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Cheng Cheng

Publications and source records attributed to Cheng Cheng.

At least 19 recordsLinked to original sources
arXiv2026

TaskFrame: Connecting Task-Level Planning with Frame-Level Control for Game Agents

Building general game agents requires both high-level task reasoning and low-latency action control in rapidly changing visual environments. These capabilities favor models with substantially different computational profiles, making them difficult to realize within a single model. In this work, we present TaskFrame, a hierarchical planner-controller framework that connects task-level planning with frame-level keyboard-and-mouse control. A vision-language task planner decomposes a high-level goal into an ordered sequence of atomic instructions and is optimized through supervised learning and dependency-aware GRPO. The hidden states of special goal tokens that terminate each atomic instruction provide continuous goal embeddings that are cached and reused across control steps. A causal Transformer controller combines the embedding of the active goal with recent observations to predict discrete keyboard and mouse-button actions and continuous camera motion at every game step. We train the system in three stages: planner training, controller pretraining, and joint alignment. Experiments in Minecraft show that TaskFrame is competitive with general-purpose vision-language models on instruction decomposition, and that in closed-loop evaluation the agent achieves a higher combat success rate than the evaluated baselines.

cs.CV↗
arXiv2026

Player-1: Learning to Play from Synthetic Experience

Computer-use agents have demonstrated strong capabilities in interpreting screens and executing keyboard and mouse actions. However, games, with their visually rich worlds and diverse mechanics, remain challenging and offer a demanding next step toward general digital control. Current game-agent learning pipelines often rely on large-scale demonstrations with limited coverage of dynamic game states, while scalable environments for interactive training remain difficult to obtain. We introduce Player-1, a post-training framework for learning game control beyond demonstrations through synthetic experience. We extend the computer-use action space with relative pointer motion, sustained inputs, and composite actions. For supervised initialization, we fine-tune computer-use models on 6,044 curated gameplay trajectories that pair recorded actions with task instructions and visual reasoning. We construct Player-1-Gym, sixteen synthetic 2D and 3D environments designed around control demands shared across games. Programmatically verifiable rewards and randomized observations and control dynamics support multi-environment reinforcement learning with PPO. The resulting Player-1-9B and Player-1-35B-A3B improve MineDojo-300 success over their SFT policies by 27.8 and 17.7 percentage points, respectively. Player-1-9B reaches 38.33% success compared with 12.63% for the strongest evaluated baseline, with further gains over SFT on ViZDoom-10 and GameWorld. Further analyses show that broader synthetic environment coverage improves transfer, while the use of composite actions that combine multiple keyboard and mouse inputs increases during RL. We demonstrate that learning from synthetic experience substantially improves game control beyond supervised initialization, with gains that generalize beyond the RL training suite.

cs.CV↗
arXiv2026

GameHorizon Suite: Multi-Horizon Data and Evaluation in Gameplay

Modern video games provide a measurable testbed for AI models, combining abilities of visual understanding, instruction decomposition, goal planning, and precise action control over multiple temporal horizons. Existing datasets and benchmarks, however, either cover a narrow range of games, lack language instructions, or rely on high-variance online rollouts. To address these challenges, we introduce GameHorizon, a unified data and evaluation suite that measures gameplay capabilities at different horizons for diverse model families. GameHorizon Suite consists of three components. First, GameHorizon-Annotator is a scalable and automated annotation pipeline for multi-horizon instructions. Second, utilizing the pipeline, we construct GameHorizon-Data, the first large-scale AAA gameplay dataset with temporally aligned videos, player actions, and multi-horizon instructions. It comprises 5,000 hours of recordings from 21 games, collected by 100 human expert players. Third, we build GameHorizon-Bench with reproducible offline and stepwise online testing. The offline track enables reproducible evaluation using thousands of standardized questions organized into three primary tasks and a series of diagnostic variants, while the online track tests whether offline scores reflect actual gameplay capabilities and localizes failures to specific steps within long-horizon gameplay. Based on our GameHorizon Suite, we evaluate 47 models through more than one million model invocations, revealing a meaningful hierarchy of task difficulty and pronounced differences in model capabilities. Our work can provide a standardized yardstick for evaluating gameplay capabilities across horizons and model families. We will release our dataset, annotator, and benchmark to facilitate future research.

cs.CV↗
arXiv2026

WeFair: Fidelity-Aware Alignment from Human Feedback for Personalized Query Rewriting in Short-Video Search

Short-video search requires compact query rewriters that infer personalized intent from brief, ambiguous queries under strict latency constraints. The challenge is to decide when personalization is warranted and generate a reformulation that preserves the user's immediate information need. A rewriter optimized for fluency or engagement may still introduce unsupported details and redirect the search toward a different intent. Existing methods often underutilize users' actual reformulations and behavioral traces, relying instead on synthetic reasoning and indirect behavioral rewards that may produce more engaging candidates but cause intent drift. To build an effective rewriter that remains faithful to user intent, we propose WeFair, Fidelity-Aware Alignment from Human Feedback for Personalized Query Rewriting in WeChat short-video search. WeFair assigns real user behavioral signals to a progression from demonstration to preference to verification. We first mine actual human reformulations from search sessions, then apply multidimensional LLM screening and contextual verification to filter unsupported transitions without extensive manual annotation. During training, we begin with a supervised curriculum that progressively builds rewriting capabilities from large-scale human personalized query demonstrations, and then use direct preference optimization to align with users' subsequent consumption. In the final on-policy optimization stage, we construct a strict verification reward based on actual human reformulations to further boost performance with group relative policy optimization. Further analysis shows that auxiliary rewards can activate more rollout groups in GRPO but reduce intent fidelity. Online A/B testing in WeChat short-video search shows a 1.576% relative increase in effective clicked-query rate (VV>10s) and a 1.731% relative reduction in query reformulation rate.

cs.AI↗
arXiv2026

The Population of Type~1 Active Galactic Nuclei in Dark Energy Spectroscopic Instrument. I. Profile-dependent Measurements of the Low-redshift Sample

We present a spectral analysis of a large sample of low-redshift type 1 active galactic nuclei (AGNs) selected from the first data release of the Dark Energy Spectroscopic Instrument (DESI DR1). For objects at $z\le0.9$, we perform spectral fitting and ML-assisted refinement, yielding 114,754 type 1 AGNs. We use two fitting schemes that differ only in the broad-line model: the broad Balmer components are fitted with either a Lorentzian function or a sum of Gaussians. The continuum and [O III] luminosity measurements agree closely between the two schemes, whereas the Gaussians scheme generally yields larger broad-H$β$ full width at half maximum (FWHM) values, larger virial black hole masses, and lower accretion rates. The adopted broad-Balmer profile also changes the decomposition of narrow Balmer emission. Specifically, the anti-correlation between [O III]$\lambda5007$/narrow H$β$ and accretion rate is stronger under the Gaussians scheme with a Spearman rank correlation coefficient $r_{\rm s}=-0.412$, than under the Lorentzian scheme with $r_{\rm s}=-0.206$; the same tendency is observed in the narrow-line Seyfert 1 galaxies (NLS1s) subset ($r_{\rm s}=-0.435$ and $-0.259$ for the Gaussians and Lorentzian schemes, respectively), suggesting that the Gaussians scheme may provide a more self-consistent separation of the broad and narrow H$β$ components. Moreover, the consistent correlations recovered under both profile prescriptions favor a continuous type 1 accretion-state sequence, with NLS1s occupying its high-accretion end rather than constituting a distinct population.

astro-ph.GA↗
arXiv2026

A Decade-Scale Ionization Echo of Black-Hole Accretion in Galactic Nuclei

Circumnuclear structures in active galactic nuclei respond to accretion variability over different spatial and temporal scales, but whether compact narrow-line gas responds on decade timescales has not been systematically established. We use repeat Sloan Digital Sky Survey and Dark Energy Spectroscopic Instrument spectroscopy of 473 variability-selected galactic nuclei with evidence for recent nuclear brightening. We identify 19 sources whose narrow [O III] $\lambda5007$ flux increased by more than a factor of two over rest-frame baselines of 12.0-20.4 yr, which we term narrow-line ionization echoes (NIEs). In equal-weight stacked spectra, [O III] increased by $229\pm22\%$, while lower-ionization lines remained nearly unchanged or declined, indicating an ionization-dependent response rather than uniform spectral rescaling. The NIE hosts also moved coherently toward higher excitation in optical diagnostic diagrams, with mean increases of $0.46\pm0.18$ dex in $\log([\mathrm{O III}]/\mathrm{H}β)$ and $0.58\pm0.13$ dex in $\log([\mathrm{O III}]/[\mathrm{O II}])$. Photoionization models reproduce this evolution with a higher ionization parameter and a harder AGN-like ionizing spectrum at fixed gas abundance. For 13 sources with well-defined mid-infrared outbursts, the integrated W1+W2 excess energy correlates strongly with the absolute [O III] luminosity change ($r=0.87$), linking the dust and narrow-line responses to the same nuclear event. The mid-infrared timescale and spectroscopic baseline constrain the variable [O III]-emitting gas to characteristic scales of 1.5-5.2 pc. Compact narrow-line gas therefore appears to form an intermediate, decade-scale response layer between the infrared dust echo and the long-lived extended narrow-line region.

astro-ph.GA↗
arXiv2026

Overdetermined problems for sum Hessian equations in the space forms

We study overdetermined problems for normalized sum Hessian equations and their quotients in the space forms. The equations and boundary conditions imply admissibility and ellipticity. For quotient equations, we prove that the domain is a geodesic ball if it is strictly star-shaped, or if the Dirichlet value satisfies an upper bound. We also prove the corresponding result for sum Hessian equations without star-shapedness. The proofs use two auxiliary functions and a Rellich--Pohožaev identity.

math.AP↗
arXiv2026

RetiWave-Mamba: A Dual-Stream Network for Retinal Disease Detection based on Multi-scale Context and Feature-Adaptive Mamba Projection

Retinal diseases are a leading cause of irreversible vision impairment, making early and accurate diagnosis essential for effective treatment. Optical Coherence Tomography (OCT) serves as a critical imaging modality for this purpose, yet its automated analysis is hindered by inherent speckle noise, varying lesion scales, and subtle inter-class similarities. To address these challenges, we propose a novel framework, RetiWave-Mamba, which integrates spatial-frequency domain learning with state-of-the-art state space models. The framework utilizes Discrete Wavelet Transform (DWT) to decompose OCT images into low- and high-frequency streams, enabling decoupled processing of structural context and fine-grained details. For the low-frequency branch, we design a Multi-scale Contextual Localization Module (MCLM), which synergizes multi-scale dilation with spatial attention to expand the global receptive field and precisely localize lesion regions. For the high-frequency branch, we introduce an Attention-Guided High-Resolution Network (AG-HRNet) equipped with an intelligent gating mechanism to suppress noise propagation during multi-scale interactions. Furthermore, a Feature-Adaptive Mamba Projector (FAMP) is incorporated to form complementary channel-wise feature paths and adaptively reweight them using Mamba-generated gates. Extensive experiments on the OCT-C8 dataset demonstrate that our approach achieves a state-of-the-art (SOTA) classification accuracy of 98.38, surpassing existing methods. These results highlight the effectiveness of RetiWave-Mamba in identifying retinal pathologies and support its potential for computer-aided OCT image analysis.

cs.CV↗
arXiv2026

Star formation and morphological trends in the Antlia cluster: Probing environmental influence out to 5R200

Galaxy evolution in dense environments such as clusters is strongly affected by environmental processes that can alter both morphology and star formation activity. We investigate these effects in the Antlia cluster out to 5R200 using Southern Photometric Local Universe Survey (S-PLUS) data. We derive H-alpha-based star formation rates from the J0660 band and Sersic indices with GALFITM. Our analysis focuses on 154 spectroscopically confirmed galaxies brighter than mr = 16. We classify galaxies as early- or late-type systems using colour and Sersic index, and as quenched or star-forming using specific star formation rate. We examine their dependence on clustercentric distance, projected phase-space, substructure, and local density. We find that star-forming galaxies dominate from 1 to 5R200, while quenched galaxies are more common within 1R200. Early- and late-type galaxies show similar radial trends, with comparable fractions within 1R200 and a higher late-type fraction at larger radii. Quenched galaxies dominate at log(Sigma10 [Mpc^-2]) >= 1.5, whereas late-type galaxies dominate at log(Sigma10 [Mpc^-2]) <= 1.0. We identify substructures extending from the cluster centre to the outskirts. Substructures outside the central region are dominated by star-forming and late-type galaxies, with fractions similar to those of galaxies not associated with detected substructures. However, when the massive central structure is included, the fraction of star-forming galaxies in substructures decreases, indicating a more environmentally processed population in the central region. Altogether, the presence of a massive central substructure together with additional substructures detected out to 5R200 indicates that Antlia is still undergoing mass assembly through group accretion, consistent with a dynamically active and young cluster that is still assembling its galaxy population.

astro-ph.GA↗
arXiv2026

GRF-Recon: Global Ray-Field Optimization for Long-Sequence Feed-forward Reconstruction

Feed-forward 3D reconstruction provides an efficient paradigm for scene modeling from image sequences. Scaling these models to large monocular scenarios are constrained by excessive GPU memory footprint, degraded local geometry, and long-term trajectory drift. Existing chunk-based optimization strategies provide limited geometric constraints and fail to maintain global consistency over extended trajectories. We present a unified framework for stable and scalable feed-forward 3D reconstruction from long monocular sequences. Our approach builds on coarse-to-fine trajectory alignment augmented by lightweight geometric prior injection. Distilling monocular geometric cues into the feed-forward backbone via LoRA adaptation improves depth accuracy on fine structures while preserving inference efficiency. We introduce a hybrid-weight sparse ray-field optimization that leverages high-frequency geometric features to guide local point-cloud refinement and enforce consistent inter-frame ray constraints. Unlike prior chunk-based methods, this establishes strong cross-frame geometric coupling while maintaining scalability. Finally, an efficient trajectory stitching strategy with joint ray-error optimization explicitly reduces accumulated drift. Extensive experiments show that our approach achieves competitive trajectory accuracy compared with representative SLAM systems, while maintaining globally consistent 3D reconstruction in large-scale scenarios.

cs.CV↗
arXiv2026

Hypergraph Enterprise Agentic Reasoner over Heterogeneous Business Systems

Applying Large Language Models (LLMs) to heterogeneous enterprise systems is hindered by hallucinations and failures in multi-hop, n-ary reasoning. Existing paradigms (e.g., GraphRAG, NL2SQL) lack the semantic grounding and auditable execution required for these complex environments. We introduce HEAR, an enterprise agentic reasoner built on a Stratified Hypergraph Ontology. Its base Graph Layer virtualizes provenance-aware data interfaces, while the Hyperedge Layer encodes n-ary business rules and procedural protocols. Operating an evidence-driven reasoning loop, HEAR dynamically orchestrates ontology tools for structured multi-hop analysis without requiring LLM retraining. Evaluations on supply-chain tasks, including order fulfillment blockage root cause analysis (RCA), show HEAR achieves up to 94.7% accuracy. Crucially, HEAR demonstrates adaptive efficiency: utilizing procedural hyperedges to minimize token costs, while leveraging topological exploration for rigorous correctness on complex queries. By matching proprietary model performance with open-weight backbones and automating manual diagnostics, HEAR establishes a scalable, auditable foundation for enterprise intelligence.

cs.AI↗
arXiv2026

FASHI DR2: A Catalog of 132 Low-Redshift HI 21 cm Absorption Systems

We present an untargeted survey of 21 cm HI absorption systems based on the second data release of the FAST All Sky HI survey (FASHI DR2), covering approximately 19,500 deg$^{2}$ at $z\lesssim0.09$. A total of 132 HI absorbers are identified, including approximately 60 new discoveries, forming one of the largest homogeneous samples of low-redshift HI absorbers assembled to date. The sample extends to continuum flux densities as low as 2.6 mJy, substantially below the limits of previous flux-limited surveys. The absorber population is dominated by narrow systems ($W_{50}<100$ km s$^{-1}$), while broad absorbers ($W_{50}>200$ km s$^{-1}$) account for 13.6% of the sample. Most absorbers are optically thin, with a median optical depth of $τ_{\rm HI}\approx0.14$. The velocity-offset distribution is broadly symmetric about the systemic velocities of the host galaxies. The associated absorbers are preferentially found in massive, actively star-forming galaxies. We find tentative evidence for a weak anti-correlation between HI column density and stellar mass, although the relation exhibits substantial scatter. These results provide the first statistical characterization of the low-redshift HI absorber population based on the FASHI DR2 sample and establish a valuable benchmark for future HI absorption surveys with next-generation radio facilities.

astro-ph.GA↗
arXiv2026

An HI study of a large sample of ultra-diffuse galaxies

By cross-matching the SMUDGes catalog with the FASHI and ALFALFA HI surveys, we construct an HI-detected sample of 112 ultra-diffuse galaxies (UDGs) and 48 low-surface-brightness (LSB) galaxies, providing HI-based redshifts for 76 galaxies for the first time. Using DESI DR1 redshifts, we assemble an HI-non-detected sample of 168 galaxies for stacking analysis, with detections in two stellar mass bins below 10^{8.5} M_sun. Combining optical, UV, and HI data, we investigate stellar masses, star formation rates, gas fractions, and kinematics. We present a systematic analysis of the HI mass--optical size relation for UDGs and LSBs, showing both populations follow a mass--optical size scaling similar to normal galaxies, suggesting an average HI surface density, under the assumption that the optical size traces the extent of the HI distribution. Their stellar mass--size relation indicates nearly constant stellar surface densities for galaxies with central surface brightness mu_{0,g}>~24mag arcsec^{-2}, independent of UDG or LSB classification. Many UDGs and LSB galaxies are systematically offset from the baryonic Tully--Fisher relation toward higher baryonic masses at a given velocity, consistent with trends found in UDGs with resolved HI kinematics. Both populations exhibit low star formation efficiencies and long gas depletion times, supporting multiple formation pathways for UDGs.

astro-ph.GA↗
arXiv2026

Magnetically Self-Sealed MR Haptic Actuator With PWM-Based Excitation and High-Fidelity Torque Control

Accurate and stable torque rendering is essential for safe and perceptive human--machine interaction. Magnetorheological fluid (MRF)-based actuators offer a compact and rapidly controllable solution for haptic feedback, but their practical implementation requires reliable fluid sealing, low-hysteresis excitation, accurate torque control, and stable long-duration operation. This article presents an integrated MRF haptic system featuring a compact magnetically self-sealed rotary actuator, low-hysteresis PWM operation, high-fidelity model-based torque rendering, and stable performance during long-time operation. Magnetostatic simulation guides the arrangement of magnetic and nonmagnetic materials to focus flux in the multidisk torque and permanent-magnet sealing regions, enabling a maximum 600 N$\cdot$mm/A output. Experiments show that higher PWM frequencies reduce hysteresis and improve repeatability. At 10 kHz, the response is represented by a nonlinear model that varies with the direction and speed of torque change. The real-time controller combines feedforward, hysteresis compensation, PI feedback, and sliding-mode correction. Compared with PID, it reduces square-wave overshoot, undershoot, and steady-state RMSE by 77.4\%, 61.9\%, and 68.3\%, respectively. It tracks sinusoidal and biomechanics-model-based references, and a 1.5-h test shows only a 2.5 $^\circ$C rise near the coil with no clear tracking loss. This high-fidelity torque rendering will fundamentally transform human--robot collaboration by making interactions safer, more efficient, and more intuitive.

cs.RO↗
arXiv2026

Halo Mass of ULIRGs at Cosmic Noon

We present a clustering analysis of $\sim 3000$ ultraluminous infrared galaxies (ULIRGs) at $z\sim 2$, uniformly selected by $24μ{\rm m}$ flux and IRAC colors in the COSMOS and BOOTES fields. We measure the angular correlation functions of ULIRGs in both fields and fit them with galaxy clustering models. Linear theory modeling shows that these ULIRGs reside in dark matter halos with characteristic masses of $\log M_{\rm h} /({h^{-1}\rm M_{\odot}}) = 12.64\pm 0.50$ in COSMOS and $12.72\pm 0.13$ in BOOTES. The halo occupation distribution (HOD) modeling yields occupation-weighted effective halo masses of $\log M_{\rm eff} /({h^{-1}\rm M_{\odot}}) =12.50^{+0.26}_{-0.28}$ for COSMOS and $12.89^{+0.11}_{-0.12}$ for BOOTES. These host halos are expected to evolve into halos with masses of $\sim 10^{13.6-13.9}\ {h^{-1}\rm M_{\odot}}$ at $z=0$. The HOD fits allow for a non-negligible satellite contribution to the clustering of galaxies in the BOOTES field, but the satellite fraction derived in the COSMOS field appears nearly zero.

astro-ph.CO↗
arXiv2026

MicroVerse: An Instrument for Measuring Self-Authored Identity Drift in Long-Horizon Multi-Agent Language-Model Simulations

Long-horizon, multi-agent language model (LM) simulations are widely proposed for studying social behavior, yet instruments to measure whether persona-conditioned agents maintain identity fidelity under sustained pressure are lacking. We present MicroVerse, a behavioral-science instrument that measures identity drift in generative agents. Agents carry an immutable "soul file" (core values, moral boundaries, personality, goals) and inhabit a resource-scarce 50 x 50 environment where water is a non-respawning survival constraint. Scarcity is operationalized via a per-tick existence-cost gradient. The eight-verb action space maps directly to moral boundaries (trade, talk, attack, scavenge). Using a three-layer memory architecture, agents periodically revise a mutable current identity against their immutable original soul via importance-triggered reflection. To mitigate survivor bias, MicroVerse decouples measurement from behavior using uniform longitudinal engine snapshots every N ticks alongside a forced-end snapshot of all living and dead agents. Identity drift is scored offline using a paraphrase-aware, value-anchored, multi-register diff rather than raw cosine similarity. We evaluate the instrument via a controlled seed run (n = 25) and a reflection-threshold sweep (thresholds {40, 80, 150}) to determine if drift dynamics are gate artifacts or threshold-robust properties. We report two primary findings: (1) Anti-self-deception emerges unprompted as the single largest semantic category of identity modification (27 of 111 added boundaries, 24%). (2) The system is threshold-robust; lower gates accelerate and increase revision frequency but preserve drift direction. All empirical results are strictly preliminary existence proofs and effect shapes (one model, one seed per arm, n = 25) rather than statistical significance claims.

cs.CL↗
arXiv2026

FITTER: Vocabulary-Agnostic Cross-Domain Inference on Temporal Knowledge Graphs

Temporal knowledge graphs are central to many uses of the Semantic Web, but existing completion methods assume the entities, relation names, and timestamps to be reasoned about are already known at training time, restricting each model to a single graph and vocabulary. We propose FITTER, the first fully-inductive structural model for temporal knowledge graph link prediction that supports cross-domain transfer: the inference graph may contain entirely unseen entities, relation names, and timestamps drawn from a different domain. FITTER represents each predicate by its interaction patterns with others and time through encodings of relative rather than absolute ordering; message-passing fuses local and global temporal context to produce vocabulary-agnostic embeddings. We prove the temporal encoding is time-shift invariant and evaluate FITTER on cross-domain, cross-graph transfer over six temporal knowledge graph benchmarks of diverse domains, granularities, and time spans. FITTER consistently outperforms inductive baselines without retraining, indicating that vocabulary-agnostic structural learning is a viable foundation for inference over the heterogeneous knowledge graphs of the Semantic Web.

cs.AI↗
arXiv2026

HI Depletion Begins Well Beyond the Virial Radius: A FAST Stacking Study of 36 Galaxy Clusters to 5R200

We present a stacking study of the neutral atomic hydrogen (HI) content in and around 36 local galaxy clusters at $z<0.07$, using a combination of the FAST all sky HI survey (FASHI) and the extensive spectroscopic catalog mainly from the Dark Energy Spectroscopic Instrument (DESI). We employ spectral stacking techniques to probe the average HI mass and HI-to-stellar mass ratio ($M_{\rm HI}/M_*$) for member galaxies down to stellar masses of $M_*\sim 10^9M_\odot$, spanning a projected cluster-centric distance to $5R_{200}$. Our analysis reveals a pronounced environmental effect: both $M_{\rm HI}$ and $M_{\rm HI}/M_*$ decrease steadily towards the cluster center, dropping by $\sim 0.5$ dex on average from the outskirts to the core. Crucially, we find that $M_{\rm HI}/M_*$ of galaxies remain lower than the field galaxies even at the $5R_{200}$. This provides direct, statistical evidence for substantial gas stripping and pre-processing in the cluster outskirts, likely occurring in infalling groups and large-scale filaments. By further splitting the sample by $g-r$ color, we show that the HI deficiency persists at fixed galaxy color: even the bluest cluster members exhibit $\sim 0.5$~dex lower $M_{\rm HI}/M_*$ than field galaxies of similar color, reflecting environmental effects on the cold gas reservoir prior to full optical transformation. The total HI mass within clusters and their outskirts agrees broadly with predictions from cosmological simulation. Our results underscore the critical role of the extended cluster environment in quenching galaxies by depleting their cold gas reservoirs well before they enter the dense cluster core.

astro-ph.GA↗