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Zihao Li

Publications and source records attributed to Zihao Li.

At least 73 records · Page 4Linked to original sources
arXiv2026

MAMMOTH-Grism: Gas-phase Metallicity Gradients of Star-forming Galaxies in Protocluster Environments at Cosmic Noon

Environment plays a crucial role in shaping galaxy formation, yet the impact of overdensities on the internal chemical structure of galaxies at cosmic noon is still under debate. Here, we present spatially resolved gas-phase metallicity gradients for 42 star-forming galaxies in three massive protoclusters at $z \sim 2.3$, derived fromHubble Space Telescope (HST) slitless grism spectroscopy from the MAMMOTH-Grism survey. We find that the majority (29 of 42, $\sim$69%) of these protocluster members exhibit positive (inverted) metallicity gradients, a fraction significantly higher than observed in field galaxies of similar mass and redshift. By examining correlations with global properties, we show that these positive gradients are strongly associated with galaxies that are metal-deficient relative to the field mass-metallicity relation, particularly among the massive population ($\log(M_*/M_\odot) > 9.95$). These trends suggest that galaxies in dense protocluster environments experience substantial, enhanced inflows of pristine gas toward their central regions, which dilute the central metallicity and produce the observed inverted gradients. Our results provide observational evidence that environmental effects actively regulate gas accretion and chemical redistribution during the peak epoch of cosmic star formation.

astro-ph.GA↗
arXiv2026

A Post-starburst Galaxy Undergoing Ram-pressure Stripping at Redshift 3.06

Understanding how galaxies ignite and extinguish their star formation remains a cornerstone question in modern astrophysics. Recent JWST surveys have revealed an overabundance of massive quiescent galaxies in the first billion years of the Universe, challenging current models of galaxy evolution. In the nearby Universe, ram pressure stripping (RPS) is a major environmental mechanism capable of rapidly shutting down star formation, yet direct observation remains scarce at redshift $z\gtrsim1$, and its role at $z>2$ is even poorly constrained by simulations. Here, we utilize JWST and ALMA observations to present direct evidence of RPS in the post-starburst galaxy A2744-JF-z3, residing in a galaxy group at redshift 3.06, the earliest such detection to date. Spectroscopic diagnostics and spectral energy distribution modeling reveal the ongoing removal of cold gas and dust, coincident with the abrupt cessation of star formation. Contrary to hydrodynamical simulations that predict a reduced incidence of RPS at high redshift, our results instead imply that RPS can operate at $z>3$, suggesting a highly stochastic and impulsive stripping within a clumpy, filamentary intra-group and circumgalactic medium. These observations extend environmental quenching well into the epoch of galaxy assembly, highlighting RPS as a previously overlooked decisive pathway to rapid quenching in nascent groups and protoclusters in the early Universe.

astro-ph.GA↗
arXiv2026

Revisiting the mass metallicity relation and the fundamental metallicity relation of dwarf galaxies at cosmic noon with NIRISS

We extend the stellar-mass gas-phase metallicity relation (MZR) at $z = 1.1-3.4$ down to the extremely low-mass regime using 183 galaxies with $\log(M_*/M_\odot) = 6.3-10.2$, based on deep JWST/NIRISS slitless spectroscopy from the NGDEEP program. The derived MZR is in excellent agreement with our previous result from 50 galaxies in the GLASS-JWST sample, underscoring the robustness and universality of this relation. Together, these datasets constitute the largest sample of dwarf galaxies yet obtained with NIRISS. The observed MZR slope, $β\simeq0.24\pm0.03$, remains constant across nearly four orders of magnitude in stellar mass. Analytical modeling of the metal-loading factor of outflows ($ζ_\textrm{out}$) indicates that, at $M_*\lesssim10^8M_\odot$, $ζ_\textrm{out}$ becomes progressively less dominant than the gas fraction ($μ_\textrm{gas}$) in regulating the MZR slope. Using this enlarged NIRISS sample, we further test the existence of the fundamental metallicity relation (FMR). We find no robust evidence for an additional SFR dependence beyond the MZR, nor any reduction in metallicity scatter when SFR is included. Examination of systematic uncertainties in \oh, $M_*$ and SFR suggests that the MZR slope ($β\sim0.22$) is robust, and that different assumptions about the strong-line calibrations or star-formation history (SFH) of the galaxies change the slope by less than 1-$σ$. At the current depth of the NIRISS data, evidence for an FMR among high-redshift dwarf galaxies remains inconclusive, highlighting the need for larger samples, and deeper observations.

astro-ph.GA↗
arXiv2026

Code as Agent Harness

Recent large language models (LLMs) have demonstrated strong capabilities in understanding and generating code, from competitive programming to repository-level software engineering. In emerging agentic systems, code is no longer only a target output. It increasingly serves as an operational substrate for agent reasoning, acting, environment modeling, and execution-based verification. We frame this shift through the lens of agent harnesses and introduce code as agent harness: a unified view that centers code as the basis for agent infrastructure. To systematically study this perspective, we organize the survey around three connected layers. First, we study the harness interface, where code connects agents to reasoning, action, and environment modeling. Second, we examine harness mechanisms: planning, memory, and tool use for long-horizon execution, together with feedback-driven control and optimization that make harness reliable and adaptive. Third, we discuss scaling the harness from single-agent systems to multi-agent settings, where shared code artifacts support multi-agent coordination, review, and verification. Across these layers, we summarize representative methods and practical applications of code as agent harness, spanning coding assistants, GUI/OS automation, embodied agents, scientific discovery, personalization and recommendation, DevOps, and enterprise workflows. We further outline open challenges for harness engineering, including evaluation beyond final task success, verification under incomplete feedback, regression-free harness improvement, consistent shared state across multiple agents, human oversight for safety-critical actions, and extensions to multimodal environments. By centering code as the harness of agentic AI, this survey provides a unified roadmap toward executable, verifiable, and stateful AI agent systems.

cs.CL↗
arXiv2026

CryptoBench: A Dynamic Benchmark for Expert-Level Evaluation of LLM Agents in Cryptocurrency

This paper introduces CryptoBench, the first expert-curated, dynamic benchmark designed to rigorously evaluate the real-world capabilities of Large Language Model (LLM) agents in the uniquely demanding and fast-paced cryptocurrency domain. Unlike general-purpose agent benchmarks for search and prediction, professional crypto analysis presents specific challenges: \emph{extreme time-sensitivity}, \emph{a highly adversarial information environment}, and the critical need to synthesize data from \emph{diverse, specialized sources}, such as on-chain intelligence platforms and real-time Decentralized Finance (DeFi) dashboards. CryptoBench thus serves as a much more challenging and valuable scenario for LLM agent assessment. To address these challenges, we constructed a live, dynamic benchmark featuring 50 questions per month, expertly designed by crypto-native professionals to mirror actual analyst workflows. These tasks are rigorously categorized within a four-quadrant system: Simple Retrieval, Complex Retrieval, Simple Prediction, and Complex Prediction. This granular categorization enables a precise assessment of an LLM agent's foundational data-gathering capabilities alongside its advanced analytical and forecasting skills. Our evaluation of ten LLMs, both directly and within an agentic framework, reveals a performance hierarchy and uncovers a failure mode. We observe a \textit{retrieval-prediction imbalance}, where many leading models, despite being proficient at data retrieval, demonstrate a pronounced weakness in tasks requiring predictive analysis. This highlights a problematic tendency for agents to appear factually grounded while lacking the deeper analytical capabilities to synthesize information.

cs.CL↗
arXiv2026

QSeqSim: A Symbolic Simulator for Qiskit While Loops Using Sequential Quantum Circuits

We present a tool QSeqSim, a Qiskit-integrated symbolic backend that fills the current gap of having no Qiskit-native support for simulating while-loop quantum programs and their induced sequential quantum circuits. QSeqSim takes Qiskit QuantumCircuit objects, translates them into OpenQASM 3 code, and organises the resulting program into a combination of combinational, dynamic, and sequential circuits, thereby assigning while-loops a precise sequential circuit semantics with explicit internal and external qubits. Building on this semantics, QSeqSim adopts a Binary Decision Diagram (BDD)-based symbolic representation and integrates weighted model counting to compute measurement probabilities efficiently by exploiting sharing in structured and sparse BDDs. On top of this Boolean backbone, it introduces dedicated symbolic operators for state composition and state retention, thereby enabling efficient symbolic execution of sequential quantum circuits. Our experiments demonstrate that QSeqSim scales to substantial while-induced sequential circuits; in particular, in the quantum random walk benchmark we successfully simulate circuits with over 1000 qubits for more than 10 loop iterations. QSeqSim is available at https://github.com/Veri-Q/QSeqSim.

quant-ph↗
arXiv2026

Heterogeneous Scientific Foundation Model Collaboration

Agentic large language model systems have demonstrated strong capabilities. However, their reliance on language as the universal interface fundamentally limits their applicability to many real-world problems, especially in scientific domains where domain-specific foundation models have been developed to address specialized tasks beyond natural language. In this work, we introduce Eywa, a heterogeneous agentic framework designed to extend language-centric systems to a broader class of scientific foundation models. The key idea of Eywa is to augment domain-specific foundation models with a language-model-based reasoning interface, enabling language models to guide inference over non-linguistic data modalities. This design allows predictive foundation models, which are typically optimized for specialized data and tasks, to participate in higher-level reasoning and decision-making processes within agentic systems. Eywa can serve as a drop-in replacement for a single-agent pipeline (EywaAgent) or be integrated into existing multi-agent systems by replacing traditional agents with specialized agents (EywaMAS). We further investigate a planning-based orchestration framework in which a planner dynamically coordinates traditional agents and Eywa agents to solve complex tasks across heterogeneous data modalities (EywaOrchestra). We evaluate Eywa across a diverse set of scientific domains spanning physical, life, and social sciences. Experimental results demonstrate that Eywa improves performance on tasks involving structured and domain-specific data, while reducing reliance on language-based reasoning through effective collaboration with specialized foundation models.

cs.AI↗
arXiv2026

Constraining the nature of active galactic nuclei through circumgalactic Lya emission at z=2-3

We present a comprehensive analysis of circumgalactic Lya nebulae around 59 unobscured and 26 obscured quasars at z=2-3, observed with the Keck Cosmic Web Imager (KCWI), to constrain the nature of active galactic nuclei (AGN) at cosmic noon. We find that Lya nebulae around unobscured quasars are significantly less symmetric having a symmetry parameter of a_w=0.2-0.6 and more spatially extended having a scale length of r_h=10.7+/-0.5 kpc than those around obscured quasars (a_w=0.6-0.8; r_h=6.6-7.7 kpc).Unobscured quasars also exhibit steeply declining velocity dispersion profiles with the slope of -4.3+/-0.4 km s^-1 kpc^-1, indicative of large-scale outflows, whereas obscured quasars display flat profiles (-0.2+/-0.7 and -0.6+/-0.4 km s^-1 kpc^-1). The degree of quasar obscuration appears to be intrinsically linked to nebular asymmetry and extent, a relationship that could be in tension with the standard orientation-based AGN unified model, as it expects unobscured-quasar nebulae to be more symmetric and compact. These results naturally fit the evolutionary scenario, where AGN feedback drives a transition from an obscured to an unobscured phase-progressively redistributing gas to larger radii, introducing anisotropy, and driving turbulence. Taken together, our findings favor the evolutionary scenario over the purely orientation-based unified model for quasars at cosmic noon.

astro-ph.GA↗
arXiv2026

Keypoint-based Dynamic Object 6-DoF Pose Tracking via Event Camera

Accurate 6-DoF pose estimation of objects is critical for robots to perform precise manipulation tasks. However, for dynamic object pose estimation, conventional camera-based approaches face several major challenges, such as motion blur, sensor noise, and low-light limitation. To address these issues, we employ event cameras, whose high dynamic range and low latency offer a promising solution. Furthermore, we propose a keypoint-based detection and tracking approach for dynamic object pose estimation. Firstly, a keypoint detection network is constructed to extract keypoints from the time surface generated by the event stream. Subsequently, the polarity and spatial coordinates of the events are leveraged, and the event density in the vicinity of each keypoint is utilized to achieve continuous keypoint tracking. Finally, a hash mapping is established between the 2D keypoints and the 3D model keypoints, and the EPnP algorithm is employed to estimate the 6-DoF pose. Experimental results demonstrate that, whether in simulated or real event environments, the proposed method outperforms the event-based state-of-the-art methods in terms of both accuracy and robustness.

cs.CV↗
arXiv2026

CAST: Achieving Stable LLM-based Text Analysis for Data Analytics

Text analysis of tabular data relies on two core operations: \emph{summarization} for corpus-level theme extraction and \emph{tagging} for row-level labeling. A critical limitation of employing large language models (LLMs) for these tasks is their inability to meet the high standards of output stability demanded by data analytics. To address this challenge, we introduce \textbf{CAST} (\textbf{C}onsistency via \textbf{A}lgorithmic Prompting and \textbf{S}table \textbf{T}hinking), a framework that enhances output stability by constraining the model's latent reasoning path. CAST combines (i) Algorithmic Prompting to impose a procedural scaffold over valid reasoning transitions and (ii) Thinking-before-Speaking to enforce explicit intermediate commitments before final generation. To measure progress, we introduce \textbf{CAST-S} and \textbf{CAST-T}, stability metrics for bulleted summarization and tagging, and validate their alignment with human judgments. Experiments across publicly available benchmarks on multiple LLM backbones show that CAST consistently achieves the best stability among all baselines, improving Stability Score by up to 16.2\%, while maintaining or improving output quality.

cs.CL↗
arXiv2026

HPLT 3.0: Very Large-Scale Multilingual Resources for LLMs and MT. Mono- and Bi-lingual Data, Multilingual Evaluation, and Pre-Trained Models

We present an ongoing initiative to provide open, very large, high-quality, and richly annotated textual datasets for almost 200 languages. At 30 trillion tokens, this is likely the largest generally available multilingual collection of LLM pre-training data. These datasets are derived from web crawls from different sources and accompanied with a complete, open-source pipeline for document selection from web archives, text extraction from HTML, language identification for noisy texts, exact and near-deduplication, annotation with, among others, register labels, text quality estimates, and personally identifiable information; and final selection and filtering. We report on data quality probes through contrastive and analytical statistics, through manual inspection of samples for 24 languages, and through end-to-end evaluation of various language model architectures trained on this data. For multilingual LLM evaluation, we provide a comprehensive collection of benchmarks for nine European languages, with special emphasis on natively created tasks, mechanisms to mitigate prompt sensitivity, and refined normalization and aggregation of scores. Additionally, we train and evaluate a family of 57 monolingual encoder-decoder models, as well as a handful of monolingual GPT-like reference models. Besides the monolingual data and models, we also present a very large collection of parallel texts automatically mined from this data, together with a novel parallel corpus synthesized via machine translation.

cs.CL↗
arXiv2026

Knowledge Is Not Static: Order-Aware Hypergraph RAG for Language Models

Retrieval-augmented generation (RAG) enhances large language models by grounding outputs in retrieved knowledge. However, existing RAG methods including graph- and hypergraph-based approaches treat retrieved evidence as an unordered set, implicitly assuming permutation invariance. This assumption is misaligned with many real-world reasoning tasks, where outcomes depend not only on which interactions occur, but also on the order in which they unfold. We propose Order-Aware Knowledge Hypergraph RAG (OKH-RAG), which treats order as a first-class structural property. OKH-RAG represents knowledge as higher-order interactions within a hypergraph augmented with precedence structure, and reformulates retrieval as sequence inference over hyperedges. Instead of selecting independent facts, it recovers coherent interaction trajectories that reflect underlying reasoning processes. A learned transition model infers precedence directly from data without requiring explicit temporal supervision. We evaluate OKH-RAG on order-sensitive question answering and explanation tasks, including tropical cyclone and port operation scenarios. OKH-RAG consistently outperforms permutation-invariant baselines, and ablations show that these gains arise specifically from modeling interaction order. These results highlight a key limitation of set-based retrieval: effective reasoning requires not only retrieving relevant evidence, but organizing it into structured sequences.

cs.CL↗
arXiv2026

OWLEYE: Zero-Shot Learner for Cross-Domain Graph Data Anomaly Detection

Graph data is informative to represent complex relationships such as transactions between accounts, communications between devices, and dependencies among machines or processes. Correspondingly, graph anomaly detection (GAD) plays a critical role in identifying anomalies across various domains, including finance, cybersecurity, manufacturing, etc. Facing the large-volume and multi-domain graph data, nascent efforts attempt to develop foundational generalist models capable of detecting anomalies in unseen graphs without retraining. To the best of our knowledge, the different feature semantics and dimensions of cross-domain graph data heavily hinder the development of the graph foundation model, leaving further in-depth continual learning and inference capabilities a quite open problem. Hence, we propose OWLEYE, a novel zero-shot GAD framework that learns transferable patterns of normal behavior from multiple graphs, with a threefold contribution. First, OWLEYE proposes a cross-domain feature alignment module to harmonize feature distributions, which preserves domain-specific semantics during alignment. Second, with aligned features, to enable continuous learning capabilities, OWLEYE designs the multi-domain multi-pattern dictionary learning to encode shared structural and attribute-based patterns. Third, for achieving the in-context learning ability, OWLEYE develops a truncated attention-based reconstruction module to robustly detect anomalies without requiring labeled data for unseen graph-structured data. Extensive experiments on real-world datasets demonstrate that OWLEYE achieves superior performance and generalizability compared to state-of-the-art baselines, establishing a strong foundation for scalable and label-efficient anomaly detection.

cs.LG↗
arXiv2026

COSMOS-3D: Dense Circumnuclear Gas across Black Hole Growth Phases at z ~ 3

We report the discovery of two broad-line X-ray AGNs (cid_414 and cid_947) at z ~ 3 identified in the JWST Cycle 3 COSMOS-3D program using NIRCam F444W grism spectroscopy. Both exhibit prominent HeI+Pa$γ$ emission and absorption, indicative of circumnuclear dense gas that is traced in these systems. Complementary UV and optical spectroscopy in the COSMOS field provides Ly$α$, Si IV, and C IV measurements. Both sources are detected in MIRI F1000W, and cid_414 is also detected in F2100W, indicating hot dust emission. The two AGNs show distinct black hole and obscuration properties. The source cid_414 displays little red dots (LRD)-like V-shape spectra energy distribution (SED) shape with a turnover near the Balmer 4000 Å break, and a narrow Ly$α$ line with $\log L_{\rm Lyα}=42.49\pm0.01~\mathrm{erg\ s^{-1}}$, with no additional metal lines detected. In contrast, cid_947 exhibits a higher HeI absorption column density, larger X-ray--inferred $N_{\rm H}$, lower intrinsic 2--10 keV luminosity, and strong blueshifted features in He I, Si IV, and C IV absorption with velocity offsets exceeding $5000~\mathrm{km\ s^{-1}}$. Photoionization modeling implies gas densities of $\sim10^{9-10} \mathrm{cm^{-3}}$ and sizes comparable to the broad-line region, consistent with dense gas envelopes predicted for LRDs. Together with previous detections of HeI absorption in compact little red dots, these results suggest that dense circumnuclear gas is likely prevalent at high redshift and may regulate obscuration and black hole--host co-evolution across AGN types.

astro-ph.GA↗
arXiv2026

Flow Matching Meets Biology and Life Science: A Survey

Over the past decade, advances in generative modeling, such as generative adversarial networks, masked autoencoders, and diffusion models, have significantly transformed biological research and discovery, enabling breakthroughs in molecule design, protein generation, catalysis discovery, drug discovery, and beyond. At the same time, biological applications have served as valuable testbeds for evaluating the capabilities of generative models. Recently, flow matching has emerged as a powerful and efficient alternative to diffusion-based generative modeling, with growing interest in its application to problems in biology and life sciences. This paper presents the first comprehensive survey of recent developments in flow matching and its applications in biological domains. We begin by systematically reviewing the foundations and variants of flow matching, and then categorize its applications into three major areas: biological sequence modeling, molecule generation and design, and peptide and protein generation. For each, we provide an in-depth review of recent progress. We also summarize commonly used datasets and software tools, and conclude with a discussion of potential future directions. The corresponding curated resources are available at https://github.com/Violet24K/Awesome-Flow-Matching-Meets-Biology.

cs.LG↗
arXiv2026

Language in the Flow of Time: Time-Series-Paired Texts Weaved into a Unified Temporal Narrative

While many advances in time series models focus exclusively on numerical data, research on multimodal time series, particularly those involving contextual textual information, remains in its infancy. With recent progress in large language models and time series learning, we revisit the integration of paired texts with time series through the Platonic Representation Hypothesis, which posits that representations of different modalities converge to shared spaces. In this context, we identify that time-series-paired texts may naturally exhibit periodic properties that closely mirror those of the original time series. Building on this insight, we propose a novel framework, Texts as Time Series (TaTS), which considers the time-series-paired texts to be auxiliary variables of the time series. TaTS can be plugged into any existing numerical-only time series models and effectively enable them to handle time series data with paired texts. Through extensive experiments on both multimodal time series forecasting and imputation tasks across benchmark datasets with various existing time series models, we demonstrate that TaTS can enhance multimodal predictive performance without modifying model architectures. Our Code is available at https://github.com/iDEA-iSAIL-Lab-UIUC/TaTS.

cs.LG↗
arXiv2026

LLMs for High-Frequency Decision-Making: Normalized Action Reward-Guided Consistency Policy Optimization

While Large Language Models (LLMs) form the cornerstone of sequential decision-making agent development, they have inherent limitations in high-frequency decision tasks. Existing research mainly focuses on discrete embodied decision scenarios with low-frequency and significant semantic differences in state space (e.g., household planning). These methods suffer from limited performance in high-frequency decision-making tasks, since high-precision numerical state information in such tasks undergoes frequent updates with minimal fluctuations, and exhibiting policy misalignment between the learned sub-tasks and composite tasks. To address these issues, this paper proposes Normalized Action Reward guided Consistency Policy Optimization (NAR-CP). 1) Our method first acquires predefined dense rewards from environmental feedback of candidate actions via reward functions, then completes reward shaping through normalization, and theoretically verifies action reward normalization does not impair optimal policy. 2) To reduce policy misalignment in composite tasks, we use LLMs to infer sub-observation candidate actions and generate joint policies, with consistency loss ensuring precise alignment between global semantic policies and sub-semantic policies. Experiments on UAV pursuit, a typical high-frequency task, show our method delivers superior performance on independent and composite tasks with excellent generalization to unseen tasks.

cs.AI↗
arXiv2026

Fractional-order Spiking Neural Network

Spiking Neural Networks (SNNs) draw inspiration from biological neurons to enable brain-like computation, demonstrating effectiveness in processing temporal information with energy efficiency and biological realism. Most existing SNNs are based on neural dynamics such as the (leaky) integrate-and-fire (IF/LIF) models, which are described by first-order ordinary differential equations (ODEs) with Markovian characteristics. This means the potential state at any time depends solely on its immediate past value, potentially limiting network expressiveness. Empirical studies of real neurons, however, reveal long-range correlations and fractal dendritic structures, suggesting non-Markovian behavior better modeled by fractional-order ODEs. Motivated by this, we propose a fractional-order spiking neural network (f-SNN) framework that strictly generalizes integer-order SNNs and captures long-term dependencies in membrane potential and spike trains via fractional dynamics, enabling richer temporal patterns. We further release an open-source toolbox, spikeDE, to support the f-SNN framework across diverse architectures and real-world tasks. Experimentally, fractional adaptations of established SNNs into the f-SNN framework achieve superior accuracy, comparable energy efficiency, and improved robustness to noise, underscoring the promise of f-SNNs as an effective extension of traditional SNNs.

cs.NE↗