arXiv subjects

Ruoyu Wang

Publications and source records attributed to Ruoyu Wang.

At least 19 recordsLinked to original sources

arXiv2026

Debiased Estimating Equation Method for Robust and Efficient Mendelian Randomization Using a Large Number of Correlated Weak and Invalid Instruments

Mendelian randomization (MR) is a widely used tool for causal inference in the presence of unmeasured confounders, which uses single nucleotide polymorphisms (SNPs) as instrumental variables to estimate causal effects. However, SNPs often have weak effects on complex traits, leading to bias in existing MR analyses when weak instruments are included. In addition, existing MR methods often restrict analysis to independent SNPs via linkage disequilibrium clumping and result in a loss of efficiency in estimating the causal effect due to discarding correlated SNPs. To address these issues, we propose the Debiased Estimating Equation Method (DEEM), a summary statistics-based MR approach that can incorporate a large number of correlated, weak-effect, and invalid SNPs. DEEM effectively eliminates the weak instrument bias and improves the statistical efficiency of the causal effect estimation by leveraging information from a large number of correlated SNPs. DEEM also allows for pleiotropic effects, adjusts for the winner's curse, and applies to both two-sample and one-sample MR analyses. Asymptotic analyses of the DEEM estimator demonstrate its attractive theoretical properties. Through extensive simulations and two real data examples, we demonstrate that DEEM significantly improves the efficiency and robustness of MR analysis compared with existing methods.

stat.ME↗

arXiv2026

Sequence-to-Sequence Refinement for EEND-VC Diarization with a Shared Encoder

End-to-end neural diarization with vector clustering (EEND-VC) separates local segmentation from global speaker assignment, leaving speaker confusion (SC) despite strong segmentation. We propose shared-encoder sequence-to-sequence (S2S) refinement, which reuses DiariZen's self-supervised learning (SSL) representations for speaker-conditioned decoding and prior-constrained assignment. We study frozen-feature refinement with WavLM and structurally pruned w2v-BERT 2.0, and extend the latter to joint training of local segmentation and refinement heads. The heads learn separate layer mixtures while their losses update one acoustic encoder. Across eight domains, frozen-feature refinement reduces macro diarization error rate (DER) from 12.95% to 12.40% with WavLM and from 12.35% to 12.08% with w2v-BERT 2.0, with relative SC reductions of 17.4% and 14.6%. It adds approximately 15% to system real-time factor. The joint system further lowers macro DER to 11.90%, improving seven of eight domains over non-joint refinement. Error decomposition separates SC-driven refinement gains from detection-driven joint-system gains.

eess.AS↗

arXiv2026

AgentSnare: Learning to Delay, Divert, and Defuse Autonomous Web Penetration Agents

Large language model agents automate web penetration testing through observation-action loops, enabling defenders to influence decisions with deceptive decoy observations. Advanced agents can recognize and bypass static decoys, then refocus on real targets. We propose AgentSnare, a trajectory-adaptive deception system that expands a decoy environment alongside exploration. For online deployment, ShadowOS combines an LLM-driven decoy constructor with a virtual kernel. Conditioned on exploration, it generates decoy artifacts that the kernel materializes and handles consistently across interactions, sustaining long-horizon exploration. Because deploying a powerful LLM for decoy construction is costly and slow, we train a small LLM using an offline adversarial interaction-simulation framework. It synthesizes multi-round trajectories by generating candidates and retaining those that sustain simulated interactions. Across 15 CVE-Bench web applications and four attacker models, AgentSnare absorbs 49.9% of tool calls to delay attacks, retains 57.2% of post-entry actions in the decoy to divert trajectories, and grounds 89.5% of completion reports in decoy evidence to defuse threats; across 60 attacker-CVE pairs, no real target is exploited at pass@3.

cs.CR↗

arXiv2026

Variational Optimal Control for Diffusion Guidance

We present a variational control framework that connects sampling-time guidance and reward-based model adaptation through a shared Jacobian pullback. Starting from terminal reward maximization with a path-space KL penalty, the Gibbs variational principle and Doob transform yield a correction to the generative dynamics. A local denoising approximation exposes the pullback that transports output preferences to the controlled variable. This construction gives Variational Optimal Guidance (VOG) in state space and extends to Distilled Variational Optimal Control (DVOC) by amortizing locally improved endpoints into model parameters. The resulting controls share a local variational rule: VOG acts through the denoising Jacobian, while DVOC realizes the parameter pullback by endpoint distillation. Under a linear MMSE model, VOG's exact correction lies in the data subspace. On ImageNet, VOG reduces DiT-XL/2 FID from 2.27 to 2.04 at 250 sampling steps and from 7.58 to 4.44 at 25 steps. After 20 updates on Z-Image-Turbo, DVOC improves HPSv3++ and four held-out evaluator scores over the untrained model, with the highest corresponding means among the trained methods compared. These results connect inference-time control and reward adaptation through a common geometric principle.

cs.LG↗

arXiv2026

Denoising Data with Measurement Error Using a Reproducing Kernel-Based Diffusion Model

Recent advancements in measurement technologies have facilitated the acquisition of high-resolution data in fields such as engineering, biology, and medicine. However, these observations are frequently corrupted by measurement noise. To address this challenge, we propose a denoising framework that employs diffusion models to recover the underlying error-free data distribution, thereby enabling reliable downstream analysis. Central to our framework is a novel Reproducing Kernel Hilbert Space (RKHS)-based method that trains the diffusion model using only error-contaminated data. This method admits a closed-form solution and achieves a fast convergence rate in terms of estimation error. Theoretically, we validate our approach by deriving an upper bound on the total variation distance between the distributions of the generated denoised data and the true error-free data. Extensive simulations demonstrate the superior empirical performance of our method compared to state-of-the-art baselines. Finally, we illustrate the framework's practical utility in digital health and epidemiology by applying it to Continuous Glucose Monitor (CGM) data and the association analysis between blood lipids and coronary heart disease.

stat.ME↗

arXiv2026

Not an Accident: Exploiting Safety Filter Inadequacy in Humanoid Robots

As humanoid robots move from research laboratories toward deployment in human-centered environments such as homes, hospitals, and warehouses, safe operation around nearby people and objects becomes a fundamental requirement. Safety filters have emerged as a promising technique to address this challenge by providing mathematical guarantees that generated control actions do not result in collisions. This paper shows that these guarantees hold only when the safety parameters are configured correctly. However, correct parameter configurations are task- and environment-specific, and no general method exists to determine them across diverse deployments. As a result, safety filters shipped with humanoid robots can fail after deployment in specific scenarios. We show that attackers can exploit these failures through only normal user interactions without any additional privileges or capabilities. To demonstrate this risk, we present goal insertion attacks and develop SIREN, an automated tool that identifies vulnerable inserting goals for a given task and environment. Experimental results show that SIREN identifies attacks in all vulnerable scenarios in the SPARK dataset. Compared with a baseline approach, SIREN discovers three times as many attacks. We also show that SIREN is able to generate attacks against a real humanoid and cause it to collide while executing tasks. These results demonstrate that current safety filters introduce exploitable vulnerabilities that can cause safety violations, which reveals that existing safety mechanisms can themselves become a source of safety risk for humanoid robots.

cs.CR↗

arXiv2026

VEOcc++: Learning Persistent Voxel Feature Maps for Embodied Online Semantic Occupancy Mapping and Offline Refinement

Semantic occupancy mapping supports embodied perception during exploration and detailed scene understanding afterward, giving rise to two complementary requirements: online incremental mapping for maintaining an up-to-date global map under causal constraints, and offline global map refinement for improving the completed scene representation with additional computation. We present VEOcc++, a unified voxel-centric framework that connects both stages through a persistent sparse voxel feature map. During exploration, the map expands with newly observed regions and integrates aligned local features through reliability-guided updates, while context-aware decoding produces the current semantic occupancy map. After exploration, cached local features are consolidated as complementary evidence and combined with the completed online representation for multi-scale spatial refinement. On EmbodiedOcc-ScanNet, VEOcc++ achieves 67.12\% IoU and 58.40\% mIoU, establishing new state-of-the-art performance for online incremental mapping. With the local predictor fixed, our online mapping pipeline outperforms VEOcc fusion by 3.70 IoU and 3.74 mIoU points, demonstrating substantial gains from the proposed global mapping design. Offline global map refinement further improves the completed map to 67.79% IoU and 59.37% mIoU using only the observations acquired during exploration. Zero-shot deployment on a mobile robot further demonstrates transferability to unseen environments, onboard feasibility, and scalability to large-scale indoor mapping. Project page: https://wryzju.github.io/VEOccpp/.

cs.CV↗

arXiv2026

S-Agent 2.0: Spatial Reasoning with a General Coding Agent

Spatial reasoning asks a model to work out where objects are in 3D space, how they relate to one another, and how they move. Agentic VLMs have recently brought large improvements on such tasks by letting the model actively gather evidence with 3D perception tools instead of answering in a single pass. Even so, on many questions the tools return the right evidence and the model still does not arrive at the correct answer: the gap between model and tools, where raw readings become usable conclusions, is left unfilled. We present S-Agent 2.0, a spatial reasoning agent that equips an off-the-shelf general coding agent with a command-line perception toolkit, and we design the interface around one principle: help the VLM close the evidence loop: acquire scene evidence, remember it in a persistent scene memory, and interpret each reading in plain spatial language. This design keeps the full flexibility of a coding agent, and every reading it acts on stays grounded in the tools' geometry. On MMSI-Bench and ReVSI, the toolkit improves a strong open-source model, Qwen3.8-27B, by +13.9 and +20.5 points over the same agent without tools, and a strong closed-source model, Gemini-3.8-Flash, by +11.2 and +9.6 points. With the open backbone S-Agent 2.0 reaches 66.5 on MMSI-Bench and 82.3 on ReVSI, ahead of the specialized system SpatialClaw on the same backbone (62.6 and 74.5), and with Gemini-3.8-Flash it reaches 74.1 and 90.5 against its 67.0 and 79.7. Both tool gains shrink as the backbone grows more capable, but SpatialClaw's shrinks far more on ReVSI, from +13.2 to +2.7, while ours stays at +9.6. S-Agent 2.0 also works across different general coding agent harnesses, such as Codex and Claude Code.

cs.AI↗

arXiv2026

No, Your Tools Are Not Enough: It's Time to Bring Agents to the Hunt

Modern software systems are large and complex, demanding scalable and autonomous techniques for vulnerability discovery. Existing approaches still depend on human expertise for domain knowledge, and on human intervention throughout the process of vulnerability discovery. AI agents offer us an opportunity to make vulnerability discovery more adaptive, scalable, and autonomous. Agents bring the ability to make continuous decisions and adapt, and general knowledge that can be augmented. In this paper, we present our experience using AI agents to find vulnerabilities across diverse programs such as Windows drivers, the Linux kernel, XNU, firmware, and web applications. We use an iterative agentic workflow and find that it generalizes across these targets. We discuss tasks that agents help with, which were previously challenging, and some future research directions we consider important for advancing autonomous vulnerability discovery.

cs.CR↗

arXiv2026

Humans vs. Machines: A Comparison With AI Agents in Software Reverse Engineering

Can an autonomous AI agent reverse engineer a binary as well as a skilled human analyst? Do they adopt the same strategy, and can agents scale to real-world programs? We compare human and LLM-based software reverse engineering (SRE): nine models tackle two compact static-analysis challenges previously solved by human analysts, scored with a common understanding rubric, and we record their complete interactions with IDA Pro. This lets us compare not only what humans and agents recover, but also how they investigate a binary. The best agents (GPT-5.5 Pro, Claude Opus 4.8, and Kimi K3) achieve statistically human-competitive understanding on both tasks, while weaker models remain far behind. Ablations show that the gap depends on code representation and workflow demands, not on model capability alone: open-weight models improve substantially with richer code representations, whereas frontier models show no significant change. Similar success rates hide different workflows. Humans repeatedly revisit functions and follow local program structure; strong agents jump across the binary, retrieve information broadly, and rarely return to inspected code, a behavior we call greedy fetching. This strategy is not fixed: given semantically richer code, agents fetch less without losing understanding. Because greedy fetching aggregates large amounts of code in the agent's context, we measure context-window saturation across 4,000 real-world binaries: large windows accommodate many programs, but larger applications and lower-level representations quickly exhaust them. Autonomous reverse engineering is becoming competitive, with a large speed advantage, but scaling it will require better navigation, selective retrieval, and persistent analysis state, not merely larger context windows.

cs.SE↗

arXiv2026

A Moment-assisted Approach for Improving Subsampling-based MLE with Large-scale data

The maximum likelihood estimation is computationally demanding for large datasets, particularly when the likelihood function includes integrals. Subsampling can reduce the computational burden, but it often results in efficiency loss.This paper proposes a moment-assisted subsampling (MAS) method that can improve the estimation efficiency of existing subsampling-based maximum likelihood estimators.The motivation behind this approach stems from the fact that sample moments can be efficiently computed even if the sample size of the whole data set is huge.Through the generalized method of moments, the proposed method incorporates informative sample moments of the whole data. The MAS estimator can be computed rapidly and is asymptotically normal with a smaller asymptotic variance than the corresponding estimator without incorporating sample moments of the whole data. The asymptotic variance of the proposed estimator depends on the specific sample moments incorporated. We derive the optimal moment that minimizes the resulting asymptotic variance in terms of Loewner order. The proposed MAS estimator can achieve the same estimation efficiency as the whole data-based estimator when the optimal moment is incorporated. Numerical results demonstrate the promising performance of the proposed method in both estimation and computational efficiency compared with existing subsampling methods.

stat.ME↗

arXiv2026

Instrumental Variable Analysis in Underrepresented Subpopulations Powered by Knowledge Transfer

Instrumental variable (IV) analysis in underrepresented target populations suffers from low statistical efficiency and weak IV bias due to limited sample size. Leveraging external knowledge from a source population with larger samples offers an appealing solution to this problem. We propose in this paper the KNowledge-powered IV Estimator for underrepresented Subpopulations (KNIVES), a framework that enhances the IV analysis for underrepresented subpopulations by transferring knowledge from external source population data. KNIVES constructs a knowledge-transferred IV score by combining multiple candidate models through an effective signal-to-noise ratio criterion tailored to the downstream IV estimator, rather than prediction accuracy alone. To address weak associations between the IV score and the exposure, as well as a high correlation among candidate models in the target population, KNIVES employs a bias correction technique to mitigate the resulting bias and ensure robust performance. Theoretical investigation demonstrates that our method is robust to weak IV bias and achieves improved efficiency compared to the causal effect estimator based on any single candidate model. In addition, KNIVES avoids negative transfer by guaranteeing that its asymptotic variance is not larger than that of the estimator relying solely on target population data. Extensive numerical studies demonstrate the superior finite-sample performance of our method over existing methods. Two Mendelian randomization studies on ethnic minority subgroups in UK Biobank data further illustrate the advantages of our method.

stat.ME↗

arXiv2026

MAGiDiff: Sampling the Photospheric Vector Field from UV/EUV Filtergrams

Photospheric vector magnetic fields are foundational to modeling, understanding, and forecasting solar activity. These data are usually produced by inverting and disambiguating the full Stokes vector at multiple passbands, which is demanding. Here, we investigate how well we can estimate photospheric vector magnetograms from UV/EUV filtergrams. This problem is challenging and intrinsically ambiguous without polarization information, as the mapping from UV/EUV intensity to the magnetic field is indirect and ill-posed. We introduce MAGiDiff, a machine-learning-based method that uses denoising diffusion models to estimate vector magnetograms from UV/EUV filtergrams. As input, MAGiDiff takes a stack of filtergrams from the Solar Dynamics Observatory (SDO) / Atmospheric Imaging Assembly (AIA); as output, it is trained to estimate the disambiguated vector magnetogram as seen by Hinode / Solar Optical Telescope-Spectro-Polarimeter (SOT-SP). We show that MAGiDiff can accurately mimic the Hinode ground-truth. Additionally, we probe MAGiDiff's understanding of the physical structure and magnetic connectivity. On full-disk, we show that it produces plausible structures for active regions. MAGiDiff generalizes across solar cycles despite hemispheric polarity reversal, and can be fine-tuned to other EUV instruments including STEREO/EUVI and GOES-R/SUVI. While clearly not a substitute for a dedicated instrument, MAGiDiff opens the door to new capabilities.

astro-ph.SR↗

arXiv2026

Enhancing BGP Security by Understanding BGP's Language with LLMs

The trust-based nature of Border Gateway Protocol (BGP) makes it vulnerable to prefix hijacking and misconfigurations. Traditional BGP anomaly detection relies on manual inspection with poor scalability, while Machine/Deep Learning (M/DL)-based approaches suffer from suboptimal precision, limited generalizability, and high retraining cost. This is because existing M/DL methods focus on topological structures rather than semantic characteristics of Autonomous Systems (ASes), assigning dissimilar embeddings to functionally similar but topologically distant ASes. To address this, we propose BGPShield, a novel anomaly detection framework built on an Adaptive LLM BGP Encoder that captures each AS's Behavior Portrait and Routing Policy Rationale beyond topology. Inspired by multimodal LLMs, the encoder generates embeddings representing both routing behaviors and semantics of ASes via contrastive learning. We further introduce SAM-ED to quantify BGP-specific semantic deviations between historical and updated paths, rather than naively accumulating distances without awareness of BGP-specific structures. Evaluated on 16 real-world datasets, BGPShield detects 100% of verified anomalies with an average false discovery rate below 5%. The open-source LLMs used by BGPShield were released prior to several evaluation events, verifying generalizability on unseen events. Furthermore, BGPShield can construct the representation for a previously unseen AS within one second, significantly outperforming BEAM which demands thorough retraining (averagely 65 hours).

cs.NI↗

arXiv2026

Chemical-space completeness through iterative crystal-structure generation and model adaptation

The emergence of deep learning has brought large-scale exploration of crystalline materials closer to practical realization. Yet, universal atomistic models face a practical trade-off between chemical generality and the accuracy, efficiency, and adaptability required for intensive exploration of specific materials systems. Within a bounded chemical system, this trade-off can be relaxed by exploiting its limited chemical complexity. Guided by this intuition, we propose a chemical-system-centric strategy that couples crystal-structure generative models with machine-learned force fields (MLFFs) in an iterative generation-evaluation-refinement loop. Using Li--P--S as a test case, we generate approximately 70,000 candidate structures, including more than 10,000 stable-unique-novel structures. The diversity of near-equilibrium local environments saturates within the first few iterations, accompanied by convergence of MLFF prediction errors, providing an operational measure of chemical-space completeness within bounded systems. The exploration also recovers chemically plausible P--S motifs that are absent from the pretraining databases but supported by earlier experiments. The resulting system-adapted models and structures further enable finite-$P$--$T$ phase-stability calculations, Li-ion transport screening, and electronic-structure prediction. These results suggest that chemical-system-centric exploration provides a practical route toward data-efficient and high-fidelity modeling within bounded chemical spaces.

cond-mat.mtrl-sci↗

arXiv2026

PQ-HSA: Reusing Product-Quantized Scores for Hybrid Sparse-Approximate Attention

At each decoding step a language model attends over the key-value (KV) cache of every earlier token, so at long context the attention call is bounded by memory bandwidth. Sparse attention reads only a subset of keys chosen by a cheap score estimate, and most methods give the unread tokens zero weight. The output then draws on only a small fraction of the KV cache, and accuracy drops at small budgets, most on tasks that aggregate information across the context. An inverted-file product-quantization (IVF-PQ) index over the cached keys computes an approximate score for every indexed token in order to rank them; after ranking, those scores approximate the attention logits of the tokens left out. PQ-HSA (hybrid sparse-approximate attention) attends the selected tokens with their original keys and values, and the unselected tokens, the background, enter the same softmax through those scores, summed per inverted list and multiplied by the list's mean value. At 128K and a 1-2% retrieval budget, PQ-HSA is more accurate than Quest and SnapKV on Llama-3.1-8B and Qwen3-30B-A3B and stays close to full attention in macro accuracy; with the same selector, the background term raises macro accuracy on the 8B model from 0.71 to 0.83. In the same 128K setting, inside vLLM on one NVIDIA H20, the decode attention call runs 1.6x faster than the FlashAttention-3 kernel; the speedup grows with context length, and a cost model fitted on 8B to 30B models gives the context length at which it begins. A vLLM plugin runs PQ-HSA on two engine versions without changes to the engine source; code is available at https://github.com/KunmingSHAO/pqhsa_release.

cs.LG↗

arXiv2026

VEOcc: Voxel-Centric Online Semantic Occupancy Prediction For Embodied Scene Understanding

Crucial for autonomous exploration, online 3D occupancy prediction and mapping incrementally construct dense spatial representations on the fly. Embodied online occupancy prediction remains predominantly Gaussian-centric, despite the wide use of voxel representations for frame-wise scene completion. We present VEOcc, to the best of our knowledge, the first voxel-centric framework for online embodied semantic occupancy prediction. It incrementally maintains a sparse global semantic voxel map from monocular observations and enables open-ended expansion without predefined scene bounds. To robustly integrate noisy multi-view predictions, we further introduce a Spatio-Temporal-Aware Online Update Strategy comprising Cross-Temporal Logit Aggregation (TLA) for short-term temporal consistency, Reliability-Aware Confidence Modulation (RCM) for spatial uncertainty calibration, and Confidence-Driven Incremental State Update (CSU) for robust global state assimilation. Extensive experiments on Occ-ScanNet and EmbodiedOcc-ScanNet demonstrate state-of-the-art performance among models of comparable scale in both local and embodied settings. Moreover, onboard deployment on a mobile robot validates practical online operation and long-horizon scalability, while results on self-collected handheld sequences demonstrate zero-shot generalization to unseen real-world environments. Code and supplementary visualizations are available on our project page: https://wryzju.github.io/VEOcc/.

cs.CV↗

arXiv2026

BuildBench: Benchmarking LLM Agents on Compiling Real-World Open-Source Software

Automatically compiling open-source software (OSS) projects is a vital, labor-intensive, and complex task, which makes it a good challenge for LLM Agents. Existing methods rely on manually curated rules and workflows, which cannot adapt to OSS that requires customized configuration or environment setup. Recent attempts using Large Language Models (LLMs) used selective evaluation on a subset of highly rated OSS, a practice that underestimates the realistic challenges of OSS compilation. In practice, compilation instructions are often absent, dependencies are undocumented, and successful builds may even require patching source files or modifying build scripts. We propose a more challenging and realistic benchmark, BUILD-BENCH, comprising OSS that are more diverse in quality, scale, and characteristics. Furthermore, we propose a strong baseline LLM-based agent, OSS-BUILD-AGENT, an effective system with enhanced build instruction retrieval module that achieves state-of-the-art performance on BUILD-BENCH and is adaptable to heterogeneous OSS characteristics. We also provide detailed analysis regarding different compilation method design choices and their influence to the whole task, offering insights to guide future advances. We believe performance on BUILD-BENCH can faithfully reflect an agent's ability to tackle compilation as a complex software engineering tasks, and, as such, our benchmark will spur innovation with a significant impact on downstream applications in the fields of software development and software security.

cs.SE↗