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Yi Lu

Publications and source records attributed to Yi Lu.

At least 37 records · Page 2Linked to original sources
arXiv2026

Belobog: Move Language Fuzzing Framework For Real-World Smart Contracts

Move is a resource-oriented programming language designed for secure and verifiable smart contract development and has been widely used in managing billions of digital assets in blockchains, such as Sui and Aptos.Move features a strong static type system and explicit resource semantics to enforce safety properties such as the prevention of data races, invalid asset transfers, and entry vulnerabilities. However, smart contracts written in Move may still contain certain vulnerabilities that are beyond the reach of its type system. It is thus essential to validate Move smart contracts. Unfortunately, due to its strong type system, existing smart contract fuzzers are ineffective in producing syntactically or semantically valid transactions to test Move smart contracts. This paper introduces the first fuzzing framework, Belobog, for Move smart contracts. Belobog is type-aware and ensures that all generated and mutated transactions are well-typed. More specifically, for a target Move smart contract, Belobog first constructs a dependency graph based on Move's type system, and then generates or mutates a transaction based on the graph trace derived from the dependency graph. In order to overcome the complex checks in Move smart contracts, we further design and implement a concolic executor in Belobog. We evaluated Belobog on 109 real-world Move smart contract projects. The experimental results show that Belobog is able to detect 100% critical and 79% major vulnerabilities manually audited by human experts. We further selected two recent notorious incidents in the Move ecosystem, i.e., Cetus and Nemo. Belobog successfully reproduced full exploits for both of them, without any prior knowledge. Moreover, we applied Belobog on three ongoing auditing projects and found 2 critical, 2 major, and 3 medium new vulnerabilities, all acknowledged by the project developers.

cs.CR↗
arXiv2026

Causal Inference under Kink Bunching

Kinked policies change marginal incentives at a threshold, and agents respond by adjusting the assignment variable that determines their treatment. The bunching literature uses this response to estimate the elasticity of the assignment variable; policymakers often care about effects on other outcomes. We develop a framework for estimating causal effects of kinked policies on outcomes beyond the assignment variable when agents can fully manipulate it. Average effects are defined for two affected populations: bunchers, who locate at the kink, and shifters, who reduce their assignment values but remain above the threshold; shifter effects compare equal-mass intervals and require only rank invariance. Because a single kink identifies neither the counterfactual assignment density nor the counterfactual outcome function, identification is design-assisted: placebo groups and moving thresholds discipline the local shape of both objects, the focal group's unaffected observations pin down level and slope differences, and the restrictions are testable. Applying the framework to a kinked coinsurance schedule in China's medical insurance, we find that the loss of reimbursement above the annual cap sharply reduces outpatient visits, raises cost per visit, and shifts the composition of care toward hospitals -- effects that are invisible in a density-only bunching analysis.

econ.EM↗
arXiv2026

ClawBench: Can AI Agents Complete Everyday Online Tasks?

AI agents may be able to assist with emails and documents, but can they reliably complete everyday online workflows on real websites? Everyday online tasks offer a realistic yet unsolved testbed for evaluating the next generation of AI agents. To this end, we introduce ClawBench, an evaluation framework comprising 153 everyday online tasks that people need to accomplish regularly in their lives and work, spanning 144 platforms across 15 categories, from completing purchases and booking appointments to submitting job applications. These tasks require capabilities beyond existing benchmarks, such as obtaining relevant information from user-provided documents, navigating multi-step workflows across diverse platforms, and write-heavy operations like filling in many detailed forms correctly. Unlike existing benchmarks that evaluate agents in offline sandboxes with static pages, ClawBench operates on production websites, preserving the full complexity, dynamic nature, and interaction challenges of real-world web environments. An interception layer captures and blocks the final submission request, ensuring safe evaluation without real-world side effects. Our evaluations of 8 frontier models show that both proprietary and open-source models complete only a small portion of these tasks. For example, Claude Sonnet 4.6 achieves only 33.3%, which exposes gaps in current AI agents. Progress on ClawBench brings us closer to AI agents that can function as general-purpose assistants.

cs.CL↗
arXiv2026

Wan-Dancer: A Hierarchical Framework for Minute-scale Coherent Music-to-Dance Generation

Generating long-duration, high-definition, and rhythmically synchronized dance videos directly from music remains a significant challenge, primarily due to the temporal constraints of current diffusion models, which typically fail beyond 20 seconds. Existing approaches, whether they rely on intermediate 3D skeletons or on end-to-end video synthesis, suffer from temporal drift, identity inconsistency, and repetitive motion patterns when extended to longer horizons. To address these limitations, we propose a novel hierarchical framework for minute-scale coherent music-to-dance generation. Our method decouples the process into global keyframe planning and local temporal refinement, leveraging full-track musical context to ensure long-range coherence. Key innovations include dynamic frame rate adaptation via time-mapped RoPE embeddings for precise alignment, an optical-flow-based loss function to enhance motion continuity, and motion-speed control to preserve high-fidelity details during rapid movements. Extensive experiments demonstrate that our framework surpasses the conventional duration barrier, generating stable, 720p/30fps videos exceeding one minute with superior temporal stability. Furthermore, the model exhibits robust versatility across five distinct dance genres, conditioned on both audio and textual prompts, establishing a new state-of-the-art in coherent, long-form dance video synthesis.

cs.CV↗
arXiv2026

Latency Coding for Efficient and Low-Latency Deep Spiking Neural Networks

Spiking neural networks (SNNs) offer a biologically inspired computing paradigm with significant potential for energy-efficient neural processing. Among neural coding schemes of SNNs, Time-To-First-Spike (TTFS) coding, which encodes information through the precise timing of a neuron's first spike, provides exceptional activity sparsity and energy efficiency. However, existing TTFS models lack efficient training methods, suffering from high inference latency and limited performance, limiting their practicality on neuromorphic hardware. In this work, we propose latency coding, an extension of TTFS coding, and present a compatible framework that enables the efficient training of deep latency-coded SNNs by leveraging backpropagation through time (BPTT) algorithm. The framework includes: (1) a latency encoding (LE) module with feature extraction and straight-through estimators to address severe information loss in direct intensity-to-latency mapping; (2) relaxation of the strict single-spike constraint in intermediate layers to improve information propagation and gradient flow; and (3) a temporal adaptive decision (TAD) loss function that dynamically weights supervision signals based on the model's confidence, balancing the trade-off between speed and accuracy. Experimental results demonstrate that our method achieves competitive or superior accuracy compared with existing TTFS-coded SNNs with ultra-low inference latency and high energy efficiency. Latency-coded SNNs also demonstrate improved robustness against input perturbations. These findings highlight latency coding as a practical and hardware-friendly approach for fast and energy-efficient neuromorphic processing.

cs.NE↗
arXiv2026

Knowdit: Agentic Smart Contract Vulnerability Detection with Auditing Knowledge Summarization

Smart contracts govern billions of dollars in decentralized finance (DeFi), yet automated vulnerability detection remains challenging because many vulnerabilities are tightly coupled with project-specific business logic. We observe that recurring vulnerabilities across diverse DeFi business models often share the same underlying economic mechanisms, which we term DeFi semantics, and that capturing these shared abstractions can enable more systematic auditing. Building on this insight, we propose Knowdit, a knowledge-driven, agentic workflow for smart contract vulnerability detection. Knowdit first constructs an auditing knowledge graph from historical human audit reports, linking fine-grained DeFi semantics with recurring vulnerability patterns. Given a new project, a multi-agent pipeline leverages this knowledge through an iterative loop of specification generation, Proof-of-Concept (PoC) synthesis, PoC execution, and finding reflection, driven by a shared repository index. We evaluate Knowdit on 11 recent Code4rena projects with 84 ground-truth vulnerabilities. Knowdit detects all 21 high-severity and 90% of medium-severity vulnerabilities without false positives, fully covering eight projects, significantly outperforming all baselines. Applied to seven real-world projects, Knowdit further discovers 9 high- and 36 medium-severity previously unknown vulnerabilities, securing millions in liquidity and proving its outstanding performance.

cs.CR↗
arXiv2026

PressMimic: Pressure-Guided Motion Capture and Control for Humanoid Robot Imitation

Humanoid motion imitation requires not only accurate perception of human kinematics but also faithful reproduction of physical interactions with the environment. However, existing pipelines rely primarily on vision-based motion capture and kinematic imitation, largely ignoring contact dynamics, leading to artifacts such as foot sliding, floor penetration, and unstable behaviors. In this work, we revisit humanoid motion imitation from the perspective of physical grounding and leverage pressure as a unified modality across perception and control. We present PressMimic, a framework that integrates pressure into the full pipeline from motion capture to humanoid control. In the perception stage, we introduce FRAPPE++, a multimodal model that fuses RGB and pressure to jointly estimate 3D pose and global motion, where pressure provides explicit contact and support constraints to resolve ambiguity in vision-based estimation. In the control stage, we propose a pressure-supervised policy (PSP) that incorporates pressure-derived signals into reinforcement learning, enabling physically consistent contact patterns during execution. We further construct MotionPRO, a large-scale dataset with synchronized RGB, pressure, and motion capture data. Experiments show that pressure improves motion estimation accuracy, trajectory consistency, and execution stability. These results demonstrate that pressure serves as an effective physical grounding signal, bridging perception and control for physically consistent humanoid motion imitation.

cs.RO↗
arXiv2026

From Magnetic to Inverse Magnetic Catalysis: The Interplay of Quark and Gluon Mass Generation in Magnetic Fields

We analyze the effects of the magnetic field on the quark and gluon propagators within the functional QCD framework. By solving the coupled Dyson-Schwinger equations for the quark and gluon propagators, we find that the quark mass is generally enhanced in the presence of a magnetic field, leading to magnetic catalysis of the chiral condensate. Meanwhile, the magnetic field also induces an increase in the gluon screening mass. The enhancement of the gluon screening mass suppresses the quark-gluon interaction and thereby weakens the strength of dynamical chiral symmetry breaking, establishing a competing mechanism against magnetic catalysis. In particular, this enhancement of the gluon screening mass becomes dominant near the chiral phase transition, which in turn gives rise to inverse magnetic catalysis.

hep-ph↗
arXiv2026

$d$-wave altermagnetism revealed by resonant inelastic X-ray scattering

Altermagnetism defines a third fundamental class of collinear magnetic order, featuring compensated magnetic moments with antiparallel spin alignment, yet lifted Kramers degeneracy without the need for relativistic spin-orbit coupling. Its ability to host spin-polarized electronic bands and unconventional chiral magnons makes it a promising platform for functional materials. However, experimental verification has proven challenging; while circular dichroism in resonant inelastic X-ray scattering (RIXS) has been suggested as a signature of chiral magnons, it remains controversial whether this effect is an intrinsic property of altermagnetism or an artifact of experimental geometry. In this work, we resolve this debate and provide unambiguous experimental evidence of $d$-wave altermagnetism in the strongly correlated Lieb-lattice magnet La$_2$O$_3$Mn$_2$Se$_2$. The RIXS spectra exhibit a $d$-wave-symmetry circular dichroism in the magnetic excitations that vanishes in the paramagnetic phase. Through RIXS-operator symmetry analysis and exact-diagonalization calculations, we prove that the observed dichroism is a direct consequence of altermagnetic symmetry constraints, independent of magnon branch splitting. Our results provide definitive evidence for the experimental realization of $d$-wave altermagnetism in La$_2$O$_3$Mn$_2$Se$_2$ and establish circularly polarized RIXS as a highly symmetry-sensitive spectroscopic framework for detecting magnetic phases that evade conventional probes.

cond-mat.str-el↗
arXiv2026

Dr-DCI: Scaling Direct Corpus Interaction via Dynamic Workspace Expansion

Agentic search over large corpora relies on retriever-mediated interfaces (e.g., BM25 or ColBERT) for scalable candidate discovery. While effective at ranking relevant documents, these interfaces expose evidence only as ranked results or bounded document views, limiting agents' ability to reorganize material and verify constraints across documents. Direct Corpus Interaction (DCI) addresses this limitation by exposing shell-executable corpus operations for flexible search, filtering, comparison, and verification. However, full-corpus terminal commands become slow and unstable as the corpus grows, degrading performance and efficiency. We introduce DR-DCI, a retriever-steered DCI framework that treats retrieval as an agent-callable action for expanding a local workspace. Rather than operating directly over the full corpus, the agent dynamically pulls relevant documents into an evolving workspace and conducts DCI operations within it. This design combines retriever-level recall with DCI-style precision: retrieval keeps exploration scalable, while DCI preserves the local operations needed for effective evidence resolution. Experiments show that DR-DCI is both effective and efficient across scales. On Browsecomp-Plus, DR-DCI reaches 71.2\% accuracy, improving over raw DCI and ablated variants by up to 8.3 points while reducing tool usage, wall time, and estimated cost. With workspace-preserving context reset, accuracy further improves to 73.3\%. In corpus-scaling experiments, DR-DCI remains effective from 100K to 10M documents, whereas raw DCI becomes unstable and BM25 performs substantially worse. DR-DCI also scales to a 20M-scale file-per-document Wiki-18 QA setting, achieving an average score of 63.0 across six benchmarks and outperforming retrieval-based and trained search-agent baselines. Ablation analysis further shows that ranked previews and inter-document DCI are key to performance.

cs.AI↗
arXiv2026

Autoregression-Free Neural Operators for Time-Dependent PDEs

Neural operators learn mappings from function-dependent inputs to solutions, providing an effective framework for solving partial differential equations (PDEs). For time-dependent PDEs, existing methods typically perform long-horizon prediction through autoregressive rollout directly in high-dimensional physical field spaces, where each predicted state is recursively fed back as the input for the next step. Although effective for short-term prediction, this autoregressive rollout and the lack of continuous-time modeling lead to progressive error accumulation over long-horizon rollouts. In this work, we propose Autoregression-Free Neural Operators (AFNO), which map the time evolution of PDEs into a latent space and model continuous-time vector fields within it. AFNO uses flow matching to learn the latent vector field, thereby enabling continuous evolution over extended horizons, avoiding autoregressive rollout and capturing dynamics under varying parameter configurations through explicit conditioning on physical parameters. Theoretical analysis and extensive experiments on six PDEs demonstrate that AFNO improves long-horizon prediction stability and consistently reduces rollout errors compared with the baselines.

cs.LG↗
arXiv2026

Med-Scout: Curing MLLMs' Geometric Blindness in Medical Perception via Geometry-Aware RL Post-Training

Despite recent Multimodal Large Language Models (MLLMs)' linguistic prowess in medical diagnosis, we find even state-of-the-art MLLMs suffer from a critical perceptual deficit: geometric blindness. This failure to ground outputs in objective geometric constraints leads to plausible yet factually incorrect hallucinations, rooted in training paradigms that prioritize linguistic fluency over geometric fidelity. This paper introduces Med-Scout, a novel framework that "cures" this blindness via Reinforcement Learning (RL) that leverages the intrinsic geometric logic latent within unlabeled medical images. Instead of relying on costly expert annotations, Med-Scout derives verifiable supervision signals through three strategic proxy tasks inspired by the systematic reading and reasoning patterns of clinicians: Hierarchical Scale Localization, Topological Jigsaw Reconstruction, and Anomaly Consistency Detection. To rigorously quantify this deficit, we present Med-Scout-Bench, a new benchmark specifically designed to evaluate geometric perception. Extensive evaluations show that Med-Scout significantly mitigates geometric blindness, outperforming leading proprietary and open-source MLLMs by over 40% on our benchmark. Furthermore, this enhanced geometric perception generalizes to broader medical understanding, achieving superior results on radiological and comprehensive medical VQA tasks.

cs.CV↗
arXiv2026

MotionPRO: Exploring the Role of Pressure in Human MoCap and Beyond

Existing human Motion Capture (MoCap) methods mostly focus on the visual similarity while neglecting the physical plausibility. As a result, downstream tasks such as driving virtual human in 3D scene or humanoid robots in real world suffer from issues such as timing drift and jitter, spatial problems like sliding and penetration, and poor global trajectory accuracy. In this paper, we revisit human MoCap from the perspective of interaction between human body and physical world by exploring the role of pressure. Firstly, we construct a large-scale human Motion capture dataset with Pressure, RGB and Optical sensors (named MotionPRO), which comprises 70 volunteers performing 400 types of motion, encompassing a total of 12.4M pose frames. Secondly, we examine both the necessity and effectiveness of the pressure signal through two challenging tasks: (1) pose and trajectory estimation based solely on pressure: We propose a network that incorporates a small kernel decoder and a long-short-term attention module, and proof that pressure could provide accurate global trajectory and plausible lower body pose. (2) pose and trajectory estimation by fusing pressure and RGB: We impose constraints on orthographic similarity along the camera axis and whole-body contact along the vertical axis to enhance the cross-attention strategy to fuse pressure and RGB feature maps. Experiments demonstrate that fusing pressure with RGB features not only significantly improves performance in terms of objective metrics, but also plausibly drives virtual humans (SMPL) in 3D scene. Furthermore, we demonstrate that incorporating physical perception enables humanoid robots to perform more precise and stable actions, which is highly beneficial for the development of embodied artificial intelligence. Project page is available at: https://nju-cite-mocaphumanoid.github.io/MotionPRO/

cs.CV↗
arXiv2026

Newton-Okounkov Bodies and Jet Separation: Canonical-Free and Multipoint Generalizations

We establish three generalizations of the Küronya-Lozovanu jet separation criterion via Newton-Okounkov bodies: if an inverted standard simplex of size $n+k+\varepsilon$ is contained in all infinitesimal Newton-Okounkov bodies at $x$, then $K_X+D$ separates $k$-jets at $x$. We prove (1) a canonical-free version with a computable multiple $m(D)$; (2) a multipoint extension for simultaneous jet separation; and (3) a combination of both. Proofs use Trusiani's framework and Nadel vanishing. We conclude with explicit computations for a double cover of a product of elliptic curves.

math.AG↗
arXiv2026

Beyond Semantic Similarity: Rethinking Retrieval for Agentic Search via Direct Corpus Interaction

Modern retrieval systems, whether lexical or semantic, expose a corpus through a fixed similarity interface that compresses access into a single top-k retrieval step before reasoning. This abstraction is efficient, but for agentic search, it becomes a bottleneck: exact lexical constraints, sparse clue conjunctions, local context checks, and multi-step hypothesis refinement are difficult to implement by calling a conventional off-the-shelf retriever, and evidence filtered out early cannot be recovered by stronger downstream reasoning. Agentic tasks further exacerbate this limitation because they require agents to orchestrate multiple steps, including discovering intermediate entities, combining weak clues, and revising the plan after observing partial evidence. To tackle the limitation, we study direct corpus interaction (DCI), where an agent searches the raw corpus directly with general-purpose terminal tools (e.g., grep, file reads, shell commands, lightweight scripts), without any embedding model, vector index, or retrieval API. This approach requires no offline indexing and adapts naturally to evolving local corpora. Across IR benchmarks and end-to-end agentic search tasks, this simple setup substantially outperforms strong sparse, dense, and reranking baselines on several BRIGHT and BEIR datasets, and attains strong accuracy on BrowseComp-Plus and multi-hop QA without relying on any conventional semantic retriever. Our results indicate that as language agents become stronger, retrieval quality depends not only on reasoning ability but also on the resolution of the interface through which the model interacts with the corpus, with which DCI opens a broader interface-design space for agentic search.

cs.IR↗
arXiv2026

Make Tracking Easy: Neural Motion Retargeting for Humanoid Whole-body Control

Humanoid robots require diverse motor skills to integrate into complex environments, but bridging the kinematic and dynamic embodiment gap from human data remains a major bottleneck. We demonstrate through Hessian analysis that traditional optimization-based retargeting is inherently non-convex and prone to local optima, leading to physical artifacts like joint jumps and self-penetration. To address this, we reformulate the targeting problem as learning data distribution rather than optimizing optimal solutions, where we propose NMR, a Neural Motion Retargeting framework that transforms static geometric mapping into a dynamics-aware learned process. We first propose Clustered-Expert Physics Refinement (CEPR), a hierarchical data pipeline that leverages VAE-based motion clustering to group heterogeneous movements into latent motifs. This strategy significantly reduces the computational overhead of massively parallel reinforcement learning experts, which project and repair noisy human demonstrations onto the robot's feasible motion manifold. The resulting high-fidelity data supervises a non-autoregressive CNN-Transformer architecture that reasons over global temporal context to suppress reconstruction noise and bypass geometric traps. Experiments on the Unitree G1 humanoid across diverse dynamic tasks (e.g., martial arts, dancing) show that NMR eliminates joint jumps and significantly reduces self-collisions compared to state-of-the-art baselines. Furthermore, NMR-generated references accelerate the convergence of downstream whole-body control policies, establishing a scalable path for bridging the human-robot embodiment gap.

cs.RO↗
arXiv2026

Chiral Magnetic effect as the anomaly in the transverse axial vector Ward Identity

Through analyzing the quark propagator under the magnetic field, we establish that the axial anomaly originates from an additional Dirac structure in quark propagator induced by the magnetic field. This Dirac structure also allows one to connect the axial anomaly with the topological properties of the system by checking the axial vector Ward identity. For the tree level propagator, we reproduce the result of the anomalous axial current as in the Dirac Hamiltonian approach and kinetic theory. Particularly, we confirm that the chiral magnetic effect (CME) comes from the same term that is in charge of the axial anomaly, specifically, as the anomaly of the transversal axial vector Ward Identity. The identity guarantees that the CME conductivity $C_{\rm CME}$ is a constant as $C_{\rm CME}=\frac{1}{2π^2}$, and is robust against the temperature, chemical potential, magnetic field and also interaction. Finally, we verify this numerically by applying the full quark propagator under magnetic field calculated from the functional QCD methods.

hep-th↗
arXiv2026

$3d_{z^2}$ orbital delocalization and magnetic collapse in superconducting (La,Pr)$_3$Ni$_2$O$_{7-δ}$ films

The recent discovery of Ruddlesden--Popper (RP) nickelate thin-film superconductors has opened a new frontier in unconventional superconductivity. Its realization requires both compressive epitaxial strain and highly oxidative growth conditions, yet the microscopic pathway from the parent phase to the superconducting phase remains elusive. Here, X-ray absorption spectra and resonant inelastic X-ray scattering are employed to track this evolution by independently tuning strain and oxygen content in (La,Pr)$_3$Ni$_2$O$_{7-δ}$ thin films. We uncover a remarkable two-step narrative. First, signatures of delocalization emerge in the same way upon two independent tunings: Spectral weight transfers from a ''Upper Hubbard''-like peak to the hole-like peak associated with O $2p_z$ state, and in parallel, the initially localized Ni $3d_{z^2}$ orbital becomes more itinerant followed by the broadening and weakening of $dd$ orbital excitations. Second, as itinerancy increases, long-range spin-density-wave (SDW) order is suppressed in both intensity and correlation length, indicating direct competition with superconductivity. Yet, short-range magnons persist: they become damped but their bandwidth stays unchanged. Our results paint a coherent picture that both strain and oxygenation drive the RP bilayer nickelates towards the superconducting instability, where the O $2p_z$ and Ni $3d_{z^2}$ orbitals become delocalized. Concomitantly, the long-range magnetic order loses coherence and gets suppressed. These findings establish an orbital-selective route to RP nickelate superconductivity, in which the delocalization of the $2p_z$ and $3d_{z^2}$ orbitals and the robust short-range magnons upon the melting of SDW order are prerequisites, providing strong constraints for theory and the roadmap for designing nickelate superconductors.

cond-mat.supr-con↗