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Zhicheng He

Publications and source records attributed to Zhicheng He.

At least 19 recordsLinked to original sources
arXiv2026

Little Red Dots as a Transient Phase of Self-Interacting Dark Matter Assisted Black Hole Growth

The discovery of Little Red Dots (LRDs) with JWST has revealed a population of compact, red galaxies hosting rapidly growing black holes at early cosmic times. Their compact morphologies, broad emission lines, weak X-ray emission, and distinctive V-shaped spectral energy distributions indicate a short-lived phase of black-hole growth within a dense nuclear environment. Here we propose that LRDs arise from a transient episode of self-interacting dark matter (SIDM)-assisted black-hole growth during galaxy assembly. In this scenario, gas inflows first establish a compact nuclear thick disk, which modifies the central SIDM distribution and provides the obscuring structure around the accreting black hole. Once the SIDM density near the seed black hole becomes sufficiently enhanced, rapid SIDM accretion drives a major increase in black-hole mass, initiating the transient LRD phase. The resulting obscured growth phase naturally suppresses direct short-wavelength emission and redistributes the radiation field, producing the red continuum and V-shaped spectral signatures of LRDs. This framework links gas inflow, SIDM dynamics, and early black-hole assembly, predicting that LRDs preferentially occur in galaxies undergoing strong nuclear inflow and evolve into ordinary AGN after this transient phase. Unlike models that require globally rare halo histories, long super-Eddington gas growth, or purely phenomenological obscuration, our model ties the overmassive black hole, X-ray weakness, spectral shape, and finite duty cycle to one local gas-triggered SIDM event.

astro-ph.GA↗
arXiv2026

Morphological Decoupling-Based Skeletal Classification for Clinical Assessment of Malocclusion

Malocclusion skeletal grading is a fundamental task in orthodontics, critical for diagnosis and treatment planning. Traditionally, cone-beam computed tomography (CBCT) is used for visual measurement, and the reconstructed lateral cephalograms are handed over to expert dentists for diagnosis. However, manual review is time-consuming, labor-intensive, and subject to inter-operator variability. Therefore, an automatic CBCT-based system is needed for reliable malocclusion skeletal grading. In this case, we develop TeethGNN, a novel graph-based framework designed to combine CBCT image features with morphological information for accurate and efficient malocclusion grading. TeethGNN utilizes a decoupled learnable decoder to directly predict key morphological indicators from CBCT images, eliminating the need for manual measurements. These morphological features are then fused with image features using a graph neural network (GNN), which effectively models the relationships between the modalities. To further enhance robustness and calibration, we introduce a collaborative calibration strategy. This strategy combines multi-scale graph adversarial perturbation for explicit calibration and nonlinear topological graph calibration for implicit confidence adjustment. Extensive experiments and ablation studies on our collected clinical dataset demonstrate that our malocclusion measurement system achieves 77.08\% in accuracy and 89.61\% in AUC, outperforming the compared state-of-the-art methods. These results validate the effectiveness of graph-based multimodal fusion and collaborative calibration in improving malocclusion grading performance. Our system shows strong potential for advancing computer-aided orthodontic diagnosis, providing an accurate and reliable solution for vision-based clinical measurement and diagnosis.

eess.IV↗
arXiv2026

T-GMP: Terrain-conditioned Generative Motion Priors for Versatile and Natural Humanoid Locomotion

Achieving both anthropomorphic naturalness and rich motion diversity during terrain traversal remains a fundamental challenge in humanoid locomotion. Existing reinforcement learning approaches typically rely on fixed motion priors, limiting their adaptability to varying environments. We propose Terrain-conditioned Generative Motion Priors (T-GMP), a module that captures a terrain-conditioned latent motion manifold from a few expert state-terrain demonstrations. The learned priors enable smooth style transitions, facilitating a unified policy that adapts to terrain variations. We integrate T-GMP into an adversarial learning pipeline, where a discriminator dynamically modulates naturalness constraints conditioned on local terrain features, guiding the generation of versatile and human-like motions. We further introduce a Foothold Penalty to promote safe foot placement on challenging terrains. Experimental results demonstrate that T-GMP outperforms existing baselines in motion naturalness, motion diversity, and traversal success rates, while preserving physically coordinated motions.

cs.RO↗
arXiv2026

Evidence for the transformation from lenticular to spiral galaxies

It is widely accepted that late-type galaxies, such as spirals, evolve into early-type systems, including elliptical and lenticular galaxies, through galaxy mergers and violent disk instability processes. Throughout this morphological transformation, star formation is typically suppressed by quenching mechanisms whose detailed nature remains the subject of active investigation. Here, we present compelling evidence for an evolutionary pathway that proceeds in the reverse direction. Using the integral field unit observations, we identify a population of spiral galaxies hosting quenched central cores (QCCs). These galaxies exhibit bimodal distributions in both their stellar population properties and their dynamical properties, along with sharp changes in radial gradients near the QCC boundary. These results indicate that the QCCs and the surrounding outer disks formed at distinct cosmic epochs and through different physical processes. Remarkably, QCCs closely resemble quiescent early-type galaxies, particularly lenticular galaxies, in their mass-size and mass-velocity dispersion scaling relations, as well as in their stellar population demographics and internal kinematics. These findings provide strong support for a rejuvenation scenario in which spiral disks are reassembled around pre-existing quiescent lenticular or early-type systems. Moreover, we show that such rejuvenation, accompanied by a reverse morphological transformation from early- to late-type appearance, is quite common. This indicates that quenching in galaxies is not invariably a terminal state and can be reversed under appropriate conditions.

astro-ph.GA↗
arXiv2026

PolygMap: A Perceptive Locomotion Framework for Humanoid Robot Stair Climbing

Recently, biped robot walking technology has been significantly developed, mainly in the context of a bland walking scheme. To emulate human walking, robots need to step on the positions they see in unknown spaces accurately. In this paper, we present PolyMap, a perception-based locomotion planning framework for humanoid robots to climb stairs. Our core idea is to build a real-time polygonal staircase plane semantic map, followed by a footstep planar using these polygonal plane segments. These plane segmentation and visual odometry are done by multi-sensor fusion(LiDAR, RGB-D camera and IMUs). The proposed framework is deployed on a NVIDIA Orin, which performs 20-30 Hz whole-body motion planning output. Both indoor and outdoor real-scene experiments indicate that our method is efficient and robust for humanoid robot stair climbing.

cs.RO↗
arXiv2026

Measuring Outflow Distances in NGC 5548 Using Absorption-Line Variability Diagnostics

AGN-driven outflows serve as a key channel through which the energetic central engine influences host galaxy evolution. Among the physical properties of outflows, their radial distance from the galactic nucleus is particularly important for assessing AGN feedback. In this study, we investigate the UV outflow components in NGC 5548 by analyzing the variability of C IV absorption troughs in optical spectra obtained through multiple HST observations during 2013 and 2014. We construct a set of variability-based diagnostic events, labeled G1 and G2, which are sensitive to the recombination timescale ($t_r$) of ionized gas. By combining these with mock light curves generated using a damped random walk (DRW) model, we numerically establish a mapping between the G1 event probability and $t_r$. This approach allows us to constrain the radial distances of outflow components 1 and 6, whose absorption variability is primarily driven by changes in the incident ionizing continuum, to be $0.77^{+0.10}_{-0.10}$ and $1.72^{+1.74}_{-1.72}$ pc, respectively. These results are consistent with those obtained using a different method in our previous study, as well as with values reported in the literature.

astro-ph.GA↗
arXiv2026

Dissecting the multiple-component outflow in NGC 5548 with absorption-line Variability

AGN-driven outflows are routinely invoked as a key agent of supermassive black holes to regulate the evolution of galaxies. The radial distance from the central engine is a crucial parameter for evaluating the impact of these outflows on the host galaxy. In this work, we estimate the radial distances of ultraviolet (UV) outflow components in NGC 5548 using the most up-to-date absorption-line variability method, combined with multi-epoch HST/COS spectroscopy from the 2014 AGN STORM campaign and archival data observed in 2013. The recombination timescale (tr) of the absorbers are measured by analyzing the detection rate curves of absorption-line variability. In particular, the detection rate curves of the absorption troughs showing blended multiple velocity components are featured by distinct ``multi-step' profiles, allowing for measuring tr for individual components. Among the 6 identified outflow components, four are found to be a few pc from the center and two are 30-40 pc away. Our results agree well with the more reliable results in the literature on components 1 and 4, and show overall consistency with previous works, demonstrating the power of our new methodology especially when it is aided by densely sampled HST spectra.

astro-ph.GA↗
arXiv2026

Stellar feedback drives the baryon deficiency in low-mass galaxies

Stellar feedback, as a key process regulating the baryon cycle, is thought to greatly redistribute baryonic material inside and outside the dark matter halos (DMHs), however the observational evidences are lacking. Through stacking analyses of ~400,000 galaxy spectra from Dark Energy Spectroscopic Instrument (DESI), we find star formation driven cool outflows in Mg II absorption line. Assuming only gravity acts on the launched gas, our calculations reveal that outflows from low mass galaxies ($M_*<10^{10}\,\rm M_\odot$) are capable of escaping beyond the DMHs, which aligns well with our finding in the circumgalactic medium (CGM) absorption along the minor-axes of galaxies using background quasars. This research offers indirect evidence that stellar feedback drives the low baryon retention rate in low-mass haloes, implicating that baryonic processes within galaxies are connected with the diffuse matter beyond the DMHs.

astro-ph.GA↗
arXiv2026

Reasoning with Memory: A Temporal Granularity-Adaptive Framework for Training-Free Long Video Understanding

While Multimodal Large Language Models (MLLMs) demonstrate superior generalization in fundamental video tasks, restricted context windows limit their long video understanding. To accommodate this constraint, models typically resort to keyframe selection. However, uniform sampling or static query-guided selection often overlooks critical temporal context, failing to adapt to the varying query temporal granularities. In this paper, we propose ReMem, a temporal granularity-adaptive keyframe selection framework for training-free LongVideoQA. ReMem introduces a dual-level memory-augmented adaptation. At the query level, Memory-Driven Question Parsing leverages LLM long-term memory to decode question temporal granularity and extract semantic entities. At the video level, Synergistic Dual-Semantic Frame Alignment exploits intrinsic structural memory to align frames with query semantics, guiding Structure-Aware Dynamic Frame Routing to cluster events and optimally distribute sampling budgets. By explicitly preserving temporal information with memory mechanisms, ReMem suppresses redundancy and empowers MLLMs to perform robust multi-granular video reasoning. Evaluations across four popular LongVideoQA benchmarks using three MLLMs demonstrate highly efficient, state-of-the-art zero-shot performance; notably, LLaVA-Video with ReMem reaches 54.5% (+12.3%) on LVBench and 67.1% (+8.2%) on LongVideoBench.

cs.AI↗
arXiv2026

Radial gradients revealed by mutliscale outflows from down-the-barrel spectroscopy toward a quasar at redshift 3.4

Active galactic nucleus(AGN) feedback is a key ingredient in galaxy formation models and simulations. From an observational point of view, however, the channels of AGN feedback coupling to the interstellar medium (ISM) and circumgalactic medium (CGM) and hence the impact on galaxy evolution, are largely uncertain and remain fiercely debated, due primarily to the huge gap from nuclear to CGM scales. Here we present multi-epoch, down-the-barrel spectroscopy toward a luminous quasar at $z=3.409$ over two decades, which reveals multiscale outflows expanding from nuclear to CGM scales along with characteristic radial gradients. Most strikingly, the trends of trough depth across three different-scale, freely expanding outflows are opposite between N V and C IV, regardless of the spectral normalization and short-term variability, leading to a tenable gradient of N/C and signaling a critical transition from ejective feedback on small scales to regulative feedback on large scales. Our observations of this quasar offer valuable diagnostics to explore the realistic wind-ISM/CGM coupling, one of the most challenging tasks in state-of-the-art simulations of feedback.

astro-ph.GA↗
arXiv2026

GuideWalk: Learning Unified Autonomous Navigation and Locomotion for Humanoid Robots across Versatile Terrains

Humanoid robots have achieved strong locomotion capabilities, but reliable navigation on versatile terrains remains challenging because obstacle avoidance must be coordinated with dynamically feasible motion. In this work, we present GuideWalk, a unified end-to-end framework that integrates traversability-aware navigation guidance with terrain-adaptive locomotion teacher for humanoid navigation. Specifically, we introduce a navigation module that provides explicit velocity guidance, decoupling obstacle avoidance from terrain conditions to enable robust planning across diverse environments. We propose a composite teacher distillation scheme, where goal-directed commands and dynamically consistent actions are aggregated and distilled into a single policy. To further improve robustness, the distilled policy is refined with reinforcement learning and an auxiliary behavior cloning objective, which promotes exploration while preserving desirable teacher behaviors. Experiments demonstrate that GuideWalk achieves stable and effective navigation while maintaining stable humanoid locomotion.

cs.RO↗
arXiv2026

Rethinking the Necessity of Adaptive Retrieval-Augmented Generation through the Lens of Adaptive Listwise Ranking

Adaptive Retrieval-Augmented Generation aims to mitigate the interference of extraneous noise by dynamically determining the necessity of retrieving supplementary passages. However, as Large Language Models evolve with increasing robustness to noise, the necessity of adaptive retrieval warrants re-evaluation. In this paper, we rethink this necessity and propose AdaRankLLM, a novel adaptive retrieval framework. To effectively verify the necessity of adaptive listwise reranking, we first develop an adaptive ranker employing a zero-shot prompt with a passage dropout mechanism, and compare its generation outcomes against static fixed-depth retrieval strategies. Furthermore, to endow smaller open-source LLMs with this precise listwise ranking and adaptive filtering capability, we introduce a two-stage progressive distillation paradigm enhanced by data sampling and augmentation techniques. Extensive experiments across three datasets and eight LLMs demonstrate that AdaRankLLM consistently achieves optimal performance in most scenarios with significantly reduced context overhead. Crucially, our analysis reveals a role shift in adaptive retrieval: it functions as a critical noise filter for weaker models to overcome their limitations, while serving as a cost-effective efficiency optimizer for stronger reasoning models.

cs.IR↗
arXiv2026

Dynamic Whole-Body Dancing with Humanoid Robots -- A Model-Based Control Approach

This paper presents an integrated model-based framework for generating and executing dynamic whole-body dance motions on humanoid robots. The framework operates in two stages: offline motion generation and online motion execution, both leveraging future state prediction to enable robust and dynamic dance motions in real-world environments. In the offline motion generation stage, human dance demonstrations are captured via a motion capture (MoCap) system, retargeted to the robot by solving a Quadratic Programming (QP) problem, and further refined using Trajectory Optimization (TO) to ensure dynamic feasibility. In the online motion execution stage, a centroidal dynamics-based Model Predictive Control (MPC) framework tracks the planned motions in real time and proactively adjusts swing foot placement to adapt to real world disturbances. We validate our framework on the full-size humanoid robot Kuavo 4Pro, demonstrating the dynamic dance motions both in simulation and in a four-minute live public performance with a team of four robots. Experimental results show that longer prediction horizons improve both motion expressiveness in planning and stability in execution.

cs.RO↗
arXiv2026

Tracing Star Formation in Quasar Hosts via [O II] $λ$3727: A Kinematically Consistent Approach

Measuring star formation in quasar host galaxies is crucial for understanding the coevolution of supermassive black holes (SMBHs) and galaxies, yet remains observationally challenging due to severe contamination from active galactic nucleus (AGN) emission. In this work, we present a new method to robustly isolate the AGN contribution to the [O II] $λ$3727 emission line in quasars, based on a kinematically consistent decomposition of [O II] and the high-ionization [Ne V] $λ$3426 line. We find that the [O II] emission in quasars is primarily dominated by star formation, with only a weak AGN contribution, and thus can be reliably used as a tracer of star formation in quasar hosts. Applying this technique to a large sample of Sloan Digital Sky Survey quasars, we derive mean SFRs as a function of bolometric luminosity. We find a tight correlation between mean SFR and luminosity. Further analysis, assuming a constant dust extinction correction to [O II] emission, shows that luminosity is the primary parameter most strongly associated with star formation, rather than SMBH mass or Eddington ratio. This supports the scheme in which star formation and black hole accretion are closely linked through their common dependence on the cold gas supply.

astro-ph.GA↗
arXiv2026

Granulon: Awakening Pixel-Level Visual Encoders with Adaptive Multi-Granularity Semantics for MLLM

Recent advances in multimodal large language models largely rely on CLIP-based visual encoders, which emphasize global semantic alignment but struggle with fine-grained visual understanding. In contrast, DINOv3 provides strong pixel-level perception yet lacks coarse-grained semantic abstraction, leading to limited multi-granularity reasoning. To address this gap, we propose Granulon, a novel DINOv3-based MLLM with adaptive granularity augmentation. Granulon introduces a text-conditioned granularity Controller that dynamically adjusts the visual abstraction level according to the semantic scope of the textual input, and an Adaptive Token Aggregation module that performs granularity-guided pooling and relation-aware clustering to produce compact, semantically rich visual tokens. This design enables unified "pixel-to-fine-to-coarse" reasoning within a single forward pass. Extensive and interpretable experiments demonstrate that Granulon improves accuracy by ~30% and reduces hallucination by ~20%, outperforming all visual encoders under identical settings.

cs.CV↗
arXiv2026

Symmetry in Fundamental Parameters of Galaxies on the Star-forming Main Sequence

The Star-Forming Main Sequence (SFMS) serves as a critical framework for understanding galaxy evolution, highlighting the relationship between star formation rates (SFR) and stellar masses M_* across cosmic time. Despite its significance, the origin of the 0.3-0.4 dex dispersion in the SFMS remains a key unresolved question. Uncovering the origin of dispersion is crucial for understanding the evolution of galaxies. Using a large sample of approximately 500,000 galaxies, we reveal an unprecedented symmetry in the distribution of key structural properties-effective radius (R_{\rm e}), stellar surface density (M_*/R_{\rm e}^2), and morphology on the SFMS. This symmetry implies that galaxies with high (above SFMS) and low (below SFMS) SFRs share similar fundamental parameters. Moreover, galaxies with smaller R_{\rm e} or higher M_*/R_{\rm e}^2 exhibit greater dispersion in SFR. This dispersion reflects the response to fluctuations in cosmic accretion flows, while the SFR itself represents the time-averaged effect over the gas consumption timescale. Shorter gas consumption timescales, associated with higher M_*/R_{\rm e}^2, lead to greater SFR dispersion. Our results reveal that the variation of SFR originates from the oscillation of accretion flow and is regulated by the stellar surface density.

astro-ph.GA↗
arXiv2026

MedVAR: Towards Scalable and Efficient Medical Image Generation via Next-scale Autoregressive Prediction

Medical image generation is pivotal in applications like data augmentation for low-resource clinical tasks and privacy-preserving data sharing. However, developing a scalable generative backbone for medical imaging requires architectural efficiency, sufficient multi-organ data, and principled evaluation, yet current approaches leave these aspects unresolved. Therefore, we introduce MedVAR, the first autoregressive-based foundation model that adopts the next-scale prediction paradigm to enable fast and scale-up-friendly medical image synthesis. MedVAR generates images in a coarse-to-fine manner and produces structured multi-scale representations suitable for downstream use. To support hierarchical generation, we curate a harmonized dataset of around 440,000 CT and MRI images spanning six anatomical regions. Comprehensive experiments across fidelity, diversity, and scalability show that MedVAR achieves state-of-the-art generative performance and offers a promising architectural direction for future medical generative foundation models.

cs.CV↗
arXiv2026

ELFO: A Python Package for Emission Line Fitting Optimization in Integral Field Spectroscopy Data

Integral field spectroscopy (IFS) provides spatially resolved spectra, enabling detailed studies that address the physical and kinematic properties of the interstellar medium. A critical step in analyzing IFS data is the decomposition of emission lines, where different velocity components are often modeled with Gaussian profiles. However, conventional fitting methods that treat each spectrum independently often yield spatial discontinuities in the fitting results. Here, we present Emission Line Fitting Optimization (ELFO), a Python package for IFS spectral fitting. ELFO uses the results of neighboring spectra to determine multiple initial guesses and selects the result that exhibits spatial smoothness. We tested ELFO on IFS data of two quasars obtained from the Multi-Unit Spectroscopic Explorer, where it successfully corrected anomalous fits, revealed previously unresolved substructures, and made large-scale kinematic structures more evident. With minor modifications, this method can also be easily adapted to other IFS data and different emission lines.

astro-ph.GA↗