Multimedia
DualAffect: Dual-Path Paralinguistic Integration for Generative Audiovisual Emotion Understanding
Yibo Wang, Jisheng Dang, Bimei Wang et al. (+8 more)
Understanding human emotion is essential for
multimodal systems that interpret both the content of an interaction and the speaker's expressive state. However, connecting paralinguistic cues to generated answers remains challenging, particularly when vocal expression diverges from visual appearance. We propose DualAffect, a dual-path architecture that integrates visually conditioned acoustic features and temporally organized acoustic
tokens into a shared
language decoder. The two pathways reuse the same acoustic encoding. The fusion pathway associates sound with visual content through visual queries, while the direct pathway compresses the valid acoustic sequence into ordered
tokens through an independent projector. This design gives the decoder access to both joint audiovisual context and acoustic evidence organized along its own timeline. Mixed-task adaptation with task-dependent output preservation supports emotion recognition and general audiovisual question answering within the same model. DualAffect achieves an average metric of 67.89 on CA-MER and accuracies of 62.88% on the EmotionTalk test set and 49.87% on Daily-Omni. Pathway interventions show that the trained decoder uses direct acoustic evidence alongside existing fusion. With model parameters and fusion inputs fixed, zeroing the direct
tokens reduces EmotionTalk validation accuracy by 9.75 percentage points and the CA-MER average metric by 2.66 points. The largest CA-MER subset effect occurs when the overall emotion agrees with audio but differs from
vision. These findings identify acoustic representation organization as a practical design axis for connecting paralinguistic perception to unified audiovisual generation. Code is available at https://github.com/YiboWang2002/DualAffect.
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All authors Yibo Wang, Jisheng Dang, Bimei Wang, Junhong Liu, Jiongpei Hou, Jingwen Zhao, Wenjun Ma, Hong Peng, Bin Hu, Qi Tian, Tat-Seng Chua
Computer Vision
LensErase: A Foundation Model for Erase Anything
Yunuo Chen, Xiuyu Wu, Qinhong Yang et al. (+11 more)
We introduce LensErase, a general-purpose mask-based Erase Anything foundation model designed to cover a broad spectrum of real-world erase scenarios. LensErase supports diverse objects, visual text, watermarks, associated visual effects, multiple-object removal, and a wide range of object scales, aspect ratios, and image resolutions. First, to enable such broad coverage, we construct LensErase-100M, a large-scale
dataset containing 101.07 million paired source-mask-target examples spanning diverse erase scenarios. Second, we pretrain LensErase from the text-to-image model Lens, adapting its strong generative prior to mask-based erasing. To efficiently incorporate the source image and erase mask, we propose Mask-Aware RoPE, a 3D rotary positional encoding that directly injects mask information into the source-image
tokens without introducing a separate mask
token sequence. Finally, we refine the model through supervised fine-tuning and reinforcement learning with contract-guided, region-aware rewards, and apply TraceFusion at inference to preserve high-fidelity content outside the effective edit region. Across six public
benchmarks and the proposed LensErase-Bench, LensErase achieves substantial improvements under both reference-based objective metrics and reference-free VLM evaluation. We hope LensErase can serve as a foundational model for erase-related applications and systems.
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All authors Yunuo Chen, Xiuyu Wu, Qinhong Yang, Jinjing Zhao, Yanchen Dong, Sirui Zhang, Dong Chen, Fangyun Wei, Lei Shi, Ji Li, Dongdong Chen, Guo Lu, Yan Lu, Baining Guo
Data Structures and Algorithms
On the Parameterized Complexity of Capacitated Stack and Queue Layouts with Colored Edges
Thomas Depian, Giuseppe Liotta, Fabrizio Montecchiani et al. (+1 more)
Stack and queue layouts are two extensively studied and complementary linear representations of graphs. A stack (queue) layout $\langle \prec, \sigma \rangle$ of a graph $G$ consists of a total ordering $\prec$ of its vertices and of a partitioning $\sigma$ of its edges into pages, such that no two independent edges on the same page cross (nest). We propose a generalized model with colored edges (one color per edge) and capacitated pages. Namely, the input graph is equipped with colored edges and a prescribed budget for each page-color pair $(p,c)$ - this budget determines the maximum number of edges of color $c$ that page $p$ can accept. This model is motivated by fair allocation scenarios where budgets can be used to control the share of monochromatic edges on each page. Unfortunately, it is known that deciding whether a stack (queue) layout exists is NP-hard already with two (one) pages. Thus, we focus on the fixed-order setting - where the linear order $\prec$ is part of the input - for which an ordinary 2-page stack or $\ell$-page queue layout can be computed in
polynomial time. Despite the restrictive setting, we show that both capacitated problems are NP-complete already for two pages. Furthermore, we study the complexity of the problems when parameterized by the number of pages, colors, and/or the structure of the edge conflict graph with respect to the fixed linear order $\prec$. Our analysis paints a detailed complexity landscape and uncovers a surprisingly intricate interplay between the considered parameters.
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All authors Thomas Depian, Giuseppe Liotta, Fabrizio Montecchiani, Martin Nöllenburg
Group Theory
Groups with the fixed-point property
Nicolas Monod, Omer Tamuz and Lizzy Teryoshin
We propose the study of the class of groups that fix a point for every continuous action on any convex compact space. Oliver completed the corresponding program of classifying the finite groups that have this fixed-point property in finite dimensions. Turning to infinite groups, the Schauder–Tychonoff
theorem establishes that the integers have the fixed-point property, but this has not been shown for any other infinite, finitely generated group. We establish a local transfer principle and use it to show, as a proof of concept, that $\mathbf{Z} \times (\mathbf{Z}/p\mathbf{Z})$ belongs to this class of groups.
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Computation and Language
From Agents to Edges: Paired Optimization for Multi-Agent Collaboration
Feng Lin, Xiaotian Yu, Yifan Yang et al. (+1 more)
Strong individual
agents do not guarantee effective collaboration. Communication must align what one
agent conveys with how another interprets and uses it. This dependency makes the communication edge a natural unit of optimization. We introduce Paired Automated Communication Optimization (PACO), which jointly optimizes the instructions at both ends of each edge and their shared communication convention in systems built on black-box
language models. PACO keeps model weights,
agent roles, and workflow
topology fixed. A state-aware discounted bandit selects edges for refinement, while execution feedback guides paired revisions evaluated through staged end-to-end execution. The optimized instructions integrate into existing
agent prompts without additional model calls at deployment. On a four-
agent workflow evaluated across diverse multi-
agent benchmarks, PACO consistently outperforms leading
prompt-optimization and communication baselines. Configurations learned on HotpotQA retain their advantage in cross-
dataset transfer without target-specific optimization. These results demonstrate that optimizing how
agents communicate can substantially improve collaboration within an otherwise fixed system.
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All authors Feng Lin, Xiaotian Yu, Yifan Yang, Aijun Li
Materials Science
Hydrogen partial pressure as a key parameter in the reactive sputtering of photochromic YHO thin films
E. Strods, M. Zubkins, E. Letko et al. (+3 more)
Oxygen-containing yttrium hydride (YHO) thin films were synthesised by reactive magnetron sputtering over a wide range of hydrogen-to-argon gas flow ratios (H2/Ar) during yttrium hydride deposition, followed by post-oxidation in air. The deposition process was monitored in situ by
plasma optical emission spectroscopy. X-ray diffraction reveals a strong dependence of crystallographic texture on the H2/Ar ratio: highly <100>-textured films formed at low H2/Ar ratios progressively evolve into more polycrystalline microstructures with slightly reduced crystallite size as the hydrogen content increases. The optical transmittance gradually decreases from approximately 85 % to 70 % as the H2/Ar ratio is raised to 0.6, followed by a sharp drop at higher hydrogen contents. The photochromic contrast of 705-845 nm thick YHO films, measured after 10 min of illumination with a solar simulator, decreases from 14 % to 7 % as the H2/Ar ratio increases from 0.08 to 0.45. Thinner films (365-505 nm) exhibit a similar but less pronounced trend. Bleaching kinetics, analysed using a double-exponential model, indicate faster recovery, with effective bleaching time constants of approximately 1-15 min for films deposited at H2/Ar < 0.25. X-ray photoelectron spectroscopy reveals higher oxygen incorporation in films deposited at lower H2/Ar ratios, deviating from the commonly reported correlation between oxygen content and photochromic response in YHO. These results demonstrate that low hydrogen partial pressure during reactive sputtering is advantageous for achieving higher optical transparency, enhanced photochromic contrast, and faster bleaching kinetics in YHO thin films.
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All authors E. Strods, M. Zubkins, E. Letko, V. Vibornijs, A. Sarakovskis, J. Purans
Artificial Intelligence
WorkRover: Scaling Long-Horizon Cowork Agent Training Via Recursive Environment Evolution
Hang He, Linting Wang, Guanming Liu et al. (+14 more)
Scaling interactive environments is important for training long-horizon
agents through reinforcement learning. These environments often require further testing and refinement after initial generation, and correcting defects in a task may also require updating its work context and verifier. However, existing construction pipelines offer limited support for such iterative refinement while keeping these components aligned. We introduce WorkRover, a framework for training long-horizon cowork
agents through recursive environment
evolution. Its harness-driven synthesis enables construction
agents to jointly build and validate semantically aligned work contexts, executable tasks, and evidence-grounded verifiers, adapting their actions to workspace state and execution feedback rather than following a fixed construction workflow. To support environment
evolution, we introduce Factorial Compatibility Replay (FCR), which uses rollout feedback to evaluate combinations of component revisions through dependency-closed replay and selects the smallest validated repair among tested candidates while preserving task requirements and evaluation constraints. To provide finer-grained training signals, we propose Verifier-to-Turn Credit Assignment (VTCA), which augments trajectory-level GRPO advantages with signals from deterministic workspace-progress checks and assigns a bounded share of positive-progress credit to prerequisite turns using recorded tool-output and artifact dependencies. Using WorkRover, we post-train Qwen3.5-35B-A3B to obtain WorkRover-35B-A3B and evaluate it on several cowork-
agent benchmarks. Its reported scores exceed those of Qwen3.5-35B-A3B by 26.27 percentage points on JobBench, 29.32 percentage points on OfficeQA Pro, and 413.25 Elo points on GDPval v2. These results suggest that WorkRover offers a promising foundation for scalable training of long-horizon cowork
agents.
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All authors Hang He, Linting Wang, Guanming Liu, Bo Mao, Jinxiu Liu, Yiran Suo, Maosen Zhou, Zhiheng Xi, Guanting Dong, Zhou Shao, Rui Zheng, Jie Zhou, Chengcheng Wan, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang
Machine Learning
Uncertainty Evaluation: Bridging Machine Learning and Measurement Science
Dario Petri and Luca Mari
Reliable uncertainty evaluation is essential for assessing the trustworthiness of machine learning (ML) predictions, particularly in safety-critical applications. This paper explores conceptual analogies between uncertainty evaluation in ML and measurement science, drawing on the
evolution of metrology from error-based approaches to the framework established by the Guide to the Expression of Uncertainty in Measurement (GUM). Particular
attention is devoted to the distinction between process-level and result-level quality and to the limitations of the conventional classification of ML uncertainty into aleatoric and epistemic components. It is argued that this dichotomy, similarly to the traditional distinction between random and systematic measurement errors, is inherently model-dependent and provides an insufficient basis for a unified uncertainty evaluation framework. Further analogies between machine learning and measurement field of knowledge are identified concerning distribution shifts, anomalous operating conditions, and the validity of uncertainty evaluation. Building on the proposed considerations, the paper advocates a metrology-inspired approach based on the systematic identification, quantification, and propagation of uncertainty contributions. By extending the methodology already established in measurement science to ML, this approach could offer a potentially rigorous, consistent, and transferable framework for evaluating the trustworthiness of individual predictions.
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Number Theory
On Berndt-Straub conjectures about zeros of generalized Ramanujan polynomials
Siddhi Pathak
We settle two
conjectures of Berndt and Straub asserting that all nonzero zeros of the generalized Ramanujan
polynomials $R_k(x;\chi,\psi)$ and $S_{2n}(x;\chi,\chi)$ lie on the unit circle for real nonprincipal Dirichlet characters. We prove their
conjecture for $R_k$. For $S_{2n}$, we prove that at most four nonzero zeros lie off the unit circle, all purely imaginary. If $\chi_{163}=\left(\frac{\cdot}{163}\right)$ is the primitive quadratic character modulo $163$, then $S_6(x;\chi_{163},\chi_{163})$ has four exceptional zeros, showing that this bound is sharp. This disproves the second
conjecture and establishes that the `Riemann hypothesis for period
polynomials' can fail for Eisenstein newforms. Further, we introduce a transcendental invariant whose sign determines whether exceptional zeros of $S_{2n}(x;\chi,\chi)$ occur in all sufficiently large degrees, and show that these zeros converge to transcendental limits off the unit circle. For primitive odd characters, we relate this invariant and these limits to Fekete
polynomials.
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General Relativity and Quantum Cosmology
Pseudo-spectral methods in gravitational lensing: Spin precession in loop quantum black hole spacetimes
Xiankai Pang and Xiao-Kan Guo
Gravitational
lensing is usually treated as an initial value problem: for a given impact parameter of an unbound orbit, one computes the resulting deflection angle. In this paper we advocate the inverse point of view. For a prescribed deflection angle, the orbit is selected by two boundary conditions at spatial infinity, so that the
lensing problem becomes a well-defined two-point boundary value problem, with the impact parameter playing the role of a nonlinear
eigenvalue. We show that pseudo-spectral methods based on Chebyshev collocation solve this boundary value problem accurately at modest computational cost, and that the parallel transport equations governing the
spin precession of the particle reduce to a single linear system on the same grid. As a concrete application, we compute the
spin precession of massive particles in backward scattering off two black hole spacetimes from loop
quantum gravity: the covariant effective metric and the self-dual loop
quantum black hole. All equations are written for a general static, spherically symmetric line element with three free functions, so that both spacetimes are handled on the same footing. We find that the
quantum corrections are most pronounced for slowly moving particles, while in the ultra-relativistic limit the universal
spin flip is always recovered. The flip itself is universal, but the rate at which it is approached is not: the derivative of $\cos\xi$ with respect to the particle speed at $v=1$ is a strong-field quantity in every backward channel and depends clearly on the
quantum parameters of both spacetimes. The framework is not restricted to the models studied here and applies to any static, spherically symmetric spacetime.
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High Energy Physics (Phenomenology)
Color Lorentz force on a transversely polarized quark in the nucleon
Ziqi Zhang, Chandan Mondal, Siqi Xu et al. (+2 more)
The $x^2$ moment of the genuine twist-3 component of the unpolarized scalar parton distribution function $e(x)$ is related to the average transverse color Lorentz force experienced by a transversely polarized
quark in an unpolarized nucleon. This higher-twist quantity probes
quark–gluon correlations and provides unique insight into the nonperturbative dynamics of
quantum chromodynamics. For the first time, we study the corresponding off-forward
matrix element and derive its Lorentz-covariant decomposition in terms of generalized form factors. We calculate these form factors from light-front wave functions obtained by solving the light-front
Hamiltonian eigenvalue problem within the Basis Light-Front Quantization framework. Their
Fourier transforms provide a spatial picture of the transverse color forces in impact-parameter space. In particular, $\mathscr{F}_2$, whose forward limit is directly related to the twist-3
matrix element $e_2$, can be interpreted as the impact-parameter-space representation of the Boer–Mulders force, providing an intuitive spatial picture of the final-state interaction mechanism underlying the Boer–Mulders effect.
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All authors Ziqi Zhang, Chandan Mondal, Siqi Xu, Xingbo Zhao, James P. Vary
High Energy Physics (Phenomenology)
Covariant approach to allowed beta decay
Sergio Cruz-Alzaga and Adam Falkowski
We propose a Lorentz-covariant on-shell formalism for allowed nuclear beta decay. Nuclear
matrix elements are organized in a systematic effective field theory expansion in the recoil momentum, with the dependence on nuclear
spin and polarization encoded in little-group-covariant structures. In this
language, we construct the most general standard and exotic currents for allowed transitions through first recoil order. We then derive the differential decay distribution encoded in a set of Lorentz-invariant form factors. Specializing to the rest frame of the parent
nucleus, we relate our parameters to those used in the traditional beta-decay literature and in nucleon- and
quark-level effective field theories. The formalism provides a natural framework for extensions to higher recoil orders, radiative corrections, and forbidden transitions.
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Combinatorics
Non-additive symmetric rank-metric codes
Marta Messia and Ferdinando Zullo
We study non-additive rank-metric codes contained in the space of symmetric
matrices over a finite field. Our main focus is on symmetric $2$-codes. For odd $q$, we introduce a quotient construction for $\mathrm{Sym}_3(q)$ in which rank-one differences are described by isotropic vectors of a nondegenerate quadratic form on $\mathbb F_q^4$. This leads to a correspondence with partial ovoids of the elliptic quadric $Q^-(5,q)$. Using a construction of Cossidente and Pavese, we obtain a non-additive symmetric $2$-code in $\mathrm{Sym}_3(q)$ of cardinality $q^4+2q^3$, improving the previously known general lower bound. We also give an explicit
matrix description of this family. Lastly, we present a block-based construction for symmetric $2$-codes that extends the three-dimensional improvement to all odd dimensions. In particular, for every odd $n\geq3$ and every
prime power $q>2$, we obtain \begin{equation*} A_q^sym(n,2) \ge q^{(n^2-1)/2}+q^{(n^2-3)/2}+q^{(n^2-9)/2}, \end{equation*} which is strictly larger than the maximum cardinality of an additive symmetric $2$-code with the same parameters. This yields the first infinite family (as the
matrix dimension $n$ varies) of non-additive symmetric $2$-codes whose size is larger than the largest achievable size of an additive code with the same parameters; before this, such a gap had only been established for $n=3$.
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Computation and Language
A Benchmark for Bangla Task-Oriented Dialogue Understanding
Afrina Alam Lamia, Sumiaya Sharif, Prottoy Modak et al. (+2 more)
Task-oriented dialogue understanding remains underexplored for Bangla, particularly in multilingual settings involving English and romanized Bangla (Banglish). We introduce BanglaTOD, a parallel tri-lingual
benchmark comprising 209 multi-turn dialogues across nine Bangladesh-specific service domains and 64 intents. Each conversational turn is aligned across Bangla, English, and Banglish, yielding 4,689
language-specific turn records annotated with intents,
token-level slots, state updates, and accumulated dialogue states. We establish context-aware baselines for joint intent classification and slot filling using mBERT, BERT-base-uncased, and BanglaBERT. Cross-
language analysis reveals substantial performance differences across models and linguistic representations, while removing dialogue history reduces intent accuracy by 19.0–25.7 percentage points. BanglaTOD provides a foundation for multilingual dialogue understanding and future dialogue state tracking research in Bangladesh-specific conversational applications. The
dataset is publicly available at \href{https://huggingface.co/
datasets/afri7here/BanglaTOD}{Hugging Face}.
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All authors Afrina Alam Lamia, Sumiaya Sharif, Prottoy Modak, Ohiduzzaman Rayhan, Nafis Sadeq
Statistics Theory
A new approach for estimating the number of spikes in high-dimensional Gaussian models
Hugo Jeannin, Pascal Vallet, Florent Bouchard et al. (+2 more)
In this paper, we study the estimation of the signal subspace dimension $k$ in a $p$-variate signal-plus-noise complex Gaussian model, when the noise covariance
matrix is assumed to be diagonal or Toeplitz. We consider the high-dimensional asymptotic regime in which both the data dimension $p$ and the available sample size $n$ diverge to infinity at the same rate, while we also allow $k$ to diverge at a slower speed. In this context, the classical estimates of the literature, consistent in the high-dimensional regime, often rely on the a priori knowledge of an upper bound $k_*$ of $k$, which can be misspecified in practice. Our proposed approach relies on building estimates of the tracial moments of the noise covariance
matrix using the $p-k_*$ smallest
eigenvalues of the sample covariance
matrix (SCM), which are in turn used to build a threshold above which the $k$ largest
eigenvalues are isolated. The resulting estimate of $k$ is proved to be consistent in the high-dimensional regime, even for the misspecified situations where $k_* < k$. Numerical experiments in different challenging scenarios illustrate the improvement of the proposed approach over the aforementioned methods.
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All authors Hugo Jeannin, Pascal Vallet, Florent Bouchard, Guillaume Ginolhac, Audrey Giremus
Emerging Technologies
Two-State Dynamical Synapses as a Physical Substrate for Equilibrium Propagation
Dongshu Liu and Julie Grollier
Equilibrium Propagation (EqProp) updates each synapse by comparing a local term at the free and nudged
equilibria. Because these two values occur at different times, a physical implementation requires temporary local
memory. We show that this
memory can emerge from a two-timescale synaptic model in which neural and volatile synaptic dynamics are jointly derived from a single energy function. A volatile state $w$ relaxes toward the product of the pre- and postsynaptic activities, while a non-volatile state $M$ stores the learned parameter. At
equilibrium, $w$ equals the local term entering the EqProp update. Using its net change between phases implements phase-based EqProp, while transferring its successive changes during nudging yields continual EqProp, in which the temporal comparison is carried by the synaptic dynamics themselves. On MNIST with a single-hidden-layer network, the phase-based model reaches $2.01 \pm 0.09\%$ test error versus $2.00 \pm 0.13\%$ for standard EqProp, and the continual model reaches $2.24 \pm 0.11\%$ versus $2.28 \pm 0.16\%$ for continual EqProp. The framework further specifies the dynamical requirements that a volatile–non-volatile synaptic device must satisfy to implement this learning mechanism.
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Machine Learning
From Policy Evaluation to Off-Policy Control: Provable Emergence of In-Context Q-Learning
Yingjun Mo, Yajie Zhao and Fang Kong
Transformers can implement reinforcement-learning
algorithms in context, yet how off-policy control emerges within
Transformers remains poorly understood. We study Q-learning, a foundational model-free control
algorithm that combines off-policy bootstrapping with greedy policy improvement. We prove that a two-layer
attention-only
Transformer can implement a finite-temperature form of Q-learning, where the temperature $\tau>0$ controls the approximation to greedy action selection, recovering window Q-learning as $\tau\to0$. We train the structured
Transformer on randomly generated MDP tasks using output-only supervision. Experiments show that the theoretically predicted parameter structure emerges during training, including the internal greedy action-selection mechanism, and that the trained
Transformer achieves performance comparable to Q-learning on unseen tasks. Our theoretical analysis further shows that the equivalent realizations form a solution
manifold and that, under suitable task-diversity conditions, the training iterates converge locally to this
manifold at an exponential rate. For the random-task model analyzed here, the local worst-case number of gradient steps needed to reduce the distance to the
manifold by a factor $\epsilon$ is $\Theta\left(\tau^{-5}\log(1/\varepsilon)\right)$, quantifying the increasing optimization cost as the learned update approaches window Q-learning.
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Programming Languages
OpenMQPI: An MLIR-based Pipeline to Offload Quantum Circuits via OpenMP
Ercüment Kaya, Ekin Devim Şahinkaya, Minh Chung et al. (+1 more)
Quantum computing introduces specialized software stacks and programming interfaces that are often tightly coupled to specific ecosystems and hardware backends, limiting interoperability and software reuse. For HPCQC application developers, this complicates the integration of
quantum accelerators into existing applications. Existing
quantum frameworks also use ecosystem-specific
circuit representations and compilation pipelines, hindering integration with established heterogeneous compiler infrastructures. We propose OpenMQPI, an MLIR-based OpenMP extension for
quantum programming that leverages OpenMP's standardized target offloading infrastructure to integrate
quantum resources into HPC applications. OpenMQPI introduces QPIR, a lightweight MLIR dialect for representing
quantum circuits within OpenMP target regions, and a compiler pipeline that performs
quantum-specific optimizations and lowers
circuits to executables compatible with OpenMP offloading. At runtime, an MQSS plugin enables the generated executable to offload
quantum circuits to
quantum hardware through the OpenMP target offloading model. We evaluate OpenMQPI using the VQE, QFT, and Deutsch-Jozsa
algorithms with 5, 10, 15, and 20
qubits, comparing execution times against Qiskit and CUDA
Quantum. OpenMQPI achieves the lowest average VQE execution time across all tested
qubit configurations. At 20
qubits, it requires 5.18 s, compared with 5.83 s for Qiskit and 7.39 s for CUDA
Quantum. For QFT and Deutsch-Jozsa, OpenMQPI achieves execution times comparable to Qiskit and lower than those of CUDA
Quantum.
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All authors Ercüment Kaya, Ekin Devim Şahinkaya, Minh Chung, Martin Schulz
Machine Learning (Statistics)
Reliable Scale-Free Bandits via Empirical Likelihood
Pratham Gupta, Alhad Sethi and Shubhada Agrawal
We study
stochastic bandits without knowledge of reward scales or tail parameters. Almost-sure regret guarantees describe eventual learning but do not quantify expected performance or the risk of large regret in a finite run. We introduce $\texttt{EL-F}$, an anytime empirical-likelihood (EL) extension of the $g$-Forcing policy of Cowan and Katehakis (2020), and characterize its performance at three levels. First, for integrable rewards and non-decreasing, diverging exploration schedules $g$ with vanishing increments, $\texttt{EL-F}$ preserves the $g$-Forcing asymptotics for pseudo-regret $R(T)$: $R(T)/g(T)\to\sum_{i:\Delta_i>0}\Delta_i$ almost surely as $T\to\infty$, where $\Delta_i$ is the suboptimality gap of arm $i$. Second, on every sub-exponential instance, for non-decreasing $g(t)=\omega(\log t)$ satisfying a mild feasibility condition, expected pseudo-regret is at most $g(T)\sum_{i:\Delta_i>0}\Delta_i+C_{\underline{P}}$, where $C_{\underline{P}}$ is an instance-dependent constant independent of $T$. Moreover, we also establish finite-time tail bounds with super-
polynomial decay in regret beyond the forced-exploration budget. For slowly varying $g$, we determine the exact exponent of linear regret deviations on two-armed Gaussian instances with common variance and establish a matching lower bound among policies satisfying the same expected-regret budget on every such Gaussian instance. These quantitative guarantees also cover the greedy variant obtained at zero EL radius. A fixed positive radius introduces local optimism while preserving the leading asymptotic regret. We establish coupled pathwise dominance over the greedy variant in two-armed bandits with a deterministic suboptimal arm. Experiments demonstrate reductions in prolonged suboptimal allocation compared with $g$-Forcing on challenging instances.
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Cryptography and Security
Privacy-Preserving GNN Inference on Financial Transaction Graphs via Homomorphic Encryption
H. Çağr{\i} Bilgi, Kemal Mutluergil, Ceren Y{\i}ld{\i}r{\i}m et al. (+2 more)
Anti-money laundering (AML) in financial networks requires modeling transaction patterns that span multiple institutions. In practice, however, banks cannot share account and transaction features due to strict
privacy regulations. We propose a
privacy-preserving graph neural network (GNN) inference framework tailored to this multi-bank setting. A central server holds the graph connectivity and pretrained model weights in plaintext, while each bank keeps its transaction features encrypted. Inference is then carried out entirely on encrypted features under the CKKS homomorphic encryption scheme, so that no plaintext transaction features or predictions are exposed to the server. The key to making this practical is an edge-centric GNN architecture, wherein message passing is expressed through operations on node–edge incidence
matrices. This formulation not only preserves the edge-attributed multigraph structure of transaction networks but also reduces every aggregation step to plaintext–ciphertext
matrix products. Since CKKS can evaluate only
polynomial circuits, we also approximate each nonlinearity with a composite
polynomial of the sign function, which we calibrate per activation site. On the IBM AML
benchmark, our model achieves performance competitive with plaintext baselines, and with a consumer-grade GPU, classifies each queried transaction end-to-end in as little as 5.31 seconds, while per-transaction latency can be amortized to 1.65 seconds when optimized for throughput.
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All authors H. Çağr{\i} Bilgi, Kemal Mutluergil, Ceren Y{\i}ld{\i}r{\i}m, Kubilay Atasu, Erkay Savas
Robotics
LADDER: A Long-Horizon Robot Manipulation Benchmark
Petko Petkov, Giuliano Albanese, Nikolay Nikolov et al. (+3 more)
General-purpose
robot manipulation policies learn short-horizon skills effectively, but degrade rapidly on long-horizon tasks. Existing
benchmarks cannot isolate failures because task horizon is either too short or entangled with scene and object variations. We propose LADDER, a long-horizon
robot manipulation
benchmark scaling from 1 to 10 subgoals within the same environment over three suites (SimplerEnv-Long, RobotArena-$\infty$-Long, and RoboLab-Long), 145 tasks, eight categories, two embodiments, and 2,024 simulated episodes per system. We evaluate two orchestration paradigms: text subgoals for learned low-level policies (VoLo) and tool selection orchestration (CaP-X, GPT-Policy, PSP-
Agent, TiPToP). Given the full instruction directly, low-level policies achieve 85.4% on single-subgoal tasks, but drop to near zero on extended tasks. While orchestration remedies this, it remains bounded by physical execution errors of the low-level policy rather than high-level planning and achieves only ~25%. Tool selection orchestration with GPT-Policy on GPT-6 Astra significantly outperforms learned-policy methods, but incurs significant time and cost budget and succeeds only ~62%. Real-world experiments across two embodiments confirm the collapse of the low-level policy and the gain from orchestration. Project website: https://ladder.insait.ai
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All authors Petko Petkov, Giuliano Albanese, Nikolay Nikolov, Luc Van Gool, Jonathan Tremblay, Danda Pani Paudel
Artificial Intelligence
Bofan Xie, Chaokun Wang, Guanchen Ge et al. (+4 more)
Long-term
memory has become a core component of large
language model (LLM)
agents. As
agents accumulate personal information over long-term use, users increasingly ask them to delete sensitive or outdated facts. A
memory system should honor such requests. It should remove exactly what was requested and keep the rest of its
memory intact. However, existing
memory benchmarks evaluate only
memory writing,
retrieval, and organization, so whether
agents can forget at the user's explicit request remains under-evaluated. To address this gap, we propose Bemula, the first
benchmark dedicated to
memory unlearning. It comprises 50 multi-session dialogue instances, 135 forget requests of six types, and 1,742 test questions. Each dialogue has a control version without the requests, so that only systems that have stored the target can earn credit for forgetting. Across the paired versions, five test suites measure storage, removal of the requested information, preservation of unrelated
memories, relearning of similar facts, and resistance to extraction attacks. On Bemula, we evaluate six mainstream
memory systems under three backbone LLMs, together with the Full Context LLM on two of them. The results are negative. Across all evaluated systems and backbones, no system passes more than 1.5% of forget requests end to end, and most pass none at all. These systems either leave the requested information extractable under simple attacks or delete far more than requested.
Memory unlearning therefore remains an open challenge for
agent memory design. We release our data and code at https://github.com/Nboxff/bemula-
benchmark.
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All authors Bofan Xie, Chaokun Wang, Guanchen Ge, Songyao Wang, Fang Niu, Hang Zhang, Wei Wang
Machine Learning
inLoRA-MINT: Internal Representations for Auditing Training Data in Domain-adapted LLMs
Gonzalo Mancera, Julian Fierrez, Aythami Morales et al. (+3 more)
This work investigates the application of the Membership Inference Test (MINT) to large
language models (LLMs) fine-tuned with LoRA, aiming to determine whether specific samples were used during training and address concerns about data
privacy, leakage, and transparency. Building on our previous LoRA-MINT framework, which relies exclusively on perplexity under a strict black-box setting, we propose an approach based on intermediate model representations, including activations and parameter-dependent signals. These representations enable a MINT classifier to distinguish training from non-training samples without requiring full access to model parameters. We evaluated the method on four LLMs (Qwen, Phi-4, DeepSeek-V3, and LLaMA 3.2-3B) fine-tuned on three
datasets covering medical, mathematical, and academic
reasoning tasks. Results show consistent performance across architectures and domains, achieving scores between 95% and 98%, with robustness to variations in model configurations and data distributions. The proposed approach enables efficient auditing of sensitive training data and supports data provenance and compliance with emerging
privacy and AI governance regulations. Although our experiments focus on LoRA-based fine-tuning, the methodology can be extended to other LLM adaptation techniques and, more broadly, to other AI models.
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All authors Gonzalo Mancera, Julian Fierrez, Aythami Morales, Ruben Tolosana, Daniel DeAlcala, Francisco Jurado
Statistical Methodology
ndscape: a structural map of the nested dichotomy space
Maxwell Dix-Matthews, Melinda Hodkiewicz and Adriano Polpo
Nested dichotomies (NDs) reduce multiclass classification to a tree of binary classifiers. Tree structure can change accuracy, bias, and variance, but the full space of structures has not been mapped in aggregate. We introduce ndscape, a two-dimensional mapping of ND structures based on depth-weighted structural
tokens. We use this
embedding to conduct what is, to our knowledge, the first bias-variance analysis across the ND structure space. In simulations with 6 to 10 classes, NDs with similar tree structures exhibit similar squared bias, variance, and squared probability loss, regardless of whether the binary classifier is logistic regression or a decision tree. Variance, however, follows mean root-to-leaf depth when logistic regression is used at each node, so flatter, more balanced NDs are more stable, whereas with decision-tree node models, variance is not associated with depth and differs little across ND structures. On six
datasets with 6 to 19 classes, held-out accuracy, log loss, and prediction variance remain spatially organised on the same
topology-only map. None of the six standard construction heuristics tested selects a tree from the best-performing cluster on any
dataset, although some select high-performing individual trees. The ndscape
embedding turns ND selection from one opaque construction step into a readable comparison of class groupings, exposing the upper-level splits that distinguish high-performing and stable regions.
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Quantum Physics
Scalable long-distance quantum fan-out gates with reduced resource overhead
Wen-Qiang Liu and Hai-Rui Wei
Scalable long-distance
quantum gates are essential for distributed
quantum computation and scalable
quantum networks. We propose an efficient scheme for implementing an $n$-
qubit fan-out gate $C^n$ (also known as a controlled-NOT-$\cdots$-NOT gate) across spatially separated parties. Our protocol requires only a pre-shared $n$-
qubit Greenberger-Horne-Zeilinger state, $n$ local controlled-NOT (CNOT) gates, and feed-forward operations, thereby reducing both the CNOT and Hadamard gate counts by $(n-2)$ when compared with the standard decomposition-based approach. We further develop two optical architectures to implement the heralded and post-selected $n$-
photon fan-out gates, respectively. These schemes require fewer single
photons resources and achieve higher success probabilities than their decomposition-based counterparts, representing significant improvements. The results indicate that our approach is experimentally feasible and pave the way toward practical long-distance
quantum computation.
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Optimization and Control
A Smoothed Augmented Lagrangian Method for Mathematical Programs with Vertical Complementarity Constraints
Jia Wang, Andreas Themelis and Panagiotis Patrinos
We propose a smoothed augmented Lagrangian method (ALM) for solving mathematical programs with vertical complementarity constraints (VCCs). The augmented Lagrangian function involving the VCCs is smoothed by the Lasry–Lions double envelope, which is an extension of the Moreau envelope that enjoys enhanced smoothness properties. This construction yields a smooth unconstrained subproblem within the ALM framework that can be efficiently solved by many fast optimization
algorithms. In the limiting setting, we establish global convergence to Mordukhovich- and Clarke-stationary points. For the convergence rate, we show that, under appropriate assumptions, the smoothed ALM achieves Q-linear convergence. We further provide a worst-case iteration complexity analysis for computing an approximate stationary point of the original problem or of an associated feasibility problem. Preliminary numerical experiments demonstrate the effectiveness of the proposed method.
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Artificial Intelligence
Beyond Black Boxes: Visual Grounding for Text Embeddings
Slimane Larabi
In this paper, our goal is to represent text
embeddings as sketches for three main reasons. First, to visualize text representation and to explore their potential their potential for AI explainability. Second, to investigate the potential benefits of the sketch-based representation for information
retrieval. Third, to explore their potential for
reasoning, drawing inspiration from the concept of mental imagery. We propose a sketch-based visual grounding framework that transforms text representations into visual topographic sketches. Our approach aims to emphasis the semantics content encoded in the text
embeddings through the corresponding sketch. Evaluations are conducted on the MS-COCO
dataset to examine the utility of these sketches for text
retrieval. The comparative study of SBERT and Sketch-based method reveals that SBERT retains an advantage in strict top-1
retrieval accuracy while achieving comparable performance to the sketch-based method in top-5
retrieval. A cascaded approach combining SBERT and the sketch-based method outperforms SBERT alone, suggesting that the Sketch-based representation could offer significant potential for information
retrieval if trained directly on text.
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Astrophysics of Galaxies
Atomic hydrogen in $z=0$ galaxies in the COLIBRE simulations
Kyle A. Oman, Ruby J. Wright, Andrea Gebek et al. (+12 more)
We present an overview of the properties of atomic hydrogen (HI) in
galaxies at
redshift $z=0$ in the COLIBRE cosmological
galaxy formation simulations. COLIBRE directly simulates the multi-phase interstellar medium (ISM) and includes new methods to follow radiative cooling to $\approx 10\,\rm{K}$, non-
equilibrium chemistry coupled to dust, and dust formation, growth & destruction. We focus on the $(200\,\rm{cMpc})^3$ volume with resolution elements of $2\times 10^{6}\,\rm{M}_{\odot}$. No HI properties informed the calibration of model parameters and yet we find very good agreement with the observed $M_{\star}$-$M_{\rm{HI}}$ relation, the HI mass and velocity width functions, the HI mass-size relation, and (baryonic) Tully-Fisher relations, albeit with some mismatches at the extremes of the
galaxy population. HI surface density profiles are realistic, except at large radii around massive ($M_{\star}\gtrsim 10^{10}\,\rm{M}_{\odot}$)
galaxies where we find a population of possibly spurious circumgalactic HI clouds, whose abundances and masses decrease, however, with increasing mass resolution). Using smaller simulations to vary some of the uncertain model parameters, we find that the main scaling relations are at most mildly affected, although this may not extend to detailed HI morphokinematics. Convergence across mass resolution levels a factor of $8$ higher and lower is good: the median HI mass and HI `occupation fraction' as a function of
halo mass, and the HI mass function, are remarkably stable, although the highest resolution model overproduces HI sources by a factor of $\approx2$ at $M_{\rm{HI}}\gtrsim 3\times 10^{9}\,\rm{M}_{\odot}$. Upturns in the low-mass and low-velocity slopes of the HI mass and velocity width functions with potentially subtle observational signatures are predicted.
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All authors Kyle A. Oman, Ruby J. Wright, Andrea Gebek, Diego Dado, Katherine E. Harborne, Evgenii Chaikin, Carlos S. Frenk, Filip Huško, Claudia D. P. Lagos, Aaron D. Ludlow, Sylvia Ploeckinger, Alexander J. Richings, Matthieu Schaller, Joop Schaye, James W. Trayford
Fluid Dynamics
High-fidelity Three-Dimensional Flow Field Reconstruction Driven by Turbulent Fluid Dynamics
Hanlin Kong, Jianan Wei, Yuntian Chen et al. (+2 more)
High-fidelity three-dimensional turbulent flow fields are essential for scientific and engineering analyses, yet obtaining them through direct numerical simulation or high-resolution measurements remains expensive. Super-resolution reconstruction from coarse-grained data therefore offers a promising alternative, but remains fundamentally challenging because fine-scale structures are intrinsically three-dimensional and dynamically coupled across scales. Existing data-driven approaches still struggle to recover multiscale volumetric structures and their coherent temporal
evolution. To address these limitations, we propose a
turbulence-aware framework for time-resolved three-dimensional flow reconstruction. The framework couples a multiscale neural operator, designed to recover volumetric structures, with a state space model to promote temporal consistency in the reconstructed dynamics. In addition, Navier-Stokes residuals are incorporated as soft constraints to regularize the reconstruction toward physically plausible dynamics. A mixture-of-experts mechanism is also introduced to support unsupervised adaptation to unknown coarse-graining. Across evaluations on canonical turbulent systems and atmospheric reanalysis data, the proposed framework reduces reconstruction errors relative to learning-based and classical baselines, while better preserving multiscale spatial structure and coherent temporal
evolution. These results validate the effectiveness of the proposed framework in reconstructing high-fidelity three-dimensional flow fields from coarse-grained data.
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All authors Hanlin Kong, Jianan Wei, Yuntian Chen, Yujia Wang, Wenguan Wang
General Relativity and Quantum Cosmology
Quasiperiodic oscillations as probes of rotating black holes in exponential dark matter environments
Madina Alimkulova, Kuantay Boshkayev, Sholpan Gumarova et al. (+2 more)
We construct the rotating counterpart of a black hole embedded in a dark-matter
halo with the exponential-sphere profile $\rho=\rho_0e^{-r/r_0}$, using the Newman–Janis
algorithm in the Azreg-Aïnou form, and study its imprints on test-particle motion and quasiperiodic oscillations (QPOs). We derive closed-form orbital and epicyclic frequencies valid for any Kerr-like metric with a mass function and show that, to first order, the
halo enters them only through its central density, acting as an effective cosmological constant $\Lambda_{eff}=8\pi\rho_0$; the corrections are therefore universal for all cored profiles. The
halo moves the innermost stable circular orbit outward and produces an outer boundary of stable orbits, while the sign of the frequency shift depends on the assumed equation of state of the
halo. Fitting the relativistic precession model to the simultaneous QPO triplets of GRO~J1655$-$40 and XTE~J1859+226 with Markov-chain Monte Carlo sampling yields
spins and emission radii consistent with Kerr and $\rho_0M^2<2.9\times10^{-5}$ and $3.0\times10^{-5}$ (95%), respectively. In physical units these limits exclude only ultra-dense dark cores, $\rho_0\gtrsim10^{11}$~g\,cm$^{-3}$.
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All authors Madina Alimkulova, Kuantay Boshkayev, Sholpan Gumarova, Aliya Taukenova, Ainur Urazalina