arXiv subjects

Qi Zeng

Publications and source records attributed to Qi Zeng.

At least 19 recordsLinked to original sources

arXiv2026

Interactive 3D Cutaway Visualization for Subsurface Target Localization in Robot-Assisted Surgery

Purpose: Blended augmented reality (AR) overlays can provide ambiguous depth cues in which subsurface anatomy floats above the tissue surface. We investigate whether an interactive 3D cutaway visualization can improve robot-assisted target localization. Methods: We propose Virtual Box, an interactive 3D cutaway interface that allows users to independently manipulate the viewing perspective to inspect subsurface targets. In a controlled, counterbalanced within-subject tissue phantom study, 15 engineering students used Virtual Box and a depth-enhanced blended AR overlay to position a da Vinci instrument at virtual targets in a water tank. We used registered 3D transrectal ultrasound to measure 3D localization error and depth error. We also collected completion time and subjective ratings. Results: Virtual Box reduced mean 3D error from 12.92 to 4.96 mm (61.6% reduction) and depth error from 9.48 to 2.07 mm (78.2% reduction; both p < 0.001). Spatial understanding, depth perception, and confidence ratings were higher (all unadjusted p < 0.001), but completion time increased from 21.64 to 32.58 s (p = 0.008). Overall workload did not differ significantly. Conclusion: Virtual Box improved localization accuracy and perceived spatial understanding in this controlled task, with additional completion time, supporting further evaluation for surgical navigation.

cs.RO↗

arXiv2026

SemICP: Semantic Non-Rigid Point Cloud Registration with Elastic Energy Regularization

Purpose: Accurate point cloud registration is essential in computer-aided interventions (CAI) to align multi-modal medical images for intraoperative guidance. Classical methods, such as Iterative Closest Point (ICP), remain attractive for their explainability and minimal training requirements, but typically ignore anatomical semantics and biomechanical properties during regularization. Methods: We present Semantic ICP (SemICP), a novel non-rigid point cloud registration framework that combines semantically informed point matching with linear elastic energy regularization. Semantic labels are used to improve correspondence matching by constraining correspondences to be anatomically consistent. A novel control-point deformation representation with linear-elastic energy regularization is introduced to encourage biomechanically plausible deformations. SemICP was evaluated on a simulation phantom and four datasets against established baselines. It was also tested with labels from AI-based segmentation in a fully automatic segmentation-registration pipeline. Results: On the finite element phantom, SemICP recovered 86% of the interior displacement from surface observations alone. On the real datasets, SemICP achieved the lowest target registration error on Liver MR-US and the lowest surface distances on TORS, and ranked second on AbdominalMR-CT and HippocampusMR, where NR-ICP achieved lower surface distances. The fully automatic registration pipeline was shown to be effective for US-MR registration and to improve the alignment of expert-annotated structures. Conclusion: SemICP improves interior accuracy at competitive surface accuracy by combining semantic correspondence constraints and linear energy regularization. Combined with AI-based segmentation, SemICP provides an effective pipeline for multi-modal registration in CAI.

cs.CV↗

arXiv2026

Can Image Agents Complete Visual Commissions? A Benchmark and Modular Framework

Visual commissions require image agents to integrate heterogeneous source materials, preserve requirements across dependent stages, and produce coordinated deliverables. Existing evaluations examine specific capabilities and execution settings, leaving end-to-end commission completion less understood. We introduce Visual Commission Bench (VCB), comprising 600 tasks across 12 categories and three complementary tiers, from capability-focused tasks to complete commissions. VCB uses 5,039 requirement checks and separate visual quality ratings to assess content correctness, visual realization, constraint preservation, and delivery completeness without prescribing an internal workflow. We present V-Protocol, a reference agent that dynamically composes reusable task, cognitive, and expression modules at inference time. Task modules organize delivery, cognitive modules derive content, and expression modules guide visual realization. The modules exchange structured intermediate artifacts, while a shared execution harness tracks artifact revisions and dependencies, manages tool execution, and checks completion. With the same controller and image generator as the agent baselines, V-Protocol achieves 69.0\% strict task success on VCB, compared with 42.0\% for SCOPE. On Mind-Bench, strict accuracy increases from 39.2\% to 45.4\%.

cs.CV↗

arXiv2026

Adaptive Personalization Under Preference Epistemic Uncertainty

Human preferences change over time: an established preference may remain valid over time, become uncertain as context shifts, or be explicitly revised. Effective personalization requires conversational AI to adapt its response strategy to evolving preferences rather than relying on passive memory retrieval. We formalize this capability as adaptive personalization under epistemic uncertainty. A human preference study further confirms that people favor state-contingent response strategies as preference uncertainty changes. To evaluate whether language models mirror this adaptation, we create a multilingual benchmark spanning 250 personas, 5 languages, and 25K conversational turns that decouples strategy selection from response execution. Across eight closed- and open-weight models, we uncover a recognition-generation gap: models reliably identify appropriate strategies when selecting among candidate options, but collapse during open-ended generation, suffering a 35-46% drop in strategy calibration. Crucially, this failure is systematic: models default to exploit-first behavior, over-relying on existing preferences regardless of preference epistemic uncertainty. We show this failure is addressable: while prompting interventions fail, targeted training that supervises models' internal strategy representations toward state-conditional human preferences narrows the user acceptability generalization gap by a third on out-of-distribution personas and conversational styles.

cs.CL↗

arXiv2026

MIMESIS: Learning User Simulators as Training Environments for Interactive Agents

Training and evaluating interactive language agents typically requires rich user interactions, yet collecting human feedback is expensive and difficult to scale. Simulated users offer a scalable alternative, but they must both resemble real user behavior and provide useful learning experiences for agents. In contrast, most agent-training frameworks rely on off-the-shelf assistant LLMs, whose helpfulness can make them overly cooperative, explicit, and behaviorally homogeneous compared with real users. We introduce MIMESIS, a purpose-built user simulator trained on human conversations with explicit reasoning supervision and 13 realistic behavioral patterns derived from real user interactions. Empirically, our 9B model achieves a SOUL-Index of 65.7, surpassing the strongest frontier model. Compared with Claude-Opus-5, the strongest baseline on RealUserSim and SimulatorArena, MIMESIS improves behavioral fidelity by 13.4 points and reduces Turing distance by 3.6 points, respectively. We then freeze the simulator and train an agent by interacting with the frozen simulator using multi-turn reinforcement learning. Across eight environments, training with MIMESIS yields better agent performance than training with GPT-5.5 under all nine unseen user simulators, demonstrating stronger generalization to new user simulators. Moreover, we propose Coached On-Policy Self-Distillation (CSD), which leverages simulator-generated private reasoning traces and subsequent utterances as feedback on how well the agent addresses user needs. A coach converts this information into concise coaching notes that describe how the agent can better anticipate user needs and adapt its behavior over the course of an interaction. CSD turns this feedback into dense, token-level supervision beyond sparse task rewards, yielding further gains across all nine evaluation user models.

cs.AI↗

arXiv2026

Sharpening Tax in Post-Training

An emerging hypothesis about reinforcement learning (RL) post-training of large language models (LLMs) is that it merely sharpens existing behaviors of a base model, improving single-shot accuracy at the cost of solution coverage. Although this trade-off has been observed in math and coding tasks, it need not extend to agentic tasks, where multi-turn tool use and interaction may require capabilities newly acquired during post-training. Our surprising finding is that pre-trained LLMs, equipped with a light inference harness, can serve as capable agents. Despite far lower accuracy (pass@1), they often surpass their post-trained counterparts in solution coverage (pass@K) given a sufficient test-time budget. We further analyze the underlying mechanism and show that post-training pushes tasks toward two extremes, always solved or never solved, and thereby improves sampling efficiency and consistency at the cost of solution coverage. To measure this cost, we propose Sharpening Tax, a diagnostic metric that quantifies the loss in test-time scalability after post-training. Across 14 base/post-trained model pairs from four families and three agentic benchmarks (42 cases in total), the tax is prevalent in most settings, can be estimated from a few rollouts, and correlates well with other metrics. Finally, we present posterior-tempered group sampling (PTGS), a simple plug-and-play Bayesian sampler that adapts the sampling temperature per prompt to its estimated difficulty. Applied during RL training in two agentic environments, PTGS pays a smaller tax than the fixed-temperature baseline, solving more tasks under repeated sampling while also improving single-shot accuracy.

cs.AI↗

arXiv2026

InfoAgent: Traceable Generation and Repair of Evidence-Grounded Infographics

Reliable infographic generation requires facts, symbols, and visual relations to remain consistent through rendering and revision. Correcting one element also requires tracking its supporting evidence and the dependencies affected by the change. We present \textbf{InfoAgent}, a training-free framework for \emph{evidence-bound visual-symbolic program synthesis}. Its Infographic Visual Description (IVD) records factual payloads, evidence provenance, execution routes, and verification obligations in a typed dependency graph. Retrieved design priors guide compilation, and layered execution combines raster synthesis with editable symbolic and binding objects while retaining their traces. Dependency-aware repair localizes corrections, rechecks affected dependencies, and requires protected obligations to remain satisfied under the declared checkers. Unresolved obligations remain explicit. On IGenBench, InfoAgent achieves 93.0 Q-ACC and 59.0 I-ACC. We also introduce InfoGraphicBench-Evidence, where complete-checklist pass rates on 200 test requests increase from 21.5\% for Same-IVD Prompt to 23.5\% for the initial layered output and 28.5\% after repair, using the same evidence and initial IVD. On 120 audited repair cases, localized repair edits 12.4\% of the canvas on average, compared with 67.3\% for global regeneration.

cs.CV↗

arXiv2026

The TNG50-SKIRT Atlas: Spatially resolved synthetic galaxies from the ultraviolet to the submillimetre (DR2)

We present the second data release (DR2) of the TNG50-SKIRT Atlas (TSA), a library of synthetic, spatially resolved galaxy observables. The atlas is constructed by post-processing a stellar-mass-complete ($10^{9.8}~{\text{M}}_\odot < M_\star < 10^{12}~{\text{M}}_\odot$) sample of 1154 $z=0$ galaxies from the TNG50 cosmological hydrodynamical simulation with the Monte Carlo radiative transfer code SKIRT. Compared to the first release, TSA DR2 extends the wavelength coverage from the ultraviolet to the submillimetre, including dust emission, and incorporates updated stellar population models together with an improved treatment of dust-enshrouded star-forming regions. The atlas provides spatially resolved spectral energy distributions, broadband images, and physical property maps for multiple viewing orientations, as well as a catalogue of integrated properties enabling direct comparison with unresolved observations. We validate the data products through extensive quality control, including an assessment of Monte Carlo noise, and demonstrate their internal consistency using diagnostic relations between luminosities and star formation rates. TSA DR2 provides a versatile resource for studies of dust attenuation and emission, star formation tracers, galaxy morphology, and multi-wavelength scaling relations across spatial scales. The atlas and associated data products are publicly released and are intended to support a wide range of observationally oriented studies of galaxy evolution.

astro-ph.GA↗

arXiv2026

For-Value: Efficient Forward-Only Data Valuation for finetuning LLMs and VLMs

Data valuation is essential for enhancing the transparency and accountability of large language models (LLMs) and vision-language models (VLMs). However, existing methods typically rely on gradient computations, making them computationally prohibitive for billion-parameter models and precluding batch parallelization. In this work, we introduce For-Value, a forward-only data valuation framework that enables efficient batch-scalable value estimation while maintaining effectiveness. Leveraging the expressive power of pretrained LLMs/VLMs, we theoretically demonstrate that data valuation can be captured by the alignment between the final hidden representations and prediction errors at the last layer. In light of this insight, For-Value computes data value using a simple closed-form expression with a single forward pass, eliminating the need for costly backpropagation and enabling efficient batch calculating at scale. Extensive experiments show that For-Value matches or outperforms gradient-based baselines in detecting influential data and mislabeled data, while achieving significant efficiency improvements.

cs.CL↗

arXiv2026

NavThinker: Action-Conditioned World Models for Coupled Prediction and Planning in Social Navigation

Social navigation requires robots to act safely in dynamic human environments. Effective behavior demands thinking ahead: reasoning about how the scene and pedestrians evolve under different robot actions rather than reacting to current observations alone. This creates a coupled prediction-planning challenge, where robot actions and human motion mutually influence each other. To address this challenge, we propose NavThinker, a future-aware framework that couples an action-conditioned world model with on-policy reinforcement learning. The world model operates in the Depth Anything V2 patch feature space and performs autoregressive prediction of future scene geometry and human motion; multi-head decoders then produce future depth maps and human trajectories, yielding a future-aware state aligned with traversability and interaction risk. Crucially, we train the policy with DD-PPO while injecting world-model think-ahead signals via: (i) action-conditioned future features fused into the current observation embedding and (ii) social reward shaping from predicted human trajectories. Experiments on single- and multi-robot Social-HM3D show state-of-the-art navigation success, with zero-shot transfer to Social-MP3D and real-world deployment on a Unitree Go2, validating generalization and practical applicability. Webpage: https://hutslib.github.io/NavThinker.

cs.RO↗

arXiv2026

Atlas-Assisted Segment Anything Model for Fetal Brain MRI (FeTal-SAM)

This paper presents FeTal-SAM, a novel adaptation of the Segment Anything Model (SAM) tailored for fetal brain MRI segmentation. Traditional deep learning methods often require large annotated datasets for a fixed set of labels, making them inflexible when clinical or research needs change. By integrating atlas-based prompts and foundation-model principles, FeTal-SAM addresses two key limitations in fetal brain MRI segmentation: (1) the need to retrain models for varying label definitions, and (2) the lack of insight into whether segmentations are driven by genuine image contrast or by learned spatial priors. We leverage multi-atlas registration to generate spatially aligned label templates that serve as dense prompts, alongside a bounding-box prompt, for SAM's segmentation decoder. This strategy enables binary segmentation on a per-structure basis, which is subsequently fused to reconstruct the full 3D segmentation volumes. Evaluations on two datasets, the dHCP dataset and an in-house dataset demonstrate FeTal-SAM's robust performance across gestational ages. Notably, it achieves Dice scores comparable to state-of-the-art baselines which were trained for each dataset and label definition for well-contrasted structures like cortical plate and cerebellum, while maintaining the flexibility to segment any user-specified anatomy. Although slightly lower accuracy is observed for subtle, low-contrast structures (e.g., hippocampus, amygdala), our results highlight FeTal-SAM's potential to serve as a general-purpose segmentation model without exhaustive retraining. This method thus constitutes a promising step toward clinically adaptable fetal brain MRI analysis tools.

cs.CV↗

arXiv2026

Non-Parametric Attenuation Curves in Local Star-Forming Galaxies: Geometry Effect, Dust Evolution, and ISS

We introduce a non-parametric approach, the Stellar Population Synthesis with Equivalent Widths (SEW) method, to reconstruct spectrally-resolved attenuation curves for 169,568 star-forming galaxies from the Sloan Digital Sky Survey Data Release 7 (SDSS DR7). Composite attenuation curves, stacked by stellar mass and inclination, reveal systematic trends: a higher stellar mass correlates with steeper slopes (lower $R_V$), while edge-on galaxies exhibit flatter curves due to geometric saturation effects. This flattening occurs because, as optical depth increases along the line of sight, the observed light becomes increasingly dominated by emission from the outer, less obscured layers of the galaxy. Using a simplified radiative transfer treatment based on a uniform dust-star mixture, we find the inclination-dependent slope variations are consistent with geometric effects, whereas the mass-dependent slope steepening indicates evolution in intrinsic dust properties, suggesting feedback-driven grain fragmentation in massive galaxies. Additionally, intermediate-scale structures (ISSs) are tentatively identified in the attenuation curves at approximately 4870, 6370, and 7690 Å. These results illustrate how the interplay among dust-star geometry, grain size evolution, and the galactic environment shapes attenuation curves.

astro-ph.GA↗

arXiv2025

Explicit Temporal-Semantic Modeling for Dense Video Captioning via Context-Aware Cross-Modal Interaction

Dense video captioning jointly localizes and captions salient events in untrimmed videos. Recent methods primarily focus on leveraging additional prior knowledge and advanced multi-task architectures to achieve competitive performance. However, these pipelines rely on implicit modeling that uses frame-level or fragmented video features, failing to capture the temporal coherence across event sequences and comprehensive semantics within visual contexts. To address this, we propose an explicit temporal-semantic modeling framework called Context-Aware Cross-Modal Interaction (CACMI), which leverages both latent temporal characteristics within videos and linguistic semantics from text corpus. Specifically, our model consists of two core components: Cross-modal Frame Aggregation aggregates relevant frames to extract temporally coherent, event-aligned textual features through cross-modal retrieval; and Context-aware Feature Enhancement utilizes query-guided attention to integrate visual dynamics with pseudo-event semantics. Extensive experiments on the ActivityNet Captions and YouCook2 datasets demonstrate that CACMI achieves the state-of-the-art performance on dense video captioning task.

cs.CV↗

arXiv2025

DMTrack: Deformable State-Space Modeling for UAV Multi-Object Tracking with Kalman Fusion and Uncertainty-Aware Association

Multi-object tracking (MOT) from unmanned aerial vehicles (UAVs) presents unique challenges due to unpredictable object motion, frequent occlusions, and limited appearance cues inherent to aerial viewpoints. These issues are further exacerbated by abrupt UAV movements, leading to unreliable trajectory estimation and identity switches. Conventional motion models, such as Kalman filters or static sequence encoders, often fall short in capturing both linear and non-linear dynamics under such conditions. To tackle these limitations, we propose DMTrack, a deformable motion tracking framework tailored for UAV-based MOT. Our DMTrack introduces three key components: DeformMamba, a deformable state-space predictor that dynamically aggregates historical motion states for adaptive trajectory modeling; MotionGate, a lightweight gating module that fuses Kalman and Mamba predictions based on motion context and uncertainty; and an uncertainty-aware association strategy that enhances identity preservation by aligning motion trends with prediction confidence. Extensive experiments on the VisDrone-MOT and UAVDT benchmarks demonstrate that our DMTrack achieves state-of-the-art performance in identity consistency and tracking accuracy, particularly under high-speed and non-linear motion. Importantly, our method operates without appearance models and maintains competitive efficiency, highlighting its practicality for robust UAV-based tracking.

eess.SY↗

arXiv2025

A novel method of half-life determination for highly charged ions based on isochronous mass spectrometry

The lifetime of the isomeric state in fully stripped 94Ru44+ ions has been measured using isochronous mass spectrometry (IMS) at the experimental Cooler Storage Ring (CSRe) of the Heavy Ion Research Facility in Lanzhou (HIRFL). Previously, the isomeric lifetime was determined by analyzing the decay time points of individual decay events. In this paper, we present a novel approach to determine the isomeric lifetime based on the survival time of ions obtained from IMS. The survival lifetimes of the ground and isomeric states of 94Ru44+ were measured to be s and s in the laboratory, respectively. Given that the ground state of 94Ru44+ has a natural lifetime of approximately 75 min, its survival lifetime in the experimental setup was predominantly determined by the beam-loss lifetime, including interactions with residual gas in the storage ring and carbon foil of the detector. In contrast, the survival lifetime of 94mRu44+ was governed by its intrinsic nuclear lifetime and additional beam-loss effects. The nuclear decay lifetime of 94mRu44+ was extracted through differential survival lifetime analysis between ground and isomeric states, under the assumption that the beam-loss lifetimes for both quantum systems are identical. Using this novel methodology, the lifetime measured in the laboratory frame was s. After relativistic time-dilation corrections, the corresponding rest-frame half-life was calculated to be s. This result demonstrates excellent consistency with previous experimental results, validating the reliability of the new method. This method is suitable for determining half-lives of highly charged ions in the range of several tens of microseconds to milliseconds using IMS.

physics.ins-det↗

arXiv2025

Global strong solutions to the frame hydrodynamics for biaxial nematic phases

In this article, we consider the frame hydrodynamics of biaxial nematic phases, a coupled system between the evolution of the orthonormal frame and the Navier--Stokes equation, which is derived from a molecular-theory-based dynamical tensor model about two second-order tensors. In two and three dimensions, we establish global well-posedness of strong solutions to the Cauchy problem of frame hydrodynamics for small initial data. The key ingredient of the proof relies on estimates of nonlinear terms with rotational derivatives on $SO(3)$, together with the dissipative structure of the frame hydrodynamics.

math.AP↗

arXiv2025

SDSS-IV MaNGA: Physical Origins of Double-Peaked Narrow Emission-Line Spaxels in Barred Galaxies

The physical origins of double-peaked narrow emission-line spaxels (DPSs) in barred galaxies are explored through the analysis of a sample of 72 barred double-peaked emission-line galaxies (DPGs) extracted from the MaNGA dataset. In this study, we examine two potential scenarios: the gas inflow along the bar and the formation of a bar-induced gaseous nuclear ring. By applying a classical galactic dynamics model, we calculate the radii and rotational velocities of the nuclear rings for all barred DPGs, and compare them with the observed properties of their DPSs. Our analysis reveals a significant correlation between the predicted radii of the nuclear rings and the maximum centric distances of the DPSs, as well as a marginal correlation between the predicted rotational velocities of the nuclear rings and the observed maximum velocity differences of the DPSs. These findings provide strong evidence to support the hypothesis that the DPSs of a barred DPG in MaNGA primarily originate from the convolution of the PSF effect with its bar-induced fast-rotating gaseous nuclear ring.

astro-ph.GA↗

arXiv2025

Advances in Automated Fetal Brain MRI Segmentation and Biometry: Insights from the FeTA 2024 Challenge

Accurate fetal brain tissue segmentation and biometric analysis are essential for studying brain development in utero. The FeTA Challenge 2024 advanced automated fetal brain MRI analysis by introducing biometry prediction as a new task alongside tissue segmentation. For the first time, our diverse multi-centric test set included data from a new low-field (0.55T) MRI dataset. Evaluation metrics were also expanded to include the topology-specific Euler characteristic difference (ED). Sixteen teams submitted segmentation methods, most of which performed consistently across both high- and low-field scans. However, longitudinal trends indicate that segmentation accuracy may be reaching a plateau, with results now approaching inter-rater variability. The ED metric uncovered topological differences that were missed by conventional metrics, while the low-field dataset achieved the highest segmentation scores, highlighting the potential of affordable imaging systems when paired with high-quality reconstruction. Seven teams participated in the biometry task, but most methods failed to outperform a simple baseline that predicted measurements based solely on gestational age, underscoring the challenge of extracting reliable biometric estimates from image data alone. Domain shift analysis identified image quality as the most significant factor affecting model generalization, with super-resolution pipelines also playing a substantial role. Other factors, such as gestational age, pathology, and acquisition site, had smaller, though still measurable, effects. Overall, FeTA 2024 offers a comprehensive benchmark for multi-class segmentation and biometry estimation in fetal brain MRI, underscoring the need for data-centric approaches, improved topological evaluation, and greater dataset diversity to enable clinically robust and generalizable AI tools.

cs.CV↗