arXiv subjects

Fei Liu

Publications and source records attributed to Fei Liu.

At least 73 records · Page 4Linked to original sources

arXiv2026

EvoDR: Evolving Dispatching Rules via Large Language Model for Dynamic Flexible Assembly Flow Shop Scheduling

Dynamic flexible assembly flow shop scheduling with multi-product delivery is a critical combinatorial problem, characterized by kitting supply and machine flexibility. Genetic programming is widely used to automatically generate dispatching rules, enabling responsive scheduling that reduces manual effort while meeting high responsiveness demands. However, these methods are dependent on fixed terminal sets and have weak interpretability. In this article, we develop an evolving dispatching rules framework (EvoDR) that leverages the semantic understanding and generation capabilities of large language models to achieve cross-domain integration of algorithm design and scheduling knowledge. Firstly, multi-stage assembly supply decisions are modeled as priority sorting of directed edges based on heterogeneous graphs. A dual-expert co-evolution mechanism is implemented, where LLM-A generates code while LLM-S conducts scheduling analysis and reflection. Guided by improvements in hybrid evaluation, adaptive rules that fit dynamic features are continuously evolved. Experimental results show that the EvoDR achieves lower average tardiness than state-of-the-art approaches. In 24 scenarios with different resource configurations and disturbance levels totaling 480 instances, it consistently outperforms expert-designed competitors, demonstrating superior robustness.

cs.NE↗

arXiv2026

arg-VU: Affordance Reasoning with Physics-Aware 3D Geometry for Visual Understanding in Robotic Surgery

Affordance reasoning provides a principled link between perception and action, yet remains underexplored in surgical robotics, where tissues are highly deformable, compliant, and dynamically coupled with tool motion. We present arg-VU, a physics-aware affordance reasoning framework that integrates temporally consistent geometry tracking with constraint-induced mechanical modeling for surgical visual understanding. Surgical scenes are reconstructed using 3D Gaussian Splatting (3DGS) and converted into a temporally tracked surface representation. Extended Position-Based Dynamics (XPBD) embeds local deformation constraints and produces representative geometry points (RGPs) whose constraint sensitivities define anisotropic stiffness metrics capturing the local constraint-manifold geometry. Robotic tool poses in SE(3) are incorporated to compute rigidly induced displacements at RGPs, from which we derive two complementary measures: a physics-aware compliance energy that evaluates mechanical feasibility with respect to local deformation constraints, and a positional agreement score that captures motion alignment (as kinematic motion baseline). Experiments on surgical video datasets show that arg-VU yields more stable, physically consistent, and interpretable affordance predictions than kinematic baselines. These results demonstrate that physics-aware geometric representations enable reliable affordance reasoning for deformable surgical environments and support embodied robotic interaction.

cs.CV↗

arXiv2026

Communication to Completion: Modeling Collaborative Workflows with Intelligent Multi-Agent Communication

Multi-agent LLM systems have demonstrated impressive capabilities in complex collaborative tasks, yet most frameworks treat communication as instantaneous and free, overlooking a fundamental constraint in real world teamwork, collaboration cost. We propose a scalable framework implemented via Communication to Completion (C2C), which explicitly models communication as a constrained resource with realistic temporal costs. We introduce the Alignment Factor (AF), a dynamic metric inspired by Shared Mental Models, to quantify the link between task understanding and work efficiency. Through experiments on 15 software engineering workflows spanning three complexity tiers and team sizes from 5 to 17 agents, we demonstrate that cost-aware strategies achieve over 40% higher efficiency compared to unconstrained interaction. Our analysis reveals emergent coordination patterns: agents naturally adopt manager centric hub-and-spoke topologies, strategically escalate from asynchronous to synchronous channels based on complexity, and prioritize high value help requests. These patterns remain consistent across multiple frontier models (GPT-5.2, Claude Sonnet 4.5, Gemini 2.5 Pro). This study moves beyond simple agent construction, offering a theoretical foundation for quantifying and optimizing the dynamics of collaboration in future digital workplaces.

cs.MA↗

arXiv2026

Surface-Constrained Offline Warping with Contact-Aware Online Pose Projection for Safe Robotic Trajectory Execution

Robotic manipulation tasks that require repeated tool motion along curved surfaces frequently arise in surface finishing, inspection, and guided interaction. In practice, nominal motion primitives are often designed independently of the deployment surface and later reused across varying geometries. Directly tiling such primitives onto nonplanar surfaces introduces geometric inconsistencies, leading to interpenetration, orientation discontinuities, and cumulative drift over repeated cycles. We present a two-stage framework that separates geometric embedding from execution-level regulation. An offline surface-constrained warping operator embeds a nominal periodic primitive onto curved surfaces through asymmetric diffeomorphic deformation of dual-track waypoints and axis-consistent orientation completion, producing a surface-adapted reference trajectory. An online contact-aware projection operator then enforces bounded deviation relative to this reference using FSR-driven disturbance adaptation and a conic orientation safety constraint. Experiments across multiple analytic surface families and real-robot validation on a sinusoidal surface demonstrate improved geometric continuity, reduced large orientation jumps, and robust contact maintenance compared with direct tiling. These results show that decoupling offline geometric remapping from lightweight online projection enables stable and repeatable surface-embedded trajectory execution under sensor-lite feedbacks.

cs.RO↗

arXiv2026

Domain Walls Stabilized by Intrinsic Phonon Modes and Engineered Defects Enable Robust Ferroelectricity in HfO2

Ferroelectric $\mathrm{HfO}_2$ has attracted extensive research interest for its applications in AI era. The domain walls play a crucial role in phase structure stabilization and polarization switching of ferroelectric $\mathrm{HfO}_2$, however, a thorough understanding is still lacking. Here, we developed a unified framework based on phonon mode expansion to systematically study the effects of phonon modes and defects on domain wall structures. Using this approach combined with first-principle calculations, we revealed that the interface phonon modes play a key role in stability of domain walls; defects pin and stabilize ferroelectric domains, which in turn stabilizes the metastable orthorhombic phase and facilitates polarization switching. This provides an insight from the microscopic physics origin into the enhanced ferroelectricity in $\mathrm{HfO}_2$ by doping and defect engineering. Furthermore, the theoretically predicted domain structures and defect distributions were observed in La-doped $\mathrm{HfO}_2$ ferroelectric films by EELS and STEM experiments, which confirms the validity of our findings.

cond-mat.mtrl-sci↗

arXiv2026

Decoding the Complexity of Ferroelectric Orthorhombic HfO2: A Unified Mode Expansion Approach

The ferroelectricity in $\mathrm{HfO}_2$ thin films is widely attributed to the formation of a polar orthorhombic phase named OIII phase. However, the complexity of OIII phase originated from its low symmetry becomes an obstacle for studying ferroelectric properties of $\mathrm{HfO}_2$. Here, we developed a unified framework based on phonon mode expansion for studying ferroelectric $\mathrm{HfO}_2$. In this framework, phase structures, domain walls and switching paths of orthogonal crystal system can be studied from the same basis of mode analysis. The OIII phase and other orthogonal phases can be represented by the high-symmetry cubic phase with the excitation of cubic phonon modes, into which the complexity of orthogonal phases is faithfully coded. To present the capability of this mode expansion approach, we clarified the origin of orthorhombic stability from the energy functional of modes; enumerated inequivalent domain walls and calculated their stable criteria; and summarized all possible switching mechanisms. This unified framework can be used to simplify the study of domain wall structures and transition paths. Furthermore, it can provide a new perspective for ferroelectricity in $\mathrm{HfO}_2$ from phonon mode analysis.

cond-mat.mtrl-sci↗

arXiv2026

NavForesee: A Unified Vision-Language World Model for Hierarchical Planning and Dual-Horizon Navigation Prediction

Embodied navigation for long-horizon tasks, guided by complex natural language instructions, remains a formidable challenge in artificial intelligence. Existing agents often struggle with robust long-term planning about unseen environments, leading to high failure rates. To address these limitations, we introduce NavForesee, a novel Vision-Language Model (VLM) that unifies high-level language planning and predictive world model imagination within a single, unified framework. Our approach empowers a single VLM to concurrently perform planning and predictive foresight. Conditioned on the full instruction and historical observations, the model is trained to understand the navigation instructions by decomposing the task, tracking its progress, and formulating the subsequent sub-goal. Simultaneously, it functions as a generative world model, providing crucial foresight by predicting short-term environmental dynamics and long-term navigation milestones. The VLM's structured plan guides its targeted prediction, while the imagined future provides rich context to inform the navigation actions, creating a powerful internal feedback loop of perception-planning/prediction-action. We demonstrate through extensive experiments on the R2R-CE and RxR-CE benchmark that NavForesee achieves highly competitive performance in complex scenarios. Our work highlights the immense potential of fusing explicit language planning with implicit spatiotemporal prediction, paving the way for more intelligent and capable embodied agents.

cs.RO↗

arXiv2026

Cold source field-effect transistor with type-III band-aligned HfS$_2$/WTe$_2$ heterostructure

The cold source field-effect transistor (CSFET) is promising for reducing power dissipation in integrated circuits by engineering the density of states at the injecting source. Existing CSFET designs utilizing Dirac-source metals or p-Metal-n stacks are challenged by Schottky barriers at the metal-semiconductor interface. In this work, a 2D WTe$_2$/HfS$_2$ heterojunction with type-III band alignment is proposed to be an excellent design of cold source and CSFET. The architecture has a high band-to-band transport mechanism by removing the detrimental Schottky barrier issues. Importantly, the proposed CSFET has the same channel barrier modulation principle as conventional MOSFET to enable a high on-state current. Using first-principles-based quantum transport modeling, we predict a very high $I_{\rm on}$/$I_{\rm off}$ ratio at $\sim$ 10$^{10}$, a low subthreshold swing below the thermal limit for a wide range of gate voltages, reaching as small as 41.3 mV/dec, at low source-drain bias $V_{DS}=0.3$ $\rm V$. These findings establish a design principles for next-generation low-power nanoelectronic switches leveraging 2D van der Waals heterostructures.

cond-mat.mtrl-sci↗

arXiv2026

Multimodal LLM-assisted Evolutionary Search for Programmatic Control Policies

Deep reinforcement learning has achieved impressive success in control tasks. However, its policies, represented as opaque neural networks, are often difficult for humans to understand, verify, and debug, which undermines trust and hinders real-world deployment. This work addresses this challenge by introducing a novel approach for programmatic control policy discovery, called Multimodal Large Language Model-assisted Evolutionary Search (MLES). MLES utilizes multimodal large language models as programmatic policy generators, combining them with evolutionary search to automate policy generation. It integrates visual feedback-driven behavior analysis within the policy generation process to identify failure patterns and guide targeted improvements, thereby enhancing policy discovery efficiency and producing adaptable, human-aligned policies. Experimental results demonstrate that MLES achieves performance comparable to Proximal Policy Optimization (PPO) across two standard control tasks while providing transparent control logic and traceable design processes. This approach also overcomes the limitations of predefined domain-specific languages, facilitates knowledge transfer and reuse, and is scalable across various tasks, showing promise as a new paradigm for developing transparent and verifiable control policies. Code is publicly available at https://github.com/QingL2000/MLES.

cs.LG↗

arXiv2026

Verifiable Reasoning for LLM-based Generative Recommendation

Reasoning in Large Language Models (LLMs) has recently shown strong potential in enhancing generative recommendation through deep understanding of complex user preference. Existing approaches follow a {reason-then-recommend} paradigm, where LLMs perform step-by-step reasoning before item generation. However, this paradigm inevitably suffers from reasoning degradation (i.e., homogeneous or error-accumulated reasoning) due to the lack of intermediate verification, thus undermining the recommendation. To bridge this gap, we propose a novel \textbf{\textit{reason-verify-recommend}} paradigm, which interleaves reasoning with verification to provide reliable feedback, guiding the reasoning process toward more faithful user preference understanding. To enable effective verification, we establish two key principles for verifier design: 1) reliability ensures accurate evaluation of reasoning correctness and informative guidance generation; and 2) multi-dimensionality emphasizes comprehensive verification across multi-dimensional user preferences. Accordingly, we propose an effective implementation called VRec. It employs a mixture of verifiers to ensure multi-dimensionality, while leveraging a proxy prediction objective to pursue reliability. Experiments on four real-world datasets demonstrate that VRec substantially enhances recommendation effectiveness and scalability without compromising efficiency. The codes can be found at https://github.com/Linxyhaha/Verifiable-Rec.

cs.IR↗

arXiv2026

CLEAR: Null-Space Projection for Cross-Modal De-Redundancy in Multimodal Recommendation

Multimodal recommendation has emerged as an effective paradigm for enhancing collaborative filtering by incorporating heterogeneous content modalities. Existing multimodal recommenders predominantly focus on reinforcing cross-modal consistency to facilitate multimodal fusion. However, we observe that multimodal representations often exhibit substantial cross-modal redundancy, where dominant shared components overlap across modalities. Such redundancy can limit the effective utilization of complementary information, explaining why incorporating additional modalities does not always yield performance improvements. In this work, we propose CLEAR, a lightweight and plug-and-play cross-modal de-redundancy approach for multimodal recommendation. Rather than enforcing stronger cross-modal alignment, CLEAR explicitly characterizes the redundant shared subspace across modalities by modeling cross-modal covariance between visual and textual representations. By identifying dominant shared directions via singular value decomposition and projecting multimodal features onto the complementary null space, CLEAR reshapes the multimodal representation space by suppressing redundant cross-modal components while preserving modality-specific information. This subspace-level projection implicitly regulates representation learning dynamics, preventing the model from repeatedly amplifying redundant shared semantics during training. Notably, CLEAR can be seamlessly integrated into existing multimodal recommenders without modifying their architectures or training objectives. Extensive experiments on three public benchmark datasets demonstrate that explicitly reducing cross-modal redundancy consistently improves recommendation performance across a wide range of multimodal recommendation models.

cs.IR↗

arXiv2026

LLM4AD: A Platform for Algorithm Design with Large Language Model

We introduce LLM4AD, a unified Python platform for algorithm design (AD) with large language models (LLMs). LLM4AD is a generic framework with modularized blocks for search methods, algorithm design tasks, and LLM interface. The platform integrates numerous key methods and supports a wide range of algorithm design tasks across various domains including optimization, machine learning, and scientific discovery. We have also designed a unified evaluation sandbox to ensure a secure and robust assessment of algorithms. Additionally, we have compiled a comprehensive suite of support resources, including tutorials, examples, a user manual, online resources, and a dedicated graphical user interface (GUI) to enhance the usage of LLM4AD. We believe this platform will serve as a valuable tool for fostering future development in the merging research direction of LLM-assisted algorithm design.

cs.AI↗

arXiv2026

Computing Large Deviations of First-Passage-Time Statistics in Open Quantum Systems: Two Methods

We propose two methods for computing the large deviations of the first-passage-time statistics in general open quantum systems. The first method determines the region of convergence of the joint Laplace transform and the $z$-transform of the first-passage time distribution by solving an equation of poles with respect to the $z$-transform parameter. The scaled cumulant generating function is then obtained as the logarithm of the boundary values within this region. The theoretical basis lies in the facts that the dynamics of the open quantum systems can be unraveled into a piecewise deterministic process and there exists a tilted Liouville master equation in Hilbert space. The second method uses a simulation-based approach built on the wave function cloning algorithm. To validate both methods, we derive analytical expressions for the scaled cumulant generating functions in field-driven two-level and three-level systems. In addition, we present numerical results alongside cloning simulations for a field-driven system comprising two interacting two-level atoms.

cond-mat.stat-mech↗

arXiv2026

ABot-N0: Technical Report on the VLA Foundation Model for Versatile Embodied Navigation

Embodied navigation has long been fragmented by task-specific architectures. We introduce ABot-N0, a unified Vision-Language-Action (VLA) foundation model that achieves a ``Grand Unification'' across 5 core tasks: Point-Goal, Object-Goal, Instruction-Following, POI-Goal, and Person-Following. ABot-N0 utilizes a hierarchical ``Brain-Action'' architecture, pairing an LLM-based Cognitive Brain for semantic reasoning with a Flow Matching-based Action Expert for precise, continuous trajectory generation. To support large-scale learning, we developed the ABot-N0 Data Engine, curating 16.9M expert trajectories and 5.0M reasoning samples across 7,802 high-fidelity 3D scenes (10.7 $\text{km}^2$). ABot-N0 achieves new SOTA performance across 7 benchmarks, significantly outperforming specialized models. Furthermore, our Agentic Navigation System integrates a planner with hierarchical topological memory, enabling robust, long-horizon missions in dynamic real-world environments.

cs.RO↗

arXiv2026

Inference for High-Dimensional Local Projection

This paper rigorously analyzes the properties of the local projection (LP) methodology within a high-dimensional (HD) framework, with a central focus on achieving robust long-horizon inference. We integrate a general dependence structure into h-step ahead forecasting models via a flexible specification of the residual terms. Additionally, we study the corresponding HD covariance matrix estimation, explicitly addressing the complexity arising from the long-horizon setting. Extensive Monte Carlo simulations are conducted to substantiate the derived theoretical findings. In the empirical study, we utilize the proposed HD LP framework to study the impact of business news attention on U.S. industry-level stock volatility.

econ.EM↗

arXiv2026

ParaGSE: Parallel Generative Speech Enhancement with Group-Vector-Quantization-based Neural Speech Codec

Recently, generative speech enhancement has garnered considerable interest; however, existing approaches are hindered by excessive complexity, limited efficiency, and suboptimal speech quality. To overcome these challenges, this paper proposes a novel parallel generative speech enhancement (ParaGSE) framework that leverages a group vector quantization (GVQ)-based neural speech codec. The GVQ-based codec adopts separate VQs to produce mutually independent tokens, enabling efficient parallel token prediction in ParaGSE. Specifically, ParaGSE leverages the GVQ-based codec to encode degraded speech into distinct tokens, predicts the corresponding clean tokens through parallel branches conditioned on degraded spectral features, and ultimately reconstructs clean speech via the codec decoder. Experimental results demonstrate that ParaGSE consistently produces superior enhanced speech compared to both discriminative and generative baselines, under a wide range of distortions including noise, reverberation, band-limiting, and their mixtures. Furthermore, empowered by parallel computation in token prediction, ParaGSE attains about a 1.5-fold improvement in generation efficiency on CPU compared with serial generative speech enhancement approaches.

cs.SD↗

arXiv2026

FoMEMO: Towards Foundation Models for Expensive Multi-objective Optimization

Expensive multi-objective optimization is a prevalent and crucial concern in many real-world scenarios, where sample-efficiency is vital due to the limited evaluations to recover the true Pareto front for decision making. Existing works either involve rebuilding Gaussian process surrogates from scratch for each objective in each new problem encountered, or rely on extensive past domain experiments for pre-training deep learning models, making them hard to generalize and impractical to cope with various emerging applications in the real world. To address this issue, we propose a new paradigm named FoMEMO (Foundation Models for Expensive Multi-objective Optimization), which enables the establishment of a foundation model conditioned on any domain trajectory and user preference, and facilitates fast in-context optimization based on the predicted preference-wise aggregated posteriors. Rather than accessing extensive real-world domain experiments for training, we demonstrate that pre-training the foundation model with a diverse set of hundreds of millions of synthetic data can lead to superior generalization and optimization performance to unknown problems, without necessitating any subsequent model training or updates in the following optimization process.

cs.LG↗

arXiv2026

Few for Many: Tchebycheff Set Scalarization for Many-Objective Optimization

Multi-objective optimization can be found in many real-world applications where some conflicting objectives can not be optimized by a single solution. Existing optimization methods often focus on finding a set of Pareto solutions with different optimal trade-offs among the objectives. However, the required number of solutions to well approximate the whole Pareto optimal set could be exponentially large with respect to the number of objectives, which makes these methods unsuitable for handling many optimization objectives. In this work, instead of finding a dense set of Pareto solutions, we propose a novel Tchebycheff set scalarization method to find a few representative solutions (e.g., 5) to cover a large number of objectives (e.g., $>100$) in a collaborative and complementary manner. In this way, each objective can be well addressed by at least one solution in the small solution set. In addition, we further develop a smooth Tchebycheff set scalarization approach for efficient optimization with good theoretical guarantees. Experimental studies on different problems with many optimization objectives demonstrate the effectiveness of our proposed method.

cs.LG↗