Robotics
Energy-Regularized Imitation Learning for Force- and Work-Aware Robotic Manipulation
Toshiki Otani, Hiromu Taketsugu and Norimichi Ukita
This paper studies energy-aware manipulation as a physically grounded learning problem. We define a joint-space mechanical-work proxy from joint torque and angular displacement, and train a differentiable energy predictor that estimates this work from
robot states and actions. The predictor converts a non-differentiable simulator-side physical quantity into a differentiable regularizer for fine-tuning a pretrained manipulation policy. We instantiate the framework with RVT-2 on RLBench and evaluate 12 manipulation tasks involving object contact, articulated motion, placement, pushing, and sweeping. The proposed fine-tuning reduces the average mechanical work from 208.8J to 204.4J (i.e., 2.1% reduction), while the mean task success rate also increases slightly from 86.2% to 86.9%. These results show that work-aware policy optimization can suppress physically inefficient motion without requiring an explicit differentiable dynamics model.
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Artificial Intelligence
Time-Aligned Evolving Concept Graphs for Scientific Relation Forecasting
Fred Sun, Jingze Wang, Minkun Xu et al. (+1 more)
Forecasting scientific relations can guide discovery by identifying promising connections before they emerge. Existing approaches often model concept semantics and graph structure separately or summarize semantics over coarse historical snapshots, leaving semantic representations potentially misaligned with rapidly evolving graph evidence. We propose a time-aligned evolving concept graph framework that jointly models semantic and structural
evolution. Its core idea is to treat dated papers as shared update events, reconstructing semantic and structural states from the same publication history through each prediction time. Pair-level fusion combines these states to forecast first co-occurrence, relation formation, and conditional relation type. Holding architecture and training fixed, refreshing context alongside graph updates improves mean relation AUPRC by 16.6% over frozen context. On a graph built from 187,848 papers with 270,687 concepts and 7.45 million co-occurrence links, the complete framework improves mean relation AUROC from 0.9290 for the strongest evaluated baseline to 0.9722, with mean population-weighted AUPRC 0.005778.
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All authors Fred Sun, Jingze Wang, Minkun Xu, Shangqi Guo
Algebraic Topology
Condensed Brown Comenetz Duality
Roey Hel-Or and Amos Kaminski
We construct an involutive duality on condensed spectra with locally compact homotopy groups, extending classical Pontryagin duality. We prove that dualization induces Pontryagin duality on homotopy groups, with degrees reversed, and that the canonical biduality map is an equivalence. In particular, every ordinary spectrum, viewed as a discrete condensed spectrum, satisfies biduality, without any finiteness assumptions on its homotopy groups.
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General Economics
Why a Non-Discriminatory Royalty Surcharge Is Not Chip-Neutral: The Error in FTC v. Qualcomm
Sang-Seung Yi
Qualcomm's No License, No Chips policy let it levy a royalty surcharge on every handset, whether or not it used a Qualcomm modem chip. In FTC v. Qualcomm, the Ninth
Circuit reversed the district court after accepting Qualcomm's argument that, because the surcharge did not vary with the chip, it was "chip neutral" and left handset makers' choices undistorted. I develop an
equilibrium model of the modem chip
market and show the defense to be wrong: the surcharge's facial neutrality does not imply economic neutrality. For per handset surcharges, a surcharge and an equal government tax affect the rival's pricing identically, but not Qualcomm's: a tax is remitted to the Treasury, whereas Qualcomm collects the surcharge – including on handsets using a rival's chip. Raising its own price therefore yields Qualcomm a smaller gain under the surcharge (the surcharge it collects on the demand diverted to the rival) than under the tax (the tax it avoids on its own lost sales), because the diversion ratio is less than one. Under the very conditions that would make a tax chip neutral, the surcharge raises the rival's all in price by strictly more than Qualcomm's – and, under symmetric demand, lowers its output by more as well – tilting handset makers toward Qualcomm. For ad valorem surcharges, the defense fails for a different reason: even a non discriminatory tax is generically chip neutral only if the FRAND royalty rate is zero, so the argument's premise itself does not hold. I also analyze discriminatory surcharges.
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Astrophysics of Galaxies
A Detailed GASKAP-HI View of Shells and Loops in the Magellanic Bridge
Shin-Jeong Kim, Antoine Marchal, N. M. McClure-Griffiths et al. (+19 more)
We present 8 pc-scale GASKAP-HI observations of an HI shell (diameter \sim130 pc) associated with the H\alpha shell DEM171 in the Magellanic Bridge. The Bridge's diffuse, HI-dominated environment, with minimal
galactic shearing and fewer overlapping
star-forming regions, provides an ideal environment to study cold gas formation driven by
stellar feedback. To investigate the HI multi-phase structure and kinematics of the shell, we perform Gaussian decomposition of HI line profiles. We identify cold HI components with velocity dispersions \sigma < 2.5 km/s located within the shell. Complementary Herschel far-infrared (FIR) 250\micron and H\alpha maps show that while the central cavity is ionized, the shell walls contain cold HI, molecular gas, and
dust, suggesting that
stellar feedback has promoted cold gas formation or swept pre-existing cold gas into the shell walls. We also identify cold HI clumps outside the shell tracing a larger-scale structure, MB-Loop I. To investigate the large-scale context, we apply a
Fourier transform method to HI emission-line profiles to map the lower limit of cold-gas column densities across a section of the Bridge, revealing structures from large loops to smaller shells. The association between MB-Loop I and the HI shell suggests that cold HI gas may pre-exist before shell expansion. Finally, the shell exhibits asymmetric expansion, with a preferred orientation roughly perpendicular to the arc along MB-Loop I. Our results show that cold HI gas exists across a wide range of spatial scales in the Magellanic Bridge, highlighting the dynamic interplay that shapes the surrounding interstellar medium.
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All authors Shin-Jeong Kim, Antoine Marchal, N. M. McClure-Griffiths, J. R. Dawson, James Dempsey, Helga Dénes, John M. Dickey, Steven J. Gibson, Katie Jameson, Ian Kemp, Bumhyun Lee, Min-Young Lee, Adam K. Leroy, Callum Lynn, Yik Ki Ma, Marc-Antoine Miville-Deschênes, Eric G. M. Muller, Claire Murray, Hiep Nguyen, Nickolas Pingel, Hye-Jin Park, Jacco Th. van Loon
Optimization and Control
Sample average approximation of multiobjective stochastic optimization problems with application in insurance
Bogdan Norkin
The article describes a technique for solving multiobjective
stochastic optimization problems. As a generalized model of a
stochastic system to be optimized a vector (input-random output) system is used. Ran- dom outputs are converted into a vector of deterministic performance and risk indicators. The problem is to find those inputs that correspond to a Pareto-optimal values of output indicators. The problem is approximated by a sequence of deterministic multi-criteria optimization problems, where, for example, the objective vector function is a sample average approximation of the original one and the feasible set is a discrete sample approximation of the feasible inputs. Approximate optimal solutions are defined as weakly Pareto-efficient ones within some vector tolerance. Convergence analysis includes establishing convergence of the general approx- imation scheme and establishing conditions of convergence with probability one under proper regulation of sampling parameters. The proposed technique is illustrated on a computer system for supporting multi-criteria optimization of insurance business.
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Cryptography and Security
Bridging the Opacity: Evidence-Backed Cross-Chain Transaction Correspondence Reconstruction Across Heterogeneous Blockchains
Dan Lin, Huan Xiao, Ziwei Li et al. (+4 more)
Cross-chain bridges enable interoperability, but they also break the transaction trails needed to trace illicit funds. Third-party investigators typically cannot access the source-to-destination mappings maintained by bridge backends, and our survey of 131 bridges finds that only 16.79% provide complete public tracking. Existing approaches depend on official APIs, EVM-specific assumptions, or fragile temporal heuristics, limiting their ability to trace transfers across heterogeneous ledgers. We present XSplicer, an evidence-driven system for reconstructing cross-chain transaction correspondence (xTCR) without privileged access to bridge backends. XSplicer derives unified semantic specifications from public protocol documentation and transaction examples, translates them into lightweight parsers and verifiers, and links source and destination transactions by prioritizing hard evidence and using soft clues only when necessary. We evaluate XSplicer on seven bridge protocols spanning EVM, Bitcoin, and Solana. XSplicer achieves 92.5% global recovery rate and up to 98.61% on individual protocols. Under
adversarial noise, its hard-evidence verifier retains the correct match in 100% of tested cases, while soft-clue matching degrades as ambiguity increases. In two real-world case studies, XSplicer recovers more than 1,900 historical transaction pairs after Multichain ceased operations and identifies 754 illicit cross-chain transfers worth 105.6 million USD in the Bybit laundering incident. These results show that public protocol invariants can support practical cross-chain forensics without privileged bridge mappings.
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All authors Dan Lin, Huan Xiao, Ziwei Li, Xiapu Luo, Jiachi Chen, Jiajing Wu, Zibin Zheng
Econometrics
Tensor-BEKK: Conditional Covariance Modeling and Inference for Tensor-Valued Time Series
Huan Gong and Feiyu Jiang
Modern economic and financial data are increasingly organized as multiway arrays, with observations indexed simultaneously by geographic regions, industrial sectors, asset categories, and other economic characteristics. Representing such data as tensor-valued time series preserves their intrinsic multiway structure. Although substantial effort has been devoted to modeling the conditional mean of tensor-valued time series, comparatively less
attention has been paid to their conditional covariance dynamics. The latter remains challenging because unrestricted multivariate covariance models involve many parameters and substantial computational cost. To address these challenges, we propose the Tensor-BEKK (T-BEKK) model, a tensor-structured BEKK specification that retains the positive definite covariance recursion for the vectorized process while imposing Kronecker structures on the intercept and the ARCH and GARCH coefficient
matrices. The model reduces the parameter dimension and provides mode-specific interpretations of the covariance intercept, ARCH effects, and GARCH persistence. We establish stationarity, identification, and the asymptotic properties of the Gaussian quasi-maximum likelihood
estimator. We further develop mode-specific restricted score tests tailored to the tensor structure, inference procedures for nonzero spillover intensities within each mode, and a portmanteau diagnostic test based on quadratic form residuals. For higher-dimensional settings, we also introduce the Tensor-Factor-BEKK (TF-BEKK) model. Under a first-step negligibility condition, its feasible second-step QMLE is asymptotically equivalent to the oracle QMLE based on the latent factors. Simulations and two empirical applications, covering currency futures and Chinese equity tensor portfolio allocation, illustrate the finite-sample behavior and empirical usefulness of the proposed methods.
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Computation and Language
TeochewBench: A Human-Reviewed Benchmark for Teochew Hanzi Translation
Jianan Wu
Teochew has a substantial speaker community and exhibits distinctive lexical, syntactic, and pragmatic features, yet textual resources for evaluating large
language models remain limited. We present TeochewBench, a human-reviewed
benchmark comprising 300 Teochew Hanzi expressions for evaluating translation from Teochew Hanzi into Mandarin Chinese and English. The
dataset covers five categories: basic vocabulary; everyday sentences; Teochew-specific expressions; tone, politeness, and context; and idiomatic, ambiguous, and culturally specific expressions. A primary Teochew-speaking reviewer examined all entries individually and revised them as needed, while two additional Teochew speakers verified selected items. Our main evaluation covers 11 official general-purpose post-trained models on the reviewed
dataset in both translation directions, yielding 6,600 predictions. Two official base checkpoints provide 1,200 predictions for supplementary diagnostics, bringing the total to 13 models and 7,800 predictions. We additionally include a Hanzi-copy control, which returns the source input unchanged, to assess how shared Hanzi affect automatic scores for translation into Mandarin Chinese. Qwen3.5-27B achieved the highest overall chrF-style score among the evaluated checkpoints, at 60.63, followed by Qwen2.5-72B-Instruct at 56.61, Gemma-3-27B-IT at 56.36, and GLM-4-32B-0414 at 55.82. Across the 11 main-evaluation models, the mean chrF-style score decreased from 69.25 for low-specificity items to 27.52 for high-specificity items. High-specificity expressions received lower scores and exhibited smaller cross-model differences, suggesting that they constitute a shared low-scoring region across the model families evaluated here. The Hanzi-copy control further indicates that surface overlap in low-specificity items can substantially affect automatic scores for translation into Mandarin Chinese.
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Optics
Wavelength-Multiplexed Nonlinear Computing with a Single-Layer Diffractive Optical Processor
Yongkang Cheng, Che-Yung Shen, Yuntian Wang et al. (+2 more)
Diffractive optical processors provide a promising platform for high-throughput, low-latency analog computing by exploiting engineered wave propagation to transform optical fields. However, implementing nonlinear mappings in optical hardware remains challenging. Here, we introduce a wavelength-multiplexed encoding-and-decoding (E+D) diffractive processor that exploits multiple illumination wavelengths to enhance the nonlinear function-approximation capability of a compact single-layer diffractive architecture, without cascading multiple diffractive layers or increasing the trainable degrees of freedom. Under sequential wavelength scanning, each wavelength-detector channel implements a distinct nonlinear function at the output of the diffractive processor. Numerical simulations using 100 wavelength channels and 10,000 spatial detector regions at the output of the diffractive processor demonstrate the implementation of one million distinct nonlinear functions through wavelength multiplexing. We also demonstrate a simultaneous multiwavelength illumination configuration in which the E+D diffractive processor performs wavelength-division multiplexing and nonlinear function approximation concurrently. This multiwavelength illumination configuration is experimentally validated by optically implementing 64 distinct nonlinear functions under simultaneous illumination at eight wavelengths from 495 to 635 nm. Through numerical simulations and visible-light experimental demonstrations, our results indicate that this wavelength-multiplexed nonlinear function approximation framework provides a scalable route toward compact, high-capacity diffractive processors for large-scale analog optical computing and optical information processing.
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All authors Yongkang Cheng, Che-Yung Shen, Yuntian Wang, Shiqi Chen, Aydogan Ozcan
Information Retrieval
PageRecall: Measuring Page Selection in Literature-Grounded Question Answering
Aaditya Chauhan
We describe our system for LitTraceQA (GroundLM @ EMNLP 2026): given a research question, retrieve the relevant papers from a pool of 27,487, cite the page and the table or figure where the answer lives, and answer in a requested format. Our main finding is that evidence grounding is limited by
retrieval, not by reading. The page selector put the annotator's page, which we call the gold page, in front of the model that locates evidence only about half the time (52.6% gold-page recall), while that model, given the page, cited the right one in 45 of the 48 locators it emitted (94%). When the page was missing it rarely said so: of 45 such cases it returned nothing 14 times, a wrong page 24 times, and a correct page 7 times, so the pipeline failed quietly almost twice as often as it failed visibly. Since the failure was that the right page was never shown, the fix is to stop choosing: each retrieved paper fits in the model's context, so we show it whole. Page ranking survives only as a fallback inside papers too long to fit, which no test-split paper was, and gold-page recall reaches 100% on the papers we can parse. Separately, questions that identify their target by position rather than content, such as "the first author of the 24th reference", are served by parsing rather than
retrieval: we resolve the bibliography into an addressable list, which also supplies identifiers the evidence metric scores. The final system scores 0.762 paper $F_1$, 0.441 evidence $F_1$ and 0.920 multiple-choice accuracy on the held-out test split. Because the pipeline depends on a closed model without seed control, we release a harness that verifies the paper's central claims against committed artifacts.
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Robotics
Weiliang Deng, Zhengyang Dang, Yao Mu et al. (+1 more)
Aerial grasping is a remarkable capability exhibited by predatory birds, allowing them to capture prey through highly coordinated maneuvers in flight. Inspired by this capability, researchers have developed various formulations to reproduce such maneuvers through trajectory optimization. However, two limitations remain in practice. First, the resulting optimization problem is highly nonconvex and sensitive to initialization, making high-quality solutions difficult to obtain under a limited computational budget. Second, prescribed numerical objectives are human-designed abstractions that describe successful grasping through a limited set of mathematically tractable quantities and may not fully capture what determines task success. We investigate how learning can address these limitations within an analytical planner. Accordingly, a trajectory prior is first learned from optimized motions and then evolved through a CEM-based process that evaluates sampled initializations with the deployed optimizer and retains favorable ones as new supervision. An Execution-Aware Critic learns from contact, lift, and completion outcomes to assess whether the optimized trajectories are likely to succeed in physical execution. Its frozen energy can further serve as a differentiable grasping cost, allowing execution data to directly shape trajectory generation. Simulation and real-world experiments demonstrate improved optimization reliability, trajectory consistency, and grasping performance.
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All authors Weiliang Deng, Zhengyang Dang, Yao Mu, Ximin Lyu
Computational Engineering
The Exact Approximation Ratio of Uniformly Rotated Coordinate-wise Median in the Euclidean Plane
Song Zichen
Uniformly rotated coordinate-wise median chooses a random orthonormal coordinate system, takes a median in each coordinate, and maps the resulting point back to the Euclidean plane. We determine its exact worst-case expected approximation ratio when social cost is the $L_p$ norm of the
agents' Euclidean distances and $1<p<2$. The ratio is \(\frac{2^{2-1/p}}{\pi}\int_0^{\pi/2}(\cos^p\theta+\sin^p\theta)^{1/p}\dd\theta\). This expression was previously established as a lower bound by Chan, Lin, and Wang; our contribution is the matching upper bound. The proof establishes a strengthened coordinate-wise median inequality relative to an arbitrary reference point. Its right-hand side is linear in a sum of direction-dependent norms, which permits direct averaging over rotations without the loss incurred by passing through a $p$th-moment bound. We give all auxiliary inequalities and a self-contained proof of tightness using the established two-cluster-and-outlier construction. The upper bound holds for every finite profile and every measurable choice within the coordinate median intervals, while odd-size profiles suffice for the matching lower bound. The result characterizes this fixed mechanism, rather than the optimal approximation ratio among all randomized strategyproof mechanisms.
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Combinatorics
Demazure crystals for flagged set-valued reverse plane partitions
Siddheswar Kundu
In this paper, we build a Demazure
crystal structure on the set of all flagged set-valued reverse plane partitions of a given skew shape $\lambda/\mu$ and flag $\Phi$. Our construction provides a unified generalization of the Demazure
crystal structures previously known for the set of all flagged semi-standard set-valued tableaux and the set of all flagged reverse plane partitions. Consequently, we obtain a combinatorial expansion for the flagged hybrid Grothendieck
polynomial $H_{\lambda/\mu}(\mathbf{x}_{\Phi};\mathbf{t};\mathbf{w})$, introduced by Guo–Kang–Liu, in terms of key
polynomials. Applying this expansion, we express the hybrid Grothendieck
polynomial $H_{\lambda/\mu}(\mathbf{x};\mathbf{t};\mathbf{w})$ in terms of both the stable Grothendieck
polynomials $G_{\nu}(\mathbf{x})$ and the dual stable Grothendieck
polynomials $g_{\nu}(\mathbf{x})$.
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Econometrics
Profiled Anderson–Rubin Test: Robust Inference Allowing for Direct Effects of Instruments
Jung Hyub Lee
Instrumental variable analyses often rely on the assumption that instruments affect the outcome only through the endogenous regressor. In many applications, researchers can defend only a plausible range for direct effects of instruments, while conventional sensitivity analyses may be unreliable when instruments are weak. This paper proposes the profiled Anderson–Rubin (pAR) test, which considers all direct effects within a prespecified range and retains a candidate effect whenever at least one admissible direct effect is consistent with the data. Under the maintained sampling assumptions, the procedure controls false rejection for each compatible candidate without requiring strong instruments. The paper provides practical methods for constructing confidence sets and distinguishes substantive bounds from bounds tied to the realized instrument design. Simulations and applications to retirement saving and returns to schooling show that the procedure resembles conventional sensitivity analysis when instruments are strong but preserves substantially more uncertainty when identification is weak.
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Computational Engineering
Research on the Price Prediction Algorithms of Major Cryptocurrencies and a Basic Transaction Framework
Shengjian Chen
Through long-term observation and time series analysis of Bitcoin and Ethereum, we found the similarity in long-term consistent price trends, especially in the weekly K-line. We denoised and smoothed the historical data of their prices, and further derived the periodicity of their price change trends and the timing of buying and selling. We have for the first time proposed that the full application of central bank digital currencies (CBDC) is a key signal for investors to reduce their holdings of cryptocurrencies or even exit the
market. In addition, we proposed momentum opening/closing prices to replace the traditional nominal opening/closing prices to accurately describe the price trends of the 24/7 financial trading
market. We found that the cryptocurrency
market can be regarded as a relatively independent financial
market, thereby designing a safer and more efficient arbitrage strategy.
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Machine Learning
LIGE-GR: A Smooth Leap from Ranking to Generative Recommendation in the LLM Era
Venkat Srinivas, Chenzhang He, Sam Woodmansee et al. (+63 more)
The remarkable success of large
language models (LLMs) has provided important inspiration for the next generation of recommender systems. Structurally, recommendation and
language generation share a similarity: both aim to produce an ordered sequence that optimizes the user's experience. However, how to precisely absorb the essence of the LLM paradigm into mature industrial recommender systems remains an open problem. There are two challenges. First, it is unclear how to incorporate the LLM paradigm – sequence-level generation and optimization – into recommendation. Second, real-world recommender systems are mature systems that have been iteratively customized for years around specific products, business constraints, serving infrastructure, and organizational ownership. Replacing such systems wholesale is often technically risky and organizationally disruptive. In this paper, we propose LIGE-GR, a listwise generation and evaluation recommendation framework that upgrades from a traditional ranking system (itemwise recommendation) toward a generative recommendation paradigm. Instead of rebuilding the entire recommendation stack from scratch, LIGE-GR generalizes the existing pointwise recommendation system into a listwise generation system. This allows mature recommender systems to benefit from listwise optimization while preserving compatibility with existing models, value functions, and serving infrastructure. We validate LIGE-GR in short-
video recommendation on Instagram Reels and Facebook
Video. On these recommendation surfaces, LIGE-GR improves time spent by 1.14 percent on Instagram Reels and 0.72 percent on Facebook
Video, while requiring only modest additional inference resources.
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All authors Venkat Srinivas, Chenzhang He, Sam Woodmansee, Shawn Lian, Wenjie Hu, Renjie Jiang, Ziheng Huang, Xinyuan Zhang, Zhihao Zheng, Zhuoran Yu, Rui Li, Lei Yuan, Ziwei Li, Jimmy Jia, Mert Terzihan, Ekrem Kocaguneli, Yiming Liao, Zhichen Zhao, Yue Yin, Yue Weng, Wanli Ma, Xufeng Cai, Weimiao Wu, Yezhou Huang, Du Zhang, Yukun Ding, Aaron Johnston, Yueming Wang, Zhaojie Gong, Yuting Zhang, Serena Li, Adithya Ganesh, Boying Liu, Haichuan Yang, Xialu Li, Matt Ma, Qunshu Zhang, Andrew Ton, Nathan Berrebbi, Neel Pawar, John Joshua Miller, Jayant Subramanian, Praveen Rathinavelu, Cheng Huang, Aadhar Sachdeva, Josh Karns, Andres Aaron Gutierrez, Neil Agarwal, Gustas Pladis, Vladimir Batygin, Gopal Ray, Aditya Priyadarshi, Shantanu Patil, Zhe Wang, Penny Pan, Yiping Han, Arun Singh, Guangdeng Liao, Bi Xue, Xinyao Hu, Yang Song, Yisong Song, Meihong Wang, Haotian Wu, Deepak Agarwal, Ji Liu
Theoretical Economics
Dynamic Pooling and Regional Participation in Deceased-Donor Organ Allocation
Genta Okada
Moving from geographically fragmented to pooled waiting lists in deceased-donor organ transplantation can improve efficiency, but it raises concerns about regional
fairness and participation incentives. This paper studies Pareto gains from such transitions in a multi-class queueing model with impatient
agents, perishable items, and a tractable homogeneous compatibility friction. In contrast to standard approaches that emphasize static match quality or overlook regional incentives in dynamic contexts, this analysis identifies dynamic risk pooling as the primary source of Pareto improvements for regions. Pooling reduces organ waste resulting from
stochastic supply-demand fluctuations, temporal mismatches, and random compatibility outcomes. Although this dynamic surplus theoretically enables Pareto improvements, a purely utilitarian policy that treats all patients equally across regions often fails to achieve these improvements when regions are asymmetric. In these situations, utilitarian redistribution removes an excessive number of organs from high-supply regions and violates their participation constraints. To address this conflict, it is demonstrated that the set of achievable utilities can be represented by mixtures of static priority index policies. Building on this structure, the Constrained Pareto Frontier Search (CPFS) is proposed as a
polynomial-time procedure that computes an approximately optimal allocation policy with arbitrarily small participation-constraint violations. The results indicate that, by adjusting priority indices to ensure redistribution remains within the gains from pooled supply, demand, and compatibility risk, policymakers can achieve global efficiency while maintaining local participation incentives.
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Algebraic Geometry
On the singularities and the Kodaira dimension of unitary Shimura varieties
Shuji Horinaga and Yota Maeda
We study the geometry of Shimura varieties associated with a Hermitian form of signature $(p,q)$ over an imaginary quadratic field $E$, where $2\leq p\leq q$. We prove that when $p\geq w_E$ and $(p,q)\neq(2,2),(2,3)$, where $w_E:=\#\mathscr{O}_E^\times$, there exists a toroidal compactification with at worst canonical singularities. As an application, combining this singularity analysis with Arthur's multiplicity formula, we prove that only finitely many pairs $(p,q)$ satisfying the above conditions and $p+q\equiv1\pmod{w_E}$ give rise to
unitary Shimura varieties that are not of general type. Our method also improves the singularity bound of Gritsenko–Hulek–Sankaran (Invent.\ Math., 2007) for $\mathrm{O}^+(2,n)$ to the range $n\geq6$ and shows that this bound is sharp.
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Machine Learning
Reaching Every Position Without Searching: Rotating Sparse Wiring on the Hypercube as a Substitute for Attention
Yoshiaki Takashita
Attention pays, at every layer and for every input, the cost of searching for whom to connect. We ask how far one can get with wiring that is fixed, sparse, and simply rotated from layer to layer. Treating the $n$ positions of a sequence as the vertices of a $\log_2 n$-dimensional hypercube and connecting each position, at layer $\ell$, to its neighbour along dimension $\ell \bmod \log_2 n$, information from every position reaches every other in $\log_2 n$ layers with $2n$ links per layer instead of $n^2$. On a synthetic task that is unsolvable unless all positions are reached, this rotation matches all-to-all wiring at $1/32$ of the links, while the same sparse pattern held fixed across layers fails; what matters is that every dimension is touched, not the order. On character-level
language modelling of a public corpus (the first $12$M characters of enwik8), a hybrid that keeps two
attention layers among sixteen sparse ones reaches $0.06$ bits-per-character lower held-out loss than a fully attentive model of the same width at the same step budget (three seeds each, no overlap), with $1/7$ of the links, $42\%$ fewer parameters, and $2.4\times$ less wall-clock time; the purely rotated schedule is level with the hybrid. The same ordering holds on a second corpus of mixed Japanese, English and code, where the gap widens to $0.16$. The usable learning-rate window is four to eight times wider than
attention's on both. We also report what did not work - learned coordinates, and a "dynamics" variant whose apparent gains turned out to be an artefact of a saturated kernel - and the measurement discipline (frozen corpus, full-coverage evaluation, seed spread as the bar for ranking) that we found necessary to say anything at all at this scale.
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Rings and Algebras
Growth functions of algebras and an application to Leavitt path algebras
João Schwarz and Alfilgen Sebandal
In this paper, we consider three growth functions of
algebras: the Gelfand-Kirillov dimension, superdimension, and
entropy – as well as a variation of the latter successfully used in the study of Leavitt path
algebras. We prove results that make precise the heuristic fact that the Gelfand-Kirillov dimension is suitable for the study of
algebras with
polynomial growth, the superdimension for
algebras with subexponential growth, and the
entropy for
algebras with exponential growth. We introduce a definition of
entropy for graded modules motivated by the
entropy for graded
algebras. As an application of our study, we give a new criterion for Leavitt path
algebras to be PI.
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General Relativity and Quantum Cosmology
Horizon–orbit scale competition underlying chaos bound violation for spinning particles
Deyou Chen, Lifang Li, Chuang Yang et al. (+1 more)
In this paper, we investigate the chaos bound through the local radial instability of charged spinning test particles on unstable circular orbits in the black-bounce–Kerr–Newman spacetime. Our results show that violation of the bound is governed by the competition between the local orbital-instability scale and the surface
gravity scale, with variations of the black hole background and the particle dynamics providing two complementary routes to the same threshold crossing. Background variations affect both scales and can drive the system into the bound-violating regime when the surface
gravity is suppressed more strongly than the orbital instability. At fixed background, by contrast, the surface
gravity remains unchanged, and the transition across the threshold is driven by changes in the unstable orbit dynamics induced by the particle parameters. The background-controlled and probe-dynamics-controlled routes therefore represent two realizations of a unified horizon–orbit scale competition rather than independent mechanisms.
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All authors Deyou Chen, Lifang Li, Chuang Yang, Kangqiao Liu
Analysis of PDEs
Sharp Born–Infeld Sobolev inequality and extremal functions on the lattice graph $\mathbb Z^N$
Chao Ji and Kai Sheng
In this paper, we study the sharp Sobolev-type inequality associated with the Born–Infeld energy on the
lattice graph \(\mathbb Z^N\), \(N\geq3\): \[ \frac12\sum_{x\in\mathbb Z^N}\sum_{y\sim x} \left(1-\sqrt{1-|\nabla_{xy}u|^2}\right) \geq C_{N,\alpha} \left(\sum_{x\in\mathbb Z^N}|u(x)|^\alpha\right)^{\frac{N}{N+\alpha}}, \] for \(u\in D^{1,2}(\mathbb Z^N)\cap\ell^\alpha(\mathbb Z^N) \) satisfying \(|\nabla_{xy}u|\leq1\) for every \(x\sim y\), where \(C_{N,\alpha}\) denotes the optimal constant. We determine the exact positivity threshold and prove that \(C_{N,\alpha}>0\) if and only if \(\alpha\geq2^*:=2N/(N-2)\). At the Sobolev critical exponent \(\alpha=2^*\), we identify the optimal constant as \[ C_{N,2^*}=\frac12\mathcal S_2, \] where \(\mathcal S_2\) is the optimal discrete Sobolev constant, and show that it is not attained. In the supercritical regime, there exists \(\varepsilon_0=\varepsilon_0(N)>0\) such that \(C_{N,\alpha}\) is attained for every \(2^*<\alpha<2^*+\varepsilon_0\), with a nonnegative Schwarz symmetric extremal function. The main compactness difficulties stem from the lack of a suitable scaling on \(\mathbb Z^N\) and from the nonhomogeneity of the Born–Infeld energy. We overcome them by combining discrete Schwarz rearrangement with a \(Q_1\) discrete-to-continuum comparison and by establishing a strict separation as the \(\ell^\alpha\)-norm tends to infinity.
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Complex Variables
A solution to Berndtsson's problem and uniqueness of twisted KE currents
Yinji Li, Haoyuan Sun, Zhiwei Wang et al. (+1 more)
We prove the uniqueness
conjecture on the twisted Kähler-Einstein currents in big
cohomology classes, by solving a problem of Bo Berndtsson posed in his work on the uniqueness
theorem of Käher-Einstein metrics.
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All authors Yinji Li, Haoyuan Sun, Zhiwei Wang, Xiangyu Zhou
Physics and Society
Overlap and topology shape synergy in collective knowledge integration
Daeseong Kim and Masato S. Abe
Collective discovery often requires the integration of partially overlapping knowledge, yet the beneficial level of overlap and the role of background
topology remain unclear. We model individual knowledge as connected subnetworks of a common knowledge space and distinguish internal synergy, in which integration shortens paths between concepts, from external synergy, in which an unknown concept connects to the shared and all individual-specific regions. Internal synergy peaked at intermediate overlap across random, scale-free, modular, and small-world backgrounds, reflecting a balance between complementarity and common ground. External synergy was strongly
topology dependent and was most pronounced when small-world structure maintained overlapping local boundaries. Rewiring separated the total supply of synergistic nodes from their concentration at the accessible boundary; moderate rewiring could increase structural opportunities while dispersing them among more external alternatives and reducing their discovery. Group-size effects on internal synergy depended on
topology, whereas strict higher-order external synergy declined as its all-region criterion became more restrictive. Collective discovery therefore depends jointly on knowledge overlap and on how complementary knowledge regions are organized within the surrounding knowledge space.
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Computer Vision
Multi-View Mixture-of-Experts with Vision-Language Reranking for Cross-View Object Geo-Localization
Xuyu Fan, Qi Ming, Zhu Han et al. (+6 more)
Cross-view object geo-localization (CVOGL) locates a target in satellite imagery using drone or street-view queries. Existing methods train separate detectors for each viewpoint, leading to parameter redundancy and impeding cross-view knowledge sharing. Moreover, top-ranked satellite candidates are often visually similar, so visual appearance and categorical labels alone are insufficient to resolve such ambiguity. To address these, we propose MVLGeo, an efficient framework designed to unify multiple viewpoints and reduce model redundancy. First, we introduce environmental contextual text from the query view as cues to distinguish visually similar candidates via
Vision-
Language Reranking (VL-Rerank). Second, we design a multi-view Mixture-of-Experts architecture (MV-MoE) with a shared encoder and view-specific experts to reduce redundancy and promote knowledge sharing, while cross-view contrastive learning aligns their representations for consistency. Third, we introduce an adaptive elliptical prior (ESAM-Prior) as auxiliary positional encoding for anisotropic
geometric perception. Extensive experiments on the CVOGL
benchmarks confirm that MVLGeo, as a unified model for multiple query viewpoints, achieves state-of-the-art performance, demonstrating robustness to input degradation and generalization across viewpoints. Code and models will be available on GitHub to facilitate future work.
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All authors Xuyu Fan, Qi Ming, Zhu Han, Liuqian Wang, Siyuan Cao, Xiaohan Zhang, Xudong Zhao, Mingjing Zhao, Yuhan Zhang
Mesoscale and Nanoscale Physics
Landscape geometry of Majorana zero modes in inhomogeneous superconductors
Guo-Jian Qiao, Zhi-Lei Zhang, Kang Xu et al. (+1 more)
Spatial inhomogeneity breaks translational
symmetry and prevents conventional Bloch-band topological invariants from directly answering a practical question: do Majorana zero modes survive in a given inhomogeneous superconducting device? In this Letter, we develop a real-space landscape approach to address this question. Rather than solving the zero-energy equation as a boundary-value problem, we treat the spatial coordinate as an
evolution parameter and recast the equation as a first-order dynamical system. A Majorana zero mode is then identified with a stable trajectory that satisfies the physical boundary condition and approaches the origin at large distance. We show that the landscape geometry fixes the dimensions of the stable and unstable subspaces, while the intersection of the stable subspace with the physical boundary subspace determines the number of Majorana zero modes. In the homogeneous limit, the topological phase transition is manifested as a
geometric transition of the landscape. Applied to one-dimensional spinless $p$-wave
superconductors and nanowire–
superconductor systems, the approach yields sufficient bounds on both the amplitude and spatial gradient of the inhomogeneity, providing quantitative criteria for the design of Majorana devices.
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All authors Guo-Jian Qiao, Zhi-Lei Zhang, Kang Xu, C. P. Sun
Accelerator Physics
Ultralow Mean Transverse Energy and High Quantum Efficiency Cryogenic Bialkali Photocathode for MHz-Repetition-Rate Electron Sources
D. Wang, S. Liu, J. Liu et al. (+15 more)
Simultaneously achieving high
quantum efficiency (QE), ultralow mean transverse energy (MTE), and robust long-term operation under conditions relevant to continuous-wave (CW) X-ray free-electron
lasers (XFELs) remains a central challenge for
semiconductor photocathodes. This challenge arises from the trade-off between QE and MTE, as well as the difficulty of maintaining stable operation in high-field CW electron guns. Here we demonstrate a cryogenic K2CsSb photocathode that simultaneously achieves high QE, ultralow MTE, and robust long-term operation in a CW gun under XFEL-relevant operating conditions. Under cryogenic operation, the photocathode achieves an MTE of 50 meV while sustaining a QE of 5.4%. Milliampere-level CW current, including operation at 5 mA, was demonstrated together with an approximately 20-day operational history. The observations are consistent with improved carrier survival and/or surface escape in photocathodes prepared using the optimized recipe. These results show that the practical QE-MTE trade-off can be substantially mitigated in cryogenic bialkali photocathodes and provide a practical pathway toward high-brightness electron sources for CW XFELs and energy-recovery linacs.
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All authors D. Wang, S. Liu, J. Liu, Y. Dai, Z. Hong, J. Wang, Y. Shi, M. Tai, L. Feng, H. Xu, L. Lin, F. Wang, F. Zhu, J. Hao, S. Quan, K. Liu, H. Xie, S. Huang
Commutative Algebra
A criterion for determinantal presentations of numerical semigroup rings
Satoshi Murai and Kou Takahashi
We study numerical
semigroups whose defining ideals admit determinantal presentations by the $2\times2$ minors of a $2\times n$
matrix. A
conjecture of Cuong, Kien, Matsuoka, and Truong relates such presentations to arithmetic progressions of pseudo-Frobenius numbers. We prove a numerical criterion for this determinantal presentation, reducing the
conjecture to a problem on factorizations of maximal elements of an Apéry set in the numerical
semigroup. As an application, we verify the
conjecture in
embedding dimension four under a unique factorization assumption on an Apéry set.
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Computation and Language
DualSQL: Text-to-SQL with Multi-Agent Reinforcement Learning
Shijie Chen, Yu Gan, Yeounoh Chung et al. (+6 more)
State-of-the-art Text-to-SQL systems are typically multi-
agent pipelines centered around two fundamental tasks: schema linking and SQL generation. However, existing work trains separate models for each task, failing to leverage the synergy between these interrelated tasks. In this work, we propose DualSQL, a new Text-to-SQL system consisting of two
agents powered by a single model backbone. The
agents share the same model weights and agentic scaffold, enabling joint optimization through a robust multi-
agent reinforcement learning (RL) framework. We design three database access tools to facilitate effective multi-step
reasoning grounded to interactions with the databases. To improve training and avoid model collapse, we introduce a set of rollout guardrail mechanisms that stabilizes multi-
agent RL training, supporting DualSQL to keep improving during training. We also introduce a new SQL correctness metric, robust execution match (REX), to more accurately judge SQL correctness and assign reward signals. Being trained on only 3755 examples, DualSQL-4B achieves an impressive 68.0% execution accuracy on the BIRD development set, matching previous 7B models. DualSQL-8B further improves to 71.1%, outperforming previous state-of-the-art single-model solutions with 32B parameters. These results demonstrate the strength of joint multi-
agent reinforcement learning for building high performance Text-to-SQL pipelines.
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All authors Shijie Chen, Yu Gan, Yeounoh Chung, Jiani Zhang, Quannan Li, Sravan Babu Bodapati, Cody J. Greer, Yu Su, Fatma Ozcan