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Yichen Zhang

Publications and source records attributed to Yichen Zhang.

At least 19 recordsLinked to original sources
arXiv2026

RAVEN: A Unified Active SLAM System for Rapid Autonomous Visual Exploration with Aerial Robots under Localization Uncertainty

Active simultaneous localization and mapping (SLAM) addresses the problem of planning robot motion to build complete and accurate maps of unknown environments through efficient exploration while maintaining high localization accuracy. This raises a dilemma between exploring unknown spaces to expand coverage and exploiting visited regions to perform active loop closures. Despite numerous prior works on ground robots, aerial robots with high agility and constrained sensor suites face more significant localization drift and thereby demand dedicated active SLAM planning. Moreover, a unified strategy that adapts performance based on task-specific priorities remains an open question. In this article, we introduce RAVEN, a unified active SLAM framework for Rapid Autonomous Visual ExploratioN using quadrotors, which systematically accounts for localization uncertainty. The framework starts with identifying frontiers and active loop closure candidates, from which global tour candidates are generated. These candidates are evaluated using a utility vector that separately quantifies exploration efficiency and localization accuracy, and the final tour is selected according to a prioritization parameter ranging from $[0,1]$. Finally, a perception-aware navigation planner generates smooth execution trajectories that minimize visual state estimation error based on the online Fisher information field that provides visual localization information in real-time. Benchmark evaluations demonstrate that RAVEN outperforms state-of-the-art active SLAM methods, highlighting its advantages in producing a spectrum of performance across prioritizations. Fully onboard field experiments with a customized stereo-inertial quadrotor further validate its practical applicability in challenging environments. The source code will be released to benefit the community.

cs.RO↗
arXiv2026

Temporal-Difference Calibration of Black-Box Simulators

We develop a derivative-free temporal-difference (TD) framework for calibrating black-box simulators using one-step transitions from a target system and noisy simulator-generated continuation values. The calibration parameter is defined through Bellman orthogonality, recovering the physical parameter under correct simulator specification and yielding a near-optimal $L_2$ approximation of the policy value under misspecification. We propose three TD algorithms based on random-direction finite differences, distinguished by their query costs and reuse of noisy simulator evaluations. We establish almost-sure convergence, last-iterate mean-square error bounds, and central limit theorems for Polyak-Ruppert averages across four query-noise regimes. Our analysis reveals a specification-dependent bias phenomenon: for two algorithms, the finite-difference bias of the TD estimating equation vanishes at the calibration target under correct specification, even when the gradient surrogate remains biased. This permits a fixed perturbation radius and rate exponents approaching $1/2$ in the simulator rollout budget, whereas misspecification generally recovers the classical Kiefer-Wolfowitz rates. We also develop a few-replicate online bootstrap that yields Student-$t$ calibrated confidence intervals under negligible query noise, and identify a covariance mismatch that can invalidate conventional multiplier inference along Markov trajectories. Finally, a second-stage TD recursion corrects the remaining value discrepancy without additional simulator queries.

stat.ML↗
arXiv2026

U-STS-LLM: A Unified Spatio-Temporal Steered Large Language Model for Traffic Prediction and Imputation

Accurate spatio-temporal cellular traffic analysis is fundamental to the reliable operation of modern mobile networks, as it underpins load forecasting for congestion avoidance and missing-data imputation for continuity despite transmission errors. While deeply connected, forecasting and imputation have historically evolved as separate sub-fields. Existing models are typically task-specific, decouple temporal and spatial modeling, and rely on cascaded designs that accumulate errors and latency. Meanwhile, adapting pre-trained LLMs for this domain often leaves spatial structure under-guided and tends to overfit on limited data. To bridge these gaps, we propose U-STS-LLM, a unified framework built on a partially frozen spatio-temporally attentive (PFSTA) LLM backbone. A Dynamic Spatio-Temporal Attention Bias Generator synthesizes a persistent functional graph with transient nodal states to steer attention, while a lightweight parallel branch yields a stable local estimate fused via a Gated Adaptive Fusion, confining the LLM to residual correction. Trained under a unified multi-task objective, the model attains state-of-the-art results with low trainable-parameter overhead. Extensive experiments on real-world cellular traffic datasets demonstrate the effectiveness and transferability of U-STS-LLM, achieving average RMSE improvements of 23.7% over the strongest baselines in forecasting and 13.1% in imputation, with further gains exceeding 33% in zero-shot transfer to unseen network regions.

cs.LG↗
arXiv2026

DriveVLA-M0: Failure-Aware Memory Augmentation for Autonomous Driving

Vision-Language-Action (VLA) models have recently emerged as a promising paradigm for end-to-end autonomous driving by enabling unified reasoning across perception, language, and planning. However, existing approaches lack mechanisms to exploit past failures or adapt to distribution shifts, causing the model to persistently underperform on similar scenarios where it has previously failed. In this paper, we propose DriveVLA-M0, a retrieval-augmented VLA with failure-aware latent memory. We construct a latent memory pool that stores failure cases along with their structure scene representations and expert trajectory labels, and design a dedicated Retrieve Model that decouples static road structure and dynamic agent interactions to enable structurally grounded retrieval. At inference time, retrieved cases are injected into the model via a lightweight decoupled LoRA-based test-time training (TTT) mechanism, allowing targeted and scenario-specific correction without modifying the backbone. Extensive experiments on NAVSIMv1 and NAVSIMv2 benchmark demonstrate that our approach consistently outperforms prior methods, achieving 94.1 PDMS on Navtest and 47.0 EPDMS on Navhard with only 26.44 ms TTT backward latency overhead. Furthermore, we show that DriveVLA-M0 scales effectively with additional memory, enabling training-free performance gains through memory expansion. The code is available at https://github.com/ZebinX/DriveVLA-M0.

cs.CV↗
arXiv2026

Physical Canvas: A Vision Tactile World Model For Robot Policy Evaluation And Learning

Evaluating and post-training robot policies through physical interaction is costly and difficult to scale, motivating action-conditioned world models as interactive simulators. However, visually plausible predictions can misrepresent contact dynamics, providing unreliable feedback for policy evaluation and learning. We introduce PhysCanvas, a state-consistent visuo-tactile world model for contact-rich robot manipulation. PhysCanvas unifies multi-view RGB, tactile images, marker displacement, task state, and actions within a unified vision space. All modalities share a video VAE and pretrained generative backbone, allowing optical touch to reuse visual and temporal priors. Motion images derived through forward kinematics and camera projection provide spatially aligned action conditioning, while an initial RGB anchor and updated reference observations guide sequential generation. Our consistency-aware post-training uses the frozen Scene Reward Model, Modality Reward Model, and State Reward Model to supervise scene preservation, visual-tactile compatibility, and agreement with real task outcomes. The resulting world model supports closed-loop policy rollouts for evaluation and provides imagined interactions and rewards for GRPO policy updates. Across four real-world manipulation tasks, when policies are rolled out in the world model, State Reward Model supervision increases the arithmetic mean of task-wise Pearson correlations between world-model and real-world success rates from 0.602 to 0.885. Policy post-training in the reward-guided world model further increases $π_{0.5}$'s mean real-world success rate from a 59.1% SFT baseline to 70.8%. These results demonstrate the value of unified multimodal modeling and consistency-aware post-training for robot policy evaluation and learning. Project page: https://zebinx.github.io/PhysCanvas_HomePage/.

cs.RO↗
arXiv2026

FAUST XXXII. Chemically layered structures and ionisation in the disk and envelope of NGC 1333 IRAS 4C

Recent molecular line observations with high spatial resolutions have revealed vertical chemical stratification in Class I/II disks (i.e. chemical composition changing with height in the vertical direction of the disk), as well as a radial variation of ionisation rates. However, such chemical layered structures in Class 0 disk and envelope systems have been investigated only for limited objects, such as L1527. In the context of the ALMA Large Program FAUST, we report the results of molecular line observations toward the disk and envelope around the low-mass Class 0 protostar NGC 1333 IRAS 4C in the Perseus Molecular Cloud. Previous observations have shown that the disk rotation axis of this object is largely tilted to the line of sight, making it suitable for studying the molecular abundance distribution as a function of disk height. We found that the molecules known as gas density tracers such as C18O and H2CO are distributed around the disk midplane. In addition, molecules known as tracers of photochemical reactions such as CCH and c-C3H2 are distributed in the low-density upper layers of the disk, unlike the distribution in L1527. Moreover, the column density ratio of HCO+/CO derived from the H13CO+ and C18O line emission suggests that the ionisation degree and the ionisation rate vary with the disk radius and they are larger in the outer region of the disk and envelope.

astro-ph.EP↗
arXiv2026

Latent-WAM: Latent World Action Modeling for End-to-End Autonomous Driving

We introduce Latent-WAM, an efficient end-to-end autonomous driving framework that achieves strong trajectory planning through spatially-aware and dynamics-informed latent world representations. Existing world-model-based planners suffer from inadequately compressed representations, limited spatial understanding, and underutilized temporal dynamics, resulting in sub-optimal planning under constrained data and compute budgets. Latent-WAM addresses these limitations with two core modules: a Spatial-Aware Compressive World Encoder (SCWE) that distills geometric knowledge from a foundation model and compresses multi-view images into compact scene tokens via learnable queries, and a Dynamic Latent World Model (DLWM) that employs a causal Transformer to autoregressively predict future world status conditioned on historical visual and ego status representations. Extensive experiments on NAVSIM v2 and HUGSIM demonstrate new state-of-the-art results: 89.3 EPDMS on NAVSIM v2 and 28.9 HD-Score on HUGSIM, surpassing the best prior perception-free method by 3.2 EPDMS with significantly less training data and a compact 104M-parameter model. The code and model weights have been open-sourced at https://github.com/yukki555/Latent-WAM.

cs.CV↗
arXiv2026

Self-Evolving Agents Can Repair Failures but Struggle to Generalize from Them

Experience-driven self-evolution offers a promising path for large language model (LLM) agents to continually improve through interaction. Yet existing approaches often rely on successful trajectories or external evaluation signals, leaving unclear whether agents can evolve when such positive anchors are unavailable. We study this question through Cold-Start Failure-Only Evolution (CFOE), where agents start without accumulated experience and learn only from failed interactions without privileged guidance. Across three self-evolving paradigms, multiple LLM backbones, and four interactive environments, we uncover a consistent repair-generalization gap: failure-derived experience can help repair previously failed tasks, but rarely yields sustained improvements on unseen tasks. Further analysis shows that agents often misinterpret failures, while even verified-correct experience remains difficult to transfer. Relaxing CFOE reveals that verifier feedback and additional interaction provide limited gains, whereas successful trajectories substantially improve transfer. These findings suggest that current self-evolving agents are better at repairing observed failures than building generalizable capabilities from failure.

cs.CL↗
arXiv2026

Write, Execute, Refine: From Skill Follower to Skill Optimizer via Reinforcement Learning from Execution Feedback

Expert-written natural language skills can improve tool-using agents, yet agent-authored skills perform 8--11 points worse than using no skill. While inference-time loops can repair individual skills per task, they leave the underlying generator's refinement capability unimproved. We study how to organize execution experience from intermediate skills into training states for an optimizer. We introduce WER(Write, Execute, and Refine), a multi-phase framework that trains a Skill Optimizer outside a frozen executor. The optimizer proposes skills, a frozen agent executes each repeatedly, and a programmatic verifier scores the outcomes. These scores serve as group-relative rewards and filter for informative, mixed-outcome instances. Matched successful and failed trajectories from these records form the next phase's refinement states, so the optimizer learns from the consequences of its earlier outputs. On BFCL v4 multi-turn and tau2-bench, WER improves average Pass@1 over the no-skill baseline by 7.80 and 6.06 points, respectively. Under an identical refinement workflow, it outperforms the same backbone without optimizer training by 10.02 and 12.50 points. Notably, our trained 4B optimizer achieves 76.63\% on BFCL v4, outperforming all evaluated off-the-shelf general-purpose LLMs in the same role on average. Our code is available at https://github.com/littlepkk/wer-training.

cs.CL↗
arXiv2026

A robust digital receiver architecture for continuous-variable quantum key distribution

Continuous-variable quantum key distribution utilizes advanced digital signal processing techniques to mitigate channel impairments, driving the progress of high-performance quantum secure communications. Specifically, digital heterodyne receivers are commonly implemented using down-conversion algorithms to detect both components simultaneously where the signal is modulated onto an intermediate frequency to avoid low-frequency noise and bypass stringent optical phase-locking requirements. However, a rigorous analysis of the quantum security implications of the digital down-conversion algorithm remains absent. In this paper, we investigate the algorithm process from the perspective of quantum operator evolution, revealing that the frequency folding effect inherent in down-conversion inevitably mixes the image-frequency information into the baseband signal. To address this issue, we propose a novel operator reconstruction detection scheme based on dual-path digital heterodyne receiver. By performing a non-equal weighted combination of the output operators along two optical paths in the digital domain, this scheme perfectly cancels out the image-frequency operator physically. The robust receiver architecture preserves the baseband noise-suppression advantages, inherently achieves perfect mode balancing for asymmetric optical detection losses, and fundamentally closes the image-frequency crosstalk. Practical experiments have validated the feasibility of the proposed architecture and its effective suppression of image-frequency noise, achieving a performance of 11.4 Mbps @ 42 km. This work lays a solid theoretical and algorithmic foundation for future high-performance, highly secure digital continuous-variable quantum key distribution systems.

quant-ph↗
arXiv2026

Low-Metallicity Star Formation Survey in Sh2-284 (LZ-STAR): The Core Mass Function

We present an ALMA 1.3~mm dust continuum study of the dense core mass function (CMF) in Sh2-284, a low metallicity outer Galaxy star-forming complex with $Z\sim1/3$--$1/2~Z_\odot$. The observations cover six far-infrared bright subregions at $\sim$0.65" (3000~au) resolution. We identify a total of 91 candidate dense cores and define a robust catalog of 68 cores with Gaussian fitting. The high-mass CMF above 2.5 Msun is well described by a Salpeter-like slope, with a fiducial forward-modeled value of $α=1.22^{+0.24}_{-0.22}$ for $dN/d\log M\propto M^{-α}$. Together with existing constraints on the initial mass function (IMF) of Sh2-284, the Salpeter-like CMF is consistent with a resemblance between the CMF and IMF shapes in the outer Galaxy environments, suggesting that moderately low metallicity alone does not strongly reshape the high-mass CMF/IMF slope.

astro-ph.GA↗
arXiv2026

A High Resolution View of Three Massive Young Stellar Objects in the Magellanic Clouds

The Magellanic Clouds provide an excellent laboratory to study massive star formation beyond typical Galactic environments. Using ALMA band 7 long-baseline observations, we present the highest resolution (approximately 0.03", $1500{\rm\:au}$) view to date of three massive young stellar objects (MYSOs) in the Magellanic Clouds, i.e., Lh03 and Lh09 in the LMC, and S07 in the SMC. The 0.87 mm continuum, with a sensitivity of $\sim30\:μ$Jy beam$^{-1}$, reveals varying levels of fragmentation, from a single compact source (S07), a few (Lh03), to a cluster of compact sources (Lh09). In Lh09, we identified 17 core candidates with lowest measured mass of 2.6 Msun, and their de-projected separations peak around 6000~au. All three targets exhibit high velocity CO(3--2) outflows, including detection of a collimated bipolar jet driven by a core in Lh09. Spectral lines from HCO$^+$, SO, and SO$_2$ are detected in all sources. We observe velocity gradients that likely trace rotating toroids (Lh03) or more complex motions (Lh09, S07). Taken together, these phenomena observed on $\sim1500$~au scales are qualitatively similar to those seen in Galactic MYSOs, suggesting a degree of universality in the basic processes of massive star formation across different environments. Nevertheless, the substantial source-to-source diversity in continuum morphology and dense-gas kinematics motivates larger high-resolution surveys to determine how these properties vary with source characteristics and environment.

astro-ph.GA↗
arXiv2026

Image Profile (IMPRO) Fitting of Massive Protostars. I. Method Development and Test Cases of Cepheus A and G35.20-0.74N

Massive stars play a critical role in the evolution of galaxies, but their formation remains poorly understood. One challenge is accurate measurement of the physical properties of massive protostars, such as current stellar mass, envelope mass, outflow cavity properties, and system orientation. Spectral energy distribution (SED) fitting is widely-used to test models against observations. The far-infrared SED traces cold dust in envelopes, while the near- and mid-infrared (MIR) probes emission from outflow cavities and/or the inner envelope. However, SED fitting has degeneracy limiting its ability to yield accurate measurements of protostellar properties. Here, we develop image profile (IMPRO) fitting as a method to improve the characterization of protostars. We utilize brightness distributions from multi-wavelength MIR images of massive protostars taken by SOFIA/FORCAST as part of the SOFIA Massive Star Formation (SOMA) survey to constrain protostellar properties via comparison to a grid of radiative transfer models. We develop a fitting pipeline to extract information along the outflow axis, which is then combined with the SED fitting to yield improved constraints on protostellar properties. We apply the IMPRO fitting method on the nearby massive protostar Cepheus A, finding that its properties become more tightly constrained compared to SED fitting, especially in the inclination of the source. However, for the more distant G35.20-0.74N, we find that the spatial resolution of SOFIA/FORCAST limits the utility of this combined fitting pipeline. However, higher resolution MIR observations, e.g., with JWST, are expected to greatly expand the applicability of this fitting technique to protostars across the Galaxy.

astro-ph.SR↗
arXiv2026

Tuning-Free Covariance-Aware Sequential Shrinkage for Multi-Source Estimation

Modern statistical learning problems often involve multiple related data sets, in which learning efficiency on a target set can be improved by borrowing information from related sources, while source heterogeneity may introduce bias. Existing approaches are limited by suboptimal performance in multi-source settings, insufficient use of covariance information, or the computational burden of tuning procedures. We propose a covariance-aware shrinkage framework that constructs shrinkage directions using covariance information to improve efficiency. We establish finite-sample risk bounds that yield an explicit risk-improving interval for the shrinkage size, making the transfer strength fully data-driven. When multiple source sets are available, we further propose a sequential algorithm that applies source-specific shrinkage updates according to a priority score. Under a mixed-source separation condition, the estimator asymptotically attains the oracle risk and, when the pooled discrepancy is large, strictly improves over single-step shrinkage. The framework is further extended to a broad class of smooth M-estimation problems. Numerical studies show substantial gains over competing methods, especially when the source data sets are highly heterogeneous.

stat.ME↗
arXiv2026

Dust Seeding Molecules in a Massive Protostar -- Detection of TiO in Orion Source I

We report the first detection of TiO in star-forming regions based on Atacama Large Millimeter/submillimeter Array observations of Orion Source I, a well-characterized massive protostar. Multiple rotational transitions are identified, with emission spatially resolved within $\sim 50$ au, showing a compact distribution with a velocity structure consistent with the base of a rotating outflow. The spatial and velocity distributions of TiO are consistent with those of AlO, with both species being key dust seeding refractory molecules. The column density of TiO is derived to be $(3.0 \pm 0.4)\times10^{15}\ {\rm cm^{-2}}$, corresponding to $X_{\rm TiO/SiO} \sim 12.8 \pm 1.7 \times10^{-3}$, higher than CI chondrites and indicative of efficient dust-to-gas conversion near the protostar. The detection of TiO, a key dust seeding species, offers important constraints on refractory chemistry and the formation environments of primitive minerals, linking astrochemical processes in protostellar systems to the earliest stages of Solar System material formation.

astro-ph.GA↗
arXiv2026

An Eccentric Massive Protobinary Assembled via a Core-merger Parabolic Encounter

Most massive stars form in binary systems, which profoundly influence their subsequent evolution. However, how such systems form remains poorly understood, with several competing scenarios proposed, including disk fragmentation, core fragmentation and capture. Determining the orbital architectures of massive binaries, particularly during their earliest embedded phases, is therefore crucial for distinguishing among these formation pathways, but direct measurements of their three-dimensional motions have remained exceptionally challenging. Here we present high-resolution, multi-epoch sub-millimeter-to-centimeter ALMA and JVLA observations of the massive protobinary IRAS 07299$-$1651, complemented by JWST and VLT infrared imaging. We detect orbital proper motion of the binary components, enabling a full three-dimensional orbital reconstruction. Combining orbital fitting, multi-wavelength continuum modelling, hydrogen recombination line kinematics and jet observations, we find that the preferred orbital solutions are highly eccentric and close to parabolic, while both circumstellar disks are strongly misaligned with the orbital plane. These properties are naturally explained by a ``core-merger'' scenario in which the two protostars originated independently from initially unbound cores that recently underwent a near-parabolic encounter, producing an eccentric binary with a current separation of about 200 au. These findings suggest that the core-merger process may represent an important pathway for forming eccentric massive binaries.

astro-ph.SR↗
arXiv2026

Compactness of Solutions to Sub-Elliptic Equations with Potential on the Heisenberg Group

In this paper, we investigate the compactness of nonnegative solutions to a critical sub-elliptic equation with a nonnegative potential on the Heisenberg group. We establish compactness in all dimensions. Moreover, we show that if a sequence of solutions blows up, both the potential and its sub-Laplacian must vanish at the blow-up point. Our analysis overcomes the inherent geometric and analytical challenges posed by the Heisenberg group, including the degeneracy of the sub-Laplacian, its non-commutative structure, and the anisotropic dilation symmetry.

math.AP↗
arXiv2026

GS-Net: Heterogeneous Vehicle Data Reuse via Generalizable Plug-and-Play 3DGS Module

End-to-end autonomous driving is increasingly data-driven, yet data reuse across vehicles remains limited. Each new vehicle often requires additional data collection and retraining because camera translation, orientation, and field of view differ across sensor layouts. Cross-sensor view synthesis offers a promising route for cross-platform data reuse by synthesizing images under novel sensor configurations from existing sensor data. To realize this goal, we propose GS-Net, a lightweight plug-and-play module that aggregates local geometric context from sparse Structure-from-Motion (SfM) point clouds and expands each point into multiple dense Gaussian primitives in a single forward pass, learning a cross-scene generalizable initialization for standard 3DGS that improves rendering quality for both interpolated views along the original sensor trajectories and extrapolated views at new sensor positions. In such settings, target camera viewpoints often lie beyond the convex hull of training cameras, whereas existing methods and benchmarks predominantly evaluate in-hull interpolation, leaving the extrapolation regime underexplored. To enable quantitative evaluation under this setting, we introduce CARLA-NVS, the first benchmark explicitly designed for cross-sensor view synthesis. Unlike existing autonomous driving datasets that typically employ no more than eight cameras in fixed configurations, CARLA-NVS features 12 cameras uniformly distributed at 30-degree azimuth intervals, enabling controlled evaluation of both interpolated and extrapolated viewpoints. Experiments on CARLA-NVS show that GS-Net improves rendering quality by 2.08 dB PSNR on interpolated views and 1.86 dB on extrapolated views over standard 3DGS, while achieving a 50x faster initialization compared to MVS-based densification. These results offer a practical step toward scalable cross-vehicle data reuse in autonomous driving.

cs.CV↗