Neural and Evolutionary Computing
Predictive Suppression Layers for Communication-Efficient Spiking Neural Networks
Aidin Attar and Michele Rossi
Feedforward Spiking Neural Networks (SNNs) typically propagate every generated spike indiscriminately, disregarding whether the information is redundant from an information-theoretic perspective. This lack of selectivity induces high redundancy in inter-layer communication, creating an expensive overhead, e.g., in scenarios involving many-core neuromorphic hardware or communication-dominated Internet-of-Things (IoT) where features are transmitted wirelessly. To address this challenge, we trade localized processing for leaner network channels by introducing a minimal predictive coding framework for SNNs. We propose two layer variants sharing a predictor block: error units, which transmit signed spiking residuals, and predictive suppression, which uses residual magnitude to dynamically gate and forward only unpredictable, "surprising" activity. Evaluated on the N-MNIST and Spiking Heidelberg Digits (SHD)
datasets using diagnostic metrics that decouple local processing from cross-layer communication, our new predictive coding layers achieve significant communication savings. Numerical results reveal a three-fold reduction in communicated activity, while increasing the task accuracy for both
datasets. The latter finding is notable, and suggests that predictive coding layers not only minimize communication overhead, but also produce output feature vectors with a higher representation power.
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Soft Condensed Matter
Tunable parametric vibrations in fluid ferroelectric fibres
Alexander Dieter-Jarosik, Hajnalka Nádasi, Rachel Tuffin et al. (+2 more)
Freely suspended ferroelectric-nematic fibres combine 1D fluidity with macroscopic spontaneous polarisation. Under alternating electric fields, we observe a sequence of dynamical states, from subthreshold pulsations to polarity-dependent axial flow, and sustained transverse oscillations. We show that the oscillatory state arises from a parametric instability of a
liquid string whose effective tension is dominated by the electrical polarisation-field coupling contribution. A Floquet model incorporating finite-time polarisation switching captures the observed resonance tongues, modal selection, and square-wave response. These results establish ferroelectric-nematic fibres as electrically programmable fluid resonators coupling polar order and interfacial flow with nonlinear vibration.
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All authors Alexander Dieter-Jarosik, Hajnalka Nádasi, Rachel Tuffin, Melanie Klasen-Memmer, Alexey Eremin
Audio and Speech Processing
Towards Zero-Shot Attribution of Synthetic Speech via Audio-Text Contrastive Retrieval
Cristian-Teodor Neamtu, Serban Mihalache, Stefan Smeu et al. (+3 more)
Audio deepfake forensics is moving beyond a simple real-or-fake verdict toward attribution: which system generated the audio clip? Most source-attribution methods cast this as closed-set classification, so they cannot name a generator that was absent from training, a gap that widens with every newly released text-to-
speech (TTS) system. We instead frame attribution as cross-modal
retrieval: each generator is described in natural
language, and a clip is attributed by retrieving the description closest to it in a shared audio-text
embedding space. Adding a new system then takes nothing more than writing its description, with no retraining and no new classifier head. Our model couples a frozen Wav2Vec2-BERT audio encoder with a frozen E5 text encoder and aligns them through small trainable projection heads, using a contrastive objective that combines a cross-modal supervised-contrastive loss with intra-modal terms. We evaluate on MLAAD v9 (140 TTS models, 51
languages) under 10-fold leave-models-out cross-validation. For generators it has never encountered before, the model reaches a model-level mean reciprocal rank (MRR) of 58.4%. Even when the correct model is not identified, the audio clip is often matched to systems that share the true generator's vocoder, acoustic model, or architecture. Because the same
embedding space also answers natural-
language attribute queries, one set of descriptions covers both open-set attribution and attribute-level forensic profiling.
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All authors Cristian-Teodor Neamtu, Serban Mihalache, Stefan Smeu, Dan Oneata, Horia Cucu, Dragos Burileanu
Robotics
Tilt as a Certified Resource: Preserving Motor Wrench-Rate Authority on Articulated Multirotors
Giuseppe Silano and Martin Saska
Fully-actuated multirotor aerial vehicles must not only track nominal wrenches but retain the "readiness" to modulate them rapidly under disturbances. Classical effort-minimizing allocators ignore this dynamic limit, whereas maximizing readiness leads to topologically disconnected optimal sheets demanding physically impossible actuator rates. Enforcing a readiness safety floor on fixed-geometry symmetric platforms further encounters a zero-sum degeneracy: motor-speed redistribution cannot improve authority without conceding wrench tracking. This paper uses active morphology to break the degeneracy, treating servo tilt as a
geometric resource supplying authority-recovery directions unavailable to static rotors. We construct a configuration-dependent, motor-only readiness certificate - the log-volume of the reachable wrench-rate set - that explicitly excludes servo capacity, preventing a "ghost capacity fallacy" in which the certificate would falsely credit slow mechanical kinematic limits instead of collapsing accurately at motor saturation. The certificate is enforced as a Control Barrier Function (CBF) within a Unified Physical-Command Quadratic Program acting on motor torques and servo setpoints. Closed-loop simulations of an articulated octorotor under severe gust disturbances show classical allocators diverging and uncertified articulated allocators violating the safety floor, while the proposed CBF filter bounds the system state and preserves vehicle authority.
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Quantum Physics
Global Entanglement Quantification via Classical Shadows
João P. Engster and Eduardo I. Duzzioni
Detecting and characterizing
quantum correlations are tasks of great relevance in
quantum information. More specifically, quantifying the amount of multipartite
entanglement is a known difficult task, even for pure states. To this end, several
entanglement measures have been proposed, although there is currently no universal manner to do so. In this work, we propose the classical shadows technique to measure the generalized global
entanglement introduced in Phys. Rev. A 74, 022314. This particular multipartite
entanglement quantifier $E_G^{(n)}$ relies on the linear
entropies of all $n$-
qubit partitions of a state. As the linear
entropy can be written in terms of a complete set of observables of the subsystem, we employ classical shadows to estimate many observables with fewer measurements. We simulate the quantification of $E_G^{(n)}$ for well-known entangled states and for random states, comparing shadow estimations and grouping techniques. Our results show a clear advantage of classical shadows over direct estimation approaches, indicating that it can be a useful tool to quantify
entanglement without the need to reconstruct the density operator of the whole system.
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Cosmology and Nongalactic Astrophysics
A note on oscillons: non-negligible metric fluctuations during preheating
Daniel del-Corral, Paolo Gondolo, K. Sravan Kumar et al. (+1 more)
The dominant
lattice approach to post-inflationary preheating and oscillon formation evolves an inhomogeneous inflaton field on a spatially homogeneous Friedmann-Lemaître-Robertson-Walker background, whose expansion is sourced by a volume-averaged energy density, while metric perturbations are neglected. We examine the consistency of this approximation with General Relativity. Using the linearized Einstein constraints, we show that the suppression of the Bardeen potential, characteristic of slow-roll inflation, disappears during preheating: once the first slow-roll parameter becomes of order unity, the metric and inflaton fluctuations enter at the same perturbative order. We further show that the scalar-field equation evolved in the fixed-FLRW prescription omits leading-order contributions generated by metric fluctuations. We then derive the proper-volume average of the ADM
Hamiltonian constraint and show that it does not reduce to the Friedmann equation used in
lattice simulations: the exact averaged constraint contains additional contributions from the spatial
curvature, the variance of the local expansion, the shear, and metric corrections to the local energy density. Finally, we illustrate numerically, for Starobinsky and $\alpha$-attractor models, that the metric contribution becomes comparable to the scalar-field contribution during the amplification stage. These results directly affect the standard
lattice description of oscillon formation and motivate numerical relativity as a consistent framework for the nonlinear preheating problem.
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All authors Daniel del-Corral, Paolo Gondolo, K. Sravan Kumar, João Marto
Statistical Methodology
Phasing out Monte Carlo: exact acceptance probabilities for mean-spread release rules
Remus Osan
A large family of regulated release decisions accepts a production lot if and only if the pair (sample mean, sample standard deviation) of $n$ units falls in a fixed plane region. Content uniformity (USP <905>), capability release, variables sampling and percent-within-limits highway acceptance all take this form. The acceptance probability under a hypothesized unit-level law is not, so far as we are aware, evaluated exactly in current practice: for a general parent the joint finite-$n$ law of $(\bar X,s)$ is a constrained $n$-fold integral (Craig 1932 at $n=3,4$; Springer 1953 at general $n$) which admits no elementary reduction. Yet that probability is fixed by the law of the additive pair $(T_1,T_2)=(\sum_{i=1}^{n} X_i,\sum_{i=1}^{n} X_i^2)$, whose characteristic function is the $n$-th power of a one-observation transform. One two-dimensional
Fourier inversion therefore delivers it for an arbitrary smooth parent - exactly, as an identity, and numerically on a grid whose dimension stays two whatever $n$ is - a deterministic alternative to both the simulation and the Normality assumption. The identity is exact; what we evaluate is a finite-grid inversion of it, whose error we report per example rather than bound. At $n=3$ the Uniform parent yields the joint law of $(\bar X,s)$, the acceptance probability and the capability law in closed form; we derive these, use them as the anchor of a validation ladder, and then work three rules end to end under non-normal parents at their prescribed sample sizes ($n=10$-$30$). Against the exact answer the Normal-theory incumbent errs by enough to change the risk a plan is designed against, and errs in both directions along the operating curve, so no single constant corrects it. In general only the full distribution suffices.
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Computer Vision
GestureFAR: Streaming Co-Speech Gesture Generation with Flow Autoregression
Pinxin Liu, Haiyang Liu, Jiahao Luo et al. (+3 more)
Generating natural co-
speech gestures from streaming
speech is essential for
embodied conversational
agents, where motion must be produced while a user is still speaking. Recent streaming gesture systems make online generation possible by autoregressing over discrete motion
tokens, but this design compresses high-dimensional continuous motion into finite codebooks and can limit the realism and diversity of generated gestures. To preserve both causality and continuous expressiveness, we propose GestureFAR, a flow-autoregressive framework for streaming co-
speech gesture generation. First, GestureFAR autoregresses over causal continuous motion latents, using a
transformer to model streaming audio-motion context and a per-
token flow-matching head to sample the next latent from a continuous distribution. Second, we introduce a head-only flow
distillation strategy that freezes the causal backbone and distills the multi-step per-
token flow head into a single network evaluation using consistency and distribution-matching objectives. This keeps the model
token-causal while removing the main latency bottleneck for live interaction. Experiments on BEAT2 show that GestureFAR significantly improves the quality–latency trade-off among streaming-capable methods, preserving strong gesture quality while enabling real-time
token-causal generation. Project Page: https://andypinxinliu.github.io/GestureFAR
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All authors Pinxin Liu, Haiyang Liu, Jiahao Luo, Junhua Huang, Chunhao Zou, Luchuan Song
Optics
Programmable Intrinsic Circularly Polarized Emission
Yujie Jiao, Zhenqin Li, Yide Chang et al. (+11 more)
Circularly polarized luminescence (CPL) is central to
chiral photonics, yet programming circularly polarized emission at the nanoscale remains challenging. Here, we program intrinsic CPL at its microscopic origin in
laser-written all-inorganic perovskite nanocrystals embedded in glass. High-resolution transmission electron microscopy reveals a core-shell-like variation in interplanar spacing associated with intrinsic CPL, consistent with torsional
lattice distortion. The torsional
lattice distortion breaks inversion
symmetry, while density functional theory calculations show that it lifts the
spin degeneracy of the band-edge electronic states. Power-dependent measurements further reveal a transition from birefringence-mediated circular polarization to intrinsic CPL, accompanied by the emergence of a distinct core-shell-like
lattice distortion in the nanocrystals. By tuning the incident linear polarization angle and focal depth, we deterministically control both the handedness and magnitude of the intrinsic CPL, with |glum| of approximately 4 ^ 10^-3. These results show that programmable intrinsic CPL originates from the structural and electronic properties of the emitting nanocrystals, enabling circularly polarized emission to be controlled at its microscopic origin and spatially encoded within a monolithic material.
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All authors Yujie Jiao, Zhenqin Li, Yide Chang, Yongsen He, Xiaoyu Sun, Wei Lyu, Jiayu Ding, Zhaolin Zhong, Puxin Yang, Chi Chen, Bangjie Song, Min Qiu, Yanming Wang, Siying Peng
Optics
Frequency-swept Brillouin spectroscopy
Lin Kang, Shahrin Jahan Jaima and Jitao Zhang
Spontaneous Brillouin microscopy provides non-contact access to the viscoelastic properties of materials. Standard measurements typically utilize a single-wavelength continuous-wave
laser to illuminate a specimen and excites a spontaneous Brillouin scattering signal, which is then recorded by a spectrometer. Existing Brillouin spectrometers are mostly built around either a scanning Fabry-Pérot etalon or a virtually imaged phased array (VIPA) etalon. While performing well, these setups are bulky and demand significant optical expertise to construct and maintain. Here, we propose a new approach to conducting Brillouin spectroscopy. In this approach, a frequency-swept
laser excites a series of Brillouin spectra with incrementally shifted central frequencies, while a frequency picker, consisting of a narrow bandpass filter and a highly sensitive single-
photon detector, sequentially records each spectral component to reconstruct the full spectrum. We demonstrate that the frequency-swept Brillouin spectroscopy can run under shot-noise limited condition using standard samples. Compared with conventional spectrometers, our system features a much more compact design for portable applications and holds potential for rapid mechanical imaging through multiplexing.
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Cryptography and Security
Micro-Collaborative Poisoning: A Distributed Attack on RAG Systems
Pedro Pereira, Eva Maia and Isabel Praça
Retrieval-Augmented Generation (RAG) improves large
language models by grounding outputs in external knowledge sources, but this dependency also creates a surface for poisoning attacks. This paper introduces Micro-Collaborative Poisoning, a distributed attack in which a false target claim is divided across multiple locally plausible documents instead of being concentrated in a single malicious passage. We evaluate the attack across 108 RAG configurations by varying
dataset, retriever architecture,
retrieval depth, database composition, number of poisoned databases, and generator model. The results indicate that Micro-Collaborative Poisoning is not driven by a single dominant poisoned passage, but by the accumulation of weak
adversarial signals across retrieved sources. Increasing top-$k$ and poisoning multiple databases make it more likely that these signals will appear together in the retrieved context, while clean database diversity and stronger retrievers can reduce their influence. The document-level poisoning visibility analysis further shows that this threat is difficult to expose through isolated document inspection, since Micro-Collaborative Poisoning achieves downstream influence while leaving a weaker explicit poisoning signature than direct poisoning.
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Robotics
SABER: Learning Attention-based Semantic Affordance for Legged Locomotion
Hari Prasanth Palanivelu, Samuel Sze, Kennard Garrison Johannes et al. (+2 more)
Perceptive legged locomotion has advanced rapidly by integrating terrain geometry into learned policies, yet the integration of terrain meaning remains sparse: a pipe, a patch of grass, or a fragile box may be geometrically traversable while being inappropriate for contact. In industrial environments, where legged
robots increasingly operate, a single misplaced step can damage fragile equipment, destabilize the
robot, or endanger the site. To address this, we introduce SABER, a planner-free reinforcement-learning policy that jointly reasons about terrain geometry and semantic contact permission. The policy consumes a unified terrain-affordance map, where each
cell encodes local 3D geometry and a semantic contact cost. We augment cross-
attention with a learned, signed semantic bias: an additive term on the
attention logits, gated by the contact cost, that reweights flagged
cells by their distance from the nearest foot. A hazard therefore reshapes
attention where it can still affect the next foothold, and its influence fades where it cannot. The resulting policy selects footholds on permitted support and keeps the leg clear of forbidden regions throughout the swing phase. We perform a systematic ablation that isolates the contribution of each architectural component; removing the semantic bias alone increases forbidden contacts by 55% while velocity tracking is unchanged. We validate the policy on a Unitree B2, demonstrating sim-to-real semantic contact selection across indoor and outdoor environments and four semantic obstacle classes.
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All authors Hari Prasanth Palanivelu, Samuel Sze, Kennard Garrison Johannes, Albertus Hendrawan Adiwahono, Meng Yee Michael Chuah
Probability
Angles and volumes of simplices, boxes, and crosspolytopes in constant curvature
Zakhar Kabluchko and Philipp Schange
We determine, up to isometry, the Riemannian tangent and normal cones at arbitrary faces of boxes, acute orthocentric simplices, rectangular simplices, and asymmetric crosspolytopes in spaces of constant sectional
curvature $\kappa\in\mathbb R$. Using these descriptions, we derive explicit one-dimensional integral formulas for their internal and external angles in arbitrary dimension. In particular, our results cover regular simplices, cubes, and crosspolytopes, the three families of regular polytopes existing in every dimension. We also establish a correspondence between regular simplices and cubes with isometric local cones at corresponding faces, provided their edge-length parameters are suitably matched. Combining our angle formulas with the Poincaré relation, we obtain volume formulas for simplices and crosspolytopes in even dimensions. For boxes, we derive volume formulas valid in every dimension. Among the new results are angle formulas for spherical and
hyperbolic cubes and crosspolytopes, volume formulas for asymmetric crosspolytopes, and a one-dimensional integral representation for the internal angles of regular crosspolytopes that appears to be new even in the Euclidean case. The formulas involve the standard normal distribution function $\Phi(x)$ and its values at purely imaginary arguments $\Phi(\mathrm{i} x)$. For example, if $d\geq 2$ is even, then the volume of the ideal regular simplex in the $d$-dimensional
hyperbolic space of
curvature $\kappa=-1$ is given by the absolutely convergent integral $$ \frac{\pi^{d/2}} {\sqrt{2}\,\mathrm{i}^{d}\, \Gamma\left(\frac{d+1}{2}\right)} \int_{-\infty}^{\infty} \left[ \Phi\left(\frac{\mathrm{i} y}{\sqrt d}\right)^{d+1} + \Phi\left(-\frac{\mathrm{i} y}{\sqrt d}\right)^{d+1} \right] \mathrm{e}^{-y^2/2}\,\mathrm{d}y. $$
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Multiagent Systems
CityLearn v3: A Configurable Simulation and Evaluation Framework for Realistic Control Studies of Renewable Energy Communities
Tiago Fonseca, Luis Lino Ferreira, Armando Sousa et al. (+2 more)
Renewable energy communities (RECs) coordinate buildings, photovoltaic generation, batteries, electric vehicles and flexible loads. Controller studies often simplify changing participation, equipment availability, service deadlines and data quality, so lower cost or peak demand can conceal missed services or infeasible power requests. This paper presents CityLearn v3, a configurable simulation and evaluation framework for REC control studies under these conditions. It represents changing members and assets, flexible-load deadlines, demand-response requests, local energy sharing, and data or equipment failures within one simulation environment. Building and phase power limits constrain controllable requests, while a declared timestep preserves consistent power-to-energy accounting. The framework records controller inputs and distinguishes requested actions from those applied to the simulated equipment. Reference controllers, service- and constraint-aware performance indicators, and trajectory exports support comparisons within and across communities. Software checks and application examples examine service delivery, electrical constraints, settlement and changing scenarios; a synthetic high-frequency trace replay illustrates how aggregation can conceal short peaks without changing annual energy. Together, these records allow aggregate performance to be interpreted alongside service failures, action reductions and participant-level outcomes.
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All authors Tiago Fonseca, Luis Lino Ferreira, Armando Sousa, Ava Mohammadi, Zoltan Nagy
Cryptography and Security
Et Tu, MacBook? Unprivileged Keystroke Inference and Context Profiling via the Built-in IMU Side Channel
Jiaji He, Yi Shi, Junfeng Cai et al. (+2 more)
Recent generations of Apple MacBooks embed an inertial measurement unit (IMU) within their unibody chassis for device orientation and motion sensing. However, this IMU inadvertently captures not only intended device-level information but also subtle physical vibrations from user interactions and the surrounding environment. These signals establish a novel, previously unexplored side channel. We uncover a vulnerability allowing non-root access to IMU data via an IOKit driver, alongside two content-free system metadata interfaces (HIDIdleTime and CGEventSource) that further enrich the side-channel leakage. Through rigorous characterization of the IMU data, we reveal that the leakage spans three core dimensions: (1) keystroke identity (which key is typed), (2) desk surface (where the laptop is placed), and (3) user behavior (who is typing). Leveraging these findings, we introduce BRUTUS, the first comprehensive unprivileged side-channel attack targeting built-in IMU sensors on Apple MacBooks. BRUTUS achieves a character-level accuracy of 89.1% to 97.5% in key recovery. Furthermore, aided by
language models, it can successfully reconstruct certain sentences with 100% accuracy. For user identification and environment profiling, BRUTUS correctly discovers user and environment profiles without labels and correctly assigns subsequent segments to their corresponding profiles. Ultimately, this work highlights the urgent necessity of strictly regulating access to built-in IMU sensors.
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All authors Jiaji He, Yi Shi, Junfeng Cai, Chang Liu, Yongqiang Lyu
Probability
Monotonicity of Beckner Inequalities
Qingbo Lei, Cheng Li, Bo Wu et al. (+1 more)
In this paper, we prove a monotonicity principle for Beckner inequalities of the form \[ \frac{\mu(f^2)-\mu(f^p)^{2/p}}{2-p}\leq C\E(f,f), \qquad f\in\D(\E),\quad 1\le p\le2, \] where $\E$ is a conservative symmetric Dirichlet form. If the inequality holds for every exponent $p$, it holds with the same constant $C$ for all $q\in[1,p]$. This resolves an open question posed in Chapter~6 of Wang's monograph~\cite{Wangbook}. We also study weak Beckner inequalities of the form \[ \Psi_p(f)\le \beta(r)\E(f,f)+r\,\Osc(f)^2,\qquad f\in\D(\E),\quad r>0, \] where $\beta$ is a rate function. We show that the same monotonicity principle preserves the entire rate function. We further derive consequences for
polynomial rates,
semigroup contractivity, and concentration.
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All authors Qingbo Lei, Cheng Li, Bo Wu, Jiyang Wu
Quantum Physics
Weighted Quantum Signal Processing: Low-Depth Polynomial Approximation with Applications to Kolmogorov-Arnold Networks
Rohit Sarma Sarkar, Rupayan Bhattacharjee, Elias F. Combarro et al. (+5 more)
Quantum Signal Processing is a powerful
quantum framework for generating and approximating univariate
polynomials. However, QSP is often limited by
circuit-depth bottlenecks and parity constraints on the class of realizable
polynomials. In this work, we introduce Weighted
Quantum Signal Processing, an extension of QSP in which a weight function is assigned to the central rotation operator. This formulation provides a deeper understanding of QSP, which emerges as the special case of WQSP with unit weights. The choice of weights determines the structure and expressive capabilities of WQSP
circuits. When the weights are natural numbers greater than one, WQSP reduces to a pruned version of QSP, revealing parameter redundancies in the standard framework. Through appropriate selection of integer weights, WQSP achieves linear-to-exponential reductions in the number of parameters required to realize arbitrary bounded univariate
polynomials while preserving approximation quality. For generic weights, we establish corresponding approximation error bounds and show that, in many cases, the approximation is exact. We analyze WQSP from both a deterministic perspective, where
polynomial generation is formulated as the solution of a linear system, and a
quantum machine learning perspective, where WQSP serves as a structured and expressive
quantum learning model. We further employ this learning framework to parameterize learnable activation functions in Kolmogorov–Arnold Networks for multivariate function approximation. Our results show that WQSP provides a compact, flexible, and theoretically grounded framework for realizing arbitrary univariate
polynomials while requiring significantly fewer trainable parameters than conventional QSP. This yields expressive and parameter-efficient neural architectures, highlighting the potential of WQSP as a scalable primitive for
quantum-enhanced machine learning.
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All authors Rohit Sarma Sarkar, Rupayan Bhattacharjee, Elias F. Combarro, Michele Grossi, Lirandë Pira, Carmen G. Almudéver, Sergi Abadal, Eduard Alarcon
Information Theory
Goal-Oriented Communication and Control Co-Design via Semantic Push-Pull in Industrial IoT
Muhammad Azeem Khan, Yuriy Zacchia Lun, Aamir Mahmood et al. (+3 more)
Emerging 6G industrial IoT architectures require wireless networked control systems capable of stabilizing diverse control loops over tightly constrained
radio resources. Conventional periodic and Age-of-Information (AoI) based scheduling guarantees bounded staleness at the cost of persistent channel saturation. Conversely, pure event-triggered (PureET) strategies minimize transmissions but risk catastrophic silent deterioration when local sensor-side thresholds fail to reflect critical state
evolution. To bridge this gap, we propose a communication-control co-design framework governed by a 6G Semantic Layer that independently arbitrates uplink and downlink resources. Instead of relying on freshness, our architecture evaluates the actual control impact of a packet using the state-to-error ratio (SER). We unify this control confidence with channel reliability in terms of signal-to-noise ratio (SNR) to orchestrate a threshold-based sensor push and a state-aware controller pull mechanism. To ensure equitable resource allocation across dynamically heterogeneous plants, the proposed framework explicitly scales actuation deadbands according to local plant dynamics. Simulations over Rayleigh-faded channels demonstrate that this approach fundamentally shifts the Pareto frontier between transmission rate and control quality. The proposed scheme achieves tracking accuracy comparable to periodic schedulers at a reduced communication overhead, while mitigating the estimation errors characteristic of PureET.
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All authors Muhammad Azeem Khan, Yuriy Zacchia Lun, Aamir Mahmood, Piergiuseppe Di Marco, Mikael Gidlund, Fortunato Santucci
Logic in Computer Science
Logics of Filter Bubbles
Lei Li and Jialiang Yan
Filter bubbles can be understood as configurations characterized by within-group proximity and separation from outsiders. Personalized feeds may form or preserve such configurations. We develop hybrid-style logics for
reasoning about filter bubbles. The static logic characterizes bubble-shaped configurations, while its dynamic extension captures their formation and persistence under personalized feeds. We provide sound and strongly complete axiomatizations for the static and dynamic logics and prove their decidability.
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Databases
kgsteward: a tool for building, reproducing and maintaining distributed knowledge graphs
Marco Pagni, Robin Engler, Frederic Burdet et al. (+15 more)
Collaborative research projects in life sciences increasingly need to integrate private, embargoed consortium data with public reference databases in order to reach statistically meaningful interpretations. The Resource Description Framework (RDF) is well suited to this task: it facilitates the integration of heterogeneous data sources, and allows researchers to keep data and their documentation as metadata in the same place, provided the knowledge graph itself remains private during the time course of the project. Nevertheless, the development and long-term maintenance of a scientific knowledge graph remains a challenging, labour-intensive endeavour owing to the state of constant flux of most public resources. To tackle this challenge, we present kgsteward, a Python command-line tool that builds and maintains knowledge graphs inside RDF stores from a single, version-controlled configuration file. kgsteward supports multiple triplestores, keeps the local graph up-to-date with its external sources possibly already in RDF, or transformed into it on the fly, and uses SPARQL 1.1 UPDATE commands to amend further imported RDF on the fly. It can also validate the resulting graph with SPARQL queries that double as usage examples for both human users and AI
agents. kgsteward has already been used in several collaborative projects at the SIB Swiss Institute of Bioinformatics, and we demonstrate its applicability in two real-world international research projects: one that builds a library of plant extracts with chemical analyses and associated bio-activities, and a second that reconciles public reference resources for human metabolic-network reconstruction.
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All authors Marco Pagni, Robin Engler, Frederic Burdet, Sebastien Moretti, Loic Le Gregam, Luis Quiros-Guerrero, Jahn Nitschke, Pierre-Marie Allard, Louis-Felix Nothias, Jack McGoldrick, Ronan M.T. Fleming, Alan Bridge, Vincent Emonet, Tarcisio Mendes de Farias, Ana-Claudia Sima, Mark Ibberson, Jean-Luc Wolfender, Florence Mehl
Information Theory
On the Fourier Entropy-Influence Conjecture for Boolean Plateaued Functions
Vladimir N. Potapov
We prove the following inequality for Boolean functions: $2\sum_{x\in F_2^n}f(x)wt(x)\geq wt(f)(n-deg(f))$. Using this inequality, we establish the
Fourier Entropy-Influence (FEI)
conjecture for Boolean plateaued functions. In particular, we show that the sharp FEI constant for the class of plateaued functions is 4. We also prove the FEI
conjecture for partially bent functions and show that the corresponding sharp constant is 2. Finally, we derive several estimates for the p-biased distribution on the Boolean hypercube. Keywords:
Fourier entropy, total influence, average sensitivity, plateaued function, algebraic degree, Reed-Muller code, p-biased distribution.
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Software Engineering
GameLogicBench: Evaluating Coding Agents on Runtime Game Logic with Tick-Level State Assertions
Xinyu Che, Yunfei Ge, Shihao Li et al. (+9 more)
Coding
agents can modify and test code across large software projects. Game development is a domain where
agents must implement gameplay rules. A game can end in a valid state even after violating its rules during the run. Current game-development
benchmarks replay fixed examples, score
videos, or ask another model to judge the result. However, no existing
benchmark checks game rules throughout execution across varied evaluator-selected scenarios while ensuring exactly reproducible verdicts. We introduce GameLogicBench, a
benchmark of 72 gameplay-logic tasks in Godot projects. An automated evaluator checks each game's rules at every simulation tick. Across 403 hand-designed scenarios, seeded parameter variations produce 1,451 test cases. To ensure that the evaluator measures behavior rather than implementation choice, it must accept different correct implementations for each task while rejecting mutants, implementations with one required capability removed. The tasks span isolated mechanics, multi-system interactions, and repository-scale features. Across 20 combinations of
language models and scaffolds, the best observed run solves 52.78% of tasks. Under Claude Code, all twelve models solve fewer tasks as task scope expands from isolated mechanics, through interacting systems, to repository-scale features.
Agents inspect code more often and make more tool calls on repository-scale tasks than on isolated-mechanic tasks. Most unsuccessful submissions are runnable, but implement some required game behavior incorrectly. We compared versions of our
benchmark evaluator built with and without validation using mutants. Without this validation, incorrect
agent submissions passed. A separate analysis finds
agents copying code from public repositories when network access is open. Reliable evaluation thus depends both on what the tests reject and on what external code
agents can access.
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All authors Xinyu Che, Yunfei Ge, Shihao Li, Yanchen Liu, Hang Yan, Xinping Lei, Yanghai Wang, Zixuan Dong, Yifan Yao, Qianqian Xie, Letian Zhu, Jiaheng Liu
Machine Learning
On Repulsive and Attractive Teachers: Separating Correctness from Behavior in Self-Distillation
Anton Baumann, Akmal Ashirmatov, Leo Schmidt-Traub et al. (+4 more)
On-policy self-
distillation provides dense,
token-level supervision by conditioning a model on privileged information and distilling the resulting teacher distribution back into the model. However, privileged information can change not only what the teacher knows, but also how it behaves, entangling correctness-relevant learning signals with unintended behavioral shifts. We study this effect in
reasoning tasks by contrasting attractive self-
distillation, which moves the model toward a privileged teacher, with repulsive self-
distillation, which moves it away from a privileged teacher. We find that both objectives can induce strong and opposing behavioral shifts: attraction suppresses exploratory
reasoning and promotes shorter, more confident responses, whereas repulsion increases response length, can trigger unintended switches into a model's latent thinking mode, and ultimately becomes unstable. Motivated by these observations, we study contrastive self-
distillation, which combines attraction toward a correct-solution-conditioned teacher with repulsion from an incorrect-solution-conditioned teacher. In contrast to prior work that combines such
distillation signals with a GRPO objective, we isolate the self-
distillation objective and study its behavior on its own. We find that the shared behavioral shifts of the two teachers largely cancel, leaving a
token-level signal that more directly reflects correctness. Across non-thinking, instruct-only, and already-thinking models, this contrastive objective improves
reasoning performance while maintaining stable response lengths.
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All authors Anton Baumann, Akmal Ashirmatov, Leo Schmidt-Traub, Frederike Lübeck, Jonas Hübotter, Thomas Kleine Buening, Andreas Krause
Quantum Physics
Giant-Atom-Induced High-Order Output Zeros in a Coupled-Cavity Array
Mengxue Li, H. Z. Shen, H. D. Liu et al. (+1 more)
Coherent perfect absorption, zero transmission and zero reflection are several scattering phenomena governed by interference engineering in Hermitian and non-Hermitian systems. Higher-order coherent perfect absorption can significantly broaden the absorption bandwidth, while existing implementations rely on scattering zero degeneracy induced by exceptional points or additional momentum-dependent phase delays introduced in incident waves. We propose a scheme where a
giant atom couples to a one-dimensional coupled-cavity array at two spatially separated sites. The spatially separated coupling configuration of the
giant atom generates tunable nonlocal interference phases that dominate the scattering interference process. We further investigate the zero-reflection and zero-transmission behaviors under single-sided incidence. Remarkably, we find that for certain parameter choices, zero transmission can persist over the entire propagating band, rather than being restricted to a single momentum. Our results reveal that the
giant-atom interference mechanism enables bandwidth-enhanced coherent perfect absorption and bandwidth-enhanced zero transmission in the absence of exceptional points and incident momentum-dependent phase delays. Our work provides a physical route for coherent wave manipulation in coupled-cavity
quantum networks.
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All authors Mengxue Li, H. Z. Shen, H. D. Liu, Gangcheng Wang
Materials Science
Validation of an Ab Initio-Informed Electronic Stopping Model for Large-Scale Atomistic Simulations
Glen P. Kiely, Rafael Nuñez-Palacio and Andrea E. Sand
Accurately modeling nonadiabatic electronic energy dissipation in atomistic simulations of radiation damage remains a key challenge. Existing models often rely on arbitrary thresholds and neglect trajectory-dependent, band-structure, and nonlinear effects. A model developed by Tamm et al. (\href{https://doi.org/10.1103/PhysRevLett.120.185501}{doi:10.1103/PhysRevLett.120.185501}) offers a nonempirical description of electronic dissipation for atomistic simulations, but has not been directly benchmarked in the electronic stopping regime. We present the first such
benchmark via the comparison of atomistic simulations with experimental ion transmission experiments. The model accurately captures the magnitude and trajectory-dependence of the predicted energy losses. These results support the reliability of the model for predictive large-scale atomistic simulations of radiation cascades.
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Quantum Physics
Julio I. de Vicente
Local operations assisted by classical communication (LOCC) play a fundamental role in the resource-theoretic formulation of
entanglement theory inducing an operationally meaningful ordering in the set of entangled states. The particular class within LOCC of local
unitary (LU) transformations is obviously closed under inversion. Thus, LU-equivalent states are interconvertible by LOCC and have therefore the same
entanglement. For this reason LU-equivalence has been thoroughly studied in the context of
entanglement theory. However, as I note here with a simple example, the converse is not true: there exist entangled states that are interconvertible by LOCC but which are not LU-equivalent. This motivates studying under which conditions LOCC interconvertibility is characterized by LU-equivalence. I provide a sufficient condition for this to hold. When one considers multipartite states with full
entanglement dimensionality (as quantified by the Schmidt number vector) LOCC interconvertibility can only hold under LU-equivalence.
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High Energy Physics (Phenomenology)
Discriminating baryonium and final-state-interaction interpretations of $X(2356)$
Bing-Dong Wan and Sheng-Qi Zhang
We revisit the $X(2356)$ enhancement observed by BESIII in $e^+e^-\to\Lambda\bar\Lambda\eta$ from the viewpoint of near-threshold dynamics. Motivated by the possibility of light $\Lambda\bar\Lambda$ baryonium, we compare this interpretation with final-state-interaction (FSI) explanations within a minimal FSI-dressed pole framework. Fits of the pure-FSI, baryonium-like-pole, and mixed pole–continuum scenarios to the digitized BESIII spectrum identify the energy-dependent FSI description as providing the best balance between fit quality and model complexity. The extracted pole positions depend strongly on the amplitude parametrization, demonstrating the sensitivity of the baryonium interpretation to the treatment of the continuum. We discuss how decay channels,
spin observables, and partner searches can provide complementary constraints on the dynamics underlying the enhancement.
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Soft Condensed Matter
Phase Separation in a system of Brownian inertial rotors
Lucio Mauro Carenza, Pasquale Digregorio, Massimiliano Semeraro et al. (+3 more)
We uncover an emergent phase separation in a system of inertial dumbbells put into persistent rotation by a constant applied torque, breaking
chiral symmetry, while interacting with each other through repulsive hard-core interactions. We numerically determine the phase diagram of the model, showing that there exists a binodal and a spinodal region where the system separates into a dense
liquid and an ordered phase, which we identify as hexatic. An analysis of the system pressure unveils that the mechanism underlying the phase separation is heavily controlled by inertia. Locally injected kinetic energy is steadily stored into the system in a way that depends on density and that, at large inertia, results in a hot dilute and cool dense state, leading to a generically unstable homogeneous phase. The interplay between dumbbells' arrangement into clusters and
chiral forces produces a spontaneous rigid rotation of the clusters around their centers of mass.
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All authors Lucio Mauro Carenza, Pasquale Digregorio, Massimiliano Semeraro, Antonio Suma, Ignacio Pagonabarraga, Giuseppe Gonnella
Logic in Computer Science
Compact Partial Symmetry Breaking for Graph Search Problems
Michael Codish, Mikoláš Janota and Peter J. Stuckey
Symmetry breaking in graph search problems remains challenging: complete
symmetry breaking constraints are typically exponential in size, while partial approaches trade precision for scalability. We introduce a strength-driven, redundancy-aware methodology for constructing compact partial
symmetry breaking constraints based on graph patterns. Each pattern corresponds to a Boolean clause over equality literals and can be integrated directly into CP or SAT models. Our approach incrementally selects strong, non-redundant graph patterns, yielding partial
symmetry breaks that achieve substantial
pruning of non-canonical graphs while remaining compact in size. This provides a principled framework for deriving lightweight partial
symmetry breaking constraints which are significantly more precise than all published state-of-the-art alternatives. We evaluate the proposed constraints on graph instances with up to 25 vertices using a redundancy ratio metric. The resulting partial
symmetry breaks constitute only a small fraction of the size of complete
symmetry breaking constraints, while delivering significant performance improvements across several graph search
benchmarks compared to previously defined partial
symmetry breaking methods.
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Computation and Language
MIRAGE: Multi-Perspective Creative Language Model Reasoning with Reinforcement Learning Guidance
Arash Lagzian, Srinivas Anumasa and Dianbo Liu
Recent advances in Large
Language Models (LLMs) have revolutionized artificial intelligence and how human interact with AIs. Despite impressive advancements, LLMs struggle with complex mathematical, scientific, and logical tasks. Inspired by human cognitive flexibility - our ability to dynamically switch mental perspectives - we propose MIRAGE (Multi-perspective Inference-time
Reasoning via
Agent-Guided Exploration), a novel inference-time creative thinking framework. MIRAGE includes a Selector that prioritizes effective conceptual perspectives (e.g., algebraic, probabilistic) and a Reasoner that sequentially solves tasks until a confident solution emerges, otherwise aggregating multiple perspectives. Tested on GSM8K, MATH500, MMLU-Pro, and Game-of-24
benchmarks, MIRAGE consistently outperforms methods like Chain-of-Thought and diverse prompting ensembles, significantly boosting accuracy with minimal inference overhead, providing a scalable solution for practical applications.
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