arXiv subjects

Lixin Liu

Publications and source records attributed to Lixin Liu.

At least 19 recordsLinked to original sources

arXiv2026

Agentic Artifact Creation: Systems, Evaluation, Principles, and Opportunities

Generative models can turn natural-language prompts into images, text, code, and other content, lowering the cost of producing drafts and components. Their practical impact increasingly depends on whether those pieces can become complete, dependable deliverables. This survey examines agentic artifact creation, which we define as stateful construction in which an AI system materially constructs or revises a deliverable and intermediate observations redirect later work. Functionally, the process links an operational representation of the artifact, a construction policy, and runtime verification whose feedback can redirect later actions. We reviewed 259 works available through August 20, 2026: 230 systems meeting this definition and 29 benchmarks of agentic artifact construction. We compare six artifact families, then analyze application settings and evaluation practice as separate dimensions. Across families, construction challenges reflect not only modality but also how tightly decisions are coupled and whether failures become visible while they remain repairable. Decomposition can reduce local complexity while increasing coordination and reassembly costs. Learned judges may add little independent evidence when they share the generator's preferences or blind spots. We formulate principles for keeping commitments and responsibility explicit, turning feedback into targeted repair, and revalidating affected state after change. We also identify opportunities for sustaining coherent, accountable control as artifacts, creator intent, and construction systems evolve. A curated paper list is available at https://github.com/GeminiLight/awesome-agentic-artifact-creation.

cs.MM↗

arXiv2026

EmoTrace: An Emotion Trajectory-Centered Framework for Psychological Support Dialogue Generation

Using large language models (LLMs) to assist psychological counseling is an important task in the field of natural language processing. The construction of high-quality psychological support dialogue corpora serves as a critical foundation for training counseling-oriented conversational models. However, existing data generation approaches generally suffer from several limitations, including emotionally stable seekers, limited variation in emotional dynamics, and a high degree of compliance with counselors' guidance. These issues result in LLM that lack the capability to effectively respond to emotionally unstable scenarios. In addition, counselor responses are typically driven by problem-solving objectives, thereby overlooking the role of emotion-focused interaction, which are essential in psychological counseling. To address these gaps, we propose EmoTrace, a multi-turn dialogue corpus generation framework centered on modeling seekers' emotional trajectories. we construct seekers' cognitive profile and introduce a seeker module with emotional schemas and an associated activation mechanism, a counselor module, and an emotional trajectory control module, thereby enhancing the layering of the seeker's emotional expression and the counselor's targeted empathic expression. Experimental results demonstrate that the proposed method outperforms existing approaches in terms of emotional richness and empathy quality.

cs.CL↗

arXiv2026

Coherent terahertz magnon-phonon three-wave mixing in a layered antiferromagnet

The coherent nonlinear dynamics between collective excitations, such as magnons and phonons, drive emergent phenomena in quantum materials, yet their direct observation remains a central challenge. Here, using double-terahertz-pump optical-probe spectroscopy, we report the direct observation of coherent magnon-phonon three-wave mixing in the layered antiferromagnetic insulator FePS$_{3}$. We resolve both second- and third-order nonlinear responses of antiferromagnetic magnons and identify a suite of nonlinear couplings in two-dimensional (2D) coherent spectra, including definitive sum- and difference-frequency generation between magnons and phonons. These results lay the groundwork for exploiting coherent nonlinearities to entangle magnetic and vibrational excitations, opening avenues for quantum control and hybrid quantum technologies in the terahertz regime.

cond-mat.mtrl-sci↗

arXiv2026

Recursion formula for the volumes of moduli spaces of compact hyperbolic surfaces with cone points

Let $V_{g,m,n}(\overrightarrow L,\overrightarrow θ)$ be the Weil-Petersson volume of the moduli space of hyperbolic surfaces of genus g with m geodesic boundary components of length $\overrightarrow L=(\ell_1,...,\ell_m)$ and $n$ cone points of angle $\overrightarrow θ=(θ_1,...θ_n)$. By using the generalized McShane's identities, we show that $V_{g,m,n}(\overrightarrow L,\overrightarrow θ)$ is a polynomial of $(\ell_1,...,\ell_n,iθ_1,...,iθ_m)$. And we obtain a recursion formula for $V_{g,m,n}(\overrightarrow L,\overrightarrow θ)$, which is a generalization of Mirzakhani's result.

math.GT↗

arXiv2026

POET-X: Memory-efficient LLM Training by Scaling Orthogonal Transformation

Efficient and stable training of large language models (LLMs) remains a core challenge in modern machine learning systems. To address this challenge, Reparameterized Orthogonal Equivalence Training (POET), a spectrum-preserving framework that optimizes each weight matrix through orthogonal equivalence transformation, has been proposed. Although POET provides strong training stability, its original implementation incurs high memory consumption and computational overhead due to intensive matrix multiplications. To overcome these limitations, we introduce POET-X, a scalable and memory-efficient variant that performs orthogonal equivalence transformations with significantly reduced computational cost. POET-X maintains the generalization and stability benefits of POET while achieving substantial improvements in throughput and memory efficiency. In our experiments, POET-X enables the pretraining of billion-parameter LLMs on a single Nvidia H100 GPU, and in contrast, standard optimizers such as AdamW run out of memory under the same settings.

cs.LG↗

arXiv2025

Dielectric and gate metal engineering for threshold voltage modulation in enhancement mode monolayer MoS2 field effect transistors

Excellent gate electrostatics in field effect transistors (FETs) based on two-dimensional transition metal dichalcogenide (2D TMD) channels can dramatically decrease static power dissipation. Energy efficient FETs operate in enhancement mode with small and positive threshold voltage (Vth) for n-type devices. However, most state-of-the-art FETs based on monolayer MoS2 channel operate in depletion mode with negative Vth due to doping from the underlying dielectric substrate. In this work, we identify key properties of the semiconductor/dielectric interface (MoS2 on industrially relevant high dielectric constant (k) HfO2, ZrO2 and hBN for reference) responsible for realizing enhancement-mode operation of 2D MoS2 channel FETs. We find that hBN and ZrO2 dielectric substrates provide low defect interfaces with MoS2 that enables effective modulation of the Vth using gate metals of different work functions (WFs). We use photoluminescence (PL) and synchrotron X-ray photoelectron spectroscopy (XPS) measurements to investigate doping levels in monolayer MoS2 on different dielectrics with different WF gate metals. We complement the FET and spectroscopic measurements with capacitance-voltage analysis on dielectrics with varying thicknesses, which confirm that Vth modulation in ZrO2 devices is correlated with WF of the gate metals - in contrast with HfO2 devices that exhibit signatures of Vth pinning induced by oxide/interface defect states. Finally, we demonstrate FETs using a 2D MoS2 channel and a 6 nm of ZrO2 dielectric, achieving a subthreshold swing of 87 mV dec-1 and a threshold voltage of 0.1 V. Our results offer insights into the role of dielectric/semiconductor interface in 2D MoS2 based FETs for realizing enhancement mode FETs and highlight the potential of ZrO2 as a scalable high-k dielectric.

cond-mat.mtrl-sci↗

arXiv2025

Validation of the copper equation of state via shock loading experiments of loosely associated powders

High-fidelity shock experiments were performed on copper powders with controlled porosity via improved target fabrication and assembly. Optical velocimetry and multi-channel pyrometry were used to obtain Hugoniot data, isentropic release paths, and interface temperature histories. The results validate a modified two-phase equation of state (EOS) for copper based on the framework of Greeff et al. The measured Hugoniot shows good agreement with the present model but exhibits significant softening above ~156 GPa relative to the original Greeff EOS, indicating that reduction in lattice specific heat becomes essential when shock temperatures exceed three times the melting point (T > 3Tm). Unloading behavior matches hydrodynamic simulations incorporating the recalibrated EOS, confirming its accuracy for off-Hugoniot states. Theoretical analysis of temperature release profiles suggests that the thermal conductivity of shocked copper powders may be considerably higher than first-principles predictions. Crucially, despite heterogeneity in shock heating, the macroscopic dynamic response of copper powders with a porosity of ~1.7 is well captured by an average-density EOS model, supporting the use of porous material experiments for EOS validation under extreme conditions.

physics.app-ph↗

arXiv2025

Ultra-clean interface between high k dielectric and 2D MoS2

Atomically thin transition metal dichalcogenides (TMDs) are promising candidates for next-generation transistor channels due to their superior scaling properties. However, the integration of ultra-thin gate dielectrics remains a challenge, as conventional oxides such as SiO2, Al2O3, and HfO2 tend to unintentionally dope 2D TMDs and introduce interfacial defect states, leading to undesirable field-effect transistor (FET) performance and unstable threshold voltages. Here, we demonstrate that zirconium oxide (ZrO2), a high-k dielectric compatible with semiconductor processing, forms an ultra-clean interface with monolayer MoS2. Using soft and hard X-ray photoelectron spectroscopy and density functional theory, we find that ZrO2 does not measurably interact with MoS2, in contrast to significant doping observed for SiO2 and HfO2 substrates. As a result, back-gated monolayer MoS2 FETs fabricated with ZrO2 dielectrics exhibit stable and positive threshold voltages (0.36 plus/minus 0.3 V), low subthreshold swing (75 mV per decade), and high ON currents exceeding 400 microamperes. We further demonstrate p-type WSe2 FETs with ON currents greater than 200 microamperes per micrometer by suppressing electron doping with ZrO2 dielectrics. Atomic-resolution imaging confirms a defect-free ZrO2/MoS2 interface, which enables top-gate FETs with an equivalent oxide thickness of 0.86 nanometers and subthreshold swing of 80 mV per decade. Moreover, the ultraclean ZrO2/MoS2 interface allows for effective threshold voltage modulation in top-gate FETs via gate metal work function engineering. These findings establish ZrO2 as a highly promising, industry-compatible high-k dielectric for scalable 2D TMD-based electronics.

cond-mat.mtrl-sci↗

arXiv2025

Hunyuan-TurboS: Advancing Large Language Models through Mamba-Transformer Synergy and Adaptive Chain-of-Thought

As Large Language Models (LLMs) rapidly advance, we introduce Hunyuan-TurboS, a novel large hybrid Transformer-Mamba Mixture of Experts (MoE) model. It synergistically combines Mamba's long-sequence processing efficiency with Transformer's superior contextual understanding. Hunyuan-TurboS features an adaptive long-short chain-of-thought (CoT) mechanism, dynamically switching between rapid responses for simple queries and deep "thinking" modes for complex problems, optimizing computational resources. Architecturally, this 56B activated (560B total) parameter model employs 128 layers (Mamba2, Attention, FFN) with an innovative AMF/MF block pattern. Faster Mamba2 ensures linear complexity, Grouped-Query Attention minimizes KV cache, and FFNs use an MoE structure. Pre-trained on 16T high-quality tokens, it supports a 256K context length and is the first industry-deployed large-scale Mamba model. Our comprehensive post-training strategy enhances capabilities via Supervised Fine-Tuning (3M instructions), a novel Adaptive Long-short CoT Fusion method, Multi-round Deliberation Learning for iterative improvement, and a two-stage Large-scale Reinforcement Learning process targeting STEM and general instruction-following. Evaluations show strong performance: overall top 7 rank on LMSYS Chatbot Arena with a score of 1356, outperforming leading models like Gemini-2.0-Flash-001 (1352) and o4-mini-2025-04-16 (1345). TurboS also achieves an average of 77.9% across 23 automated benchmarks. Hunyuan-TurboS balances high performance and efficiency, offering substantial capabilities at lower inference costs than many reasoning models, establishing a new paradigm for efficient large-scale pre-trained models.

cs.CL↗

arXiv2025

High-efficiency WSe$_2$ photovoltaics enabled by ultra-clean van der Waals contacts

Layered transition metal dichalcogenide semiconductors are interesting for photovoltaics owing to their high solar absorbance and efficient carrier diffusion. Tungsten diselenide (WSe$_2$), in particular, has emerged as a promising solar cell absorber. However, defective metal-semiconductor interfaces have restricted the power conversion efficiency (PCE) to approximately 6%. Here we report WSe$_2$ photovoltaics with a record-high PCE of approximately 11% enabled by ultra-clean indium/gold (In/Au) van der Waals (vdW) contacts. Using grid-patterned top vdW electrodes, we demonstrate near-ideal diodes with a record-high on/off ratio of $1.0\times 10^9$. Open-circuit voltage (VOC) of 571 +/- 9 mV, record-high short-circuit current density (JSC) of 27.19 +/- 0.45 mA cm$^{-2}$ -- approaching the theoretical limit (34.5 mA cm$^{-2}$) -- and fill factor of 69.2 +/- 0.7% resulting in PCE of 10.8 +/- 0.2% under 1-Sun illumination on large active area (approximately 0.13x0.13 mm$^2$) devices have been realised. The excellent device performance is consistent with the high external quantum efficiency (up to approximately 93%) measured across a broad spectral range of 500-830 nm. Our results suggest that ultra-clean vdW contacts on WSe$_2$ enable high-efficiency photovoltaics and form the foundation for further optimisation.

cond-mat.mtrl-sci↗

arXiv2025

Large Scale Manufacture of Phase Pure Two-Dimensional Metallic MoS2 Nanosheets

Metallic monolayered [or two - dimensional (2D)] MoS2 nanosheets show tremendous promise for energy storage and catalysis applications. However, state-of-the-art chemical exfoliation methods require > 48 hours to produce milligrams of metallic 2D MoS2. Further, chemically exfoliated MoS2 nanosheets are a mixture of metallic (1T or 1T prime 50% to 70%) and semiconducting (2H 30% to 50%) phases. Here, we demonstrate large-scale and rapid (>600 grams per hour) production of purely metallic phase 2D MoS2 (and WS2, MoSe2) nanosheets using microwave irradiation. Atomic resolution imaging shows 1T or 1T prime metallic phase in basal plane - consistent with close to 100% metallic phase concentration measured by X-ray photoelectron spectroscopy. The high 1T phase concentration results in the highest exchange current density of 0.175 plus-minus 0.03 mA cm-2 and among the lowest Tafel slopes (39 - 43 mV dec-1) measured to date for the hydrogen evolution reaction. In supercapacitors and lithium-sulfur pouch cell batteries, record-high volumetric capacitance of 753 plus-minus 3.6 F cm-3 and specific capacity of 1245 plus-minus 16 mAh g-1 (at exceptionally low electrolyte to sulfur ratio = 2 microlitre g-1), respectively, are obtained. Our method provides a practical pathway for producing high quality purely metallic phase 2D materials for high performance energy devices.

cond-mat.mtrl-sci↗

arXiv2025

VisualSimpleQA: A Benchmark for Decoupled Evaluation of Large Vision-Language Models in Fact-Seeking Question Answering

Large vision-language models (LVLMs) have demonstrated remarkable achievements, yet the generation of non-factual responses remains prevalent in fact-seeking question answering (QA). Current multimodal fact-seeking benchmarks primarily focus on comparing model outputs to ground truth answers, providing limited insights into the performance of modality-specific modules. To bridge this gap, we introduce VisualSimpleQA, a multimodal fact-seeking benchmark with two key features. First, it enables streamlined and decoupled evaluation of LVLMs in visual and linguistic modalities. Second, it incorporates well-defined difficulty criteria to guide human annotation and facilitates the extraction of a challenging subset, VisualSimpleQA-hard. Experiments on 15 LVLMs show that even state-of-the-art models such as GPT-4o achieve merely 60%+ correctness in multimodal fact-seeking QA on VisualSimpleQA and 30%+ on VisualSimpleQA-hard. Furthermore, the decoupled evaluation across these models highlights substantial opportunities for improvement in both visual and linguistic modules. The dataset is available at https://huggingface.co/datasets/WYLing/VisualSimpleQA.

cs.CL↗

arXiv2024

Instability of Self-Driving Satellite Mega-Constellation: From Theory to Practical Impacts on Network Lifetime and Capacity

Low Earth Orbit (LEO) satellite mega-constellations aim to enable high-speed Internet for numerous users anywhere on Earth. To safeguard their network infrastructure in congested outer space, they perform automatic orbital maneuvers to avoid collisions with external debris and satellites. However, our control-theoretic analysis and empirical validation using Starlink's space situational awareness datasets discover that, these safety-oriented maneuvers themselves can threaten safety and networking via cascaded collision avoidance inside the mega-constellation. This domino effect forces a dilemma between long-term LEO network lifetime and short-term LEO network capacity. Its root cause is that, the decades-old local pairwise maneuver paradigm for standalone satellites is inherently unstable if scaled out to recent mega-constellation networks. We thus propose an alternative bilateral maneuver control that stabilizes self-driving mega-constellations for concurrent network lifetime and capacity boosts. Our operational trace-driven emulation shows a 8$\times$ network lifetime extension in Starlink without limiting its network capacity.

cs.NI↗

arXiv2024

Conifer: Improving Complex Constrained Instruction-Following Ability of Large Language Models

The ability of large language models (LLMs) to follow instructions is crucial to real-world applications. Despite recent advances, several studies have highlighted that LLMs struggle when faced with challenging instructions, especially those that include complex constraints, hindering their effectiveness in various tasks. To address this challenge, we introduce Conifer, a novel instruction tuning dataset, designed to enhance LLMs to follow multi-level instructions with complex constraints. Utilizing GPT-4, we curate the dataset by a series of LLM-driven refinement processes to ensure high quality. We also propose a progressive learning scheme that emphasizes an easy-to-hard progression, and learning from process feedback. Models trained with Conifer exhibit remarkable improvements in instruction-following abilities, especially for instructions with complex constraints. On several instruction-following benchmarks, our 7B model outperforms the state-of-the-art open-source 7B models, even exceeds the performance of models 10 times larger on certain metrics. All the code and Conifer dataset are available at https://www.github.com/ConiferLM/Conifer.

cs.CL↗

arXiv2024

Open-World Semi-Supervised Learning for Node Classification

Open-world semi-supervised learning (Open-world SSL) for node classification, that classifies unlabeled nodes into seen classes or multiple novel classes, is a practical but under-explored problem in the graph community. As only seen classes have human labels, they are usually better learned than novel classes, and thus exhibit smaller intra-class variances within the embedding space (named as imbalance of intra-class variances between seen and novel classes). Based on empirical and theoretical analysis, we find the variance imbalance can negatively impact the model performance. Pre-trained feature encoders can alleviate this issue via producing compact representations for novel classes. However, creating general pre-trained encoders for various types of graph data has been proven to be challenging. As such, there is a demand for an effective method that does not rely on pre-trained graph encoders. In this paper, we propose an IMbalance-Aware method named OpenIMA for Open-world semi-supervised node classification, which trains the node classification model from scratch via contrastive learning with bias-reduced pseudo labels. Extensive experiments on seven popular graph benchmarks demonstrate the effectiveness of OpenIMA, and the source code has been available on GitHub.

cs.LG↗

arXiv2023

Continual Transfer Learning for Cross-Domain Click-Through Rate Prediction at Taobao

As one of the largest e-commerce platforms in the world, Taobao's recommendation systems (RSs) serve the demands of shopping for hundreds of millions of customers. Click-Through Rate (CTR) prediction is a core component of the RS. One of the biggest characteristics in CTR prediction at Taobao is that there exist multiple recommendation domains where the scales of different domains vary significantly. Therefore, it is crucial to perform cross-domain CTR prediction to transfer knowledge from large domains to small domains to alleviate the data sparsity issue. However, existing cross-domain CTR prediction methods are proposed for static knowledge transfer, ignoring that all domains in real-world RSs are continually time-evolving. In light of this, we present a necessary but novel task named Continual Transfer Learning (CTL), which transfers knowledge from a time-evolving source domain to a time-evolving target domain. In this work, we propose a simple and effective CTL model called CTNet to solve the problem of continual cross-domain CTR prediction at Taobao, and CTNet can be trained efficiently. Particularly, CTNet considers an important characteristic in the industry that models has been continually well-trained for a very long time. So CTNet aims to fully utilize all the well-trained model parameters in both source domain and target domain to avoid losing historically acquired knowledge, and only needs incremental target domain data for training to guarantee efficiency. Extensive offline experiments and online A/B testing at Taobao demonstrate the efficiency and effectiveness of CTNet. CTNet is now deployed online in the recommender systems of Taobao, serving the main traffic of hundreds of millions of active users.

cs.IR↗

arXiv2023

A new boundary of the mapping class group

Based on the action of the mapping class group on the space of measured foliations, we construct a new boundary of the mapping class group and study the structure of this boundary. As an application, for any point in Teichmuller space, we consider the orbit of this point under the action of the mapping class group and describe the closure of this orbit in the Thurston compactification and the Gardiner-Masur compactification of Teichmuller space. We also construct some new points in the Gardiner-Masur boundary of Teichmuller space.

math.GT↗

arXiv2021

Fractal Rosette: A Stable Space-Ground Network Structure in Mega-Constellation

We present F-Rosette, a stable space-ground network structure for low-earth orbit (LEO) satellite mega-constellations at scale. Due to the dynamic many-to-many space-ground mapping in high mobility, existing LEO mega-constellations with IP protocol stack suffer from frequent user IP address changes (every 133~510s per user) and network routing re-convergence (<20% network usability). To provably stabilize the space-ground network under high mobility and many-to-many dynamics, F-Rosette adopts a recursive structure over the Rosette constellation, derives a hierarchical and time-invariant network address space from a new geographical coordinate system, and ensures efficient and stable routing via geographical-to-topological routing embedding without re-convergence. Our hardware-in-the-loop, trace-driven emulations validate F-Rosette's stability, near-optimal routing (<1.4% additional delays), and marginal overhead (<1% CPU, <2MB memory) for resource-constrained satellites.

cs.NI↗