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Cong Zhang

Publications and source records attributed to Cong Zhang.

At least 55 records · Page 3Linked to original sources
arXiv2026

Mamba Meets Scheduling: Learning to Solve Flexible Job Shop Scheduling with Efficient Sequence Modeling

The Flexible Job Shop Problem (FJSP) is a well-studied combinatorial optimization problem with extensive applications for manufacturing and production scheduling. It involves assigning jobs to various machines to optimize criteria, such as minimizing total completion time. Current learning-based methods in this domain often rely on localized feature extraction models, limiting their capacity to capture overarching dependencies spanning operations and machines. This paper introduces an innovative architecture that harnesses Mamba, a state-space model with linear computational complexity, to facilitate comprehensive sequence modeling tailored for FJSP. In contrast to prevalent graph-attention-based frameworks that are computationally intensive for FJSP, we show our model is more efficient. Specifically, the proposed model possesses an encoder and a decoder. The encoder incorporates a dual Mamba block to extract operation and machine features separately. Additionally, we introduce an efficient cross-attention decoder to learn interactive embeddings of operations and machines. Our experimental results demonstrate that our method achieves faster solving speed and surpasses the performance of state-of-the-art learning-based methods for FJSP across various benchmarks.

cs.LG↗
arXiv2026

Quantum Geometry Effects in Quantum Field Theory: Hamiltonian constraint Generates Gravity-Matter Entanglement

In this paper, we address a foundational challenge in quantum field theory on curved spacetime by developing a consistent framework within loop quantum gravity. We introduce a methodology for defining meaningful superpositions of quantum geometry and matter states. This is achieved by identifying a restricted subspace of the gravitational phase space, which ensures unitary equivalence among Fock representations of a scalar field across different quantum geometries. Within the resulting well-defined state space, we derive weak solutions to the quantum Hamiltonian constraint of general relativity. Furthermore, we generalize the Hartle-Hawking vacuum state to this quantum geometric framework. The resulting state exhibits the inherent entanglement between geometry and matter, which arises from the quantum Hamiltonian constraint of general relativity. This work establishes a principled framework for studying geometry-matter entanglement and offers new insights into the quantum foundations of the black hole information paradox.

gr-qc↗
arXiv2026

An Agentic Framework with LLMs for Solving Complex Vehicle Routing Problems

Complex vehicle routing problems (VRPs) remain a fundamental challenge, demanding substantial expert effort for intent interpretation and algorithm design. While large language models (LLMs) offer a promising path toward automation, current approaches still rely on external intervention, which restrict autonomy and often lead to execution errors and low solution feasibility. To address these challenges, we propose an Agentic Framework with LLMs (AFL) for solving complex vehicle routing problems, achieving full automation from problem instance to solution. AFL directly extracts knowledge from raw inputs and enables self-contained code generation without handcrafted modules or external solvers. To improve trustworthiness, AFL decomposes the overall pipeline into three manageable subtasks and employs four specialized agents whose coordinated interactions enforce cross-functional consistency and logical soundness. Extensive experiments on 60 complex VRPs, ranging from standard benchmarks to practical variants, validate the effectiveness and generality of our framework, showing comparable performance against meticulously designed algorithms. Notably, it substantially outperforms existing LLM-based baselines in both code reliability and solution feasibility, achieving rates close to 100% on the evaluated benchmarks.

cs.AI↗
arXiv2026

A mixed fractional CIR model: positivity and an implicit Euler scheme

We consider a Cox--Ingersoll--Ross (CIR) type short rate model driven by a mixed fractional Brownian motion. Let $M=B+B^H$ be a one-dimensional mixed fractional Brownian motion with Hurst index $H>1/2$, and let $\mathbf{M}=(M,\mathbb{M}^{\mathrm{It\hat{o}}})$ denote its canonical Itô rough path lift. We study the rough differential equation \begin{equation}\label{eqn1} \dd r_t = k(θ-r_t)\,\dd t + σ\sqrt{r_t}\,\dd\mathbf{M}_t,\qquad r_0>0, \end{equation} and prove that, under the Feller condition $2kθ>σ^2$, the unique rough path solution is almost surely strictly positive for all times. The proof relies on an Itô type formula for rough paths, together with refined pathwise estimates for the mixed fractional Brownian motion, including Lévy's modulus of continuity for the Brownian part and a law of the iterated logarithm for the fractional component. As a consequence, the positivity property of the classical CIR model extends to this non-Markovian rough path setting. We also establish the convergence of an implicit Euler scheme for the associated singular equation obtained by a square-root transformation.

math.PR↗
arXiv2026

Comparing methods to assess treatment effect heterogeneity in general parametric regression models

This paper reviews and compares methods to assess treatment effect heterogeneity in the context of parametric regression models. These methods include the standard likelihood ratio tests, bootstrap likelihood ratio tests, and Goeman's global test motivated by testing whether the random effect variance is zero. We place particular emphasis on tests based on the score-residual of the treatment effect and explore different variants of tests in this class. All approaches are compared in a simulation study, and the approach based on residual scores is illustrated in a clinical trial with time-to-event outcome comparing treatment versus placebo. Our findings demonstrate that score-residual based methods provide practical, flexible and reliable tools for exploring treatment effect heterogeneity and treatment effect modifiers, and can provide useful guidance for decision making around treatment effect heterogeneity.

stat.AP↗
arXiv2026

RFM-Pose:Reinforcement-Guided Flow Matching for Fast Category-Level 6D Pose Estimation

Object pose estimation is a fundamental problem in computer vision and plays a critical role in virtual reality and embodied intelligence, where agents must understand and interact with objects in 3D space. Recently, score based generative models have to some extent solved the rotational symmetry ambiguity problem in category level pose estimation, but their efficiency remains limited by the high sampling cost of score-based diffusion. In this work, we propose a new framework, RFM-Pose, that accelerates category-level 6D object pose generation while actively evaluating sampled hypotheses. To improve sampling efficiency, we adopt a flow-matching generative model and generate pose candidates along an optimal transport path from a simple prior to the pose distribution. To further refine these candidates, we cast the flow-matching sampling process as a Markov decision process and apply proximal policy optimization to fine-tune the sampling policy. In particular, we interpret the flow field as a learnable policy and map an estimator to a value network, enabling joint optimization of pose generation and hypothesis scoring within a reinforcement learning framework. Experiments on the REAL275 benchmark demonstrate that RFM-Pose achieves favorable performance while significantly reducing computational cost. Moreover, similar to prior work, our approach can be readily adapted to object pose tracking and attains competitive results in this setting.

cs.CV↗
arXiv2026

Using Individualized Treatment Effects to Assess Treatment Effect Heterogeneity

Assessing treatment effect heterogeneity (TEH) in clinical trials is crucial, as it provides insights into the variability of treatment responses among patients, influencing important decisions related to drug development. Furthermore, it can lead to personalized medicine by tailoring treatments to individual patient characteristics. This paper introduces novel methodologies for assessing treatment effects using the individual treatment effect as a basis. To estimate this effect, we use a Double Robust (DR) learner to infer a pseudo-outcome that reflects the causal contrast. This pseudo-outcome is then used to perform three objectives: (1) a global test for heterogeneity, (2) ranking covariates based on their influence on effect modification, and (3) providing estimates of the individualized treatment effect. We compare our DR-learner with various alternatives and competing methods in a simulation study, and also use it to assess heterogeneity in a pooled analysis of five Phase III trials in psoriatic arthritis. By integrating these methods with the recently proposed WATCH workflow (Workflow to Assess Treatment Effect Heterogeneity in Drug Development for Clinical Trial Sponsors), we provide a robust framework for analyzing TEH, offering insights that enable more informed decision-making in this challenging area.

stat.AP↗
arXiv2026

Relativistic Position Verification with Coherent States

Determining the position of an entity is a fundamental prerequisite for nearly all activities. Classical means, however, have been proven incapable of providing secure position verification, meaning that a prover can mislead verifiers about its actual position. In this work, we propose and experimentally realize a secure position-verification protocol that leverages quantum optics and relativity within an information-theoretic framework. Using phase-randomized weak coherent states, two verifiers separated by 2 km securely verify the prover's position with an accuracy better than 75 meters. These results establish secure position-based authentication as a practical possibility, paving the way for applications in financial transactions, disaster response, and authenticated secure communications.

quant-ph↗
arXiv2025

Magnetic order and novel quantum criticality in the strongly interacting quasicrystals

We present the sign-problem-free quantum Monte Carlo study of the half-filled Hubbard model on two-dimensional quasicrystals, revealing how specific aperiodic geometries fundamentally dictate quantum criticality. By comparing the Penrose and Thue-Morse quasicrystals, we demonstrate that the nature of the magnetic phase transition is controlled by the electronic density of states (DOS): while the singular DOS of the Penrose tiling induces magnetic order at infinitesimal interaction strengths, the Thue-Morse lattice requires a finite critical interaction to drive the transition. Crucially, through a novel boundary construction strategy and rigorous finite-size scaling, we identify a quantum critical point on the Thue-Morse quasicrystal with critical exponents ($ν\approx 0.94$, $β\approx 0.72$ and $z\approx 1.51$) that deviate significantly from the conventional $(2+1)$D Heisenberg $O(3)$ class. These findings establish the existence of a novel universality class driven by the interplay between electronic correlations and aperiodic geometry, challenging standard paradigms of magnetic criticality in two dimensions.

cond-mat.str-el↗
arXiv2025

Ultrahigh-Q chiral resonances empowered by multi-head attention deep learning

High quality (Q) factor optical chiral resonators are indispensable for many chiral photonic devices. Designing ultrahigh Q-factors in chiral metasurfaces traditionally relies on extensive parameter scanning, which is time-consuming and inefficient. While deep learning now provides a rapid design alternative, conventional models still face challenges in accurately predicting ultrahigh Q-factor spectral characteristics. In this study, we introduce a multi-head attention network (MuHAN) to accelerate the design of ultrahigh Q-factor optical chiral resonators in bilayer metasurfaces. MuHAN achieves forward spectral predictions in approximately 10ms, thousands of times faster than finite-difference time-domain simulations, boasting 99.85% and 99.9% accuracy for forward and inverse predictions, respectively. By transferring the learned physical principles, we perform inverse design of nanoscale structures with ultrahigh Q-factors (up to 2.9910E5) based on chiral quasi-bound states in the continuum (quasi-BICs) at minimal computational cost. Our rapid design tool, based on MuHAN, enables high-performance encryption imaging, bridging deep learning with high-Q chiral metasurfaces for advanced sensing, laser, and detection applications.

physics.optics↗
arXiv2025

Dynamical State Feedback Control for Linear Input Delay Systems, Part I: Dissipative Stabilization via Semidefinite Programming

We propose an SDP-based framework to address the stabilization of input delay systems while taking into account dissipative constraints. A key to our approach is the introduction of the concept of parameterized linear dynamical state feedbacks (LDSFs), which draws inspiration from recent advancements in the analyses of distributed delays. The parameterized LDSFs generalize conventional predictor controllers, where the interpretation of state prediction is concealed and their degree of parameterization can be increased by adjusting the integral kernels. A sufficient condition for the existence of dissipative LDSFs is formulated as matrix inequalities by constructing a complete type Krasovski\uı functional. To solve the bilinear matrix inequality in the synthesis condition, we employ an off-line inner convex approximation algorithm that can be initialized using the gains of predictor controllers obtained via explicit construction. So the unknowns of our LTDS can be computed by solving convex semidefinite programs. Numerical examples and simulations were experimented to demonstrate the validity and effectiveness of our methodology.

math.OC↗
arXiv2025

SizeGS: Size-aware Compression of 3D Gaussian Splatting via Mixed Integer Programming

Recent advances in 3D Gaussian Splatting (3DGS) have greatly improved 3D reconstruction. However, its substantial data size poses a significant challenge for transmission and storage. While many compression techniques have been proposed, they fail to efficiently adapt to fluctuating network bandwidth, leading to resource wastage. We address this issue from the perspective of size-aware compression, where we aim to compress 3DGS to a desired size by quickly searching for suitable hyperparameters. Through a measurement study, we identify key hyperparameters that affect the size -- namely, the reserve ratio of Gaussians and bit-width settings for Gaussian attributes. Then, we formulate this hyperparameter optimization problem as a mixed-integer nonlinear programming (MINLP) problem, with the goal of maximizing visual quality while respecting the size budget constraint. To solve the MINLP, we decouple this problem into two parts: discretely sampling the reserve ratio and determining the bit-width settings using integer linear programming (ILP). To solve the ILP more quickly and accurately, we design a quality loss estimator and a calibrated size estimator, as well as implement a CUDA kernel. Extensive experiments on multiple 3DGS variants demonstrate that our method achieves state-of-the-art performance in post-training compression. Furthermore, our method can achieve comparable quality to leading training-required methods after fine-tuning.

cs.CV↗
arXiv2025

Covariant dynamics from static spherically symmetric geometries

This work reveals a fundamental link between general covariance and Birkhoff's theorem. We extend Birkhoff's theorem from general relativity to a broad class of generally covariant gravity theories formulated in the Hamiltonian framework. Conversely, we show that each one-parameter family of static, spherically symmetric spacetimes determines a class of covariant theories, each of which has that family of spacetimes as its entire vacuum solution space. Our systematic and model-independent framework applies to a wide range of spacetimes, including observationally inferred, quantum-gravity-inspired, and regular black holes. It provides a universal tool for probing their dynamical origins and enables the reconstruction of the underlying covariant theories from observational data, including gravitational-wave and black-hole-shadow measurements.

gr-qc↗
arXiv2025

Divide-and-Conquer Decoupled Network for Cross-Domain Few-Shot Segmentation

Cross-domain few-shot segmentation (CD-FSS) aims to tackle the dual challenge of recognizing novel classes and adapting to unseen domains with limited annotations. However, encoder features often entangle domain-relevant and category-relevant information, limiting both generalization and rapid adaptation to new domains. To address this issue, we propose a Divide-and-Conquer Decoupled Network (DCDNet). In the training stage, to tackle feature entanglement that impedes cross-domain generalization and rapid adaptation, we propose the Adversarial-Contrastive Feature Decomposition (ACFD) module. It decouples backbone features into category-relevant private and domain-relevant shared representations via contrastive learning and adversarial learning. Then, to mitigate the potential degradation caused by the disentanglement, the Matrix-Guided Dynamic Fusion (MGDF) module adaptively integrates base, shared, and private features under spatial guidance, maintaining structural coherence. In addition, in the fine-tuning stage, to enhanced model generalization, the Cross-Adaptive Modulation (CAM) module is placed before the MGDF, where shared features guide private features via modulation ensuring effective integration of domain-relevant information. Extensive experiments on four challenging datasets show that DCDNet outperforms existing CD-FSS methods, setting a new state-of-the-art for cross-domain generalization and few-shot adaptation.

cs.CV↗
arXiv2025

Every Activation Boosted: Scaling General Reasoner to 1 Trillion Open Language Foundation

We introduce Ling 2.0, a series reasoning-oriented language foundation built upon the principle that every activation boosts reasoning capability. Designed to scale from tens of billions to one trillion parameters under a unified Mixture-of-Experts (MoE) paradigm, Ling 2.0 emphasizes high sparsity, cross-scale consistency, and efficiency guided by empirical scaling laws. The series includes three non-thinking (instruct) models - Ling-mini-2.0, Ling-flash-2.0, and Ling-1T - ranging from 16B to 1T total parameters and achieving up to 7-fold active-compute efficiency compared with dense counterparts. Ling 2.0 integrates coordinated innovations across model architecture, pre-training, post-training, and infrastructure: a high-sparsity MoE with MTP for efficient reasoning, reasoning-oriented data and mid-training CoT activation, reinforcement-based fine-tuning (DFT, Evo-CoT), and full-scale FP8 training with fine-grained heterogeneous pipelines. At the trillion scale, Ling-1T establishes a new Pareto frontier of reasoning accuracy versus computational efficiency, demonstrating that sparse activation, when properly aligned with reasoning objectives, enables scalable and efficient intelligence. Collectively, Ling 2.0 provides a coherent, open, and efficient foundation for advancing future reasoning and thinking models, including the Ring series built upon the same base.

cs.CL↗
arXiv2025

Refining Hybrid Genetic Search for CVRP via Reinforcement Learning-Finetuned LLM

While large language models (LLMs) are increasingly used as automated heuristic designers for vehicle routing problems (VRPs), current state-of-the-art methods predominantly rely on prompting massive, general-purpose models like GPT-4. This work challenges that paradigm by demonstrating that a smaller, specialized LLM, when meticulously fine-tuned, can generate components that surpass expert-crafted heuristics within advanced solvers. We propose RFTHGS, a novel Reinforcement learning (RL) framework for Fine-Tuning a small LLM to generate high-performance crossover operators for the Hybrid Genetic Search (HGS) solver, applied to the Capacitated VRP (CVRP). Our method employs a multi-tiered, curriculum-based reward function that progressively guides the LLM to master generating first compilable, then executable, and finally, superior-performing operators that exceed human expert designs. This is coupled with an operator caching mechanism that discourages plagiarism and promotes diversity during training. Comprehensive experiments show that our fine-tuned LLM produces crossover operators which significantly outperform the expert-designed ones in HGS. The performance advantage remains consistent, generalizing from small-scale instances to large-scale problems with up to 1000 nodes. Furthermore, RFTHGS exceeds the performance of leading neuro-combinatorial baselines, prompt-based methods, and commercial LLMs such as GPT-4o and GPT-4o-mini.

cs.LG↗
arXiv2025

Adaptive Overclocking: Dynamic Control of Thinking Path Length via Real-Time Reasoning Signals

Large Reasoning Models (LRMs) often suffer from computational inefficiency due to overthinking, where a fixed reasoning budget fails to match the varying complexity of tasks. To address this issue, we propose Adaptive Overclocking, a method that makes the overclocking hyperparameter $α$ dynamic and context-aware. Our method adjusts reasoning speed in real time through two complementary signals: (1) token-level model uncertainty for fine-grained step-wise control, and (2) input complexity estimation for informed initialization. We implement this approach with three strategies: Uncertainty-Aware Alpha Scheduling (UA-$α$S), Complexity-Guided Alpha Initialization (CG-$α$I), and a Hybrid Adaptive Control (HAC) that combines both. Experiments on GSM8K, MATH, and SVAMP show that HAC achieves superior accuracy-latency trade-offs, reducing unnecessary computation on simple problems while allocating more resources to challenging ones. By mitigating overthinking, Adaptive Overclocking enhances both efficiency and overall reasoning performance.

cs.LG↗