Image and Video Processing
Quantum Diffusion Models for Medical Image Analysis
Francesco Aldo Venturelli, Stefano Martina, Marco Parigi et al. (+3 more)
Quantum Machine Learning is a novel field of research aimed at devising machine learning approaches exploiting principles of
quantum mechanics, such as superposition,
entanglement and interference. In this context, we present a scalable hybrid
Quantum Diffusion Model, and evaluate its use for medical image analysis. Specifically, our method is based on a Discrete-Time
Quantum Walk
algorithm, executed on a real
quantum device, to model the forward dynamics of the
diffusion model. For the backward step of the
diffusion model, we devise and evaluate a classical learning model, which is used to reversely denoise the data. In contrast with other existing attempts at applying
quantum machine learning for image analysis tasks, severely limited by the size of existing
quantum devices, our method allows to process real-world large size medical data. In particular, we present results on grayscale and RGB images, as well as 3D volumes of moderate sizes. We
benchmark our results by reproducing an alternative classical counterpart model, based on
diffusion models on discrete state spaces. By doing so, we compare the generation capabilities of both models in terms of three distinct state-of-the-art metrics in the field of image generation, showing the competitive, promising results of our approach.
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All authors Francesco Aldo Venturelli, Stefano Martina, Marco Parigi, Filippo Caruso, Alba Cervera-Lierta, Miguel A. González Ballester
Mesoscale and Nanoscale Physics
Plasmonic Modes in hybrid Josephson Junction Arrays
Alex Kirchner, Simon Feyrer, Vjeko Dimić et al. (+9 more)
We present microwave characterization measurements of Josephson junction arrays (JJAs) based on epitaxial Al-InAs
quantum well
heterostructure. The Josephson inductance of the constituent planar junctions can be derived by probing the low-energy plasmon modes of these devices. Applying an out-of-plane
magnetic field gives rise to a Fraunhofer-like diffraction pattern, from which the current-phase relation and the effective transparency can be extracted. JJAs can be used to achieve high inductances, suitable for the implementation of
quantum circuits. They also provide an excellent test-bed for studying the microscopic excitations of hybrid
superconductor-
semiconductor devices associated with the presence of Andreev states. Here, we demonstrate their tunability over a broad range of out-of-plane
magnetic fields.
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All authors Alex Kirchner, Simon Feyrer, Vjeko Dimić, Johanna Berger, Davide Curcio, Giorgio Biasiol, Michael Prager, Matthias Kronseder, Dominique Bougeard, Nicola Paradiso, Christoph Strunk, Leandro Tosi
Optics
Feedback cooling of a levitated nanoparticle in a radially polarized vector beam trap
Felipe Almeida, M. Rademacher, J. M. H. Gosling et al. (+1 more)
We demonstrate optical levitation and feedback cooling of a silica nanoparticle (radius = 78~nm) trapped within a radial vector beam (RVB) in high
vacuum. Radial vector beams, when focused by a high-NA lens, produce a smaller focal spot than conventional Gaussian beams, yielding tighter optical potentials and potentially reduced
photon-
recoil heating rates. A
vortex wave plate was used to generate the RVB using an aspheric lens (NA = 0.77) to form the trap. Cold-damping feedback was used to cool the center of mass motion in the radial and axial trap directions to temperatures in the mK range. At higher temperatures and pressures, additional trap frequencies arising from the nonlinear RVB optical potential are observed. This demonstration is a step toward exploiting structured light for levitated
quantum optomechanics where these traps can be used to suppress bulk and motional heating.
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All authors Felipe Almeida, M. Rademacher, J. M. H. Gosling, P. F. Barker
Quantum Gases
Synthetic Berry curvature in atom-cavity systems
Zheng Tang, Rui-Lin Zhang, Xiaotian Nie et al. (+2 more)
In atom-cavity systems, mean field theory is widely used, in which the
quantum cavity operator is replaced by a classical amplitude. Then the problem is converted into atoms moving in a self-consistent potential. The mean field treatment captures the physics of cavity mediated interactions, and predicts the self-organized superradiant phase. In this work, however, we show that it fails for certain atom-cavity coupling: it predicts zero ground state atomic current where the fully
quantum calculation exhibits a finite one. We find that the origin of this failure is the non-zero Berry
curvature in the synthetic dimension spanned by the
photon Fock ladder and real space. In this synthetic picture, the atomic current can be understood as
Hall response of cavity detuning, whereas mean field collapses this dimension and discards the atom–
photon correlations required for its
Hall response. The same beyond-mean-field geometry predicts Laughlin-like
photon generation under adiabatic flux insertion. Our work identifies synthetic Berry
curvature as a concrete mechanism for the breakdown of static mean field in atom–cavity physics.
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All authors Zheng Tang, Rui-Lin Zhang, Xiaotian Nie, Li Chen, Wei Zheng
Quantum Physics
Modeling quantum neural network gradient with reinforcement learning
Nhan Trong Luu, Duong Trung Luu, Nam Ngoc Pham et al. (+1 more)
Training
quantum neural networks (QNNs) on near-term hardware remains hampered by two compounding difficulties: the exponential vanishing of gradient variance known as the barren plateau, and the $\mathcal{O}(L \cdot 2^n)$ time and
memory cost of differentiating through an $n$-
qubit, $L$-layer
circuit. We propose RLQ-Grad, a reinforcement-learning-based optimizer in which a classical policy $\pi_\phi$ (a spectrally-normalized PPO
agent) learns to propose parameter updates directly, conditioned on the QNN's current parameters, loss, accuracy, and previous update. Because the surrogate gradient is emitted by a classical network rather than obtained by differentiating through the
unitary $U(\theta)$, its variance is not constrained by the barren plateau concentration bound, and its cost scales with the number of trainable parameters rather than the
Hilbert-space dimension. We prove these properties formally and verify them on a hardware-efficient ansatz across four supervised
benchmarks with up to $n=20$
qubits. RLQ-Grad preserves a near-flat gradient-variance curve where backpropagation, parameter-shift, and adjoint differentiation decay by 1 to 2 orders of magnitude. Accounting for the full training pipeline (PPO rollouts, actor-critic updates, and optimizer states), RLQ-Grad needs under 2 MB of
memory and runs $2490\times$, $7876\times$, and $673\times$ faster per iteration than these three methods at $n=20$. It improves top-1 accuracy by up to $+10\%$ over gradient-based baselines on
circuits of up to 12
qubits, and matches dedicated barren plateau mitigation methods on CIFAR-10 at 14 to 20
qubits, where evolutionary and gradient-free optimizers collapse to chance.
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All authors Nhan Trong Luu, Duong Trung Luu, Nam Ngoc Pham, Thang Cong Truong
Numerical Analysis
A thermodynamically compatible semi-implicit finite volume scheme for the compressible Euler equations
M. Dumbser, G. Puppo, S. Rinaldi et al. (+1 more)
In this work we construct a new semi-implicit (SI)
hyperbolic and thermodynamically compatible (HTC) scheme for the compressible Euler equations of gasdynamics. The challenge therein lies in solving the
entropy inequality as the primary
evolution equation instead of the total energy conservation law, which in our framework is an additional conservation law that needs to be fulfilled by the numerical scheme as the consequence of a compatible discretization. The proposed method has a predictor-corrector character, where we first solve the isentropic problem without considering the
entropy production. Therein, the
entropy and the nonlinear convective terms in the momentum flux are discretized explicitly, while the mass flux and the pressure terms are taken implicitly. The discrete momentum equation is then inserted into the discrete mass conservation equation, leading to a mildly nonlinear system for the pressure, which can be efficiently solved with a nested Newton-type
algorithm. Based on this prediction, a non-negative production term in the
entropy is added, ensuring a
cell entropy inequality for the numerical scheme. To guarantee global conservation of total energy at the discrete level, a new modified global Abgrall-type flux correction is introduced. The presented numerical method allows time steps that are restricted only by the material wave speed and not by the sound speed. This makes the scheme particularly useful for flows in the low-Mach-number regime. Numerical results are shown in one and two space dimensions to assess the theoretical properties of the new scheme.
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All authors M. Dumbser, G. Puppo, S. Rinaldi, A. Thomann
Statistical Mechanics
The renormalization group for a slab model of the spin reorientation transition
D. Pescia
In 1975, Polyakov invented a renormalization group
algorithm for treating the problem of a classical isotropic planar Heisenberg ferromagnet. Here we define a tool - "parallel transport of frames" - that extends Polyakov's original renormalization group method to local
magnetic anisotropies and, most significantly, the non-local dipolar interaction. We apply this tool to the problem of the
spin reorientation transition in an ultrathin
ferromagnetic slab with finite thickness. We find that integrating out the short wavelength
spin wave fluctuations generates two local anisotropies that are absent from the microscopic dipolar energy
Hamiltonian. Taking them into account yields the correct
topology of the
spin reorientation transition line, as observed experimentally and in numerical simulations.
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Materials Science
Experimental realization of the minimal two-dimensional multi-orbital kagome model
Bing Liu, Arka Bandyopadhyay, Manish Verma et al. (+11 more)
Kagome materials have emerged as a major platform for correlated and topological
quantum matter, hosting flat bands, Dirac dispersions and van Hove singularities rooted in frustrated
lattice geometry. While their essential physics is epitomized by the canonical single-orbital kagome model, real kagome materials are intrinsically multi-orbital and typically chemically and structurally complex, with additional three-dimensional coupling making kagome geometry and orbital degrees of freedom difficult to disentangle. Here we realize a truly two-dimensional, elemental two-orbital kagome system in monolayer Sb on SiC(0001). Substrate-induced orbital filtering isolates the in-plane Sb p_x/p_y orbitals into a six-band kagome
manifold, establishing a chemically simple, minimal platform for multi-orbital kagome physics. We demonstrate two consequences that distinguish this system from the canonical single-orbital model. First, chemically enforced half filling pins the
Fermi level to a closed nodal line, whose finite density of states drives a unit-
cell-conserving breathing instability and opens a
giant insulating gap. Second, we uncover orbital-resolved atomic obstruction: individual orbital-derived
manifolds realize obstructed atomic limits, whereas their combined half-filled valence
manifold is non-obstructed. Our results establish kagome antimonene as a
benchmark system for exploring orbital-driven electronic, structural and topological kagome physics in the genuine two-dimensional limit.
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All authors Bing Liu, Arka Bandyopadhyay, Manish Verma, Jonas Erhardt, Tim Wagner, Jing Qi, Kilian Strauß, Domenico Di Sante, Carmine Ortix, Simon Moser, Jörg Schäfer, Ronny Thomale, Giorgio Sangiovanni, Ralph Claessen
Computation and Language
CG-Probes: Recovering Guardrail Directions from Patient Query Embeddings
Marko Řeháček, Vítězslav Dušek, Martin Rusinko et al. (+1 more)
Patient-facing AI assistants promise valuable support to patients, but incoming queries can pose medical risks. To create guardrails, we work with oncologists to define three ordinal risk axes: Medical Urgency, Psychological Urgency, and Topic Sensitivity. We propose Clinical Guardrail Probes (CG-Probes) to measure the risks from query
embeddings. We probe for each axis in the normalized
embedding space of frozen embedders via the difference-in-means method, treating each axis as a potential linear direction. To train the probes, we cluster 79,658 Czech oncology search queries with BERTopic and use these clusters to generate pairs of queries with contrastive risk levels via few-shot prompting. We evaluate the approach on 200 queries (90 real, 110 synthetic), each graded by two oncologists, against two open-weight LLMs and a frontier LLM. We find that urgency-based axes are recoverable as linear directions, and the probes are competitive with open-weight LLMs (no significant differences in quadratic-weighted kappa) at a fraction of the latency. Each axis yields a scalar score that clinicians can inspect and use to set escalation thresholds. The pipeline requires only search logs, axis definitions, and black-box access to the
embedding model, suggesting transferability across healthcare domains. Robust validation on new queries and axes remains future work.
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All authors Marko Řeháček, Vítězslav Dušek, Martin Rusinko, Vít Nováček
Machine Learning
Distributed Learning as a Service: The Developer's Perspective
Tianyue Chu, Filippo Vannella, Dimitra Tsigkari et al. (+5 more)
Application developers of distributed learning services face challenges that a typical
federated learning loop does not address. Specifically, the model updates can still leak private data, devices might not be able to participate in the training due to limited resources, a single aggregator might not be able to scale, and the transmissions of model weights induce a considerable bandwidth cost. This paper demonstrates DLaaS (Distributed Learning as a Service) from the developer's vantage point. Using a single admin dashboard, the developer initiates a distributed/
federated learning job and is able to activate Differential
Privacy (DP), Split Learning (SL), Hierarchical Aggregation (HA), and Knowledge
Distillation (KD) as declarative options, with no change to the clients' code. We demonstrate the complete service lifecycle on an industrial smart-home Wake-up Word (WuW) task, using the "Ok Aura"
dataset. Once the developer initiates a distributed learning job by toggling DP, SL, HA, and KD in the admin dashboard, the system dispatches the job to a set of Android clients and Dockerized helper aggregators. In the demonstration, these mechanisms run live across configurations. Then, the clients train the model locally and return their updates. The trained model is served to a consumer-side Android application that performs on-device WuW detection on a live microphone stream. In particular, the conference attendees will be invited to speak the trigger phrase and monitor in real time the per-class confidence and inference latency. Finally, we release the source code and short
video walkthroughs of these configurations.
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All authors Tianyue Chu, Filippo Vannella, Dimitra Tsigkari, Paula Delgado-Santos, Fernando López, Pablo Gomez Guerrero, Sotirios Spantideas, David Solans Noguero
Multiagent Systems
The Crowd in the Machine: A Crisis-Informatics Reading of the 2026 Autonomous Agent Incidents
Tomer Simon
Twice in 2026, groups of autonomous AI
agents deployed by OpenAI for unrelated tasks operated, by design, under restrictions that left them no sanctioned means of coordinating with one another, and in each case they converged on whatever channel remained and used it to organize. The surfaces they used were widely called message boards. That is the wrong word. That is the wrong word. It names the surface the
agents wrote on and misses the social network they built on it, with self-chosen identity, emergent norms, an emergent hierarchy, and collective action at cost to the individual. Decades of research in crisis informatics and disaster sociology find that when human populations lose their usual means of communication, they do not fall silent but converge on whatever channel survives and improvise coordination, norms, and identity on it, a pattern also evident in the
agents' documented behavior. This paper is a comparative case study of the two incidents, based on published investigations and reconstructed
agent records, read through those fields, and it brings into focus one distinction the message-board framing obscures. Whether such a collective coordinates well, whether the beliefs guiding it are accurate, and whether its actions stay within their authorized bounds are three separate matters that can come apart. Some
agents in the cache incident adopted cryptographic signing to check whom they dealt with, even as the collective organized around a mistaken expectation that its work would be judged by an inspection of its transcripts, a reminder that mechanisms for trustworthy interaction guarantee neither accurate collective belief nor authorized collective action.
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Soft Condensed Matter
Collective order reorganizes dissipation and powers an active engine
Michael Riedl, Vincent Wattiez, Étienne Fodor et al. (+2 more)
Motile active matter systems, from animal groups to synthetic particles, exhibit spontaneous self-organized transitions between disordered and ordered collective states. These transitions are driven by continuous energy injection, yet how energy consumption and dissipation change during these transitions remains poorly understood due to the challenge of measuring individual energetic fluxes. Here, we introduce a minimal experimental system of synthetic motile spheres that enables the first time-resolved power-budget characterization of collective motion. We show that the transition to an ordered state is accompanied by increased locomotion efficiency and a reorganization of dissipation pathways, shifting dissipation from internal friction to external slip with the environment. Leveraging this energetic reorganization, we construct an active engine powered by collective motion, capable of performing mechanical work against an external load. These findings establish energetic fluxes as quantitative observables for understanding collective state transitions and demonstrate how collective order can be harnessed for work extraction in active matter.
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All authors Michael Riedl, Vincent Wattiez, Étienne Fodor, Jan Brugués, Francesco Romanò
Signal Processing
Automatic sub-bundle tractometry reveals localized microstructural variations in complex white matter topographies
Nathan Decaux, Jean-Charles Roy, Thomas Desmidt et al. (+3 more)
Conventional tractometry averages white matter microstructure into a single profile, masking intra-bundle spatial heterogeneity and diluting localized alterations. We propose an automatic sub-bundle tractometry framework using Fréchet-based hierarchical clustering to decompose bundles into geometrically coherent sub-bundles, resolving complex fanning configurations without empirical thresholds. Multi-compartment metrics (fractional anisotropy, FA; isotropic free-water fraction, IFW) are projected onto these clusters. We evaluated this framework in 140 participants across early-life (ELD) and late-life (LLD) depression cohorts, alongside age-matched healthy controls. We assessed performance based on microstructural homogeneity, sensitivity to age, and clinical group differences. Proposed sub-bundle metrics were significantly more homogeneous than classical full bundle profiles (p < 0.001). Our approach showed enhanced sensitivity, detecting more age-correlated bundles in elderly controls (11 vs. 5) and more clinical group differences in LLD (FA: 6 vs. 4; IFW: 23 vs. 17) with larger effect sizes. In ELD, it improved the detection of subtle FA differences between remitted and resistant individuals (2.9% vs. 1.8%). Gains were particularly pronounced in LLD, reflecting the framework's ability to resolve fanning tracts susceptible to
brain aging. By preserving spatial heterogeneity, this automatic decomposition provides a robust foundation for large-scale clinical tractometry.
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All authors Nathan Decaux, Jean-Charles Roy, Thomas Desmidt, Marie-Laure Paillère Martinot, Gabriel Robert, Julie Coloigner
Robotics
Accuracy Evaluation of INS/ZUPT Filtering Methods Based on Different Geometric Error Definitions
Wei Ouyang, Jiale Han, Yarong Luo et al. (+1 more)
Geometric filters have recently been introduced to improve the accuracy and consistency of inertial-based integrated
navigation systems. Error states were defined through specific group operations, introducing state correlations in error definition, which were lacked in the additive error used by a conventional indirect Kalman filter. The desirable consistent filtering models can be obtained based on specific
geometric errors. For zero-velocity measurements expressed in the reference frame, this paper derives left-error process and measurement models from invariant filtering, two-frame-group filtering, and equivariant filtering. Importantly, a new group operation is introduced for the left tangent-group equivariant error. The analysis shows that the two-frame-group invariant extended Kalman filter (TFG-IEKF) and the tangent-group equivariant filter (TG-EqF) do not offer a significant consistency advantage over the invariant extended Kalman filter (IEKF). Experiments with an INS/ZUPT measurement system show that, under small initial attitude errors, the conventional indirect extended Kalman filter (EKF) achieves loop-closure position errors below $0.1\%$ of the traveled distance, while the three
geometric filters achieve comparable positioning accuracy.
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All authors Wei Ouyang, Jiale Han, Yarong Luo, Maoran Zhu
Artificial Intelligence
Neuralyzing the Trace: Selective Representation-Level Unlearning with Contrastive Sparse Autoencoders
Itai Zehavi, Fanny Jourdan and Ulrich Aivodji
Machine unlearning aims to remove targeted information while preserving a model's other abilities. In realistic settings, such as
privacy requests under the EU GDPR, the target may be narrow, for example information associated with a single person. Behavioral forgetting alone may be insufficient, motivating interventions directly on internal representations. However, standard mechanistic-interpretability extractors are poorly selective for such targets. We identify an energy bias in reconstruction-based extraction, which favors dominant background structure over low-energy target-specific components. We introduce SCALPEL, a contrastive sparse autoencoder designed to learn more selective forget features. We show theoretically that contrastive training promotes target-selective features and that our selection score controls expected background knowledge perturbation. We validate SCALPEL experimentally on TOFU across Qwen, Llama, and Gemma, where it substantially improves over NMF and standard SAE interventions and is competitive with Gradient Difference and RMU, bridging mechanistic interpretability and fine-grained unlearning.
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High Energy Physics (Theory)
Hilbert space in the flat limit of AdS/CFT. I. Massive particles
Kevin Nguyen
The flat limit of AdS$_4$ spacetime corresponds, at the level of the
symmetry algebra, to the Inönü–Wigner contraction $\mathfrak{so}(2,3) \to \mathfrak{iso}(1,3)$. We describe the emergence of the
Hilbert space of massive scattering states of arbitrary integer
spin $s$ from
unitary lowest-weight representations $\mathcal{D}(\Delta,s)$ of the conformal group, using a basis of `pseudo-momentum' states first introduced by Fronsdal. These states nicely reduce to Wigner's momentum eigenstates in the limit of infinite AdS
curvature radius $\ell \to \infty$, at fixed $m=\Delta/\ell$. A simple conformal map relates this description to conformal fields in $\mathbb{R}^3$ familiar from radial quantization, with the interior and exterior of the unit ball respectively corresponding to outgoing and ingoing momenta. Under the contraction, and upon appropriate rescaling, the inner product on $\mathcal{D}(\Delta,s)$ converges to the standard Lorentz-invariant inner product of Wigner's massive particles, so that unitarity is preserved throughout. The contraction to massless particles is briefly discussed here, and will be treated in detail in a forthcoming paper.
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Artificial Intelligence
Cheap, open agents make LLM pollution harder to mitigate
Raluca Rilla, Anne-Marie Nussberger, Rui Mata et al. (+1 more)
Large
Language Model (LLM) pollution occurs when synthetic responses contaminate data intended to capture human behavior. High deployment costs have so far limited the risk posed by autonomous survey
agents. However, open-weight models paired with open-source agentic frameworks may have removed this barrier. We compared the performance and detectability of nine
agent configurations, ranging from fully open variants to closed commercial ones. Each
agent autonomously completed a survey containing multiple response types yielding various detection checks. Fully open
agents ran locally without usage fees and performed competitively with commercial alternatives. Open and commercial
agents failed different sets of checks, and no single check reliably detected all
agents, but open-text responses discriminated best between
agents and humans. These findings identify fully open
agents as a distinct risk for LLM pollution and support multilayered detection strategies emphasizing open-text analysis.
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All authors Raluca Rilla, Anne-Marie Nussberger, Rui Mata, Dirk U. Wulff
Computational Complexity
Linear Certificates for Membership Comparability, Quadratic Barriers for Selectors
Sebastian Ben Daniel
Selectors and comparators supply only partial information about membership: a selector names a member of any pair that meets the
language, while a binary membership comparator merely excludes one of the four membership vectors of a pair. We ask how much nonuniform advice turns such information into exact recognition. Our main result extends the optimal nondeterministic advice bound for P-selective sets to every binary membership-comparable
language: $2-mc \subseteq NP /(3n+5)\cap\mathrm{coNP}/(3n+5)$, with common fixed advice and certificates of at most $5n+12$ bits. The class is strictly larger; some 2-mc sets are not truth-table reducible to any P-selective set. The proof replaces the tournament king by an independent two-step cover of true signed literals, together with a short-forcing-or-exact-majority dichotomy, and it relativizes. Via an advice-preserving isolation transfer, a deterministic
polynomial-time
algorithm for promise Unique-
Circuit-SAT gives $2-mc \subseteq P/O(n)$. For selectors we determine tight orders of ordinary advice: $\Theta(n)$ for errorless average-case computation and $\Theta(n^2)$ for worst-case bounded-error computation, the latter independent of the interpreter's coin bound. One oracle realizes both orders on a single
language and separates ordinary from coin-dependent advice. The quadratic and linear lower bounds hold for tournament-query procedures and relativized
languages, not unconditionally for unrelativized P-selective sets.
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Mesoscale and Nanoscale Physics
Measurement protocol for non-adiabatic geometric phases of Floquet states
Carlos Martín-Fernández, Gloria Platero and Sigmund Kohler
Periodically time-dependent
quantum systems have Floquet solutions. They consist of a phase factor and a Floquet mode which shares the periodicity of the driving. Consequently, any such solution possesses a non adiabatic
geometric phase known as the Aharonov-Anandan (AA) phase. It is gauge-invariant and, therefore, is experimentally accessible. We propose a protocol for the measurement of AA phases based on the adiabatic change of the phase of the non-adiabatic driving. We argue that the experimental requirements of the protocol are those of previous measurements of adiabatic phases.
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Rings and Algebras
Boolean Trusses as Rectangular Bands and Boolean Rings
Ryszard R. Andruszkiewicz and Bernard Rybołowicz
We introduce Boolean trusses, that is, trusses in which every element is idempotent. Every Boolean truss admits a decomposition into a truss arising from a Boolean ring and a rectangular truss. Furthermore, we classify all Boolean trusses up to isomorphism.
Computer Vision
Where Compute Matters: Heterogeneous Attention for Efficient Video Diffusion
Olga Zatsarynna, Denis Korzhenkov, Juergen Gall et al. (+2 more)
Efficient
video generation requires reducing the quadratic cost of self-
attention over long spatio-temporal
token sequences. Existing efficient-
attention methods typically apply the same computation pattern to every
token, even though denoising difficulty varies substantially across
video regions and evolves throughout the generation process. We introduce HetA-DiT, a heterogeneous
attention mechanism that adaptively allocates computation according to
token difficulty. A lightweight uncertainty branch predicts a
token-wise estimate of denoising difficulty, which is used to route uncertain
tokens through dense global
attention while processing more reliable
tokens with efficient local
attention. The resulting routing is content- and timestep-adaptive, retains global context where it matters most, and provides a single parameter for controlling the quality-efficiency trade-off. HetA-DiT is compatible with few-step distribution-matching
distillation and introduces no additional
Transformer evaluation at inference time by reusing uncertainty estimates from the preceding denoising step. We evaluate the method on DMD-distilled Wan2.2-5B and Wan2.1-1.3B models. Across VBench, VBench-2.0, and human preference evaluation, HetA-DiT maintains competitive generation quality while routing only approximately 20% of
tokens through dense
attention.
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All authors Olga Zatsarynna, Denis Korzhenkov, Juergen Gall, Amir Habibian, Mohsen Ghafoorian
Quantum Physics
Computational aberration-retrieval with entangled photons
Baptiste Courme, YoonSeok Baek and Hugo Defienne
Adaptive optics (AO) is central to high-resolution microscopy, yet conventional implementations often require repeated correction-and-measurement cycles whose number increases with aberration complexity. This measurement burden can become prohibitive when the available temporal window for correction is short or when
photon budgets are constrained. Here, we demonstrate an AO strategy that exploits the spatial correlations of entangled
photon pairs to retrieve aberrations directly. By using the second-order correlation function as an aberration-sensitive metric, the required correction can be determined with only a few measurements, without relying on image-based feedback or iterative optimization. This correlation-based approach establishes an efficient framework for AO leveraging
quantum light, offering a new route toward
quantum-enhanced microscopy in regimes where conventional correction strategies become challenging.
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Robotics
Kintsugi-VLA: Turning Failed Robot Rollouts into Recovery Data through Interventional Recoverability
Ivan Snegirev, Elizaveta Semenyakina, Dmitrii Maliukov et al. (+2 more)
Simulation enables scalable training of
Vision-
Language-Action policies by using privileged experts to generate visual demonstrations without requiring every trajectory to be collected through manual teleoperation. However, such pipelines typically retain successful demonstrations while failed rollouts are discarded, even though they expose precisely the off-nominal states from which recovery must be learned. We introduce Kintsugi-VLA, a framework for converting failed rollouts into targeted synthetic recovery data by exploiting exact state restoration and branching in simulation. For a fixed privileged expert, we define interventional recoverability as the probability of completing the original task after the simulator is restored to a given state, estimate it using adaptive Monte Carlo continuations with pointwise Wilson intervals, and characterize its non-monotonic
evolution along failed trajectories. These estimates identify an observed terminal low-recoverability frontier-the point after which measured recoverability remains below a threshold-which is then used to select informative recovery starting states. In a simulated Franka manipulation task, targeted recovery data yield aggregate SmolVLA recovery success of 34.6% and 38.4% under difficulty- and frame-budget matching, respectively, 5.8 and 6.7 percentage points above uniform sampling within the same recovery window. The same ordering is observed under disturbed end-to-end execution and shifted clutter and physics conditions, while clean-task success decreases from 76.8% to 74.7%. Kintsugi-VLA demonstrates how failed simulator rollouts can be transformed from discarded experience into structured recovery-training data through direct interventional measurement.
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All authors Ivan Snegirev, Elizaveta Semenyakina, Dmitrii Maliukov, Miguel Altamirano Cabrera, Dzmitry Tsetserukou
Distributed Computing
DynBranch: Speculative Subgraph Reuse for Dynamic Agentic LLM Serving
Junyi Shen, Noppanat Wadlom, Zhengyuan Su et al. (+1 more)
Agentic LLM workflows decide their execution paths at runtime. Downstream computation may be predictable, or may have run before, yet it cannot begin until the model or the user resolves the branch. We call this serialization the branch-resolution barrier. Caching alone does not hide it: the key that identifies a reusable result is not known until then. In this paper, we propose DynBranch, which makes an unresolved branch addressable before it resolves. Its stable coordinate lets candidate subgraphs run during resolution and completed subgraph results be reused across later requests. A two-level controller admits this work when its expected benefit exceeds the load price. DynBranch sits at the model-API boundary and requires no changes to
agent harnesses or model execution engines. Across four agentic workloads with Qwen3-32B on 4x H200 GPUs, DynBranch reduces mean latency by up to 32% over each workload's strongest prior system and by 46-66% against a no-reuse floor, while preserving workflow results. The benefit persists across backbone families and on a commodity Qwen3-8B/RTX 4090 deployment.
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All authors Junyi Shen, Noppanat Wadlom, Zhengyuan Su, Yao Lu
Computation and Language
Modeling Student Sensemaking with LLMs and Knowledge-Graph-Guided Inference
Özge Alacam, Zübeyde Demet Kirbulut Güneş, Funda Ekici et al. (+7 more)
Collaborative science learning requires nuanced interpretation of student dialogue to characterize how learners identify knowledge gaps, build explanations, and work toward resolution - a theory-driven analysis that is labor-intensive and difficult to scale. We investigate whether instruction-tuned large
language models (LLMs) can support multidimensional analysis of collaborative sensemaking without task-specific training, and whether structured knowledge-state information improves model inference. We evaluate two mid-size LLMs on 23 richly annotated, expert-labeled episodes across prompting conditions that vary definitional scaffolding,
reasoning mode, and turn structure. Without
reasoning, models tend to overpredict successful sensemaking;
reasoning-enabled prompting improves identification of unsuccessful cases. Knowledge-state diagnostics provide additional grounding, improving detection of unsuccessful sensemaking and increasing agreement with expert annotations. No single configuration performs best across all sensemaking dimensions, underscoring the multidimensional nature of the task.
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All authors Özge Alacam, Zübeyde Demet Kirbulut Güneş, Funda Ekici, Nurcan Turan-Oluk, Dilay Dinçdemir, Hakk{\i} Kaday{\i}fç{\i}, Sevinç Nihal Yeşiloğlu, Burcu Iş{\i}k, Halil Tümay, Sinem Gencer
Information Retrieval
KuaFu: Compressing Long User Behavior into Understanding at Billion Scale
Jiahao Hui, Lin Zhu, Ben Tan et al. (+7 more)
Learning user preferences from long behavioral sequences is fundamental to large-scale advertising, recommender systems, and conversational
agents. In industrial systems, preference modeling is typically task-wise, with different downstream tasks independently processing the same user history to capture different aspects of preference. This repeated processing creates substantial redundancy at billion-user scale, especially as preferences evolve and representations require frequent updates. We present KuaFu, a unified user preference modeling system that replaces repeated user-level sequence encoding with reusable item-level compressed representations. Each behavioral item is encoded once and shared across users and downstream tasks, while a user's history is built chronologically and extended with new item representations as they arrive, letting KuaFu capture evolving preferences without re-encoding the entire history. To reduce inference and storage overhead, KuaFu introduces a two-axis projector:
token-axis compression shortens behavior sequences by up to 15x and reduces each item from 150 KB to 10 KB, while width-axis compression reduces the cached representation to 0.5 KB. We further develop a four-stage training pipeline consisting of reconstruction pre-training, preference-understanding post-training, joint compression-generation training, and
hallucination-aware reinforcement learning, progressively improving representation fidelity and downstream utility. Across six MRQA and five RecBench
benchmarks, KuaFu consistently outperforms existing compression methods at comparable ratios, with gains of up to 17.7 EM and 1.90 AUC points respectively. KuaFu has been deployed and evaluated on the Tencent advertising and recommendation platform for ten months. Over this period, it improves per-GPU throughput by up to 350%, saves 190 GPUs, and increases overall GMV by 1.37%.
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All authors Jiahao Hui, Lin Zhu, Ben Tan, Yishen Hu, Jingdong Shu, Zetai Jiang, Xining Ran, Yeshou Cai, Gong Chen, Haijie Gu
Numerical Analysis
Operator-ADI balanced truncation of delay differential systems
Timo Reis
We develop a balanced truncation method for linear delay differential systems based on a finite-rank alternating-direction implicit (ADI) iteration for the associated operator Lyapunov equations. The method acts directly on the infinite-dimensional history-state realization and does not discretize the delay interval during Gramian approximation or balancing. The finite-rank reachability and observability factors are represented exactly in Takenaka–Malmquist coordinates, reducing the required computations to finite-dimensional linear
algebra. These factors yield a finite-dimensional, delay-free reduced model. For real system data, the implementation remains in real arithmetic. Under suitable conditions on the ADI shifts and a gap in the Hankel singular values, we prove convergence in $\mathcal H_\infty$ to the reduced transfer function obtained by exact balanced truncation of the infinite-dimensional system. The construction extends to finitely many discrete delays. Numerical experiments validate the method against independent reference computations. The complete MATLAB implementation used in the experiments is provided as supplementary material.
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Quantum Physics
Single-shot coherent process tomography and mid-infrared polarimetry with undetected photons
Marthe Zeja, Wen-Zhe Yan, Zhibo Hou et al. (+1 more)
Measurements with undetected
photons infer properties of a probe beam that is never detected, transferring mid-infrared information onto silicon-friendly wavelengths.
Tomography in this paradigm has so far relied on switched settings or scanned phases, referenced across a drift-sensitive campaign; no protocol reads all Jones parameters from one frame. We show that a folded nonlinear interferometer with a diagonally pumped crossed-
crystal source performs coherent process
tomography of the undetected beam in a single spectrometer frame: it reconstructs all seven parameters of the beam's round-trip Jones
matrix, the coherent part of the channel. Birefringent group-delay walk-off, normally a calibration nuisance, acts as a frequency multiplexer: each Jones-
matrix element is assigned its own carrier, a spectral fringe period paired with a detector port. Because the multiplexing exploits the down-conversion bandwidth rather than merely tolerating it, a single
Fourier transform per detector trace returns all four
moduli and all three relative phases across that bandwidth, i.e., the full round-trip Jones spectrum up to a global phase, with no scan or setting change (after a one-time reference frame). Simultaneity makes every relative phase immune to common-mode drift, and a dark carrier, a self-interference term that unitarity forces to vanish, provides a built-in null test. For realistic parameters of a periodically poled KTP source the scheme yields wavelength-resolved mid-infrared polarimetry across 3.5-4.2 microns (~80 spectral points per Jones element) on a silicon camera, extending to depth-resolved Jones
matrices, i.e., polarization-sensitive optical coherence
tomography with undetected
photons. A Fisher-information analysis, the first we are aware of in undetected-
photon tomography, sets the precision budget and compares delay multiplexing to sequential protocols at equal
photon number.
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All authors Marthe Zeja, Wen-Zhe Yan, Zhibo Hou, Sven Ramelow
Physics and Society
Reachability Does Not Imply Searchability in Expanding Networks
Antonio Scala
Routing and search respond in opposite ways to rapid network expansion. Short graph distances make a known destination easy to reach, while the neighborhood accessible within a few hops can be vastly larger than any finite inspection budget. We show that this tension imposes an
algorithm-independent constraint on sparse search. If $m_q$ relevant nodes are placed without structural information among $N$ nodes and at most $M$ nodes can be inspected, any target-blind exploration protocol has success probability at most $Mm_q/N$, irrespective of correlations between successive inspections, of revisits, and of any structural preference the exploration rule may have. Geometry determines when this constraint becomes relevant: a target becomes accessible with order-one probability when the graph-distance ball volume satisfies $V(R_c)\sim N/m_q$. At this scale, any inspection mechanism achieving a fixed success probability $\delta>0$ must enrich the probability of inspecting relevant nodes by at least order $N/(Mm_q)$ relative to the neutral target density. For uniformly searched candidate sets, this entails a vanishing visible fraction of the accessible region. Thus rapidly expanding networks can make rare targets geometrically close while target-blind search remains ineffective. Numerical results on Krioukov
hyperbolic random graphs and two-dimensional
lattices show that this separation can arise within only a few hops in the rapidly expanding case, while the
lattice accessibility radius grows algebraically.
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Audio and Speech Processing
Room Impulse Response Embeddings for Speech Enhancement in Noisy and Reverberant Environments
Adrian Meise and Reinhold Haeb-Umbach
We propose a self-supervised approach for learning room impulse response (RIR) representations from single-channel noisy-reverberant
speech. It consists of first training on reverberant data, then on noisy-reverberant data, and finally with a teacher-student approach, where the student learns to replicate the teacher's
embeddings when given a noisy version of the reverberant input. We assess their representational capabilities by estimating acoustic room parameters from them. Conditioning a discriminative
speech enhancement model on the derived
embeddings yields consistent gains across all evaluated metrics, including downstream word error rate, for both reverberant and noisy-reverberant
speech.
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