Machine Learning
What Must Survive? Exact Task-Information–State Frontiers Under Partial Task Revelation
Ronald Katende
A state may need to be compressed before its exact downstream task is known. For a finite family of linear task operators $\{T_u\}_{u\in\U}$, we characterize exactly how much state must survive when only one of $K$ coarse task messages is available at compression time: $$p^*(K)= \min_{\substack{\Pcal\text{ partition of }\U\\|\Pcal|\le K}} \max_{C\in\Pcal}\rank(T_C),$$ where $T_C$ stacks the tasks that remain unresolved within
cell $C$. Thus task information reduces retained state precisely by separating tasks whose joint task-visible subspace is expensive. We derive the dual bit frontier, an irreducible common-core floor, and a sharp singular-value characterization for nonzero tolerance. For overlapping task families, a cumulative overlap deficit places the exact frontier within an additive $\Delta$ of weighted load balancing. Optimal partitioning is strongly NP-hard, yet direct-sum families admit classical approximation guarantees and a PTAS. We stress-test the theory on learned task operators from CIFAR-10, Fashion-MNIST, SVHN, and Burgers observables. For Burgers with $K=2$ and $\eps=0.35$, a constructive partition attains $p=4$, while balanced random partitions require median $p=6$, with held-out worst standardized RMSE $0.0368$. The results distinguish uniform task sufficiency from distributional and decision-level performance.
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Computer Vision
Refine Then Fusion: Training-Free 3D Point Cloud Adaptation with Priority Refinement and Multi-Modal Knowledge Fusion
Hang Cheng, Yan Chen, Mingyu Fan et al. (+1 more)
Recent pre-trained foundation models provide rich multi-modal priors for downstream 3D
vision tasks. However, the effectiveness of these representations in few-shot scenarios is limited by two fundamental challenges: High-dimensional features often contain substantial channel redundancy and task-irrelevant noise, while the reliability of different modalities varies across samples. Consequently, direct aggregation of heterogeneous representations overlooks sample-dependent modality reliability and may obscure the discriminative cues essential. To address these limitations, we propose Refine Then Fusion(RTF), a training-free framework for few-shot 3D recognition. RTF first identifies discriminative feature channels by jointly modeling inter-class similarity and intra-class stability, thereby decoupling domain-specific knowledge refinement from the cached representations of pre-trained models. It then introduces a reliability-aware fusion mechanism that estimates sample-wise modality reliability from the distribution shifts induced by feature refinement, enabling adaptive aggregation of multi-modal representations. Furthermore, RTF constructs a
memory cache that integrates instance-level support features with class-level prototypes to infer query labels. Extensive experiments on five
benchmarks demonstrate that RTF consistently outperforms single-modal baselines, partial-fusion variants, and existing lightweight adaptation methods, achieving state-of-the-art few-shot 3D recognition performance without gradient optimization, additional training data, auxiliary training, or parameter updates.
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All authors Hang Cheng, Yan Chen, Mingyu Fan, Long Zeng
Computer Vision
VidOmni-Bench: A Benchmark for Fine-Grained Video Understanding via Spatio-Temporal Event Verification across Complexity and Duration
Changbeen Kim, Junwon Chang, Kipyo Kim et al. (+3 more)
While
Video Large
Language Models (
Video-LLMs) have recently demonstrated strong performance, reliably evaluating their fine-grained
video understanding remains challenging. Existing
benchmarks often rely on question answering or ground-truth caption matching, where models may succeed through superficial cues and incomplete annotations. To this end, we introduce VidOmni-Bench, a
benchmark that requires models to verify whether each event in dense
video captions is supported by the
video. VidOmni-Bench consists of 500
videos spanning five complexity types and diverse durations from 4 seconds to 90 minutes. After collecting
videos along these axes, we use diverse
Video-LLMs to generate dense captions and obtain human-verified sentence-level labels, where sentences containing incorrect events serve as hard negatives for evaluation. Our experiments on VidOmni-Bench reveal three key findings: (i)
Video-LLMs frequently generate hallucinated descriptions in dense
video captioning; (ii) they also struggle as verifiers, failing to reliably detect plausible but incorrect event descriptions; and (iii) model weaknesses vary across
video complexity and duration, revealing diverse, model-specific bottlenecks in current
Video-LLMs.
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All authors Changbeen Kim, Junwon Chang, Kipyo Kim, Risa Shinoda, Kuniaki Saito, Donghyun Kim
Nuclear Experiment
First observation of the fine structure of the Pygmy Dipole Resonance in a nucleus away from stability via the $\beta^-$ decay of $^{92}$Rb to $^{92}$Sr
P. Spagnoletti, M. Ramalho, S. Tahtela et al. (+52 more)
A comprehensive $\gamma$-ray spectroscopy study of excited states in $^{92}$Sr populated via $\beta^-$ decay of $^{92}$Rb ($J^{\pi}=0^-$, $Q_{\beta}=8095(6)$~keV) was performed with the GRIFFIN spectrometer at the Isotope Separator and Accelerator (
ISAC) radioactive ion beam facility at TRIUMF. The high $\gamma$-ray efficiency of the GRIFFIN spectrometer combined with the intense rubidium beams produced allowed for 864 $\gamma$-ray transitions to be placed in the level scheme with 190 excited levels populated, most of them identified for the first time. The excitation energies of low-
spin states in $^{92}$Sr are well reproduced by large-scale Shell Model calculations up to 5~MeV. The $\beta$-feeding intensities are in very good agreement with results from a recent study employing Modular Total Absorption Spectroscopy, indicating a significant suppression of the Pandemonium effect. The experimental picture reveals the energy level dependence of log~$ft$ values in unprecedented detail. These log~$ft$ values are well reproduced by Multiple-Commutator Model calculations that identify the features in the excited levels' wavefunctions that enable the population of high-lying levels with a low effective Q-value that belong to the Pygmy Dipole Resonance.
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All authors P. Spagnoletti, M. Ramalho, S. Tahtela, E. Kauppinen, J. Suhonen, C. Andreoiu, M. Scheck, V. Vedia, Z. Ahmed, D. Annen, G.C. Ball, G. Benzoni, S.S. Bhattacharjee, H. Bidaman, V. Bildstein, S. Buck, R. Caballero-Folch, R.J. Coleman, S. Devinyak, I. Dillmann, I. Djianto, F.H. Garcia, A.B. Garnsworthy, P.E. Garrett, B. Greaves, C.J. Griffin, G.F. Grinyer, E.G. Fuakye, G. Hackman, D. Hymers, D. Kalaydjieva, R. Kanungo, K. Kapoor, E. Kasanda, W. Korten, N. Marchini, K. Mashtakov, A. Nannini, K. Ortner, B. Olaizola, C. Natzke, C.M. Petrache, M. Polettini, A. Radich, M. Rocchini, N. Saei, M. Satrazani, M. Siciliano, M. Singh, C.E. Svensson, D.A. Torres, R. Umashankar, E. Wadge, T. Zidar, M. Zielinska
Artificial Intelligence
Learning-to-Optimize as the Missing Architectural Layer of AI-Native Networks
Giambattista Amati, Federica Mangiatordi, Pierpaolo Salvo et al. (+2 more)
Artificial Intelligence (AI) is becoming a fundamental design principle of future AI-native communication networks, enabling autonomous resource management, adaptive control, and zero-touch network operation. While current AI-native architectures increasingly embed intelligence across network functions, they provide little guidance on how optimisation knowledge should be systematically generated, transferred, and exploited by AI models. This paper argues that the Learning-to-Optimize (L2O) represents the missing architectural layer between optimisation and AI-native intelligence. Rather than viewing optimisation merely as an online decision engine, the proposed paradigm redefines optimisation
algorithms as offline knowledge generators that produce high-quality supervisory information for neural surrogate models. The resulting models inherit optimisation expertise while enabling low-latency runtime inference suitable for dynamic network environments. A generic four-stage L2O workflow is introduced, comprising optimisation, knowledge generation, surrogate learning, and runtime inference. Unlike existing Learning-to-Optimize approaches, which primarily focus on
algorithm acceleration, the proposed framework establishes L2O as an architectural abstraction applicable across heterogeneous communication and computing systems. The proposed paradigm is illustrated by an NR-V2X relay-selection problem, in which optimisation-generated solutions from a Mixed-Integer Linear Programming (MILP) solver are used to train a Graph Neural Network that can reproduce near-optimal decisions in real time. The presented perspective positions Learning-to-Optimize as a key architectural enabler for future AI-native networks.
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All authors Giambattista Amati, Federica Mangiatordi, Pierpaolo Salvo, Emiliano Pallotti, Simone Angelini
Differential Geometry
The metric extension problem under positive and negative curvature conditions
Wenlong Wang and Jintian Zhu
We first prove that every smooth boundary metric on a compact
manifold extends to a metric with any prescribed positive lower bound for
Ricci curvature, whereas extensions under stronger positive \(k^{\mathrm{th}}\)-intermediate
Ricci curvature conditions may fail due to local obstructions. For negative sectional
curvature, we identify a global obstruction to extension. We then consider the class of compact
manifolds defined by the existence of a metric with negative sectional
curvature and boundary index at most one. On every
manifold in this class, we prove that any smooth boundary metric extends to a metric with negative sectional
curvature and strictly convex umbilical boundary. We further show that every such initial metric admits a complete asymptotically
hyperbolic isometric extension with the same
curvature condition and any prescribed conformal infinity. This class of
manifolds is closed under boundary connected sums. As a
geometric application of these extension results, every smooth metric on \(\mathbb S^n\) admits a strictly convex isometric
embedding into \(\mathbb R^{n+1}\) equipped with a complete metric of negative sectional
curvature. The proofs combine neck constructions with corner smoothing for upper
curvature bounds.
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Medical Physics
Voxel-Matching NORDIC: Non-local patch formation by time-series similarity increases tSNR in high-resolution BOLD fMRI
Alessandro Nigi, Natalia Petridou and Jeroen C.W. Siero
Submillimeter functional
magnetic resonance imaging (fMRI) based on blood-oxygenation-level-dependent (BOLD) signal enables the study of
brain function at the submillimeter level, uncovering insights into fine-scale organisations like cortical layers and columns. However, its inherently low contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR) often limit its reliability and applicability. Noise Reduction with Distribution Corrected Principal Components Analysis (NORDIC PCA) is a locally low-rank denoising
algorithm that reduces thermal noise levels in BOLD fMRI in a local patch manner. However, local patches often contain a mixture of signals from multiple tissues that negatively affects the low-rank structure of the patches, which limits the denoising capabilities of the
algorithm. We propose an alternative approach for patch formation by gathering similar non-local voxels, dubbed voxel-matching (VM) NORDIC. The results on submillimeter-resolution BOLD fMRI data indicate that VM-NORDIC effectively promotes the low rank of the patches by boosting signal redundancy, allowing for more efficient noise attenuation. Moreover, the method barely affects spatial smoothness due to the non-local voxel selection based on time-series similarity. In particular, VM-NORDIC outperforms standard NORDIC with default local patching (Standard-NORDIC) in terms of temporal SNR (tSNR) (~9-90% larger than Standard-NORDIC; ~23-250% larger than the original) and spatial smoothness estimates (~20% of the smoothness induced by Standard-NORDIC). These improvements are fundamental to improving the validity and precision of fMRI studies at submillimeter resolutions.
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Computer Vision
2D GauSS-MI: Efficient Active Scene Reconstruction with Balanced Visual and Geometric Quality
Yuhan Xie and Jia Pan
Active reconstruction requires efficient active view selection to achieve high-quality reconstruction within limited onboard computational resources. Existing methods face challenges in adequately balancing visual and
geometric quality with the computational efficiency required for real-time operation. In this work, we present an active reconstruction framework based on 2D Gaussian Splatting (2DGS). We develop an efficient online 2DGS mapping pipeline for incremental RGB-D observations and introduce a probabilistic reliability model that characterizes the view-dependent reconstruction quality of individual 2D Gaussian splats. Building on this model, we formulate 2D Gaussian Splatting Shannon Mutual Information (2D GauSS-MI), a mutual-information-based metric that exploits the explicit surface orientation of 2DGS to evaluate the expected information gain of candidate views. The proposed metric enables active view selection to account for both visual and
geometric reconstruction quality. We evaluate the proposed system against three state-of-the-art baselines on eight Replica scenes. Experimental results demonstrate that our method achieves a favorable balance between visual and
geometric reconstruction quality with substantially lower computational cost and competitive model storage.
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Cryptography and Security
ServeGuard: Verifiable, Bounded-Residual Confinement of Operator-Invisible Channels Without Revealing the Certified Read Factor
Dominik Dahlem and Rui Vieira
Third-party adapters for open-weight
language models ship as opaque weight
matrices; a recipient cannot check whether an adapter hides a
backdoor without trusting the publisher or inspecting the weights, the publisher's core asset. For one important class (payloads placed where a safety monitor is structurally blind), detection is unsound as a defense: every detector that factors through the declared monitor is invariant on its blind subspace, and honest and backdoored adapters overlap on every blind-subspace statistic we evaluate, because benign adaptation uses that subspace too. Rather than detect this channel, we make it structurally absent and prove that we did. The publisher builds the adapter to read the input only through directions the monitor covers and proves this in zero knowledge, revealing nothing about the read factor it certifies. The certificate is cheap because the expensive part, identifying the monitor's blind spot, is a deterministic function of the public base model, so only one linear identity is proved; the served residual is the base model's own public floor, not a prover-chosen tolerance. The result is ServeGuard, a supply-chain primitive: the publisher ships a proof-carrying adapter whose proof lets a consumer or regulator verify, without the certified read factor and without trusting the publisher, that the adapter carries no hidden channel of this class relative to the declared monitor; an admission-time typing guard binds the guarantee to the adapter bytes admitted at serving time. Across eight checkpoints up to 7B from four families, the monitoring budget is architectural: the measured frontier saturates at the value-path rank on grouped-query checkpoints but not on multi-head ones. On a 0.5B model confinement is nearly free for benign adaptation, making monitor quality the security lever.
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Robotics
Skel-WAM: A Hand-Skeleton-Conditioned World Action Model for Human-to-Robot Manipulation Transfer
Zetao Cai, Yaping Li, Yiqun Wang et al. (+8 more)
Robot demonstrations are expensive to collect and often provide limited distributional coverage of task variations. Human
videos offer a low-cost source of complementary manipulation experience, but learning from them requires bridging embodiment gaps in visual appearance and action spaces. We introduce Skel-WAM, a world action model that bridges these differences through a unified hand-skeleton motion interface. The key insight is to align human and
robot motion through a common hand
topology, combining skeleton overlays that ground motion in the scene with structured 2.5-D keypoints that encode explicit hand kinematics.
Video and Keypoint Experts jointly learn visual and skeletal dynamics through a Mixture-of-
Transformers, while a separate
robot-trained Action Expert maps these predictions to executable controls. This separation enables human and
robot demonstrations to directly supervise shared dynamics without requiring
robot action labels for human
videos. Across four real-world bimanual tasks and seven simulated tasks, Skel-WAM achieves average success rates of 79.86% and 63.29%, surpassing the strongest baseline by 22.22 and 8.28 percentage points, respectively. Human-
robot cotraining more than doubles real-world success on task variations absent from
robot training data, from 38.89% to 86.11%. These results demonstrate that a shared skeletal interface enables joint learning across human and
robot data and expands
robot task coverage through complementary human demonstrations.
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All authors Zetao Cai, Yaping Li, Yiqun Wang, Xinyu Zhan, Yuyin Yang, Haoxiang Ma, Kailin Li, Tao Lu, Jiangmiao Pang, Linning Xu, Dahua Lin
Algebraic Topology
Continuous Comparison of Free Simplicial Prounipotent and Pro-$p$ Resolutions
Andrey Mikhovich
We give a continuous version of the comparison
theorem for free simplicial resolutions in the categories of prounipotent groups over a field of characteristic zero and of pro-$p$ groups. The resolutions carry free bases compatible with degeneracies; their ranks may be infinite. In characteristic zero the lifting step follows from continuous linear splittings and the universal property of completed free Lie
algebras. In the pro-$p$ case it follows from the classical projectivity of free pro-$p$ groups. A relative lifting argument and an explicit simplicial cylinder establish homotopy uniqueness. We formulate the free bases using actual surjections in the simplex category, so degeneracy words related by simplicial identities are identified from the outset, and we spell out the full matching objects used in the lifting argument. The question arose naturally in our preceding work on Bousfield–Kan completions of subcontractible presentations.
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Numerical Analysis
FrFNO:Injecting the analytic Mittag-Leffler propagator into a resolution-robust neural operator for space-time fractional PDEs
Guofei Pang
Fractional partial differential equations couple a
memory-dependent time derivative with a nonlocal fractional
Laplacian, and their repeated solution under varying fractional orders, initial data, or diffusivity fields is computationally expensive. Neural operators offer a fast surrogate, but standard architectures must relearn the dominant linear fractional
evolution from data. We propose the Fractional
Fourier Neural Operator (FrFNO), a resolution-robust conditional operator. It injects the analytic Mittag–Leffler response of the frozen linear part as a once-precomputed, resolution-independent propagator table, and trains a spectral convolutional network only on the residual induced by variable coefficients and nonlinear advection, conditioned continuously on both fractional orders. The central theoretical result is that under zero-shot super-resolution the injected propagator fills the out-of-band modes that a standard
Fourier neural operator sets to zero, replacing the out-of-band full-field tail by the smaller residual tail (a $K$-independent constant-factor reduction under weak perturbation). The residual and the full solution carry the same Sobolev order, so the advantage is amplitude reduction rather than a steeper tail. Mesh refinement drives the error to a residual-controlled floor. On a nonlinear two-dimensional space–time fractional Burgers problem FrFNO achieves the lowest relative $L^2$ error among six baselines (FNO, PINO, PDNO, CNO, DeepONet, U-Net) at the training resolution and under zero-shot super-resolution, yields the smallest spectral phase error, and remains the best at the integer-order limit and for long integration windows. The same construction extends to coupled fractional systems such as fractional Allen-Cahn and Navier–Stokes equations. Code, training scripts, and reference outputs are available at https://github.com/Derek2021Pang/FrFNO.
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Robotics
2nd Place Solution to the HANDS 2026 Workshop Challenge-Dexterous Grasp Motion Track: Single-Shot Trajectory Warping for Grasp Motion Generation
Muneeb A. Khan, Woojin Kim, Shinwoo Kim et al. (+3 more)
This report describes our 2nd place solution to the HANDS 2026 workshop challenge (Dexterous Grasp Motion track) in conjunction with ECCV 2026. In this challenge, we address grasp motion generation for the 12-DoF LinkerHand O6, aiming to produce physically plausible reach-and-lift trajectories for unseen objects from randomized initial hand poses in simulation. This task is particularly challenging because each grasp requires a per-step policy to make approximately $70$ twelve-dimensional decisions, with errors accumulating over time, while test objects and physical dynamics may differ from those encountered during training. To address these challenges, we propose editing a single successful GraspM3 demonstration instead of generating the motion step by step: a policy observes the object once and outputs a 12-D warp of the demonstration, which is then replayed open-loop. Moreover, we train the warp policy with one-step PPO over all $4{,}824$ training objects in parallel. As a result, our method achieved success rates of $94.61\%$ on the easy track, the highest of all submissions, and $57.18\%$ on the hard track of the private test set.
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All authors Muneeb A. Khan, Woojin Kim, Shinwoo Kim, Muhammad Munsif, Binod Bhattarai, Seungryul Baek
Metric Geometry
Measure contraction property on isometric leaves and monotone fibres
Krzysztof J. Ciosmak
For finite measures with positive densities on convex Euclidean supports, we prove that $MCP(\kappa,N)$ passes with unchanged parameters to almost every isometric leaf of an arbitrary nonexpansive map. The proof rests on a sharp contraction inequality for
geometric conditional densities, with exponent equal to the leaf codimension. The inherited dimension parameter is optimal. A total-variation limit on resolvent graphs extends the result to inverse fibres of maximal monotone relations, including convex gradient fibres. We also disprove Klartag's
curvature-dimension inheritance
conjecture by a firmly nonexpansive example in dimension three and a gradient example in dimension four. In codimension one, affinity of the
geometric density yields
curvature-dimension inheritance. The first example also gives failure on monotone fibres. Both constructions admit arbitrarily large
curvature loss, including for a fixed Gaussian ambient measure on families of leaves of positive quotient measure.
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Artificial Intelligence
The Communication Bottleneck: A Round-Trip Study of Tree-Structured Expression Serialization in Language Models
Xavier Suau, Alex Ferrando de las Morenas, Luca Zappella et al. (+1 more)
When
language models reason in chain-of-thought or exchange free-text intermediates, they serialize structured information into natural
language. How much tree-structured compositional content survives this bottleneck? We propose a round-trip protocol that answers this question empirically for tree-structured expressions. A generator converts a procedurally generated arithmetic expression into a word problem, a separate extractor recovers the expression from the word problem alone, and symbolic equivalence provides an exact oracle. Evaluating all pairwise combinations of sixteen models yields a communication
matrix whose marginals separate generation quality from extraction quality. Three main findings emerge. First, the channel is lossy and asymmetric: swapping which model generates and which extracts shifts accuracy by up to 60.4 points, and the best pair reaches 92.9% by combining different models on each end rather than the same model on both. Second, at least 73.6% of round-trip failures originate at generation, and difficulty is driven by tree structure (operator count, depth, right-branching) rather than model family. Third, the channel is trainable: ~3600 fine-tuning examples that share the evaluation's operators and tree shapes lift every open-weight model above untrained Gemini-3.1-Pro, an upper bound under matched semantics. A disjoint-domain regime with new operators and vocabulary also raises every open-weight model, confirming the gain is not an artifact of matched semantics, though a gap to the frontier remains. Together these results identify tree-structured expression serialization as a primary limiting factor when models communicate hierarchical structure through natural
language.
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All authors Xavier Suau, Alex Ferrando de las Morenas, Luca Zappella, Samy Bengio
Statistical Mechanics
The charge dependent hard-sphere model: Polycrystals as low-energy configurations
Leonard Kreutz and Timo Ziereis
We investigate the emergence of rigid polycrystalline structures in atomistic ionic particle systems as low-energy configurations. The interaction between particles of opposite charge is modeled by hard spheres that interact when they are tangential. The interaction between particles of same charge is modeled as a hard repulsion that forces a minimal distance between them. The atomistic energy is frame invariant, and no underlying reference
lattice is assumed on the ionic configurations. The asymptotic behavior of configurations with finite surface energy scaling is identified by means of $\Gamma$-convergence. The related continuum theory is described by piecewise constant fields that encode the local orientation of the configuration. The limiting energy is local and concentrates at grain boundaries, which correspond to the boundaries of the regions where the underlying configuration has a constant orientation. The limiting energy density is anisotropic and depends on the relative misorientation of the two grains, their translation misfit, and the normal to their interface. Furthermore, we perform a fine analysis of surface energies for solid-solid and solid-
vacuum phase transitions and determine energetically favorable orientation mismatches. This relies on a structure result for our grain boundaries, which shows that, due to the rigid setup, interpolating layers near the grain interface are energetically not favorable.
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Combinatorics
The Diametral Metric Dimension of Generalized Corona Graph
Anis Nur Fitria, Liliek Susilowati, Yayuk Wahyuni et al. (+1 more)
This study investigates the diametral metric dimension of generalized corona graphs, where the main graph is connected graph and the branch graphs form a sequence of connected graphs. The concept of diametral metric dimension is an extension of the metric dimension concept, requiring the resolving set to contain all diametral vertices. To determine the diametral metric dimension of a generalized corona graph, one must first identify the distance of each vertex in the graph and the graph's diametral set. Subsequently, a resolving set containing the diametral set is determined. The results show that the diametral metric dimension of the generalized corona graph depends on the diameter of the main graph and the metric dimensions of the graphs in the sequence. These concepts provide theoretical insights into resolving structures in the planning infrastructure and motivate further studies on other graph families and graph operations.
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All authors Anis Nur Fitria, Liliek Susilowati, Yayuk Wahyuni, Nor Haniza Sarmin
Optics
Mechanical Activation of Terahertz Tunneling in Metallic Nanogaps
Dasom Kim, Dukhyung Lee, Young-Mi Bahk et al. (+1 more)
Metallic nanogaps concentrate terahertz (THz) fields into deep subwavelength volumes and support field-driven electron tunneling when the insulating barrier becomes sufficiently narrow. Here, we demonstrate mechanical control of tunneling-mediated nonlinear THz transmission in a flexible nanogap metasurface. The metasurface consists of Au/PMMA/Au nanogaps fabricated on a polyethylene terephthalate substrate, enabling continuous tuning of the gap geometry through macroscopic bending. In the flat state, the resonant transmission exhibits only a weak dependence on the incident THz field strength. Upon bending, increasing the incident field strength induces pronounced resonance suppression accompanied by saturation of the voltage developed across the nanogaps. This nonlinear response is consistent with the opening of a field-dependent tunneling conduction channel through the mechanically narrowed PMMA barriers. Simmons-model calculations illustrate the strong increase in tunneling current density and the associated dissipative gap response as the local gap width approaches the few-nanometer regime. These results establish mechanical deformation as a macroscopic means of controlling tunneling-mediated THz nonlinearities in flexible metasurfaces.
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All authors Dasom Kim, Dukhyung Lee, Young-Mi Bahk, Dai-Sik Kim
Algebraic Geometry
Bounds on automorphism groups of surfaces of general type with negative c_2
Xiaokun Zhong
Let $k$ be an algebraically closed field of characteristic $p>0$, and let $S$ be a minimal smooth projective surface of general type over $k$. If the second Chern class satisfies $c_2(S)<0$, then the order of the
automorphism group satisfies $|\mathrm{Aut}_k(S)|<2598(K_S^2)^4$, and the order of every abelian subgroup $G\subset \mathrm{Aut}_k(S)$ satisfies $|G|<81(K_S^2)^3$.
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Robotics
DPed-VLN: A Benchmark for Socially Compliant Vision-and-Language Navigation in Dynamic Pedestrian Environments
Haojie Dai, Xiangyi Wang, Liuyi Wang et al. (+5 more)
Vision-and-
language navigation (VLN) has advanced rapidly in static indoor environments, but
robots operating in human-populated spaces must ground
language while responding to moving pedestrians and social-safety constraints. We present DPed-VLN, a Habitat 3.0
benchmark for dynamic-pedestrian VLN that couples 33,093
navigation episodes with paired global and prior-augmented instructions, ORCA-controlled
humanoid pedestrians, socially constrained expert paths, and metrics that jointly assess
navigation efficiency and social safety. DPed-VLN separates ordinary goal-oriented route guidance from prior-augmented instructions that expose dynamic-pedestrian cues for controlled analysis. To instantiate the
benchmark, we introduce DPet (Dynamic Pedestrian-aware Network), a pedestrian-aware policy network trained with reinforcement learning and imitation learning. We further adapt representative state-of-the-art VLM-based
navigation models, including NaVILA and StreamVLN, to DPed-VLN through LoRA fine-tuning. Experiments show that LoRA adaptation improves zero-shot VLM baselines in several success and safety metrics, especially reducing StreamVLN's collision rate. Among the evaluated methods, DPet-RL achieves the highest SR, SPL, and STL.
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All authors Haojie Dai, Xiangyi Wang, Liuyi Wang, Kai Sheng, Zongtao He, Chengju Liu, Wei Ye, Qijun Chen
Algebraic Geometry
On Fano varieties of index one that are linear sections of Grassmannians of lines
Michele Bolognesi, Ciro Ciliberto and Alessandro Verra
In this paper we consider the general $n-2$ dimensional linear section $X$ of the Grassmannian $\mathbb G(1,n)$ of lines in $\mathbb{P}^n$, that is a Fano variety of index 1. We first prove that for $n$ odd $X$ is birational to a hypersurface defined by a pfaffian determinant of order $n+1$ of linear forms in $\mathbb{P}^{n-1}$, and we study this birational transformation in some detail. Then for $n$ even, we prove that $X$ is unirational and birational to a hypersurface defined by a pfaffian determinant of order $n$ of linear forms in $\mathbb{P}^{n-1}$. Moreover we prove that the hypersurface of $\mathbb{P}^n$ described by the lines corresponding to the points in $X$ has degree $n-1$ and is rational, and we find a rational parametrization of it.
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Computer Vision
Adaptive World Memory 3D Foundation Model for Scalable 3D Mapping, Localization, and Rendering
Tianchen Deng, Guole Shen, Yilin Shen et al. (+7 more)
Recent 3D foundation models enable generalizable
geometric reasoning from RGB images but remain limited in persistent
memory, scalability, and renderable scene modeling. We present a
memory-centric 3D foundation model for scalable robotic localization, reconstruction, and Gaussian rendering. Its core is an adaptive world
memory mechanism that combines
transformer-based gated updates with test-time temporal-spatial regulation. Learned gates control recurrent
memory propagation, while temporal state
evolution and spatial observation-state consistency regulate
token-wise updates and forgetting over long image sequences. To support large-scale mapping, we organize
memory into local submaps and integrate progressive mapping and tracking, loop closure, and SL(4)-based global refinement to maintain local accuracy and global consistency. A Gaussian reconstruction head decodes
memory-enhanced features into renderable primitives, unifying camera pose estimation, dense point-cloud reconstruction, and photorealistic rendering within a single model. Experiments on public
benchmarks and self-collected
datasets from diverse robotic platforms demonstrate improved trajectory accuracy, reconstruction completeness, and rendering quality over existing 3D foundation reconstruction and SLAM baselines. These results support adaptive
memory as a foundation for persistent robotic world modeling. The
dataset and code will be made publicly available at \href{https://github.com/dtc111111/AWM-3DFM}{https://github.com/dtc111111/AWM-3DFM}.
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All authors Tianchen Deng, Guole Shen, Yilin Shen, Wenhua Wu, Yilin Fang, Ziqi Ma, Tianjun Zhang, Shenghai Yuan, Wolfram Burgard, Hesheng Wang
Quantum Physics
Optimal control for duty-cycle-limited interferometry with single-NV centers
Ugur Tamer, Sina Zeytinoglu and Ozgur E. Mustecaplioglu
Stimulus-responsive hydrogels convert temperature changes into
magnetic-field shifts detectable by nitrogen-vacancy (NV) centers, enabling nanoscale thermometry in soft and biological environments. Existing hydrogel-nanodiamond demonstrations rely on NV ensembles, whose high
photon throughput is accompanied by gradient-induced inhomogeneous broadening, while idealized single-NV projections assume high-fluence fluorescence/ODMR readout. Here we study a pulsed single-NV route for the same class of sensors and ask whether decoherence-aware coherent control can improve thermometric performance over optimized Ramsey interrogation at equal detected-
photon budget. Using a sigmoidal volume-phase-transition model, dipolar
magnetic transduction, and Lindblad master-equation simulations, we find a reproducible 25-27% per-shot sensitivity gain over optimized Ramsey, i.e., a 57-60% gain in Fisher information (1.57-1.60). The same gain carries over to the
photon-normalized Fisher information. The rate gain is governed by the measurement duty cycle, the fraction of the experimental cycle spent accumulating signal rather than initializing, reading out or waiting, and becomes largest when the overhead or optical-dose constraint dominates the cycle time. The optimized trajectories reveal a response-shaping mechanism in which phase accumulation is concentrated near the end of the sequence, and a closed-form depth-two solution reproduces the numerical optimum and exhibits that mechanism analytically. The advantage is most pronounced when the dephasing time is short compared with the measurement overhead or dose-limited waiting time, which is the operating regime targeted by hydrogel-transduced single-
spin biosensing.
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Systems and Control
Model-Free Control for Residential Heating: Deployment and Simulation of Nonlinear Data-Enabled Predictive Control
Sebastian Zieglmeier, Chris Verhoek, Jaap Eising et al. (+1 more)
Residential heating accounts for a large share of building energy use, and predictive control strategies can reduce it by anticipating rather than reacting to the room temperature alone. The usual manner of implementing predictive control, model predictive control (MPC), requires an accurate model of each individual residential unit for heating control. Obtaining this model is time-consuming to obtain manually or even impossible, and such models do not transfer across a heterogeneous building stock. Data-enabled predictive control (DeePC) removes this modeling step by designing the controller directly using measured trajectories of the system. The foundations of DeePC, however, are built on the class of deterministic linear time-invariant systems, which is an unrealistic assumption for residential heating. This work applies three recently proposed nonlinear extensions of DeePC, collectively referred to as data-driven predictive control (DPC): Select-DPC, gain-scheduling DPC, and linear parameter-varying DPC. These are compared against standard DeePC and the hysteresis controller, the industry standard in residential heating. The comparison runs over a full heating season on a calibrated digital twin of an occupied research unit, the NEST research building in Switzerland, assessed by heating energy and comfort-band violation. Moreover, gain-scheduling DPC was deployed on the real residential apartment and validated the conclusions from the simulations. Each of the DPC controllers consumes significantly less energy than the hysteresis controller, amounting to roughly $11\%$ over the season, validating the use of DPC. The nonlinear methods further outperform linear DeePC in all assessed scenarios, and we analyze the relative costs and benefits of each nonlinear method.
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All authors Sebastian Zieglmeier, Chris Verhoek, Jaap Eising, Mathias Hudoba de Badyn
Nuclear Experiment
Direct measurement of Enhanced octupole collectivity in 148Dy
P. Spagnoletti, V. Vedia, E. Yuksel et al. (+36 more)
Excited states in $^{148}_{~66}$Dy were populated via $\beta^+/EC$ decay of $^{148m}$Ho using the GRIFFIN spectrometer at the TRIUMF
ISAC-I facility. A combined measurement of the mean lifetime of the $3_1^-$ level using the Generalized Centroid Difference (GCD) method and branching fraction of the $3_1^-\rightarrow0_1^+$ $\gamma$-ray decay has been performed. From these results, an enhanced electric octupole $B(E3;3_1^-\rightarrow0_1^+)$ transition strength of 46(3)~W.u. has been determined in $^{148}_{~66}$Dy. This is the largest measured value across the closed
neutron shell at $N=82$ and provides direct evidence of enhanced octupole collectivity beyond $Z=64$. The
evolution of the $B(E3; 3^-_1 \rightarrow 0^+_1)$ strength along the $N=82$ isotonic chain is compared with quasiparticle random-phase approximation (QRPA) calculations using the SkI3 and SkM$^*$ Skyrme energy-density functionals, as well as with large-scale shell-model (SM) calculations. This result extends the boundaries of enhanced octupole collectivity far from the so-called `octupole magic numbers' $Z=56$ and $N=88$.
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All authors P. Spagnoletti, V. Vedia, E. Yuksel, Y. X. Yu, G. J. Fu, R. Umashankar, G. Andreetta, C. Andreoiu, A.A. Avaa, G.C. Ball, V. Bildstein, S. Buck, E. Cantacuzene, G. Colombi, I. Dillmann, L. P. Gaffney, A.B. Garnsworthy, P.E. Garrett, G.F. Grinyer, G. Hackman, J. Liu, A. Ludlam, L.L. Luperi, Madhu, T. La Marca, D. Movilla-Quintero, S. Murillo Morales, R. Nicolas del Alamo, M.M. Rajabali, P.H. Regan, M. Rocchini, M. Scheck, J. Seger, S. Sekal, D. Stramaccioni, C.E. Svensson, A. Tsantiri, J. Williams, F. Wu
Computer Vision
VoxelTTO: Voxel-Aligned Feed-Forward 3D Gaussian Splatting with Test-Time Optimization
Yibin Zhao, Yihan Pan, Yangwen Li et al. (+2 more)
Recent feed-forward 3D Gaussian Splatting (3DGS) methods typically regress pixel-aligned Gaussian primitives, often causing excessive overlap and artifacts, while inaccuracies in predicted camera poses can lead to misalignment in novel-view synthesis (NVS). We present VoxelTTO, a feed-forward framework for reconstructing geometrically accurate 3DGS scenes from an arbitrary number of images and optional camera parameters. VoxelTTO aggregates dense image features into a global voxel representation and decodes Gaussians from voxel features, breaking the pixel-to-Gaussian correspondence. To exploit known camera parameters while keeping the pretrained visual foundation model (VFM) parameters frozen, we introduce test-time optimization (TTO) that adapts lightweight LoRA modules using pose supervision. We further replace vanilla 3DGS rasterization with
stochastic solid volume rendering during training and inference, improving
geometric fidelity. Training updates only the voxel-aligned Gaussian reconstruction modules, requiring 80 GPU hours. Experiments on Replica, Tanks and Temples, and DTU demonstrate improved RGB-D NVS and camera-pose estimation relative to prior methods.
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All authors Yibin Zhao, Yihan Pan, Yangwen Li, Jun Nan, Jianjun Yi
Robotics
FORTE: Task-Adaptive Force Capability Optimization for Mobile Manipulators
Xiao Wang, Heng Zhang, Gokhan Solak et al. (+2 more)
Effective physical interaction control in robotic manipulation requires not only kinematically feasible motion but also sufficient force-interaction capability. Existing redundancy resolution methods often ignore task-specific force demands or maximize the force capability indiscriminately, sacrificing dexterity when large force margins are unnecessary. We propose a task-oriented force capability optimization framework for redundant mobile manipulators. A
Vision-
Language Model (VLM) infers object physical properties from an RGB image and a task description, generating a desired task-force sequence that captures gravitational and inertial demands. We then define a task-oriented force capability metric as the signed distance between a task-force uncertainty ball and the dynamic residual force polytope (RFP), quantifying compatibility between task demands and the
robot's remaining actuation capacity. This metric is incorporated, alongside manipulability, joint-limit avoidance, trajectory smoothness, and base-oscillation suppression, into a whole-body multi-objective trajectory-optimization problem. Experiments on a mobile manipulator performing lifting and single-point-holding tasks under varying payload conditions demonstrate that the proposed method provides sufficient force capability for heavy loads while preserving high manipulability for light loads. This yields a task-adaptive balance that fixed capability-maximizing baselines (RFP inscribed radius, RFP cone) and manipulability-only optimization fail to achieve. The core implementation is publicly available at https://github.com/yeying256/FORTE.
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All authors Xiao Wang, Heng Zhang, Gokhan Solak, Fei Zhao, Arash Ajoudani
Social and Information Networks
Two Fault Lines: Latent Polarity Geometry in X Community Notes
Andreas Andreou and Michael Sirivianos
Community Notes is X's crowdsourced fact-checking system. A note is published beneath the post it corrects only when raters who usually disagree both rate it helpful, a design called bridging. To apply that rule, the system learns who disagrees with whom from the ratings alone, placing every rater and note on one line, the polarity axis. Every scorer in the production pipeline uses a single axis. Refitting the base model these scorers share on the full public data (212.9M ratings, 2.33M notes, 1.07M raters), we find that one axis is too few. The space is at least two-dimensional. The first axis is left/right politics, while the second, which we interpret as trust in institutions, is largely independent of the first. A held-out test confirms that the second axis improves prediction of unseen ratings, while a third adds little. A second rater dimension learned from one set of topics predicts how raters judge COVID and Ukraine notes excluded from the fit, so it does not merely restate subject matter. Among heavily rated notes that barely divide raters politically, the published share falls from 71.5% to 11.7% as second-axis disagreement grows. A one-axis fit records these notes only as weakly polarised and less helpful; the information that raters at one end of the second axis support them is lost. Authors write notes matching their own position on both axes (r = 0.538 and 0.358), and a small minority of raters cast most ratings (Gini = 0.718). Fewer notes are published in the smallest
language communities, but the shortfall is in ratings received, not in how the rule treats them. Keeping ratings per note constant, only Hindi stays below the global rate of 10.85%, and Greek moves from 7.76% to 11.68%. We argue for a bridging model with more than one axis of disagreement, and for recruiting raters in the
languages the current design reaches least.
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Statistical Computation
Scentree: a framework for generating scenario trees for multistage stochastic programming
Cristian Pachón-García, Albert Solà Vilalta and F.-Javier Heredia
We present scentree, an open-source Python package for constructing scenario trees for multistage
stochastic programming models using either historical data or samples drawn from a probability distribution. It combines machine learning and multivariate time series models to obtain a scenario fan that captures dependencies in the
stochastic processes. This scenario fan is subsequently reduced into a scenario tree that preserves representativeness of the
stochastic process, providing a flexible and extensible framework for uncertainty modeling in
stochastic programming problems. The framework can also be naturally applied to two-stage
stochastic programming problems. Key contributions are the joint modeling of the
stochastic process, preserving correlations between all random variables, and the automation of the complete workflow, including
forecasting model selection, parameter estimation, scenario fan generation, and scenario tree construction. In addition, scentree does not assume any underlying data distribution, reducing the statistical expertise required to produce a scenario tree, and is modular by design, allowing external forecasts and other partial executions of the workflow.
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Solar and Stellar Astrophysics
The independence of the mid-infrared RR Lyrae Period-Luminosity relation from metallicity from a study of three globular clusters in the Large Magellanic Cloud
Steve Ardern, Vicky Scowcroft and Stijn Wuyts
RR Lyrae are pulsating variable
stars tracing old ($>10$ Gyr)
stellar populations, and exhibit a strong mid-infrared Period-Luminosity (PL) relation that can be calibrated with known parallaxes to infer distances. Here we present Period-Luminosity relations and distance
moduli for three isolated globular clusters in the Large Magellanic Cloud: Reticulum, NGC 1841, and NGC 1466. Our analysis uses legacy Spitzer Space
Telescope images obtained by the Carnegie RR Lyrae Program, and an internally self-consistent sample restricted to RRab
stars, with cluster membership confirmed using
Gaia DR3 proper motions and photometry. In the Spitzer 3.6~$\mu$m band, we simultaneously fit a PL for these clusters using a slope derived from
Galactic Globular Clusters, yielding extinction-corrected distance
moduli of $18.47\pm0.09$ mag for Reticulum, $18.29\pm0.09$ mag for NGC 1841, and $18.61\pm0.09$ mag for NGC 1466. The latter two are the first RR Lyrae PL-based distance
moduli for these clusters, and all three are consistent with literature values from other techniques. Additionally we fit a PL with slope as a free parameter, and find that this LMC-derived PL is consistent with that derived from
Galactic GCs. Simultaneously fitting a PLZ for the three clusters yields a
metallicity coefficient $c= -0.03\pm0.05~\mathrm{mag}~\mathrm{dex}^{-1}$ which can be considered a negligible dependence of the PL on
metallicity ($|c| <0.1$ mag dex$^{-1}$). Modelling an intrinsic width, $W$, to the PL/ PLZ yields a consistent value $W\approx0.1\pm0.02~\mathrm{mag}$, suggesting intrinsic width is not driven by
metallicity.
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