-free multi-
robot task allocation framework based solely on local observations. In this study, tasks are defined as reaching target locations. Each
robot estimates the positions of neighboring
robots using a Labeled Multi-Bernoulli (LMB) filter and independently assigns tasks through a greedy auction-based strategy. By continuously updating state estimates and reallocating tasks during execution, the proposed method enables decentralized coordination without explicit communication. Monte Carlo simulations demonstrate that the proposed method enables effective cooperative task allocation without inter-
robot communication while remaining robust to measurement clutter and observation uncertainty.
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Quantum Physics
Reservoir- and Measurement-free Microwave Initialization of Semiconductor Spin Qubits
Leon C. Camenzind, Ik Kyeong Jin, Akito Noiri et al. (+4 more)
Scalable
quantum processors require repeated
qubit initialization throughout large arrays. In
semiconductor spin qubits, fast initialization commonly relies on local reservoir access or measurement-based feedback, requiring dedicated infrastructure that becomes increasingly difficult to distribute as processors scale. Here, we demonstrate reservoir- and measurement-free initialization of a silicon
spin-
qubit pair in an industrially fabricated Si/SiGe
quantum-dot device using a fixed sequence of microwave and baseband pulses. Odd
spin-parity states relax to the singlet charge state, whereas blocked even
spin-parity states are microwave-driven through the triplet
manifold and subsequently converted to the singlet by singlet-triplet mixing and charge hybridization. Repeated cycles produce the singlet-associated charge outcome with a median probability of 99.4% across the sampled preparation states, while exchange spectroscopy independently verifies mapping to the target $|\uparrow\downarrow\rangle$ operational state. Microwave spectroscopy and time-domain measurements identify the dark-state-limited single-cycle transfer and the subsequent blockade-lifting dynamics that set the initialization time scale. The demonstrated pumping sequence uses approximately $12\,\mu\mathrm{s}$ of microwave bursts and mixing dwells, while we project sub-microsecond initialization under improved device conditions. These results establish fixed-sequence microwave initialization as a scalable control primitive for
semiconductor spin-
qubit processors, based on singlet-triplet physics that can be adapted to platforms with suitable Pauli-blockade transitions.
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All authors Leon C. Camenzind, Ik Kyeong Jin, Akito Noiri, Kenta Takeda, Takashi Nakajima, Takashi Kobayashi, Seigo Tarucha
Cryptography and Security
SEW: Style-Encoded Watermarking of LLM-Generated Code
Soohan Lim, Hyundong Jin and Yo-Sub Han
Code watermarking supports provenance tracking for code generated by LLMs. Modifying
token selection to embed watermarks as an LLM generates code can create a trade-off between detectability and functional correctness. Other methods instead watermark completed code using predefined transformations or trained neural models. Recurring patterns can make watermark choices predictable across programs, while treating patterns common in unwatermarked code as watermark evidence can cause false detections. We therefore introduce SEW, which embeds and detects watermarks in already generated code through three components: (i) code style rules collected from style guides and transformation rules, with style choices determined by a secret key and each program's structural context; (ii) style-preference calibration, which evaluates watermark evidence using style probabilities estimated from human-written code; and (iii) context-aware style aggregation, which combines evidence from structurally matching locations assigned the same code style choice, preventing repeated applications of that choice from inflating watermark evidence. On CodeContests across three LLMs and three programming
languages, SEW achieves a mean relative improvement of 12.44% in TPR@FPR5% over the baselines and is robust to four non-LLM code-editing attacks, with only a 0.94% mean relative decrease. Our code is available at https://github.com/suhanmen/SEW.
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Human-Computer Interaction
Aligning the Incomplete: Joint Distribution Calibration for Multimodal EEG-Eye Emotion Recognition
Yang Wu and Jinpeng Li
The success of cross-subject
multimodal emotion recognition hinges on maintaining the consistency of the joint data distribution across individuals. However, real-world deployment frequently triggers the asymmetric joint distribution collapse:
EEG signals suffer from severe cross-subject distribution shifts, while eye movements sensors are susceptible to packet loss and tracking failures. Existing methods treat domain adaptation and missing-modality imputation as disjoint tasks. Consequently, they fail to resolve the compounded errors when both degradations co-occur, either propagating domain shifts through imputed signals or destroying the joint decision boundary. To tackle this unified challenge, we propose GUARD (Gradient-guided Unsupervised Asymmetric Recovery of Distributions). First, GUARD establishes a reliable anchor
manifold in the source domain by employing a theoretically grounded gradient-weighted objective, which forces the robust
EEG modality to preemptively entangle task-discriminative ocular features. Next, to structurally recover the collapsed joint distribution, we constrain a generative module with downstream perceptual losses, prioritizing
emotion-discriminative semantics over mere signal fidelity. Finally, we formulate target-domain adaptation as an ill-posed inverse problem. By driving a cycle-consistent flow, we achieve unsupervised calibration of the recovered joint distribution directly on the target-domain
manifold. Extensive experiments demonstrate that GUARD significantly outperforms state-of-the-art methods, maintaining resilient discriminative performance even under complete auxiliary modality failure. Our code and models are made publicly available to ensure complete reproducibility.
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Databases
Recommendation Systems for Exploratory Data Tasks
Anna Fariha and Whanhee Cho
Exploratory Data Tasks (EDTs) involve iteratively steering workflows as users refine their subjective goals based on newly emerging insights. EDTs are performed by millions of users with varying levels of expertise to understand unfamiliar data, discover trends, and identify evidence that informs critical decision-making. However, a key challenge in EDTs is the enormous space of possible actions that one can take at each step: users struggle to choose among thousands of joins, transformations, and aggregations, leading to exploration paralysis. Because EDT workflows are interconnected, each choice shapes subsequent exploration, and suboptimal choices can lead to inefficiency, missed insights, confirmation bias, and incomplete coverage. This calls for intelligent recommendations that efficiently guide users toward optimal EDT actions. We envision recommendation as a core capability of data systems, proactively guiding users toward promising actions that lower the barrier to EDTs. This capability becomes even more critical as AI
agents emerge as a new class of users that perform EDTs on behalf of humans. EDT recommendation is challenging because the action space is combinatorial and actions are data-dependent, which require costly materialization. Moreover, recommendation often involves bundles or sequences of actions across interdependent tasks, requiring coordination across tasks. In this paper, we present our
vision of EDT recommendation systems along two axes: single-task vs. multi-task settings and single-action vs. multi-action recommendations. We outline a research agenda that progresses from recommending individual EDT actions to constrained bundles and sequences of actions, and ultimately to coordinated recommendations across interconnected EDTs. We identify research directions for incorporating various contexts, addressing efficiency challenges, and coordinating across tasks.
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Quantum Physics
Genuine Multipartite Nonlocality Is Fermionic Magic
Jamal Slim
Free
fermion dynamics is classically simulable, and a single non-Gaussian state makes it universal. We show that the same boundary governs
quantum nonlocality. Measured as fermionic
qubits, fermionic Gaussian states reach at most the biseparable bound of the Mermin inequality for three parties and, for any number of parties, violate the Mermin family by exactly the factor of a single entangled pair. They are never genuinely multipartite nonlocal. Genuine multipartite nonlocality and computational universality are thus one resource for
fermions, the opposite of the stabilizer case. The mechanism is
fermion parity. The bound is monogamous and certifies non-Gaussianity from measured correlations alone. On superconducting processors we watch nonlocality beyond free
fermions switch on under an interaction between densities, confirm the monogamy law on 96
qubits, and measure the ground state of twelve interacting
fermions on a ring cross the bound as the interaction grows, detecting the charge density wave transition.
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Soft Condensed Matter
The hidden side of paintbrushes: a geometry shaped by power laws
Raphaelle Taub, Jérôme Crassous, Thomas Salez et al. (+2 more)
Since prehistoric times, painters have used brush-like tools made from animal and plant materials to apply pigments to rock surfaces. Over time, techniques for creating brushes evolved, and converged towards optimized shapes. Our study performed over hundreds of brushes reveals universal scaling laws. In particular, we uncover a relation between bristle length and diameter linked to the shape of the bristle ends, which governs their mechanical response with and without paint. For round brushes, we found that the size of the paint stain scales with the compression distance, meaning it only depends on the force applied by the painter and not on the mechanical responses of the brushes. These results shed light on the underlying universal physical principles governing brush design and their impact on artistic techniques.
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All authors Raphaelle Taub, Jérôme Crassous, Thomas Salez, Frédéric Restagno, Christophe Poulard
Probability
Regular points and the boundary-value problem for a hypoelliptic process in a general smooth domain
Mouad Ramil and Mathias Rousset
We fully describe the set of regular and irregular boundary points for a general hypoelliptic
stochastic process $(X(t))_{t\geq0}$ absorbed outside a $C^{3,\,1}$ domain $\Omega$. The well-posedness of the associated boundary-value problem on $\Omega$ is also established in the classical sense. Let us emphasize that, throughout this work, no assumption is made regarding the inherent structure of the boundary of $\Omega$. In particular, characteristic and non-characteristic points may coexist on $\partial\Omega$, thus extending previous results obtained in the literature. We show that, unlike previous settings, the solution to this boundary-value problem may exhibit discontinuity at the boundary $\partial\Omega$. In certain cases involving a $C^\infty$ domain $\Omega$ and $C^\infty$ coefficients, the discontinuity set is even of positive Lebesgue surface measure on $\partial\Omega$. Finally, we highlight a cone condition under which the continuity is preserved at the boundary.
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Machine Learning
Awakening of the Buddha: Subspace Learning During Population-Loss Plateaus
Akash Kumar
Population loss can remain nearly constant while a neural network learns a substantially more predictive representation. We establish this separation for two-layer ReLU and leaky-ReLU networks trained on Gaussian inputs by simultaneous fixed-step population gradient descent on all parameters. For structured additive teachers whose links are positive mixtures of Gaussian-damped cubics in $H^1(\gamma)$, we give explicit conditions under which small IID Gaussian initialization yields a high-probability guarantee: at a checkpoint during a high-loss plateau, minimum
alignment between the rank-$r$ teacher subspace and the leading $r$-dimensional eigenspace of the predictor's average gradient outer product (AGOP) increases by at least $1/2$, and the minimum refit MSE under unchanged coefficient budgets decreases by more than $0.399$, both relative to initialization. The same trajectory subsequently attains a trained loss below every value in the plateau window. A complementary result treats unequal-weight cubic teachers and small additive Sobolev perturbations using projected-feature refits. For SwiGLU networks with an exactly fitted intercept, we prove leading-AGOP
alignment during a loss plateau at fixed width and dimension as Gaussian initialization vanishes, for square-integrable teachers with nonzero Hermite content of degree one, two, or three. A rank-one cubic specialization also gives simultaneous unrestricted-refit gains at a prescribed width. An approximation lower bound further shows that certain interaction targets retain nonzero error when ridge
neurons are restricted to shared orthogonal axes within the teacher subspace. Population-moment experiments with ReLU students across 21 teachers and 50 initializations per teacher complement the analysis.
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Combinatorics
Nice Partitions, Supersolvability, and Freeness in Deformations of Graphic Arrangements
Weikang Liang, Suijie Wang and Yue Zhou
We study affine deformations $\mathcal{A}(G_{\mathcal{S}})$ of graphic arrangements and their cones. Here $G=([n],E(G))$ is a simple graph, $\mathcal{S}=(S_{ij})$ is a family of finite gain sets, and $\mathcal{A}(G_{\mathcal{S}})$ consists of the hyperplanes $x_i-x_j=a$ with $a\in S_{ij}$. We call $G_{\mathcal{S}}$ blockwise admissible if every block has a vertex ordering $v_1,\ldots,v_m$ satisfying $S_{v_kv_j}-S_{v_kv_i}\subseteq S_{v_iv_j}$ for every $k$ and all distinct $i,j>k$. Every such ordering is a perfect elimination ordering. We prove, for arbitrary $G$, that the following are equivalent: (i) $G_{\mathcal{S}}$ is blockwise admissible; (ii) the cone $c\mathcal{A}(G_{\mathcal{S}})$ is supersolvable; and (iii) $\mathcal{A}(G_{\mathcal{S}})$ admits a nice partition. We give a direct arrangement-theoretic proof of these equivalences. For a block, the equivalence between admissibility and supersolvability is already contained in Zaslavsky's characterization of supersolvable graphic-lift
lattices. We further show that every nice partition on a block is induced by an admissible ordering and that its induced edge classes are
stars with distinct centers. Under these equivalent conditions, we construct a maximal modular chain through the hyperplane at infinity. We also prove that if $c\mathcal{A}(G_{\mathcal{S}})$ is free, then $G$ is chordal.
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Artificial Intelligence
Inferring Causal Relations between Two Sequences of Events with Language Models
Nishchal Prasad, Eric Gaussier, Emilie Devijver et al. (+3 more)
Causal AI is a branch of Artificial Intelligence which helps understand and reason about cause and effect relationships, not just patterns or correlations. Causal discovery aims to infer elements of the underlying causal structure–often represented as a directed graph–from observational and, when available, interventional data. While causal discovery is the fundamental step for moving beyond mere associations toward genuine understanding, and thus the basic building block of causal AI, it becomes intrinsically difficult when causal relations must be inferred from single observations. In such situations, standard causal discovery methods cannot be used and one has to identify causal relations from limited amount of information. This is typically the case for, e.g., sequences of events produced by different alarms which need to be analyzed on the fly to detect abnormal phenomena, which are usually rare. We show in this study that it is possible to leverage the predictive power of Large
Language Models (LLMs) to infer causal relations between only two sequences of events. This approach, which is validated on both synthetic and real data, provides better results than standard causal discovery
algorithms on several time series data, even though these data were converted into smaller, single observed sequences.
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All authors Nishchal Prasad, Eric Gaussier, Emilie Devijver, Alexander Obeid Guzman, Armen Aghasaryan, Gregor Gössler
Machine Learning
No Task Vector Is an Island: A Comprehensive Study on the Composability of Task Vectors from On-Policy Distillation
Jingang Zhou, Feiyu Han, Han Zhu et al. (+6 more)
Task vectors provide a simple mechanism for composing learned capabilities through model merging. However, the composability of task vectors produced by on-policy
distillation (OPD) remains largely unexplored. OPD trains a student using teacher feedback on student-generated trajectories, yielding parameter updates that differ from those produced by the teacher model, usually by reinforcement learning (RL). We therefore ask whether OPD task vectors can complement their RL teacher updates and compose effectively across tasks. Across five domains and two model architectures, we find evidence for both forms of composability. Within a task, merging OPD and RL task vectors can outperform both constituent models, even when the OPD student is weaker than its RL teacher. Across tasks, OPD task-vector compositions achieve higher average scores than corresponding RL compositions in seven of eight backbone-merging-rule comparisons. Parameter-space analyses reveal substantial non-collinearity between OPD and RL updates. Experiment in CODE domain on SMOLLM3-3B shows that the combined direction outperforms either constituent direction at the tested global update norm, supporting directional complementarity in this configuration. Across tasks, OPD updates also show lower overlap among the top-10% feed-forward channels ranked by update energy. Together, these results show that weaker standalone performance does not imply weaker task-vector composability. OPD task vectors can complement stronger RL teacher updates and combine effectively across tasks, highlighting composability as a distinct property for understanding and evaluating post-training updates.
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All authors Jingang Zhou, Feiyu Han, Han Zhu, Yuyi Zhou, Ruiyang Zhang, Jian Xu, Sirui Gao, Qingpei Guo, Xu-Yao Zhang
Strongly Correlated Electrons
Chiral quantum chaos around exponentially many zero modes in the quantum breakdown model
Kohei Kawabata, Kinya Guan and Hosho Katsura
The
quantum breakdown model is a model of randomly interacting
fermions, motivated by the physics of dielectric breakdown. Here, we establish classification of
symmetry and
quantum chaos in a zero-dimensional, all-to-all-interacting version of the
quantum breakdown model. It exhibits the $\mathbb{Z}_4$ periodicity with respect to the number of fermionic modes, reminiscent of the
symmetry classification of the Sachdev-Ye-Kitaev model. A unique feature of the
quantum breakdown model is that it realizes all the five classes with
chiral symmetry and hosts an exponentially large number of many-body zero modes protected by the
chiral index. We elucidate the separation and scaling of the spectral gap and demonstrate the
symmetry-enriched hard-edge spectral statistics as signatures of
quantum chaos in the
chiral symmetry classes.
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Robotics
IronMind: Scaling Humanoid Dexterous Manipulation via Camera-Space Ego-Centric Pretraining
Huimin Pan, Yufan Ren, Kunpeng Song et al. (+16 more)
Egocentric human
video offers a scalable data source for dexterous manipulation, yet using it to train
humanoid robots presents two challenges: (1) an embodiment gap, as human hands differ structurally from
robot end-effectors and low-cost
egocentric recordings lack the torso kinematics required by conventional retargeting; and (2) heterogeneous data quality, including noisy hand-pose tracking and weakly aligned text annotations. We introduce IronMind, a
vision-
language-action (VLA) model that uses
egocentric human
video and heterogeneous
robot data to pretrain policies for
humanoid dexterous manipulation. To bridge the embodiment gap, IronMind bypasses explicit body-retargeting by using a camera-space action representation, the native reference space of
egocentric video, and semantically aligning
robot and human action dimensions. Across total pretraining budgets from 250 to 10,000 hours, validation loss decreases approximately log-linearly with data scale. Larger pretraining budgets also improve out-of-distribution real-
robot manipulation after post-training: across six challenging tasks with unseen objects, affordances, and
reasoning prompts, the 10,000-hour model achieves a 55.0% success rate, compared with at most 11.7% for every pretraining budget up to 5,000 hours and 5.0% without pretraining. At the same pretraining budget, the camera-space action representation also outperforms the torso-frame baseline. Together, these findings support pretraining with a camera-space action representation on large-scale human
egocentric data as a scalable foundation for
humanoid robot manipulation.
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All authors Huimin Pan, Yufan Ren, Kunpeng Song, Siyang Wang, Xiwen Zhang, Xiaoyun Hu, Zhuoxu Duan, Hanrui Zheng, Jialeng Ni, Nathan Zhao, Sibo Ma, Zhenxuan Fan, Zhongyang Che, Danny Bao, Jiacheng Wei, Jerry Bai, Xiaoyu Yue, Xiaoyang Guo, Chenyi Chen
Artificial Intelligence
Advancing Entropy-Level Credit Assignment in RLVR via Proximal Entropy Policy Optimization
Yun Kim and Nojun Kwak
Value-model-free RLVR methods such as GRPO assign uniform advantages to all
tokens in a rollout, ignoring that
tokens contribute unequally. Recent methods use
token entropy as an importance proxy but compute it globally across the batch, conflating importance with
prompt difficulty and positional trends. We argue that importance should instead be measured relative to the local context of each
token. We introduce proximal
entropy, a local measure of
token importance relative to neighboring
tokens, and prove it is invariant to both confounders. Proximal
Entropy Policy Optimization (PEPO) uses it to weight per-
token advantages and outperforms GRPO and
entropy-based baselines on mathematical
reasoning across Qwen3-1.7B, Qwen3-4B, and Llama-3.2-3B-Instruct. We also show the formulation generalizes to other
algorithms where substituting proximal
entropy into existing methods improves, and applying it to single-stream RL succeeds where global
entropy fails.
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Statistical Mechanics
Square-root growth of operator entanglement in an integrable brickwork circuit
Balázs Pozsgay
It is widely expected that the von Neumann
entanglement of a local operator grows at most logarithmically in integrable many-body systems in infinite volume. We give a counterexample in a four-state brickwork
circuit whose gate is a permutation
matrix solving the constant Yang–Baxter equation. The von Neumann operator
entropy grows as $(\log2)\sqrt{t/\pi}+O(\log t)$, whereas fixed-index Rényi
entropies grow linearly below index one and logarithmically above it. At fixed relative
Hilbert–Schmidt error below one,
matrix product operator simulations require a bond dimension growing at least as $\exp(c\sqrt t)$ for some $c>0$.
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Instrumentation and Detectors
Measurement of the $^{39}$Ar specific activity in atmospheric argon with DArT at the Canfranc Underground Laboratory
P. Agnes, H. O. Back, S. Bharat et al. (+41 more)
We report the measurement of the $^{39}$Ar specific activity of atmospheric argon using DArT, a low-background single-phase
liquid-argon detector read out by cryogenic silicon photomultipliers, at the Canfranc Underground Laboratory (LSC) in Spain. DArT was first filled with atmospheric argon and then with a sample of underground argon of known radioactivity from the DarkSide-50 experiment. The underground argon serves as a background reference in this measurement, making the result robust because we do not rely on background simulations. We measure the $^{39}$Ar specific activity with both a cut-and-count method and a binned maximum-likelihood fit, yielding consistent results. We obtain $a_{\text{AAr}}= 0.955 \pm 0.008~\text{Bq}/\text{kg}$. This result agrees with previous measurements from other experiments and provides the most precise determination of the $^{39}$Ar specific activity in atmospheric argon to date. It also validates DArT as a key component of the DArTInArDM experiment at LSC, which aims to measure the $^{39}$Ar activity of the argon extracted from deep underground wells in Colorado (USA) for the Darkside-20k and LEGEND-1000 experiments at Laboratori Nazionali del Gran Sasso in Italy.
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All authors P. Agnes, H. O. Back, S. Bharat, W. M. Bonivento, M. Boulay, M. Caboni, N. Canci, N. Cargioli, P. Castello, S. Cebrián, L. Consiglio, R. Crampton, D. Diaz Mairena, D. Franco, I. Fuente-Ortega, F. Gabriele, D. Gahan, C. Galbiati, P. Garcia Abia, T. Hessel, S. Horikawa, A. Ianni, G. Korga, M. Kuźniak, L. Luzzi, A. Marasciulli, M. Martínez, P. Organtini, A. Ortiz de Solórzano, R. Pavarani, P. Pegoraro, V. Pesudo Fortes, M. Razeti, O. U. Rehman, L. Romero, D. Sablone, E. Sánchez García, R. Santorelli, C. Seoane, J. Sosiak, S. Sulis, C. Tierney, S. Tullio, G. Vera Díaz
Astrophysics of Galaxies
Yao-Yu Li, Shihong Liao, Yue-Lin Sming Tsai et al. (+1 more)
Primordial magnetic fields (PMFs), potentially generated during
cosmic inflation or early-universe phase transitions, can modify
cosmic structure formation by enhancing the post-recombination matter power spectrum. In this work, we investigate the impact of PMFs on
galaxy formation using a suite of high-resolution cosmological hydrodynamical simulations evolved to $z=0$. Our simulations are initialized with PMF-induced enhancements to the matter power spectrum and include more comprehensive baryonic subgrid models than previous work, incorporating both
stellar and AGN feedback. We find that PMFs systematically accelerate early structure formation, producing more abundant
halos and
galaxies, higher
baryon fractions, enhanced
star formation rates, and rapid growth of supermassive black holes at high
redshifts ($z \sim 10$). However, the resulting enhanced
stellar and AGN feedback efficiently processes and expels gas, reducing the late-time differences between PMF and standard $\Lambda$CDM models. The PMF signatures are therefore strongest at high
redshift and gradually weaken toward low
redshift as nonlinear
evolution and baryonic feedback dominate over the initial PMF-induced enhancements. The impact of PMFs depends strongly on the characteristic peak scale, $k_{\rm peak}$, of the initial power enhancement, with models yielding similar non-linear
evolution if they share a similar $k_{\rm peak}$, even when other PMF parameters differ. Our results highlight the critical role of comprehensive baryonic physics in accurately quantifying PMF signatures.
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All authors Yao-Yu Li, Shihong Liao, Yue-Lin Sming Tsai, Yi-Zhong Fan
Systems and Control
Sharing the Gains of Aggregation: Cooperative Imbalance Cost Allocation
Asmus Winther Eriksen and Jalal Kazempour
A facilitator is an intermediary that offers renewable producers and consumers fixed-price contracts and, acting as their balance responsible party, manages the residual imbalances in the
market. Pooling imperfectly correlated residuals nets consumers' imbalances and reduces the portfolio's total imbalance cost, but raises an allocation question: how should these savings be divided among heterogeneous consumers? We formulate this as a cooperative game, the imbalance netting game, and compare six allocation mechanisms under a two-price imbalance settlement in terms of computational requirements, budget balance, group rationality, and additivity. We establish three analytical results: a necessary and sufficient condition for budget balance of the marginal cost contribution mechanism, group rationality of the marginal cost contribution and Vickrey-Clarke-Groves mechanisms, and a necessary and sufficient condition for when the Gately point is well-defined. On Danish 2024 data for 19 consumers, aggregation reduces imbalance cost by 10%, but how these savings are shared depends strongly on the allocation rule. A flat-rate allocation proportional to consumption, used as a
benchmark for socialized imbalance pricing, charges some consumers more than twice their standalone cost, whereas all the game-theoretic mechanisms studied produce stable allocations in practice.
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Machine Learning (Statistics)
Towards Optimal Inventory Control under Censored Demand: A Biased Sample-Average Approximation Approach
Yuxuan Han, Xiaoyu Fan, Jiawei Zhang et al. (+1 more)
We study data-driven multi-period lost-sales inventory control under censored demand, where a stockout reveals only that demand exceeded the stocking level. We develop a unified, model-based framework for policy learning from censored data, built on a new cost decomposition for base-stock policies and a biased sample-average approximation (SAA) approach. The cost decomposition allows us to propose a new coverage condition under which censored observations are informative enough for sample-efficient policy learning. Guided by this coverage condition, we design two biased SAA
algorithms: an upper-biased one that achieves near-optimal sample complexity under the offline coverage condition, and a lower-biased one that actively generates the required coverage and achieves near-optimal regret online. More broadly, this biased SAA approach provides a general principle for implementing pessimism and optimism under censored feedback, which may be of independent interest.
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All authors Yuxuan Han, Xiaoyu Fan, Jiawei Zhang, Zhengyuan Zhou
Systems and Control
Learning to Fix: Optimisation-Aware Machine Learning for Accelerated Unit Commitment
Benjamin Fritz, Andreas Makrides, Maryam Fetanat et al. (+1 more)
Unit Commitment is a computationally demanding mixed-integer linear optimisation problem requiring many binary commitment decisions across a scheduling horizon. To reduce this computational burden, machine learning approaches can predict a subset of these decisions, thereby shrinking the search space explored by the optimisation solver. Existing confidence-based approaches determine which variables to fix based on user-defined probability thresholds, which are agnostic to the downstream optimisation problem. We address this limitation by introducing an optimisation-aware framework that yields generator-specific confidence thresholds based on the impact of fixing errors on Unit Commitment solution quality. This approach won first place in the 2025 EPRI AI-ccelerating Unit Commitment competition. The proposed framework achieves a mean optimality gap below 0.5% while also delivering an average speed-up of more than 20x. It therefore provides a general mechanism for translating probabilistic predictions into controlled reductions of mixed-integer search spaces, directly linking learning decisions to their downstream computational and economic consequences.
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All authors Benjamin Fritz, Andreas Makrides, Maryam Fetanat, Pierre Pinson
Quantum Physics
Logical Operator Decomposition for Distance Analysis of Bivariate Bicycle Codes
Mohammad Rowshan and Simon Devitt
Bivariate bicycle (BB)
quantum codes are a prominent finite-length family of
quantum low-density parity-check codes, but their minimum distance is usually established numerically rather than read from the defining
polynomials. We study the $Z$-logical quotient $K/S$ over $\mathbb F_2[x,y]/(x^\ell-1,y^m-1)$ and show that it fits into a short exact sequence with an annihilator quotient as kernel and a colon quotient as cokernel. The sequence gives an explicit logical basis, a dimension formula, and a componentwise distance identity $d_Z=\min(d_{\mathcal A},d_{\mathcal C})$. Using the Frobenius structure of the finite group
algebra, we prove $r_{\mathcal A}=r_{\mathcal C}=k/2$ for every BB code, including repeated-root cases. The algebraic component of a logical class is distinct from the support shape of its lightest representatives: an annihilator class can have a lighter two-block representative, and a colon class can have a one-sided minimum. For lower bounds we show that every proper subset of a minimum-weight logical operator has nonzero syndrome, and that this property persists inside the colon component but not inside the annihilator component. A translation-anchored cluster search built on it proves the distances $4,6,10,10,12,18$ of the six standard BB codes of lengths $18$ to $288$ and enumerates every minimum-weight logical operator. The resulting census shows that $[[108,8,10]]$ is the only one of the six whose distance is attained in a single component, with $d_{\mathcal C}=10$ and $d_{\mathcal A}=12$.
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Artificial Intelligence
Can Computation from Earlier Problems Help LLMs Solve New Ones?
Jipei He, Wenhui Tan, Xiaoyi Yu et al. (+4 more)
Large
language models often solve independent problems in the same conversation. Can computation from earlier problems help them solve new ones? To answer this question, we first conduct preliminary experiments showing that retained history can raise or lower later-turn accuracy, even within the same domain. To understand these effects, we use controlled replay to isolate internal state changes specific to each problem-history pairing. Across different histories, these changes preserve similar relationships among current problems. To improve
reasoning under retained history, we introduce STAIR (Stale-
Token Attention for Inter-query Reuse). STAIR captures keys and values from earlier response generation in a fixed bank. It learns to redirect current queries when they read this bank during
prompt processing. The base model remains frozen; only 12,288 parameters are trained. Across three Qwen models and four
benchmarks, STAIR improves average later-turn accuracy by up to 11.67 percentage points over the unmodified model with history.
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All authors Jipei He, Wenhui Tan, Xiaoyi Yu, Enver Sangineto, Fiorenzo Parascandolo, Rita Cucchiara, Ruihua Song
Quantum Physics
Spectral and Impedance Matching for Multi-excitation Charging of an Interacting Quantum Battery via an Anharmonic Mediator
Sujay Mondal, Anumita Mukhopadhyay, Siddhartha Dutta et al. (+1 more)
We investigate a finite-time
quantum-battery charging architecture in which a driven two-level working system transfers nonequilibrium resources to an interacting two-
qubit battery through a weakly anharmonic three-level mediator. Finite-time driving generates coherence and ergotropy, while excitation-number conservation organizes the transfer into distinct dynamical sectors. The mediator then provides spectrally selective charging channels: its lower transition enables nearly complete single-excitation transfer with negligible residual mediator energy, whereas initial mediator loading activates a cross-matched resonant two-excitation cascade toward the doubly excited battery state. In the latter sector, the mediator anharmonicity compensates the interaction-induced non-equidistance of the battery spectrum, enabling simultaneous resonance of the two transfer channels. At exact double resonance, unequal collective
matrix elements impose the analytical bound $P_{\rm max}=8/9$; unit transfer is recovered under the impedance-matching condition $|G_1|=|G_2|$, as confirmed numerically. Detuning scans quantify the spectral tolerance, and the full mixed-state dynamics reveals distinct energy- and ergotropy-transfer profiles. Finite-time compression, thermal reset, and switching work close the cycle-level energy balance to numerical precision. The resulting framework combines nonequilibrium resource generation, spectral matching, impedance matching, and coherent multilevel charging, and provides a basis for extending these principles to collective and many-body
quantum batteries.
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All authors Sujay Mondal, Anumita Mukhopadhyay, Siddhartha Dutta, Abhijit Bandyopadhyay
Artificial Intelligence
Experimental Experience Modeling for Autonomous Research
Wenda Wei, Yingchen Zhang, Ruqing Zhang et al. (+3 more)
Autonomous research
agents can generate hypotheses and conduct experiments, but experimentation remains a major source of computational cost. A fundamental challenge is deciding which experiments are worth running, particularly when prior evidence is insufficient to resolve uncertainty. Yet current research
agents lack a systematic way to leverage experimental experience when making such decisions. We introduce Experimental Experience Modeling (EEM), a framework for making informed experimental decisions by acquiring, reusing, and accumulating experimental experience. EEM extracts decision-relevant records from earlier experimental trajectories, distills them into reusable experience, and organizes them in an experience library. For a new experimental decision, EEM retrieves relevant historical experience and assesses whether it provides sufficient support for deciding whether a candidate direction warrants further investment. When historical experience is insufficient, EEM conducts a targeted, low-cost pilot experiment to acquire the missing decision-relevant experience on demand. It then combines this newly acquired experience with retrieved historical experience to determine whether the direction warrants full-scale evaluation, which requires substantial resources. The resulting experimental outcomes are further distilled into reusable experience, allowing the library to continually grow through iterative accumulation. Experiments on autonomous research
benchmarks show that EEM improves research performance while reducing model interaction overhead, demonstrating the value of reusing accumulated experience and acquiring additional experience only when needed.
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All authors Wenda Wei, Yingchen Zhang, Ruqing Zhang, Jiafeng Guo, Daiting Shi, Xueqi Cheng
Fluid Dynamics
Rigorous vertical averaging for pseudo-3D modeling of precipitation obstructed fluid flow
David Krach, Felix Weinhardt, Holger Class et al. (+2 more)
Computing effective hydraulic properties of porous materials with a dynamically changing pore morphology presents a complex and critical challenge in both theoretical and applied sciences. This paper presents a tool to effectively compute the intrinsic permeability k(x,t) for domains with both temporally and spatially varying pore structures, by utilizing a sample of snapshots of microfluidic experiments with calcium carbonate precipitation. To compute large domains at reasonable cost, a pseudo-3D Stokes solver with an additional viscous drag term is employed. In order to simulate the intricate domains with highly heterogeneous pore space, we derive vertically averaged governing equations including a vertically averaged drag term from scratch and without limiting assumptions. For exemplary precipitates (semi-spheres) of varying sizes within a rectangular channel with fixed solid boundaries, results are compared across multiple modeling approaches, each employing different drag force formulations. In addition, the performance of the derived models is examined for segments of an EICP experiment in terms of accuracy (comparison with 3D solvers) and computational efficiency. Finally, an entire experimental domain is used to demonstrate the capabilities offered by the solver thanks to its efficiency as well as accuracy and the permeability tensor k is computed and compared with experimental results. In all these applications, the vertically averaged drag term proves to be the optimal solution in terms of speed and accuracy, thus allowing for an efficient analysis of the non-uniformity of local fluid flow fluctuations resulting from alterations in the pore space.
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All authors David Krach, Felix Weinhardt, Holger Class, Holger Steeb, Martin Schneider
Machine Learning
Decoupled and Distilled: Task-Adaptive LoRA-Teachers with Ensemble Knowledge Transfer for Few-Shot Class-Incremental Learning
Hongwei Zhao, Rui Liu, Yansong Liu et al. (+2 more)
Few-Shot Class-Incremental Learning (FSCIL) addresses the challenge of learning new classes from very limited samples while retaining knowledge of previously learned ones. Although parameter-efficient fine-tuning methods with pre-trained models show promise for class-incremental learning, strict gradient-based constraints can be unreliable under severe data scarcity, while multi-expert approaches can impose substantial inference-time costs. We propose TALON (Task-Adaptive LoRA-Teachers with Ensemble Knowledge Transfer), an inference-efficient FSCIL framework. TALON dynamically allocates an independent LoRA-Teacher to each incremental task for task-specific representation learning, then distills multiple frozen teachers into a unified LoRA-Student through Ensemble Knowledge Transfer, eliminating runtime module selection or generation. A semantic-guided
distillation strategy weights teacher contributions by feature-space similarity to mitigate catastrophic forgetting and overfitting. Across three class-order runs, TALON achieves comparable or better mean average accuracy across four FSCIL
benchmarks, obtaining 86.68 +/- 1.22% on CUB200, 90.39 +/- 0.27% on CIFAR100, 78.38 +/- 0.94% on ImageNet-R, and 96.34 +/- 0.33% on miniImageNet. TALON uses up to 33x fewer deployment parameters and reduces average inference time per task to 26.7 s, a 41.70% reduction relative to ASP.
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All authors Hongwei Zhao, Rui Liu, Yansong Liu, Zhiyuan Zou, Yong Chen
Classical Analysis and ODEs
Natural reparameterization and continuity of the solution map for polynomials
Adam Parusiński and Armin Rainer
The optimal Sobolev regularity of the roots of a smooth curve of monic complex
polynomials is $W^{1,q}$, for $q \in [1,\frac{d}{d-1})$, where $d$ is the degree. This result is stable in the sense that the solution map from $C^d$ coefficients to $W^{1,q}$ roots is continuous. We prove that, after a natural Lipschitz reparameterization, the roots are Lipschitz and the map from $C^d$ coefficients to reparameterization and reparameterized roots is continuous with respect to the $W^{1,q}$
topology on the target spaces, for all $q \in [1,\infty)$. The result is based on a convergence and reparameterization
theorem for Almgren's $Q$-valued Sobolev functions.
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