Statistical Methodology
Improved Forecasting for Multivariate Time Series using GNAR models with General Inverse Distance Weights
Hengxu Liu, Guy P. Nason, Sourav Das et al. (+1 more)
Many multivariate time series arise on networks where relationships between variables depend on physical or more general distances. Standard vector autoregressive models struggle in such settings due to their quadratic parameter growth, while generalised network autoregressive (GNAR) models offer a parsimonious alternative by explicitly incorporating network structures. Existing GNAR formulations assume fixed weights, typically uniform or reciprocal distances, which may not reflect the true influence of distance in real systems. We introduce a novel model with general inverse distance weights (GNARID) that extends GNAR by introducing a tunable exponent parameter that controls the strength of distance penalisation. We establish theoretical results on the consistency and asymptotic normality of new GNARID
estimators, including an extension to the setting where the number of nodes grows. We analyse the effect of misspecifying the exponent on forecasting error, which helps assess how much GNAR models could be improved by our tunable weights. Simulation studies confirm the theoretical properties and show measurable benefits from correct weight specification. Using the Brumadinho ground displacement
dataset, we show that proper tuning of the weight exponent improves predictive performance, highlighting the practical value of flexible distance-based weighting in network time series models.
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All authors Hengxu Liu, Guy P. Nason, Sourav Das, Stephen Grebby
Machine Learning
MolHop: Scaffold-Hopping Molecular Design with Agentic Reinforcement Learning
Yanyi Su, Chenyan Wang, Yulei Gong et al. (+3 more)
Molecular design is often formulated as an optimization problem over existing molecular spaces. However, molecular optimization and the discovery of structurally distinct replacement molecules represent two fundamentally different search problems. LLM-based molecular design
agents typically perform the former, conducting local refinement within the neighborhood of existing structures to improve target properties through structural modifications. In contrast, the latter requires exploration of a broader chemical space to discover molecules with entirely different scaffolds while preserving the functional characteristics of reference molecules. We propose MolHop, an agentic framework for cross-scaffold molecular discovery that enables models to navigate and edit within a pre-organized chemical space. MolHop organizes approximately 1.6 million molecular scaffolds from commercial molecules into a coarse-to-fine hierarchical action space. The
agent selects new exploration regions through scaffold navigation actions and adjusts candidate properties through molecular editing. The framework maintains historical trajectory
memory to support long-horizon search processes. On drug molecule and commercial compound tasks, connecting Grok-4.6 and DeepSeek-Flash-V4.1 to the MolHop framework substantially improves their cross-scaffold joint success rates. We develop MolHop-9B through agentic reinforcement learning, and MolHop-9B outperforms generative baselines including GPT-5.6-sol, Claude-5-Opus, and Gemini-3.1-Pro, while generating candidate molecules with larger scaffold differences from the reference molecules. These results demonstrate that effective molecular discovery beyond the traditional optimization paradigm requires structured chemical and action spaces, enabling
agents to explore new structural regions rather than merely refine existing molecules.
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All authors Yanyi Su, Chenyan Wang, Yulei Gong, Yeyun Chen, Xiaohui Yang, Jun Cheng
Machine Learning
Beyond DFT electronic band structures via dual-space orbital learning
Minhyuk Kang, Beom Seok Kang, Nikolai Argatoff et al. (+4 more)
Accurate electronic band structures are essential for predicting material properties, but high-fidelity calculations are computationally demanding. We introduce O-kRystal, a dual-space orbital-learning framework that uses a lower-cost Perdew-Burke-Ernzerhof (PBE) calculation as both an energy baseline and a source of structured orbital information. Combining PBE band energies with real- and reciprocal-space orbital
matrices, O-kRystal predicts higher-level corrections for individual electronic states as a function of
crystal momentum and
spin. For states within 5 eV of the band-edge midpoint, it predicts Heyd-Scuseria-Ernzerhof (HSE06) band energies with a mean absolute error of $0.171 \pm 0.009$ eV across nearly 6,000 three-dimensional materials spanning insulating, metallic,
magnetic, and nonmagnetic systems. On a separate
benchmark of six strongly correlated materials, it reduces the corresponding PBE band-energy MAE by 68.7% and resolves orbital-dependent changes in the relative placement of transition-metal d and ligand p states. Despite training only at high-
symmetry points, it maintains comparable accuracy at unseen points along band paths and reconstructs HSE06 densities of states from dense Brillouin-zone predictions. Finally, training O-kRystal on $G_0W_0$ quasiparticle energies for two-dimensional materials demonstrates its applicability across levels of theory and the potential of orbital learning for higher-fidelity, momentum-resolved electronic-structure prediction beyond density functional theory.
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All authors Minhyuk Kang, Beom Seok Kang, Nikolai Argatoff, Miguel Nouman, Danish Khan, William A. Goddard III, Anima Anandkumar
Systems and Control
On the Mechanism of Multi-Cluster System Separation Under Extreme Events
Nikolaos Sofos, Georgios Tzounas and Federico Milano
Historical blackout events demonstrate that, under complex multi-event contingencies, interconnected power systems can split into multiple areas exhibiting distinct dynamic behavior while remaining physically connected. This work provides a rationale for this phenomenon. Typical N-1 contingency analysis leads to a response dominated by a single unstable mode, supporting the two-cluster
conjecture used by transient stability assessment tools such as the SIME method. However, extreme events, often involving cascading N-k contingencies, can excite multiple unstable modes. We establish a proof of concept based on linear modal expansion to show how such concurrent excitation leads to multiple coherent areas. The resulting behavior is illustrated through simulations on the standard WSCC 9-bus, IEEE 14-bus, and IEEE 39-bus systems, demonstrating the limitations of conventional two-cluster representations under extreme events.
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High Energy Physics (Phenomenology)
The Semileptonic B Frontier: LHC, B-factories, Lattice QCD & the Hunt for Precision
F. De Fazio, P. De Simone, M. Fedele et al. (+33 more)
This review document summarizes the main outcomes and key theoretical and experimental developments discussed at the workshop “The Semileptonic $B$ Frontier: LHC, $B$-Factories,
Lattice QCD & the Hunt for Precision”, held at Laboratori Nazionali di Frascati on 16–18 February, https://agenda.infn.it/event/49469/. Bringing together experimentalists and theorists, the meeting addressed recent advances and outstanding challenges in both charged-current ($b \to c/u\,\ell\nu$) and neutral-current ($b \to s\,\ell^+\ell^-$) semileptonic $b$-
hadron decays. We synthesize current experimental measurements from Belle, Belle~II, and LHCb with state-of-the-art theoretical inputs—including
Lattice QCD determinations, Heavy
Quark Expansion (HQE) frameworks, and QED radiative corrections. In addition to reviewing the status of CKM
matrix elements, hadronic form factor extractions, and Lepton Flavor Universality (LFU) tests, this report highlights novel observables and fitting techniques designed to maximize the sensitivity of future analyses and guide upcoming data-taking campaigns.
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All authors F. De Fazio, P. De Simone, M. Fedele, G. Finocchiaro, F. Mescia, M. Rotondo, A. Barone, B. Capdevila, M. De Cian, L. Dreyfus, M. Fael, G. Finauri, G. Gagliardi, G. Gaudino, D. Giusti, N. Gubernari, A-M. Heyn, F. Manganella, E. Minucci, B. Mitreska, E. Manoni, T. Martinov, L. Paolucci, Z. Polonsky, A. Poluektov, M. Palutan, F. Sanfilippo, S. Simula, A. Smecca, N. Tantalo, A. Tinari, A. Vaquero, R. Vazquez Gomez, M. Valli, L. Vittorio, V. Vobbilisetti
Optimization and Control
On the Regularization of Primal-Dual Dynamics in Time-Dependent Nonconvex Optimization
Miguel Vaquero and Jorge Cortés
We study continuous-time primal-dual dynamics for tracking solutions of time-dependent constrained optimization problems. First, for uniformly strongly convex-strongly concave saddle functions, we establish explicit finite-time tracking and additive-disturbance bounds. For convex problems, we then introduce a directional primal regularization, combined with dual Tikhonov regularization, and quantify the resulting optimization bias and constraint violation. For smooth nonconvex problems, we couple a proximal local convexification with a two-time-scale primal-dual flow and obtain a local tracking result under constraint qualification, second-order sufficiency, and temporal regularity. Numerical traffic-signal experiments at a single intersection evaluate the proposed method under vehicle-throughput and pedestrian-aware objectives, comparing it with standard signal-control schemes.
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Computation and Language
Not All Criteria Matter: Importance-Aware Criteria Weighting for Rubric-Based LLM Evaluation
Haocheng Yang, Licheng Pan, Yiming Dong et al. (+7 more)
Rubric-based evaluation assesses the responses of large
language models (LLMs) by aggregating the verification results of explicit criteria, yet not all criteria matter equally. However, existing methods commonly aggregate the verification results with uniform weighting, which assigns minor and overlapping criteria the same weight as essential ones and thus produces misleading evaluation scores. Although some methods
prompt an LLM to directly assign weights, direct weighting requires a holistic judgment over all criteria that LLMs struggle to make reliably. To address these limitations, we propose Importance-Aware Criteria Weighting (ICW), which derives the weights of criteria from pairwise importance comparisons made by an LLM. Specifically, ICW transforms the holistic judgment over all criteria into a series of pairwise importance comparisons, checks and revises inconsistent comparisons, and derives the weights from the principal eigenvector of the comparison
matrix. Extensive experiments demonstrate that ICW consistently outperforms uniform weighting and competitive weighting methods, and translates into better policy models in downstream reinforcement learning.
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All authors Haocheng Yang, Licheng Pan, Yiming Dong, Xiaoxi Li, Yuan Lu, Zhenhao Chen, Jiajun Fan, Shuai Shao, Chunyuan Zheng, Hao Wang
Analysis of PDEs
Persistence of the reaction term in an unbalanced fast reaction limit
Yuki Tsukamoto
We study a fast reaction limit for a two-component reaction-
diffusion system in which one component diffuses and the other does not. In the regime considered here, the nondiffusing component converges to zero as the reaction rate tends to infinity, while the reaction term has a nonzero limit. As a result, the limiting equation for the diffusing component retains a time-integral term. Our results cover both homogeneous Dirichlet and Neumann boundary conditions. We prove convergence of the diffusing component and determine the limiting behavior of the reaction term and the rescaled nondiffusing component. The proof combines monotonicity with respect to the reaction rate with barrier estimates adapted to the time-integral term. Under an additional separation condition on the initial data, we also prove uniqueness of the limiting initial-boundary value problem.
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Robotics
MUCO: A Mani-Unit-Structured Manipulation Framework for Compositional Long-Horizon Robot Tasks
Xiangyi Wei, Hanzhe Yao, Jingwen Shang et al. (+3 more)
Learning and executing long-horizon
robot manipulation as complete task trajectories raises data-collection costs, lim?its capability reuse, and causes local failures to cascade into later task stages. We introduce manipulation units (MUs) as reusable operations, each defined by a local goal, an initi?ation condition, and observable success and failure criteria. MUs are learned and verified individually. During execution, consecutive MUs remain coupled through shared environ?ment dynamics. We formulate MU-Structured Manipulation (MUSM) and realize it through MU-Centric Orchestration (MUCO). Given
language and
vision, MUCO constructs an MU sequence, matches each MU to compatible policy models in a frozen capability registry, and dispatches the model with the highest calibrated success rate. Visual verification checks MU outcomes against success and failure criteria and governs transitions, enabling retry or fallback to another model from the current state without re-executing verified MUs. Canonical keys enable checkpoint reuse for equivalent MUs across tasks without new demonstrations or updates. We evaluate MUCO on RLBench, RoboTwin 2.0, and five real-world tasks. MUCO achieves higher complete-task success and verified progress, stronger failure containment, and lower error propagation than baselines. Results demonstrate cross-task MU reuse and het?erogeneous policy composition without joint training, shared backbones, or common action representations.
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All authors Xiangyi Wei, Hanzhe Yao, Jingwen Shang, Zhenhong Wu, Yang Li, Changbo Wang
Computer Vision
Quantifying Annotation Agreement in Object Detection with Optimal Transport
Kirill Prokhorov and Alexandr A. Kalinin
Reliable validation of machine learning models is a crucial step in designing robust and trustworthy systems. Yet validation relies on annotated test sets treated as ground truth, even though annotations can be noisy or ambiguous. In object detection, sources may disagree on object presence, box location, or class label. Standard agreement metrics usually binarize box overlap before computing a summary score, obscuring the degree and structure of disagreement between annotation sets. Here, we study an Optimal Transport (OT)-based metric for measuring agreement between object detection annotations. Instead of binarizing bounding box overlap, this metric assigns graded costs to matched and unmatched labeled boxes. Through theoretical analysis and experiments on real and synthetically perturbed annotations, we show that OT captures graded changes in agreement and distinguishes annotation pairs that thresholded metrics score identically. Our results suggest that OT provides a richer measure of annotation agreement in object detection, especially for domains with many small or ambiguous objects. Such agreement estimates can support more reliable validation when labels are noisy or conflicting.
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Machine Learning
CARD: Closing the Teacher-Student Loop for Adversarial Robustness Distillation
Zhi Li, Haowei Liu, Hongchen Yang et al. (+5 more)
Adversarial robustness
distillation typically treats a robust teacher as a fixed knowledge source and transfers robustness to a compact student in a one-way manner. This paradigm terminates teacher-student interaction once the student is distilled, thereby discarding potentially useful feedback from the distilled student. However, exploiting such feedback is nontrivial because indiscriminate weak-to-strong transfer may propagate student errors to the teacher. To address this issue, we propose Cyclic
Adversarial Robustness
Distillation (CARD), a closed-loop framework that turns the distilled student into a feedback source for refining the robust teacher and subsequently transfers the teacher gains back to the same student. CARD first performs strong-to-weak
adversarial distillation and then uses reliability-enhanced student feedback to refine the robust teacher. The refinement combines inverse
adversarial student feedback with teacher-student discrepancy maximization, after which the refined teacher is used to continue training the same student under the unchanged base
distillation objective. We further establish a sample-level sufficient condition for reliable weak-to-strong feedback transfer and derive a distribution-level upper bound on the refined teacher's risk. Experiments on CIFAR-10, CIFAR-100, and TinyImageNet-10 across multiple teacher-student architectures and base
distillation methods show that CARD improves
adversarial robustness in most evaluated settings while generally preserving clean accuracy. These results show that the distilled student can provide useful feedback to the teacher, enabling robustness transfer to proceed beyond the conventional one-way teacher-to-student paradigm.
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All authors Zhi Li, Haowei Liu, Hongchen Yang, Xiaoxuan Wang, Song Gao, Zhenli He, Wei Zhou, Shaowen Yao
Machine Learning
When Non-Adversarial Noise Hurts Learning: Benchmarking Skill Self-Evolution in Agents
Tao Yu, Zhongtian Luo, Yuchen Long et al. (+19 more)
Large
language model
agents increasingly improve by distilling experience into reusable skills, but errors in that experience can persist in learned rules and affect later tasks. Existing evaluations largely examine clean learning or perturbations encountered after skills are frozen, leaving robustness during self-
evolution insufficiently understood. We introduce RSE-Bench, to our knowledge the first
benchmark for evaluating skill self-
evolution under ordinary, non-
adversarial noise. It spans four domains and six
benchmarks, with controlled perturbations to instructions, environmental evidence, execution records, and outcome–record pairings that preserve the underlying tasks and native scoring rules. The protocol separates noise during learning from input-side noise at evaluation, testing frozen skill stores on held-out tasks. We also introduce RSEClaw, a method-agnostic framework supporting consistent noise injection and process diagnostics of actual exposure and skill updates. Across three methods and two backbones, noisy learning retains only $25.5\%$–$42.1\%$ of the aggregate gain achieved by clean self-
evolution, depending on the noise type. Perturbations confined to execution records and outcome–record pairings cause average losses comparable to input-side noise, despite leaving the original executions and evaluations intact. Diagnostics further identify differences in candidate skill text that coexist with small changes in mean held-out performance.
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All authors Tao Yu, Zhongtian Luo, Yuchen Long, Yiheng Lu, Yuxuan Zhou, Yanpei Gong, Siyi Cao, Yuqing Wu, Haopeng Jin, Zhenghao Zhang, Yujia Yang, Jingmin Zhu, Yunyun Hou, Xinming Wang, Hongzhu Yi, Jiachen Liu, Sirui Wang, Nan Duan, Yan Huang, Liang Wang, Tianjin Huang, Yanyan Zou
Instrumentation and Methods for Astrophysics
RPCA: a Resolved PSF reconstruction method for undersampled images
Lin Nie, Huanyuan Shan, Guoliang Li et al. (+11 more)
The Point-Spread Function (PSF) is a critical component in various astrophysical research areas, including
galaxy morphology, photometric
redshift determination, and weak
lensing studies. As the next generation of space
telescopes prepares for operations, including the China Space Station
Telescope (CSST), accurate PSF modeling has become increasingly important. However, accurately modeling the PSF remains challenging, particularly for poorly sampled images. To construct reliable PSF models based on
star images, we aim to develop a novel and robust method, which accounts for the pixel integration effect in modeling for reconstructing high-resolution PSFs from undersampled or poorly resolved images. In this paper, we present Resolved Principal Components Analysis (RPCA), a novel approach that accounts for pixel integration effects to enable the direct reconstruction of resolved PSFs from undersampled images via an Expectation-Maximization PCA (EMPCA) technique. Our method has been tested on a simulated PSF
dataset for the China Space Station
Telescope (CSST), where both observational and detector-induced effects were considered during simulation. The results demonstrate that RPCA achieves superior precision in PSF shape parameters compared to existing methods such as Multi-CCD (MCCD), Resolved Components Analysis (RCA), and PSFEx. Notably, RPCA {could reduce the bias of $\Delta R^2/R^2$ by approximately one order of magnitude with respect to MCCD, RCA and PSFEx. RPCA exhibits robust stability under realistic observational conditions, including varying signal-to-noise ratios (SNRs) and contamination from
cosmic rays and bad pixels. This suggests that RPCA could be a promising candidate for future CSST PSF modeling efforts.
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All authors Lin Nie, Huanyuan Shan, Guoliang Li, Tobías I. Liaudat, Chengliang Wei, Peng Wei, Tianmeng Zhang, Hekun Li, Lei Wang, Han Qu, Zhaojun Yan, Nan Li, You Wu, Peng Jia
Analysis of PDEs
A starlike 3-dimensional counterexample to the hot spots conjecture
Rami Atar and Krzysztof Burdzy
We construct a bounded Lipschitz domain in $\mathbb{R}^3$ that is starlike about the origin and whose first positive Neumann
eigenvalue is simple. A corresponding eigenfunction, after a choice of sign, has its unique global maximum at the origin. The construction uses domains with increasingly fine angular structure and convergence to a weighted
eigenvalue problem whose first positive eigenfunction is radial.
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Artificial Intelligence
Sparse Transformer: a Controlled Testbed for Mechanistic Interpretability
Anton Korznikov, Alexey Dontsov and Elena Tutubalina
Mechanistic interpretability aims to decompose neural network computations into human-understandable features, yet progress is limited by the absence of ground-truth units to evaluate against. We address this with a Sparse
Transformer (ST): a 5-layer
language model trained on TinyStories whose wide, TopK-sparse MLP forces
neuron specialization. We show that ST
neurons are highly interpretable: they outperform a loss-matched dense
transformer by 43% on AutoInterp, and
circuits of a median of 12
neurons suffice to reproduce individual model predictions. This testbed allows us to directly compare feature learning methods: Transcoder, which approximates the MLP input-output mapping, recovers ST's
neuron vectors with 52.6% higher cosine similarity than Sparse Autoencoder, which only reconstructs outputs. Furthermore, the ST enables a controlled study of polysemanticity: distilling its sparse MLP into a dense counterpart reveals how compression leads to feature superposition. These results show how a known reference basis sharpens evaluation and understanding, a role we hope the ST can serve for the field.
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Computation and Language
Beyond Benchmark Accuracy: A Diagnostic Test for LLM Chemical Prediction
Yanyi Su, Yu Zeng, Hongshuai Wang et al. (+3 more)
LLMs have achieved strong performance on chemical prediction
benchmarks. However, existing evaluation methods typically provide target entity identity information directly, making it difficult to determine whether model predictions arise from transferable structure-property
reasoning capabilities or rely on target-related contextual information or
memory retrieval. We propose the Identity-Blinded
Reasoning Diagnostic framework to evaluate chemical prediction models when target entity identity information is hidden. In this framework, the target is no longer directly defined by its entity identity, but instead by a known source entity and an explicit structural edit operation. This setting preserves the information required to determine the target while reducing the opportunity for models to access target identity cues. We evaluate this framework across six tasks spanning odor prediction, molecular property prediction, retrosynthesis, and reaction yield prediction. The results show that models generally achieve better prediction performance when source and target entity properties are consistent. Providing explicit source entity labels further strengthens this dependence. Moreover, while keeping the target entity, structural change, and ground-truth label unchanged, replacing only the source entity label causes some model predictions to systematically shift toward the substituted source labels. These results indicate that high
benchmark accuracy in chemical prediction tasks does not necessarily imply that LLMs possess reliable structure-property
reasoning capabilities. Our diagnostic framework provides a controlled evaluation approach for testing whether models can genuinely update predictions according to entity changes. The framework can be extended to other AI for Science tasks, particularly those where targets can be defined through transformations of known entities.
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All authors Yanyi Su, Yu Zeng, Hongshuai Wang, Chenyan Wang, Zhifeng Gao, Xiaohui Yang
Quantum Physics
On the structure of low-rank PPT entangled states violating Peres conjecture
Gurvir Singh and Manik Banik
Bound-entangled states with positive partial transpose (PPT) can exhibit
quantum steering and Bell nonlocality, disproving the long-standing Peres
conjecture and its stronger steering counterpart. These counterexamples, obtained through explicit constructions or numerical searches, raise the question of whether they share a common structural origin. We address this question in the low-rank regime by asking whether known counterexamples can be obtained, up to invertible local filtering, from the complement of an unextendible product basis (UPB). We find that the answer depends crucially on the local dimension. In $3\otimes3$, the rank-four structure of PPT entangled states implies that both the steerable PPT state of Moroder et al. and the Bell-nonlocal PPT state of Vértesi and Brunner can be obtained from UPB-complement states under invertible local filtering. For three
qubits, in contrast, rank-four states that are PPT across every bipartition are separable across each cut. Moreover, comparison of the product-vector structure of the relevant kernels shows that the known three-
qubit Bell-nonlocal PPT states cannot be obtained by invertible local filtering from the complement of any three-
qubit UPB. Going beyond rank four, we further show that a known rank-eight three-qutrit state, supported on the complement of a UPB and PPT across every bipartition, is genuinely multipartite entangled. These results show that UPBs provide a unifying structural origin for important low-rank PPT entangled states in $3\otimes3$, but that nonlocal bound
entanglement is not, in general, a UPB phenomenon.
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Quantum Physics
Single and coupled quantum van der Pol oscillators: Wigner-function dynamics, exact qutrit solutions, and synchronization
Rohitashwa Chattopadhyay, Vinayak Pendse and Shamik Gupta
We study
quantum van der Pol oscillator in which single-
photon pumping competes with a nonlinear two-
photon dissipative channel to produce a
quantum limit cycle, and the question of how two such oscillators with non-identical intrinsic frequencies synchronize in the sense of phase locking when coupled dissipatively to one another. For a single oscillator, starting from a Lindblad description of the dynamics, we derive the exact Wigner-function
evolution equation, and show that its Fokker–Planck approximation admits in the appropriate regime an equivalent classical Itô
stochastic description that reproduces remarkably well the full Lindblad dynamics. Notably, the
stochastic approach provides an efficient means of capturing system dynamics in the semiclassical regime, where a large number of energy eigenstates contribute and direct numerical simulations become computationally demanding. Restricting to the three lowest Fock states and using a parity
symmetry, we obtain in closed form the stationary-state density
matrix and its Wigner-function representation that displays pronounced nonclassical features. For two non-identical oscillators, we study the full Lindblad dynamics numerically and characterize synchronization through the stationary-state order parameter as a function of frequency mismatch and dissipative coupling. We find that increasing the effective nonlinear dissipative strength suppresses the synchronized region. Restricting the two oscillators to the three-level qutrit subspace, a parity
symmetry reduces the stationary-state problem to an exact $41$-dimensional linear system, allowing to compute the synchronization order parameter. In this reduced description, results in the limit of large dissipation agree closely with those from the full dynamics, while exhibiting in the limit of small dissipation a double-peak synchronization structure accompanied by a phase reversal.
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Statistics Theory
A New Perspective on Cumulative Residual Rényi Entropy
Shubham Babasaheb Alte, Swaroop Georgy Zachariah and Mohd. Arshad
This paper introduces a new cumulative residual Rényi
entropy (CRRE), which addresses limitations of the existing cumulative residual Rényi
entropy. The conventional measure suffers from two primary drawbacks: its failure to converge to the cumulative residual
entropy (CRE) as the order parameter approaches unity, and its lack of guaranteed positivity. Our proposed CRRE resolves these issues, offering a theoretically consistent and always nonnegative measure of uncertainty in residual lifetimes. We explore its mathematical properties, including its relationship with existing information measures. Furthermore, the concept is extended to its dynamic version, and its time-dependent characteristics are investigated. A non-parametric
estimator of the proposed CRRE is developed, its almost sure convergence is established, and is validated through Monte Carlo simulation. The effectiveness of CRRE as a tool for quantifying uncertainty is confirmed through its application in chaotic theory and financial data.
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Earth and Planetary Astrophysics
On-sky performance and prospects of near-infrared CMOS detectors in exoplanet detection: first year of SPIRIT
Clàudia Janó-Muñoz, Matthew J. Hooton, Peter P. Pedersen et al. (+8 more)
Ultracool dwarfs are promising targets for detecting and characterising temperate terrestrial
exoplanets. Their flux peaks in the near-infrared, beyond the wavelength cut-off of the silicon-based CCDs used by ground-based surveys dedicated to observing them, such as SPECULOOS. We present the on-sky performance of SPIRIT (SPeculoos InfraRed Imager for Transits), an InGaAs CMOS detector on the Callisto
telescope at the SPECULOOS Southern Observatory, during its first year of operation, from mid-2022 to 2023. We use 328 visits of 73 targets to optimise our CMOS-specific reduction process, including our approach to glow effects and bad-pixel handling. We find the measured precision to be consistent with our theoretical noise model, achieving sub-part-per-thousand precision under good observing conditions across targets spanning $J$-band magnitudes of 10–13. We further illustrate SPIRIT's capabilities with two case studies: a simultaneous SPIRIT $zYJ$ and CCD $I + z$ observation of a TRAPPIST-1\,h transit, and monitoring observations of the fast-rotating dwarf LP~944-20. Our results confirm that InGaAs infrared CMOS detectors are a cost-effective and competitive technology for ground-based transit photometry of cool
stars, and that they show promising prospects to become a core part of the next generation of instruments.
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All authors Clàudia Janó-Muñoz, Matthew J. Hooton, Peter P. Pedersen, Didier Queloz, Khalid Barkaoui, Yasmin T. Davis, Michaël Gillon, Benjamin V. Rackham, Mathilde Timmermans, Amaury H. M. J. Triaud, Sebastián Zúñiga-Fernández
Computer Vision
Multimodal Medical Image Parsing with Task-Specific Low-Rank Adapters
Taha Sabir, Alberto Montero Molina, Thinh Quang Ly et al. (+3 more)
Adapting a single
vision-
language model to medical image parsing requires training choices that reflect the demands of each task. We study task-specific adaptation with Qwen3.5-9B for
FLARE 2026 Task 3, covering six task families and seven imaging modalities. Our system couples a shared, frozen 4-bit backbone with LoRA adapters selected from the input question. We customize adapter ranks and
prompts, rebalance rare classification targets, and supplement regression training with dermatology classification examples. On the fixed internal holdout, the task-specific adapters achieve a
dataset-macro balanced accuracy of 0.7791 for classification, a
dataset-macro micro-F1 of 0.6286 for multi-label classification, a counting mean absolute error (MAE) of 11.73, a
dataset-macro detection F1 of 0.8914, a
dataset-macro regression MAE of 9.78, and a report-generation CRIMSON score of 0.8204. On the organizer's 1,703-example hidden-validation set, the submitted system achieves 0.8496 classification balanced accuracy, 0.5338 sample-averaged multi-label F1, and 14.5950 regression MAE. The final 30.1 GB container averages 3.1 seconds per case, including model loading, and produces identical outputs across repeated verification runs. The benefits vary across tasks, supporting the use of task-specific adapter configurations and image budgets within a shared medical
vision-
language model. Code is available at https://github.com/TahaSabir0/
FLARE26-Hexapus.
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All authors Taha Sabir, Alberto Montero Molina, Thinh Quang Ly, Veronika Dmitrenko, Mohammad Yaqub, Tausifa Jan Saleem
Artificial Intelligence
How GUI Agents Fail: GraceBench for Execution Stability Diagnosis
Ridong Zhang, Shuxian Liang, Chaoqun Wan et al. (+2 more)
As GUI
agents are increasingly deployed for mobile task automation, their reliability depends not only on final task outcomes, but also on whether they reach these outcomes through stable interaction trajectories. In automation workflows, unnecessary actions, detours, and execution errors can add noise to collected data, introduce irreversible side effects, or undermine trust, even when the
agent eventually completes the task. It is therefore important to systematically diagnose the execution stability, including whether an
agent deviates from a feasible path, enters a loop, becomes stagnant, or recovers from unstable actions. Existing
benchmarks, however, primarily assess final task success or a small set of intermediate milestones, leaving step-level execution behavior unclear. To bridge this gap, we introduce GraceBench, an online evaluation framework that represents the reachable interaction space of mobile applications as UI Transition Graphs (UTGs), providing a structural reference for step-level execution diagnosis. To support reliable
alignment between dynamic UI observations and graph states, GraceBench uses
video-grounded trajectory evaluation over this graph-aligned representation, assessing process stability beyond final success with metrics such as deviation rate, loop burden rate, and recovery ability. GraceBench contains 169 tasks spanning 10 applications and constructs 10 UTGs with 2,909 canonical GUI states and 3,116 observed transitions. Across 3,718 trajectories, GraceBench shows that outcome scores hide execution instability: GPT-5.6 Sol achieves the highest success rate (SR) but the lowest recovery ability, 51.24% of successful runs still contain a deviation or loop, and trajectories with the same success rate (SR) and completion rate (CR) can differ substantially in recovery behavior. Code and data will be released on https://iaie-physicalai.github.io/GraceBench.
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All authors Ridong Zhang, Shuxian Liang, Chaoqun Wan, Zhen Huang, Xian-Sheng Hua
Machine Learning
Universal Sample-Based Embeddings of Higher-Order Objects
Radovan Haluška, Christopher Solinas, Nathan R. Sturtevant et al. (+2 more)
Neural networks operate naturally on finite-dimensional vectors, yet many problems require
reasoning about higher-order objects such as distributions, functions, and graphs. Such objects capture relationships within a ground set and rarely admit an explicit compact representation. Moreover, we typically want a compact representation not of a single higher-order object but of an entire family of such objects, including members unseen during training. While a few approaches exist for particular object types, none is designed to work across object types. We propose a unified framework that represents any higher-order object as a fixed-size
embedding vector learned over a family of such objects. An encoder maps an object, seen only through a finite set of samples, to an
embedding, and a domain-specific decoder supplies the training signal by modeling the object conditioned on that
embedding. Since the encoder works only from samples, one architecture serves different object types. Because the decoder is a model already suited to single instances of the object type, the framework inherits that model's native operations and inductive biases. We instantiate the framework on distributions, functions, and graphs. In each case we show that a single trained model represents an entire family and generalizes to members unseen during training. Furthermore, we demonstrate the downstream utility of these
embeddings: interpolating distributions in
embedding space, solving differential equations in
embedding space, and predicting unseen edges from a graph's
embedding.
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All authors Radovan Haluška, Christopher Solinas, Nathan R. Sturtevant, Martin Schmid, Michael Bowling
Cryptography and Security
Bilinear Compressive Security with Finitely Many Samples
Axel Flinth, Semira Einsele, Matilda Kylberg et al. (+2 more)
Bilinear Compressive Security (BCS) is a lightweight encryption scheme that combines compressed sensing with a fresh random convolution filter to improve resistance against key-recovery attacks under key reuse. Previous work derived information-theoretic guarantees for BCS by relating key identifiability to phase
retrieval under the assumption that an adversary has access to the full ciphertext distribution induced by the random filter. In practice, however, an adversary only observes finitely many encryptions, leaving the security of BCS in the finite-sample regime largely unexplored. In this paper, we study key recovery from only finitely many observations. We first develop a covariance-based attack that estimates the linear measurements from repeated encryptions of the same plaintext and subsequently recovers the secret measurement
matrix. We analyze the estimation procedure and derive finite-sample guarantees together with corresponding sample-complexity bounds. We further investigate the complete attack experimentally and compare its observed recovery behavior with the previously established information-theoretic identifiability results. Our experiments show two qualitatively different recovery regimes on the structure of the measurement
matrix. For dense
matrices, including Gaussian
matrices, the number of measurements required for successful recovery appears to grow linearly with the signal dimension. This differs from the worst-case behavior suggested by information theory. In contrast, for
matrices with sparse rows, we observe a quadratic growth consistent with the theoretical bound. These results suggest that the sparsity structure of the measurement
matrix plays an important role in the security of BCS and should be taken into account when selecting parameters and designing practical instances of the scheme.
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All authors Axel Flinth, Semira Einsele, Matilda Kylberg, Hubert Orlicki, Gerhard Wunder
Quantum Physics
Trading energy for coherence in mode discrimination
Shuyang Meng and Valerio Scarani
Energy is arguably the main resource in classical physics; coherence is the prototypical
quantum resource. In this paper, we study the interplay between these two resources in the task of optimal mode discrimination. This task, motivated by cryptography and metrology, consists of finding in which mode a signal has been encoded, for a given finite set of possible modes. After recalling that the task can be cast as a semi-definite program, we solve completely the case of two-mode discrimination under a hard energy constraint $n\leq N$. Specifically, we identify the region of modes overlap, for which the maximal energy state (the Fock state with N excitations) is the best choice for discrimination. For other overlaps, having access to coherence leads to better discrimination.
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Robotics
Active Perception of Upper-Limb Functional Mobility through Human-Robot Handovers
Francesco Semeraro, Marta Lagomarsino, Alberto Ranavolo et al. (+1 more)
The reintegration of people in the workplace after an upper-limb injury is often a long process. Collaborative
robots and humanoids operating in shared workspaces must adapt their behavior to the physical capabilities of their human partners. Achieving this requires
robots to autonomously estimate task-relevant human mobility during interaction, without relying on external clinical assessments. In this work, we formulate the estimation of upper-limb functional mobility as an active perception problem. We model task-available upper-limb functional ranges of motion through impairment coefficients that parameterize reductions of healthy joint limits, and integrate this representation into a human model used by the
robot during interaction. Based on this formulation, we propose an online active perception pipeline in which the
robot actively probes the user through repeated handover requests while progressively updating a
Bayesian belief over the impairment coefficients. This enables the
robot to continuously refine its internal model of the user's joint-level capabilities through interaction rather than passive observation. The proposed framework was evaluated through simulation studies and a proof-of-concept physical validation with a robotic platform and enacted human impairments, demonstrating that the pipeline can accurately recover the impairment coefficients during real human-
robot handovers.
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All authors Francesco Semeraro, Marta Lagomarsino, Alberto Ranavolo, Arash Ajoudani
Software Engineering
Systematic Detection of Energy Regressions in Software Version Histories
Jérôme Maquoi, François Bechet, Luís Cruz et al. (+2 more)
Context: Green software engineering is emerging as a crucial response to the rising energy impact of information technology, especially in continuous development. However, challenges remain in devising automated methods to efficiently identify energy regressions across commits. In particular, little effort has been put into automatically detecting regressions at the commit level by identifying statistically significant changes in energy consumption. Objectives: This paper presents EnergyTrackr, an approach for detecting energy regressions across multiple commits. Methods: We empirically evaluated EnergyTrackr on 1,932 commits from five Java projects, assessing its ability to identify significant energy changes. We also compared a straightforward linear
algorithm that analyzes the entire commit history with a more efficient dichotomous search
algorithm. Results: The evaluation demonstrates the effectiveness of EnergyTrackr in identifying significant energy changes across commits. Our findings highlight the efficiency of the dichotomous
algorithm in uncovering overall software energy trends, achieving results comparable to the linear approach while reducing resource consumption and processing time. Conclusion: We expect EnergyTrackr to help developers accurately monitor energy regressions and improvements in their projects and optimize their source code to reduce software energy consumption.
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All authors Jérôme Maquoi, François Bechet, Luís Cruz, Benoît Vanderose, Xavier Devroey
Signal Processing
DeepPV-MRF: End-to-End Partial-Volume Fraction Estimation from MR Fingerprinting Time Series
Tianyi Ding, Yang Gao, Zhuang Xiong et al. (+4 more)
Background: Estimating subvoxel tissue fractions from
magnetic resonance fingerprinting (MRF) is challenging. Conventional dictionary matching and segmentation pipelines can propagate errors from intermediate parametric maps into tissue fraction estimates. Purpose: We introduce DeepPV-MRF, a deep learning framework that directly estimates voxel-wise tissue fractions from raw MRF signals. Methods: A cascaded spatial-temporal network was trained on subject-derived Bloch simulations with radial k-space undersampling. The network combines temporal encoding and spatial U-Net refinement to estimate cerebrospinal fluid (CSF), gray matter (GM), and white matter (WM) fractions without intermediate T1/T2 maps. We compared DeepPV-MRF with partial-volume dictionary matching (PV-DM) and pseudoinverse estimation (PV-Pinv) using simulated and in vivo data. Results: DeepPV-MRF outperformed both methods across acquisition settings. In single-shot simulations, CSF/WM/GM mean absolute errors were 0.0418/0.0744/0.0757, versus 0.1281/0.2443/0.2958 for PV-DM and 0.2021/0.2793/0.2630 for PV-Pinv. Corresponding structural similarity values were 0.960/0.916/0.922. In vivo results showed sharper anatomical boundaries and smoother tissue transitions, while cross-shot analysis indicated improved consistency across volunteers. Conclusions: DeepPV-MRF enables accurate and efficient direct tissue fraction estimation from MRF signals, particularly with limited shots. Further validation is needed before clinical deployment.
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All authors Tianyi Ding, Yang Gao, Zhuang Xiong, Feng Liu, Martijn A. Cloos, Jin Jin, Hongfu Sun
Sound
Few-Shot Adaptation for Anomalous Sound Detection at Low False-Positive Rate
Simone Lugani, Christof Pichler, Hannes Wegleiter et al. (+3 more)
Reliable fault detection is crucial for industrial machinery in large-scale facilities, where unexpected failures can cause costly downtime and production interruptions. Although acoustic monitoring provides a scalable solution for machine condition monitoring, its deployment is often constrained by high false-positive rates. We propose a two-step approach to automatic anomalous sound detection: first, when only normal operating audio samples are available, a regularized unsupervised model is deployed; then, after the first machine fault, a few-shot adaptation strategy exploits the anomalous samples acquired during the fault to improve the system. This adaptation increases the area under the precision-recall curve (AUPR) from 23.0 to 64.0 and improves detection in the low false-positive-rate region, with the partial area under the receiver operating characteristic curve (pAUROC) increasing from 39.6 to 72.8. We also show that covariance regularization improves both prediction accuracy and training stability in the unsupervised model. These results provide a practical strategy for acoustic anomaly detection systems, enabling improved accuracy and reduced false alarms.
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All authors Simone Lugani, Christof Pichler, Hannes Wegleiter, Christoph Feilmayr, Rodolfo Zunino, Edoardo Ragusa
Machine Learning
On the Limits of Representation-Based Test-Time Adaptation for Multimodal LLMs
Mikołaj Piórczyński, Wojciech Masarczyk, Damian Sójka et al. (+2 more)
Vision-
language models suffer significant accuracy drops under common corruptions. While test-time adaptation (TTA) successfully mitigates these shifts in CLIP-like contrastive models, its application to generative
multimodal large
language models (MLLMs) remains underexplored and uniquely challenging due to their sequential
token generation. In this work, we demonstrate that representation-based TTA fails to recover performance in MLLMs. Our experiments reveal that even when violating standard TTA assumptions by leveraging target data offline, sample-blind feature
alignment recovers only marginal accuracy. We attribute this failure to the nature of corruption shifts: unlike CLIP-like architectures, which benefit from a shared domain-shift direction within a single pooled classification
embedding, MLLM visual
token sequence shifts are nearly orthogonal and span the full
embedding space.These findings bound what any sample-blind, position-shared correction at a single encoder site can achieve, establishing that representation-space interventions alone might be insufficient to ensure corruption robustness in MLLMs.
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All authors Mikołaj Piórczyński, Wojciech Masarczyk, Damian Sójka, Sebastian Cygert, Bartłomiej Twardowski