arXiv subjects

Xiang Chen

Publications and source records attributed to Xiang Chen.

At least 199 records · Page 11Linked to original sources

arXiv2025

EBT-Policy: Energy Unlocks Emergent Physical Reasoning Capabilities

Implicit policies parameterized by generative models, such as Diffusion Policy, have become the standard for policy learning and Vision-Language-Action (VLA) models in robotics. However, these approaches often suffer from high computational cost, exposure bias, and unstable inference dynamics, which lead to divergence under distribution shifts. Energy-Based Models (EBMs) address these issues by learning energy landscapes end-to-end and modeling equilibrium dynamics, offering improved robustness and reduced exposure bias. Yet, policies parameterized by EBMs have historically struggled to scale effectively. Recent work on Energy-Based Transformers (EBTs) demonstrates the scalability of EBMs to high-dimensional spaces, but their potential for solving core challenges in physically embodied models remains underexplored. We introduce a new energy-based architecture, EBT-Policy, that solves core issues in robotic and real-world settings. Across simulated and real-world tasks, EBT-Policy consistently outperforms diffusion-based policies, while requiring less training and inference computation. Remarkably, on some tasks it converges within just two inference steps, a 50x reduction compared to Diffusion Policy's 100. Moreover, EBT-Policy exhibits emergent capabilities not seen in prior models, such as zero-shot recovery from failed action sequences using only behavior cloning and without explicit retry training. By leveraging its scalar energy for uncertainty-aware inference and dynamic compute allocation, EBT-Policy offers a promising path toward robust, generalizable robot behavior under distribution shifts.

cs.RO↗

arXiv2025

From Nucleobases to DNA: Clustering-Triggered Emission and Pressure-Induced Emission Enhancement

The photophysical properties of deoxyribonucleic acid (DNA) are fundamental to life sciences and biophotonics. While previous studies have generally been restricted to fluorescence, attributing it to pi-pi* transitions and charge transfer within nucleobases in dilute solution, these understandings fail to explain the pronounced visible emission in physiological and aggregated states, and moreover, ignore the possible phosphorescence. Addressing this critical gap, we systematically investigate native DNA across its structural hierarchy, from nucleobases to single-stranded chains, under varying states. We demonstrate that DNA exhibits excitation-dependent emission in aggregates and moreover room-temperature phosphorescence (RTP) in the solid state. These behaviors are rationalized by the clustering-triggered emission (CTE) mechanism, where nucleobases and electron-rich nonaromatic moieties like sugar and phosphate synergistically contribute to DNA photophysics. High-pressure experiments reveal a 207-fold luminescence enhancement for nucleotides at 26 GPa, largely retained after decompression, underscoring the precise control of emission by intermolecular interactions. This study not only elucidates the intrinsic luminescence mechanism of DNA and but also establishes pressure modulation as a versatile approach for developing new nucleic acid-inspired luminescent materials.

physics.chem-ph↗

arXiv2025

X-Reflect: Cross-Reflection Prompting for Multimodal Recommendation

Large Language Models (LLMs) have been shown to enhance the effectiveness of enriching item descriptions, thereby improving the accuracy of recommendation systems. However, most existing approaches either rely on text-only prompting or employ basic multimodal strategies that do not fully exploit the complementary information available from both textual and visual modalities. This paper introduces a novel framework, Cross-Reflection Prompting, termed X-Reflect, designed to address these limitations by prompting Multimodal Large Language Models (MLLMs) to explicitly identify and reconcile supportive and conflicting information between text and images. By capturing nuanced insights from both modalities, this approach generates more comprehensive and contextually rich item representations. Extensive experiments conducted on two widely used benchmarks demonstrate that our method outperforms existing prompting baselines in downstream recommendation accuracy. Furthermore, we identify a U-shaped relationship between text-image dissimilarity and recommendation performance, suggesting the benefit of applying multimodal prompting selectively. To support efficient real-time inference, we also introduce X-Reflect-keyword, a lightweight variant that summarizes image content using keywords and replaces the base model with a smaller backbone, achieving nearly 50% reduction in input length while maintaining competitive performance. This work underscores the importance of integrating multimodal information and presents an effective solution for improving item understanding in multimodal recommendation systems.

cs.IR↗

arXiv2025

Rethinking Nighttime Image Deraining via Learnable Color Space Transformation

Compared to daytime image deraining, nighttime image deraining poses significant challenges due to inherent complexities of nighttime scenarios and the lack of high-quality datasets that accurately represent the coupling effect between rain and illumination. In this paper, we rethink the task of nighttime image deraining and contribute a new high-quality benchmark, HQ-NightRain, which offers higher harmony and realism compared to existing datasets. In addition, we develop an effective Color Space Transformation Network (CST-Net) for better removing complex rain from nighttime scenes. Specifically, we propose a learnable color space converter (CSC) to better facilitate rain removal in the Y channel, as nighttime rain is more pronounced in the Y channel compared to the RGB color space. To capture illumination information for guiding nighttime deraining, implicit illumination guidance is introduced enabling the learned features to improve the model's robustness in complex scenarios. Extensive experiments show the value of our dataset and the effectiveness of our method. The source code and datasets are available at https://github.com/guanqiyuan/CST-Net.

cs.CV↗

arXiv2025

Concise and Sufficient Sub-Sentence Citations for Retrieval-Augmented Generation

In retrieval-augmented generation (RAG) question answering systems, generating citations for large language model (LLM) outputs enhances verifiability and helps users identify potential hallucinations. However, we observe two problems in the citations produced by existing attribution methods. First, the citations are typically provided at the sentence or even paragraph level. Long sentences or paragraphs may include a substantial amount of irrelevant content. Second, sentence-level citations may omit information that is essential for verifying the output, forcing users to read the surrounding context. In this paper, we propose generating sub-sentence citations that are both concise and sufficient, thereby reducing the effort required by users to confirm the correctness of the generated output. To this end, we first develop annotation guidelines for such citations and construct a corresponding dataset. Then, we propose an attribution framework for generating citations that adhere to our standards. This framework leverages LLMs to automatically generate fine-tuning data for our task and employs a credit model to filter out low-quality examples. Our experiments on the constructed dataset demonstrate that the propose approach can generate high-quality and more readable citations.

cs.CL↗

arXiv2025

EDIT: Enhancing Vision Transformers by Mitigating Attention Sink through an Encoder-Decoder Architecture

In this paper, we propose EDIT (Encoder-Decoder Image Transformer), a novel architecture designed to mitigate the attention sink phenomenon observed in Vision Transformer models. Attention sink occurs when an excessive amount of attention is allocated to the [CLS] token, distorting the model's ability to effectively process image patches. To address this, we introduce a layer-aligned encoder-decoder architecture, where the encoder utilizes self-attention to process image patches, while the decoder uses cross-attention to focus on the [CLS] token. Unlike traditional encoder-decoder framework, where the decoder depends solely on high-level encoder representations, EDIT allows the decoder to extract information starting from low-level features, progressively refining the representation layer by layer. EDIT is naturally interpretable demonstrated through sequential attention maps, illustrating the refined, layer-by-layer focus on key image features. Experiments on ImageNet-1k and ImageNet-21k, along with transfer learning tasks, show that EDIT achieves consistent performance improvements over DeiT3 models. These results highlight the effectiveness of EDIT's design in addressing attention sink and improving visual feature extraction.

cs.CV↗

arXiv2025

On a Class of Berndt-type Integrals and Related Barnes Multiple Zeta Functions

This paper investigates a class of special Berndt-type integral calculations where the integrand contains only hyperbolic cosine functions. The research approach proceeds as follows: Firstly, through contour integration methods, we transform the integral into a Ramanujan-type hyperbolic infinite series. Subsequently, we introduce a $θ$-parameterized auxiliary function and apply the residue theorem from complex analysis to successfully simplify mixed-type denominators combining hyperbolic cosine and sine terms into a normalized Ramanujan-type hyperbolic infinite series with denominators containing only single hyperbolic function terms. For these simplified hyperbolic infinite series, we combine properties of Jacobi elliptic functions with composite analytical techniques involving Fourier series expansion and Maclaurin series expansion. This ultimately yields an explicit expression as a rational polynomial combination of $Γ(1/4)$ and $π^{-1/2}$. Notably, this work establishes a connection between the integral and Barnes multiple zeta functions, providing a novel research pathway for solving related problems.

math.NT↗

arXiv2025

StyleDecipher: Robust and Explainable Detection of LLM-Generated Texts with Stylistic Analysis

With the increasing integration of large language models (LLMs) into open-domain writing, detecting machine-generated text has become a critical task for ensuring content authenticity and trust. Existing approaches rely on statistical discrepancies or model-specific heuristics to distinguish between LLM-generated and human-written text. However, these methods struggle in real-world scenarios due to limited generalization, vulnerability to paraphrasing, and lack of explainability, particularly when facing stylistic diversity or hybrid human-AI authorship. In this work, we propose StyleDecipher, a robust and explainable detection framework that revisits LLM-generated text detection using combined feature extractors to quantify stylistic differences. By jointly modeling discrete stylistic indicators and continuous stylistic representations derived from semantic embeddings, StyleDecipher captures distinctive style-level divergences between human and LLM outputs within a unified representation space. This framework enables accurate, explainable, and domain-agnostic detection without requiring access to model internals or labeled segments. Extensive experiments across five diverse domains, including news, code, essays, reviews, and academic abstracts, demonstrate that StyleDecipher consistently achieves state-of-the-art in-domain accuracy. Moreover, in cross-domain evaluations, it surpasses existing baselines by up to 36.30%, while maintaining robustness against adversarial perturbations and mixed human-AI content. Further qualitative and quantitative analysis confirms that stylistic signals provide explainable evidence for distinguishing machine-generated text. Our source code can be accessed at https://github.com/SiyuanLi00/StyleDecipher.

cs.CL↗

arXiv2025

Interlayer coupling enhanced superconductivity near 100 K in La$_{3-x}$Nd$_x$Ni$_2$O$_7$

Systematically controlling the superconducting transition temperature ($T_\text{c}$) in the bilayer Ruddlesden-Popper nickelate La$_3$Ni$_2$O$_7$ remains a significant challenge. Here, we address this by synthesizing high-quality polycrystalline La$_{3-x}$Nd$_x$Ni$_2$O$_7$ ($0 \leq x \leq 2.4$) with record-level rare-earth substitution. Nd doping compresses the lattice, particularly along the $c$ axis, enhances the spin density wave transition temperature, and elevates the pressure required for the orthorhombic-to-tetragonal structural transition. Superconductivity is observed across all doping levels under high pressures, with the onset $T_\text{c}$ rising to $\sim$93~K for $x = 2.1$ and $2.4$ from the electronic transport measurement. Using the radio-frequency transmission technique, newly applied to nickelate superconductors, we detect signatures of superconductivity at $98 \pm 2$~K in the $x=2.4$ compound, pushing the $T_\text{c}$ frontier further. We identify a universal linear relationship where $T_\text{c}$ decreases with the $c$-axis lattice parameter at a rate of approximately $-28$~K/Å, demonstrating that enhanced interlayer magnetic exchange coupling is the dominant mechanism for superconducting pairing. Our work establishes the critical role of magnetism and provides a unified structural descriptor for elevating $T_\text{c}$ in bilayer nickelates.

cond-mat.supr-con↗

arXiv2025

Superconductivity in monolayer-trilayer phase of La$_3$Ni$_2$O$_7$ under high pressure

The discovery of 80 K superconductivity in pressurized bilayer Ruddlesden-Popper (RP) nickelate La$_3$Ni$_2$O$_7$ has established a new high-temperature superconductor family. The quest to understand the governing principles of RP nickelate superconductivity has become a central focus in condensed matter physics. Here, we report a critical advance by synthesizing and investigating a distinct structural polymorph of the same compound: the monolayer-trilayer (1313) hybrid phase of La$_3$Ni$_2$O$_7$. Under high pressure, synchrotron X-ray diffraction and Raman spectroscopy reveal a structural transition from the orthorhombic $Cmmm$ to the tetragonal $P4/mmm$ space group at 13~GPa. Above 19 GPa, the phase exhibits a clear superconducting transition, confirmed by a zero-resistance state, albeit at a significantly reduced temperature of 3.6 K. The stark contrast with the 80 K transition in the bilayer phase provides a uniquely clean experimental comparison. Our results demonstrate that the superconducting transition temperature is directly governed by the nature of the interlayer coupling, and the bilayer NiO$_6$ block as the essential structural motif for achieving high-$T_\text{c}$ superconductivity in the RP nickelates.

cond-mat.supr-con↗

arXiv2025

Tailored Teaching with Balanced Difficulty: Elevating Reasoning in Multimodal Chain-of-Thought via Prompt Curriculum

The effectiveness of Multimodal Chain-of-Thought (MCoT) prompting is often limited by the use of randomly or manually selected examples. These examples fail to account for both model-specific knowledge distributions and the intrinsic complexity of the tasks, resulting in suboptimal and unstable model performance. To address this, we propose a novel framework inspired by the pedagogical principle of "tailored teaching with balanced difficulty". We reframe prompt selection as a prompt curriculum design problem: constructing a well ordered set of training examples that align with the model's current capabilities. Our approach integrates two complementary signals: (1) model-perceived difficulty, quantified through prediction disagreement in an active learning setup, capturing what the model itself finds challenging; and (2) intrinsic sample complexity, which measures the inherent difficulty of each question-image pair independently of any model. By jointly analyzing these signals, we develop a difficulty-balanced sampling strategy that ensures the selected prompt examples are diverse across both dimensions. Extensive experiments conducted on five challenging benchmarks and multiple popular Multimodal Large Language Models (MLLMs) demonstrate that our method yields substantial and consistent improvements and greatly reduces performance discrepancies caused by random sampling, providing a principled and robust approach for enhancing multimodal reasoning.

cs.CL↗

arXiv2025

Two-loop QCD-corrections to $e^{+} e^{-} \rightarrow Z^{\ast} \rightarrow \boldsymbol{J /ψ}+\boldsymbol{J/ ψ}$

We present a next-to-next-to-leading-order calculation within the nonrelativistic QCD framework for the process of $e^{+}e^{-} \rightarrow Z^{\ast} \rightarrow J/ψ+J/ψ$ . We find that the NNLO contribution is 2-3 times larger than the next-to-leading-order contribution, which itself is already 3-5 times larger than the leading-order result. In the high-energy limit, we provide analytic expressions for the leading-power coefficients in the asymptotic expansion of the two-loop amplitudes. Our results are directly applicable to the bottomonium process $Z^{\ast} \rightarrow Υ+Υ$. Using the obtained hadronic amplitudes, we predict the decay width of the $Z$ boson into these rare di-charmonium and di-bottomonium final states.

hep-ph↗

arXiv2025

New Physics Search at the CEPC: a General Perspective

The Circular Electron-Positron Collider (CEPC), a proposed next-generation Higgs factory, provides new opportunities to explore physics beyond the Standard Model (SM). With its clean electron-positron collision environment and the ability to collect large samples of Higgs, W, and Z bosons, the CEPC enables precision measurements and searches for new physics. This white paper outlines the CEPC's discovery potential, including studies of exotic decays of the Higgs, Z, and top quarks, dark matter and dark sector phenomena, long-lived particles, supersymmetry, and neutrino-related signatures. Advanced detector technologies and reconstruction techniques, such as one-to-one correspondence reconstruction and jet origin identification, significantly improve sensitivity to rare and weakly interacting processes. The CEPC is particularly well suited to probe the electroweak phase transition and test models of electroweak baryogenesis and dark sector interactions. In addition, global fit analyses highlight the CEPC's complementary role in constraining a wide range of new physics scenarios. These features position the CEPC as a powerful tool for exploring the next frontier in fundamental particle physics in the post-Higgs discovery era.

hep-ex↗

arXiv2025

Mitigating Forgetting Between Supervised and Reinforcement Learning Yields Stronger Reasoners

Large Language Models (LLMs) show strong reasoning abilities, often amplified by Chain-of-Thought (CoT) prompting and reinforcement learning (RL). Although RL algorithms can substantially improve reasoning, they struggle to expand reasoning boundaries because they learn from their own reasoning trajectories rather than acquiring external knowledge. Supervised fine-tuning (SFT) offers complementary benefits but typically requires large-scale data and risks overfitting. Recent attempts to combine SFT and RL face three main challenges: data inefficiency, algorithm-specific designs, and catastrophic forgetting. We propose a plug-and-play framework that dynamically integrates SFT into RL by selecting challenging examples for SFT. This approach reduces SFT data requirements and remains agnostic to the choice of RL or SFT algorithm. To mitigate catastrophic forgetting of RL-acquired skills during SFT, we select high-entropy tokens for loss calculation and freeze parameters identified as critical for RL. Our method achieves state-of-the-art (SoTA) reasoning performance using only 1.5% of the SFT data and 20.4% of the RL data used by prior SoTA, providing an efficient and plug-and-play solution for combining SFT and RL in reasoning post-training.

cs.CL↗

arXiv2025

Determination of the Strong Coupling Constant $α_s$ from Inclusive Semi-leptonic $B$ Meson Decays

We demonstrate the feasibility of determining the strong coupling constant, $α_s$, from the inclusive semileptonic decay width of $B$ mesons. We express the semileptonic $B$ decay width as a function of $α_s(5\mathrm{\,GeV})$, the Cabibbo-Kobayashi-Maskawa matrix element $|V_{cb}|$, $b$- and $c$-quark masses in the $\overline{\mathrm{MS}}$ scheme. We fit $α_s(5\mathrm{\,GeV})$ to current world averages of the $B^{\pm}$ and $B^{0}$ semileptonic decay widths. This yields $α_s(5\mathrm{\,GeV}) = 0.245 \pm 0.009$, corresponding to a 5-flavor extrapolation of $α_s(m_{Z}) = 0.1266 \pm 0.0023$. The primary uncertainty contributions arise from the uncertainty on the perturbative expansion and the value of $|V_{cb}|$. Future advancements including higher-order perturbative calculations, and precise measurements of $|V_{cb}|$ and $B$ decay widths from upcoming $B$ and $Z$ factories, could enable this method to determine $α_s(m_{Z})$ with a competitive precision of $Δα_s(m_{Z}) \sim 0.0018$. This precision is comparable to the current accuracy of $α_s(m_{Z})$ measurements from $τ$-lepton decays, which is regarded as the most precise experimental approach.

hep-ph↗

arXiv2025

Fiber-to-chip grating couplers for Lithium Niobate on Sapphire

Lithium Niobate on Sapphire (LNOS) is an emerging platform for photonic integrated circuits, offering unique properties such as a wide transparency window, high nonlinearity, and strong electro-optic, nonlinear, and acousto-optic effects. Efficient light coupling between optical fibers and on-chip waveguides is crucial for practical applications. We present the design, simulation, and experimental demonstration of high-efficiency fiber-to-chip grating couplers for LNOS. The grating coupler design employs a self-imaging approach with a fixed period and linearly diminished filling factor, enabling a negative diffracted angle to match the fiber array and suppress higher-order diffraction. Numerical simulations predict a coupling efficiency of 42% at 1550 nm wavelength. The grating couplers are fabricated on an X-cut, 400 nm thick LN film with a 220 nm etching depth using electron beam lithography and inductively coupled plasma etching. Experimental characterization using a fiber array and a 6-axis displacement stage reveals a single-end coupling efficiency exceeding 20%, confirming the effectiveness of the design. The demonstrated grating couplers pave the way for efficient light coupling in LN-on-Sapphire photonic circuits, enabling diverse applications in classical and quantum information processing, sensing, and nonlinear optics.

physics.optics↗

arXiv2025

FastViDAR: Real-Time Omnidirectional Depth Estimation via Alternative Hierarchical Attention

In this paper we propose FastViDAR, a novel framework that takes four fisheye camera inputs and produces a full $360^\circ$ depth map along with per-camera depth, fusion depth, and confidence estimates. Our main contributions are: (1) We introduce Alternative Hierarchical Attention (AHA) mechanism that efficiently fuses features across views through separate intra-frame and inter-frame windowed self-attention, achieving cross-view feature mixing with reduced overhead. (2) We propose a novel ERP fusion approach that projects multi-view depth estimates to a shared equirectangular coordinate system to obtain the final fusion depth. (3) We generate ERP image-depth pairs using HM3D and 2D3D-S datasets for comprehensive evaluation, demonstrating competitive zero-shot performance on real datasets while achieving up to 20 FPS on NVIDIA Orin NX embedded hardware. Project page: \href{https://3f7dfc.github.io/FastVidar/}{https://3f7dfc.github.io/FastVidar/}

cs.CV↗

arXiv2025

ASIC-based Compression Accelerators for Storage Systems: Design, Placement, and Profiling Insights

Lossless compression imposes significant computational over head on datacenters when performed on CPUs. Hardware compression and decompression processing units (CDPUs) can alleviate this overhead, but optimal algorithm selection, microarchitectural design, and system-level placement of CDPUs are still not well understood. We present the design of an ASIC-based in-storage CDPU and provide a comprehensive end-to-end evaluation against two leading ASIC accelerators, Intel QAT 8970 and QAT 4xxx. The evaluation spans three dominant CDPU placement regimes: peripheral, on-chip, and in-storage. Our results reveal: (i) acute sensitivity of throughput and latency to CDPU placement and interconnection, (ii) strong correlation between compression efficiency and data patterns/layouts, (iii) placement-driven divergences between microbenchmark gains and real-application speedups, (iv) discrepancies between module and system-level power efficiency, and (v) scalability and multi-tenant interference is sues of various CDPUs. These findings motivate a placement-aware, cross-layer rethinking of hardware (de)compression for hyperscale storage infrastructures.

cs.AR↗