arXiv subjects

Jian Huang

Publications and source records attributed to Jian Huang.

At least 37 records · Page 2Linked to original sources

arXiv2026

Auto-Scaling Heterogeneous Neural Processing Units for Energy and Cost-Efficient LLM Serving

To meet the ever-increasing computing demands of large language model (LLM) services, modern cloud platforms have widely deployed neural processing units (NPUs). These NPU chips have been developed and evolved at an incredibly fast pace, this inevitably produces heterogeneous compute pools backed by different versions of NPU chips. Unfortunately, due to the lack of system and architecture support for managing NPU heterogeneity in the cloud, it is unclear how to best utilize heterogeneous NPUs to maximize the energy and cost efficiency for LLM services. In this paper, we first conduct a characterization study of various generations of real NPU chips to demonstrate the potential benefits on energy/cost efficiency and performance by utilizing heterogeneous NPU chips. To realize these benefits, we present NeuScale, an auto-scaling framework to automatically exploit heterogeneous NPUs for cloud platforms. NeuScale manages heterogeneous NPU resources with a new vPod abstraction, which abstracts the core hardware parameters of different NPU versions and provides compatibility with existing ML frameworks. It makes the best-fit vPod allocations for different LLM inference requests using an intuitive and lightweight roofline-based analysis. It supports fine-grained dynamic NPU resource provisioning by adjusting both the vPod configuration (i.e., scaling up/down) and the number of vPods (e.g., scaling in/out). To validate the benefits of NeuScale at scale, we implement it with a production-level NPU simulator. Our evaluation with popular LLMs shows that NeuScale can significantly improve cost efficiency and service-level objective (SLO) satisfaction rate by best utilizing heterogeneous NPU resources.

cs.AR↗

arXiv2026

Slope-Guided Mamba and Angular-Refined Transformer for Light Field Super-Resolution

Light Field Super-Resolution (LFSR) necessitates accurate modeling of spatial-angular correlations while preserving intrinsic 4D ray coherence. However, maintaining such high-dimensional consistency remains challenging, primarily due to two inherent limitations in prevailing modeling paradigms. First, spatial and angular dimensions are often modeled in a decoupled manner, restricting early cross-dimensional interaction and leading to geometric inconsistencies. Moreover, although continuous sequence modeling paradigms show promise in representing epipolar structures, their rigid scanning mechanisms fundamentally conflict with epipolar geometry, limiting geometry-aware feature aggregation. To address these challenges, we propose a hybrid light field super-resolution network, termed SMART, which integrates a Slope-Guided Mamba and an Angular-Refined Transformer to effectively overcome these limitations. Specifically, we introduce an angular-modulated spatial module to bridge the decoupling gap, incorporating angular priors to strengthen spatial-angular correlation modeling. To mitigate the scan-geometry mismatch, we propose a manifold-aligned trajectory module that enables geometry-consistent sequence modeling along epipolar structures. Experiments on five benchmarks demonstrate that SMART achieves state-of-the-art performance, surpassing previous methods by 0.42 dB (PSNR) with significantly reduced artifacts.

cs.CV↗

arXiv2026

Benchmarking LLM Serving Systems for Agentic AI Workloads with XPerf

We present XPerf, a benchmarking framework that load-tests LLM serving systems with diverse agentic AI workloads. It provides detailed profiling of the serving system and hardware, enabling users to identify performance bottlenecks introduced by agentic workloads. Benchmarking LLM serving systems under agentic workloads is challenging - agentic applications rely on nondeterministic LLM outputs to guide their control flow; therefore, workload patterns vary unpredictably from run to run. XPerf minimizes this workload variation with a fine-grained trace replay approach: it enables users to easily collect traces from real agentic applications, synthesize new workloads with various patterns if needed, and reproducibly replay them on different LLM serving systems. XPerf includes eight agentic applications across diverse use cases (e.g., coding, deep research, and Q&A) by default. Our empirical study using these workloads shows that XPerf accurately replays agentic workloads, provides detailed performance breakdowns, scales to larger serving systems, and assists in serving system debugging. We will open-source XPerf on GitHub.

cs.DC↗

arXiv2026

Spatiotemporal Magnonic Vortex Beams with Alternating Transverse Orbital Angular Momentum

Recent theoretical and experimental advances have demonstrated spatiotemporal photonic and acoustic vortex beams in free space. Such spatiotemporal vortex beams possess orbital angular momentum oriented perpendicular to the wave propagation direction. Herein, we report the discovery of spatiotemporal magnonic vortex beams in a confined ferromagnetic nanostrip geometry. The spatiotemporal magnonic vortex beam features stationary phase dislocations and exhibits wave propagation along a zigzag-like trajectory. Notably, the transverse orbital angular momentum carried by these phase dislocations displays spatial alternation. Our results differ distinctly from their photonic and acoustic counterparts, offering new insights into the fundamental research of spatiotemporal vortex beams.

cond-mat.mes-hall↗

arXiv2026

The Curious Case of PHL 1811: Heavy Obscuration Versus Intrinsic X-ray Weakness

We present a systematic X-ray analysis of the narrow-line Type 1 quasar PHL 1811, which has long been regarded as the prototype of intrinsically X-ray weak quasars. A critical breakthrough came with the first detection of a bright X-ray flare from this source by the Einstein Probe (EP) in 2024. We utilize archival X-ray observations spanning 2001-2024, including the post-flare EP and Swift data. We confirm that PHL 1811 shows X-ray weakness factors $f_{\rm weak} \approx 23$-179 across all epochs before 2024. The 2024 EP flare marks the first detection of an X-ray nominal state with $f_{\rm weak} \approx 0.63$, followed by a rapid flux decline. We identify three key observational signatures that strongly support heavy obscuration: (1) a significant hard X-ray excess above $\approx5$ keV in the 2015 XMM-Newton spectrum; (2) relatively flat spectral shapes in two Swift observations; and (3) transitions between X-ray nominal and multiple X-ray weak states without corresponding optical/infrared variability, consistent with expectations from obscuration by a clumpy dust-free absorber. Fitting with a partial-covering obscuration model reproduces all multi-epoch spectra well. The observed steep spectra are dominated by a small leaked/scattered fraction of the intrinsic continuum, and variability is driven by changes in the leakage fraction and column density. Our results strongly favor the scenario where PHL 1811 is obscured by a radiatively driven accretion-disk wind from super-Eddington accretion, unifying PHL 1811 with the broader population of super-Eddington accreting AGNs under a single obscuration framework.

astro-ph.HE↗

arXiv2026

LAP: An Agent-to-Instrument Protocol for Autonomous Science

Autonomous science is moving from demonstration to infrastructure. Large language model agents now plan experiments, and self-driving laboratories execute them. Yet every such system rebuilds the link between the reasoning agent and the physical instrument from scratch, against fragmented vendor SDKs and standards built for deterministic software clients rather than probabilistic, goal-directed agents. Recent agent-interoperability protocols clarify two of the three edges of an agentic ecosystem (Anthropic's Model Context Protocol (MCP) standardizes the agent-to-tool edge, and Google's Agent2Agent (A2A) the agent-to-agent edge), but neither models the agent-to-instrument edge, where operations are stateful, safety-critical, exclusively owned, physically embodied, and produce measurements with units, calibration, and uncertainty. We present the Lab Agent Protocol (LAP), a protocol design that fills this gap. LAP retains A2A's peer-to-peer, discovery-first, task-lifecycle structure and adds four physical-world primitives: (i) the InstrumentCard, a signed capability and physical-limit description; (ii) first-class reservation for exclusive instrument and sample locking; (iii) a safety-fence handshake with operator-confirmation tokens cryptographically bound to a specific task and its parameters, gating hazardous and irreversible operations; and (iv) a MeasurementResult schema that makes every result physically typed (QUDT/UCUM), calibration-anchored, uncertainty-bearing, and reproducible by construction. We specify roles, a six-layer architecture, the JSON-RPC method set, the task and safety state machines, the error model, and cross-laboratory federation, and walk a closed-loop autonomous campaign through the protocol end-to-end. LAP is transport-compatible with the A2A/MCP ecosystem and encapsulates rather than replaces existing device standards such as SiLA 2 and OPC-UA.

cs.AI↗

arXiv2026

Vegas: Self-Speculative Decoding with Verification-Guided Sparse Attention

Long-context large language model (LLM) inference has become the norm for today's AI applications. However, it is severely bottlenecked by the increasing memory demands of its KV cache. Previous works have shown that self-speculative decoding with sparse attention, where tokens are drafted using a subset of the KV cache and verified in parallel against the full KV cache, speeds up inference in a lossless manner. However, they rely on a standalone KV selection algorithm to select the KV entries used for drafting and overlook the fact that the criticality of each KV entry is inherently computed during verification. In this paper, we propose Vegas, a self-speculative decoding method with verification-guided sparse attention. Vegas identifies critical KV cache entries as a byproduct of verification and computes attention only over these entries when drafting subsequent tokens. This not only improves the draft token acceptance rate but also incurs low KV selection overhead, thereby improving decoding throughput. Vegas achieves a 1.25$\times$-2.81$\times$ speedup in decoding throughput over default vLLM and a 1.15$\times$-1.29$\times$ speedup over state-of-the-art sparse attention-based self-speculative decoding methods. Our code is available at https://github.com/platformxlab/vegas.

cs.LG↗

arXiv2026

Selective Fermi-Level Pinning: A Design Strategy for Giant Rectification in Molecular Junctions

Molecular rectifiers are key functional components of molecular-scale integrated circuits, yet achieving high rectification ratios remains a longstanding challenge due to the intrinsic symmetry of resonant tunneling and the complexity of interfacial energy-level alignment. Here, we propose a rectifier design strategy based on selective Fermi-level pinning that breaks transport symmetry via pinning interactions between molecular frontier orbitals and electrodes. This framework enforces tunneling transport to be predominantly governed by unoccupied molecular orbitals, while substantially suppressing contributions from occupied states, thereby establishing a simplified and highly controllable rectification mechanism. The resulting cyclo[n]carbon-based molecular junctions exhibit giant rectification ratios exceeding 103, while retaining exceptional structural robustness against variations in both donor chain length and carbon ring size. This work reveals the critical role of selective Fermi-level pinning in molecular junctions and provides a general design principle for engineering functional single-molecule electronic devices.

cond-mat.mes-hall↗

arXiv2026

Phase-tunable remote nonreciprocal charging in waveguide QED

Remote quantum batteries require directional and controllable energy transfer between spatially separated quantum nodes, yet most existing protocols rely on direct charger-battery Hamiltonian couplings. Here we propose a phase-tunable waveguide-QED architecture for remote quantum-battery charging, in which a driven charger and a remote battery are coupled solely via engineered waveguide-mediated interference, without any direct local interaction. We systematically compare four configurations: two-giant-emitter and giant-small-emitter hybrids, each with open or mirror-terminated waveguides. By engineering the propagation and coupling phases, the waveguide-mediated coherent exchange interaction and collective dissipation can be balanced to suppress the backward channel while retaining a finite forward channel, thereby realizing cascaded-like unidirectional charging. Our analysis shows that nonreciprocity and storage efficiency can be independently engineered, offering design flexibility for different quantum network scenarios. The giant-small-emitter mirror-terminated configuration simultaneously achieves perfect nonreciprocity and battery-dominated storage, while both giant-small-emitter configurations exhibit distance-insensitive directionality. Extending the scheme to quadratic driving, we show that anomalous second moments render the battery state non-passive, making ergotropy a performance metric distinct from stored energy. These results establish phase-tunable waveguide networks as a versatile platform for remote quantum-energy transfer and provide design principles for directional and work-extractable energy storage in quantum networks.

quant-ph↗

arXiv2026

Strong X-ray Variability of I Zwicky 1: Obscuration from Clumpy Accretion-Disk Winds

Obscuration from clumpy accretion-disk winds has been invoked to explain the extreme X-ray weakness and X-ray variability observed in a substantial fraction of super-Eddington accreting quasars. We present a comprehensive study of the strong X-ray variability of the super-Eddington accreting active galactic nucleus (AGN) I Zwicky 1 (I Zw 1), a prototypical narrow-line} Seyfert 1 galaxy (NLS1), to test the disk-wind obscuration scenario as the underlying mechanism and characterizing the disk-wind absorber properties. We focus on spectral and temporal analyses of simultaneous XMM-Newton and NuSTAR observations in 2020, and a 100-day NICER monitoring campaign in 2022. Despite strong X-ray variability by factors of $\approx3$ and $\approx6$ on short-term and long-term timescales, respectively, the XMM-Newton Optical Monitor observations do not show contemporaneous significant UV variability, and archival data reveal only mild long-term optical/infrared variability ($\approx30\%$), indicating a stable accretion process in I Zw 1. The strong X-ray variability thus likely arises from variable absorption of relatively stable coronal emission. We perform time-resolved X-ray spectroscopy utilizing a partial-covering absorption model with a stable corona and varying ionized absorbers. We identify three distinct absorbers whose variations in the column density and covering factor successfully explain the observed X-ray ``flares'' in 2020 and the longer-term spectral evolution in 2022. Our results support a unified scenario in which obscuration from clumpy disk winds produces the strong X-ray variability observed in super-Eddington accreting AGNs. This scenario may be applicable to other NLS1s exhibiting strong X-ray variability to better characterize the disk winds driven by super-Eddington accretion.

astro-ph.HE↗

arXiv2026

A Truthful Multiunit Profit-Optimal Mechanism for Synthesizing Social Laws

This paper studies Social Law Synthesis (SLS) in strategic multi-agent environments as a new multi-unit mechanism design problem. We model SLS as a Bayesian single-parameter procurement auction based on Alternating-time Temporal Logic (ATL) and aim to design a truthful, individually rational, and profit-optimal mechanism. We first prove a representation lemma showing that any valuation respecting alternating bisimulation can be compactly expressed as a feature set of ATL formulae. We then reduce payment determination to allocation determination in polynomial time, resolving the irregular payment issue inherent in multi-unit settings. We further show that allocation determination is $FP^{NP}$-complete and encode ATL semantics into integer linear programming (ILP) constraints to make the problem tractable with standard solvers. Based on these results, we present the $\mathcal{PO\text{-}ASL}$ mechanism, which is incentive-compatible, individually rational, and maximizes expected profit. Theoretical guarantees and examples confirm that our approach provides an effective and computationally feasible solution for synthesizing optimal social laws under strategic agent behavior.

cs.GT↗

arXiv2026

TRACE: Transport Alignment Conformal Prediction via Diffusion and Flow Matching Models

Constructing valid and informative conformal prediction regions for multi-dimensional outputs remains a fundamental challenge. While conformal prediction provides finite-sample, distribution-free coverage guarantees, its practical performance critically depends on the choice of nonconformity score. Existing approaches often rely on restrictive geometric assumptions or require explicit likelihood evaluation and invertible transformations, limiting their applicability in complex generative settings. In this work, we introduce TRACE (TRansport Alignment Conformal Estimation), a conformal prediction framework that defines nonconformity through transport alignment in diffusion and flow matching models. Rather than evaluating likelihoods, we measure how well a candidate output aligns with the learned generative dynamics by averaging denoising or velocity-matching errors along stochastic transport trajectories. The resulting transport-based scores are scalar-valued and can be calibrated using split conformal prediction, yielding valid marginal coverage under exchangeability. We further analyze the statistical properties of the proposed scores and their sensitivity to computational budget. Experiments on synthetic and real datasets demonstrate valid coverage and show that the resulting regions adapt naturally to multimodal and non-convex conditional distributions.

stat.ML↗

arXiv2026

CONTRA: Conformal Prediction Region via Normalizing Flow Transformation

Density estimation and reliable prediction regions for outputs are crucial in supervised and unsupervised learning. While conformal prediction effectively generates coverage-guaranteed regions, it struggles with multi-dimensional outputs due to reliance on one-dimensional nonconformity scores. To address this, we introduce CONTRA: CONformal prediction region via normalizing flow TRAnsformation. CONTRA utilizes the latent spaces of normalizing flows to define nonconformity scores based on distances from the center. This allows for the mapping of high-density regions in latent space to sharp prediction regions in the output space, surpassing traditional hyperrectangular or elliptical conformal regions. Further, for scenarios where other predictive models are favored over flow-based models, we extend CONTRA to enhance any such model with a reliable prediction region by training a simple normalizing flow on the residuals. We demonstrate that both CONTRA and its extension maintain guaranteed coverage probability and outperform existing methods in generating accurate prediction regions across various datasets. We conclude that CONTRA is an effective tool for (conditional) density estimation, addressing the under-explored challenge of delivering multi-dimensional prediction regions.

stat.ML↗

arXiv2026

RepFlow: Representation Enhanced Flow Matching for Causal Effect Estimation

Estimating causal effects from observational data has become increasingly critical in diverse fields including healthcare, economics, and social policy. The fundamental challenge in causal inference arises from the missing counterfactuals and the selection bias. Existing methods are largely limited to point estimates and lack the capacity for distribution modeling. In this work, we propose RepFlow, a novel framework that formulates causal effect estimation as a joint optimization problem integrating representation learning with Conditional Flow Matching (CFM). RepFlow mitigates selection bias by minimizing the entropically regularized Wasserstein distance between treated and control representations. To enhance numerical stability, we further introduce an $L_2$ normalization constraint on latent representations. This balanced representation enables the flow model to accurately capture the distribution of potential outcomes. Extensive experiments across a wide range of benchmarks demonstrate that RepFlow consistently outperforms existing methods in both point and distributional causal effect estimation.

cs.LG↗

arXiv2026

K2MUSE: A human lower-limb multimodal walking dataset spanning task and acquisition variability for rehabilitation robotics

The natural interaction and control performance of lower limb rehabilitation robots are closely linked to biomechanical information from various human locomotion activities. Multidimensional human motion data significantly deepen the understanding of the complex mechanisms governing neuromuscular alterations, thereby facilitating the development and application of rehabilitation robots in multifaceted real-world environments. However, existing lower limb datasets are inadequate for supplying the essential multimodal data and large-scale gait samples necessary for the development of effective data-driven approaches, and the significant effects of acquisition interference in real applications are neglected. To fill this gap, we present the K2MUSE dataset, which includes a comprehensive collection of multimodal data, comprising kinematic, kinetic, amplitude mode ultrasound (AUS), and surface electromyography (sEMG) measurements. The proposed dataset includes lower-limb multimodal data collected from two cohorts, including 30 able-bodied young adults and 12 older adults, across different inclines (0$^\circ$, $\pm$5$^\circ$, and $\pm$10$^\circ$), speeds (0.5 m/s, 1.0 m/s, and 1.5 m/s), and representative non-ideal acquisition conditions (muscle fatigue, electrode shifts, and interday differences). The kinematic and ground reaction force data were collected with a Vicon motion capture system and an instrumented treadmill with embedded force plates, whereas the sEMG and AUS data of thirteen muscles on the bilateral lower limbs were synchronously recorded. K2MUSE is released with the corresponding structured documentation, preprocessing pipelines, and example code, thereby providing a comprehensive resource for rehabilitation robot development, biomechanical analysis, and wearable sensing research. The dataset is available at https://k2muse.github.io/.

cs.RO↗

arXiv2026

DRIVE: Modeling Skills at the Reasoning and Interaction Levels for Web Agents under Continual Learning

Web agents require both high-level reasoning (for task decomposition) and low-level interactions (for page elements manipulation) to conduct different tasks. However, these knowledge types differ fundamentally: reasoning knowledge (e.g., booking a flight requires first searching for routes) is abstract and transferable across websites, while interaction knowledge (e.g., clicking the Search button at a specific coordinate on Site A) depends heavily on page-specific contexts. Existing methods store experiences uniformly. This creates a dilemma: abstract representations lose executability on concrete pages, while concrete representations fail to generalize across domains. This entanglement limits capability accumulation: on new websites, agents either fail to recognize reusable task logic due to surface-level differences or attempt infeasible actions from outdated page structures. To disentangle them, we propose DRIVE, a dual-level skill modeling framework separating historical experience into natural language reasoning skills, which capture transferable task logic, and programmatic interaction skills, grounding abstract actions to executable operations. A scene-aware coordination mechanism adaptively retrieves and invokes these dual-level skills based on task semantics. DRIVE also uses skill-level reflection to identify hierarchy-specific failure modes, enabling targeted skill library expansion and refinement. Experiments across five WebArena domains show DRIVE attains an average task success rate of 52.8%, exceeding the skill-free baseline by 7.3 percentage points. Further ablations show reasoning and interaction skills provide distinct, complementary benefits, supporting separation of transferable task logic from executable page-level operations.

cs.AI↗

arXiv2026

Decoupled Travel Planning with Behavior Forest

Behavior sequences, composed of executable steps, serve as the operational foundation for multi-constraint planning problems such as travel planning. In such tasks, each planning step is not only constrained locally but also influenced by global constraints spanning multiple subtasks, leading to a tightly coupled and complex decision process. Existing travel planning methods typically rely on a single decision space that entangles all subtasks and constraints, failing to distinguish between locally acting constraints within a subtask and global constraints that span multiple subtasks. Consequently, the model is forced to jointly reason over local and global constraints at each decision step, increasing the reasoning burden and reducing planning efficiency. To address this problem, we propose the Behavior Forest method. Specifically, our approach structures the decision-making process into a forest of parallel behavior trees, where each behavior tree is responsible for a subtask. A global coordination mechanism is introduced to orchestrate the interactions among these trees, enabling modular and coherent travel planning. Within this framework, large language models are embedded as decision engines within behavior tree nodes, performing localized reasoning conditioned on task-specific constraints to generate candidate subplans and adapt decisions based on coordination feedback. The behavior trees, in turn, provide an explicit control structure that guides LLM generation. This design decouples complex tasks and constraints into manageable subspaces, enabling task-specific reasoning and reducing the cognitive load of LLM. Experimental results show that our method outperforms state-of-the-art methods by 6.67% on the TravelPlanner and by 11.82% on the ChinaTravel benchmarks, demonstrating its effectiveness in increasing LLM performance for complex multi-constraint travel planning.

cs.LG↗

arXiv2026

Stability-Weighted Decoding for Diffusion Language Models

Diffusion large language models (dLLMs) enable parallel text generation by iteratively denoising a fully masked sequence, unmasking a subset of masked tokens at each step. Existing decoding strategies rely on static confidence metrics computed at a single denoising step, ignoring temporal history and often leading to premature unmasking of unstable tokens. In this work, we theoretically establish that a token's temporal instability, quantified by the KL divergence between consecutive prediction distributions, provides a strict lower bound on its mutual information with the remaining masked context, indicating that temporally unstable tokens are inherently unsafe to unmask. Based on this insight, we propose Stability-Weighted Decoding (SWD), a training-free, plug-and-play strategy that incorporates temporal stability into token scoring and acts as a universal modulator for arbitrary score-based decoding policies. Experiments on code generation and mathematical reasoning benchmarks demonstrate that SWD consistently improves generation accuracy across representative scoring metrics and selection policies, and exhibits exceptional robustness, maintaining a significant performance lead over standard baselines across varying acceleration ratios.

cs.CL↗