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Jingjing Li

Publications and source records attributed to Jingjing Li.

At least 19 recordsLinked to original sources
arXiv2026

Gaia DR3 Compact Binary Candidates Constrained by Radial Velocity Measurements

Astrometry enables the most direct determination of the orbital elements and component masses of compact binaries. The potential of the current Gaia data for this purpose has not yet been fully explored. In this work, we combine Gaia astrometric and radial velocity (RV) measurements to perform joint orbital fitting for 1,835 Gaia astrometric binaries with available RV data. Among the 518 systems with at least three effective RV epochs, we identify 171 compact binary candidates, including four with inferred dark companion masses exceeding 2.1 $M_{\odot}$ and 22 between 1.4 and 2.1 $M_{\odot}$. These candidates provide valuable targets for future follow-up observations and Gaia DR4 analyses. We also flag 1,317 additional potential compact binary candidates with effective RV epochs less than three, including 84 with inferred companion masses above 2.1 $M_{\odot}$ and 173 between 1.4 and 2.1 $M_{\odot}$. More importantly, our results point to a large, currently hidden population of black hole and neutron star binaries that can be further explored with the forthcoming Gaia DR4 data and complementary follow-up observations.

astro-ph.SR↗
arXiv2026

Frequency-Domain Decomposition and Recomposition for Robust Audio-Visual Segmentation

Audio-visual segmentation (AVS) plays a critical role in multimodal machine learning by effectively integrating audio and visual cues to precisely segment objects or regions within visual scenes. Recent AVS methods have demonstrated significant improvements. However, they overlook the inherent frequency-domain contradictions between audio and visual modalities--the substantial noise often present in audio high-frequency signals vs. the structurally rich details in visual high-frequency signals. Ignoring these differences can result in suboptimal performance. In this paper, we rethink the AVS task from a deeper perspective by reformulating AVS task as a frequency-domain decomposition and recomposition problem. To this end, we introduce a novel Frequency-Aware Audio-Visual Segmentation (FAVS) framework consisting of two key modules: Frequency-Domain Enhanced Decomposer (FDED) module and Synergistic Cross-Modal Consistency (SCMC) module. FDED module employs a residual-based iterative frequency decomposition to discriminate modality-specific semantics and structural features, and SCMC module leverages a mixture-of-experts architecture to reinforce semantic consistency and modality-specific feature preservation through dynamic expert routing. Extensive experiments demonstrate that our FAVS framework achieves state-of-the-art performance on three benchmark datasets, and abundant qualitative visualizations further verify the effectiveness of the proposed FDED and SCMC modules. The project repository is available at https://github.com/happylife-pk/FAVS.

cs.CV↗
arXiv2026

Cascade Failure Attribution for Multi-Agent Systems

Large language model (LLM)-based agents have shown strong potential in solving complex tasks through multi-step reasoning, yet they remain vulnerable to execution failures. Accurate failure attribution is therefore critical for improving agent reliability. Existing topology- and spectrum-based methods exploit trajectory structures but often overlook fine-grained semantics, while LLM-based attribution methods capture semantic cues but suffer from long-context degradation over lengthy trajectories. To address these challenges, we propose DuoTrace, a plug-and-play detection filter for LLM-based failure attribution. DuoTrace follows a detect-before-attribute paradigm: it first detects anomalous executions and then supplies focused trajectory evidence to downstream LLM-based attribution methods. For effective VAE-based anomaly detection on agent trajectories, DuoTrace integrates dual-view semantic-structural node representations, a Tree-LSTM-based trajectory encoder, and data augmentation through outcome-verified LLM replay and root-to-node path extraction to handle heterogeneous nodes, hierarchical execution structures, and limited normal execution data. Experiments with six LLM-based attribution baselines show that DuoTrace improves agent-level and step-level attribution accuracy by 11.2% and 7.7%,respectively.

cs.AI↗
arXiv2026

Counterfactual Neighborhood Risk Fields: Evidence-Grounded Selective Safety for Vision-Language Models

Representation-based safety defenses for vision-language models (VLMs) often learn to separate harmful from benign inputs, but separation alone does not establish what a risk score means or when it should be trusted. We introduce Counterfactual Neighborhood Risk Fields (CNRF), an evidence-grounded framework for selective VLM safety. CNRF constructs context-matched benign-harmful counterfactual pairs, uses their representation differences as capability-related risk directions, and estimates neighborhood support separately from risk. This separation prevents unfamiliarity from being conflated with harmfulness and enables selective intervention rather than blanket rejection. A victim-adaptive interface accommodates different safety observations across VLM architectures and routes selected requests for same-victim response review, which either releases acceptable candidates or produces constrained alternatives with reduced harmful capability. Across three VLMs and five multimodal jailbreak families, CNRF achieves 86.06-89.30% macro defense success rate while retaining substantial benign-task utility. Controlled experiments further show that query-matched references and preserved context-direction correspondence materially improve risk ranking in the low-false-positive regime. CNRF thus reframes representation-based VLM safety from learning harmfulness boundaries to constructing applicable evidence for selective risk decisions.

cs.AI↗
arXiv2026

High-Priority Compact-Object Candidates in Young Clusters from the \textit{Gaia} DR3 Non-Single-Star Catalog

The search for massive compact objects such as black holes (BHs) or neutron stars (NSs) in star clusters is longstanding challenge in astrophysics, yet no definitive identification of such compact objects in young clusters has been reported. In this Letter, we construct a sample of high-priority candidates for follow-up investigation. By cross-matching the cluster catalog with the Gaia Non-Single-Star database and estimating companion masses, we identify 13 systems with companion masses exceeding 1.4~$M_{\odot}$, three of which have masses above 2.1~$M_{\odot}$. Applying our newly developed Astrometric Mass-Ratio Function (AMRF) classification based on cluster-specific isochrones, we identify three systems whose companions cannot readily be explained by a single main-sequence star or an unresolved close binary. These systems have inferred companion masses exceeding 2.1~$M_{\odot}$, suggesting that they may be high-mass compact-object candidates. Joint Gaia and radial velocity orbit fitting for a comparison subset yields companion masses broadly consistent with the astrometric estimates, supporting the use of our framework for target prioritization. These unconfirmed systems are valuable targets for multi-epoch spectroscopy, high-resolution imaging, and future Gaia epoch astrometry.

astro-ph.GA↗
arXiv2026

CerebroSim: Scalable Whole-Brain Simulator at 100-Trillion-Synapse Scale on the LineShine Supercomputer

Building executable brain models is essential for moving neuroscience from description to mechanism and prediction. Human-brain-scale spiking simulation is constrained by highly irregular communication, multithreaded spike delivery, and the memory cost of sparse connectivity. We present CerebroSim, a scalable framework for whole-brain simulation. CerebroSim combines Delay-aware Spike Broadcast (DSB) for aggregated delay-aware communication, Race-free Synaptic Dynamics Computation (RSDC) for lock/atomic-free multithreaded delivery with HBM-aware optimization, and Sparse Synapse Storage Compression (3SC) for compact indexing with deterministic synapse regeneration. Using a model derived from magnetic resonance imaging and diffusion-weighted imaging, CerebroSim simulates 86 billion neurons and 100 trillion synapses on 18,432 nodes across 11.2 million cores of the LineShine Supercomputer, sustaining 24.44 PFlop/s, 91% weak-scaling efficiency, and 94% strong-scaling efficiency. This capability makes biologically constrained human-brain models practical for mechanistic studies of brain disorders and controlled in silico testing of intervention hypotheses.

cs.DC↗
arXiv2026

LongRCA Bench: Root-Cause Localization in Long-Horizon Agent Trajectories

In long agent executions, an early error can persist through later actions and checks, while evidence needed to trace its origin is dispersed across the history. Short histories offer limited tests of recovering error origins across substantial subsequent execution. We introduce LongRCA Bench: 1,140 complete failed trajectories from five sources, all human-annotated for responsible roles and earliest decisive root-cause steps. Reference roots precede completion by a median of 48 recorded steps; 28.4% of trajectories contain over 100 subsequent steps. With DeepSeek-V4-Flash on the full benchmark, the strongest of five evaluated baselines achieves 13.2% exact root-step accuracy. We propose Root-Cause Trajectory Attribution (RCTA), a training-free method that organizes original candidate records and explicit handoff instructions for attribution. Segment summaries and a trajectory outline guide candidate retrieval; available handoff records supply upstream instruction context for the final instruction-execution comparison. With the same backbone and scoring protocol, RCTA reaches 24.1% exact root-step accuracy and 51.1% responsible-role accuracy. LongRCA-Mini provides 200 fixed trajectories for lower-cost comparative screening. Even with RCTA, fewer than one quarter of reference roots are recovered exactly.

cs.AI↗
arXiv2026

VOLTA: Improving Generative Diversity by Variational Mutual Information Maximizing Autoencoder

The natural language generation domain has witnessed great success thanks to Transformer models. Although they have achieved state-of-the-art generative quality, they often neglect generative diversity. Prior attempts to tackle this issue suffer from either low model capacity or over-complicated architectures. Some recent methods employ the VAE framework to enhance diversity, but their latent variables fully depend on the input context, restricting exploration of the latent space. In this paper, we introduce VOLTA, a framework that elevates generative diversity by bridging Transformer with VAE via a more effective cross-attention-based connection, departing from conventional embedding concatenation or summation. Additionally, we propose integrating InfoGAN-style latent codes to enable input-independent variability, further diversifying the generation. Moreover, our framework accommodates discrete inputs alongside its existing support for continuous inputs. We perform comprehensive experiments with two types of Transformers on six datasets from three different NLG tasks to show that our approach can significantly improve generative diversity while maintaining generative quality.

cs.CL↗
arXiv2026

Phase stability and mechanical response of Ag-interlayered Al/Cu resistance spot-welded joints

Dissimilar Al/Cu joints are essential to battery-pack assemblies; however, their mechanical strength is limited by brittle Al-Cu intermetallic compounds (IMCs) such as Al2Cu and Al4Cu9. Interlayer strategies to suppress these phases remain largely empirical, lacking a predictive framework linking interlayer chemistry to the phases that form and to their intrinsic mechanical character. Here an Ag interlayer is introduced and combines computational thermodynamics, first-principles calculations, microstructural characterization, and mechanical testing into a single self-consistent description of the joint. CALculation of PHAse Diagrams (CALPHAD) equilibrium and Scheil simulations predict the solidification path of the Al-rich Al-Ag fusion zone and explain why Cu incorporation is limited when Ag is present; energy-dispersive X-ray spectroscopy (EDS) and electron backscatter diffraction (EBSD) confirm an FCC Al-Ag solid solution as the dominant constituent. First-principles phonon calculations within the quasiharmonic approximation yield finite-temperature entropy and Gibbs energy, benchmarked against CALPHAD, while elastic constants assess ductility via the Pugh criterion (i.e., the bulk/shear (B/G) modulus ratio). All Al-Ag phases, including the observed solid solution, exceed the Pugh threshold of 1.75, whereas the targeted Al-Cu IMCs do not, giving a mechanistic basis for the interlayer's effectiveness. This microstructural change translates into improved performance: nominal strength rises from 47.9 to 67.4 MPa. Nanoindentation gives a fusion-zone reduced modulus of 82.8 GPa (Young's modulus 82.0 GPa), versus a calculated 0 K Voigt-Reuss-Hill value of 71.4 GPa. The present work establishes a transferable CALPHAD, first-principles, and experiment workflow for rational interlayer selection in dissimilar-metal joining.

cond-mat.mtrl-sci↗
arXiv2026

Agent System Operations: Categorization, Challenges, and Future Directions

As the reasoning capabilities of Large Language Models (LLMs) continue to advance, LLM-based agent systems offer advantages in flexibility and interpretability over traditional systems, garnering increasing attention. However, despite the widespread research interest and industrial application of agent systems, these systems, like their traditional counterparts, frequently encounter anomalies. These anomalies lead to instability and insecurity, hindering their further development. Therefore, a comprehensive and systematic approach to the operation and maintenance of agent systems is urgently needed. Unfortunately, current research on the operations of agent systems is sparse. To address this gap, we have undertaken a survey on agent system operations with the aim of establishing a clear framework for the field, defining the challenges, and facilitating further development. Specifically, this paper begins by systematically defining anomalies within agent systems, categorizing them into intra-agent anomalies and inter-agent anomalies. Next, we introduce a novel and comprehensive operational framework for agent systems, dubbed Agent System Operations (AgentOps). We provide detailed definitions and explanations of its four key stages: monitoring, anomaly detection, root cause localization, and resolution.

cs.MA↗
arXiv2026

UniTexture: Cross-Task Universal Adversarial Textures for Vision-Language-Action Models

Vision-Language-Action (VLA) models have emerged as generalist robotic policies capable of following diverse language instructions and performing a wide range of manipulation tasks. However, their direct control over embodied agents also exposes them to adversarial interference that may cause unsafe physical behaviors. Existing attacks on robotic policies are typically optimized for a single task or instruction, leaving the cross-task vulnerabilities of multitask VLAs largely unexplored. We introduce UniTexture, a cross-task universal adversarial texture attack that uses a single textured 3D object to induce targeted deviations in VLA action predictions across multiple tasks. UniTexture backpropagates gradients from the policy's action outputs to surface texture parameters through a differentiable renderer. It jointly optimizes the shared texture over a distribution of tasks, instructions, states, and viewpoints using a targeted action-space objective, steering predicted actions toward attacker-defined targets without optimizing a separate texture for each task. We evaluate UniTexture on OpenVLA and $π_{0.5}$ across diverse manipulation tasks and multiple evaluation settings. UniTexture reduces the mean task success rate from 90.0% under benign conditions to 48.4% under attack, induces target-aligned action shifts, and further exhibits cross-suite and cross-model transfer without re-optimization. Together, these findings reveal shared cross-task vulnerabilities in multitask VLAs that can be systematically exploited through a single adversarial surface texture.

cs.CV↗
arXiv2026

MSP-Net: Manifold-Guided Spectral Prompt Network for Hyperspectral Object Tracking

Hyperspectral object tracking leverages abundant spectral information to provide unique advantages for target discrimination in complex scenes. However, existing methods typically treat hyperspectral images as multi-channel extensions of RGB images, performing feature fusion in fixed band order. This approach leads to models dependent on specific sensor configurations while neglecting manifold relationships between bands, making generalization to heterogeneous sensors difficult. Moreover, the discriminative contribution of bands dynamically changes with target attributes and scene variations, further limiting the representational capacity of static fusion strategies. To address this, we propose the Manifold-Guided Spectral Prompt Network (MSP-Net). This network first reconstructs band relationships and forms adaptive spectral grouping through graph-driven manifold routing, then jointly integrates grouped spectral statistics with template appearance to construct target-related dynamic conditional prompts, enhancing target features while suppressing background interference. Furthermore, as tracking progresses, spectral conditions continuously evolve based on intermediate target representations, enabling target prompts to adapt in real-time to appearance and scene changes. Meanwhile, reliable historical states are used to constrain target localization and scale fluctuations, significantly improving temporal stability in cross-sensor tracking. Experiments on HOT2020 and HOT2023 demonstrate that MSP-Net achieves AUC and Precision exceeding 0.80 and 0.96, respectively, exhibiting exceptional robustness under heterogeneous sensors, target deformation, and complex background conditions. The code will be released at https://github.com/GGML668897/MSP-Net.

cs.CV↗
arXiv2026

SAMOT: State-Aware Step Modulation and Optimal Transport Matching for Audio-Visual Instance Segmentation

Audio-Visual Instance Segmentation (AVIS) aims to simultaneously classify, segment, and track sounding objects within video sequences. Unlike Audio-Visual Semantic Segmentation (AVS), AVIS involves instance-level modeling across longer video sequences, introducing two key challenges: (1) complex modality-state changes disrupt long-range modeling, and (2) substantial structural and distributional discrepancies between modalities hinder precise instance-level association. Existing methods rely on fixed-step Transformers and recursive Mamba models, lacking adaptability to modality-state changes. In addition, methods performing implicit matching ignore the inherent distributional inconsistencies. To address these issues, we propose a framework with Adaptive Dynamic Step Modulation (ADSM) and Optimal Transport-based Matching Modulation (OT-MM). ADSM adaptively modulates Mamba step sizes using temporal variation, cross-modal discrepancy, and historical context, balancing rapid response to modality-state changes with stable long-range modeling. OT-MM explicitly formulates instance-level cross-modal matching as an entropy-regularized optimal transport problem solved via log-domain Sinkhorn iterations, and further enforces distribution-level coherence with an MMD regularizer. Extensive experiments demonstrate state-of-the-art performance on the AVIS benchmark (+3.76 FSLA, +2.75 HOTA, +2.58 mAP), verified through comprehensive qualitative visualizations. The code and model are available at https://github.com/happylife-pk/SAMOT.

cs.MM↗
arXiv2026

Hear to See: Discerning Stateful Listening for Audio-Visual Instance Segmentation

Audio-visual instance segmentation (AVIS) requires accurately identifying and tracking individual sounding objects with pixel-level masks. Existing methods struggle to match overlapping acoustic events with visual instances and handle asynchronous audio-visual dynamics. Therefore, two critical questions arise: how can a model establish precise correspondence between overlapping sound sources and visual instances, and how can a model maintain robust tracking when audio and visual signals are temporally misaligned?This paper proposes Hear to See (H2S), addressing these challenges through two mechanisms. The Acoustic-Semantic Projector (ASP) disentangles mixed audio and establishes hierarchical correspondence from semantic to spatial domains. The Asynchronous Dynamics Modulator (ADM) adaptively adjusts state transitions via audio-modulated Mamba, prioritizing current information during dynamic variations and maintaining continuity in stable periods.Experiments on AVISeg show H2S achieves SOTA performance, attaining 48.54 mAP with a COCO pretrained ResNet50 and surpassing the previous by 7.8\%. The code will be open-sourced once the paper is accepted. The source code will be publicly available at https://github.com/leiyeliu/H2S.

cs.MM↗
arXiv2026

ActFovea: Runtime Safeguarding for VLA Policies via Spatiotemporal Visual-Action Consistency

Vision-language-action (VLA) policies achieve strong performance in robotic manipulation but remain vulnerable to runtime disturbances that break the temporal alignment among visual observations, robot states, and executed actions. We introduce ActFovea, a plug-and-play safeguarding framework that detects and mitigates such failures without retraining or modifying the underlying VLA policy. ActFovea uses robot kinematics, proprioceptive states, and recent actions to construct action-conditioned foveated regions that retain contact-relevant areas and predicted motion corridors while suppressing task-irrelevant visual content. It detects runtime risks by evaluating whether visual motion and observation freshness remain consistent with geometric, proprioceptive, and action transitions. For recoverable disturbances, ActFovea constructs disturbance-specific candidate observations and accepts a recovery only after verifying the resulting action chunk. When stale or replayed observations make reliable recovery impossible, it invokes a bounded safe-failure procedure. In closed-loop evaluations of $π_0$ across multiple LIBERO suites, ActFovea increases success under localized visual overlays from 49.3\% to 90.3\%, closing 93.7\% of the gap to clean performance. It further improves success under action drift and visual delay by 7.0 and 9.8 percentage points, respectively, while preserving clean-task performance. Under frozen-observation replay, ActFovea triggers timely safe failure in all trials, with no unprotected failures. These results demonstrate that spatiotemporal visual-action consistency provides an effective basis for runtime safeguarding of VLA policies.

cs.RO↗
arXiv2026

Don't Predict, Prioritize: Rethinking GPU Reliability Assessment

The reliability of Graphics Processing Units (GPUs) is a criticalbottleneck for modern large-scale AI infrastructure, where a sin-gle node failure can disrupt synchronous training jobs and causesignificant financial losses. While predictive maintenance is widelyused in other hardware domains, we demonstrate that accuratelypredicting the exact timing of GPU failures is inherently difficult.Through an in-depth analysis of telemetry data from a productioncluster, we find that major GPU failures, including Double Bit Er-rors (DBEs) and GPU Lost events, exhibit strong stochasticity andlow signal-to-noise ratios in time-series telemetry, which makesconventional time-based prediction ineffective. This insight motivates a paradigm shift: instead of attempting topredict the absolute timing of a failure, we propose a more robustapproach focused on ranking nodes by their relative failure risk. Wepropose HeaRank (Health Rank), a Learning-to-Rank (LTR) frame-work that leverages stable historical failure patterns to computea global risk ranking of GPU nodes. Evaluated on a production-scale cluster with thousands of GPUs, HeaRank achieves an AUCof 0.83, significantly outperforming both heuristic baselines andstate-of-the-art ranking algorithms. In online deployment, HeaRanksuccessfully captures 64% of future failures within the top 5% ofranked nodes, compared to only 21% by the incumbent productionsystem. These results suggest that relative risk ranking can serveas a robust alternative in environments where absolute failure pre-diction is inherently limited. Our work highlights the importanceof risk-aware scheduling and proactive resource management inmodern GPU clusters.

cs.DC↗
arXiv2026

Fast, Slow, and Tool-augmented Thinking for LLMs: A Review

Large Language Models (LLMs) have demonstrated remarkable progress in reasoning across diverse domains. However, effective reasoning in real-world tasks requires adapting the reasoning strategy to the demands of the problem, ranging from fast, intuitive responses to deliberate, step-by-step reasoning and tool-augmented thinking. Drawing inspiration from cognitive psychology, we propose a novel taxonomy of LLM reasoning strategies along two knowledge boundaries: a fast/slow boundary separating intuitive from deliberative processes, and an internal/external boundary distinguishing reasoning grounded in the model's parameters from reasoning augmented by external tools. We systematically survey recent work on adaptive reasoning in LLMs and categorize methods based on key decision factors. We conclude by highlighting open challenges and future directions toward more adaptive, efficient, and reliable LLMs.

cs.CL↗
arXiv2026

AdaSurvMamba: Dynamic Fusion and Semantic Scanning for Multimodal Survival Analysis

Multimodal survival analysis utilizing whole slide images (WSIs) and genomic profiles is fundamental for cancer prognosis. Recently, state-space models like Mamba have emerged as powerful tools for sequence modeling. However, translating this success to complex multimodal tasks is hindered by two critical limitations. First, conventional fusion strategies assume a static multimodal interaction strength, ignoring the fluctuating diagnostic importance of each modality across different patients and local regions. Second, the standard Mamba architecture processes tokens along predefined physical paths. This rigid scanning disrupts the semantic continuity of spatially scattered medical features and exacerbates long-range decay. To address these challenges, we introduce AdaSurvMamba as a novel adaptive framework for multimodal survival analysis. The framework features a Dual-Scale Importance-Aware Reconstruction (DSIR) module to dynamically modulate cross-modal interaction strength. It evaluates diagnostic importance at both the sequence and token levels to reconstruct the input representations. Furthermore, we propose a Semantic Aggregation Scanning (SAS) module to overcome contextual fragmentation. The SAS module dynamically reorganizes discrete tokens into semantically continuous sequences via a shared prototype pool. It explicitly modulates the state transition step size using global modality context and semantic priors to adaptively control the information absorption rate. Experiments across five TCGA cohorts demonstrate consistent gains over existing methods. Code is available at https://github.com/zjlGO/AdaSurvMamba.

cs.LG↗