arXiv subjects

Jingyu Wu

Publications and source records attributed to Jingyu Wu.

At least 19 recordsLinked to original sources

arXiv2026

Quantum Lower Bounds for Collision Problems

We establish tight lower bounds on the adaptive and non-adaptive quantum query complexities of Promised $k$-to-one Finding on an exactly $k$-to-one input string $x\in\mathbb Z_q^n$, for every fixed $k\ge2$ and every feasible alphabet size $q\ge n/k$. The lower bounds are $Ω_k(n^{1/2-1/(2(2^k-1))})$ for unrestricted adaptive algorithms and $Ω_k(n^{1-1/k})$ for one-round non-adaptive algorithms. The former matches the adaptive quantum algorithm of Liu and Zhandry [LZ19], whereas the latter matches classical birthday sampling even when the parallel queries are arbitrarily entangled. We also establish the non-adaptive lower bound $Ω_k(\sqrt{n/k})$ for $k$-to-one Testing when $q\ge n$, resolving an open question posed by Koiran, Landes, Portier, and Yao [KLPY10]. Our proofs generate the balanced input from a uniformly random permutation and use compressed permutation oracles to describe the information learned by the algorithm. The adaptive proof bounds the growth of partial collisions through successive queries. The one-round proof instead analyzes the state after the parallel queries directly, bounding the probability that the compressed database contains a full collision. In both proofs, a short verification step accounts for correct outputs that include unrecorded positions. For the decision problem, we also compare one-round states with independent and distinct output labels.

quant-ph↗

arXiv2026

X-ray Emission Study of TeV Blazars

In this work, we present a systematic X-ray study of 85 TeV-detected blazars based on archival \emph{Swift}-XRT and \emph{NuSTAR} observations available up to May 2024. We derive unabsorbed X-ray fluxes and photon indices through spectral fitting, and investigate their light curves, spectral energy distributions, flux distributions, power spectral densities, and flux-index correlations. Using the fractional variability method, we identify 78 sources as variable in the soft X-ray band and 14 sources as variable in the hard X-ray band. For well-sampled soft X-ray light curves, the flux distributions are generally better described by log-normal functions, while the PSD slopes span $β_{\rm PSD}=0.1$--$1.6$, with most values between 1.0 and 1.6, where the PSD is defined as $P(f)\propto f^{-β_{\rm PSD}}$. We find positive correlations between TeV and X-ray luminosities, supporting a leptonic emission scenario. We also find a correlation between X-ray and disk luminosities, whereas no significant correlation is found between X-ray luminosity and black hole mass.

astro-ph.HE↗

arXiv2026

The Judge Can Debias Itself: Awareness and Self-Debiasing for Reliable LLM-as-a-Judge

LLM-as-a-Judge systems are increasingly employed for automated evaluation, yet remain susceptible to systematic biases that undermine their reliability. We demonstrate that the judge can effectively debias itself without additional training. We propose two lightweight strategies: bias-aware prompting, which explicitly alerts the judge to potential target biases prior to evaluation, and self-debiasing, in which the judge critiques the initial judgment for bias influence. To broaden the evaluation scope, we introduce three novel benchmarks--authority bias (fabricated citations), confidence bias (assertive phrasing), and formatting bias (structured presentation)--and evaluate across seven bias types in total. Across three frontier models, self-debiasing improves performance in 20 out of 21 model-bias combinations with gains up to 15 percentage points, while bias-aware prompting also yields consistent improvements. The recent fine-tuned external critic, however, does not generalize as reliably to the newly-introduced bias types (degradation observed in 5 out of 9 cases). Our findings demonstrate that prompting a judge to reason about bias, either preemptively or retrospectively, is a robust and generalizable approach for enhancing evaluation integrity.

cs.AI↗

arXiv2026

Quantum Property Testing for Bounded-Degree Directed Graphs

We study quantum property testing of directed graphs whose maximum in-degree and out-degree are bounded by a fixed constant $d$. For a proximity parameter $\varepsilon$, we prove that every property testable with $O_{\varepsilon,d}(1)$ quantum queries in the bidirectional model, where both incoming and outgoing neighbors are accessible, can also be tested in the quantum unidirectional model, where only outgoing neighbors are accessible, using $n^{1/2-Ω_{\varepsilon,d}(1)}$ queries. This gives an almost quadratic quantum speedup over the best known generic classical transformation. Our proof has two main ingredients. First, we show that, in the bidirectional model for bounded-degree digraphs, the class of properties testable with constantly many classical queries coincides with the class testable with constantly many quantum queries. Second, we give a transformation from classical bidirectional testers to quantum unidirectional testers by designing a quantum unidirectional algorithm for estimating the frequency vector of constant-radius rooted discs. The algorithm combines quantum counting and Grover search with a correction procedure that removes false local appearances of smaller disc types inside larger neighborhoods. We further show that this transformation is essentially tight. For every sufficiently small fixed $\varepsilon>0$, there is a degree bound $d=d(\varepsilon)$ and an explicit property $P_\varepsilon$ for $d$-bounded-degree digraphs that is $\varepsilon$-testable with $O_\varepsilon(1)$ classical, and hence quantum, bidirectional queries, but requires $ \widetildeΩ_\varepsilon \left(n^{1/2-f'(\varepsilon)}\right) $ quantum queries in the unidirectional model, where $f'(\varepsilon)\to 0$ as $\varepsilon\to 0$.

quant-ph↗

arXiv2026

Exceptional Gamma-Ray Flaring Activity of the Blazar S4 0954+65 in Early 2025

S4 0954+65 (4FGL J0958.7+6534) is a TeV-detected blazar at a redshift of $z = 0.3694 \pm 0.0011$, classified as an intermediate-synchrotron-peaked BL Lac object. In early 2025, it entered an exceptional $γ$-ray high state. We aim to investigate the origin and physical properties of the exceptional 2025 flare, and to constrain the emission processes responsible for this flaring activity. We performed a multi-wavelength analysis using $γ$-ray, X-ray, optical/UV, radio, and 43 GHz Very Long Baseline Array (VLBA) observations. We examined multi-band correlations with the z-transformed discrete correlation function (zDCF), analyzed the spectral evolution and parsec-scale jet kinematics, and modeled the broadband spectral energy distributions (SEDs). The $γ$-ray variations lead the optical and radio emission by 3.22 days and possibly 18.26 days, respectively, while no significant correlation is found between the $γ$-ray and X-ray emission. The $γ$-ray spectra show a harder-when-brighter behavior, consistent with enhanced particle acceleration during the active state. A similar spectral trend is also observed in the X-ray band, where the photon index is anti-correlated with flux. During the major $γ$-ray flare, VLBA images reveal the emergence of a new superluminal radio knot from the core, whose extrapolated ejection time is consistent with the peak of the $γ$-ray flare. The temporal and structural evolution during the 2025 flare is consistent with a shock-in-jet scenario, in which a newly emerging disturbance propagates downstream and drives the flare. Broadband SED modeling shows that a leptonic synchrotron self-Compton plus external Compton model with dusty torus seed photons reproduces the multi-wavelength observations with physically plausible parameters. A lepto-hadronic interpretation remains possible but requires substantially higher jet power.

astro-ph.HE↗

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

CAGE: Coherence-Aware Graph Encoding for Retrieval-Augmented Generation

Traditional Retrieval-Augmented Generation (RAG) systems score each passage independently against the query, assembling context sets that may be individually relevant yet collectively incoherent. We introduce Coherence-Aware Graph Encoding (CAGE), a reranking framework that models "between-chunk coherence" across four dimensions: Intra-Domain Relevance, Noise Resistance, Informational Bonding, and Factual Consistency. Our pipeline transforms retrieved passages into directed heterogeneous entity graphs, amplifies factual anchors via min-out-degree reweighting, encodes structural patterns through a Relational Graph Convolutional Network, and fuses inter-chunk coherence with query relevance for final ranking. Evaluated across four multi-hop benchmarks, CAGE matches or outperforms strong baselines including monoT5 in Recall@5 on bridge-dominated datasets and consistently improves downstream Exact Match, demonstrating that structurally coherent context yields more precise answers even when retrieval recall is comparable or lower.

cs.CL↗

arXiv2026

Towards Valid B-Rep Generation: Training-Free Wireframe Anomaly Detection and Repair

Multi-stage boundary representation (B-Rep) generation leverages intermediate wireframes to synthesize CAD models. However, geometric and topological risks in these wireframes -- such as self-intersections, edge collapses, and disconnected vertices -- can propagate to invalid final B-Reps. Mitigating such failures by retraining large generative models is computationally prohibitive. We propose Wireframe Detection and Repair (WDR), a training-free framework that intervenes at the intermediate wireframe stage to improve downstream B-Rep validity. WDR features a Geometric-Topology Anomaly Detector (GTAD) that combines parallel VLM-based coarse screening with geometric and topological detectors to predict downstream invalidity risk and route generation to dedicated branches. An Energy-Guided Geometric-Topology Repair (EGGTR) module then performs detector-triggered guided regeneration through geometry and topology branches. By scaling test-time computation via Energy-Guided Resampling and training-free guidance for diffusion models, WDR can be integrated into autoregressive and diffusion pipelines without retraining. Extensive experiments demonstrate consistent improvements in kernel-checked validity while largely retaining the measured diversity and distributional quality of synthesized CAD models. The code will be made publicly available upon acceptance.

cs.CV↗

arXiv2026

$L^{2}$-Supercritical Nonlinear Klein-Gordon System with Quadratic Asymmetric Interaction

In this paper we investigate the global existence, blow-up and standing waves for the $L^{2}$-supercritical nonlinear Klein-Gordon equations with quadratic asymmetric interaction (LSNKG). First, by introducing a suitable auxiliary functional, using concavity analysis and virial estimates, we obtain a finite time blow-up result for solutions to the Cauchy problem of (LSNKG) when the initial energy is negative. Next, by defining appropriate functionals, manifolds and a constrained variational problem, we employ variational method and the Lagrange multiplier method to derive the existence of ground state solutions for the corresponding nonlinear elliptic (steady-state) system, thereby to establish the existence of standing wave with the ground state for (LSNKG). Then, using the variational characterization of the ground state solutions and constructing invariant sets under the flow generated by the Cauchy problem for (LSNKG), we combine the potential well argument with concavity analysis to establish a sharp threshold between blow-up in finite time and global existence. Finally, by exploiting the variational characterization of the ground state, introducing appropriate scalings, and choosing suitable initial data based on the ground state, we justify the instability of standing wave with the ground state for (LSNKG).

math.AP↗

arXiv2026

SAFARI: Scaling Long Horizon Agentic Fault Attribution via Active Investigation

As autonomous agents tackle increasingly complex multi-step, multi-agent tasks, their execution trajectories have scaled beyond the constraints of even the largest context windows. Current methods for effectively diagnosing agent failures load the full trajectory into an LLM's context window, which suffers from attention dilution and fails when agentic traces inevitably exceed context limits. To address this, we introduce SAFARI (Scaling long-horizon Agentic Fault AttRibution via active Investigation), a framework that replaces linear context loading with a tool-augmented diagnostic loop. By equipping LLMs with a specialized toolbox to read and search trajectory segments alongside a persistent Short-Term Memory (STM) for cross-turn reasoning, SAFARI effectively decouples diagnostic accuracy from architectural context limits. Our experiments demonstrate that SAFARI outperforms state-of-the-art results by 20% on the Who&When dataset within a 1M token budget, and by 19% on TRAIL GAIA subset on a 25K token budget. Most significantly, SAFARI maintains a 0.58 precision even when the target fault resides 5x beyond the model's native context window, a scenario where traditional evaluators fail entirely.

cs.AI↗

arXiv2026

Bridging Time and Space: Decoupled Spatio-Temporal Alignment for Video Grounding

Spatio-Temporal Video Grounding requires jointly localizing target objects across both temporal and spatial dimensions based on natural language queries, posing fundamental challenges for existing Multimodal Large Language Models (MLLMs). We identify two core challenges: \textit{entangled spatio-temporal alignment}, arising from coupling two heterogeneous sub-tasks within the same autoregressive output space, and \textit{dual-domain visual token redundancy}, where target objects exhibit simultaneous temporal and spatial sparsity, rendering the overwhelming majority of visual tokens irrelevant to the grounding query. To address these, we propose \textbf{Bridge-STG}, an end-to-end framework that decouples temporal and spatial localization while maintaining semantic coherence. While decoupling is the natural solution to this entanglement, it risks creating a semantic gap between the temporal MLLM and the spatial decoder. Bridge-STG resolves this through two pivotal designs: the \textbf{Spatio-Temporal Semantic Bridging (STSB)} mechanism with Explicit Temporal Alignment (ETA) distills the MLLM's temporal reasoning context into enriched bridging queries as a robust semantic interface; and the \textbf{Query-Guided Spatial Localization (QGSL)} module leverages these queries to drive a purpose-built spatial decoder with multi-layer interactive queries and positive/negative frame sampling, jointly eliminating dual-domain visual token redundancy. Extensive experiments across multiple benchmarks demonstrate that Bridge-STG achieves state-of-the-art performance among MLLM-based methods. Bridge-STG improves average m\_vIoU from $26.4$ to $34.3$ on VidSTG and demonstrates strong cross-task transfer across various fine-grained video understanding tasks under a unified multi-task training regime.

cs.CV↗

arXiv2026

RAFFLES: Reasoning-based Attribution of Faults for LLM Systems

The advent of complex, interconnected long-horizon LLM systems has made it incredibly tricky to identify where and when these systems break down. Evaluation capabilities that currently exist today are limited in that they often focus on simple metrics, end-to-end outcomes, and are dependent on the perspectives of humans. In order to match the increasing complexity of these many component systems, evaluation frameworks must also be able to reason, probe, iterate, and understand the nuanced logic passing through these systems. In this paper, we present RAFFLES, an offline evaluation architecture that incorporates iterative reasoning. Specifically, RAFFLES operates as an iterative, multi-component pipeline, using a central Judge to systematically identify faults and a set of specialized Evaluators to assess the quality of the candidate faults as well as rationales of the Judge. We evaluated RAFFLES with several benchmarks - the Who&When dataset to identify step-level faults in multi-agent systems and the ReasonEval datasets to diagnose step-level mathematical reasoning errors. RAFFLES outperforms strong baselines, achieving an accuracy of over 20% and 50% on the Who&When Hand-Crafted and Algorithmically-Generated datasets, and over 80% on the ReasonEval datasets. These results demonstrate a key step towards introducing automated fault detection for autonomous systems over labor-intensive manual review.

cs.AI↗

arXiv2026

DF-RAG: Query-Aware Diversity for Retrieval-Augmented Generation

Retrieval-augmented generation (RAG) is a common technique for grounding language model outputs in domain-specific information. However, RAG is often challenged by reasoning-intensive question-answering (QA), since common retrieval methods like cosine similarity maximize relevance at the cost of introducing redundant content, which can reduce information recall. To address this, we introduce Diversity-Focused Retrieval-Augmented Generation (DF-RAG), which systematically incorporates diversity into the retrieval step to improve performance on complex, reasoning-intensive QA benchmarks. DF-RAG builds upon the Maximal Marginal Relevance framework to select information chunks that are both relevant to the query and maximally dissimilar from each other. A key innovation of DF-RAG is its ability to optimize the level of diversity for each query dynamically at test time without requiring any additional fine-tuning or prior information. We show that DF-RAG improves F1 performance on reasoning-intensive QA benchmarks by 4-10 percent over vanilla RAG using cosine similarity and also outperforms other established baselines. Furthermore, we estimate an Oracle ceiling of up to 18 percent absolute F1 gains over vanilla RAG, of which DF-RAG captures up to 91.3 percent.

cs.CL↗

arXiv2025

OmniGround: A Comprehensive Spatio-Temporal Grounding Benchmark for Real-World Complex Scenarios

Spatio-Temporal Video Grounding (STVG) aims to localize target objects in videos based on natural language descriptions. Despite recent advances in Multimodal Large Language Models, a significant gap remains between current models and real-world demands involving diverse objects and complex queries. We attribute this to limited benchmark scope, causing models to exhibit category bias, oversimplified reasoning, and poor linguistic robustness. To address these limitations, we introduce OmniGround, a comprehensive benchmark with 3,475 videos spanning 81 categories and complex real-world queries. We propose the Forward-Backward-Refinement annotation pipeline that combines multi-directional tracking with intelligent error correction for high-quality labels. We further introduce DeepSTG, a systematic evaluation framework quantifying dataset quality across four complementary dimensions beyond superficial statistics. Evaluations reveal performance average drop of 10.4% on complex real-world scenes, particularly with small/occluded objects and intricate spatial relations. Motivated by these, we propose PG-TAF, a training-free two-stage framework decomposing STVG into high-level temporal grounding and fine-grained spatio-temporal propagation. Experiments demonstrate PG-TAF achieves 25.6% and 35.6% improvements in m\_tIoU and m\_vIoU on OmniGround with consistent gains across four benchmarks.

cs.CV↗

arXiv2025

LLM Optimization Unlocks Real-Time Pairwise Reranking

Efficiently reranking documents retrieved from information retrieval (IR) pipelines to enhance overall quality of Retrieval-Augmented Generation (RAG) system remains an important yet challenging problem. Recent studies have highlighted the importance of Large Language Models (LLMs) in reranking tasks. In particular, Pairwise Reranking Prompting (PRP) has emerged as a promising plug-and-play approach due to its usability and effectiveness. However, the inherent complexity of the algorithm, coupled with the high computational demands and latency incurred due to LLMs, raises concerns about its feasibility in real-time applications. To address these challenges, this paper presents a focused study on pairwise reranking, demonstrating that carefully applied optimization methods can significantly mitigate these issues. By implementing these methods, we achieve a remarkable latency reduction of up to 166 times, from 61.36 seconds to 0.37 seconds per query, with an insignificant drop in performance measured by Recall@k. Our study highlights the importance of design choices that were previously overlooked, such as using smaller models, limiting the reranked set, using lower precision, reducing positional bias with one-directional order inference, and restricting output tokens. These optimizations make LLM-based reranking substantially more efficient and feasible for latency-sensitive, real-world deployments.

cs.CL↗

arXiv2025

Detection of Quasi-periodic Oscillations in the $γ$-Ray Light Curve of 4FGL J0309.9-6058

In this work, we report, for the first time, a quasi-periodic oscillation (QPO) in the $γ$-ray band of 4FGL J0309.9-6058, also known as PKS 0308-611. We employed three analytical methods (the Lomb-Scargle periodogram, REDFIT, and the weighted wavelet Z-transform) to analyze the QPO signal using \textit{Fermi} $γ$-ray light curve data. The analysis reveals a potential QPO during MJD 57983$-$60503, with a period of approximately 550 days and a maximum local significance of 3.72$σ$ and global significance of 2.72$σ$ derived from the WWZ analysis. To validate this result, we applied Gaussian Process (GP) to the same light curve, which independently confirms the presence of QPO signal consistent with our Fourier-based results. We further extended the analysis to the full duration of the \textit{Fermi} observations, and the results consistently support and strengthen the presence of this QPO signal. Additionally, a time lag between the optical and $γ$-ray bands indicates separate emission regions for these two bands. Given the year-like timescale of the QPO signal and the fact that a QPO signal with local significance over 3$σ$ for full \textit{Fermi}-LAT observed time, we suggest that the QPO is most likely caused by a precessing jet.

astro-ph.HE↗

arXiv2025

Harmonizing Diverse Models: A Layer-wise Merging Strategy for Consistent Generation

Retrieval-Augmented Generation (RAG) systems leverage Large Language Models (LLMs) to generate accurate and reliable responses that are grounded in retrieved context. However, LLMs often generate inconsistent outputs for semantically equivalent inputs, a problem compounded by the scarcity of consistency-focused training data and the limitations of current fine-tuning techniques in enhancing output consistency. We propose a new approach combining systematic synthetic data generation, triplet loss for better embeddings, and a novel layer-wise model merging approach. Using consistency-aware weights derived from intermediate layer activations, our method effectively integrates knowledge from specialized models. Experimental results how that our merged model significantly enhances output consistency, achieving a ~47.5\% improvement in response similarity over the baseline, thus offering a practical solution for increasing the reliability of an industrial RAG system.

cs.CL↗

arXiv2025

CFPFormer: Feature-pyramid like Transformer Decoder for Segmentation and Detection

Feature pyramids have been widely adopted in convolutional neural networks and transformers for tasks in medical image segmentation. However, existing models generally focus on the Encoder-side Transformer for feature extraction. We further explore the potential in improving the feature decoder with a well-designed architecture. We propose Cross Feature Pyramid Transformer decoder (CFPFormer), a novel decoder block that integrates feature pyramids and transformers. Even though transformer-like architecture impress with outstanding performance in segmentation, the concerns to reduce the redundancy and training costs still exist. Specifically, by leveraging patch embedding, cross-layer feature concatenation mechanisms, CFPFormer enhances feature extraction capabilities while complexity issue is mitigated by our Gaussian Attention. Benefiting from Transformer structure and U-shaped connections, our work is capable of capturing long-range dependencies and effectively up-sample feature maps. Experimental results are provided to evaluate CFPFormer on medical image segmentation datasets, demonstrating the efficacy and effectiveness. With a ResNet50 backbone, our method achieves 92.02\% Dice Score, highlighting the efficacy of our methods. Notably, our VGG-based model outperformed baselines with more complex ViT and Swin Transformer backbone.

cs.CV↗