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All of arXiv

Monday 5 October

Numerical Analysis

A higher order family of iterative methods for approximating inverse and generalized inverse matrices

Alicia Cordero, Bushra Rani and Juan R. Torregrosa

The development of efficient iterative schemes for approximating the inverse and generalized inverse matrices is important in my research fields. To this end, a family of fourth-order matrix iterative schemes for approximating the inverse and generalized inverse matrices is established by incorporating an appropriate weight function into the scalar Heun method. Moreover, a fifth-order matrix iterative scheme is obtained as a particular member of the proposed family. Under suitable conditions, the theoretical order of convergence is determined for the proposed family, whose stable members are identified through dynamical analysis. To assess their performance and verify the consistency of the theoretical results, some numerical tests are performed on matrices of different sizes.
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Optics

Conductivity-driven chirality in geometrically achiral plasmonic nanostructures and metasurfaces

Yulong Duan, Longzhu Liu, Suraya Kazi et al. (+7 more)

Resonant nanostructures are powerful for chiral control of light, but widely assumed to require achiral geometry. Here we demonstrate that optical chirality can emerge in geometrically achiral nanostructures, through the misalignment of independent linear responses. By rotating the conductivity tensor of an anisotropic plasmonic medium relative to a geometrically symmetric nanostructure, we generate a spin-selective optical response that can be continuously tuned both in magnitude and handedness, including reversible switching between left- and right-handed behaviour. We experimentally realize this concept using an aligned plasmonic conducting polymer and by controlling the orientation of its polymer backbone relative to otherwise identical rectangular nanoantennas. This leads to strong planar chirality that can also be reversibly modulated by varying the carrier density of the polymer. Moreover, the rotation angle of rectangular meta-atoms made through this principle encodes no longer only the local geometric phase but simultaneously the local optical chirality. This enables a new design paradigm for phase-gradient metasurfaces , enabling simultaneous control of optical chirality and wavefronts through a single rotational degree of freedom.
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All authors Yulong Duan, Longzhu Liu, Suraya Kazi, Harry Miyosi Silalahi, Roger Magnusson, Xing Xing, Zhe Zhang, Dongqing Lin, Wanlin Wang, Magnus P. Jonsson

Machine Learning

Learning to Simulate Individuals from Macro Social Signals

Yining Zhao, Bushi Liu, Haofei Yu et al. (+5 more)

Large language models are increasingly used to simulate how individuals respond to new situations, yet the behavioral reasoning behind these responses is either inherited from pretraining or learned from individual-level annotations, which offer limited behavioral diversity and little supervision of the reasoning itself. We propose to learn behavioral reasoning from prediction markets, whose price trajectories record how populations respond to real-world events at scale. We introduce macro2mind, which trains a language model with GRPO using market signals. A social behavioral decomposition makes behavioral reasoning an explicit step of forecasting: the model infers representative groups of market participants, predicts how each interprets the news and updates its beliefs, reasons about their interactions, and aggregates these responses into a price. A hindsight-regret curriculum with difficulty-aware sampling focuses training on transitions where hindsight-identified groups substantially improve the forecast while prioritizing examples that remain learnable for the current policy. The learned reasoning applies to user simulation without further training. On SWM-Bench, macro2mind achieves state-of-the-art directional accuracy and correlation on Polymarket. Trained on market data, it transfers zero-shot to four user-simulation benchmarks (Humanual, OvertonBench, PRISM, and CAD) and has competitive performance among zero-shot methods. Used as a data generator, macro2mind also raises a downstream simulator's accuracy on unseen users by 15.5 points, outperforming data generated by its backbone by 13.2 points.
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All authors Yining Zhao, Bushi Liu, Haofei Yu, Zhengyang Qi, Shanyong Wang, Chuyue Li, Yuxiang Liu, Jiaxuan You

Sound

ImpactMat: Continuous Material Estimation for Inverse Impact Sound Rendering

Hyebin Cho, Bumsoo Kim and Joon Son Chung

Impact sound rendering synthesizes the sound produced when a 3D object is struck, but practical renderers often rely on fixed material presets such as wood, plastic, or steel. These presets limit the range of impact sounds a renderer can express, while manually adjusting the underlying material parameters remains difficult without expertise in material acoustics. We therefore study inverse impact sound rendering: predicting material parameters from a reference impact sound so that a simulator can recreate a similar material response. To support this task, we introduce ImpactMat, a dataset and benchmark of single and blended material impact sounds paired with ground-truth material parameters. We further propose a feed-forward model that predicts these parameters from one or more recordings, using blended materials to learn smooth transitions between material types. Experiments show that our method outperforms competitive baselines and enables re-rendering from real recordings without manual parameter tuning. The project page is available https://material-from-impact.github.io/material-from-impact/.
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Machine Learning

Skillful Data-Driven Subseasonal Soil Moisture Forecasting: Prospects and Limits for Flash Drought Prediction

Noelia Otero, Atahan Özer, Miguel-Ángel Fernández-Torres et al. (+1 more)

Despite substantial progress in short-to-medium-range weather forecasting, predicting high-impact events such as flash droughts remains a key challenge for both early warning operations and physically-based subseasonal-to-seasonal (S2S) prediction systems. Here we demonstrate that, for S2S soil-moisture forecasting over Europe, forecast skill depends as much on how the prediction problem is formulated as on the forecasting model itself. Using a Vision Transformer-based architecture with dual-pathway temporal and spatial attention, we show that residual learning is essential to outperform persistence. This advantage is realized only when forecasting root-zone soil moisture in physical units rather than standardized anomalies, revealing that the target representation itself constrains predictability. A probabilistic extension via quantile-head fine-tuning further provides well-calibrated predictive distributions. Benchmarked against deep-learning and operational ECMWF S2S baselines over 2021-2022, our model achieves the highest deterministic and probabilistic skill at all lead times and reliably detects anomalously dry root-zone states (below the 20th percentile). Yet flash drought onset, defined by multi-pentad intensification criteria, remains a fundamental challenge shared across all current S2S systems. These findings advance data-driven S2S soil-moisture forecasting while highlighting the remaining challenge of predicting rapid drought development.
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All authors Noelia Otero, Atahan Özer, Miguel-Ángel Fernández-Torres, Jackie Ma

Computer Vision

Crop Yield Prediction for Punjab, Pakistan: A Tree-Ensemble and Leaf-Health Prototype, and What Random Validation Hides

Amina Asif, Qurat Ul ain Asif, Noor Bakhat Asif et al. (+1 more)

Yield forecasts help planners and farmers decide on inputs, storage and imports, but small agricultural tables can make reported accuracy fail on a new season. We built a crop-yield prototype for Punjab, Pakistan that combines Random Forest, XGBoost, support vector regression and a Ridge-stacked ensemble with a MobileNetV2 leaf-health classifier, and deployed it as a web application with SHAP explanations. A random 80/20 split of a merged Kaggle-derived table (414 rows) gives the ensemble an R2 of 0.991. An audit showed that the table contains only 46 independent observations: a join with nine temperature records per year copied every crop-year nine times. Holding out whole years takes XGBoost on the same rows from R2 = 0.994 to -0.20. On deduplicated data, and on a longer FAOSTAT table (1990-2024, 70 observations), a per-crop linear trend (leave-one-year-out RMSE 0.29 Ton/Ha) beats every model not given the year (0.84-1.06). Neither pesticide use nor national temperature change explains the trend residuals. An apparent pesticide gain in the Kaggle table disappears on FAOSTAT, where the pesticide series is mostly imputed and the two releases disagree. An independent district-level wheat panel (36 districts, 13 seasons) shows that most variation in Punjab is spatial and that a district mean with a common trend matches the learned models. The leaf classifier reaches 99.87% accuracy on held-out PlantVillage images and recalls 96.4% of 336 unseen diseased leaves after near-duplicates were removed. However, no leaf image is paired with a yield record, so the health score used in the yield models had to be constructed and adds nothing. We report these negative findings together with the prototype.
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All authors Amina Asif, Qurat Ul ain Asif, Noor Bakhat Asif, Kasa-e-Fatima

Nuclear Experiment

Evolution of high-spin states in neutron-rich $^{195-202}$Au isotopes approaching the $N=126$ shell closure

Y. Cho, Y. H. Kim, A. Navin et al. (+48 more)

Background: The nuclear structure in the region southwest of the doubly magic $^{208}$Pb is important to benchmark theoretical models relevant for neutron-rich heavy element formation and for the origin of the $A\approx195$ peak in the mass abundance distribution. Although neutron-rich Pb, Tl, and Hg ($Z=80-82$) isotopes are relatively well studied, experimental data for neutron-rich Au isotopes ($Z=79$) remain largely limited due to experimental challenges. Purpose: The purpose of this work is to investigate the evolution of the high-spin structure in neutron-rich Au isotopes near the $N=126$ shell closure and to characterize the underlying shell-model configurations, with a particular focus on the roles of the high-$j$ unique-parity $\pi h_{11/2}$ and $\nu i_{13/2}$ orbitals. Methods: Neutron-rich $^{195-202}$Au isotopes were produced using multi-nucleon transfer reactions of $^{198}$Pt($^{136}$Xe, $^{x}$I)$^{y}$Au at a beam energy of 7 MeV/u. %between a 7 MeV/u $^{136}$Xe beam and a $^{198}$Pt target at GANIL. Prompt and delayed $\gamma$-ray spectroscopy of isotopically identified reaction products was performed with a unique experimental setup combining the VAMOS++ large acceptance magnetic spectrometer, the AGATA HPGe $\gamma$-ray tracking array, and the CATLIFE detection system.
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All authors Y. Cho, Y. H. Kim, A. Navin, M. Rejmund, A. Lemasson, D. Ramos, E. Clement, C. Yuan, M. Liu, P. van Isacker, A. N. Andreyev, J. Dudouet, S. Choi, Y. Son, A. Mukherjee, D. Ackermann, G. de Angelis, S. Bae, R. Banik, S. Bhattacharya, S. Bhattacharyya, K. Chae, F. Didierjean, C. Fougeres G. de France, G. Fremont, J. Goupil, G. M. Gu, J. Ha, K. I. Hahn, Y. Hirayama, S. Jeong, C. H. Kim, M. J. Kim, S. H. Kim, W. Korten, P. Marini, H. Miyatake, M. Mukai, G. Mukherjee, T. Niwase, T. Otsuka, J. Park, R. M. Perez-Vidal, F. Recchia, K. Rezynkina, P. Schury, J. C. Thomas, D. T. Kattikat Melcom, I. Tsekhanovich, A. Utepov, Y. X. Watanabe

Machine Learning (Statistics)

sHAIL-Causal: A Sequential Staircase Procedure for Invariant Causal Predictor Discovery

Ernest Fokoué

We introduce sHAIL-Causal, the causal specialization of the Saturated Hierarchical Atomic Incremental Learning (sHAIL) paradigm: a sequential staircase procedure that ascends a nested hierarchy of hypothesis classes H_0 < H_1 < ... < H_K once a saturation signal indicates that mastery of the current stage has plateaued. Where general sHAIL leaves the saturation criterion open, sHAIL-Causal instantiates it with a joint criterion of goodness-of-fit saturation and cross-environment invariance, replacing the complexity control of Structural Risk Minimization. We show, theoretically and by simulation, that complexity-only staircases are seduced by confounded predictors that lower empirical risk without reflecting stable causal structure, whereas an invariance-gated staircase provably halts at the true causal predictor set under a per-variable Richness condition. We show that naive greedy search fails to recover the causal set even under Richness, trace the failure to non-monotonicity of the invariance statistic along single-variable paths, and validate a fix combining bounded-exhaustive block-seeding with a calibrated acceptance threshold. We then extend the guarantee to environments arriving sequentially, yielding a confidence guarantee that stays valid at every arrival, which one-shot exhaustive search cannot offer without repeating its full combinatorial search. We close by formalizing the intervention of a wise teacher who lifts a saturated learner off a plateau of boredom.
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Robotics

Behavioral Cloning Mystery

Seohong Park and Sergey Levine

Behavioral cloning (BC), despite its simplicity, exhibits many counterintuitive phenomena in the real world. For example, the performance of BC often keeps increasing as the model overfits more to the dataset, and fully closed-loop policies often completely fail without action chunking. Unfortunately, properly studying these anecdotal phenomena ("behavioral cloning mysteries") is challenging: in the real world, datasets and experiments are costly and not fully controllable; in simulation with synthetic data, these phenomena are often not easily observed partly due to the discrepancy between scripted policies and human demonstrations. In this work, we propose OCBench, a robotic manipulation benchmark with controllable scripted policies that have similar properties to human demonstrations. We show that, by mimicking key properties of human demonstrations, OCBench reproduces many anecdotal BC-related phenomena in controlled settings. With its GPU-accelerated environments and scripted policies, we demonstrate how OCBench enables scientific studies of previously reported BC-related phenomena by analyzing and refuting various hypotheses. Project page: https://seohong.me/projects/ocbench
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Computational Complexity

Tarski Fixed Points in Quasi-FPT Queries

Xi Chen, Ruiquan Gao, Yuhao Li et al. (+2 more)

We study the query complexity of finding a Tarski fixed point over $[n]^k$. Previous work has left a large gap between $\smash{{\Omega}(\log^2 n)}$ and $\smash{\log^{O(k)}n}$. We show that both of the previous upper and lower bounds were far from tight: for every $k\geq 3$, \[ \Omega\left((\log n)^{\frac{1}{2}\lceil \log k\rceil}\right)\le \operatorname{Tarski}(n,k)\le O\left(5^k(\log n)^{\lceil \log k\rceil}\right). \] Succinctly, up to the fixed-parameter factor of $5^k$, the complexity is settled at $(\log n)^{\Theta(\log k )}$. In particular, we obtain the first super-polynomial query lower bound for this problem.
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All authors Xi Chen, Ruiquan Gao, Yuhao Li, Aviad Rubinstein, Mihalis Yannakakis

High Energy Physics (Experiment)

First observation of the electromagnetic Dalitz decay $\psi(3686) \rightarrow \mu^+ \mu^- \eta^\prime$

BESIII Collaboration: M. Ablikim, M. N. Achasov, P. Adlarson et al. (+777 more)

Utilizing $(2712.4 {\pm} 14.3)\times10^{6}~\psi(3686)$ events collected by the BESIII detector at the symmetric $e^+ e^-$ collider BEPCII, we report the first observation of the electromagnetic Dalitz decay $\psi(3686) \to \mu^+ \mu^-\eta^{\prime} $ with a statistical significance of 6.1$\sigma$. The branching fraction is determined to be $\mathcal{B}(\psi(3686) \to \mu^+ \mu^- \eta^{\prime})=(4.1 \pm 1.0_{\rm stat.} \pm 0.4_{\rm syst.})\times 10^{-7}$. The ratio to the branching fraction of the radiative decay $\psi(3686) \to \gamma \eta^{\prime}$ is estimated to be $(3.3\pm0.9)\times10^{-3}$, which is consistent with the prediction of the vector meson dominance model within $1\sigma$. Furthermore, using the branching fraction of $\psi(3686) \to e^+ e^- \eta^{\prime}$ previously measured by the BESIII experiment, the ratio between the muon and the electron channels is evaluated to be $0.22\pm0.07$, which is consistent with the calculation of the vector meson dominance model within $1\sigma$, and no significant violation of lepton flavor universality is found.
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All authors BESIII Collaboration: M. Ablikim, M. N. Achasov, P. Adlarson, X. C. Ai, C. S. Akondi, R. Aliberti, A. Amoroso, Q. An, M. S. Anderson, Y. Bai, O. Bakina, H. R. Bao, X. L. Bao, M. Barbagiovanni, V. Batozskaya, K. Begzsuren, N. Berger, M. Berlowski, M. B. Bertani, D. Bettoni, F. Bianchi, E. Bianco, A. Bortone, I. Boyko, R. A. Briere, A. Brueggemann, D. Cabiati, H. Cai, M. H. Cai, X. Cai, X. K. Cai, G. F. Cao, N. Cao, S. A. Cetin, X. Y. Chai, J. F. Chang, T. T. Chang, G. R. Che, Y. Z. Che, C. H. Chen, Chao Chen, G. Chen, H. S. Chen, H. Y. Chen, M. L. Chen, S. J. Chen, S. M. Chen, T. Chen, W. Chen, X. R. Chen, X. T. Chen, X. Y. Chen, Y. B. Chen, Y. Q. Chen, Z. K. Chen, J. Cheng, L. N. Cheng, S. K. Choi, X. Chu, G. Cibinetto, F. Cossio, J. Cottee-Meldrum, H. L. Dai, J. P. Dai, X. C. Dai, A. Dbeyssi, R. E. de Boer, D. Dedovich, C. Q. Deng, Z. Y. Deng, A. Denig, I. Denisenko, M. Destefanis, F. De~Mori, E. Di Fiore, X. X. Ding, Y. Ding, Y. X. Ding, J. Dong, L. Y. Dong, M. Y. Dong, X. Dong, Z. J. Dong, M. C. Du, S. X. Du, Shaoxu Du, X. L. Du, Y. Q. Du, Y. Y. Duan, Z. H. Duan, P. Egorov, G. F. Fan, J. J. Fan, K. X. Fan, Y. H. Fan, J. Fang, Jin Fang, S. S. Fang, W. X. Fang, Y. Q. Fang, L. Fava, F. Feldbauer, C. Q. Feng, J. H. Feng, Q. X. Feng, Y. T. Feng, M. Fritsch, C. D. Fu, J. L. Fu, Y. W. Fu, H. Gao, Xu Gao, Y. Gao, Y. N. Gao, Y. Y. Gao, Yunong Gao, Z. Gao, S. Garbolino, I. Garzia, L. Ge, P. T. Ge, Z. W. Ge, C. Geng, A. Gilman, K. Goetzen, J. Gollub, J. B. Gong, J. D. Gong, L. Gong, W. X. Gong, B. Gou, W. Gradl, M. Greco, M. D. Gu, M. H. Gu, C. Y. Guan, A. Q. Guo, H. Guo, J. N. Guo, L. B. Guo, M. J. Guo, R. P. Guo, X. Guo, Y. P. Guo, Z. Guo, A. Guskov, J. Gutierrez, J. Y. Han, T. T. Han, X. Han, F. Hanisch, J. Y. Hao, K. D. Hao, X. Q. Hao, F. A. Harris, C. Z. He, K. K. He, K. L. He, F. H. Heinsius, C. H. Heinz, Y. K. Heng, C. Herold, P. C. Hong, G. Y. Hou, X. T. Hou, Y. R. Hou, Z. L. Hou, H. M. Hu, J. F. Hu, Q. P. Hu, S. L. Hu, T. Hu, Y. Hu, Y. X. Hu, Z. M. Hu, G. S. Huang, K. X. Huang, L. Q. Huang, P. Huang, X. T. Huang, Y. P. Huang, Y. S. Huang, T. Hussain, N. Hüsken, N. in der Wiesche, J. Jackson, Q. Ji, Q. P. Ji, W. Ji, X. B. Ji, X. L. Ji, Y. Y. Ji, L. K. Jia, X. Q. Jia, D. Jiang, S. J. Jiang, X. S. Jiang, Y. Jiang, J. B. Jiao, J. K. Jiao, Z. Jiao, L. C. L. Jin, S. Jin, Y. Jin, M. Q. Jing, X. M. Jing, T. Johansson, S. Kabana, X. L. Kang, X. S. Kang, B. C. Ke, V. Khachatryan, A. Khoukaz, O. B. Kolcu, B. Kopf, L. Kröger, L. Krümmel, Y. Y. Kuang, M. Kuessner, X. Kui, N. Kumar, A. Kupsc, W. Kühn, Q. Lan, W. N. Lan, T. T. Lei, M. Lellmann, T. Lenz, C. Li, C. H. Li, C. K. Li, Cong Li, D. M. Li, F. Li, G. Li, H. B. Li, H. J. Li, H. L. Li, H. N. Li, H. P. Li, Hui Li, J. N. Li, J. S. Li, J. W. Li, K. Li, K. L. Li, L. J. Li, L. K. Li, Lei Li, M. H. Li, M. R. Li, M. T. Li, P. L. Li, P. R. Li, Q. M. Li, Q. X. Li, R. Li, S. Li, S. X. Li, S. Y. Li, Shanshan Li, T. Li, T. Y. Li, W. D. Li, W. G. Li, X. Li, X. H. Li, X. K. Li, X. L. Li, X. Y. Li, X. Z. Li, Y. Li, Y. H. Li, Y. B. Li, Y. C. Li, Y. G. Li, Y. P. Li, Yi Li, Z. H. Li, Z. J. Li, Z. L. Li, Z. X. Li, Z. Y. Li, Zaiyi Li, C. Liang, H. Liang, Y. F. Liang, Y. T. Liang, Z. Z. Liang, G. R. Liao, L. B. Liao, M. H. Liao, Y. P. Liao, J. Libby, A. Limphirat, C. C. Lin, C. X. Lin, D. X. Lin, T. Lin, B. J. Liu, B. X. Liu, C. Liu, C. X. Liu, F. Liu, F. H. Liu, Feng Liu, G. M. Liu, H. Liu, H. B. Liu, H. M. Liu, Huihui Liu, J. B. Liu, K. Liu, K. Y. Liu, Ke Liu, Kun Liu, L. Liu, L. C. Liu, Lu Liu, M. H. Liu, P. L. Liu, Q. Liu, S. B. Liu, T. Liu, W. M. Liu, W. T. Liu, X. Liu, X. K. Liu, X. L. Liu, X. P. Liu, X. T. Liu, X. Y. Liu, Y. Liu, Y. B. Liu, Yi Liu, Z. A. Liu, Z. D. Liu, Z. Q. Liu, Z. X. Liu, Z. Y. Liu, Ziqiang Liu, X. C. Lou, H. J. Lu, J. G. Lu, X. L. Lu, Y. Lu, Y. H. Lu, Y. P. Lu, Z. H. Lu, C. L. Luo, J. R. Luo, J. S. Luo, M. X. Luo, T. Luo, X. L. Luo, Z. Y. Lv, X. R. Lyu, Y. F. Lyu, Y. H. Lyu, C. L. Ma, F. C. Ma, H. L. Ma, Heng Ma, J. L. Ma, L. L. Ma, L. R. Ma, Q. M. Ma, R. Q. Ma, R. Y. Ma, T. Ma, X. T. Ma, X. Y. Ma, F. E. Maas, I. MacKay, M. Maggiora, S. Maity, S. Malde, L. M. Mansur, Y. J. Mao, Z. P. Mao, S. Marcello, A. Marshall, F. M. Melendi, Y. H. Meng, Z. X. Meng, G. Mezzadri, H. Miao, T. J. Min, R. E. Mitchell, X. H. Mo, B. Moses, N. Yu. Muchnoi, J. Muskalla, Y. Nefedov, F. Nerling, H. Neuwirth, Z. Ning, S. Nisar, Q. L. Niu, W. D. Niu, Y. Niu, C. Normand, S. L. Olsen, Q. Ouyang, I. V. Ovtin, S. Pacetti, Y. Pan, C. Y. Pang, A. Pathak, Y. P. Pei, M. Pelizaeus, G. L. Peng, H. P. Peng, X. J. Peng, Y. Y. Peng, K. Peters, K. Petridis, J. L. Ping, R. G. Ping, S. Plura, V. Prasad, L. Pöpping, F. Z. Qi, H. R. Qi, L. Y. Qian, S. Qian, W. B. Qian, C. F. Qiao, J. H. Qiao, J. J. Qin, J. L. Qin, L. Q. Qin, L. Y. Qin, P. B. Qin, X. P. Qin, X. S. Qin, Z. H. Qin, J. F. Qiu, Z. H. Qu, J. Rademacker, K. Ravindran, C. F. Redmer, A. Rivetti, M. Rolo, G. Rong, S. S. Rong, F. Rosini, Ch. Rosner, M. Q. Ruan, W. R. Ruangyoo, N. Salone, A. Sarantsev, Y. Schelhaas, M. Schernau, K. Schoenning, M. Scodeggio, W. Shan, X. Y. Shan, Z. J. Shang, J. F. Shangguan, L. G. Shao, M. Shao, C. P. Shen, H. F. Shen, W. H. Shen, X. Y. Shen, B. A. Shi, Ch. Y. Shi, H. Shi, J. L. Shi, J. Y. Shi, M. H. Shi, S. Shi, S. Y. Shi, X. Shi, H. L. Song, J. J. Song, M. H. Song, T. Z. Song, W. M. Song, Zirong Song, S. Sosio, S. Spataro, S. Stansilaus, F. Stieler, M. Stolte, S. S Su, G. B. Sun, G. X. Sun, H. Sun, H. K. Sun, J. F. Sun, K. Sun, L. Sun, R. Sun, S. S. Sun, T. Sun, W. Y. Sun, Y. C. Sun, Y. H. Sun, Y. J. Sun, Y. Z. Sun, Z. Q. Sun, Z. T. Sun, H. Tabaharizato, N. T. Tagsinsit, C. J. Tang, G. Y. Tang, J. Tang, J. J. Tang, L. F. Tang, Y. A. Tang, Z. H. Tang, L. Y. Tao, M. Tat, J. X. Teng, J. Y. Tian, W. H. Tian, Y. Tian, Z. F. Tian, K. Yu. Todyshev, I. Uman, E. van der Smagt, B. Wang, Bin Wang, Bo Wang, C. Wang, Chao Wang, Cong Wang, D. Y. Wang, F. K. Wang, H. J. Wang, H. R. Wang, J. Wang, J. H. Wang, J. J. Wang, J. P. Wang, K. Wang, L. L. Wang, L. W. Wang, M. Wang, Mi Wang, N. Y. Wang, S. Wang, Shun Wang, T. Wang, W. Wang, W. P. Wang, X. F. Wang, X. L. Wang, X. N. Wang, Xin Wang, Y. Wang, Y. D. Wang, Y. F. Wang, Y. H. Wang, Y. J. Wang, Y. L. Wang, Y. N. Wang, Yanning Wang, Yaqian Wang, Yi Wang, Yuan Wang, Z. Wang, Z. L. Wang, Z. Q. Wang, Z. Y. Wang, Zhi Wang, Ziyi Wang, D. Wei, D. H. Wei, D. J. Wei, H. R. Wei, F. Weidner, H. R. Wen, S. P. Wen, U. Wiedner, G. Wilkinson, M. Wolke, J. F. Wu, L. H. Wu, L. J. Wu, Lianjie Wu, S. G. Wu, S. M. Wu, X. W. Wu, Z. Wu, H. L. Xia, L. Xia, B. H. Xiang, D. Xiao, G. Y. Xiao, H. Xiao, Y. L. Xiao, Z. J. Xiao, C. Xie, K. J. Xie, Y. Xie, Y. G. Xie, Y. H. Xie, Z. P. Xie, T. Y. Xing, D. B. Xiong, G. F. Xu, H. Y. Xu, Q. J. Xu, Q. N. Xu, T. D. Xu, X. P. Xu, Y. Xu, Y. C. Xu, Z. S. Xu, F. Yan, L. Yan, W. B. Yan, W. C. Yan, W. H. Yan, W. P. Yan, X. Q. Yan, Y. Y. Yan, H. J. Yang, H. L. Yang, H. X. Yang, J. H. Yang, L. Y. Yang, N. Yang, R. J. Yang, X. Y. Yang, Y. Yang, Y. G. Yang, Y. H. Yang, Y. M. Yang, Y. Q. Yang, Y. Z. Yang, Youhua Yang, Z. B. Yang, W. J. Yao, Z. P. Yao, M. Ye, M. H. Ye, Z. J. Ye, K. Yi, Junhao Yin, Z. Y. You, B. X. Yu, C. X. Yu, G. Yu, J. S. Yu, L. W. Yu, T. Yu, X. D. Yu, Y. C. Yu, Yongchao Yu, C. Z. Yuan, H. Yuan, J. Yuan, Jie Yuan, L. Yuan, M. K. Yuan, S. H. Yuan, Y. Yuan, C. X. Yue, Ying Yue, A. A. Zafar, F. R. Zeng, S. H. Zeng, X. Zeng, Y. J. Zeng, Yujie Zeng, Y. C. Zhai, Y. H. Zhan, B. L. Zhang, B. R. Zhang, B. X. Zhang, D. H. Zhang, G. Y. Zhang, Gengyuan Zhang, H. Zhang, H. C. Zhang, H. H. Zhang, H. L. Zhang, H. Q. Zhang, H. R. Zhang, H. Y. Zhang, Han Zhang, J. Zhang, J. J. Zhang, J. L. Zhang, J. Q. Zhang, J. S. Zhang, J. W. Zhang, J. X. Zhang, J. Y. Zhang, J. Z. Zhang, Jianyu Zhang, Jin Zhang, Jiyuan Zhang, L. M. Zhang, Lei Zhang, N. Zhang, P. Zhang, Q. Zhang, Q. Y. Zhang, Q. Z. Zhang, R. Y. Zhang, S. H. Zhang, S. N. Zhang, Shulei Zhang, X. M. Zhang, X. Y. Zhang, Y. T. Zhang, Y. H. Zhang, Y. P. Zhang, Y. P. Zhang, Yao Zhang, Yu Zhang, Yu Zhang, Z. Zhang, Z. D. Zhang, Z. H. Zhang, Z. L. Zhang, Z. R. Zhang, Z. X. Zhang, Z. Y. Zhang, Zh. Zh. Zhang, Zhaoke Zhang, Zhilong Zhang, Ziyang Zhang, Ziyu Zhang, G. Zhao, J.-P. Zhao, J. Y. Zhao, J. Z. Zhao, L. Zhao, Lei Zhao, M. G. Zhao, R. P. Zhao, Y. B. Zhao, Y. L. Zhao, Y. P. Zhao, Y. X. Zhao, Z. G. Zhao, A. Zhemchugov, B. Zheng, B. M. Zheng, J. P. Zheng, W. J. Zheng, W. Q. Zheng, X. R. Zheng, Y. H. Zheng, B. Zhong, C. Zhong, X. Zhong, H. Zhou, J. Q. Zhou, S. Zhou, X. Zhou, X. K. Zhou, X. R. Zhou, X. X. Zhou, X. Y. Zhou, Y. X. Zhou, Y. Z. Zhou, A. N. Zhu, J. Zhu, K. Zhu, K. J. Zhu, K. S. Zhu, L. X. Zhu, Lin Zhu, S. H. Zhu, W. D. Zhu, W. J. Zhu, W. Z. Zhu, Y. C. Zhu, Z. A. Zhu, X. Y. Zhuang, M. Zhuge, J. H. Zou, J. Zu

High Energy Physics (Phenomenology)

$\lambda$-$\rho$ mode mixing and radial Regge trajectories for triply heavy baryons $\Omega_{ccc}$ and $\Omega_{bbb}$ in the diquark picture

Jiao-Kai Chen, Xin-Ru Liu and Qi Liu

The mixed-mode trajectories arising from permutation-enforced $\lambda$-$\rho$ mode mixing are first investigated in the diquark picture. By accounting for the internal structure and substructure of baryons, we propose an approach to construct the mixed-mode Regge trajectories for baryons composed of three identical quarks. For triply heavy baryons $\Omega_{ccc}$ and $\Omega_{bbb}$, naive diquark-quark basis states without full permutation symmetrization violate the exchange symmetry required by the Pauli exclusion principle for three identical fermions; consequently, physical eigenstates arise as permutation-enforced $\lambda$-$\rho$ mode admixtures. Under the minimal-mixing assumption, we explicitly perform permutation symmetrization for the $J^{P}=(3/2)^{+}$ radially-excited S-wave states and construct mixed-mode Regge trajectory formulas for these symmetrized states. Unlike triply heavy baryons $\Omega_{ccb}/\Omega_{cbb}$ in the fixed diquark $(cc)b/(bb)c$ picture where well-defined pure $\rho$- and $\lambda$-trajectories exist, genuine pure-mode trajectories no longer survive for $\Omega_{ccc}$ and $\Omega_{bbb}$ due to permutation-driven cross-mode mixing. The resulting effective radial trajectories exhibit a novel sprouting-branch-like behavior. Both the original unmixed trajectories and the mixed-mode effective trajectories exhibit behavior of $M\sim x^{2/3}$ (where $x=n_r,\,N_r,\,n_r+N_r$). Radially excited masses are crudely estimated via the mixed-mode Regge trajectories, which show reasonable agreement with existing theoretical predictions.
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Robotics

BRACE: Adapting Whole-Body References for Force and Terrain Aware Humanoid Motion Tracking

Sudarshan Harithas, Chen Yu, Juan Borbon et al. (+5 more)

Whole-body tracking has become the interface through which operators drive humanoid robots, yet the references it consumes are recorded on level ground and carrying nothing, so the tracker is aware of neither the forces the robot must exchange with objects nor the terrain it must stand on. Existing controllers address one side of this gap: force-capable policies command an end-effector force but prescribe no whole-body pose, while terrain-adaptive trackers treat loads as disturbances to reject rather than wrenches to command. We present BRACE, a whole-body tracker that exerts and compensates commanded hand forces from diverse poses while following a flat-ground reference on sloped terrain. Rather than leaving the tracker to absorb the load and the slope, BRACE folds both into the reference it follows: terrain conformance lifts footholds and root onto the local surface, and a wrench transformation resolves the hand displacement that produces a force jointly with the center-of-mass and center- of-pressure shifts it induces, bounded by teacher-specific arm-effort limits. Separate exertion and compensation teachers are distilled by DAgger into one flow-matching student that runs on proprioception alone, without a height map or measured wrench, so a teleoperator (even in remote locations) can supply a flat- ground trajectory and the robot resolves slope and load onboard. We extensive experiments on the Unitree-G1 to demonstrate the ability of BRACE to exert, compensate force and handle diverse terrain in simulation and real robot experiments.
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All authors Sudarshan Harithas, Chen Yu, Juan Borbon, Shubhankar Mondal, Winston Zha, Srinath Sridhar, Dingqi Zhang, Jiuguang Wang

Combinatorics

Extremal subspace covers in finite vector spaces

Mohsen Aliabadi

Let $V$ be an $n$-dimensional vector space over the finite field $\mathbb F_q$, where $n\ge 2$. It is classical that $q+1$ proper subspaces are necessary and sufficient to cover $V$. We study extremal refinements of this covering theorem. For $1\le m\le q+1$, we determine the maximum possible size of the union of $m$ proper subspaces of $V$, proving that $$ \max_{W_1,\ldots,W_m<V} \left|W_1\cup\cdots\cup W_m\right| = q^{n-2}\bigl(1+m(q-1)\bigr). $$ We also classify all equality cases: for $m\ge 2$, equality holds precisely when the subspaces are distinct hyperplanes containing a common codimension-two subspace. As consequences, we obtain a structural classification of minimum covers of $V$ by proper subspaces and a sharp defect estimate for unions of $q$ proper subspaces. We then introduce basis-blocking families, namely families of proper subspaces whose union meets every basis of $V$. We prove that the minimum size of such a family is $q$ and classify all extremal families of this size. Finally, we establish the affine analogue and give an elementary recognition criterion for extremal families. Together, these results provide a unified extremal-combinatorial description of coverings of finite vector spaces by proper subspaces.
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Soft Condensed Matter

Spontaneous Motion Generates Reversible Nonreciprocity in an Achiral Active Elastic Ring

Wen-de Tian, Kang Chen and Tianhui Zhang

Nonreciprocal mechanical response is usually associated with built-in directional couplings, structural chirality, or external driving. Whether an achiral active body can instead generate and reverse such directionality through its own motion remains less clear. Here we show that spontaneous rotation makes an active elastic ring with reciprocal passive interactions mechanically nonreciprocal. Off-center elastic forces reorient propulsion, coupling deformation back to motion. Reversing the rotation reverses the antisymmetric response, while a compensating control preserves finite directionality but strongly suppresses the near-Hopf resonance. Approaching Hopf, the selected response grows inversely with distance to onset and becomes a self-sustained traveling deformation above threshold. Thus nonreciprocity is selected by the dynamical state, while active feedback controls its critical amplification.
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Combinatorics

On the structure of uniform Turán densities

Heng Li and Xizhi Liu

Motivated by parallel developments in ordinary and $\ell$-degree Turán problems, we study the set of $(r-2)$-uniform Turán densities of possibly infinite families of $r$-uniform hypergraphs. We prove that, for all sufficiently large $r$, these densities exhibit a phase transition at $4r^{-r}$, from a countable set of algebraic values below this threshold to the full interval $[4r^{-r},1]$. For every $r\ge3$, the densities below the threshold are exactly the finite-palette Lagrangians in that range, and each is realized by a finite forbidden family. Adapting a method of Pikhurko, we show that, for every $r\ge3$, the set of uniform densities omits continuum many of its limit points below the threshold and is therefore not closed.
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Machine Learning (Statistics)

Data Fusion for Errors-in-Variables

Huali Zhao, Molei Liu and Tianying Wang

We study errors-in-variables problems in which a target study contains only a single error-prone surrogate of an unobserved exposure, while an external source study provides repeated surrogate measurements from a different population. The measurement error distribution is allowed to depend on the observed error-free variables, and the error-free variable distribution itself may differ between studies. We introduce a conditional transportability assumption that enables the use of external repeated measurements under source-target heterogeneity. Together with additional replicate-error conditions, it identifies the target conditional measurement-error distribution. Building on this identification result, we develop a data-fusion estimator for a broad class of target functionals. The estimator combines conditional deconvolution, flexible nuisance estimation, and orthogonal correction that reduces first-order sensitivity to nuisance estimation. For the proposed estimator, we develop a unified spectral theory covering both diffuse-spectrum and finite atomic-spectrum target functionals, derive a general asymptotic expansion, and establish consistency and target-specific convergence-rate bounds. The resulting convergence-rate bounds depend jointly on the spectral properties of the measurement error, the latent exposure, and the target functional. For finite atomic-spectrum targets, we further establish joint Gaussian and bootstrap limits, yielding inference for smooth moment transformations under an additional centering condition. In the reported simulations, Fuse-EIV has small bias for the primary exposure-related coefficient. Applications to the National Health and Nutrition Examination Survey illustrate how accounting for population heterogeneity and error heteroscedasticity can change empirical conclusions.
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Audio and Speech Processing

SEAL: Mixture-Closed Additive Reconstruction and Refinement-Aware Expert Routing for Efficient Speech Separation

Shao-Chun Hu, Zi-Xiang Lin, Jeih-Weih Hung et al. (+1 more)

Compact time-frequency separators that mask the mixture and refine through a shared cell face two limits. First, a bounded multiplicative mask only scales a mixture bin, so where overlapping components cancel, the estimate stays small. Second, a shared cell applies the same weights to every time-frequency token at every step, so enlarging it adds compute everywhere. We present SEAL (Sparse Expert routing with Additive Latent reconstruction) to address both. For reconstruction, a zero-sum additive residual bounded by the local mixture amplitude lets estimates be nonzero where components cancel yet still sum to the mixture. For routing, a query built from acoustic and inter-step evidence sends each token to one of six residual experts, and a norm cap keeps the step cue from overriding clear acoustic evidence. On EchoSet, SEAL (small) surpasses TIGER (small) by 0.31 dB SI-SDRi with 28% fewer parameters and 2.9 times fewer MACs, and SEAL (large) is within 0.07 dB SI-SDRi of TIGER (large) at 3.1 times fewer MACs.
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All authors Shao-Chun Hu, Zi-Xiang Lin, Jeih-Weih Hung, Hung-Shin Lee

Audio and Speech Processing

GIVE-KWS: Gated Injection of Visual Evidence for Noise-Robust Query-by-Example Keyword Spotting

Ming-Hsiang Hu, Kuan-Tang Huang, Hung-Shin Lee et al. (+1 more)

Visual speech promises noise-robust keyword spotting, yet a visual stream is not necessarily used. On a tri-modal query-by-example keyword spotting (QbyE-KWS) benchmark, we find that a system with a task-trained visual encoder comes within 2 percentage points of a text-and-audio system in equal error rate (EER) at -10 dB, and link this gap to the encoder's lack of phonemic information. We present GIVE-KWS, whose fusion stage, GIVE (Gated Injection of Visual Evidence), conditions query audio on lip motion through gated cross-attention. We show that visual robustness depends on two interacting conditions: a phoneme-bearing visual representation, and fusion that injects visual evidence rather than rescaling audio features. Under a phoneme-bearing encoder, injection yields an effective SNR gain of 4.0-9.3 dB over masking at -10 dB, whereas under a phoneme-poor one it nearly vanishes. Relative to the benchmark system, GIVE-KWS reduces unseen-keyword EER by 72.9% at -10 dB and 62.8% on average.
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All authors Ming-Hsiang Hu, Kuan-Tang Huang, Hung-Shin Lee, Berlin Chen

Statistical Methodology

Multi-Task Active Learning with Efficient Resource Allocation

Hanwen Ye, Jiuchen Zhang and Annie Qu

Many scientific studies allow costly auxiliary information to be collected during data labeling but not at deployment. Examples include diagnostic tests, laboratory assays, and expert evaluations. We study prediction under this deployment asymmetry, where auxiliary variables are selectively acquired during labeling under a budget constraint but systematically unavailable at prediction time, creating a missing-by-design problem that couples data acquisition, surrogate construction, and prediction. In this work, we introduce Active Learning with Cost-Adaptive Task Resource Allocations (ALCATRAs), a unified framework for selectively acquiring auxiliary information under resource constraints and leveraging that information to improve downstream prediction. ALCATRAs consists of two main components: a task-selection policy which strategically selects a sequence of cost-effective tasks for unlabeled data to perform, and a surrogate learning procedure which transfers knowledge from completed tasks to enhance model predictions. In theory, we show the effectiveness of surrogate models and sample-efficient task policies in improving the model's prediction error bound. Simulation studies and an application to the UCI heart disease cohort demonstrate improved sample efficiency of the proposed ALCATRAs framework relative to baselines under the studied settings.
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Formal Languages and Automata

The compress-with-another threshold of Szykuła's Figure 3 family

Qichao Wang

We give a self-contained pair-automaton proof of the exact compress-with-another threshold of the corrected Figure 3 family from a recent survey of open problems in synchronizing automata. For every $p \geq 3$, the automaton has $n = 3p$ states and $\mu(q_0) = 4p = 4n/3$. The word $(ba)^p(ab)^p$ attains this value. Two entrance potentials and an excluded region yield the lower bound, with the endpoint exception at $p = 3$ treated explicitly. A separate reset construction proves $\operatorname{rt}(A_p) \leq 3p^2 + 4p - 1$ for every parameter. Reproducible computations verify the transition and entrance identities; the all-parameter results follow from the explicit proofs.
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Machine Learning

TRIAGE: Direction-Aware Mismatch Stabilization of Native NVFP4 Reinforcement Learning

Zhen Li, Shuai Zhang, Yanggan Gu et al. (+7 more)

Low-precision execution can substantially accelerate reinforcement learning (RL) for large language models, but discrepancies between learner and sampler execution can destabilize policy optimization. In this paper, we characterize the interaction between mismatch and the policy-gradient direction, distinguishing locally amplifying from contracting update contributions that mismatch magnitude alone cannot identify. In native NVFP4 runs, we observe an early imbalance between the two amplifying regions, favoring negative-advantage, negative-gap updates. Their tail tokens become concentrated in a small fraction of response segments before mismatch spreads globally. Motivated by these findings, we introduce TRIAGE, a direction-aware stabilization method that uses segment-level diagnosis to selectively rebalance policy-gradient updates and applies bounded repair to residual severe mismatch. TRIAGE modifies the optimization objective while retaining native NVFP4 weight-and activation 4-bit (W4A4) forward execution on both the sampler and learner. Experiments on Qwen3-4B and Qwen3-30B-A3B show stable optimization throughout the evaluated training horizon and achieve full precision level performance across five mathematical reasoning benchmarks, while native NVFP4 with TRIAGE provides up to 2.3x higher rollout throughput than BF16.
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All authors Zhen Li, Shuai Zhang, Yanggan Gu, Yiming Zhang, Yang Yu, Mingfa Feng, Congkai Xie, Shuang Yu, Junjie Lai, Hongxia Yang

Instrumentation and Methods for Astrophysics

XuYi Interferometer Observation Simulator

M. Hadjara, T. Xu, Y. Sun et al. (+21 more)

The XuYi Interferometer is the first Chinese long-baseline optical interferometer currently under development by the National Institute of Astronomical Optics and Technology (NIAOT) of the Chinese Academy of Sciences. In preparation for future scientific operations, we developed the XuYi Interferometer Observation Simulator (XIOS), a lightweight MATLAB-based software package designed to assist observers in planning interferometric observations. The simulator combines target observability calculations, interferometric baseline geometry, delay-line constraints, (u,v)-coverage prediction, and visibility estimation into a single graphical user interface. It incorporates astronomical routines from the MAAT toolbox and adopts visualization concepts inspired by ASPRO2. The software allows users to evaluate observing opportunities, predict interferometric performances, and generate realistic observing configurations before telescope operations. We present the architecture of the simulator, its principal modules, and representative simulation results obtained for the current three-telescope XuYi interferometric configuration operating in the near-infrared H band.
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All authors M. Hadjara, T. Xu, Y. Sun, W. Wei, C. Liu, H. Zhang, J. Huang, C. Zeng, J. Yuan, J. Perdigon, A. Boskri, S. Lagarde, R. Petrov, R. Ligi, B. Lopez, F. Millour, J. Dejonghe, F. Baron, J. Wang, X. Wu, H. Zhao, Y. Hou, Z. Hua, X. Cui

Instrumentation and Methods for Astrophysics

BRICS Southern Observatory (BRICS-SO) Project: Invitation to African countries to invest in Chile

Massinissa Hadjara, Amokrane Berdja, Swagat R. Das et al. (+6 more)

Recently, the BRICS countries created a promising astronomy consortium (https://www.bricsastronomy.org/), which was joined last year by some emerging countries, including two African countries, namely: Egypt and Ethiopia. Certainly, other African countries will join BRICS+ in the years to come. Thus, by pooling their efforts, emerging countries (BRICS+) are sure to quickly catch up with their technological delay in the field of astronomy, particularly through ambitious and large-scale projects. Chile, due to the exceptional climatic conditions of its North, is recognized as the world capital of astronomy where all the largest consortia have settled there and applied all their know-how. The official launch of BRICS (and then its expansion to other countries; BRICS+) is welcomed as an excellent opportunity to think big in the development of astronomy. China has shown great determination in being the first country in the South to establish astronomical facilities in Chile, thus opening a possible opportunity for BRICS+ astronomers. Ambitious astronomical instrumentation is essential for the future of Astronomy and Astrophysics. Access to existing facilities is becoming more and more difficult because of saturation. Existing programs like the VLT-I/ELT and their instruments necessitated decades of collaboration between international institutions. It is therefore necessary for the BRICS+ astronomical community to collaborate together on ambitious projects without wasting time in order to offer a new pole with new opportunities for the researchers. This pole (BRICS-SO), in addition to attracting highly qualified people, will also help train new generations in all our countries, and encourage exchanges and collaborations.
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All authors Massinissa Hadjara, Amokrane Berdja, Swagat R. Das, Elisson S. G. de Almeida, Slobodan Jankov, Vladmir Lukin, Abdelkarim Boskri, Zouhair Benkhaldoun, Jinghui Zheng

Optimization and Control

Optimization by Reparametrization: Time-Warped Mirror Flows with Singular Geometry

Cristian Vega, Cesare Molinari, Lorenzo Rosasco et al. (+1 more)

Solving data-driven problems requires defining complex models and fitting them to data, neural networks being a motivating example. The fitting procedure can be seen as an optimization problem, which is often non-convex, and hence optimization guarantees are hard to derive. An opportunity is provided by viewing the model of interest as a redundant reparameterization–an overparameterization–of some simpler model for which optimization results are easier to achieve. In this paper, after formalizing the above idea, we revisit some recent results and derive new ones. In particular, we consider the gradient flow of some classes of linear overparameterizations and show that they correspond to suitable mirror flows on the original parameters. Our main contribution relates to the study of the latter, for which we establish well-posedness and convergence. Several specific instances are provided, including fully connected linear networks and networks with weight normalization. The results yield insight into the role of overparameterization in optimization and corresponding implicit regularization properties.
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All authors Cristian Vega, Cesare Molinari, Lorenzo Rosasco, Silvia Villa

Human-Computer Interaction

Tellimation: Making Narrative Gaps Visible in Children's Storytelling with Just-in-Time Animation

Vincent Cavez, Marielle Zheng, Momin Siddiqui et al. (+4 more)

Illustrated scenes are used to prompt children's stories, yet children with language difficulties often omit the details, actions, and relationships that make a story coherent. We introduce Tellimation, which animates scene elements a child has omitted or misdescribed, drawing attention to a narrative gap without saying what to tell. Its design space covers eight kinds of gaps (who is in the scene, how many, what they are like, what they are doing, where, when, how they relate, and what lies beyond the picture, such as speech and thoughts), instantiated through 20 parameterized animations grounded in classical animation principles. A real-time pipeline detects discrepancies between utterance and scene, then selects and parameterizes an animation from the scene's narrative potential and the child's history. Adults interpreted most animations without instruction (N=120); children (N=12) resolved 46% of the gaps the system identified with animations, against 13% without.
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All authors Vincent Cavez, Marielle Zheng, Momin Siddiqui, Abbie L Olszewski, Srirangaraj Setlur, Maneesh Agrawala, Hari Subramonyam

Analysis of PDEs

On the Dirichlet problem for the degenerate $2$-Hessian equation

Yasheng Lyu

In this paper, we establish global $C^{1,1}$ solvability of the Dirichlet problem for the degenerate $2$-Hessian equation on bounded strictly mean convex $C^{3,1}$ domains $\Omega$, with general $C^{3,1}$ boundary values and nonnegative right-hand sides $f\in C^{1,1}(\overline{\Omega})$, resolving the $k=2$ case of a longstanding open problem. We uncover a global semiconvexity structure for general $2$-admissible solutions by proving a lower bound for $\sigma_{3}[D^{2}u]$ independent of $\inf_{\Omega}f$. For $3\leq k\leq n-1$, we also establish global semiconvexity for $k$-admissible solutions on the unit ball with $C^{3}$ boundary values and $f^{1/(k-1)}\in C^{1,1}(\overline{B}_{1})$, with a bound independent of $\inf_{B_{1}}f$. A counterexample shows that the $C^{3,1}$ boundary value assumption for global $C^{1,1}$ solvability is sharp in the Hölder scale.
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Computer Vision

One Figure, Every Canvas: Editable Flowchart Relayout via Agentic Pipeline

Shih-Chen Tseng, Chih-Hsuan Chen, Ryan Yang et al. (+3 more)

Pipeline figures in ML papers must be repurposed across many canvases, including paper columns, 16:9 slides, portrait posters, 1:1 social teasers, 9:16 phone previews. Each format imposes a different aspect ratio on the same computational graph, where any silently broken connection misrepresents the method. We formulate aspect-ratio-adaptive flowchart relayout as a distinct task: given a raster flowchart and a target ratio, produce a structurally faithful, hallucination-free, editable layout. Existing methods fail characteristically: image-to-image models stretch blocks and reject extreme ratios, text-to-image agentic systems hallucinate content, and parse-then-render systems mis-route edges. We propose an agentic pipeline factored into Parse, Style, and Layout stages, each pairing a main agent with a critic that combines deterministic constraint checks with VLM visual feedback so connectivity is explicitly checked and prevented from being silently broken. Outputs are draw.io-editable mxGraph XML. On a curated benchmark of 100 flowcharts at five aspect ratios, evaluated by Gemini 3.1 Pro and validated against human judgments, our method reaches 68.6% Content Fidelity versus 11.2-41.4% for prior work. Project page: https://onefigureeverycanvas.vercel.app/
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All authors Shih-Chen Tseng, Chih-Hsuan Chen, Ryan Yang, Hsi-An Chen, Chun-Wei Tuan Mu, Yu-Lun Liu

Machine Learning

Base Models Can Reason By Taking a Cue From Training Data

Sophie L. Wang, Amil Dravid, Rulin Shao et al. (+3 more)

In this paper, we study how training data creates associations between the tokens at the start of a base model's response and the reasoning behavior that follows. First, we demonstrate that fixing particular starting token cues makes a base model's performance competitive with that of its reinforcement learning (RL)-trained counterparts on math and coding. For instance, the cue ".\n\nOkay" raises Olmo-3-7B's MATH-500 pass@1 accuracy from 42% to 78%, while "Alright," raises Qwen3-14B's from 72% to 87%. Second, RL makes these cues more likely, while fixing them recovers much of its performance gain over the base model. Third, we trace the reasoning effects of token cues to the training data. We perform causal data interventions to turn an arbitrary word, such as "chicken", into an effective reasoning cue, or remove an existing cue's effect. A similar edit makes the prompt instruction "Think duck duck goose" as effective as "Think step by step" at eliciting reasoning. We also find that the hidden state representations induced by different cues correlate with different document types from the training set. Finally, we extend our study of token cues with a case study in language model safety, finding that different cues elicit distinct refusal and compliance behaviors that correspond to different types of training data.
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All authors Sophie L. Wang, Amil Dravid, Rulin Shao, Kevin Farhat, Sewon Min, Alexei A. Efros

Robotics

InterMimicGen: Scaling Humanoid Loco-Manipulation through Self-Evolving Motion Imitation

Yucheng Zhang, Sirui Xu, Jinhong Li et al. (+8 more)

Captured human-object interactions provide rich supervision for humanoid loco-manipulation, but they are sparse, heterogeneous, and not directly executable by robots. We introduce InterMimicGen, a self-evolving motion-imitation framework in which robot motion data and a tracking policy improve each other. First, we consolidate motion-captured human-object interaction datasets and retarget them into humanoid robot references while preserving whole-body coordination and dexterous hand-object relationships. This produces a large and diverse humanoid robot reference collection for dexterous whole-body loco-manipulation. Second, we train a physics-based generalist tracker that executes these references in simulation on a humanoid with dexterous hands, covering a scale and diversity beyond prior humanoid tracking systems for loco-manipulation. Third, we close a data flywheel: each round makes small, task-preserving changes to where an interaction takes place and how the body performs it, fine-tunes the tracker on them, and keeps only the variants whose simulated execution completes the task, which seed the next round. With more iterations, these small edits compound into broader coverage around the sparse original demonstrations while preserving task semantics and motion quality. Experiments show contact-preserving retargeting across robot configurations, broad tracking with a single generalist policy, executable motions that keep growing over augmentation rounds, and transfer to real robots. InterMimicGen provides a unified path from heterogeneous human demonstrations to a continually expanding motion resource for humanoid robot learning.
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All authors Yucheng Zhang, Sirui Xu, Jinhong Li, Liuyu Bian, Anatulya Nandi, Derek Zhang, Xiangchen Liu, Xueting Li, Umar Iqbal, Yu-Xiong Wang, Liang-Yan Gui