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Fu Li

Publications and source records attributed to Fu Li.

At least 19 recordsLinked to original sources
arXiv2026

Learn to Modify: Self-evolving Multi-agent Refinement Framework for Conversational Text-to-Motion Generation

Despite rapid progress in text-to-motion generation, existing methods are commonly designed for single-shot generation. When users are dissatisfied with specific motion details, they are often forced to regenerate the entire sequence from scratch, which is computationally inefficient and potentially destroys previously established kinematic context. A practical generation system should instead support iterative user feedback and refine only the affected spatio-temporal regions while preserving the remaining motion. Therefore, we introduce a conversational motion generation paradigm that requires the system to coordinate instruction understanding, motion generation, localized editing, and quality assessment. Recent advances in LLM-driven multi-agent systems provide a promising foundation for such role-specialized coordination. To tackle these challenges, a Self-evolving Multi-Agent RefinemenT Framework (SMART) is proposed for conversational motion generation. Within SMART, to preserve semantic consistency, we design a semantic-guided masked motion editing mechanism, which precisely grounds natural language feedback into spatiotemporal motion regions to enable localized refinements without altering unedited regions. To reduce redundant exploration, we propose a memory-based self-evolving strategy that dynamically accumulates motion editing knowledge from interaction history and reuses past successful experience. Extensive experiments demonstrate that SMART effectively supports multi-turn conversational motion generation and enables efficient motion refinement.

cs.CV↗
arXiv2026

Coexistence of Altermagnetic and Compensated Ferrimagnetic Motifs in a Noncollinear Multiferroic

Collinear compensated magnets with nonrelativistic spin splitting comprise altermagnets (ALMs), whose rotation- or mirror-related opposite-spin sublattices produce momentum-selective splitting that vanishes at $Γ$, and compensated ferrimagnets (CFIMs), whose symmetry-inequivalent sublattices allow splitting throughout the Brillouin zone, including at $Γ$. Here, we show that the coplanar noncollinear magnetic state of multiferroic boracite Ni$_3$B$_7$O$_{13}$Cl hosts both motifs on distinct Ni sublattices in real space. The electronic structure and spin texture exhibit coexisting $d$-wave-like and $s$-wave-like spin-polarization components associated with the ALM and CFIM motifs, respectively. Opposite ferroelectric states exhibit reversed band-resolved spin polarizations, indicating magnetoelectric coupling, and sign reversals of linear shift and circular injection currents.

cond-mat.mtrl-sci↗
arXiv2026

Periodic-Pulse Control of Collective Emission and Photon Statistics in Interacting Quantum Emitters

We investigate the collective emission spectra and photon correlations of two identical dipole-dipole-coupled emitters under continuous-wave (CW) and periodic pulsed driving. Our results show that a periodic train of instantaneous $π_x$ pulses can generate emission spectra whose dominant features closely resemble those obtained under CW excitation. We find that the structure of the collective emission spectra reflects the interplay among the resonance conditions, dipole-dipole coupling, and the inter-pulse delay $τ_D$. We further demonstrate that finite-duration $π_x$ pulses with sufficiently high Rabi frequencies can closely reproduce the ideal instantaneous-pulse limit. In contrast to the emission spectra, the photon correlations $g^{(2)}(t,τ)$ are governed primarily by $τ_D$ and, unlike under CW driving, become increasingly insensitive to the resonance conditions in the short-pulse limit. These results establish periodic pulsed driving as a versatile means of tailoring both the emission spectra and the photon statistics of coupled two-emitter systems.

quant-ph↗
arXiv2026

Tree-VQ: Progressive Image Compression from Pretrained Vector Quantizers

Progressive image compression requires a single embedded representation whose received prefixes can be decoded without re-encoding the source. Modern vector-quantized (VQ) image models provide strong discrete endpoint representations, but conventional flat codeword indices do not define meaningful intermediate states for a neural decoder. We present Tree-VQ, a post-hoc conversion of a pretrained flat VQ tokenizer into a fine-grained, arbitrary-prefix progressive representation while preserving its encoder assignments and every learned leaf vector. The key idea is to organize the original codebook into a balanced binary hierarchy, associate explicit representations with internal nodes, and transmit branch decisions in depth-major order. Consequently, once the image header is available, every payload prefix uniquely specifies a valid latent state: each additional branch bit refines exactly one token, and transmission can therefore be truncated at essentially any payload position rather than only at a small number of stage boundaries. We further adapt one shared decoder on the complete-depth and mixed-depth latent states encountered under such arbitrary truncation, making these densely spaced prefixes useful for reconstruction rather than merely syntactically decodable. On Kodak, Tree-VQ achieves a DISTS-based BD-rate saving of 52.1% relative to ProGIC, while exposing thousands of valid arbitrary-prefix operating points from a single embedded bitstream.

cs.CV↗
arXiv2026

Magnetic-Order-Driven Nonlinear Photocurrents in PT-Broken Antiferromagnetic MnS2 and Altermagnetic MnSe2

Nonlinear photocurrent responses generally require inversion-symmetry breaking, but its origin can be magnetic rather than structural. In this study, we investigate the bulk photovoltaic effect in antiferromagnetic MnS2 and altermagnetic MnSe2, two pyrite-type semiconductors whose magnetic ordering breaks inversion symmetry P and its combination with time reversal PT, while the accompanying lattice distortion is minute. We calculate the shift current (SC) and injection current (IC) responses under linearly and circularly polarized light with and without spin-orbit coupling (SOC) and identify their symmetry-allowed tensor components using the corresponding magnetic and spin point groups, respectively. Linear SC and circular IC are symmetry-allowed in both MnS2 and MnSe2 irrespective of SOC, whereas linear IC and circular SC remain forbidden in MnS2 but become allowed in MnSe2 only when SOC is included. These results highlight the importance of SOC and the corresponding MPG analysis for a complete description of nonlinear charge photocurrents in magnetic materials. Beyond charge responses, MnSe2 supports linear shift and circular injection spin photocurrents in the nonrelativistic limit, whereas all spin-photocurrent responses are forbidden in MnS2. Our findings therefore establish inversion symmetry breaking induced by compensated magnetic order as a route to generating and controlling nonlinear photocurrents.

cond-mat.mtrl-sci↗
arXiv2026

Mara Chain: Rethinking Failure as a Stepping Stone for AI System Auto-Evolution

Optimizing deployed AI systems increasingly amounts to editing prompts, skills, harnesses, and code rather than model weights. Existing approaches commonly optimize these artifacts through propose-evaluate-select procedures, where candidate configurations are evaluated and only those meeting an acceptance criterion are selected. Yet our analysis shows that discarded candidates often contain information critical for subsequent optimization. Discarding them causes later proposals to revisit the same failure modes. We introduce Mara Chain, a refinement procedure that turns rejected candidates into stepping stones. Rather than discarding a rejected candidate, Mara Chain retains and iteratively refines it using evidence accumulated across preceding attempts. The procedure limits each refinement chain to a fixed depth and applies Pareto-filtered Top-N selection to bound the candidate pool. Across AppWorld skill optimization, TerminalBench 2.1 harness optimization, and MuSiQue retrieval-pipeline optimization, Mara Chain delivers greater task-performance gains with fewer rollouts. It outperforms GEPA, ACE, and SkillOpt-Lite by up to 20.5% in relative performance on AppWorld, reaching the target score with 65.5% fewer rollouts than GEPA. It improves the pass rate by 20.2 and 22.5 percentage points over AHE and Meta-Harness on TerminalBench 2.1, respectively, and improves MuSiQue test nDCG@10 and Recall@10 by 0.104 and 0.131 over a hand-written retrieval pipeline.

cs.LG↗
arXiv2026

High-Throughput Photovoltaic Screening and Spacer-Dependent Shift Current in Two-Dimensional Hybrid Perovskites

High-throughput first-principles screening of structurally diverse two-dimensional hybrid organic-inorganic perovskites (2D HOIPs) provides a route to systematically explore their photovoltaic properties. Here, we develop a computational workflow with two distinct objectives: spectroscopic limited maximum efficiency (SLME) screening to identify materials with promising photovoltaic potential and nonlinear shift current calculations to characterize their bulk photovoltaic response. Applying this workflow to a curated dataset of experimentally reported 2D HOIPs, we identify more than 25 compounds with SLME values above 25\% and more than 25 compounds exhibiting shift current responses exceeding 10~$μ$A/V$^2$. We then focus on the Pb-I-based 2D HOIPs with $n=1$ to investigate the influence of spacer chemistry on the shift current response, revealing substantial spacer-dependent variations in both its magnitude and spectral peak position. Overall, these results highlight the potential of high-throughput first-principles calculations to accelerate the discovery of experimentally relevant 2D HOIPs with promising photovoltaic properties while revealing how organic spacer chemistry influences their nonlinear shift photocurrent response.

cond-mat.mtrl-sci↗
arXiv2026

Strain-Tunable Shift Current and Magneto-Optical Kerr Effect in Multiferroic Altermagnet Fe2Mo3O8

Altermagnetism has recently emerged as a compelling frontier in spintronics, seamlessly merging the agile tunability of ferromagnets with the hallmark merits of antiferromagnets. As a prototypical polar multiferroic featuring distinctive altermagnetism, Fe2Mo3O8 hosts an ideal playground for exploring the intricate interplay among ferroelectric polarization, altermagnetic order, and spin-dependent responses. Here, employing first-principles calculations, we systematically investigate the coupling among polarization, spin splitting, shift current, and magneto-optical responses in Fe2Mo3O8. Our findings reveal that switching the ferroelectric polarization not only inverts the sign of the shift current but also comprehensively reshapes the momentum-space spin-splitting texture. Furthermore, the shift current and magneto-optical spectrum exhibits strong tunability under mechanical strain. Remarkably, the application of a-axis uniaxial strain breaks the crystalline symmetry, thereby activating a finite magneto-optical Kerr effect that is otherwise forbidden in the pristine phase.

cond-mat.mtrl-sci↗
arXiv2026

Medium-Throughput Evaluation of Quantum Geometry-Driven Topological Transports in Altermagnets

Altermagnets provide a promising platform for a wide spectrum of applications integrating advantages of conventional ferromagnets and antiferromagnets. In this work, we implement a medium-throughput first-principles workflow and evaluate topological transport properties driven by quantum geometry for 135 altermagnets in the MAGNDATA database. Based on automated Wannier construction, both linear and nonlinear responses, including the anomalous Hall effect, magneto-optical Kerr effect, and bulk photovoltaic effect, are evaluated with further symmetry verifications. Detailed analysis is done on representative cases like metallic VNb3S6 with enhanced anomalous Hall conductivity, CaIrO3 with giant MOKE, and CuFeS2 with large shift current in non-centrosymmetric. These results establish a symmetry-guided computational route for identifying experimentally accessible fingerprints and functional transport properties in altermagnets.

cond-mat.mtrl-sci↗
arXiv2026

From Experience to Skill: Multi-Agent Generative Engine Optimization via Reusable Strategy Learning

Generative engines (GEs) are reshaping information access by replacing ranked links with citation-grounded answers, yet current Generative Engine Optimization (GEO) methods optimize each instance in isolation, unable to accumulate or transfer effective strategies across tasks and engines. We reframe GEO as a strategy learning problem and propose MAGEO, a multi-agent framework in which coordinated planning, editing, and fidelity-aware evaluation serve as the execution layer, while validated editing patterns are progressively distilled into reusable, engine-specific optimization skills. To enable controlled assessment, we introduce a Twin Branch Evaluation Protocol for causal attribution of content edits and DSV-CF, a dual-axis metric that unifies semantic visibility with attribution accuracy. We further release MSME-GEO-Bench, a multi-scenario, multi-engine benchmark grounded in real-world queries. Experiments on three mainstream engines show that MAGEO substantially outperforms heuristic baselines in both visibility and citation fidelity, with ablations confirming that engine-specific preference modeling and strategy reuse are central to these gains, suggesting a scalable learning-driven paradigm for trustworthy GEO. Code is available at https://github.com/Wu-beining/MAGEO

cs.AI↗
arXiv2026

FoleyDirector: Fine-Grained Temporal Steering for Video-to-Audio Generation via Structured Scripts

Recent Video-to-Audio (V2A) methods have achieved remarkable progress, enabling the synthesis of realistic, high-quality audio. However, they struggle with fine-grained temporal control in multi-event scenarios or when visual cues are insufficient, such as small regions, off-screen sounds, or occluded or partially visible objects. In this paper, we propose FoleyDirector, a framework that, for the first time, enables precise temporal guidance in DiT-based V2A generation while preserving the base model's audio quality and allowing seamless switching between V2A generation and temporally controlled synthesis. FoleyDirector introduces Structured Temporal Scripts (STS), a set of captions corresponding to short temporal segments, to provide richer temporal information. These features are integrated via the Script-Guided Temporal Fusion Module, which employs Temporal Script Attention to fuse STS features coherently. To handle complex multi-event scenarios, we further propose Bi-Frame Sound Synthesis, enabling parallel in-frame and out-of-frame audio generation and improving controllability. To support training and evaluation, we construct the DirectorSound dataset and introduce VGGSoundDirector and DirectorBench. Experiments demonstrate that FoleyDirector substantially enhances temporal controllability while maintaining high audio fidelity, empowering users to act as Foley directors and advancing V2A toward more expressive and controllable generation.

cs.SD↗
arXiv2026

2D Ferroelectric Ruddlesden-Popper Perovskites: an Emerging Fully Electronically Controllable Shift Current and Persistent Spin Helix

Two-dimensional (2D) hybrid organic--inorganic perovskites (HOIPs) are promising candidates for next-generation optoelectronic and spintronic applications. This work systematically investigates the relationship between structural distortions and functional responses in three $C_{2v}$-symmetric Ruddlesden--Popper (RP) ferroelectric perovskites, $(4,4\text{-DFPD})_{2}\mathrm{PbI}_{4}$, $(\mathrm{DFCHA})_{2}\mathrm{PbI}_{4}$, and PEPI, using first-principles calculations combined with irreducible representation decomposition and wave-vector point-group symmetry (WPGS) analysis. The results reveal that the lead--iodide framework yields shift-current (SC) magnitudes comparable to, and in specific cases even an order of magnitude larger than, those of traditional ferroelectric oxides, with PEPI reaching a maximum of $69.16\ μ\mathrm{A}/\mathrm{V}^{2}$. The SC magnitude correlates positively with the octahedral distortion index ($D_i$), while a competition mechanism is identified between covalent bond strength and structural asymmetry, where increased average bond lengths can offset the enhancement induced by $D_i$. Regarding spintronics, $C_{2v}$ symmetry-protected persistent spin textures (PST) are identified. A transition to $C_2$-protected quasi-PST occurs in monoclinic $(4,4\text{-DFHHA})_{2}\mathrm{PbI}_{4}$, leading to a persistent spin helix (PSH) with long-distance spin transport. The synergy among ferroelectricity, SC, and PST enables nonvolatile electrical control of both photocurrent direction and spin configurations. This work provides evaluation criteria and practical guidance for designing high-performance integrated spintronic--photovoltaic devices.

cond-mat.mtrl-sci↗
arXiv2026

CODER: Coupled Diversity-Sensitive Momentum Contrastive Learning for Image-Text Retrieval

Image-Text Retrieval (ITR) is challenging in bridging visual and lingual modalities. Contrastive learning has been adopted by most prior arts. Except for limited amount of negative image-text pairs, the capability of constrastive learning is restricted by manually weighting negative pairs as well as unawareness of external knowledge. In this paper, we propose our novel Coupled Diversity-Sensitive Momentum Constrastive Learning (CODER) for improving cross-modal representation. Firstly, a novel diversity-sensitive contrastive learning (DCL) architecture is invented. We introduce dynamic dictionaries for both modalities to enlarge the scale of image-text pairs, and diversity-sensitiveness is achieved by adaptive negative pair weighting. Furthermore, two branches are designed in CODER. One learns instance-level embeddings from image/text, and it also generates pseudo online clustering labels for its input image/text based on their embeddings. Meanwhile, the other branch learns to query from commonsense knowledge graph to form concept-level descriptors for both modalities. Afterwards, both branches leverage DCL to align the cross-modal embedding spaces while an extra pseudo clustering label prediction loss is utilized to promote concept-level representation learning for the second branch. Extensive experiments conducted on two popular benchmarks, i.e. MSCOCO and Flicker30K, validate CODER remarkably outperforms the state-of-the-art approaches. Our code is available at: https://github.com/BruceW91/CODER.

cs.CV↗
arXiv2026

Learning from Brain Topography: A Hierarchical Local-Global Graph-Transformer Network for EEG Emotion Recognition

Understanding how local neurophysiological patterns interact with global brain dynamics is essential for decoding human emotions from EEG signals. However, existing deep learning approaches often overlook the brain's intrinsic spatial organization, failing to simultaneously capture local topological relations and global dependencies. To address these challenges, we propose Neuro-HGLN, a Neurologically-informed Hierarchical Graph-Transformer Learning Network that integrates biologically grounded priors with hierarchical representation learning. Neuro-HGLN first constructs a spatial Euclidean prior graph based on physical electrode distances to serve as an anatomically grounded inductive bias. A learnable global dynamic graph is then introduced to model functional connectivity across the entire brain. In parallel, to capture fine-grained regional dependencies, Neuro-HGLN builds region-level local graphs using a multi-head self-attention mechanism. These graphs are processed synchronously through local-constrained parallel GCN layers to produce region-specific representations. Subsequently, an iTransformer encoder aggregates these features to capture cross-region dependencies under a dimension-as-token formulation. Extensive experiments demonstrate that Neuro-HGLN achieves state-of-the-art performance on multiple benchmarks, providing enhanced interpretability grounded in neurophysiological structure. These results highlight the efficacy of unifying local topological learning with cross-region dependency modeling for robust EEG emotion recognition.

cs.HC↗
arXiv2025

DreamFoley: Scalable VLMs for High-Fidelity Video-to-Audio Generation

Recent advances in video generation have achieved remarkable improvements in visual content fidelity. However, the absence of synchronized audio severely undermines immersive experience and restricts practical applications of these technologies. To address this challenge, several pioneering works have explored diffusion transformer architectures for generating plausible video-synchronized audio, including Kling-foley, HunyuanVideo-foley and Thinksound. Distinct from existing works, we introduce an autoregressive audio generation architecture (DreamFoley) that harnesses the capabilities of large vision-language models (VLMs) to jointly model sequential interactions among video, audio, and text modalities. Our approach features a dual-visual encoder module that effectively captures both audio-aligned and text-aligned visual features. Additionally, we employ a Residual Vector Quantization audio tokenizer with a delay-pattern generation scheme to balance the trade-off between training efficiency and audio quality. Moreover, we introduce the classifier-free guidance strategy into VLMs to bootstrap generated audio quality. Furthermore, we establish an efficient data production pipeline to scale audio-video-text triple collection. Finally, extensive experiments are conducted to validate the effectiveness of our model, achieving promising performance across popular benchmarks. We hope that the findings in this study provide a strong foundation for future video-to-audio generation research. We also release the previously missing audio-visual textual descriptions from the public benchmark, aiming to facilitate subsequent researchers in conducting more convenient and effective evaluations and comparisons.

cs.SD↗
arXiv2025

Ferroelectricity-driven altermagnetism in two-dimensional van der Waals multiferroics

Altermagnets (AMs) are a recently identified class of unconventional collinear compensated antiferromagnets that exhibit momentum-dependent spin splitting despite having zero net magnetization. This unconventional magnetic order gives rise to a range of phenomena, including the anomalous Hall effect, chiral magnons, and nonlinear photocurrents. Here, using spin space group (SSG) symmetry analysis and first-principles calculations, we demonstrate an efficient strategy to control altermagnetism in two-dimensional multiferroics through ferroelectric polarization and interlayer sliding. For material realization, we find that monolayer and bilayer FeCuP2S6 exhibit finite spin splitting when ferroelectric sublattices are connected by nonsymmorphic screw-axis operations rather than pure translation or inversion symmetry. Interlayer sliding further enables reversible switching or suppression of spin splitting through modifications of the SSG. Our calculations further reveal that the anomalous Hall response serves as a direct probe of these spin-split states. These findings establish two-dimensional van der Waals multiferroics as promising platforms for realizing electrically controllable altermagnetism and advancing next-generation spintronic and magnetoelectric technologies.

cond-mat.mtrl-sci↗
arXiv2025

High-throughput screening of spin Hall conductivity in 2D materials

Two-dimensional (2D) materials with large spin Hall effect (SHE) have attracted significant attention due to their potential applications in next-generation spintronic devices. In this work, we perform high-throughput (HTP) calculations to obtain the spin Hall conductivity (SHC) of 4486 non-magnetic compounds in the \texttt{2Dmatpedia} database and identify six materials with SHC exceeding $500\,(\hbar/e)\,(\mathrm{S/cm})$, surpassing those of previously known materials. Detailed analysis reveals that the significant SHC can be attributed to spin-orbit coupling (SOC)-induced gap openings at Dirac-like band crossings. Additionally, the presence of mirror symmetry further enhances the SHC. Beyond the high-SHC materials, 57 topological insulators with quantized SHCs have also been identified. Our work enables rapid screening and paves the way for experimental validation, potentially accelerating the discovery of novel 2D materials optimized for spintronics applications.

cond-mat.mtrl-sci↗
arXiv2025

BeSimulator: A Large Language Model Powered Text-based Behavior Simulator

Traditional robot simulators focus on physical process modeling and realistic rendering, often suffering from high computational costs, inefficiencies, and limited adaptability. To handle this issue, we concentrate on behavior simulation in robotics to analyze and validate the logic behind robot behaviors, aiming to achieve preliminary evaluation before deploying resource-intensive simulators and thus enhance simulation efficiency. In this paper, we propose BeSimulator, a modular and novel LLM-powered framework, as an attempt towards behavior simulation in the context of text-based environments. By constructing text-based virtual environments and performing semantic-level simulation, BeSimulator can generalize across scenarios and achieve long-horizon complex simulation. Inspired by human cognition paradigm, it employs a ``consider-decide-capture-transfer'' four-phase simulation process, termed Chain of Behavior Simulation (CBS), which excels at analyzing action feasibility and state transition. Additionally, BeSimulator incorporates code-driven reasoning to enable arithmetic operations and enhance reliability, and reflective feedback to refine simulation. Based on our manually constructed behavior-tree-based simulation benchmark, BTSIMBENCH, our experiments show a significant performance improvement in behavior simulation compared to baselines, ranging from 13.60% to 24.80%. Code and data are available at https://github.com/Dawn888888/BeSimulator.

cs.RO↗