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Ye Tian

Publications and source records attributed to Ye Tian.

At least 19 recordsLinked to original sources
arXiv2026

Turning Multipath into Positioning Information: Persistent Electromagnetic-Twin DoA Localization

Reflected paths can support localization when line-of-sight (LoS) links are blocked, but their usefulness depends on how accurately the stored model predicts their directions. This paper develops electromagnetic-twin (ET) direction-of-arrival (DoA) localization with a persistent, physically calibrated path-angle field. A nominal floor plan supplies virtual-path geometry, and sparse reference probes at known positions learn a compact directional correction. Field order and regularization are selected from calibration data alone. For each user, unordered DoAs are processed by joint position estimation and one-to-one path association, using assignment-aware multistart search and robust Gauss--Newton refinement. New trusted probes update the stored correction through recursive least squares (RLS), without reprocessing all previous measurements. The analysis establishes calibration rank conditions, objective descent, and conditional information gains from reflected paths, while also distinguishing the decay of initialization bias from the convergence of localization error. Synthetic experiments use 500 queries per operating point. At 50% LoS blockage and $1.5^\circ$ DoA error, the calibrated ET achieves 0.39 m root-mean-square error, versus 0.55 m for nominal geometry. After a wall change, its final error is 0.43 m, versus 0.63 m for a frozen calibrated map. Equal-evidence comparisons show competitive calibrated alternatives and faster abrupt-change recovery with sliding-window updates than with the configured RLS. The results support reusable directional calibration, not a universally superior map or update rule, and validation is limited to synthetic first-order reflections with trusted calibration labels.

eess.SP↗
arXiv2026

ClueTracer: Question-to-Vision Clue Tracing for Training-Free Hallucination Suppression in Multimodal Reasoning

Large multimodal reasoning models solve challenging visual problems via explicit long-chain inference: they gather visual clues from images and decode clues into textual tokens. Yet this capability also increases hallucinations, where the model generates content that is not supported by the input image or the question. To understand this failure mode, we identify \emph{reasoning drift}: during clue gathering, the model over-focuses on question-irrelevant entities, diluting focus on task-relevant cues and gradually decoupling the reasoning trace from visual grounding. As a consequence, many inference-time localization or intervention methods developed for non-reasoning models fail to pinpoint the true clues in reasoning settings. Motivated by these insights, we introduce ClueRecall, a metric for assessing visual clue retrieval, and present ClueTracer, a training-free, parameter-free, and architecture-agnostic plugin for hallucination suppression. ClueTracer starts from the question and traces how key clues propagate along the model's reasoning pathway (question $\rightarrow$ outputs $\rightarrow$ visual tokens), thereby localizing task-relevant patches while suppressing spurious attention to irrelevant regions. Remarkably, \textbf{without any additional training}, ClueTracer improves all \textbf{reasoning} architectures (including \texttt{R1-OneVision}, \texttt{Ocean-R1}, \texttt{MM-Eureka}, \emph{etc}.) by $\mathbf{1.21\times}$ on reasoning benchmarks

cs.CV↗
arXiv2026

Phase-Coordinated Model Predictive Control for AI Training Power Smoothing

Periodic power fluctuations from artificial intelligence (AI) training workloads can excite oscillations in power systems. This paper proposes a model predictive control (MPC) framework that exploits short start-time delays to mitigate power fluctuations at the computing source. By coordinating the relative phases of independent training jobs, the proposed method reduces the superposition of oscillatory power components without interrupting running jobs or requiring additional power-smoothing hardware. To obtain a tractable optimization formulation, cosine and sine projections are employed to quantify the dominant oscillatory power components, and their magnitudes are used to construct a convex quadratic power-smoothing objective. The receding-horizon framework updates launch decisions as new workload information becomes available while maintaining scheduling feasibility under limited computation time. Numerical results demonstrate reductions of 74.39\% in mean-square power fluctuations within the 0.2--2-Hz band and 46.6\% in power standard deviation, with an average launch delay of 2.188~s. All launch deadlines are satisfied, and the maximum end-to-end computation time is 0.390~s, below the 0.50-s MPC update period.

eess.SY↗
arXiv2026

Ultrafast light-electron modulation for free-electron quantum optics: fundamentals and applications

Coherent optical modulation of free-electron wavepackets offers a route to ultrafast beam steering, energy exchange, temporal compression, and quantum-state shaping. Recent advances in photon-induced near-field electron microscopy (PINEM), ultrafast Kapitza-Dirac effect (KDE) and dielectric laser acceleration (DLA) have demonstrated strong light-electron coupling, but longitudinal energy modulation, transverse momentum transfer, and wavepacket-dependent diffraction are still commonly described with separate models. Here, we develop a unified Light-Electron Modulation (LEM) framework that treats optical near fields as programmable phase modulators for free-electron wavepackets. Within this framework, we identify longitudinal and transverse modulation channels, analogous to electro-optic phase modulation and acousto-optic diffraction of light, respectively, and show how their operation depends on wavepacket extent, recoil, phase matching, and optical-field geometry. This formulation organizes PINEM, Raman-Nath and Bragg diffraction, DLA-like acceleration, and anomalous PINEM as limiting regimes of a common modulation picture. The resulting framework provides design principles for free-electron modulators that can control energy spectra, temporal density, transverse wavefronts, and electron-photon quantum interfaces, establishing LEM as a device-level platform for programmable femtosecond-to-attosecond matter-wave control.

physics.optics↗
arXiv2026

Maps between circle bundles: Fiber-preserving, Finiteness and Realization of mapping degree sets

Let $E_i$ be an oriented circle bundle over a closed oriented $n$-manifold $M_i$ with $i=1,2$ and assume that $M_2$ is aspherical. We prove the following geometric realization result, which extends the classical result for maps between circle bundles over surfaces to all dimensions $\geq 2$. Let $f\colon E_1\to E_2$ be a map. If the induced homomorphism $f_*$ on $π_1$ sends the fiber subgroup of $π_1(E_1)$ to the fiber subgroup of $π_1(E_2)$, then there is a fiber-preserving map in the homotopy class of $f$. Using the above result and a computation of the mapping degree set of fiber-preserving maps between $E_1$ and $E_2$, we obtain the following results on the finiteness and the realization problems of mapping degree sets. ($\mathcal F$) The mapping degree set $D(E_1, E_2)$ is finite if $M_2$ is hyperbolic and the Euler class of $E_2$ is not torsion. ($\mathcal R$) For any finite set $A$ of integers containing $0$ and any $n>2$, $A$ is the mapping degree set $D(M,N)$ for some closed oriented $n$-manifolds $M$ and $N$. Item ($\mathcal F$) extends the previously known $3$-dimensional case to all dimensions $\geq 3$. Item ($\mathcal R$) gives a complete answer to the realization problem on finite sets of integers (containing 0) in all dimensions, including in particular the previously unknown cases in dimensions $ 4$ and $5$.

math.GT↗
arXiv2026

Lamarck's Driving School: Discovering Autonomous Driving Training Strategies through Evolutionary Competition

Autonomous driving capabilities depend strongly on the distribution of scenarios encountered during training. Existing methods commonly construct or dynamically adapt training scenario distributions using surrogate criteria such as realism, difficulty, or risk. However, these predefined surrogates may misrepresent training value, leading to inefficient use of training resources. To address this limitation, we propose a Lamarckian evolutionary framework that replaces surrogate-based guidance with competition among candidate distributions. We formulate training strategy discovery as a multi-stage bilevel optimization problem and use Lamarckian evolution algorithm to approximate its solution. At the outer level, Darwinian crossover, mutation, and selection explore the scenario distribution space; at the inner level, policy learning acquires new capabilities, and Lamarckian inheritance transfers them to subsequent stages, allowing scenario distributions and policy capabilities to co-evolve. The resulting evolutionary trajectories reveal recurring stage-wise regularities among high-value distributions, characterized by capability accumulation through stage-wise challenge rotation. We further distill these regularities into a lightweight, reusable Lamarckian Training Strategy. Experiments show that, compared with the baseline, the complete framework reduces performance loss by up to 25.07%, while the lightweight strategy still achieves a 19.13% reduction. These results demonstrate that evolutionary competition can both discover effective training strategies and reveal reusable stage-wise patterns in how the value of training distributions changes with policy capability. Code is available on GitHub.

cs.AI↗
arXiv2026

Superposed Inference for Hyperdimensional Computing

Hyperdimensional computing (HDC) is attractive for efficient and robust learning, but conventional inference still encodes every query independently, repeatedly paying the cost of high-dimensional projection. We introduce SupHDC, a new inference paradigm that processes multiple queries through a shared encoding computation. SupHDC assigns lightweight random slot keys, superposes the keyed queries before encoding, and uses slot-specific classifiers to recover their individual predictions. A random-feature kernel view explains why exact recovery of each hypervector is unnecessary: inference only needs to preserve the class evidence that determines the prediction. Across ten datasets, SupHDC achieves 1.39x analytical speedup with no average accuracy loss, and up to 2.08x speedup with only a 2.67 percentage-point mean accuracy loss. On a Raspberry Pi~5, it delivers 2.01x measured wall-clock speedup with a 2.26 percentage-point loss in mean prediction accuracy. SupHDC shows that high-dimensional redundancy can be used not only for robustness, but also as capacity for shared inference.

cs.LG↗
arXiv2026

Controlling Atom Array in an Ultra-high-cooperativity Optical Cavity

Neutral-atom array and cavity quantum electrodynamics offer complementary strengths for quantum science: scalable, reconfigurable qubit architectures and strong coherent light-matter coupling. Combining them in a single platform requires an optical cavity with simultaneously high cooperativity, sufficient mode volume to accommodate atom array, and ample side optical access for atom trapping, imaging, cooling, and rearrangement, a combination that is challenging to achieve. Here we realize an atomic array integrated with a millimeter-scale Fabry--Pérot cavity whose optically-characterized single-atom cooperativity reaches $η_{\mathrm{cav}}=125\pm13$. Atom-cavity transmission spectra of trapped atoms yield an effective spectroscopic cooperativity $η_{\mathrm{spec}}=106\pm 3$, providing an in-situ verification of strong coupling in the integrated platform. In this integrated system, we achieve a state-measurement fidelity of $99.925^{+15}_{-18}\%$ for a single atom with a 4-$μ$s readout time. We also demonstrate simultaneous coupling of up to 21 individually trapped atoms to the antinode of the cavity mode. The key technical advance is a two-step mirror-fabrication method combining precision mechanical shaping and carbon-dioxide laser polishing, which produces concave fused-silica mirrors with sub-millimeter radii of curvature and residual roughness below $2~Å$. Our results establish a regime of cavity-integrated atomic array that simultaneously provides high cooperativity, large mode volume, and flexible manipulation of individual atoms, opening opportunities for cavity-assisted quantum state readout and long-range entanglement-engineering in atom-array platforms.

quant-ph↗
arXiv2026

A proof of Sylvester's conjecture

We prove Sylvester's conjecture, originating in his 1879 study of ternary cubic equations, that every prime $p\equiv4,7,8\pmod9$ is a sum of two rational cubes. Elkies announced a proof for the classes $4$ and $7$ in 1994, and Yin recently supplied a complete proof. For the remaining class $p\equiv8\pmod9$, we prove that the elliptic curve $E_p:y^2=x^3+p^2/4$ has analytic rank one, as predicted by the Birch and Swinnerton-Dyer conjecture, and so $p$ is a sum of two rational cubes. The proof begins by adapting the auxiliary Rankin--Selberg construction from the authors' work on the rank one converse for CM elliptic curves. The Rankin--Selberg $L$-function factors as the $L$-function of $E_p$ times a complementary $L$-function. Chan's $3$-isogeny descent and the rank zero converse show that the complementary central $L$-value is non-zero, and so it suffices to prove that a cubic component of the associated Heegner point is non-torsion. A basic difficulty is that the unweighted Hecke trace of the underlying CM orbit vanishes. Our decisive idea is to take $λ$-division before taking the trace, where $λ=1-ω$ and $ω$ is a primitive cube root of unity. We prove that the resulting division boundary is non-zero by analysing Frobenius at $p$. The Galois action on the CM orbit and ramification theory then transfer this non-vanishing to the cubic component.

math.NT↗
arXiv2026

Design and Simulation Study of the Hadronic Calorimeter for the EicC Zero Degree Calorimeter

The Zero-Degree Calorimeter (ZDC) at the proposed Electron-Ion Collider in China (EicC) is essential for detecting forward-going neutral particles and supporting the core nucleon spin and 3D imaging physics programs. In this work, a highly compact Spaghetti Calorimeter (SPACAL) architecture is proposed and optimized as the baseline design for the ZDC hadronic section. To systematically and quantitatively evaluate its physics capabilities, a comprehensive end-to-end Geant4 simulation framework was developed, integrating energy deposition, optical photon transport, photo-detection, and modeled front-end signal digitization. The optimized detector demonstrates excellent performance for neutron detection, achieving an energy resolution of $33.42\%/\sqrt{E/\mathrm{GeV}} + 1.47\%$ and a timing resolution of approximately 500 ps, outperforming the targeted specification. Furthermore, full-system simulations incorporating the upstream electromagnetic calorimeter yield a sub-centimeter transverse position resolution. Moreover, topological shower shape analysis across the full detector system provides robust particle identification (PID), with photon-neutron separation accuracy reaching over $99\%$ for high-energy incident particles. These quantitative results confirm that the SPACAL design fully satisfies the stringent operational and physics requirements of the EicC forward kinematic region.

physics.ins-det↗
arXiv2026

Harnessing Intrinsic Subject-Aware Attention for Controllable Multi-Subject Video Generation

Multi-subject video generation faces two key challenges: uncontrollable fidelity strength and potential semantic drift. We address these by analyzing the internal mechanisms of Diffusion Transformers (DiTs). We found that certain attention blocks naturally form an Intrinsic Spatial Grounding Map (ISGM) that precisely locates reference subjects. Building on this insight, we propose Dual-phase Intrinsic Attention Leveraging (DIAL), a framework that uses these internal signals for both training and inference. In low-noise stages, we use ISGM to guide the attention mechanism, allowing precise control over fidelity strength during inference without retraining. In high-noise stages, we use these same maps to automatically build preference pairs at no additional cost for Reinforcement Learning (RL). This RL procedure effectively anchors the model's attention to reference subjects and mitigates semantic drift. Extensive experiments show that DIAL significantly outperforms baseline models on the OpenS2V-Eval benchmark, consistently improving identity consistency and enabling controllable fidelity strength.

cs.CV↗
arXiv2026

HiPHI: A Large-Scale Benchmark for High-Precision Human Motion and Object-Interaction

Humanoid intelligence requires learning over an extremely diverse space of whole-body motions and physically grounded interactions. However, existing embodied datasets remain fundamentally limited: internet-scale video data lack precise physical states and interaction grounding, while laboratory motion datasets provide high fidelity but only narrow behavioral coverage. This mismatch creates a critical bottleneck for scalable humanoid policy learning. We present HiPHI, a 600+ hour scale high-fidelity whole-body human motion dataset designed to systematically maximize coverage of the human motion and interaction manifold. HiPHI is theoretically guided by FrameNet, a linguistic framework organizing human primitives. Created using an optical motion capture pipeline, HiPHI provides sub-millimeter spatial marker tracking accuracy for full-body human motion and mesh-level object trajectories. We further introduce a benchmark suite evaluating motion-space diversity, interaction grounding, object consistency, and physical AI applications. Our analyses demonstrate that HiPHI significantly expands motion coverage compared to existing motion datasets while maintaining high-fidelity interaction quality, and establishes a scalable data foundation for training, evaluating, and generalizing humanoid policies in real-world embodied tasks, where similar extensions are also applicable to motion prior models in computer graphics. Project page: https://noitom-robotics.github.io/hiphi/

cs.RO↗
arXiv2026

LifeAgentBench: Benchmarking LLMs for Long-Horizon, Cross-Dimensional Lifestyle Health Reasoning

Personalized lifestyle health analysis requires long-horizon, multi-dimensional reasoning over heterogeneous lifestyle signals, and recent advances in mobile sensing and large language models (LLMs) make such support increasingly feasible. However, the capabilities of current LLMs in this setting remain insufficiently understood due to the lack of systematic benchmarks. In this paper, we introduce LifeAgentBench, a large-scale QA benchmark for long-horizon, cross-dimensional, and multi-user lifestyle health reasoning, containing 22,573 questions spanning from basic retrieval to complex reasoning. We release an extensible benchmark construction pipeline and a standardized evaluation protocol, deriving verifiable answers through executable queries and programs to support reliable assessment. We then systematically evaluate 13 representative LLMs on LifeAgentBench and identify key bottlenecks in long-horizon aggregation and cross-dimensional reasoning. Motivated by these findings, we propose LifeAgent, a tool-augmented reasoning baseline that decomposes complex queries, performs multi-step evidence retrieval, and invokes tools for deterministic aggregation. LifeAgent substantially enhances LLMs' capabilities on challenging reasoning tasks, achieving clear improvements over widely used baselines and showing potential for health reasoning in everyday scenarios. The benchmark is publicly available.

cs.AI↗
arXiv2026

FLARE: A Failure-Aware Framework for Autonomous Correction and Recovery in Visual-Language Robotic Manipulation

Vision-Language-Action Models~(VLAs) have demonstrated significant promise in generalizing to complex, long-horizon robotic manipulation tasks. However, their performance remains brittle, as they are typically trained on trajectory-monotonic, failure-free demonstrations. This reliance on ``perfect" data leaves them unable to recover from common execution errors, such as a missed grasp, a dropped object, or an unexpected collision. In this paper, we propose FLARE, a novel framework that endows VLAs with robust error recovery capabilities through a ``Retry" and ``Reset" paradigm. First, we introduce a ``Retry" mechanism by injecting perturbation and bridging segments that decouple robot pose from environment state into demonstrations, enabling the policy to autonomously handle execution deviations. Second, to address critical, state-breaking (OOD) failures, we introduce a ``Reset" pipeline. We leverage an MLLM for offline failure analysis to automatically identify OOD states from execution videos. This analysis enables the efficient, targeted collection of a small library of object-centric ``Reset" skills, which are trained to restore the environment to a task-valid state. Our full framework integrates these learned policies. At inference, an online MLLM monitor arbitrates between task execution and ``Reset" skills. Experiments on challenging, contact-rich manipulation tasks show our approach significantly improves task success and robustness.

cs.RO↗
arXiv2026

On-Policy Self-Distillation in Diffusion Models

Reinforcement learning can align diffusion models with human preferences and task-specific objectives, but endpoint rewards do not specify how an intermediate denoising prediction should change. We introduce DiffusionOPSD as an on-policy self-distillation framework that converts image-level reward guidance into explicit targets for clean-output predictions at sampled queries. At each outer iteration, a frozen behavior policy generates trajectories and supplies query states and anchors. Reward gradients construct bounded positive and negative targets around each anchor. The trainable policy fits these targets as detached supervision through finite fitting before an exponential moving average update refreshes the behavior policy. This setup lets us measure target construction and finite realization separately. Controlled same-query experiments show that larger target-construction gains do not necessarily translate into larger realized gains after a single fitting update. Across SD 3.5-M and the step-distilled Z-Image-Turbo, our approach achieves the best final held-out scores in 19 of 20 reward-matched settings across two backbones and ten evaluators. It outperforms the strongest competing method by up to 44.0% and reduces training GPU-hours relative to DiffusionNFT by 40% on SD 3.5-M and 63% on Z-Image-Turbo. These results support on-policy self-distillation as an efficient and analyzable approach to diffusion post-training by converting image-level reward guidance into explicit and continually refreshed intermediate supervision, thereby opening a path toward more efficient and diagnosable alignment.

cs.CV↗
arXiv2026

Sufficient Dimesion Reduction via Generalized Stein's Lemma

Sufficient dimension reduction (SDR) seeks the minimal subspace of the predictors that captures the full conditional distribution of the response, which is known as the central subspace (CS). When the response is multivariate, the problem becomes considerably more challenging, particularly when the sample size is limited. Existing methods face different limitations:inverse regression approaches rely on strong distributional assumptions and matrix inversion, and their multi-response extensions suffer from severe slice sparsity; forward regression methods depend on computationally intensive iterative smoothing whose cost grows with the response dimension; and deep learning-based approaches demand large amounts of labeled data. To circumvent these shortcomings, we propose an SDR framework based on the generalized Stein's lemma. Our method constructs a cross-moment matrix between the multivariate response and the marginal score function of the predictors, and recovers the CS via its singular value decomposition. The proposed method does not rely on the linearity condition, avoids matrix inversion as well as iterative smoothing, and can leverage unlabeled data when available. We establish convergence guarantees for the proposed estimator under standard regularity conditions. Moreover, we propose a practical rank-selection algorithm to estimate the dimension of the CS. Extensive simulation studies and a real data application demonstrate that the proposed methods consistently outperform existing approaches across a variety of settings, particularly in moderate-dimensional, label-scarce scenarios with high noise levels.

stat.ML↗
arXiv2026

High Precision Fundamental Physics Experiments at JLab with Spin-transparent Storage Rings of Low-energy Polarized Electron Beams

A breakthrough in fundamental physics experiments measuring particle spin precession may happen if spin-transparent storage rings become adopted tools for such experiments. We present a new design of highly specialized table-sized storage rings, which use low-energy polarized electron beams and Mott polarimetry. Based on the spin transparency ansatz, the spin precession stemming from the magnetic dipole moment is canceled at any beam energy after an electron's turn along the periodic orbit in the ring. Meanwhile, a spin precession induced by the fundamental physics of interest, e.g., the electron's permanent electric dipole moment (EDM) and/or ultralight-dark-matter-mediated forces such as axions, will accumulate. However, capitalizing on such types of rings is not only desirable for measurements of EDMs and axion searches relevant to $CP$ violation and matter-antimatter asymmetry in the Universe, but may also find very promising applications in quantum computing.

nucl-ex↗
arXiv2026

Bridging Source Code and Bytecode for Smart Contract Vulnerability Detection via Dual-Perspective Cross-Modal Distillation

Smart contract vulnerabilities have caused substantial financial losses, yet most deployed contracts are closed-source, forcing detection to operate on bytecode --- which lacks the high-level semantic information available in source code. To compensate, recent cross-modal methods distill knowledge from source-code models into bytecode detectors by aligning the two modalities. However, these methods align source and bytecode only at the graph level through global embedding matching, whereas vulnerability is a property of specific nodes and control-flow regions --- so the student preserves global structure but loses the fine-grained node-level correspondences that decide a contract's safety. We propose ExDoS, a dual-focus cross-modal distillation framework that addresses this limitation from three angles. First, We introduce aligned vulnerability patterns that mark corresponding nodes in both source and bytecode graphs, establishing the missing node-level supervision and cross-modal correspondence; with these correspondences in place, we then propose a dual-attention graph network that applies relation-aware attention and adaptive node weighting so that vulnerable nodes survive into the graph-level embedding rather than being diluted by uniform aggregation; given preserved node-level signals and established correspondences, we also propose a dual-focus distillation objective whose global loss retains whole-graph alignment while its local loss matches expert-paired nodes to enforce region-level consistency. On real-world contracts, ExDoS reaches F1 of 90.86%, 90.23%, and 83.94% for reentrancy, timestamp dependency, and infinite loop --- improving by 2.7--5.1 points over the strongest per-type baseline. Our ablations confirm that the pattern annotations, local alignment, and attentive encoding each address a distinct limitation of prior distillation-based approaches.

cs.CR↗