High Energy Physics (Phenomenology)
Top-Quark Mass Scheme Uncertainties in Higgs Boson Pair Production via Gluon Fusion
Clara Del Pio, Sebastian Jaśkiewicz, Rebecca von Kuk et al. (+1 more)
We investigate theoretical uncertainties arising from the choice of the top-
quark mass scheme in
Higgs boson pair production via gluon fusion. First, we implement the resummation of the top-
quark mass logarithms obtained for the $gg\to HH$ amplitude at high energy and study its phenomenological impact on cross sections. Second, we formulate a threshold-aware treatment of the top-
quark mass based on short-distance mass definitions, combining an MSR description near the top-pair threshold with an $\overline{\mathrm{MS}}$ treatment in the high-energy regime. Within the dynamical prescription, the combined mass-scheme variation of the SM inclusive NLO cross section at $\sqrt{s}=14$ TeV is $^{+5.2\%}_{-0.2\%}$. This estimate provides an alternative to the conventional OS-$\overline{\mathrm{MS}}$ envelope.
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All authors Clara Del Pio, Sebastian Jaśkiewicz, Rebecca von Kuk, Robert Szafron
Nuclear Theory
Prospects for modelling odd and odd-odd nuclei using energy density functionals
A.V. Afanasjev, B. Bally, M. Bender et al. (+10 more)
About half of particle-bound
nuclei have an odd number of nucleons. About one quarter more are odd-odd, meaning they have an odd number of
neutrons and an odd number of
protons. In this context, it is about time to shift the 70-odd-year focus of theories using nuclear energy density functionals (EDFs) away from the remaining one-quarter of even-even
nuclei. This White Paper aims to present open, unresolved problems and promising research directions in nuclear EDFs that were discussed during the 2024 ESNT workshop in Saclay, France, on “The microscopic modelling of odd-mass and odd-odd
nuclei”.
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All authors A.V. Afanasjev, B. Bally, M. Bender, J. Bonnard, J. Dobaczewski, M. Kortelainen, K. Nomura, S. Péru, N. Pillet, L.M. Robledo, R. Rodríguez-Guzmán, W. Ryssens, W. Younes
Multimedia
Frame Rate, Not the Grid: A Confound in Single-Clip Optical ENF Video Forensics
Ryan Wyton and Bacha Rehman
The optical electric network frequency, the faint mains-driven flicker imprinted on rolling-shutter
video, is increasingly proposed for verifying where footage was captured and for flagging manipulated or generated
video. This letter shows that reading the mains frequency from a single short clip is confounded by the camera frame rate: at the rates in common use, a harmonic of that rate lands in the same narrow band as the true flicker, so the recovered grid is set by the frame rate, not the supply, and the
estimator returns a confident value instead of abstaining. The underlying aliasing is known; its forensic consequence is not, and is the contribution here. On the 804 clips with a measurable in-band line the flicker feature does not separate genuine from generated
video (area under the curve 0.509), while the recovered grid tracks the frame rate exactly for the generators, and a controlled synthesis confirms the mechanism against ground truth for genuine grids. Single-clip grid, and hence region, identification is therefore unreliable at the standard rates, and a comb-aware
estimator that models the frame-rate harmonics restores the distinction between a genuine grid and a frame-rate comb under controlled conditions.
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Computation and Language
PerSo: Investigating Facets of LLMs' Social Intelligence in Real-World Persuasion Scenarios
Ali Zamani, Soroush Esfahanian, Erfan Shahabi et al. (+3 more)
Understanding the social intelligence of large
language models (LLMs) is increasingly important for applications involving human-AI interaction and multi-
agent environments. Social intelligence comprises two aspects: cognitive intelligence, the ability to infer others' mental states, and behavioral intelligence, the ability to select appropriate social actions based on these inferences to achieve social goals. Persuasion provides a natural testbed in which both aspects are simultaneously at play, yet prior
benchmarks typically evaluate them in isolation using synthetic scenarios. We introduce PerSo, a
benchmark that evaluates both aspects on real-world persuasion dialogues from Reddit's ChangeMyView community, using an LLM-in-the-loop pipeline to construct the evaluation tasks, validated through human annotation. Our evaluation of diverse LLMs shows that performance generally scales with model size but varies across dimensions. The strongest LLM slightly surpasses the best observed human performance on the behavioral task, while the best human participant remains ahead on the cognitive task.
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All authors Ali Zamani, Soroush Esfahanian, Erfan Shahabi, Mohammad Taher Pilehvar, Azadeh Shakery, Yadollah Yaghoobzadeh
General Relativity and Quantum Cosmology
Cubic Quasinormal Modes in the Black-Hole Ringdown
Neev Khera and Huan Yang
We formulate and numerically solve the nonlinear Teukolsky equations through third order, obtaining a first-principles calculation of cubic quasinormal-mode amplitudes for Kerr black holes. Our approach combines lower-order metric reconstruction in split light-cone gauges with a hyperboloidal, compactified formulation solved on complex radial contours.
Spin-dependent complex coupling coefficients fix the cubic amplitudes from products of their linear parent amplitudes. We apply these predictions to a nonprecessing numerical-relativity waveform, including cubic products of the $(2,2)$ fundamental mode, its first overtone, and their conjugates. The predicted cubic terms improve agreement over selected early-ringdown fitting windows and shift inferred linear-overtone amplitudes without adding fitting parameters. $3M$ after the peak of $|h_{22}|$, the summed cubic contribution reaches approximately $38\%$ of the total $h_{22}$ signal; $M$ is the initial total mass. These results identify an appreciable cubic response in early ringdown.
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Solar and Stellar Astrophysics
Dynamics of a switchback-like magnetic deflection induced by a propagating coronal jet
Jade Touresse, Etienne Pariat, Clara Froment et al. (+1 more)
Parker Solar Probe (PSP) revealed the ubiquity of switchbacks (SBs), sharp, localised
magnetic deflections in the nascent solar wind, renewing interest in their origin. One proposed mechanism involves solar jets: collimated, impulsive features driven by
magnetic reconnection that launch Alfvénic torsional wavefronts into the heliosphere. We test whether realistic spacecraft sampling of a propagating solar jet produces in-situ signatures consistent with PSP observations. We also examine the 3D
magnetic topology of the resulting structures and how deflection amplitudes evolve with the ambient
plasma environment. We perform a 3D magnetohydrodynamic simulation of a solar jet in the ambient solar wind,
embedding a virtual spacecraft trajectory based on PSP's actual 22nd-perihelion orbit to generate synthetic measurements. The simulation reproduces SB-like signatures with durations and profiles consistent with PSP observations, confirming earlier results from simplified trajectories. We visualise the fully 3D
magnetic topology of a jet-driven SB region, identifying two field-line families: one with a single deflection and another with two sequential deflections along the same field line. Both are outward-propagating Alfvénic structures. Deflection amplitudes depend strongly on background
plasma $\beta$, increasing substantially in low-$\beta$ regions and evolving more moderately where $\beta \sim 1$. These results show that solar jets can produce Alfvénic, SB-like structures along realistic spacecraft trajectories. Their
topology is inherently 3D and structurally diverse, and their
evolution depends on ambient
plasma $\beta$. Complete
magnetic reversals would require longer residence times in low-$\beta$ environments. Our findings support a solar origin for a fraction of SBs and constrain interpretations of their underlying
magnetic structure.
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All authors Jade Touresse, Etienne Pariat, Clara Froment, Peter F. Wyper
Combinatorics
The $k$-color poset Ramsey number of the diamond is $2k$
Tom Bohman, Will Cong, Quentin Dubroff et al. (+3 more)
The $k$-color Ramsey number of a poset $P$ is the smallest dimension $d$ such that any $k$-coloring of the $d$-dimensional Boolean
lattice contains a monochromatic induced copy of $P$. For $ j\ge 2$ the $j$-diamond is the poset that consists of a maximum element, a minimum element and an antichain of size $j$. We prove that the $k$-color Ramsey number of the $j$-diamond is at most $jk$. Colorings of the Boolean
lattice that assign sets of the same size the same color provide matching lower bounds for $j \in \{2,3,4\}$.
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All authors Tom Bohman, Will Cong, Quentin Dubroff, Teresa Huang, Zheyuan Li, BangTam Ngo
Machine Learning
Detection of Potato Plant Leaf Diseases using Convolutional Neural Networks
Haimanot Bitew Alemayehu, Simachew Melaku Ayalew, Chilotie Desalew Mengesha et al. (+3 more)
Potato is an important food-security crop in Ethiopia, but its production is affected by foliar diseases, particularly early blight and late blight. This study develops a lightweight convolutional neural network (CNN) to classify potato leaves into three categories: healthy, early blight, and late blight. The proposed model was trained using 2,215 images from the PlantVillage
dataset and evaluated on an independent field
dataset containing 247 images collected from potato farms in Banja, Awi Zone, Ethiopia. On PlantVillage, the model achieved 99.04% training accuracy, 98.21% validation accuracy, and 98.66% test accuracy. On the field
dataset, it achieved 90.28% accuracy. The study also compares the proposed CNN with VGG16 and MobileNetV2 in terms of classification performance and computational efficiency. The proposed model contains 446,787 trainable parameters, occupies approximately 1.70 MB, and requires an average of 5.58 seconds per training epoch in the reported experimental setup, compared with 9.96 seconds for VGG16 and 7.68 seconds for MobileNetV2. The results demonstrate the potential of a compact CNN for potato leaf disease classification and highlight the importance of independent field evaluation, where image conditions are more variable than in controlled
benchmark datasets. The proposed approach may support preliminary disease identification for smallholder farmers in Ethiopia and similar agricultural settings.
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All authors Haimanot Bitew Alemayehu, Simachew Melaku Ayalew, Chilotie Desalew Mengesha, Ayisheshim Almaw Bogale, Anteneh Birhan Yenew, Abrham Tegegne Bogale
Computer Vision
Reliability-Aware Multimodal Sentiment Prediction Under Local Modality Missingness
Minghao Kong, Deng Yao, Xi Zhang et al. (+4 more)
Multimodal sentiment models are usually evaluated on complete utterances, yet streams often lose a contiguous span of text, audio, or
video. We study local missingness on aligned CMU-MOSEI features. Our reliability-aware network (RM-Net) keeps the original temporal positions, represents unobserved positions with a learned
token, outputs bounded per-modality intensity contributions gated by observation quality, and is trained with dual-view masking of contiguous segments. We fuse five RM-Net seeds with a LoRA-adapted BERT text expert, using a fusion weight that depends only on the visible fraction of content
tokens. Architecture, checkpoint, and fusion choices were made on the validation split. On the held-out split, the fused model reaches a macro-F1 of 0.655 on clean inputs and 0.634 under 40% mixed missingness; its two-view average exceeds the auxiliary-head baseline by 0.024 (95%
video-cluster bootstrap interval [0.003, 0.045]). Because this split was inspected during development, these numbers are not a blind test. For explanations, exact Shapley attributions over the eight modality subsets and position-level occlusion show that top-ranked positions reduce the predicted-class probability faster than random positions (AUC gap 0.113 on validation). We also describe an auditable word-level
alignment pipeline for raw clips; its outputs do not feed the predictive models.
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All authors Minghao Kong, Deng Yao, Xi Zhang, Ying Gao, Jiurun Chen, Zirong Liu, Rongjie Wang
Optimization and Control
The quasi-convex Rockafellar theorem
Luigi De Pascale and Paul Pegon
Rockafellar showed that every cyclically monotone operator is contained in the subdifferential of a convex function, and characterized the maximal ones. We establish the quasi-convex analogue of this
theorem, with no assumption on the operator: in any separable Banach space, a setting we show to be sharp, every cyclically quasi-monotone operator is contained in the normal cone operator of a quasi-convex function. In finite dimension, we characterize maximal cyclically quasi-monotone operators and show that they are maximal quasi-monotone. Unlike in the convex case, the potential is not unique, and we build it from a preference relation obtained through successive preorders. As applications, we extend Afriat's
theorem on revealed preferences in economics to infinite data sets, and obtain Kantorovich-type potentials for a supremal optimal transport problem.
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Earth and Planetary Astrophysics
Revealing the Spatial Distribution of Dust and Plasma Solitary Waves in Jupiter's Innermost Magnetosphere Using Juno Waves Observations
Wenyu Zhang, Hao Cao, William S. Kurth et al. (+6 more)
Jupiter's faint ring system contains micron- and submicron-sized dust whose dynamics are influenced by the
planet's rapidly rotating
magnetic field. Optical observations have established the morphology of the main ring and
halo, but tenuous dust close to Jupiter remains difficult to characterize. We analyze nearly a decade of in-situ Juno Waves electric-field measurements using a two-stage convolutional neural network to identify and classify dust impacts, solitary waves, and other
plasma waves. These in-situ dust-impact detections reveal a radially evolving structure extending well inside the traditionally observed
halo. In the innermost region, dust is concentrated in a compact, equatorially symmetric layer, indicating that the optical inner boundary does not mark the end of the dust population. Farther outward, the layer expands vertically into a broad
halo before contracting toward the main ring, directly revealing the toroidal morphology of the Jovian
halo. Dust number density is enhanced within the
halo and near the main-ring–
halo transition. The vertical distribution also develops hemispheric asymmetries that vary with radial distance, consistent with an influence of Jupiter's
magnetic geometry on charged-dust dynamics. In contrast, solitary waves occur predominantly at high latitudes, and their mapped
magnetic footprints show a strong spatial correspondence with Jovian auroral structures, particularly the main auroral oval. Together, these in-situ measurements extend the remote-sensing picture of Jupiter's rings and reveal previously unresolved dust and
plasma structures in its innermost magnetosphere.
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All authors Wenyu Zhang, Hao Cao, William S. Kurth, Mitchell M. Shen, Darrelle Wilkinson, Donglai Ma, Martin Brennan, George Hospodarsky, Scott J. Bolton
Analysis of PDEs
Fast separation of Euler and Navier–Stokes flows in two dimensions
Siming He, Yupei Huang and Jincheng Yang
We study the long-time validity of the inviscid limit problem by measuring when the two-dimensional ideal fluid ceases to accurately approximate a slightly viscous fluid with the same initial datum. We construct a stationary solution of the Euler equation from which the Navier–Stokes solution separates at a time scale proportional to the logarithm of the inverse viscosity, achieving the fastest separation from Euler steady states.
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Optimization and Control
A Decomposition-Based Data-Driven Branch-and-Bound Approach for Black-Box Global Optimization
Damilola Fasiku and Wentao Tang
Many scientific and engineering applications require the global optimization of expensive black-box functions. In high-dimensional settings, however, achieving computational scalability is a major challenge. To address this challenge, we propose a distributed spatial branch-and-bound framework that exploits partially separable structure for the global optimization of box-constrained black-box problems. The proposed framework adopts a two-layer structure in which branching is performed only on a lower-dimensional set of coupling variables in an outer layer. Within each outer node, the remaining non-overlapping (private) variables, which appear independently across blocks, have their domains partitioned and explored in parallel within an inner layer, resulting in a naturally parallelizable framework that is well suited for implementation on multi-core CPU architectures. At each node, valid upper bounds are obtained via sampling, with optional refinement using a local solver, while lower bounds are computed using data-driven convex underestimators constructed over partitions of the private variables. A residual-based correction and finite-sample validity analysis provide conditions under which the partitionwise underestimators are valid lower bounds, while the proposed branching and refinement mechanism establishes convergence to an $\epsilon$-global optimum. Numerical studies on
benchmark and ODE-constrained parameter-estimation problems demonstrate improvements in both sampling efficiency and computational performance relative to monolithic approaches.
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Computation and Language
PrismaCoder: Pushing the Boundaries of Open Multimodal Code Intelligence through Decomposed Rubric Synthesis
Qiushi Sun, Jingyang Gong, Yang Liu et al. (+14 more)
Multimodal code intelligence connects the symbolic world of programs with the visual world their execution creates. Interest in this capability is burgeoning, as models are increasingly asked to produce charts, webpages, animations, and semi-structured documents. Recently, proprietary models have pushed far ahead, producing interactive artifacts even from a text description or a screenshot alone. Yet their data and training remain closed, and open progress is held back by the quantity and quality of available data and by the pipelines and methods for producing it. Quality is the harder of the two, since a sample must be correct as a program, well built, and above all visually right once rendered. No existing signal covers all three. Execution often returns just a pass or fail, code similarity does not map to the visual output, and task-specific verifiers do not generalize. To push open progress further, we start from the data side with PrismaForge, a hierarchical data pipeline centered on a synthesized rubric that decomposes these factors into checkable criteria. The same rubric serves as both filter and reward: it curates the supervised corpus, then drives reinforcement learning on visual-coding tasks. Leveraging PrismaForge for data synthesis and filtering, we build PrismaCoder-2M, the most diverse open
dataset for
multimodal code intelligence and the largest to date. On the model side, we train PrismaCoder-9B and PrismaCoder-35B on this data and release both. They lead the open state of the art by a wide margin and rival leading proprietary systems. Our analyses further show that the proposed rubric-based pipeline improves
multimodal coding performance across model backbones. We release our code and data at https://qiushisun.github.io/PrismaCoder-Home/.
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All authors Qiushi Sun, Jingyang Gong, Yang Liu, Qiaosheng Chen, Zhangyue Yin, Zixian Huang, Hanxu Hu, Jing Xiong, Fangzhi Xu, Haochen Tan, Haoli Bai, Lifeng Shang, Xiaoguang Li, Ben Kao, Qipeng Guo, Lingpeng Kong, Jiahui Gao
Quantum Physics
Quantum-Aware Key Rank for Side-Channel Attacks evaluation
Yevgen Kotukh
Side-channel attacks on block ciphers rarely recover the key outright: they leave a residual key space ordered by subkey posteriors, and evaluation laboratories report the key rank of the true key against thresholds near 2^48 and 2^60. No evaluation framework models an adversary who collects traces classically and finishes the search on a
quantum computer, although
quantum search with advice provides exactly that. We give such an adversary a cost model and a metric. For a device whose key has rank r, any
quantum algorithm with classical access to the posteriors needs Omega(sqrt(r)) cipher evaluations, and block search achieves O(sqrt(r)); the per-instance advantage is exactly quadratic, and the super-quadratic exponents reported for search with advice are averages over the prior that apply to no single device. Under the NIST MAXDEPTH budget D and a Grover iteration of g gates and depth d, the quadratic advantage holds only for ranks up to (4D/(pi d))^2 and collapses to a constant factor beyond; with the best published AES-128 oracle and D = 2^40 the window closes near rank 2^58.6. We define the
quantum-aware key rank, the classical rank of equal cost, computable from the rank
estimators laboratories already run, and derive the classical rank a device must retain to keep a given threshold against each depth budget. Block search and amplitude amplification from the product-form posterior state are within a factor of two to four of each other on Hamming-weight posteriors, so the cost of coherent indexing decides between them. Experiments on simulated leakage and a toy cipher confirm the predicted window: at SNR 0.125 a depth-limited
quantum adversary is worth about 22 traces to a classical one, however large its gate budget.
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Quantum Physics
Intermediate memory costs of quantum channel synthesis with a protected subsystem
Kai Liu and Deguang Han
A
quantum memory can preserve a subsystem perfectly while discarding information needed by a later operation. We study the total intermediate dimension required for channel synthesis when the first stage protects a $b$-dimensional subsystem and has a prescribed noncommutative graph imposing full confusability on an $a$-dimensional control register. For a finite family of classically controlled branches, exact synthesis is possible if and only if the target graph contains the prescribed graph of the first stage. For every feasible family, we construct a single processor that implements all branches exactly. The processor uses pairwise nonorthogonal pure program states in a register of dimension at most $a$. A target graph may be realizable with a carrier of dimension $b$, while synthesizing a channel with that graph can require dimension $ab$. We also characterize the closure of the attainable channels, distinguishing exact synthesis from arbitrarily accurate approximation. Finally, for a family built from the trine communication task, a four-dimensional carrier permits exact synthesis with
quantum programs under output depolarization. At the same total carrier dimension and without free shared randomness, classical programs have optimal normalized diamond error $(1-\eta)/4$, where $0\leq\eta\leq1$ is the depolarization strength. For $\eta<1$, the gap persists under sufficiently small perturbations of the target branches.
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High Energy Physics (Theory)
Thermal AdS two-point functions and loop induced effective one-point vertices
Astha Kakkar
We present a study of thermal two-point functions from Witten diagrams in thermal AdS$_{d+1}$, defined with the conformal boundary $S_\beta^1 \times \mathbb{R}^{d-1}$, with an aim to establish a connection between thermal CFT data and perturbative bulk dynamics. We analyze one-loop tadpole and bench diagrams arising from the cubic $\phi\phi\chi$ interaction in the bulk, segregating the different relative thermal winding sectors for the bench, and also extend the analysis to include quartic scalar contact diagrams with and without derivative interactions. We also analyze the spinning exchange thermal two-point function. Utilizing the split representation of bulk propagators and the method of images we reorganize the thermal Witten diagrams into the standard AdS integrals and obtain spectral and image-sum representations that make KMS periodicity of the correlators manifest. Additionally, we show possible position dependent effective one-point vertices of scalar and spinning fields in the bulk arising from the reduction of the coincident- point thermal bulk to bulk propagator loop, allowing tadpole-type diagrams to be reconstructed from effective local bulk interactions. The bench diagram however retains an intrinsically bi-local structure. We further investigate the thermal one-point coefficients and perturbative OPE data encoded in these thermal correlators, including contributions associated with anomalous dimensions. Finally, we examine their relation to thermal dispersion relations and the role of contributions that are not determined by discontinuity data alone. Our results provide a set of explicit bulk constructions for exploring the analytic structure of thermal CFT correlators and their connection to the thermal conformal bootstrap.
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Machine Learning
Revisiting Simple Baselines for Relational Deep Learning
Viet The Nguyen, Vijay Prakash Dwivedi and Gunther Gust
Relational deep learning has emerged as a powerful paradigm for learning end-to-end predictive models over relational databases by representing them as temporal heterogeneous graphs. Recent advances based on graph neural networks and
Transformers have achieved strong performance across diverse domains. However, the effectiveness of simpler relational baselines remains underexplored, making it difficult to assess how much architectural complexity is needed to achieve strong predictive performance. Motivated by the observation that predictive signals arise from target attributes, relational attributes, and historical labels, we revisit how effectively these signals can be exploited using simple propagation methods. We adapt fixed low-pass feature propagation to relational entity graphs and train an MLP for prediction. The resulting predictions are further refined using Correct-and-Smooth, a graph-based label propagation
algorithm that incorporates historical labels over task-specific meta-graphs connecting target entities. Across classification and regression tasks on RelBench, an MLP trained on neighborhood-filtered features outperforms one using only target-entity features on most tasks and recovers a substantial portion of the performance of heterogeneous GNNs. Correct-and-Smooth provides further gains from historical labels, although applying it requires a separately constructed meta-graph because connections among target entities are not directly specified by the relational entity graph. Together, our studies revisit the effectiveness of established propagation methods as simple RDL baselines while identifying canonical meta-graph construction as a key challenge for effective label propagation.
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Machine Learning
Generative Representation Learning for Scalable Latent Tokenization
Alan Baade, Kyle Sargent, Vincent Sitzmann et al. (+4 more)
We introduce an approach for self-supervised representation learning designed to be used as a tokenizer for generative modeling. Our objective supervises a representation loss by factorizing the pointwise mutual information of the data, which we estimate by taking information differences between conditional predictions from a learned generative model. We justify our
algorithm theoretically and empirically, demonstrating that our generative approach to representation learning avoids dimensional collapse and feature suppression in cases where commonly used methods like DINO and LeJEPA fail. Moreover, we show our representation learning technique is scalable; expending more compute leads to predictably better latent tokenization. For the first time, we perform a careful analysis of the combined flop efficiency of tokenizer and generative model and demonstrate that our approach is a compute-optimal improvement over direct
diffusion for pixel-space generation, obtaining an FID-50K of 2.18 with JiT-L. Applied to ImageNet and the text-to-image
dataset GPIC, we show tokenizer encoder scaling laws for diffusability, where extrapolating our approach achieves representations in 7x fewer FLOPs than DINOv2 on ImageNet and 8x fewer FLOPs than WebSSL on GPIC.
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All authors Alan Baade, Kyle Sargent, Vincent Sitzmann, Eric Ryan Chan, Justin Johnson, Li Fei-Fei, Gordon Wetzstein
Artificial Intelligence
ActiveEvidence: Decision-Focused Evidence Graphs with Verifiable Acquisition Gaps
Miaobo Hu, Shuhao Hu, Xiaobo Guo et al. (+5 more)
A
multimodal agent must decide whether visual evidence is worth acquiring, which candidate or resolution to query, and when further interaction can no longer change its decision. We study this problem under a strict free-metadata/paid-observation boundary,
stochastic vector costs, and a frozen execution trace, and treat it as decision-certifiable active evidence acquisition. A Counterfactual Evidence Interaction Graph (CEIG) represents unary evidence value, signed second-order interaction, and directed conditional harm while maintaining explicit score enclosures for unresolved interactions; a decision-weighted enclosure objective tightens the bounds that can change action identity. Gap-directed refinement evaluates only interactions that can displace the incumbent acquisition and stops once an $\epsilon$-decision certificate shows that unresolved terms cannot change the selected action beyond a declared tolerance. The certificate is model-relative: it verifies policy-consistent optimality under the pinned learned score, and semantic quality is measured separately through post-freeze labels. Paired continuation rollouts learn the downstream value of paid previews, an online operational cost filter is separated from independent finite-policy-family trajectory certification, and an acquisition monitor with an independently implemented verifier binds every certificate to the frozen trace. On 640 cases split into 384/128/128 training, validation, and test cases, the controller reaches task score 0.842, grounding F1 0.816, evidence recall 0.873, 1.74 accepted calls, 286.5K visual
tokens, and 742ms p95 latency. Dense replay of issued certificates shows 0.992 action agreement and a 0.988 certificate rate while evaluating 19.6 instead of 191.4 expensive interaction terms per decision.
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All authors Miaobo Hu, Shuhao Hu, Xiaobo Guo, Xin Wang, Bokun Wang, Peng Zhang, Daren Zha, Jun Xiao
Robotics
EarlyCommit: Calibrated Word-by-Word Commitment for Robots Acting Before You Finish Speaking
Muhammad Haris Khan
Human listeners can often identify the intended object several words before an instruction ends.
Robots, however, are typically designed to wait until the speaker has finished before deciding how to act. We present EarlyCommit, an open-source tool that lets a
robot commit to the intended object while the instruction is being spoken. After every recognized word, a fine-tuned text encoder scores each object in the scene. The
robot acts when the calibrated probability of the best object reaches a threshold tau set by the user, so 1 - tau is the accepted risk of choosing the wrong object. Otherwise, it waits or asks. The tool includes a streaming
speech front end, a scene and instruction generator with exact word-by-word ground truth, and scripts to train, calibrate, and evaluate. On instructions containing words never seen in training, EarlyCommit commits correctly 1.6 to 2 times as often as keyword matching, is right at least 92 percent of the time when it commits at tau = 0.9, and needs 31 to 35 ms per word on consumer GPUs.
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Systems and Control
FedRIC: Heterogeneity-Aware Federated Learning with Hard Deadline Constraints in Open RAN
Adhwaa Al-Chaab, Ayman Younis and Dario Pompili
Open
Radio Access Networks (O-RANs) bring intelligence to disaggregated RANs through virtualized platforms and open interfaces, but the stringent latency budgets of the RAN Intelligent Controller (RIC) and heterogeneous traffic conditions make real-time deployment costly. Centralized learning is ill-suited to the RIC due to its computational burden, timing constraints, and communication overhead, motivating
Federated Learning (FL) as a distributed alternative. We formulate FL in O-RAN as a Mixed-Integer Nonlinear Program (MINLP) that minimizes communication cost and learning time under heterogeneity in User Equipment (UE) compute, traffic, and channel conditions, and decompose it into a UE Assignment (UA) subproblem and a Computation and Bandwidth Allocation (CBA) subproblem. We then propose FedRIC, a heterogeneity-aware FL framework combining compressed gradient descent, slice-aware resource allocation, and multilevel hierarchical aggregation. Experiments show that FedRIC outperforms FedCOM, FedAvg, and QFedAvg in convergence rate, communication cost, and latency.
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High Energy Physics (Phenomenology)
Effects of quadratic frequency chirping and carrier-envelope phase on Schwinger pair production in polarized electric fields
Abhinav Jangir
We investigate electron-positron pair production in spatially homogeneous, time-dependent polarized electric fields using the Dirac-Heisenberg-Wigner formalism, focusing on the effects of symmetric and asymmetric quadratic frequency chirping and the carrier-envelope phase (CEP). Our analysis reveals how these field parameters govern both the momentum-space structure and the overall yield of the produced pairs. Frequency chirping substantially reshapes the momentum distributions by modifying their interference patterns and peak occupations. The momentum distributions are particularly sensitive to weak chirping, whereas strong chirping produces pronounced oscillatory and ring-like structures across all polarization states. The total pair yield increases with chirp strength, with symmetric chirping consistently producing a higher yield than asymmetric chirping for the parameter range considered. Moreover, the sensitivity of the total number density to polarization decreases progressively with increasing chirp strength, becoming negligible in the strongly chirped regime. The CEP further modifies the momentum distributions by altering their shapes and relative peak occupations, with the extent of these changes depending on the polarization state. Our findings establish a theoretical framework for exploring external-field configurations that optimize the yield of Schwinger pair production.
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Quantum Physics
Quantum Work Statistics from Entanglement-Assisted Invariance
Akira Sone, Akram Touil, Emery Doucet et al. (+1 more)
We demonstrate that the one-time measurement scheme is more consistent than the standard two-time measurement scheme with respect to the fundamental
symmetry of
entanglement, namely
entanglement-assisted invariance (envariance). In particular, the work statistics obtained from the one-time measurement scheme arise naturally as a direct consequence of envariance for any driving
unitary, without involving an explicit measurement postulate, whereas the analogous identification for the two-time measurement scheme is possible only for a Haar-measure-zero set of
unitaries. This envariance-based formulation eliminates the ambiguity in work statistics related to the energetic cost of measurement.
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All authors Akira Sone, Akram Touil, Emery Doucet, Sebastian Deffner
Databases
JetStream: Generating Verified Query Accelerators for Existing Database Systems
Akhilesh Balasingam, Amadou Ngom, Geoffrey X. Yu et al. (+1 more)
Recent work has shown that LLMs can synthesize highly specialized database systems for fixed workloads, but these systems replace the DBMS's native storage and assume static data: users must migrate their data, often give up ad-hoc queries, and lose acceleration once the data changes. We present JetStream, which instead generates query-specific accelerators that extend an existing DBMS. Given a set of parametrized query templates, a staged agentic workflow synthesizes accelerators that run directly over the DBMS's native tables and may maintain custom auxiliary state, while the DBMS remains responsible for storage, transactions, recovery, and all other queries. A fixed, engine-neutral substrate handles engine-specific hooks and keeps auxiliary state transactionally consistent under writes. JetStream generates two kinds of accelerators: tested accelerators, which may use any optimization and are validated by differential testing, and verified accelerators which trade some performance for a machine-checked proof that every answer satisfies a trusted specification derived from the SQL. On TPC-H at SF=20, JetStream achieves a $315 \times$ geomean read speedup over DuckDB ($93 \times$ verified) and retains $120 \times$ under a continuous update stream of 10,000 modified rows per second.
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All authors Akhilesh Balasingam, Amadou Ngom, Geoffrey X. Yu, Tim Kraska
Multimedia
Lluna: Collaborative Unimodal Models for Missing-Modality Inference
Haoyue Bai, Yinyi Luo, Heqing Huang et al. (+3 more)
Pretrained unimodal models offer a flexible route to
multimodal prediction through collaboration, but this collaboration can become less accurate than a surviving unimodal model when modalities are missing. We study this setting using fixed surviving-model anchors and find that collaborative gains can weaken or reverse as inputs disappear, even when the anchor's own input is unchanged. Our diagnostics further reveal reduced recovery of modality-specific information after aggregation and state-dependent collaborative representations. We propose Lluna, which combines a direct private prediction path from adapted unimodal features with a common graph-based collaboration branch that provides a residual correction. Progressive Collaboration Consistency organizes richer-to-poorer guidance along single-modality removals, aligning normalized common representations while task supervision adapts predictions to the available evidence. Across eight
datasets spanning affective computing, scene understanding, and medical prediction, Lluna achieves the best evaluated average performance over non-full input states, improving Mean-Missing by 4.26% on average relative to the strongest evaluated baseline. The fixed-anchor diagnostic also shows positive gains in mean collaboration under minimal inputs on all eight tasks. Code is available at https://github.com/AIFrontierLab/Lluna.
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All authors Haoyue Bai, Yinyi Luo, Heqing Huang, Xiaoyuan Yi, James Evans, Jindong Wang
Machine Learning
The Physics Is the Network: Fast, Training-Free Neural Emulation
Luka Todorovic, Kaushal Gianchandani and Sai Ravela
Neural emulation need not require learning a substitute for known physics. We investigate PolyNets that couple a continuous-time neural representation of
polynomial dynamics with a simple recurrent network for discrete-time integration. When the governing coefficients are known, the resulting tensor graph requires no training and preserves the prescribed numerical scheme while enabling efficient hardware execution and parallel simulation. Using the one-dimensional Korteweg-de Vries equation, we compare PolyNet with CPU and GPU finite-difference Runge-Kutta solvers and three trained neural models: a state-map emulator, a physics-informed predictor, and a learned-tendency integrator. Analytical
soliton solutions provide a reference for amplitude and phase errors across grid resolutions, 10^6 dispersively scaled time steps, ensembles of up to 10^4 trajectories, and varying initial amplitudes. PolyNet achieves accuracy comparable to the numerical solvers, including at amplitudes outside the learned models' training conditions. It combines this fidelity with high GPU throughput, approximately constant over the tested resolution range, and favorable ensemble scaling.
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Robotics
Physical Time as a Missing Dimension of Robot Policy Memory Dynamics
Yushen Liang, Yue Peng, Baosheng Jin et al. (+6 more)
Existing
robot policy
memories typically evolve with observation sequences, implicitly treating each update as the same amount of physical time. Yet identical observation histories can span different durations and require different actions, exposing physical time as a missing dimension of policy
memory. We introduce Temporal Flow Policy (TFP), which learns
memory dynamics to retain past events and evolve the
memory state over measured time intervals. Observations and prior
memory determine the content and time scales of each transition; elapsed time determines how far the state advances. TFP conditions
diffusion and flow-matching policies through this
memory state and is trained with their native imitation objectives. We evaluate TFP with
Diffusion Policy and $\pi_{0.5}$ through controlled comparisons of
memory designs. These experiments show that successful timing depends on both measured intervals and retained history. TFP achieves higher average success than the recurrent and state-space
memories we equip with the same intervals and than a variant that feeds the interval to a learned gate.
Memory readouts recover the time since an event and motion information after occlusion. Changing the supplied intervals systematically delays or advances actions, linking time inputs to behavior. Real-
robot tasks requiring timing or
memory, together with LIBERO evaluations, demonstrate benefits in physical and general manipulation. These results suggest viewing
robot memory not as stored observation history, but as a state that retains past events and evolves with physical time.
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All authors Yushen Liang, Yue Peng, Baosheng Jin, Tianluo Zhang, Xinyu Zhang, Shuyi Zhou, Zhuoran Chen, Xinqi Liu, Shenji Wan
Materials Science
Growth-induced trade-off between polarity, electrical isolation, and structural quality in AlN grown on Si(111) by plasma-assisted molecular beam epitaxy
Hossein Yazdani, Nhu Tran, Xin Du et al. (+3 more)
We investigate the effects of substrate temperature and Al flux, at a fixed N flux, on the polarity, electrical properties, and structural quality of AlN films grown on high-resistivity Si(111) by
plasma-assisted molecular beam epitaxy. N-rich conditions result in relatively highly resistive Al-polar AlN/Si, whereas Al-rich conditions promote N-polarity and improved structural quality but simultaneously lead to increased p-type conductivity of AlN/Si stacks, consistent with unintentional Al doping of the Si substrate during AlN growth. Increasing the substrate temperature further improves the structural quality while enhancing the unwanted conductivity, revealing a growth-induced trade-off among polarity, electrical isolation, and structural quality. By reducing the substrate temperature to 575{\deg}C while maintaining slightly Al-rich conditions, an N-polar AlN/Si stack with a sheet resistance of 276 k{\Omega}/sq is achieved. These results provide guidelines for optimizing AlN/Si templates for various nitride device
heterostructures.
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All authors Hossein Yazdani, Nhu Tran, Xin Du, Vit Hornik, Roman Gröger, YongJin Cho
Strongly Correlated Electrons
Anyons at impurities: Fractional charge measurement and "wrong sign" binding
Haydn S. Adlong, Arthur Christianen, Atac Imamoglu et al. (+1 more)
Recent photoluminescence experiments in twisted MoTe$_2$ have reported signatures of anyons binding to trapped trions. The measured binding energies near the fractional Chern
insulators at fillings of $\nu=-2/3$ and $-3/5$ have a ratio close to the fractional-charge ratio $5/3$, even though such scaling is not generally guaranteed for the expected short-range trion-anyon interaction. We model the trapped trion as a fixed impurity, and investigate anyon-impurity binding using a lowest-Landau-level model with exact diagonalization and Jain wavefunctions. Surprisingly, we find that the anyon-impurity binding energy ratio between $\nu=-2/3$ and $-3/5$ is close to $5/3$ for either short- or long-ranged impurity-electron interactions. However, the ratio departs strongly from the charge ratio at intermediate ranges or different Jain states. Remarkably, we also uncover "wrong-sign" binding, whereby both quasiholes and quasiparticles can bind to either sign of a short-range impurity potential by selecting orbital channels with favorable local density oscillations. Our findings show that localized
excitons or trions can be used to probe the internal density profile of anyons.
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All authors Haydn S. Adlong, Arthur Christianen, Atac Imamoglu, Glenn Wagner