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The Mordell conjecture asserts that there are only finitely many rational points on a smooth projective curve of genus at least two over a number field. The uniform Mordell problem asks for suitable upper bounds on the number of rational points in the Mordell conjecture, and has been solved by combining works of Vojta, Dimitrov--Habegger--Gao and Kuhne. In this survey, we will introduce a quantitative version of the uniformity problem proved by the recent work of Yu--Yuan--Zhou.

Spatial proteomics enables single-cell-resolution characterization of protein expression within tissue architecture, playing a critical role in understanding tumor microenvironments and guiding precision medicine. However, current analysis workflows remain fragmented, requiring expert manual orchestration of heterogeneous tools and limiting research scalability and reproducibility. We present SP-Mind, the first autonomous AI agent designed to unify the spatial proteomics analysis pipeline, from raw multiplexed tissue imaging to downstream phenotype discovery. Equipped with expert-curated biological analysis skills and specialized computational tools, SP-Mind converts natural-language queries into end-to-end analytical workflows without task-specific fine-tuning. To rigorously evaluate its capabilities, we introduce SP-Bench, a comprehensive benchmark spanning diverse tissue types, comprising 102 tasks across 18 distinct categories. Through extensive evaluation on SP-Bench and established downstream tasks, SP-Mind achieves state-of-the-art performance compared to existing open-source biomedical agent baselines.

Using $20.3~{\rm fb}^{-1}$ of $e^+e^-$ collision data collected with the BESIII detector at $\sqrt{s}=3.773~{\rm GeV}$, we perform the first amplitude analysis of the decay $D^+\toπ^+ηη$. The intermediate process $D^+\to a_0(980)^+η$, $a_0(980)^+\toπ^+η$, is observed as the only significant component in the amplitude analysis, and its branching fraction is measured to be $(3.67\pm0.12_{\rm stat}\pm0.06_{\rm syst})\times10^{-3}$. The $π^+η$ mass spectrum associated with $a_0(980)^+η$ production exhibits a line shape that differs substantially from those observed in $D_{(s)}\to a_0(980)π$ and $D^0\to a_0(980)^-e^+ν_e$ decays. We examine several conventional descriptions of the $a_0(980)$ amplitude, including Flatté, dispersively modified Flatté, $T$-matrix, and $K$-matrix parameterizations. With reference $a_0(980)$ parameters, neither these models nor their extensions including additional small resonant or non-resonant amplitudes reproduce the observed line shape satisfactorily. When the $a_0(980)$ parameters are allowed to float, satisfactory fits can be obtained, but the pole mass is driven well above the $K\bar K$ threshold, inconsistent with the near-threshold character of the $

We search for the reaction channel $e^+ e^- \to ηη\,J/ψ$ in a data sample with center-of-mass energies from 4.226 to 4.950~GeV, which was collected by the BESIII detector operating at the Beijing Electron Positron Collider (BEPCII). The data analysis is performed with two different methods, exclusively and semi-inclusively, which enabling a comparison and combination of the results. Only in a few cases a statistical significance of the cross sections with more than $3σ$ is observed with one of the methods. Only at 4.750~GeV the significance of the cross section measurement is 8.9$σ$ (observation) with the exclusive analysis and 3.4$σ$ (evidence) with the semi-inclusive analysis. Therefore, the corresponding upper limits of the cross section at the 90% confidence level are determined. The energy dependent results show clear deviations from the the line shape expected from three-body phase space alone. Since the statistical significance for almost all center-of-mass energies is low, the upper limits for the reaction channel $e^+ e^- \to ηη\,J/ψ$ also serve as limits for the existence of a possible isospin partner to the charmonium-like isospin triplet $Z_{\rm c}(3900)$ which decays t

This paper reports the first multi-channel joint analysis to identify the properties of the exotic charmonium-like state $T_{c\bar{c}}(4020)$ via the electron-positron annihilation process $e^{+}e^{-}\toπ^{+}T_{c\bar{c}}(4020)^{-}+c.c$. A partial wave analysis is performed simultaneously in three decay channels $T_{c\bar{c}}(4020)^{-}\to {D}^{*0}D^{*-}$, $π^{-}J/ψ$, and $π^{-}h_{c}$, based on data samples taken at $\sqrt{s}=4.395$ and $4.416\,\mathrm{GeV}$ with an integrated luminosity of $1598.9\,\mathrm{pb}^{-1}$ collected with the BESIII detector operating on the BEPCII collider. For the first time, the spin-parity of the $T_{c\bar{c}}(4020)^{-}$ is determined to be $J^{P}=1^{+}$ with a significance $11.7σ$. Pole positions are extracted on the Riemann sheets with three branch points in the complex energy plane. Furthermore, the relative branching fractions are obtained as $\mathcal{B}[T_{c\bar{c}}(4020)^{-}\toπ^{-}J/ψ]/\mathcal{B}[T_{c\bar{c}}(4020)^{-}\to{D}^{*0}D^{*-}]=(3.6\pm0.6\pm1.6)\times10^{-3}$ and $\mathcal{B}[T_{c\bar{c}}(4020)^{-}\toπ^{-}h_{c}]/\mathcal{B}[T_{c\bar{c}}(4020)^{-}\to{D}^{*0}D^{*-}]=(8.9\pm1.3\pm2.3)\times10^{-2}$, where the first uncertainties are stati

Vision-Language Models (VLM) exhibit strong reasoning capabilities, showing promise for end-to-end autonomous driving systems. Chain-of-Thought (CoT), as VLM's widely used reasoning strategy, is facing critical challenges. Existing textual CoT has a large gap between text semantic space and trajectory physical space. Although the recent approach utilizes future image to replace text as CoT process, it lacks clear planning-oriented objective guidance to generate images with accurate scene evolution. To address these, we innovatively propose MindDriver, a progressive multimodal reasoning framework that enables VLM to imitate human-like progressive thinking for autonomous driving. MindDriver presents semantic understanding, semantic-to-physical space imagination, and physical-space trajectory planning. To achieve aligned reasoning processes in MindDriver, we develop a feedback-guided automatic data annotation pipeline to generate aligned multimodal reasoning training data. Furthermore, we develop a progressive reinforcement fine-tuning method to optimize the alignment through progressive high- level reward-based learning. MindDriver demonstrates superior performance in both nuScences

This paper re-organizes Vojta's proof of the Mordell conjecture (i.e. Faltings' theorem) in terms of Arakelov geometry. A new ingredient is to replace an application of Gillet--Soule's arithmetic Riemannn--Roch theorem by that of Yuan's arithmetic Siu inequality.

A measurement of the CKM angle $γ$ and related strong-phase parameters is performed using a novel, model-independent approach in ${B^{\pm}\rightarrow D(\rightarrow K^{0}_{\rm S} h^{\prime+}h^{\prime-}) h^{\pm}}$ decays, where $h^{(\prime)} \equiv π, K$. The analysis uses a joint data sample of electron-positron collisions collected by the BESIII experiment at the Beijing Electron-Positron Collider II during 2010--2011 and 2021--2022, corresponding to an integrated luminosity of 8 fb$^{-1}$, and proton-proton collisions collected by the LHCb experiment at the Large Hadron Collider during 2011--2018, corresponding to an integrated luminosity of 9 fb$^{-1}$. The two datasets are analyzed simultaneously by applying per-event weights based on the amplitude variation over the $D$-decay phase space to enhance the sensitivity to $C\!P$-violating observables. The CKM angle $γ$ is determined to be $γ= (71.3\pm 5.0)^{\circ}$, which constitutes the most precise single measurement to date.

The indicator matrix plays an important role in machine learning, but optimizing it is an NP-hard problem. We propose a new relaxation of the indicator matrix and prove that this relaxation forms a manifold, which we call the Relaxed Indicator Matrix Manifold (RIM manifold). Based on Riemannian geometry, we develop a Riemannian toolbox for optimization on the RIM manifold. Specifically, we provide several methods of Retraction, including a fast Retraction method to obtain geodesics. We point out that the RIM manifold is a generalization of the double stochastic manifold, and it is much faster than existing methods on the double stochastic manifold, which has a complexity of \( \mathcal{O}(n^3) \), while RIM manifold optimization is \( \mathcal{O}(n) \) and often yields better results. We conducted extensive experiments, including image denoising, with millions of variables to support our conclusion, and applied the RIM manifold to Ratio Cut, we provide a rigorous convergence proof and achieve clustering results that outperform the state-of-the-art methods. Our Code in \href{https://github.com/Yuan-Jinghui/Riemannian-Optimization-on-Relaxed-Indicator-Matrix-Manifold}{here}.

Methods based on diffusion backbones have recently revolutionized novel view synthesis (NVS). However, those models require pretrained 2D diffusion checkpoints (e.g., Stable Diffusion) as the basis for geometrical priors. Since such checkpoints require exorbitant amounts of data and compute to train, this greatly limits the scalability of diffusion-based NVS models. We present Next-Scale Autoregression Conditioned by View (ArchonView), a method that significantly exceeds state-of-the-art methods despite being trained from scratch with 3D rendering data only and no 2D pretraining. We achieve this by incorporating both global (pose-augmented semantics) and local (multi-scale hierarchical encodings) conditioning into a backbone based on the next-scale autoregression paradigm. Our model also exhibits robust performance even for difficult camera poses where previous methods fail, and is several times faster in inference speed compared to diffusion. We experimentally verify that performance scales with model and dataset size, and conduct extensive demonstration of our method's synthesis quality across several tasks. Our code is open-sourced at https://github.com/Shiran-Yuan/ArchonView.

We study a joint routing-assignment optimization problem in which a set of items must be paired one-to-one with a set of placeholders while simultaneously determining a Hamiltonian cycle that visits every node exactly once. Both the assignment and routing decisions are optimized jointly to minimize the total travel cost. In this work, we propose a method to solve this problem using an exact MIP formulation with Gurobi, including cutting-plane subtour elimination. With analysis of the computational complexity and through extensive experiments, we analyze the computational limitations of this approach as the problem size grows and reveal the challenges associated with the need for more efficient algorithms for larger instances. The dataset, formulations, and experimental results provided here can serve as benchmarks for future studies in this research area. GitHub repository: https://github.com/QL-YUAN/Joint-Assignment-Routing-Optimization

We analyze the decay of $η\rightarrow \ell^+\ell^-(\ell=e, μ)$ via $J/ψ\rightarrowγη'$ and $η'\rightarrowπ^+π^-η$, based on (10087 $\pm$ 44) $\times$ 10$^{6}$ $J/ψ$ events collected with the BESIII detector at the BEPCII storage rings. The branching fraction of $η\rightarrowμ^+ μ^-$ is measured to be $(5.8 \pm 1.0_{\rm stat} \pm 0.2_{\rm syst}) \times 10^{-6}$, which is consistent with the previous measurements and theoretical expectations. In addition, no significant $η\to e^+e^-$ signal is observed in the $e^+ e^-$ invariant mass spectrum, and an improved upper limit of ${\cal B}(η\to e^+ e^-) < 2.2 \times 10^{-7}$ is set at 90\% confidence level.

We search for a possible $Λ_{c} \bar{Σ}_{c}$ bound state, denoted as $H_{c}^{\pm}$, via the $ e^{+}e^{-} \to π^{+} π^{-} Λ_{c}^{+}\barΛ_{c}^{-}$ process for the first time. This analysis utilizes 207.8 and 159.3 pb$^{-1}$ of $e^{+}e^{-}$ annihilation data at the center-of-mass energies of 4918.02 and 4950.93 MeV, respectively, collected with the BESIII detector at the BEPCII collider. No statistically significant signal is observed. The upper limits of the product of Born cross section and branching fraction $σ(e^{+}e^{-} \to π^{+} H_c^{-} + c.c.) \times \mathcal{B}(H_c^{-} \rightarrow π^{-}Λ_{c}^{+}\barΛ_{c}^{-})$ at a 90\% confidence level are reported at each energy point and for various $H_{c}$ mass hypotheses (4715, 4720, 4725, 4730, and 4735 MeV/$c^{2}$) and widths (5, 10, or 20 MeV), with the upper limits ranging from 1.1 pb to 6.4 pb.

This paper works on heuristic solver for joint assignment and routing optimization problem. Study on previous works shows that MIP based exact solvers can only provide efficient solutions for small to moderate size problems, due to exponentially growing computational complexity. This paper proposes to start with high quality initial guess through Hungarian algorithm based assignment and heuristic cycle merging algorithm. Subsequently, the solution is improved based on a proposed shaking algorithm to improve the assignment and routing sequence. In addition, the shaking approach also enables the Simulated Annealing algorithm to further improve the solution, which is very difficult if it is purely based on random sampling updates of item and placeholder sequences. Extensive experimental validation comparing with ground truth from the previously shared database shows that the introduced solver is much more efficient than the Gurobi solver especially for large size problems, with a 1000 node pair problem being solved within 1 min in Python implementation. The solution accuracy is within a percent in general as compared with ground truth in database. Although there are spaces for the pro

Using $(10.087\pm0.044)\times10^9$ $J/ψ$ events collected with the BESIII detector at the $e^+e^-$ BEPCII collider, we present the first amplitude analysis of $J/ψ\toγp\bar{p}$ with the $p\bar p$ invariant mass in the $η_c$ mass region $[2.70,3.05]$~GeV/$c^2$. The product branching fraction $\mathcal{B}(J/ψ\toγη_c)\times\mathcal{B}(η_c\to p\bar{p})$ is precisely determined to be $(2.11\pm0.02_{\rm stat}\pm0.07_{\rm syst})\times10^{-5}$. Combining with the product branching fractions $\mathcal{B}(η_c\to p\bar{p})\times\mathcal{B}(η_c\to γγ)$ and $\mathcal{B}(J/ψ\toγη_c)\times\mathcal{B}(η_c\to γγ)$, the branching fractions of $\mathcal{B}(J/ψ\toγη_c)$ and $\mathcal{B}(η_c\toγγ)$ are calculated to be $(2.29\pm0.01_{\rm stat}\pm0.04_{\rm syst}\pm0.18_{\rm opbf})\%$ and $(2.28\pm0.01_{\rm stat}\pm0.04_{\rm syst}\pm0.18_{\rm opbf})\times10^{-4}$, respectively, which are consistent with the latest lattice quantum chromodynamics calculations. Here, opbf is the uncertainty from the other product branching fractions used in the calculation.

The electromagnetic structure of the nucleon can be determined from the scattering of electrons off a nucleon target. However, to study its axial structure, neutrino beams are required. The results from these experiments should be extrapolated to zero energy-momentum transfers to access the static properties of the nucleon. For baryons with strange quarks, hyperons, the static limit can instead be approached in semi-leptonic decays, which give direct access to the weak magnetism and axial-vector coupling strengths that are inaccessible in electromagnetic interactions. The axial-vector coupling as while weak magnetism coupling and the overall normalization, given by form factor $f_1$, are being determined with increased precision from the theory of strong interactions using a first principles formulation on the space--time lattice. Furthermore, the probability of the semi-leptonic hyperon decay is approximately proportional to $|V_{us}|^2\cdot (f_1^2+3g_1^2)$, where $V_{us}$ is the CKM matrix element responsible for the transition between an $s$ and a $u$ quark. Current determinations of $|V_{us}|$ come from kaon decays, but the results are not consistent and could indicate a deviat

An amplitude analysis of the decay $D^0 \to K_S^0 π^0 π^0$ is performed to determine the relative magnitudes and phases of different intermediate processes. The analysis uses $e^+e^-$ collision data collected at the center-of-mass energy of 3.773 GeV by the BESIII detector corresponding to an integrated luminosity of 20.3 $\rm fb^{-1}$. The absolute branching fraction of $D^0 \to K^0_S π^0 π^0$ is measured to be $(1.026 \pm 0.008_{\rm{stat.}} \pm 0.009_{\rm{syst.}}) \%$. The dominant intermediate process is $D^0 \to \bar{K}^{*}(892)^{0}(\to K^0_S π^0) π^0$, with a branching fraction of $(4.22\pm0.09_{\rm{stat.}}\pm0.14_{\rm{syst.}})\times 10^{-3}$.

In this report, we introduce the Gemini 2.X model family: Gemini 2.5 Pro and Gemini 2.5 Flash, as well as our earlier Gemini 2.0 Flash and Flash-Lite models. Gemini 2.5 Pro is our most capable model yet, achieving SoTA performance on frontier coding and reasoning benchmarks. In addition to its incredible coding and reasoning skills, Gemini 2.5 Pro is a thinking model that excels at multimodal understanding and it is now able to process up to 3 hours of video content. Its unique combination of long context, multimodal and reasoning capabilities can be combined to unlock new agentic workflows. Gemini 2.5 Flash provides excellent reasoning abilities at a fraction of the compute and latency requirements and Gemini 2.0 Flash and Flash-Lite provide high performance at low latency and cost. Taken together, the Gemini 2.X model generation spans the full Pareto frontier of model capability vs cost, allowing users to explore the boundaries of what is possible with complex agentic problem solving.