arXiv subjects

Yicheng Wang

Publications and source records attributed to Yicheng Wang.

At least 19 recordsLinked to original sources

arXiv2026

Intracavity THz generation using a thin lithium niobate plate in a compact Kerr-lens mode-locked Yb:CALGO bulk oscillator

We demonstrate intracavity terahertz (THz) generation via optical rectification in a 50-m-thick lithium niobate crystal placed inside a compact diode-pumped Kerr-lens mode-locked (KLM) Yb:CALGO bulk oscillator. The oscillator operates at a repetition rate of 85 MHz and delivers 83-fs pulses with up to 71 W of average intracavity power, obtained with only 21.4 W of low-cost multimode diode pump power. We generate single-cycle THz pulses with a spectrum extending up to 3 THz, detected by electro-optic sampling with 60 dB dynamic range within 156 s of measurement time (313 averaged traces) and up to 120 W of THz average power. This work combines the high damage threshold, power-handling capability, and cost-effectiveness of thin LN plates with simplicity, compactness, and low-cost multimode diode-pumped solid-state bulk lasers, offering an attractive alternative for high-repetition-rate THz time-domain spectroscopy systems.

physics.optics↗

arXiv2026

CollabSkill: Evaluating Human-Agent Collaboration On Real-World Tasks

AI agents are reshaping the workspace, leading to drastic change of how humans work. Despite the considerable potential of human-agent collaboration both in preserving human agency and generating economic value, this paradigm remains largely absent from occupational task evaluation, hindered by the difficulty of gathering real human data and accounting for inter-human variability. We introduce CollabSkill, a framework for evaluating human-agent collaboration on real-world occupational tasks. CollabSkill pairs real human workers with AI agents on tasks matched to their occupational background, collecting data that capture the complexity of economically valuable tasks and the usage patterns of real workers. To account for inter-human variability, CollabSkill employs a Bayesian skill rating system to disentangle and quantify the skill contributions of both humans and AI agents. Drawing on over 1,500 prompts from 386 working sessions contributed by 93 human workers, our analysis yields insights on two fronts: on the agent side, rankings on CollabSkill diverge meaningfully from those of existing fully autonomous benchmarks where Codex leads, with Claude Code ranking first; on the human side, CollabSkill reveals that practical experience emerges as the primary driver of collaboration skill, with hands-on collaboration meaningfully shifting workers' AI literacy. Together, we hope CollabSkill enables the community to invest in systematic evaluation of human-agent collaboration and spurs development efforts aimed at building AI agents that genuinely augment human workers.

cs.HC↗

arXiv2026

Cosmological Constraints on the DGP Model in light of DESI DR2 2025 Data

We present updated constraints on both flat and non-flat Dvali-Gabadadze-Porrati (DGP) cosmological models using the latest baryon acoustic oscillation (BAO) measurements from the Dark Energy Spectroscopic Instrument Data Release 2 (DESI DR2), in combination with cosmic chronometer (CC), Type Ia supernova (SNIa), and cosmic microwave background (CMB) distance priors. For the non-flat DGP model, we obtain $H_0 = 64.05 \pm 0.27\, \rm{kms^{-1}Mpc^{-1}}$, $\Omega_m = 0.3264 \pm 0.0043$, and $\Omega_k = 0.0088 \pm 0.0016$, corresponding to a transition redshift $z_t \sim 0.41$. For the flat case, the constraints are $H_0 = 63.28 \pm 0.25\, \rm{kms^{-1}Mpc^{-1}}$ and $\Omega_m = 0.3303 \pm 0.0036$. In both scenarios, the inferred Hubble constant is significantly lower than the Planck $\mathrm{\Lambda}$CDM value, indicating that the DGP framework does not alleviate the Hubble tension. Current observations strongly disfavor the DGP framework, primarily due to its inability to simultaneously accommodate DESI BAO and CMB constraints.By incorporating the latest high-precision DESI observations within a unified analysis framework, this work provides updated and more stringent limits on the DGP scenario, offering a consolidated assessment of its viability in the context of current cosmological data.

astro-ph.CO↗

arXiv2026

Diagnosing and Mitigating Retrieval Bottlenecks in LLM-Based Cold-Start Recommendation

Large language models (LLMs) are increasingly used as rerankers in recommender systems, with the expectation that semantic understanding will help in cold-start and long-tail regimes. We test this assumption with a five-domain benchmark that explicitly separates reranking quality from retrieval coverage. In a positive-controlled regime where the gold item is guaranteed present, calibrated LLM rerankers fail to consistently outperform strong collaborative and content baselines under natural traffic, and within-family scaling from Qwen3-8B to Qwen3-32B narrows but does not close the gap on most domains. In a retrieval-realistic regime where the gold item is not injected, the bottleneck is more severe: standard single retrievers place the gold item in a 200-item pool only 4.6-22.9% of the time, largely because 32-91% of cold-start targets are brand-new items with no training interactions. We introduce LHF, a validation-trained learned hybrid fusion layer over a multi-retriever union pool, as a retrieval-side realizability baseline. LHF is the only combiner we test that beats every single retriever on all five domains and recovers 17-61% of oracle coverage headroom on content-rich domains, but only 5-7% on collaboratively strong domains. End-to-end experiments reveal the remaining mismatch: learned non-LLM ranking exploits the LHF pool, while prompt-level LLM reranking often degrades it. LLMs exhibit pockets of semantic cold-start advantage, especially in text-rich domains when the item is already present, but this advantage is largely unreachable in current retrieve-then-rerank pipelines. We release the benchmark protocol, splits, prompts, evaluation tooling, and archived reproducibility artifacts: data at https://doi.org/10.5281/zenodo.20991039 and code at https://doi.org/10.5281/zenodo.20993306.

cs.IR↗

arXiv2026

Know Before You Fetch: Calibrated Retrieval-Budget Allocation for Retrieval-Augmented Generation

Retrieval-augmented generation (RAG) typically retrieves a fixed number of passages for every query. This is wasteful when the reader already knows the answer, and it can be harmful when irrelevant or partially relevant passages distract the reader. We formulate adaptive RAG as calibrated retrieval-budget allocation: given a query, decide whether to answer closed-book, retrieve a compact context (k=1), retrieve a full context (k=5), or abstain. The contribution is a probability interface rather than a new raw uncertainty signal. We calibrate sequence log-probability and prefix-logit uncertainty signals into probabilities of correctness, then use these probabilities for graded context selection, selective abstention, and explicit latency/token trade-offs. Across core QA experiments on TriviaQA, Natural Questions, and MS MARCO, with auxiliary PopQA motivation and Qwen/Llama family checks, diagnostic out-of-fold calibration improves probability quality dramatically: for sequence log-probability, ECE drops from 0.275 to 0.062 on TriviaQA, 0.643 to 0.009 on NQ, and 0.711 to 0.031 on MS MARCO. Graded retrieval improves full-context and passage-budget frontiers for both our signal and TARG-style prefix entropy/margin, while retrieval-call AUC remains essentially tied with binary gating because k=1 is still a retrieval call. Held-out train/validation/test threshold experiments report deployable operating points. At matched-accuracy frontier operating points, a measured cost model reveals that gating is not universally faster: it increases latency by about 27% on Qwen3-8B but saves about 8% on Qwen3-32B. These results support a nuanced view of adaptive RAG: calibrated confidence is best understood as a reusable interface for allocating retrieval budget under task and system constraints.

cs.IR↗

arXiv2026

Cosmological constraints on the big bang quantum cosmology model

The big bang quantum cosmology model introduces the trace $J$ of the Schouten tensor as a form of dynamic dark energy. Together with cold dark matter, these components form the so-called $J$CDM cosmology model, proposed by M.H.P.M. van Putten (J. High Energy Astrophys., 45, 2025, 194), which offers a potential resolution to the Hubble tension. We derive the constraints on the $J$CDM cosmology model, utilizing early- and late-time cosmological data including cosmic microwave background (CMB), baryon acoustic oscillations (BAO) released by the Dark Energy Spectroscopic Instrument (DESI), cosmic chronometers (CC), and type Ia supernovae (SNIa). For a flat universe, the $J$CDM model yields $ H_0 = 66.95 \pm 0.51 \, \rm{km~s^{-1}~Mpc^{-1}} $ and $ Ω_m = 0.3419 \pm 0.0065 $, results that are consistent with early-universe observations but exhibit a higher $ Ω_m $ compared to the $Λ$CDM model. In the case of a non-flat universe, $J$CDM favors a slightly curved geometry with $ Ω_k = 0.0154 \pm 0.0027 $, leading to $ H_0 = 69.13 \pm 0.56 \, \rm {km~s^{-1}~Mpc^{-1}} $ and $ Ω_m = 0.3477 \pm 0.0074 $. The increase in $ H_0 $ in the non-flat scenario suggests a geometric degeneracy between spatial curvature and $ H_0 $. We also investigate the internal inconsistencies present in DESI data and evaluate their impacts on cosmological parameter constraints. Our analysis shows that while the $J$CDM model, which is constructed from first principles without free parameters beyond those of $Λ$CDM, agrees excellently with late-time cosmology, it struggles to simultaneously match early-universe observations in a fully self-consistent manner.

astro-ph.CO↗

arXiv2026

Optimization of Laser Irradiation Uniformity for the Double-Cone Ignition Scheme with MULTI-3D simulations

The double-cone ignition (DCI) scheme holds a promising perspective for laser driven fusion energy and astrophysics. However, optimizing the laser irradiation uniformity under the constraints of limited laser beams and a given cone angle remains to be explored. We utilized the three-dimensional radiation hydrodynamics program MULTI-3D to simulate the interaction process between the laser and plasma shell. By employing Bayesian optimization for the pointing position of the incident laser beams, we achieved a laser irradiation scheme with nonuniformity less than 5%. This study can provide references for experiments and offer valuable insights for other laser fusion schemes.

physics.plasm-ph↗

arXiv2026

Qianfan-OCR: A Unified End-to-End Model for Document Intelligence

We present Qianfan-OCR, a 4B-parameter end-to-end vision-language model that unifies document parsing, layout analysis, and document understanding within a single architecture. It performs direct image-to-Markdown conversion and supports diverse prompt-driven tasks including table extraction, chart understanding, document QA, and key information extraction. To address the loss of explicit layout analysis in end-to-end OCR, we propose Layout-as-Thought, an optional thinking phase triggered by special think tokens that generates structured layout representations -- bounding boxes, element types, and reading order -- before producing final outputs, recovering layout grounding capabilities while improving accuracy on complex layouts. Qianfan-OCR ranks first among end-to-end models on OmniDocBench v1.5 (93.12) and OlmOCR Bench (79.8), achieves competitive results on OCRBench, CCOCR, DocVQA, and ChartQA against general VLMs of comparable scale, and attains the highest average score on public key information extraction benchmarks, surpassing Gemini-3.1-Pro, Seed-2.0, and Qwen3-VL-235B. The model is publicly accessible via the Baidu AI Cloud Qianfan platform.

cs.CV↗

arXiv2026

All-optical intracellular thermal profiling using nanodiamond-based "thermal radar"

The local thermal conductivity (\k{appa}) is a pivotal biophysical parameter, governing intracellular heat flux and underlying functional processes like metabolic regulation and stress response. However, label-free mapping with sub-micron resolution in living cells remains challenge. Here, we present frequency-domain fluorescence thermometry (FD-FTM), an all-optical method based on a hybrid nanodiamond-on-gold-membrane platform, which enables quantitative mapping of \k{appa} in biological systems. Fluorescence nanodiamonds (FNDs) are deposited on substrates coated with a 50 nm gold membrane, where FNDs function as nanoscale thermometers, and the gold membrane serves as a photothermal heat source. We validate FD-FTM across reference materials and biological media, with fitting uncertainties of ~10%. By varying the modulation frequency, we tune the thermal penetration depths, enabling controlled heat propagation from the substrate to the cell nucleus. The method delivers sensitivity sufficient to resolve changes in biofluid thermal conductivity on the order of 16% relative to water. Using these capabilities, we demonstrate non-invasive thermal profiling across scales: at the cellular level, nuclear chromatin packing yields \k{appa} higher by ~10% relative to the cytoplasm; at the organelle level, we resolve \k{appa} variations associated with protein aggregates formed during liquid-liquid phase separation in an amyotrophic lateral sclerosis disease model. Temporal measurements in living cells over 30 minutes further reveal spatially resolved intracellular responses to osmotic stress, linking nanoscale thermal dynamics to biomolecular condensates. These results establish FD-FTM as a label-free, robust, and quantitative platform for thermally decoding intracellular processes, opening avenues for studying metabolic heterogeneity, disease mechanisms, and therapeutic responses.

physics.bio-ph↗

arXiv2026

Redshift evolution of the Hubble constant: Constraints and new insights from an interacting dark energy model

We develop a modified interacting dark energy (IDE) model to study the redshift evolution of the Hubble constant ($H_0$), in light of the Hubble tension. In this framework, the energy exchange between dark energy and dark matter induces a redshift dependence of $H_0$. We evaluate the model against a comprehensive suite of observations, including baryon acoustic oscillations (BAO) from DESI DR2 and SDSS, cosmic chronometers, type Ia supernovae from the Pantheon sample, and Planck CMB distance priors. Analysis of late-Universe data yields $\alpha = 0.0107^{+0.0032}_{-0.011}$, with the best-fit value on the order of $10^{-2}$, revealing a decreasing trend of $H_0$ with redshift. This supports a power-law evolution beyond $\Lambda$CDM. Incorporating CMB data further tightens the constraint to the order of $10^{-5}$, which we attribute to the suppression of dark-sector interactions at high redshifts, a consequence of the strong baryon--photon coupling. These results indicate that the IDE framework provides a theoretically consistent and observationally viable mechanism for describing the redshift evolution of $H_0$, offering a promising avenue toward alleviating the Hubble tension.

astro-ph.CO↗

arXiv2026

Ho3+-doped CALGO crystals for high-power ultrafast 2.1-μm lasers

Ho3+-doped disordered CaAlGdO4 (CALGO) crystals have recently emerged as a promising gain material platform for next-generation high-power ultrafast 2.1-μm laser systems. This laser gain material offers a unique combination of high-gain, small quantum defect, inhomogeneously broadened spectra, and good thermal conductivity, enabling ultrashort pulse generation and amplification at high-average power and high pulse energy. Many systems, including mode-locked oscillators and amplifiers with state-of-the-art performance, have been demonstrated in the last few years that promise to meet growing application demands for efficient ultrafast laser technology in this wavelength region. In this review paper, we summarize recent achievements using this gain material both in oscillators and amplifiers and place these results in the state-of-the-art of 2-μm ultrafast laser technology, present detailed spectroscopic characterization of this material, and discuss future perspectives of further performance scaling of Ho:CALGO lasers.

physics.optics↗

arXiv2025

Branching Strategies Based on Subgraph GNNs: A Study on Theoretical Promise versus Practical Reality

Graph Neural Networks (GNNs) have emerged as a promising approach for ``learning to branch'' in Mixed-Integer Linear Programming (MILP). While standard Message-Passing GNNs (MPNNs) are efficient, they theoretically lack the expressive power to fully represent MILP structures. Conversely, higher-order GNNs (like 2-FGNNs) are expressive but computationally prohibitive. In this work, we investigate Subgraph GNNs as a theoretical middle ground. Crucially, while previous work [Chen et al., 2025] demonstrated that GNNs with 3-WL expressive power can approximate Strong Branching, we prove a sharper result: node-anchored Subgraph GNNs whose expressive power is strictly lower than 3-WL [Zhang et al., 2023] are sufficient to approximate Strong Branching scores. However, our extensive empirical evaluation on four benchmark datasets reveals a stark contrast between theory and practice. While node-anchored Subgraph GNNs theoretically offer superior branching decisions, their $O(n)$ complexity overhead results in significant memory bottlenecks and slower solving times than MPNNs and heuristics. Our results indicate that for MILP branching, the computational cost of expressive GNNs currently outweighs their gains in decision quality, suggesting that future research must focus on efficiency-preserving expressivity.

cs.LG↗

arXiv2025

A Hierarchical, Model-Based System for High-Performance Humanoid Soccer

The development of athletic humanoid robots has gained significant attention as advances in actuation, sensing, and control enable increasingly dynamic, real-world capabilities. RoboCup, an international competition of fully autonomous humanoid robots, provides a uniquely challenging benchmark for such systems, culminating in the long-term goal of competing against human soccer players by 2050. This paper presents the hardware and software innovations underlying our team's victory in the RoboCup 2024 Adult-Sized Humanoid Soccer Competition. On the hardware side, we introduce an adult-sized humanoid platform built with lightweight structural components, high-torque quasi-direct-drive actuators, and a specialized foot design that enables powerful in-gait kicks while preserving locomotion robustness. On the software side, we develop an integrated perception and localization framework that combines stereo vision, object detection, and landmark-based fusion to provide reliable estimates of the ball, goals, teammates, and opponents. A mid-level navigation stack then generates collision-aware, dynamically feasible trajectories, while a centralized behavior manager coordinates high-level decision making, role selection, and kick execution based on the evolving game state. The seamless integration of these subsystems results in fast, precise, and tactically effective gameplay, enabling robust performance under the dynamic and adversarial conditions of real matches. This paper presents the design principles, system architecture, and experimental results that contributed to ARTEMIS's success as the 2024 Adult-Sized Humanoid Soccer champion.

cs.RO↗

arXiv2025

Wafer-scale integration of single nanodiamonds via electrostatic-trapping

Nanodiamonds (NDs) are key materials for building nanoscale quantum sensing, imaging and communication devices. Scalable configuration of single NDs on heterogeneous platforms, forming photonic quantum source arrays, will be an essential solution towards realizing next-generation practical and industrial quantum devices. However, NDs are challenging to manipulate because their size, shape and surface chemistry vary substantially. Here, we show a simple method based on electrostatic-trapping to rapidly and reliably pattern single ND arrays on arbitrary substrates at scale. Our method, which uses carefully engineered microscale hole templates and electrostatic force, captures single NDs across 8-inch wafers with 82.5% yields within 5 min. Systematic experimental and theoretical studies show the number of deposited NDs primarily depends on the diameter of the hole trap. The method is compatible with mature CMOS technologies, enabling the mass production of scalable and integrable quantum devices. This advancement is expected to accelerate the commercialization and industrial adoption of ND-based technologies.

physics.optics↗

arXiv2025

Self-Powered, Ultra-thin, Flexible, and Scalable Ultraviolet Detector Utilizing Diamond-MoS$_2$ Heterojunction

The escalating demand for ultraviolet (UV) sensing in space exploration, environmental monitoring, and agricultural productivity necessitates detectors that are both environmentally and mechanically resilient. Diamond, featuring its high bandgap and UV absorption, exceptional mechanical/chemical robustness, and excellent thermal stability, emerges as a highly promising material for next-generation UV detection in various scenarios. However, conventional diamond-based UV detectors are constrained by rigid bulk architectures and reliance on external power supplies, hindering their integration with curved and flexible platforms and complicating device scalability due to auxiliary power requirements. To tackle these challenges, herein, we firstly demonstrated a large-scale, self-powered, and flexible diamond UV detector by heterogeneously integrating a MoS$_2$ monolayer with an ultrathin, freestanding diamond membrane. The fabricated device operates at zero external bias, and simultaneously exhibits a high responsivity of 94 mA W$^{-1}$ at 220 nm, and detectivity of 5.88 x 109 Jones. Notably, mechanical bending enables strain-induced bandgap modulation of the diamond membrane, allowing dynamically tunable photoresponse-a capability absent in rigid diamond counterparts. To validate its practicality and scalability, a proof-of-concept UV imager with 3x3 pixels was demonstrated. This newly developed configuration will undoubtedly open up new routes toward scalable, integrable, flexible, and cost-effective UV sensing solutions for emerging technologies

physics.ins-det↗

arXiv2025

Laser-driven few-cycle Terahertz sources with high average power

Ultrafast laser-driven terahertz sources are gaining in popularity in an increasingly wide range of scientific and technological applications. However, many fields continue to be severely limited by the typically low average power of these sources, which restricts speed, signal-to-noise ratio, and dynamic range in numerous measurements. Conversely, the past two decades have seen spectacular progress in high average power ultrafast laser technology based on Ytterbium lasers, rendering hundreds of watts to kilowatts of average power available to this community to drive THz sources. This has opened the young field of high-average-power laserdriven THz time-domain spectroscopy, which holds the potential to revolutionize the applications of THz time-domain systems. In this perspective article, we discuss this young field and emphasize recent advancements in broadband terahertz sources utilizing high-power Yb-based ultrafast lasers as drivers, which are nearing watt-level average power. We discuss various approaches explored thus far, current challenges, prospects for scaling, and future research areas that will accelerate their implementation in applications.

physics.optics↗

arXiv2025

Exclusive Style Removal for Cross Domain Novel Class Discovery

As a promising field in open-world learning, \textit{Novel Class Discovery} (NCD) is usually a task to cluster unseen novel classes in an unlabeled set based on the prior knowledge of labeled data within the same domain. However, the performance of existing NCD methods could be severely compromised when novel classes are sampled from a different distribution with the labeled ones. In this paper, we explore and establish the solvability of NCD with cross domain setting under the necessary condition that the style information needs to be removed. Based on the theoretical analysis, we introduce an exclusive style removal module for extracting style information that is distinctive from the baseline features, thereby facilitating inference. Moreover, this module is easy to integrate with other NCD methods, acting as a plug-in to improve performance on novel classes with different distributions compared to the labeled set. Additionally, recognizing the non-negligible influence of different backbones and pre-training strategies on the performance of the NCD methods, we build a fair benchmark for future NCD research. Extensive experiments on three common datasets demonstrate the effectiveness of our proposed style removal strategy.

cs.CV↗

arXiv2025

New cosmological constraints on the evolution of dark matter energy density

We constrain the evolution of dark matter energy density over time, specifically focusing on deviation from the standard model represented by the equation $ρ_{m}\propto(1+z)^{3-\varepsilon}$, where $\varepsilon$ is a constant parameter. Utilizing a diverse array of observational datasets, including baryon acoustic oscillations (BAO) data from the first release of the Dark Energy Spectroscopic Instrument (DESI), distance priors derived from cosmic microwave background (CMB) observations by the Planck satellite, Hubble rate data obtained through the cosmic chronometers (CC) method, type Ia supernova (SNIa) data from the Panthon sample, and the data from the redshift space distortion (RSD) measurements ($fσ_8$), we derive stringent constraints on the deviation parameter. We find that for the model under consideration, the deviation parameter is constrained to be $\varepsilon = -0.0073^{+0.0029}_{-0.0033}$, indicating a deviation of approximately $2.4σ$ from the scenario where dark matter and vacuum dark energy do not interact. When compared with previous studies and alternative analyses, our findings provide corroborative evidence for an interaction between dark matter and vacuum dark energy, particularly in light of the release of BAO data from DESI.

astro-ph.CO↗