arXiv subjects

Juan Feng

Publications and source records attributed to Juan Feng.

9 recordsLinked to original sources

arXiv2026

MCPO: Modality-Contrastive Preference Optimization for Multimodal Chain-of-Thought Compression

Recently, multimodal large-scale reasoning models have demonstrated remarkable capabilities in solving complex tasks through long Chains-of-Thought (M-CoT). However, excessively long reasoning trajectories incur substantial computational costs and significant KV-cache pressure. Existing CoT compression and alignment paradigms mainly rely on static rules or single-dimensional preferences, lacking fine-grained cross-modal constraints; as a result, they are prone to inducing visual laziness and hallucinatory reasoning. To address these issues, we propose Modality-Contrastive Preference Optimization (MCPO), a highly sample-efficient two-stage length-compression method that requires fewer than 900 training samples. In the compression stage, we introduce a step-level Normalized Cross-Modal Mutual Information (NCMI) pruning algorithm, which automatically identifies and removes visual-independent reasoning steps by comparing the reasoning discrepancies between with-image and no-image contexts. This significantly reduces redundancy and hallucinatory content in the reasoning chains. In the alignment stage, the model first undergoes supervised fine-tuning to achieve domain-adaptive initialization, followed by optimization using an asymmetric multimodal length-controlled preference loss. This objective adopts a highly nonlinear odds-ratio formulation that provides steep gradients in the with-image context to reinforce length constraints for preferred trajectories, while applying a scaled, flat-gradient linear difference in the no-image context to maintain modality consistency, thereby achieving stable cross-modal preference alignment. Extensive experiments on mainstream base models such as Qwen3-VL-Thinking show that our method can reduce CoT length by up to 69.5% and achieve up to 3.34x end-to-end inference speedup while preserving original accuracy.

cs.CV↗

arXiv2026

Optical Quasi-symmetry Groups for Meron Lattices

We introduce quasi-symmetry groups in optics emerging from the commutation between mirror operation and the spin-orbit interaction (SOI) of light. Contrary to the principle of symmetry inheritance in free-space optics, where the symmetry of any structured field is strictly constrained by that of its source, we show that strong SOI enables quasi-symmetry-protected formation of meron lattices even when the underlying optical sources violate the nominal rotational symmetry. By analyzing the Hermiticity of the electric-dipole radiation amplitude in a circular polarization basis, we derive an effective mirror operator acting only on a subset of C3 polarized dipole emitters, forming a quasi-symmetry group that commutes with SOI. This quasi-symmetry guarantees exact C3 merons and gives rise to a robust polarization zone within which continuously varying input polarizations generate identical topological textures. Our work establishes quasi-symmetry as a new fundamental principle in optical physics and opens pathways to engineered topological structures of light beyond conventional symmetry constraints.

physics.optics↗

arXiv2024

Reconstructing Quantitative Cerebral Perfusion Images Directly From Measured Sinogram Data Acquired Using C-arm Cone-Beam CT

To shorten the door-to-puncture time for better treating patients with acute ischemic stroke, it is highly desired to obtain quantitative cerebral perfusion images using C-arm cone-beam computed tomography (CBCT) equipped in the interventional suite. However, limited by the slow gantry rotation speed, the temporal resolution and temporal sampling density of typical C-arm CBCT are much poorer than those of multi-detector-row CT in the diagnostic imaging suite. The current quantitative perfusion imaging includes two cascaded steps: time-resolved image reconstruction and perfusion parametric estimation. For time-resolved image reconstruction, the technical challenge imposed by poor temporal resolution and poor sampling density causes inaccurate quantification of the temporal variation of cerebral artery and tissue attenuation values. For perfusion parametric estimation, it remains a technical challenge to appropriately design the handcrafted regularization for better solving the associated deconvolution problem. These two challenges together prevent obtaining quantitatively accurate perfusion images using C-arm CBCT. The purpose of this work is to simultaneously address these two challenges by combining the two cascaded steps into a single joint optimization problem and reconstructing quantitative perfusion images directly from the measured sinogram data. In the developed direct cerebral perfusion parametric image reconstruction technique, TRAINER in short, the quantitative perfusion images have been represented as a subject-specific conditional generative model trained under the constraint of the time-resolved CT forward model, perfusion convolutional model, and the subject's own measured sinogram data. Results shown in this paper demonstrated that using TRAINER, quantitative cerebral perfusion images can be accurately obtained using C-arm CBCT in the interventional suite.

eess.IV↗

arXiv2024

MiniFed : Integrating LLM-based Agentic-Workflow for Simulating FOMC Meeting

The Federal Funds rate in the United States plays a significant role in both domestic and international financial markets. However, research has predominantly focused on the effects of adjustments to the Federal Funds rate rather than on the decision-making process itself. Recent advancements in large language models(LLMs) offer a potential method for reconstructing the original FOMC meetings, which are responsible for setting the Federal Funds rate. In this paper, we propose a five-stage FOMC meeting simulation framework, MiniFed, which employs LLM agents to simulate real-world FOMC meeting members and optimize the FOMC structure. This framework effectively revitalizes the FOMC meeting process and facilitates projections of the Federal Funds rate. Experimental results demonstrate that our proposed MiniFed framework achieves both high accuracy in Federal Funds rate projections and behavioral alignment with the agents' real-world counterparts. Given that few studies have focused on employing LLM agents to simulate large-scale real-world conferences, our work can serve as a benchmark for future developments.

cs.SI↗

arXiv2024

Enhanced Second-Harmonic Generation in Thin-Film Lithium Niobate Circular Bragg Nanocavity

Second-order nonlinearity gives rise to many distinctive physical phenomena, e.g., second-harmonic generation, which plays an important role in fundamental science and various applications. Lithium niobate, one of the most widely used nonlinear crystals, exhibits strong second-order nonlinear effects and electro-optic properties. However, its moderate refractive index and etching sidewall angle limit its capability in confining light into nanoscales, restricting its application in nanophotonics. Here, we exploit nanocavities formed by second-order circular Bragg gratings, which support resonant anapole modes to achieve highly enhanced SHG in thin film lithium niobate. The CBG nanocavity exhibits a record-high normalized conversion efficiency of $1.21\times10^{-2}\mathrm{cm^2/GW}$ under the pump intensity of $1.9$ $\mathrm{MW/cm^2}$. An SHG enhancement of $42,000$ is realized compared to TFLN. Besides, we also show s- and p-polarization independent SHG in elliptical Bragg nanocavities. This work could inspire studying nonlinear optics at the nanoscale on TFLN as well as other novel photonic platforms.

physics.optics↗

arXiv2024

Spin Hamiltonians in the Modulated Momenta of Light

Photonic solvers that are able to find the ground states of different spin Hamiltonians can be used to study many interactive physical systems and combinatorial optimization problems. Here, we establish a real-and-momentum space correspondence of spin Hamiltonians by spatial light transport. The real-space spin interaction is determined by modulating the momentum-space flow of light. This principle is formulated as a generalized Plancherel theorem, allowing us to implement a simple optical simulator that can find the ground states for any displacement-dependent spin interactions. Particularly, we use this principle to reveal the exotic magnetic phase diagram from a J1-J2-J3 model, and we also observe the vortex-mediated Berezinskii-Kosterlitz-Thouless dynamics from the XY model. These experiments exhibit high calculation precision by subtly controlling spin interactions from the momentum space of light, offering a promising scheme to explore novel physical effects.

physics.optics↗

arXiv2021

A 15-user quantum secure direct communication network

Quantum secure direct communication (QSDC) based on entanglement can directly transmit confidential information. However, the inability to simultaneously distinguish the four sets of encoded entangled states limits its practical application. Here, we explore a deterministic QSDC network based on time-energy entanglement and sum-frequency generation. 15 users are in a fully connected QSDC network, and the fidelity of the entangled state shared by any two users is greater than 97%. The results show that when any two users are performing QSDC over 40 kilometers of optical fiber, the fidelity of the entangled state shared by them is still greater than 95%, and the rate of information transmission can be maintained above 1Kbp/s. Our Letter demonstrates the feasibility of a proposed QSDC network, and hence lays the foundation for the realization of satellite-based long-distance and global QSDC in the future.

quant-ph↗

arXiv2021

A two-way photonic quantum entanglement transfer interface

A quantum interface for two-way entanglement transfer between orbital angular momentum degree of freedom in free space and time-energy degree of freedom in optical fibers, provides a novel way toward establishing entanglement between remote heterogeneous quantum nodes. Here, we experimentally demonstrate this kind of transfer interface by using two interferometric cyclic gates. By using this quantum interface, we perform two-way entanglement transfer for the two degrees of freedom. The results show that the quantum entangled state can be switched back and forth between orbital angular momentum and time-energy degrees of freedom, and the fidelity of the state before and after switching is higher than 90%. Our work demonstrates the feasibility and high performance of our proposed transfer interface, and paves a route toward building a large-scale quantum communication network.

quant-ph↗

arXiv2019

A Causal Inference Method for Reducing Gender Bias in Word Embedding Relations

Word embedding has become essential for natural language processing as it boosts empirical performances of various tasks. However, recent research discovers that gender bias is incorporated in neural word embeddings, and downstream tasks that rely on these biased word vectors also produce gender-biased results. While some word-embedding gender-debiasing methods have been developed, these methods mainly focus on reducing gender bias associated with gender direction and fail to reduce the gender bias presented in word embedding relations. In this paper, we design a causal and simple approach for mitigating gender bias in word vector relation by utilizing the statistical dependency between gender-definition word embeddings and gender-biased word embeddings. Our method attains state-of-the-art results on gender-debiasing tasks, lexical- and sentence-level evaluation tasks, and downstream coreference resolution tasks.

cs.CL↗