arXiv subjects

Zihao Wu

Publications and source records attributed to Zihao Wu.

At least 19 recordsLinked to original sources

arXiv2026

A Comprehensive Review of Multimodal Large Language Models: Performance and Challenges Across Different Tasks

In an era defined by the explosive growth of data and rapid technological advancements, Multimodal Large Language Models (MLLMs) stand at the forefront of artificial intelligence (AI) systems. Designed to seamlessly integrate diverse data types-including text, images, videos, audio, and physiological sequences-MLLMs address the complexities of real-world applications far beyond the capabilities of single-modality systems. In this paper, we systematically sort out the applications of MLLM in multimodal tasks such as natural language, vision, and audio. We also provide a comparative analysis of the focus of different MLLMs in the tasks, and provide insights into the shortcomings of current MLLMs, and suggest potential directions for future research. Through these discussions, this paper hopes to provide valuable insights for the further development and application of MLLM.

cs.AI↗

arXiv2026

OASIS I: Spectroscopy of Mini-Quenched and Lulling Galaxies Constrains the Quiet Side of Bursty Star Formation at z > 4

JWST has revealed that star formation in early galaxies is bursty, including episodes of violent suppression, so-called mini-quenching. But these were largely serendipitous discoveries in samples of UV-bright, emission-line-dominated galaxies, so that the quiet side of the burst cycle has remained poorly explored. With the JWST Cycle 3 program OASIS (GO 5997), we searched for such galaxies deliberately, using deep 28-hour NIRSpec/PRISM spectroscopy across the full burst cycle at z>4. We identify three mini-quenched galaxies with log(M*/Msun) = 8.3-8.9 and no detectable Balmer emission, at least 1.2-1.7 dex below the star-forming main sequence (SFMS), five lulling galaxies with weak Balmer emission above the quenching threshold, and 17 nearly-main-sequence galaxies just below the SFMS. Finding them required spectroscopy: seven of our ten photometric candidates for suppressed (mini-quenched and lulling) galaxies turned out to be nearly-MS or above the SFMS, and photometric fits overestimate their current star-formation rates and cannot distinguish lulling from mini-quenched ones. The suppressed galaxies are common: five of the eight come from tiers not selected on star-formation state, where they make up 29% of the galaxies at z = 4-5.5 and log(M*/Msun) = 8-9.5, a number density of ~1.6x10^-3 Mpc^-3 dex^-1. Simulations predict them to be an order of magnitude rarer at these masses and to occur mainly at lower masses. The suppressed galaxies are also old: Balmer-break strength and stellar age both increase as galaxies fall further below the SFMS, with light-weighted ages of 5 Myr for galaxies above it, ~30 Myr for the lulling galaxies and above 100 Myr for the mini-quenched ones. They show little stellar reddening (E(B-V) < 0.2), consistent with depleted gas reservoirs. Suppressed phases at z>4 are therefore neither rare nor brief, but a common and lasting part of early galaxy growth.

astro-ph.GA↗

arXiv2026

OASIS II: The Ionized Gas Traces Feedback-Driven Declines into the Quiet Side of Bursty Star Formation at z > 4

Models of bursty star formation in low-mass (10^8-10^9.5 Msun) galaxies at z>4 differ most in the suppressed phases: what drives the decline, how fast star formation falls, and how long it stays low. OASIS Paper I showed that such quiet galaxies are common and old; here we measure how they got there and how their ionized gas responds. We compare Balmer-line star-formation rates (SFRs) with 100-Myr averages from the stellar continuum, in NIRSpec/PRISM spectra of 54 OASIS galaxies at z>4, including three mini-quenched galaxies below the quenching threshold and five lulling galaxies at least 0.6 dex below the star-forming main sequence (SFMS) but still star-forming. Galaxies above the SFMS rise and fade equally often, whereas those below it are mostly fading: the lulling galaxies formed little more than a tenth of their mass in the last 100 Myr, and their decline began tens to ~100 Myr before observation. Suppressed phases thus last of order 100 Myr, as long as or longer than in most simulations, whereas recovery is much faster: few galaxies below the SFMS are rising. The ionized gas tracks these changes in star formation. [OIII]/[OII] falls from galaxies above the SFMS to the lulling ones, even at fixed mass and after dust correction, and follows the recent change in SFR (SFR_10/SFR_100) more closely than the position relative to the SFMS: two rising galaxies below the SFMS are highly ionized. This is expected if fewer and softer ionizing photons than in strongly star-forming galaxies meet gas depleted by earlier star formation. Lulling galaxies show ISM conditions distinct from those of strongly star-forming galaxies. The declines are faster than passive gas exhaustion predicts, favoring feedback that expels the gas or keeps it from forming stars. Quiet phases are thus long, feedback-driven episodes, and the ionized gas traces where a galaxy is in its burst cycle.

astro-ph.GA↗

arXiv2026

OASIS III: Bursty star formation drives observed galaxy sizes at $z\geq4$

We employ JWST/NIRSpec MSA PRISM spectroscopy together with NIRCam imaging to investigate the impact of star-formation variability on galaxy sizes for a representative sample of 51 galaxies at $z\geq4$. The OASIS survey enables us to probe low-star-formation-rate (SFR) lulling and mini-quenched galaxies down to $\log_{10}(M_\star/M_\odot)=8.3$ and $\log_{10}(\mathrm{SFR}/M_\odot\,\mathrm{yr}^{-1})<-1.6$, i.e. more than one order of magnitude below the star-forming main sequence (MS). This enables us to probe objects that are often missed by shallower spectroscopic surveys, in addition to the MS and starburst systems, yielding an SFR- and mass-representative sample at high redshift. We derive galaxy half-light sizes using ForcePho, enabling us to resolve the highly compact nature of these sources, and we analyse their star-formation histories and interstellar medium (ISM) properties with the full-spectral-fitting code ppxf. We find a clear anti-correlation between burstiness, as traced by the ratio of the SFR from H$α$ compared to that from non-parametric spectral fitting averaged over 100 Myr, and galaxy UV/optical half-light radii as measured with ForcePho from a combination of well-sampled NIRCam photometry. Using partial correlation coefficients we show that the anti-correlation between burstiness (as traced by the SFR ratio) and size cannot simply be explained by secondary dependencies on stellar mass or redshift. We interpret this relation as the effect of central starbursts in SFR-rising (bursting) systems, where intense central star formation reduces measured galaxy half-light sizes. This burstiness then regulates galaxy sizes at high redshift. Should the same mechanism be present at $z>10$, this could explain the size bimodality seen in the UV luminous galaxies as simply stemming from a more fundamental relationship between burstiness and size.

astro-ph.GA↗

arXiv2026

Fine-Grained Caching for Diffusion Transformers with Few Calibration Conditions

Diffusion transformers require repeated denoiser evaluations, making image and video generation computationally expensive. We propose a training-free framework that uses a few calibration conditions to construct a fixed, fine-grained module-reuse schedule without schedule search. The design is motivated by an empirical effect along deterministic sampling trajectories: with initial noise fixed, condition-dependent deviations in several module outputs evolve similarly across adjacent steps, so temporal differencing attenuates much of their variation. We term this effect Conditional Common-Mode Rejection (CCMR). Motivated by this observation, we rank timestep--layer--module locations using normalized ratios of consecutive feature displacements and construct a Cache Book through two-stage calibration. The second stage updates a scorer-only reference with each provisional bit after recording its indexed score, while retaining full-compute trajectories. At inference, the fixed schedule requires no per-input policy estimation and can be nested within fixed step-wise cache policies. Module caching alone achieves $1.66$--$1.72\times$ speedup on the evaluated image models and $1.54$--$1.68\times$ on the video models. Combining it with MagCache or SeaCache provides further acceleration at a model-dependent cost in paired fidelity.

cs.CV↗

arXiv2026

Source--Line Associations in JWST/NIRSpec Dense-Shutter Spectroscopy

We demonstrate the feasibility of source--line association in JWST/NIRSpec dense-shutter spectroscopy, with $\sim$2000 sources observed simultaneously per pointing regardless of spectral overlap. While JWST wide-field slitless spectroscopy (WFSS) can disentangle overlapping spectra using two grisms with perpendicular dispersion directions, NIRSpec can achieve this using gratings with different dispersions. Compared with WFSS, NIRSpec dense-shutter spectroscopy offers substantially higher sensitivity and broader wavelength coverage. We develop a Bayesian framework that combines cross-dispersion offsets, wavelength consistency, and changes in relative line positions between gratings to jointly determine source associations and redshifts. We test the method using mock observations based on the observed source distribution and emission-line fluxes from the JWST Advanced Deep Extragalactic Survey (JADES), accounting for the NIRSpec optical model, MSA operability, measurement noise, false-positive detections, and bad pixels. Although the spectral traces overlap extensively, fewer than 0.5% of the simulated emission lines are blended with another line on the detectors in NIRSpec grating spectroscopy. Applying our method to these simulations, we obtain redshifts, at an accuracy of 98%, for the $\sim$1800 sources that have detectable emission lines, of which $\sim$1500 are classified as confident and are 99.9% accurate. We further investigate how the association accuracy depends on the number of grating configurations. By combining high multiplexing, high sensitivity, and broad wavelength coverage, NIRSpec dense-shutter spectroscopy enables efficient, highly complete spectroscopic surveys for the high-redshift Universe.

astro-ph.IM↗

arXiv2026

Evaluation of OpenAI o1: Opportunities and Challenges of AGI

This comprehensive study evaluates the performance of OpenAI's o1-preview large language model across a diverse array of complex reasoning tasks, spanning multiple domains, including computer science, mathematics, natural sciences, medicine, linguistics, and social sciences. Through rigorous testing, o1-preview demonstrated remarkable capabilities, often achieving human-level or superior performance in areas ranging from coding challenges to scientific reasoning and from language processing to creative problem-solving. Key findings include: -83.3% success rate in solving complex competitive programming problems, surpassing many human experts. -Superior ability in generating coherent and accurate radiology reports, outperforming other evaluated models. -100% accuracy in high school-level mathematical reasoning tasks, providing detailed step-by-step solutions. -Advanced natural language inference capabilities across general and specialized domains like medicine. -Impressive performance in chip design tasks, outperforming specialized models in areas such as EDA script generation and bug analysis. -Remarkable proficiency in anthropology and geology, demonstrating deep understanding and reasoning in these specialized fields. -Strong capabilities in quantitative investing. O1 has comprehensive financial knowledge and statistical modeling skills. -Effective performance in social media analysis, including sentiment analysis and emotion recognition. The model excelled particularly in tasks requiring intricate reasoning and knowledge integration across various fields. While some limitations were observed, including occasional errors on simpler problems and challenges with certain highly specialized concepts, the overall results indicate significant progress towards artificial general intelligence.

cs.CL↗

arXiv2026

JADES: Dynamical Measurements of Dark Matter Halo Masses at $z \approx 7$ Using Galaxy Pairs

We present dynamical measurements of dark matter halo masses at $z\approx7$ using galaxy pairs. Galaxy pairs in the early Universe are usually in their first infall, before substantial orbital evolution or phase mixing. Their relative velocities closely trace the halo mass, especially when the pair separation is near the halo virial radius. In the TNG100 simulation, we find that dynamical mass estimators can recover halo masses with an intrinsic scatter of $\sim$0.2 dex. We apply this method to 10 pair systems at z=6-8 with [O III] spectroscopy from the JWST Advanced Deep Extragalactic Survey (JADES). We infer the stellar-to-halo mass relation while marginalizing over projection effects, measurement uncertainties, and intrinsic scatter. We find a mean halo mass of $\log\,(M_{200}/M_\odot)=11.06\pm0.21$ at $\log\,(M_{*}/M_\odot)=9$. These galaxies do not appear to inhabit unusually massive halos. Our results instead indicate a high stellar-to-halo mass ratio, corresponding to an integrated star formation efficiency of $5^{+3}_{-2}\%$, about twice the TNG100 prediction, although the current statistical significance is limited. Future observations with larger samples will tighten these constraints and directly examine whether enhanced star formation efficiency drives the overabundance of luminous galaxies at cosmic dawn.

astro-ph.GA↗

arXiv2026

The Roman eXtreme Deep Field (RXDF)

The Roman eXtreme Deep Field (RXDF) program is one of the five General Astrophysics Survey (GAS) programs approved for observing time with the Nancy Grace Roman Space Telescope in Cycles 1 and 2. It has been allocated 386.41 hours to carry out an imaging survey to AB = 30 mag (5-sigma) over ~140x larger area than the Hubble eXtreme Deep Field (HXDF) full-depth area (ACS+WFC3/IR). The RXDF will cover the full Roman wavelength range with 7 bands, reaching AB = 30 mag in RZYJH, 29 mag in F, and 28 mag in K, over a full-depth area of 678.75 arcmin^2 embedded in a total area of 1,243 arcmin^2, and far exceeding the depths of the Roman Core Community Surveys (CCS). The RXDF is within the Euclid Ultra Deep Field (EUDF) near the North Ecliptic Pole (NEP), a strategic long-term field for generational space facilities, with a wealth of multi-wavelength data including extensive coverage from the James Webb Space Telescope (JWST) NEXUS Treasury program. The observations will cover 3 epochs at a 1-year cadence, each epoch divided into 3 sub-epochs ~10 days apart, enabling time-domain studies on time baselines from ~10 days to over ~2 years. The RXDF is uniquely positioned to address critical questions in reionization, large scale structure (LSS), growth of supermassive black holes (SMBHs), little red dots (LRDs), and high-z supernovae (SNe); the volumes probed by HST+JWST are too small at these extreme depths, and even the deepest CCS tiers are too shallow. In addition to our key objectives, a wealth of additional science will be enabled by engaging the community with our rapidly released datasets, revolutionizing a wide range of science for a lasting legacy. This short document, which is converted from the approved RXDF proposal, aims to provide the community with a summary of the program.

astro-ph.GA↗

arXiv2026

WarpSAC: Towards the Pinnacle of Scalable Off-policy RL by Rethinking Exploration and Exploitation

Massively parallel simulation changes the data regime in which off-policy reinforcement learning (RL) is trained, challenging stabilizers designed for data-limited replay. Through controlled experiments across eight benchmark families, we show that these stabilizers are data-regime-dependent: parameter normalization helps with narrow replay coverage but restricts value fitting when data are abundant, while clipped double-Q can be relaxed in high-throughput manipulation. Age-biased replay weighting improves learning efficiency across regimes, especially with limited network capacity. Based on these findings, we propose WarpSAC, a regime-aware family of off-policy RL algorithms. WarpSAC uses Sample Weight Decay for efficient exploitation and provides two variants: WarpSAC-L (Norm ON, clipped double-Q) for data-limited CPU-scale training, and WarpSAC-A (Norm OFF, single-Q) for data-abundant GPU-parallel training. WarpSAC improves normalized score--step AUC over FlashSAC by 4.5% across nine CPU-scale environments and 23.1% across fourteen GPU-parallel environments. It increases UnitreeG1TransportBox-v1 success rate from 19.8% to 96.4%, improves mean normalized wall-time AUC on MuJoCo Playground by 19.1%, and achieves 36.4% faster sim-to-real deployment on Unitree G1 than FlashSAC. These results show that scalable off-policy RL should adapt its stabilizers to the available data regime.

cs.LG↗

arXiv2026

BioMed-Agent-RL: A Meta Learning, All You Need for Biomedical Applications

The current progress of Clinical Vision Large Language Models (C-VLLMs) has substantially improved digital diagnostics, still these frameworks often endure lesion noises, modality misalignment, hallucination, and missed contextual grounding in complex clinical cases. Moreover, prevailing agent systems usually depend on static and non-adaptable pipelines and lack the versatility necessary for complex medical reasoning. To resolve these difficulties, we present BioMed-Agent-RL, a unified medical agent that incorporates adaptive orchestration, policy, and reward-based reinforcement learning (RL) models for biomedical applications. To ensure reliability, it invokes clinical context-aware preference optimization (CPO), direct preference optimization (DPO), and group relative policy optimization (GRPO) with dynamic entropy regulation. This pipeline utilizes a multimodal meta-learning approach that operates as a field-specific expert and human judgment synthesizer. The agent adaptively utilizes a set of model-level expertise, such as clinical grounding and reasoner, lesion segmenter, and field-specific synthesizer, across various clinical modalities (e.g., X-ray) by utilizing an iterative and adaptive RL approach. The agent learns to seriously synthesize misleading, conflicting vision cues and trust in inherent reasoning, while specialist advice is faulty. An intensive ablation study is conducted across multiple benchmarks, and the agent significantly outperforms existing state of the art models, such as GPT-5, attaining up to ~73% accuracy (gain of ~5%) over contemporary baselines. As a result, the framework suggests a new standard for building factual, reliable, robust, and expert-like intelligent agent systems for independent clinical reasoning.

cs.LG↗

arXiv2026

Low Ly$α$ Visibility in Galaxy Overdensities: Reionization Topology and Neutral-Fraction Ceilings from DIVER over $4.8<z<11$

Ly-alpha emission is widely used to trace cosmic reionization, but its interpretation depends on how Ly-alpha visibility varies with galaxy environment. We use deep JWST/NIRSpec observations from Deep Insights into UV Spectroscopy at the Epoch of Reionization (DIVER) in GOODS-N to measure Ly-alpha visibility for 250 galaxies at 4.8 25 A. We combine these measurements with H-alpha and [O III] emitters from JWST/NIRCam wide-field slitless spectroscopy to map the density field around each DIVER galaxy. Galaxies with high Ly-alpha equivalent widths (W_Lyalpha>25 A) or high effective Ly-alpha escape fractions (f_esc,Lyalpha^eff>0.05) tend to lie farther from nearby H-alpha and [O III] emitters than galaxies with lower Ly-alpha visibility. The clearest signal occurs near the prominent GOODS-N overdensity at z~5.2, where fewer than 15% of galaxies show strong Ly-alpha emission. This trend is opposite to the simplest inside-out reionization expectation that overdensities produce larger ionized regions and enhance Ly-alpha visibility. Possible explanations include circumgalactic and local intergalactic opacity, dense absorbers, and gas kinematics. We also derive an empirical upper envelope for f_esc,Lyalpha^eff and calibrate it with reionization simulations. Interpreting this envelope as a limiting IGM-attenuation signal gives neutral-fraction ceilings of _max=0.36, 0.76, 0.74, 0.84, and 1.0 at z~5.2, 5.8, 6.7, 7.7, and 9.8, respectively. The z~8 ceiling disfavors an almost completely neutral IGM at this epoch. These results support patchy reionization already underway by z~8 and show that galaxy Ly-alpha visibility encodes both large-scale ionization topology and near-source gas structure.

astro-ph.GA↗

arXiv2026

Thinking with Gaze: Sequential Eye-Tracking as Visual Reasoning Supervision for Medical VLMs

Vision--language models (VLMs) process images as visual tokens, yet their intermediate reasoning is often carried out in text, which can be suboptimal for visually grounded radiology tasks. Radiologists instead diagnose via sequential visual search; eye-tracking captures this process as time-ordered gaze trajectories that reveal how evidence is acquired over time. We use eye-gaze as supervision to guide VLM reasoning by introducing a small set of dedicated gaze tokens. These tokens are trained to predict gaze-selected image patch indices in temporal order, encouraging the model to follow human-like evidence acquisition and integration. Experiments on MIMIC-EYE and multiple external zero-shot benchmarks show consistent gains over baselines, achieving state-of-the-art in-domain performance and improved out-of-domain robustness. These results highlight temporally ordered gaze as an effective supervision signal for learning visually grounded medical reasoning.

cs.CV↗

arXiv2026

Revolutionizing Finance with LLMs: An Overview of Applications and Insights

In recent years, Large Language Models (LLMs) like ChatGPT have seen considerable advancements and have been applied in diverse fields. Built on the Transformer architecture, these models are trained on extensive datasets, enabling them to understand and generate human language effectively. In the financial domain, the deployment of LLMs is gaining momentum. These models are being utilized for automating financial report generation, forecasting market trends, analyzing investor sentiment, and offering personalized financial advice. Leveraging their natural language processing capabilities, LLMs can distill key insights from vast financial data, aiding institutions in making informed investment choices and enhancing both operational efficiency and customer satisfaction. In this study, we provide a comprehensive overview of the emerging integration of LLMs into various financial tasks. Additionally, we conducted holistic tests on multiple financial tasks through the combination of natural language instructions. Our findings show that GPT-4 effectively follow prompt instructions across various financial tasks. This survey and evaluation of LLMs in the financial domain aim to deepen the understanding of LLMs' current role in finance for both financial practitioners and LLM researchers, identify new research and application prospects, and highlight how these technologies can be leveraged to solve practical challenges in the finance industry.

cs.CL↗

arXiv2026

PhotoIFU: NIRCam as a Photometric Integral Field Unit for Mapping Feedback in Galaxies

We present PhotoIFU, a workflow that uses deep multi-band imaging as a low-resolution photometric integral field unit. Applied to PSF-matched JWST/NIRCam imaging, PhotoIFU treats each spatial pixel as a coarse SED element and fits the pixel SEDs with Prospector to map resolved stellar-population and ISM-related properties. We apply this approach to three galaxies at $z=1.3$--3.7 in JADES: two systems with extended ionized line emission and one post-starburst galaxy with an exceptionally strong neutral outflow. Pixel-by-pixel SED fitting gives maps of stellar-mass surface density, specific star formation rate, dust attenuation, gas-phase metallicity, and recent star-formation history. We find that regions selected from the extended-emission or outflow geometry occupy distinct parts of the resolved SED-property distribution compared with the full host. In the systems with extended ionized emission, these regions are generally less dusty, consistent with ionized emission being observed along dust-poor, low-column-density pathways through the host. In the neutral-outflow system, the selected regions show enhanced recent star formation, suggesting that compact rejuvenation may mark the aftermath of an earlier energetic phase. These results show that galactic outflows and extended emission-line structures can be spatially associated with measurable differences in resolved host-galaxy stellar populations and ISM-related properties. PhotoIFU provides an imaging-based method for resolved SED mapping of feedback-related structures in larger galaxy samples where full spectroscopic integral-field mapping is unavailable.

astro-ph.GA↗

arXiv2026

WuYuEval: A Multi-Level Benchmark for Large Language Models in Solid Waste Management

Large language models (LLMs) are increasingly used as technical assistants, but their competence in solid waste management (SWM) remains difficult to assess because existing benchmarks emphasize general knowledge rather than professional decisions under engineering, environmental, and policy constraints. We introduce WuYuEval, a multi-level benchmark for evaluating LLMs in SWM across foundational knowledge, domain reasoning, and expert decision-making. After quality auditing, WuYuEval contains a Foundation Module with 4,590 closed-ended multiple-choice questions across six task types and eight domain categories, together with an Expert Module with 247 scenario-based open-ended questions involving multi-objective optimization, constraint trade-offs, and system design. For expert tasks, we combine anchor-calibrated LLM-as-a-Judge scoring with Elo-based pairwise comparison. Across 33 LLMs, performance varied widely. The leading model reached 94.64\% accuracy on the Foundation Module, but average accuracy still fell from 84.14\% on easy questions to 42.50\% on hard questions, with lower performance concentrated in calculation, experimental design, urban planning, and open-ended expert tasks. Reasoning-oriented Thinking modes improve most matched model pairs after auditing, but the gains depend on baseline capability and are not uniformly positive. These results suggest that visible deliberation helps only when it remains anchored to units, assumptions, and engineering constraints; otherwise, it may drift from decisive answer boundaries. WuYuEval therefore provides both an evaluation resource and an empirical basis for developing SWM-oriented foundation models with professional reasoning chains and explicit constraint control.

cs.CL↗

arXiv2026

Beyond the Dot: an LRD-like nucleus at the Heart of an IR-Bright Galaxy and its implications for high-redshift LRDs

Little Red Dots (LRDs) are compact, red sources discovered by JWST at high redshift ($z \gtrsim 4$), marked by distinctive 'V-shaped' spectral energy distributions (SEDs) and often interpreted as rapidly accreting Active Galactic Nuclei (AGNs). Their true nature remains unclear, however, and their evolutionary connection to their lower-redshift counterparts is still poorly constrained. Thus, we present WISEA J123635.56+621424.2, here dubbed {\it the Saguaro}, a $z=2.0145$ galaxy in GOODS-North, as a possible analog of high-redshift LRDs and a potential missing link in their evolutionary path toward lower-redshift systems. It features a compact LRD-like nucleus surrounded by a face-on spiral host. Its connection to LRDs includes that: (1) its nuclear spectrum shows a clear `V-shaped'' SED; and (2) when redshifted to $z=7$, surface-brightness dimming makes the host undetectable, thus mimicking an LRD. This suggests that high-redshift LRDs may be embedded in extended hosts. To test this, we stack rest-frame UV images of 99 photometrically selected LRDs, revealing faint, diffuse emission. Stacking in redshift bins reveals mild radial growth, consistent with the expected galaxy size evolution. A simple analytic model confirms that surface-brightness dimming alone can explain their compact appearance. Lastly, we show that {\it the Saguaro} is not unique by describing similar objects from the literature at $z\lesssim3.5$. Taken together, our results support a scenario in which LRDs may not be a distinct population, but could instead be the visible nuclei of galaxies undergoing a short-lived, perhaps AGN-dominated, evolutionary phase, with their compact, red appearance driven largely by observational biases.

astro-ph.GA↗

arXiv2026

JADES: the mass-metallicity relation at $z=1-10$. New calibrations, extremely metal-poor galaxies, and chemical diversity

We present gas-phase metallicities of star-forming galaxies at $z=1$-10 with deep JWST/NIRSpec spectra from the JADES full data release, Dark Horse, and OASIS programmes. We stack $\sim$1500 medium-resolution spectra, yielding detections of the [OIII]$λ$4363 auroral line down to $12+\log(\mathrm{O/H})=7.0$ to derive stack-based strong-line calibrations over the metallicity range $12+\log(\mathrm{O/H})=7.0$-8.7. At a fixed metallicity, our stacks exhibit [OIII]$λ$5007/H$β$ and [OIII]$λ$5007/[OII]$λλ$3726,3729 values generally lower than calibrations based on high-$z$ individual auroral-line emitters, suggesting an observational bias towards higher excitation introduced when requiring auroral line detections in individual spectra. Based on our new calibrations, we obtain canonical mass-metallicity relations (MZRs) at z$=$1-10, identifying a decrease in metallicities from $z\sim0$ to z$\sim$4-10, without significant change in slope. Moreover, we identify 50 promising candidates of extremely metal-poor galaxies (EMPGs) with $12+\log(\mathrm{O/H})=6.7$-7.3 (1-4\% solar metallicity) at $z=1.2$-9.1. The MZRs of EMPGs are characterised by a large scatter, with those having lower metallicities generally exhibiting lower sSFRs, opposite of what expected from the local Fundamental Metallicity Relation. These results support a stochastic star-formation history involving gas consumption/ejection and metal-poor inflow, strongly affecting metallicities of low-mass galaxies. Furthermore, we identify two Little Red Dots in our EMPG candidates, both exhibiting broad H$α$ and prominent Ly$α$, offering insights into the early black-hole growth in extremely metal-poor environments.

astro-ph.GA↗