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Xiaohu Yang

Publications and source records attributed to Xiaohu Yang.

At least 19 recordsLinked to original sources
arXiv2026

Black Hole-Galaxy Correlations in Cluster Zoomed-in Simulations: GIZMO-SIMBA and TNG-Cluster

We investigate the co-evolution of supermassive black holes (SMBHs) and central galaxies in massive clusters using the GIZMO-SIMBA and TNG-Cluster zoom-in simulations at $z=0-5$. We find that the distinct subgrid physics of these two models suggest fundamentally different evolutionary pathways. On the one hand, GIZMO-SIMBA, employs torque-limited accretion and predicts a supply-driven scenario where the SMBHs rapidly assemble synchronized with dark matter halo ($M_{200c}$) growth (i.e. the halo mass-BH mass relation is set by $z=3.0$ and similar to the present-day relationship). On the other hand, TNG-Cluster, exhibits a feedback-regulated growth phase delayed by an early thermal suppression. While both models successfully reproduce some local black hole-galaxy scaling relations, they imply significantly different evolution for these relations. Analysis of the BH mass-gas mass ratio relations suggests that TNG-Cluster's isotropic kinetic winds efficiently deplete cold gas, resulting in a "hard quench" of star formation. In the black hole accretion rate (BHAR)-star formation rate (SFR) relation we find that both simulations successfully reproduce the decoupling of BHAR and star formation observed in recent massive cluster ellipticals. The divergent evolutionary trends emphasize the importance of the multiphase intracluster medium; while these subgrid models do not have the necessary resolution and employ distinct formalisms, the sustained BHAR in quenched systems resemble outcomes broadly consistent with modern multiphase feeding paradigms such as chaotic cold accretion in turbulent cluster cores. Furthermore, we demonstrate that for both models, black hole mass is a primary regulator of atomic and molecular gas depletion in galaxy clusters.

astro-ph.GA↗
arXiv2026

ELUCID-DESI II. Revealing dark matter mass, tidal, and velocity (MTV) fields using galaxy group phase information

We introduce a novel method for reconstructing the cosmic mass, tidal, and velocity (MTV) fields over the redshift range $0 < z < 0.6$ using the phase information of galaxy groups. This approach replaces the explicit theoretical bias correction typically needed to relate galaxy groups to the underlying dark matter density field with a simulation-calibrated statistical mapping, reducing a major source of systematic uncertainty and making the method directly applicable to spectroscopic redshift surveys such as the DESI Bright Galaxy Survey (BGS). We evaluate the performance of our MTV reconstruction pipeline with mock redshift surveys that include a comprehensive set of observational selection effects. The galaxy groups used as tracers are identified with an extended halo-based group finder applied to the DESI mock galaxy catalogue with an apparent magnitude limit of $m_z < 19.65$, yielding a galaxy number comparable to that of the DESI BGS faint sample ($m_r < 20.175$). Our tests show that the reconstructed velocities are accurate and unbiased, with a residual dispersion of $\sim 120\ \mathrm{km\,s^{-1}}$ across the redshift bins. The recovered velocity field allows us to shift galaxy groups to their real-space positions, thereby correcting for the Kaiser effect. By iteratively applying this Kaiser correction to the galaxy groups, we further reconstruct the tidal field and the mass-density distribution. The reconstruction is stable with respect to the grid resolution. Overall, our results demonstrate that this group-based phase-space reconstruction provides a robust pathway to recovering the dark matter MTV fields, with strong prospects for application to DESI BGS data.

astro-ph.CO↗
arXiv2026

Multi-tracer mass bias in matched cosmic voids from SDSS DR7 and the ELUCID constrained simulation

Cosmic voids provide a unique environment for studying the relationship between galaxies, subhaloes, and dark matter in the underdense Universe. Using the SDSS galaxy catalogue and the ELUCID constrained simulation, we establish an observationally anchored framework for measuring multi-tracer mass bias within matched cosmic voids. A sample of 102 matched void pairs is constructed to directly compare galaxy, subhalo, and dark matter mass distributions within an observationally constrained realisation of the local Universe. We find that both the galaxy-to-dark matter and subhalo-to-dark matter mass ratios decrease toward void centres, indicating that luminous and halo tracers become increasingly depleted relative to the underlying matter distribution in the deepest underdensities. In contrast, the galaxy-to-subhalo mass ratio exhibits substantially larger statistical uncertainties within the inner void regions ($r/R_{\rm v}\lesssim0.5$). By comparing measurements obtained using independent and common coordinate frameworks, we show that coordinate offsets contribute to the observed scatter but cannot fully account for the large uncertainties. The remaining uncertainty primarily arises from the severe scarcity of massive subhaloes ($\log_{10}(M_{\rm sub}/h^{-1}M_\odot)\ge11.8$) within void interiors, which greatly reduces the number of statistically valid measurements near void centres. Our results provide a direct measurement of multi-tracer mass bias in observationally constrained cosmic environments and highlight the fundamental statistical limitations of multi-tracer studies in extreme underdense regions.

astro-ph.CO↗
arXiv2026

Demystifying Solana Bots: From GitHub Blueprints to On-Chain Fingerprints

Solana is an emerging blockchain platform designed for high throughput and low transaction fees, making it inexpensive to submit transactions at scale and, consequently, increasing exposure to bot spamming and related financial exploitation. Solana bots are typically off-chain software systems that operate in a competitive on-chain execution environment by constructing and submitting transactions, and the bot-related transactions on the decentralized exchanges exceed 250 million dollars in daily trading volume in January 2026. Prior studies on Solana have examined system performance, smart-contract security, and specific on-chain phenomena. However, we still lack a systematic understanding of what Solana bots implement in practice and how these implementations manifest as observable on-chain execution fingerprints. To address this gap, we performed a large-scale empirical study of Solana bots from two complementary views: (i) 586 bot repositories collected from GitHub, and (ii) 200 bot addresses on Solana, with over 44 million on-chain transactions. Our study derives an implementation-grounded taxonomy of Solana bots comprising 15 categories grouped into five domains (e.g., Trading Operations, MEV, and On-chain Analytics), identifies a largely shared five-stage operational pipeline manifested in bot implementations, and uncovers systematic variation in on-chain trading behaviors of Solana bots across diverse trading platforms and assets. Based on our findings, we highlight future research directions, and provide recommendations for building and operating bots on the Solana blockchain.

cs.SE↗
arXiv2026

Quantifying Environmental Effects on Galaxy Properties using Non-spherical Voids Identified from SDSS DR7

Cosmic voids provide a distinct low-density region for studying the environmental effects of galaxy properties. Using the SDSS DR7 catalog, we identify non-spherical voids via Voronoi tessellation and the watershed algorithm, and classify void galaxies based on their local volume. We compare and find that void galaxies classified by this method are systematically less massive, fainter, bluer, and have higher specific star formation rate (sSFR) than non-void galaxies and all galaxy samples. We then divide void and non-void galaxies into stellar mass bins to focus on the environmental dependence of $g-r$ color and sSFR. By further classifying galaxies into blue/red and star-forming/quiescent populations, we calculate the ratio of blue to red and star-forming to quiescent for void and non-void galaxies separately. Comparing the ratio of the void value to the non-void value for both metrics presents an overall decreasing trend with stellar mass $M_*$ over the $9.4-10.4$ range in $\log[M_*/\mathrm{M}_\odot]$, indicating a stronger environmental effect in lower-mass systems. These results show that our classification of void galaxies in non-spherical voids based on local volume offers a robust approach for quantifying the influence of underdense environments on galaxy evolution.

astro-ph.GA↗
arXiv2026

Weak Evolution of Cosmic Atomic Hydrogen over the Past 4.5 Billion Years

The cosmic star formation rate density (CSFRD) has declined sharply toward the present day, but the roles of the atomic and molecular gas reservoirs remain uncertain. We measure the cosmic HI density, $\Omega_{\mathrm{HI}}$, over $0<z<0.41$ by combining HI spectra from the Five-hundred-meter Aperture Spherical Telescope with optical spectroscopy from the Dark Energy Spectroscopic Instrument for $\sim2.5$ million galaxies across $\sim12,000\,{\rm deg}^2$. We measure a raw decrease in $\Omega_{\mathrm{HI}}$ by a factor of $1.35\pm0.10$ over the past 4.5 Gyr. Even after applying the conservative systematic corrections from our forward model, the inferred decline is only $1.12\pm0.10$ -- still far weaker than the CSFRD decline (a factor of 2.46). The molecular gas density, in contrast, is known to evolve more closely with star formation. At fixed stellar mass, the average HI gas fraction evolves by less than 0.2 dex, showing that the weak evolution is present across the galaxy population. These quantitative differences rule out rapid depletion of galaxy HI as the primary driver of the late-time CSFRD decline, and provide a stringent benchmark for models of gas accretion, phase conversion and star-formation regulation.

astro-ph.GA↗
arXiv2026

Refploit: Facilitating Exploit Construction via Code-Agent Trajectory Repair

Vulnerability exploits play a crucial role in assessing the downstream impact of Java library vulnerabilities. While some vulnerabilities are accompanied by disclosed exploit references, automatically reproducing such references into runnable exploits remains challenging because they are often incomplete, unstructured, or only describe partial reproduction steps. Recent code agents provide a promising way to automate this process, but our study shows that their generated exploits often appear successful without triggering the actual vulnerable logic, such as replacing vulnerable APIs with self-implemented functions. To address this, we propose Refploit, an LLM-based trajectory recovery framework for facilitating vulnerability reproduction from public exploit references. The key insight is that a failed agent trajectory is not entirely useless. It may have already completed some reproduction subtasks while also revealing misleading directions that should be avoided. Refploit first validates an agent-generated exploit through differential execution. When the exploit is ineffective, Refploit analyzes its reproduction progress, locates the trajectory segments associated with the reproduction progress, and derives constraints to guide focused recovery. We evaluate Refploit on three open-source Java vulnerability datasets, covering 172 exploit references for 143 vulnerabilities. Under DeepSeek-V4-Flash, Refploit successfully reproduces 138 exploits, achieving a reproduction rate of 80.2%. It achieves a 64.3% relative improvement over the initially generated trajectories and outperforms both the SOTA exploit-generation method PoCGen and advanced code agents such as Codex with GPT-5.4. We further adapt Refploit to another code agent and observe consistent improvements, demonstrating its generality.

cs.SE↗
arXiv2026

Mitigating Package Hallucinations in Large Language Models via Model Editing

Large language models (LLMs) have demonstrated strong capabilities in software engineering tasks, such as code generation, library recommendation, and dependency configuration. However, recent studies show that LLMs may suffer from package hallucination, where they generate non-existent or invalid package names. These hallucinations can be exploited in software supply chain attacks, as attackers may register malicious packages under hallucinated names. Therefore, mitigating package hallucination is important for improving the reliability and security of LLM-assisted software development. In this paper, we introduce BOUND, a lightweight localized model editing framework for mitigating package hallucinations in LLMs. BOUND formulates package hallucination mitigation as a package-validity boundary editing problem, where the boundary refers to the model's ability to distinguish valid packages from hallucinated package names under a given task context. It first locates modules related to package hallucination through a risk-aware localization strategy, and then edits these modules with lightweight LoRA adapters using a boundary-aware objective that reinforces valid packages, suppresses hallucinated packages, and preserves locality behavior. Experimental results show that BOUND effectively reduces package hallucinations while preserving valid package recommendations. In the package recommendation task, BOUND reduces package-level hallucination rate (Package-HR) by 79.9% on edit prompts and by 65.4% on unseen prompts. The learned package-validity boundary further generalizes to other package-related tasks, reducing Package-HR by 12.8% in code generation and by 34.0% in pip install recommendation. These results show that BOUND refines the package-validity boundary of LLMs and improves the reliability of package-related outputs.

cs.SE↗
arXiv2026

IntentTester: Intent-Driven Multi-agent Framework for Cross-Library Test Migration

Unit tests capture both functional checks and domain-specific knowledge, but this knowledge remains locked within individual projects and is rarely reused across libraries with overlapping functionality. Existing migration techniques based on structural code mappings (e.g., API signatures) often break down under divergent designs or cross-language settings, resulting in non-executable migrated tests. In this paper, we present IntentTester, a multi-agent framework for intent-driven test reuse. Instead of translating raw code, IntentTester abstracts tests into a language-agnostic Test Description Language (TDL), aligns them with semantically related entities and dependencies in a repository graph, and synthesizes executable tests through LLM-guided reasoning and iterative validation. This design enables cross-library and cross-language migration without manual intervention, producing migrated tests that existing structure-mapping approaches cannot achieve. We evaluate IntentTester on nine open-source projects across three domains (JSON, HTML, and Time) and two languages (Java and Python). IntentTester generates 2,776 syntactically correct tests with 85\% correctness; in comparison, the two baselines achieve 51\% and 43\%. Among them, 2,410 tests executed successfully, yielding a 74\% effectiveness rate. Beyond higher success rates, IntentTester also surfaced previously unknown defects including stack overflows, null dereferences, and parsing inconsistencies, several of which have been acknowledged or patched by maintainers. Our results show that intent-driven migration shifts the focus from code mappings to semantic alignment, allowing practical cross-library and cross-language test reuse while improving test quality and exposing implementation flaws.

cs.SE↗
arXiv2026

The Web4 Agent Economy: A Large-Scale Empirical Study of the Landscape, Challenges, and Opportunities

The Internet is transitioning from Web3 toward Web4, where autonomous agents serve as independent economic actors. These agents can now hold crypto wallets, execute on-chain trades, and pay for external API calls. This transition calls for a new infrastructure stack capable of supporting key agent operations, including agent-to-tool interaction, agent-to-agent payments, and verifiable agent identity, represented by emerging protocols such as the Model Context Protocol, x402, and EIP-8004. Despite growing industrial interest in these protocols, the real-world Web4 agent ecosystem remains largely underexplored. To bridge this gap, we conduct the first large-scale empirical study of the Web4 ecosystem. Specifically, our study targets three interconnected questions: how Web4 agents are deployed and used in practice; what engineering challenges developers face when building Web4 agents; how current project communities respond to these challenges. To answer these questions, we analyze 99,448 multi-chain identity registrations, 317,596,323 transaction logs, the source code of 341 MCP projects, and 349 filtered GitHub issues. Our findings reveal that autonomous agents have established a highly active machine-to-machine payment economy, processing millions of daily transactions. However, this growth is built on immature infrastructure, including identity/authorization practice, cross-environment operation, and payment interoperability. Our follow-up analysis shows that community responses are visible but unevenly distributed across repositories, and payment interoperability remains the most persistent unresolved bottleneck. Overall, this study reveals a critical gap between the rapid growth of the Web4 agent economy and its fragile underlying infrastructure, highlighting future directions for building a more secure Web4 agent ecosystem.

cs.SE↗
arXiv2026

Beyond Similarity: Trustworthy Memory Search for Personal AI Agents

Personal AI agents increasingly rely on long-term memory to provide persistent personalization across sessions. However, existing memory pipelines are largely driven by semantic similarity: memory data close to the current query is retrieved and injected into the model context. This creates a critical trustworthiness gap, since a semantically related memory may still be contextually inappropriate, leading to threats such as cross-domain leakage, sycophancy, tool-call drift, or memory-induced jailbreaks. In this paper, we study memory search as a trust boundary in personal AI agents. We evaluate representative agentic memory frameworks, including A-Mem, Mem0, and MemOS, together with OpenClaw, a real-world personal-agent environment with persistent state and tool-use capability. Our results show that long-term memory is not merely a utility layer, but a durable control channel that can reshape how agents interpret tasks and execute actions, leaving them highly susceptible to the aforementioned threats. To mitigate these vulnerabilities, we propose MemGate, a lightweight and deployable memory plug-in for trustworthy memory search, with only 9M parameters and a 35.1MB footprint. MemGate is inserted between the vector memory store and the backbone LLM, requiring no LLM modification, memory-database rewriting, or inference-time LLM judge. It applies a query-conditioned neural gate to candidate memory representations, turning raw similarity search into task-conditioned memory admission. Across multiple mainstream memory frameworks, real-world agent settings, and diverse LLM backbones, MemGate reduces memory-induced threats while preserving long-term memory utility.

cs.AI↗
arXiv2026

CSST large-scale structure analysis pipeline: IV. Cosmic Voids Identified from Galaxy Group Samples as Probes of the Large-scale Structure

Because groups are directly associated with halos, they allow for considerably simpler theoretical modeling than approaches based on individual galaxies. We therefore propose to use voids identified in galaxy group catalogs, referred to as group-voids, to investigate the cosmic large-scale structure (LSS). Using the reference mock galaxy redshift survey (MGRS) designed for the Chinese Space-station Survey Telescope (CSST), we build two galaxy group catalogs representing ideal and realistic scenarios, derived from galaxy samples with 100\% and roughly 30\% spectroscopic redshift completeness, respectively. We then identify voids in these two mock group catalogs, as well as in the underlying halo catalog, and measure two void statistics, the void size function (VSF) and the void density profile, within five redshift intervals spanning $z=0$ to $1.0$. We compare the statistics obtained from two kinds of voids: those defined by galaxy groups (group-voids) and those defined by dark matter halos (halo-voids). In the void-finding process, we adopt the brightest central galaxy (BCG) as the group center to improve the accuracy of the inferred void centers. Our analysis shows that void statistics derived from group-voids with spectroscopic redshift completeness of at least 40\% can faithfully reproduce the corresponding statistics from halo-voids. Even when the redshift completeness of galaxies falls to as low as 30\%, we can still reliably describe group-voids via halo-voids by incorporating a redshift error term. This indicates that group-voids are a promising tool for probing LSS and offer a valuable complement to standard void studies, which is especially advantageous for emulator-based methods.

astro-ph.CO↗
arXiv2026

Efficient Generation of Neutrons Based on Ultrashort Laser-driven Direct Acceleration in Microwire-Array Targets

We report on an experimental demonstration of efficient neutron generation based on direct laser acceleration in microwire-array targets irradiated by ultrashort (tens of femtoseconds) laser pulses. The optimal array period was identified, at which the maximum proton energy and the number of protons with energies exceeding $1~\mathrm{MeV}$ were significantly increased. Using a $1~\mathrm{PW}$, $\sim25~\mathrm{fs}$ laser at a moderate intensity of $\sim10^{20}~\mathrm{W/cm^2}$, a high neutron yield of up to $(8.33\pm0.84)\times10^{6}~\mathrm{n/sr/J}$ was detected from the LiD converter via $^7\mathrm{Li}(p,n)$ and $\mathrm{D}(p,n+p)$ nuclear reactions. Self-consistent integrated simulations reproduced the experimental results and predicted that with a Be converter, a forward pulsed neutron source with an unprecedented yield per joule of $3.67\times10^{7}~\mathrm{n/sr/J}$ can be obtained under identical laser conditions. This type of neutron source is favorable for applications that require a high repetition rate utilizing compact and economical laser systems.

physics.plasm-ph↗
arXiv2026

A graph-based Neural Network surrogate model for accelerating semi-analytical model of galaxy formation and evolution

Understanding how galaxy populations emerge and evolve from the growth of dark matter structure is a central challenge in galaxy formation theory. Semi-analytic models (SAMs) provide an efficient framework to address this problem, but exploring large ensembles of merger trees across broad parameter spaces remains computationally demanding. We develop a conditional graph neural network surrogate model that combines merger tree information with SAM parameters to predict galaxy properties across cosmic time. Using merger trees of dark matter halos from the Uchuu simulation and the Galacticus SAM, the model predicts stellar mass, luminosity, angular momentum, gas metal mass, and specific star formation rate across the wide redshift range of 0 <= z <= 5. For instance, the model can predict stellar mass at 0 <= z <= 3 with a scatter of 0.19-0.28 dex and coefficient of determination R^2 of 0.946-0.973 (R^2 close to 1 indicates prediction closely matching the truth). The results show that a single graph based model can reproduce these galaxy properties with good accuracy over multiple SAM realizations, merger trees and redshifts. This catalog-level model provides a practical route for accelerating SAM based studies of galaxy formation to enable a more detailed investigation of the model parameter space. The inference code, trained models, and example data products are publicly available at https://github.com/MutongCat/sam2galaxy-gnn.

astro-ph.GA↗
arXiv2026

The peculiar velocity correlation function of the Cosmicflows-4 catalog

We present an analysis of the parallel peculiar velocity correlation function using data from the Cosmicflows-4 (CF4) survey. CF4 significantly extends the depth of the peculiar velocity measurements, mitigating the impact of observers on the cosmic variance. We examine the distribution of cosmic variance using different velocity correlation estimators. The combination of the large peculiar velocity uncertainties and the anisotropy distribution of the CF4 data across the northern and southern hemispheres results in substantial statistical uncertainties in the velocity correlation function. To address this, we test different weighing schemes in the velocity correlation function and implement a more accurate peculiar velocity estimator that reduces velocity uncertainties, consequently decreasing the statistical uncertainty. Using the CF4 group dataset, we derive a growth rate of $f\sigma_8=0.384^{+0.116}_{-0.194}$ and a local growth rate of $f\sigma_8=0.569^{+0.054}_{-0.06}$ through a Markov Chain Monte Carlo method.

astro-ph.CO↗
arXiv2026

What can galaxy clustering really tell us about the galaxy-halo connections?

Subhalo abundance matching (SHAM) is a commonly used framework for modeling the galaxy-halo connection. Yet, its standard implementation has difficulty reproducing the observed galaxy clustering with high accuracy (e.g., $\chi^2/\mathrm{dof} \approx 1$). To overcome this issue, we propose a novel CS-SHAM framework, in which central and satellite galaxies are independently matched to main and satellite subhalos in simulations. Within this scheme, we introduce three free parameters to explicitly characterize the satellite fraction, $f_{\mathrm{sat}}$, as a function of stellar mass or absolute magnitude. To evaluate the performance of CS-SHAM, we apply it to two sets of mock galaxy catalogs built with the conventional SHAM method but using different subhalo mass proxies, $M_{\mathrm{peak}}$ and $V_{\mathrm{peak}}$, as well as two additional galaxy samples generated from a SAM and from TNG-300. We demonstrate that CS-SHAM reliably reproduces galaxy clustering whether $M_{\mathrm{peak}}$ or $V_{\mathrm{peak}}$ is used as the subhalo mass proxy. We also find that the models are unable to place robust constraints on $f_{\mathrm{sat}}$ if different mass proxies are employed. Indeed, within the CS-SHAM framework the halo occupation distribution (HOD) and conditional luminosity or stellar mass function (CLF/CSMF) are accurately recovered. Furthermore, we demonstrate for the first time that galaxy clustering constrains the HOD and CLF/CSMF primarily for relatively massive halos. Because the halo bias is nearly constant for low-mass halos, galaxy clustering is generally not very sensitive to the satellite population residing in these low-mass systems.

astro-ph.GA↗
arXiv2026

The Sinking Statistics of Dark Matter Subhalos Across Hierarchical Levels

We investigate the mergers among subhalos in a $\Lambda$CDM simulation, focusing on two fundamental aspects overlooked by previous studies: 1) how to identify mergers robustly; 2) the statistics of mergers across the subhalo hierarchy. To this end, we make use of the HBT+ subhalo finder that tracks subhalo evolution across hierarchy levels, identifying the coalescence of subhalo cores in phase space as a "sinking" event. This coalescence marks a distinct stalled phase in orbital decay, providing a physically motivated and natural definition of a resolved merger. Moreover, the phase transition occurs over a very short timescale, making the statistics robust to numerical resolutions. Our main findings are as follows. (1) More than 90% of sinking events occur between adjacent subhalo levels, while cross-level pathways arise from tidal stripping, group accretion, and numerical constraints. (2) Resolved sinking events are predominantly major mergers (mass ratios > 1/10), whereas the contribution from minor mergers decreases with the dynamical age of the host halo. (3) Although deep-level subhalos typically have low mass ratios relative to the host halo, their mass ratios relative to their direct parents are substantially larger, significantly enhancing their sinking probability. Consequently, the satellite-satellite sinking rate can rival or even exceed the central-satellite sinking rate at lower mass thresholds. (4) Satellite-satellite sinking events are spatially biased toward the outer regions of the host halo, suggesting that the central tidal field suppresses orbital decay between satellite systems.

astro-ph.GA↗
arXiv2026

Galaxy populations in groups and clusters-II. Conditional luminosity functions at redshifts from z~1 to z~0

Using DESI SV3 spectroscopic group centrals and HSC photometric data, we measure conditional luminosity functions (CLFs) of central and satellite galaxies for red and blue populations in dark matter haloes spanning $M_h\sim10^{12}- 10^{15}M_{\odot}$ and $0<z<1$. HSC depth permits measurements to $M_r \approx -15$ at $0.2 \leqslant z < 0.5$ and $M_r \approx -17$ at $0.5 \leqslant z < 1.0$. We find satellite CLFs evolve weakly over $0<z<1$. Blue satellite CLFs are well described by a single Schechter function across halo masses and redshifts, with a nearly constant slope of $-1.25\lesssim \alpha\lesssim -1.2$. In contrast, red satellite CLFs exhibit a pronounced faint-end upturn in all halo mass and redshift bins, with little evolution in the faint-end slope ($-1.8\lesssim \alpha_f\lesssim -1.7$). The low-mass red sequence was therefore already established in clusters/groups by $z\sim1$. The lack of faint-end-slope evolution favors models where the steep upturn originates from early formation processes at $z\gtrsim2$, rather than environmental quenching after infall. Satellite characteristic magnitudes and central galaxy luminosities fade with time. Red central galaxies are consistent with passive evolution, whereas blue-central luminosity evolution is dominated by ongoing star formation. Satellites evolve more rapidly than predicted by simple stellar population models, highlighting environmental effects. Satellite quenched fractions as a function of stellar mass exhibit a minimum at $M_{*} \sim 10^9M_{\odot}$ that is consistent across halo masses and redshifts. We discuss possible interpretations of these results and their implications for galaxy formation and evolution.

astro-ph.GA↗