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Liang Chen

Publications and source records attributed to Liang Chen.

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

Light Field Primitive for Novel View Synthesis

We present Light Field Primitives (LFP), a formulation for novel view synthesis that replaces the dense ray database with a compact set of differentiable primitives in the classical two-plane parameterization. Each primitive condenses a group of rays into one learned record, and its response to a query is governed by how closely that query belongs to the group. Rendering a camera ray then reduces to compositing all responses it elicits, and a scene can be optimized directly from posed images and rendered in real time with rays. Beyond its competitive performance on standard benchmarks, the main advantage of LFP is structural: its primitives reside directly in the 4D ray space, so optical and appearance effects that are already operations on the light field become behaviors of a single shared renderer. With minimal changes to that renderer, LFP supports multi-scale anti-aliasing, defocus deblurring with refocusing, rendering for fisheye cameras, and even transparent object reconstruction with ray refraction, matching specialized frameworks that devote substantial machinery to these effects.

cs.CV↗
arXiv2026

A Helical Trajectory Interpretation of a Multi-wavelength Event in a Blazar S5 0716+714

Relativistic jets in blazars exhibit strong variability across multiple timescales, but the connection between temporal variability and the evolution of jet structures remains poorly understood. In this work, we investigate the $γ$-ray variability of the BL Lac object S5 0716+714 from July 2014 to June 2015 and explore its possible connection with the parsec-scale jet evolution. We identify an escalating periodic oscillation (EPO) in the $γ$-ray light curve, characterized by a sequence of successive flux enhancements with progressively increasing temporal separations. Combining \textit{Fermi}-LAT data with contemporaneous 43 GHz Very Long Baseline Array (VLBA) observations, we find a temporal association between the EPO and the downstream propagation of a superluminal knot. A helical trajectory model simultaneously fitting the $γ$-ray variability and the two-dimensional VLBA component positions reproduces the observed flux modulation and component motion. These results suggest that geometric Doppler modulation caused by the changing viewing angle of a moving jet component provides a possible explanation for the observed EPO. This work provides a framework of connecting blazar variability to the knot evolution of relativistic jets.

astro-ph.HE↗
arXiv2026

SG-FSM: A Self-Guiding Zero-Shot Prompting Paradigm for Multi-Hop Question Answering Based on Finite State Machine

Large Language Models with chain-of-thought prompting, such as OpenAI-o1, have shown impressive capabilities in natural language inference tasks. However, Multi-hop Question Answering (MHQA) remains challenging for many existing models due to issues like hallucination, error propagation, and limited context length. To address these challenges and enhance LLMs' performance on MHQA, we propose the Self-Guiding prompting Finite State Machine (SG-FSM), designed to strengthen multi-hop reasoning abilities. Unlike traditional chain-of-thought methods, SG-FSM tackles MHQA by iteratively breaking down complex questions into sub-questions, correcting itself to improve accuracy. It processes one sub-question at a time, dynamically deciding the next step based on the current context and results, functioning much like an automaton. Experiments across various benchmarks demonstrate the effectiveness of our approach, outperforming strong baselines on challenging datasets such as Musique. SG-FSM reduces hallucination, enabling recovery of the correct final answer despite intermediate errors. It also improves adherence to specified output formats, simplifying evaluation significantly.

cs.CL↗
arXiv2026

A local cohomology obstruction to small Cohen--Macaulay modules

We propose a local-cohomology obstruction to the existence of small Cohen--Macaulay modules over completed section rings of surfaces. Let $Y$ be a smooth connected complex projective surface, and let $H$ be an ample globally generated divisor with $K_Y\equiv4H$ numerically. Put $c(Y,H)=15H^2/8-χ(\OO_Y)$. The main claim is that, when $c(Y,H)>0$, every nonzero finite reflexive module $M$ over the completed vertex local ring of $\bigoplus_{n\geq0}H^0(Y,\OO_Y(nH))$ satisfies $\dim_\C H^2_\mm(M)\geq c(Y,H)\rk M$. The argument combines elementary transformations of reflexive extensions, Harder--Narasimhan slopes, Bogomolov's inequality, and a two-section Koszul estimate. A fixed-source kernel estimate transfers the resulting cohomology to the punctured spectrum without requiring a grading on $M$. For the degree-six member of the six-line Hirzebruch--Kummer family, $H^2=1080$ and $χ(\OO_Y)=1926$, so the bound is $99\rk M$. We give an explicit complete-intersection model for this application and spell out the argument leading to the claimed nonexistence of nonzero finite maximal Cohen--Macaulay modules.

math.AG↗
arXiv2026

SP-MoMamba: Superpixel-driven Mixture of State Space Experts for Efficient Image Super-Resolution

State space models (SSMs) have emerged as an efficient paradigm for single-image super-resolution (SR) due to their linear complexity and long-range modeling capabilities. However, existing visual SSMs mainly focus on improving how densely represented image features are traversed, while the construction of the visual sequence itself remains largely tied to predefined spatial layouts. Inspired by Gestalt perceptual grouping, we propose SP-MoMamba, a superpixel-driven mixture of state space experts for efficient SR. Instead of performing state-space modeling over densely serialized pixel features, the proposed Superpixel-driven State Space Model (SP-SSM) organizes spatially coherent features into compact region-level tokens and performs global sequence modeling over these content-aware representations, reducing redundant computation while facilitating long-range structural interaction. To accommodate image structures with varying representation granularities, we further develop a Multi-Scale Superpixel Mixture of State Space Experts (MSS-MoE), where scale-specific SP-SSM experts model region-level representations at different granularities and a sparse router dynamically selects an appropriate modeling scale. In addition, a Local Spatial Modulation Expert (LSME) complements region-level global modeling by refining local high-frequency details. Extensive experiments demonstrate that SP-MoMamba achieves strong reconstruction performance with a favorable trade-off among model size, computational cost, and inference efficiency.

cs.CV↗
arXiv2026

Evolution of the High-Energy Excess in Swift J1727.8-1613 during Its 2023 Flare State

We investigate the evolution of the high-energy excess in Swift J1727.8-1613 during its 2023 flare state using joint Insight-HXMT and INTEGRAL/ISGRI observations covering 2-500 keV. Fits to the broadband spectra with a model including thermal Comptonization and disk reflection leave systematic positive residuals above ~150 keV. Adding a power-law component improves the fits and yields photon indices of 2.08-2.61, demonstrating that the high-energy excess beyond the thermal Comptonization continuum is present throughout the flare state. We also show that all spectra can be well described by the hybrid Comptonization model eqpair, without requiring any additional high-energy component. The fitting results indicate that the energy supply to the electrons transitions from predominantly thermal heating to non-thermal acceleration, and that the electron injection spectrum steepens abruptly near the onset of the flare state. During this evolution, the radio spectral index changes from approximately zero to negative values, accompanied by the ejection of three transient jet knots. Based on this temporal association, we discuss a possible connection between the increase in non-thermal power indicated by our spectral analysis and the ejection of the transient jet.

astro-ph.HE↗
arXiv2026

Towards Characterizing Scientific Image Utility and Upgradability

Scientific images function as critical evidence in research communication, yet their integrity faces unprecedented threats from AI-generated content that introduces subtle but consequential errors. Existing evaluation paradigms prove inadequate: perceptual quality metrics poorly correlate with scientific validity, while language models lack domain-specific verification capabilities. To address this gap, we propose the \textbf{S}cientific \textbf{I}mage \textbf{U}tility and \textbf{U}pgradability \textbf{A}ssessment (\textbf{SIU$^2$A}) framework, which introduces two complementary dimensions for scientific image evaluation. \textbf{Utility} encompasses \textit{error detection} (identifying scientific inaccuracies) and \textit{correction feasibility} (assessing whether errors can be reliably repaired). \textbf{Upgradability} measures the quality of correction. We categorize scientific image corruption into four fundamental types: Detail Distortion, Incompleteness, False Content, and Entity Confusion. Based on this taxonomy, we construct SIU$^2$A-Benchmark, a dataset with expert annotations for error identification and repair. The framework implements a two-stage evaluation protocol: the \textit{Utility} stage evaluates error detection capability and repair instruction generation, while the \textit{Upgradability} stage assesses whether corrections faithfully restore scientific validity without compromising existing accurate information. Experiments reveal that current multimodal systems exhibit significant limitations in both scientific error assessment and faithful correction, exposing a fundamental gap between visual perception and scientific usability.

cs.CV↗
arXiv2026

Smallest Singular Value Estimates for Nonuniform Fourier Matrices via Periodic Nonuniform Sampling

We study the smallest singular value of nonuniform Fourier matrices in two settings: clustered nodes and perturbations of an equispaced grid. By reducing the problem to spectral norm estimates for periodic nonuniform interpolation matrices, we obtain nearly optimal bounds in both cases. For clustered nodes, we derive the first local separation condition in which each required gap depends only on the sizes of the two neighboring clusters. For perturbations with the bound \(1/4\leq L<1/2\), our result confirms the conjecture of Austin and Trefethen on the \(2\)-norm Lebesgue constant up to a logarithmic factor.

math.NA↗
arXiv2026

Analysis of Electromagnetic Scattering from Semiconductor Nanostructures by Solving Coupled Volume Integral and Two-fluid Hydrodynamic Equations

Semiconductor-based plasmonic nanostructures support localized surface plasmon modes in the infrared region. Unlike metallic nanostructures, they support both free electrons and holes, requiring a two-fluid hydrodynamic Drude equation (HDE) to accurately capture spatial dispersion effects and low-frequency acoustic plasmon modes that cannot be described by single-fluid models. In this work, a volume integral equation (VIE)-based solver is proposed for the analysis of electromagnetic scattering from semiconductor nanostructures. The proposed approach couples the VIE, formulated in terms of the electric flux density and the free-electron and hole polarization currents, with the two-fluid HDE. The coupled system is discretized using a tetrahedral mesh and solved efficiently using a two-level iterative solver. In contrast to finite-element-based methods, the proposed VIE-based approach does not require domain-wide meshing and inherently satisfies the radiation condition, thereby eliminating artificial absorbing boundaries. Numerical results for InSb-type semiconductor nanostructures demonstrate the accuracy and efficiency of the proposed VIE-based solver and its ability to capture unique optical phenomena, such as acoustic plasmon resonances and the blueshift of localized surface plasmon resonances, that cannot be described by the single-fluid HDE or classical Drude-based models.

physics.optics↗
arXiv2026

LHAASO-WCDA observed a $\sim$ 5 days TeV-delayed flaring event in blazar 1ES 1959+650

We report a day-scale hard lag between GeV and TeV $γ$-ray emission from the HBL 1ES~1959+650 in early 2024. Since the LHAASO-WCDA real-time monitoring system began operation in late 2023, multiple TeV flares from this source have been triggered, including the 1st trigger flare on 2024 February 9. A Bayesian-block analysis of the WCDA light curve identifies three TeV flares in 2024. For the second triggered flare, a discrete cross-correlation analysis reveals a $>3\,σ$ correlation (relative to uncorrelated red-noise simulations) at a time delay of $Δt = 5.0_{-2.1}^{+2.1}$ days, with the TeV emission lagging the GeV. Time-resolved spectroscopy shows that this flare has the softest TeV spectrum among these flares (intrinsic spectral index $Γ=3.16\pm0.18$), while the 1st trigger flare is harder ($Γ=2.48\pm0.21$). The observed five-day hard lag is difficult to reconcile with a purely cooling-driven temporal ordering and is consistent with scenarios in which particle energization and/or transport may contribute to the evolution. However, the current data do not uniquely identify the underlying mechanism.

astro-ph.HE↗
arXiv2026

J-LAW: Joint Localization and Action-Conditioned World Modeling via Coupled Latent Factor Graphs

Classical simultaneous localization and mapping (SLAM) estimates metric poses and a geometric map but does not provide an action-conditioned predictive state. Action-conditioned world models learn compact latent dynamics but ignore global metric consistency and accumulate drift under open-loop rollout. We introduce J-LAW (Joint Localization and Action-Conditioned World Modeling), a unified factor-graph formulation that connects metric pose variables, predictive latent states, and persistent latent landmarks in this letter.J-LAW represents each image as a compact predictive state and combines it with pose or motion measurements through a separately learned mapping. Its maximum a posteriori (MAP) factor graph enforces consistency between these complementary sources of information over time. Experiments on PushT and WildGS show that J-LAW's factor-graph representation can improve long-horizon latent consistency and recover more reliable predictive states under partial observations, forming a foundation for future integrated localization and planning systems.

cs.RO↗
arXiv2026

CVE-SAI: Counterfactual Visual Evidence-Guided Selective Attribute Indexing for Risk-Controlled E-commerce Search

Multimodal product models can complete missing e-commerce attributes, yet current methods still optimize attribute-answer accuracy without verifying visual support, conflate transient prediction with persistent index admission, and lack explicit risk control over factually incorrect or visually unsupported values. We address these gaps with Counterfactual Visual Evidence-Guided Selective Attribute Indexing (CVE-SAI), which first infers and freezes an ontology-constrained candidate from the primary image and attribute question without catalog text, and then decides whether that candidate should enter the index. Focus-Zone Distortion (FZD) constructs an attribute-specific visual-dependence proxy through a controlled counterfactual intervention, and Evidence-Guided Attention Redistribution (EGAR) uses the proxy to refine ontology-constrained scoring. The canonical candidate is frozen before evidence necessity, evidence retention, nuisance-transformation stability, and candidate-specific catalog-text conflict audits; catalog text can only tighten admission and cannot revise the candidate. Independent family-level calibration selects one policy with a simultaneous one-sided finite-sample bound under a 5% unsafe-admission budget. Experiments on five visual attributes derived from Amazon Berkeley Objects show that CVE-SAI improves attribute inference and evidence localization, achieves the highest certified admission coverage under the shared risk protocol, and yields the strongest controlled retrieval performance with the lowest unsafe auto-induced exposure among automatic-admission systems. Separating inference from admission therefore enables visually supported attribute completion to improve retrieval while limiting persistent index contamination.

cs.AI↗
arXiv2026

One Polluted Page Is Enough: Evaluating Web Content Pollution in LLM Recommenders

Search-augmented LLMs increasingly mediate everyday consumer recommendations by retrieving live web content. This creates a new risk: LLM recommenders may consume web content that Generative Engine Optimization (GEO) operators have polluted to mislead them. We ask: to what extent do they become unwitting promoters of fake products? We introduce FORGE (Fake Online Recommendations in Generative Environments), which locally rewrites real products in a frozen set of retrieved web pages into fake ones and measures how often the LLM recommends the fake product, across 225 real products in 15 categories and 5 consumer scenarios. Across 12 commercial and open-weights LLMs, all models are vulnerable: a single polluted page yields fooled rates of up to 27%, while the full top-3 replacement raises this to 73.8%. Vulnerability varies across categories, increasing when models lack stable prior knowledge of the products. Reasoning does not mitigate this vulnerability; instead, it often generates spurious social proof to justify false recommendations. None of the four defenses is adequate: the skepticism prompt can exacerbate vulnerability much like reasoning, the two consensus filters risk suppressing legitimate products, and credibility re-ranking helps every model but removes only a sixth of the fakes. We release the FORGE benchmark and the evaluation code at https://github.com/leoluolol/forge-benchmark.

cs.CL↗
arXiv2026

DART: Dual-Axis Airborne Reachability-Gated Torque-Reaction for Off-Road Vehicle Jumps

Traversing crests, ledges, and ditches at high speed often launches vehicles into the air, and a mishandled landing presents a substantial crash hazard. We show that the airborne phase is barely controllable: on a 1383 kg platform the wheel angular-momentum budget caps the recoverable pitch-rate change at roughly $9$-$13^\circ$/s in the tighter nose-up direction under drive at typical takeoff wheel speeds, and at about twice that in the reverse-inclusive braking direction; driving the wheels to their drivetrain hard limit raises the measured nose-up ceiling to only $16$-$18^\circ$/s. Takeoff pitch-rate disturbances beyond this directional budget are physically unrecoverable in flight, so the decisive leverage lies before takeoff. DART (Dual-Axis Airborne Reachability-Gated Torque-Reaction) back-propagates the landing constraint into a closed-form certified feasible-takeoff set, which supplies a conservative go/no-go condition and a pre-takeoff speed-shaping law. In flight, DART regulates pitch and roll via steer-resolved wheel-reaction torque, governed by a per-flight roll latch derived from the yaw-coupling analysis. In deterministic full-scale simulation in BeamNG.tech, a calibrated pre-takeoff speed regulator reduces touchdown speed by 36% and raises on-target landings from 0/30 to 30/30. Under the same steep-lip approach the airborne law completes 29/30 safe landings under crash-avoidance bounds versus 0/30 for reaction-wheel-style PD (RW-PD) and time-optimal bang-bang (TOBB). On banked run-ups DART holds the median pitch error at or below $2^\circ$ at every cross-slope, with the largest baseline separation at $γ=12^\circ$. Across disturbance regimes, the latch preserves pitch-only allocation on low-disturbance entries and enables dual-axis control when roll becomes binding. All results are from simulation; hardware validation remains open.

cs.RO↗
arXiv2026

DART-S: Reachability-Audited Active-Suspension Preconditioning for Off-Road Vehicle Jumps

Airborne torque reaction cannot recover takeoff errors beyond the wheel angular-momentum budget. DART-S applies ramp-face suspension preconditioning to change pitch, pitch rate, and wheel spin before liftoff, thereby shifting the queried state and altering the remaining authority budget. To predict how each suspension action reshapes this state-budget pair, DART-S employs a local calibration map. A support-aware selector combines the predicted shift with local outcome evidence and an interval-reachability screen; an exact-pair audit reports residual authority. Across 600 new runs in 72 independent BeamNG sessions, every positive, negative, and boundary query follows its prespecified branch. At the confirmed 40°/13 m/s boundary, DART-S attains 24/24 post-touchdown attitude-criterion successes versus 0/24 for DART (session-level Holm-adjusted p=0.0234). At 11.5 m/s, a 0.35 s timing action attains 23/24 versus 0/24 for the static preset (p=0.0156). The 200 rad/s command guard keeps drivetrain hard-limit exceedance at zero across all 600 runs. The source code will be available at https://github.com/MeridianCAS/DART-S

cs.RO↗
arXiv2026

Hundred-hertz quantum circuit iteration rate in a reusable neutral-atom array

Neutral-atom quantum processors have rapidly advanced in scale and coherence, yet their practical performance remains constrained by limited quantum circuit iteration rates (qCIRs) and information throughput. Here we experimentally demonstrate a high-throughput neutral-atom system based on non-destructive readout and atom reuse. By integrating a chip-based photonic interface with a 10-qubit array, we implement non-destructive readout with a retention probability of 99.7%, and further achieve a raw qCIR of 101Hz and a post-selected qCIR of 74.8Hz. More importantly, we verify a general throughput optimization methodology and obtain a normalized Fisher information rate of 57.7Hz, improving the achievable throughput by more than one order of magnitude compared with conventional methods. Our results establish a practical route toward high-throughput neutral-atom quantum processors.

quant-ph↗
arXiv2026

Magnetic rigidity reveals the PeVatron acceleration region in SS 433

PeVatrons are cosmic accelerators capable of driving particles to petaelectronvolt (PeV) energies. Recently, microquasar jets have emerged as compelling Galactic PeVatron candidates. This is especially the case for SS 433 as its $>100$ TeV gamma-ray emission is spatially coincident with an atomic cloud. However, the exact region where PeV protons are accelerated and injected within these jets remains unresolved. Here we report, using archival, multi-frequency VLBA observations, the magnetic field profile $B(H)$ along the SS 433 inner jet on tens of AU scale, where $H$ is the distance from the central compact object. We find that the field declines as $B(H) \propto H^{-0.50\pm0.12}$, demonstrating that the magnetic rigidity $B(H)R_{\rm acc}$ grows with $H$ for a conical jet. This implies the Hillas limit ($E_{\rm max} \propto BH$) to lie well beyond a PeV at a few hundred-AU scale, which becomes a highly potential site for accelerating protons to energies $E_{\rm cut} \simeq 2.6$ PeV inferred from the LHAASO gamma-ray spectrum. These results reveal a hidden PeVatron within the baryonic ejecta of microquasar SS 433, well upstream of the extended TeV-emitting lobes.

astro-ph.HE↗