arXiv subjects

Yan Liang

Publications and source records attributed to Yan Liang.

At least 37 records · Page 2Linked to original sources

arXiv2026

LexInstructEval: Lexical Instruction Following Evaluation for Large Language Models

The ability of Large Language Models (LLMs) to precisely follow complex and fine-grained lexical instructions is a cornerstone of their utility and controllability. However, evaluating this capability remains a significant challenge. Current methods either rely on subjective and costly human evaluation or on automated LLM-as-a-judge systems, which suffer from inherent biases and unreliability. Existing programmatic benchmarks, while objective, often lack the expressiveness to test intricate, compositional constraints at a granular level. To address these limitations, we introduce LexInstructEval, a new benchmark and evaluation framework for fine-grained lexical instruction following. Our framework is built upon a formal, rule-based grammar that deconstructs complex instructions into a canonical triplet. This grammar enables the systematic generation of a diverse dataset through a multi-stage, human-in-the-loop pipeline and facilitates objective verification via a transparent, programmatic engine. We release our dataset and open-source evaluation tools to facilitate further research into the controllability and reliability of LLMs.

cs.CL↗

arXiv2026

Trust the Unreliability: Inward Backward Dynamic Unreliability Driven Coreset Selection for Medical Image Classification

Efficiently managing and utilizing large-scale medical imaging datasets with limited resources presents significant challenges. While coreset selection helps reduce computational costs, its effectiveness in medical data remains limited due to inherent complexity, such as large intra-class variation and high inter-class similarity. To address this, we revisit the training process and observe that neural networks consistently produce stable confidence predictions and better remember samples near class centers in training. However, concentrating on these samples may complicate the modeling of decision boundaries. Hence, we argue that the more unreliable samples are, in fact, the more informative in helping build the decision boundary. Based on this, we propose the Dynamic Unreliability-Driven Coreset Selection(DUCS) strategy. Specifically, we introduce an inward-backward unreliability assessment perspective: 1) Inward Self-Awareness: The model introspects its behavior by analyzing the evolution of confidence during training, thereby quantifying uncertainty of each sample. 2) Backward Memory Tracking: The model reflects on its training tracking by tracking the frequency of forgetting samples, thus evaluating its retention ability for each sample. Next, we select unreliable samples that exhibit substantial confidence fluctuations and are repeatedly forgotten during training. This selection process ensures that the chosen samples are near the decision boundary, thereby aiding the model in refining the boundary. Extensive experiments on public medical datasets demonstrate our superior performance compared to state-of-the-art(SOTA) methods, particularly at high compression rates.

cs.CV↗

arXiv2026

Thinking on the Fly: Test-Time Reasoning Enhancement via Latent Thought Policy Optimization

Recent advancements in Large Language Models (LLMs) have shifted from explicit Chain-of-Thought (CoT) reasoning to more efficient latent reasoning, where intermediate thoughts are represented as vectors rather than text. However, latent reasoning can be brittle on challenging, out-of-distribution tasks where robust reasoning is most critical. To overcome these limitations, we introduce Latent Thought Policy Optimization (LTPO), a parameter-free framework that enhances LLM reasoning entirely at test time, without requiring model parameter updates. LTPO treats intermediate latent "thought" vectors as dynamic parameters that are actively optimized for each problem instance. It employs an online policy gradient method guided by an intrinsic, confidence-based reward signal computed directly from the frozen LLM's own output distributions, eliminating the need for external supervision or expensive text generation during optimization. Extensive experiments on five reasoning benchmarks show that LTPO not only matches or surpasses strong baselines on standard tasks but also demonstrates remarkable robustness where others fail. Most notably, on highly challenging AIME benchmarks where existing latent reasoning baselines collapse to near-zero accuracy, LTPO delivers substantial improvements, showcasing a unique capability for complex reasoning.

cs.CL↗

arXiv2026

DTC: Real-Time and Accurate Distributed Triangle Counting in Fully Dynamic Graph Streams

Triangle counting is a fundamental problem in graph mining, essential for analyzing graph streams with arbitrary edge orders. However, exact counting becomes impractical due to the massive size of real-world graph streams. To address this, approximate algorithms have been developed, but existing distributed streaming algorithms lack adaptability and struggle with edge deletions. In this article, we propose DTC, a novel family of single-pass distributed streaming algorithms for global and local triangle counting in fully dynamic graph streams. Our DTC-AR algorithm accurately estimates triangle counts without prior knowledge of graph size, leveraging multi-machine resources. Additionally, we introduce DTC-FD, an algorithm tailored for fully dynamic graph streams, incorporating edge insertions and deletions. Using Random Pairing and future edge insertion compensation, DTC-FD achieves unbiased and accurate approximations across multiple machines. Experimental results demonstrate significant improvements over baselines. DTC-AR achieves up to $2029.4\times$ and $27.1\times$ more accuracy, while maintaining the best trade-off between accuracy and storage space. DTC-FD reduces estimation errors by up to $32.5\times$ and $19.3\times$, scaling linearly with graph stream size. These findings highlight the effectiveness of our proposed algorithms in tackling triangle counting in real-world scenarios. The source code and datasets are released and available at \href{https://github.com/wayne4s/srds-dtc.git}{https://github.com/wayne4s/srds-dtc.git}.

cs.DS↗

arXiv2025

Competing Lattice and Defect Dynamics Govern Terahertz-Induced Ferroelectricity in Quantum Paraelectric SrTiO$_3$

Intense terahertz (THz) pulses induce transient inversion-symmetry breaking in quantum paraelectric SrTiO$_3$, yet the underlying mechanism remains controversial. Using fields up to $\sim$1.1 MV/cm, we reveal spatially inhomogeneous THz-field-induced second harmonic generation (TFISH) governed by competing lattice and defect dynamics. Short-lived coherent antiferrodistortive (AFD) modes suppress dipole correlations within $\sim$5 ps, while heavily damped soft/AFD modes and a defect-induced low-frequency mode ($\sim$0.1-0.3 THz) jointly prevent long-range ferroelectric coherence in oxygen-vacancy-rich regions. Collective modes manifested by oscillatory TFISH components exhibit softening followed by hardening below a critical temperature $T^*\simeq$28 K, confirming transient ferroelectric order where defects are sparse. These results reconcile conflicting interpretations, establish defect-mediated competition as a central regulator of light-induced ferroelectricity, and open routes to ultrafast control of quantum materials.

cond-mat.mtrl-sci↗

arXiv2025

The Impact of Orbital Anisotropy Assumptions in Lensing-Dynamics Modeling

We investigate potential systematic biases introduced by assumptions regarding stellar orbital anisotropy in joint lensing-dynamics modeling. Our study employs the massive early-type galaxies from the TNG100 simulation at redshifts z = 0.2, 0.5, and 0.7. Based on the simulated galaxies, we generate a self-consistent mock dataset containing both lensing and stellar kinematic observables. This is achieved through taking the potential composed of both dark matter and baryons of the simulated galaxies, plus the radial variation of the stellar orbit anisotropy depicted by a logistic function. By integrating constraints from both lensing and stellar kinematics, we separate the contributions of stars and dark matter inside the galaxies. Under three commonly adopted stellar anisotropy assumptions (isotropic orbits, constant anisotropy, and the Osipkov-Merritt profile), the model inferences suggest that the systematic biases in the total stellar mass and central dark matter fraction are not significant. Specifically, the total stellar mass on average is underestimated by less than $0.03\pm0.10$ $\rm dex$ while the dark matter fraction experiences only a statistically insignificant increase of less than $2\%\pm10\%$ at the population level. The dark matter inner density slope in our tests is over-predicted by $0.15\pm0.2$. Additionally, these lacks of significant biases are insensitive to the discrepancies between the assumed anisotropy in modeling and the ground truth orbital anisotropy of mock sample. Our results suggest that conventional assumptions regarding orbital anisotropy, such as an isotropic profile or the Osipkov-Merritt model, would not introduce a significant systematic bias when inferring galaxy mass density distribution at the population level.

astro-ph.GA↗

arXiv2025

Multimodal 3D Genome Pre-training

Deep learning techniques have driven significant progress in various analytical tasks within 3D genomics in computational biology. However, a holistic understanding of 3D genomics knowledge remains underexplored. Here, we propose MIX-HIC, the first multimodal foundation model of 3D genome that integrates both 3D genome structure and epigenomic tracks, which obtains unified and comprehensive semantics. For accurate heterogeneous semantic fusion, we design the cross-modal interaction and mapping blocks for robust unified representation, yielding the accurate aggregation of 3D genome knowledge. Besides, we introduce the first large-scale dataset comprising over 1 million pairwise samples of Hi-C contact maps and epigenomic tracks for high-quality pre-training, enabling the exploration of functional implications in 3D genomics. Extensive experiments show that MIX-HIC can significantly surpass existing state-of-the-art methods in diverse downstream tasks. This work provides a valuable resource for advancing 3D genomics research.

cs.LG↗

arXiv2025

Joint Deblurring and 3D Reconstruction for Macrophotography

Macro lens has the advantages of high resolution and large magnification, and 3D modeling of small and detailed objects can provide richer information. However, defocus blur in macrophotography is a long-standing problem that heavily hinders the clear imaging of the captured objects and high-quality 3D reconstruction of them. Traditional image deblurring methods require a large number of images and annotations, and there is currently no multi-view 3D reconstruction method for macrophotography. In this work, we propose a joint deblurring and 3D reconstruction method for macrophotography. Starting from multi-view blurry images captured, we jointly optimize the clear 3D model of the object and the defocus blur kernel of each pixel. The entire framework adopts a differentiable rendering method to self-supervise the optimization of the 3D model and the defocus blur kernel. Extensive experiments show that from a small number of multi-view images, our proposed method can not only achieve high-quality image deblurring but also recover high-fidelity 3D appearance.

cs.CV↗

arXiv2025

BEAM: Brainwave Empathy Assessment Model for Early Childhood

Empathy in young children is crucial for their social and emotional development, yet predicting it remains challenging. Traditional methods often only rely on self-reports or observer-based labeling, which are susceptible to bias and fail to objectively capture the process of empathy formation. EEG offers an objective alternative; however, current approaches primarily extract static patterns, neglecting temporal dynamics. To overcome these limitations, we propose a novel deep learning framework, the Brainwave Empathy Assessment Model (BEAM), to predict empathy levels in children aged 4-6 years. BEAM leverages multi-view EEG signals to capture both cognitive and emotional dimensions of empathy. The framework comprises three key components: 1) a LaBraM-based encoder for effective spatio-temporal feature extraction, 2) a feature fusion module to integrate complementary information from multi-view signals, and 3) a contrastive learning module to enhance class separation. Validated on the CBCP dataset, BEAM outperforms state-of-the-art methods across multiple metrics, demonstrating its potential for objective empathy assessment and providing a preliminary insight into early interventions in children's prosocial development.

cs.LG↗

arXiv2025

Robustness Enhancement of Universal Noncyclic Geometric Gates via Evolution Optimization

Noncyclic geometric gates aim to overcome the stringent constraints of conventional cyclic conditions and enhance the flexibility in evolution choice. Conceptually, they can also avoid the error problems arising from the violation of cyclicity, thus holding significance for improving the fault tolerance of quantum gates. However, current research on noncyclic geometric gates lacks a comprehensive exploration of their flexibility in evolution choice and validation of their effectiveness in resilience against multiple error sources present in practical quantum systems. In this paper, we systematically evaluate all noncyclic evolution conditions, elucidate their corresponding potential geometric trajectories, and identify optimal conditions for enhancing gate robustness by quantifying the resilience of constructed noncyclic geometric gates against universal systematic errors and residual crosstalk error. The optimized gates demonstrate significant robustness advantages over representative dynamical Rabi gates and conventional cyclic geometric gates. Furthermore, we validate the physical feasibility of high-fidelity noncyclic geometric gates in superconducting quantum circuits, with focused investigation into the impacts of intrinsic leakage errors and decoherence effects. Therefore, this work establishes a critical foundation for robust gate implementation toward practical fault-tolerant quantum processors.

quant-ph↗

arXiv2025

Phase-engineered Non-degenerate Sliding Ferroelectricity Enables Tunable Photovoltaics in Monolayer Janus In2S2Se

Two-dimensional sliding ferroelectrics, with their enhanced efficiencies of charge separation and tunability, constitute promising platforms for next-generation photovoltaic devices. However, recent systems predominantly exhibit dual degenerate polarization states with weak intensity, hindering the optimal manipulations of photovoltaic effects through sliding ferroelectricity. Here, we address this limitation by introducing two strengthened and distinct non-degenerate sliding ferroelectric phases (WZ' and ZB') in Janus In2S2Se, which can be achieved by Se-to-S substitution in monolayer In2Se3. First-principles calculations validate the experimental synthesis of this structure and its capability for reversible phase transitions triggered by atomic layer sliding, and a series of superior photovoltaic performances are demonstrated in such unique Janus In2S2Se, accompanied by a detailed analysis of how non-degenerate sliding ferroelectricity modulates distinct photovoltaic characteristics. The WZ' to ZB' transition can increase the carrier mobility and moderate the band gap while inducing an indirect-to-direct transition, yielding a marked red-shift and enhancement of the photocurrent peak in the infrared spectrum. Conversely, the WZ' phase, benefiting from enhanced polarization, delivers superior photoelectric conversion efficiency in the visible light region. This work establishes a phase-engineered framework of how non-degenerate sliding ferroelectricity orchestrates distinct photovoltaic behaviors, and the intrinsic physical correlations may offer novel perspectives for designing and regulating innovative photovoltaic devices.

cond-mat.mtrl-sci↗

arXiv2025

X-Intelligence 3.0: Training and Evaluating Reasoning LLM for Semiconductor Display

Large language models (LLMs) have recently achieved significant advances in reasoning and demonstrated their advantages in solving challenging problems. Yet, their effectiveness in the semiconductor display industry remains limited due to a lack of domain-specific training and expertise. To bridge this gap, we present X-Intelligence 3.0, the first high-performance reasoning model specifically developed for the semiconductor display industry. This model is designed to deliver expert-level understanding and reasoning for the industry's complex challenges. Leveraging a carefully curated industry knowledge base, the model undergoes supervised fine-tuning and reinforcement learning to enhance its reasoning and comprehension capabilities. To further accelerate development, we implemented an automated evaluation framework that simulates expert-level assessments. We also integrated a domain-specific retrieval-augmented generation (RAG) mechanism, resulting in notable performance gains on benchmark datasets. Despite its relatively compact size of 32 billion parameters, X-Intelligence 3.0 outperforms SOTA DeepSeek-R1-671B across multiple evaluations. This demonstrates its exceptional efficiency and establishes it as a powerful solution to the longstanding reasoning challenges faced by the semiconductor display industry.

cs.CL↗

arXiv2025

Ferromagnetic Two-dimensional Electron Gases with Magnetic Doping and Proximity Effects

The advent of magnetic two-dimensional electron gases (2DEGs) at oxide interfaces has provided new opportunities in the field of spintronics. The enhancement of magnetism in 2DEGs at oxide interfaces continues to be a significant challenge, as exemplified by the relatively weak magnetism observed in the classical LaAlO3/SrTiO3 interface. Here, we present ferromagnetic (FM) 2DEGs at the interface fabricated between the FM insulator EuTiO3 (ETO) and the strong spin-orbit coupled (SOC) perovskite insulator KTaO3 (KTO). With the combined effects of magnetic atom doping and magnetic proximity from ETO films, the coercive field of 2DEGs can be significantly enhanced. Magnetoresistance (MR) curve with a high coercive field of 1000 Oe has been observed, in conjunction with a temperature-dependent unambiguous hysteresis loop in the anomalous Hall effect (AHE). Furthermore, within the 2DEGs, we have identified a synergetic interplay between magnetic scattering and the weak antilocalization (WAL) effect on transport. This study provides fresh insights into the formation of FM 2DEGs at ETO/KTO interfaces, and introduce an innovative pathway for creating high-performance magnetic 2DEGs at oxide interfaces.

cond-mat.mtrl-sci↗

arXiv2025

An Empirical Study of LLM Reasoning Ability Under Strict Output Length Constraint

Recent work has demonstrated the remarkable potential of Large Language Models (LLMs) in test-time scaling. By making models think before answering, they are able to achieve much higher accuracy with extra inference computation. However, in many real-world scenarios, models are used under time constraints, where an answer should be given within a certain output length. It is unclear whether and how the reasoning ability of different LLMs remain effective under strict constraints. We take a first look at this problem by conducting an in-depth empirical study. Specifically, we test 30 LLMs on common reasoning datasets under a wide range of output length budgets, and we analyze the correlation between the inference accuracy and various properties including model type, model size, prompt style, etc. We also consider the mappings between token budgets and actual on-device latency budgets. The results have demonstrated several interesting findings regarding the budget-aware LLM reasoning ability that differ from the unconstrained situation, e.g. the optimal choices of either model size or prompt style change under different budgets. These findings offer timely evaluation to this area and practical guidance for users to deploy LLMs under real-world latency constraints.

cs.AI↗

arXiv2025

Data Center Cooling System Optimization Using Offline Reinforcement Learning

The recent advances in information technology and artificial intelligence have fueled a rapid expansion of the data center (DC) industry worldwide, accompanied by an immense appetite for electricity to power the DCs. In a typical DC, around 30~40% of the energy is spent on the cooling system rather than on computer servers, posing a pressing need for developing new energy-saving optimization technologies for DC cooling systems. However, optimizing such real-world industrial systems faces numerous challenges, including but not limited to a lack of reliable simulation environments, limited historical data, and stringent safety and control robustness requirements. In this work, we present a novel physics-informed offline reinforcement learning (RL) framework for energy efficiency optimization of DC cooling systems. The proposed framework models the complex dynamical patterns and physical dependencies inside a server room using a purposely designed graph neural network architecture that is compliant with the fundamental time-reversal symmetry. Because of its well-behaved and generalizable state-action representations, the model enables sample-efficient and robust latent space offline policy learning using limited real-world operational data. Our framework has been successfully deployed and verified in a large-scale production DC for closed-loop control of its air-cooling units (ACUs). We conducted a total of 2000 hours of short and long-term experiments in the production DC environment. The results show that our method achieves 14~21% energy savings in the DC cooling system, without any violation of the safety or operational constraints. Our results have demonstrated the significant potential of offline RL in solving a broad range of data-limited, safety-critical real-world industrial control problems.

cs.AI↗

arXiv2024

Credible fusion of evidence in distributed system subject to cyberattacks

Given that distributed systems face adversarial behaviors such as eavesdropping and cyberattacks, how to ensure the evidence fusion result is credible becomes a must-be-addressed topic. Different from traditional research that assumes nodes are cooperative, we focus on three requirements for evidence fusion, i.e., preserving evidence's privacy, identifying attackers and excluding their evidence, and dissipating high-conflicting among evidence caused by random noise and interference. To this end, this paper proposes an algorithm for credible evidence fusion against cyberattacks. Firstly, the fusion strategy is constructed based on conditionalized credibility to avoid counterintuitive fusion results caused by high-conflicting. Under this strategy, distributed evidence fusion is transformed into the average consensus problem for the weighted average value by conditional credibility of multi-source evidence (WAVCCME), which implies a more concise consensus process and lower computational complexity than existing algorithms. Secondly, a state decomposition and reconstruction strategy with weight encryption is designed, and its effectiveness for privacy-preserving under directed graphs is guaranteed: decomposing states into different random sub-states for different neighbors to defend against internal eavesdroppers, and encrypting the sub-states' weight in the reconstruction to guard against out-of-system eavesdroppers. Finally, the identities and types of attackers are identified by inter-neighbor broadcasting and comparison of nodes' states, and the proposed update rule with state corrections is used to achieve the consensus of the WAVCCME. The states of normal nodes are shown to converge to their WAVCCME, while the attacker's evidence is excluded from the fusion, as verified by the simulation on a distributed unmanned reconnaissance swarm.

cs.CR↗

arXiv2024

Modeling biases from constant stellar mass-to-light ratio assumption in galaxy dynamics and strong lensing

A constant stellar mass-to-light ratio $M_\star/L$ has been widely-used in studies of galaxy dynamics and strong lensing, which aim at disentangling the mass distributions of dark matter and baryons. However, systematic biases arising from constant $M_\star/L$ assumption have not been fully quantified. In this work, we take massive early-type galaxies from the TNG100 simulation to investigate possible systematic biases in the inferences due to a constant $M_\star/L$ assumption. We construct two-component matter density models, where one component describes the dark matter, the other for the stars, which is made to follow the light profile by assuming a constant $M_\star/L$. We fit the two-component model directly to the {\it total} matter density distributions of simulated galaxies to eliminate systematics coming from other model assumptions. We find that galaxies generally have more centrally-concentrated stellar mass profile than their light distribution. Given the light profiles adopted (i.e., single- and double-S{é}rsic profiles), the assumption of a constant $M_\star/L$ would artificially break the model degeneracy between baryons and dark matter {\it for non-constant} $M_\star/L$ systems. For such systems, without knowing the true $M_{\star}/L$ but assuming a constant ratio, the two-component modeling procedure tend to generally overestimate $M_{\star}/L$ by $30\%-50\%$, and underestimate the central dark matter fraction $f_{\rm DM}$ by $\sim 20\%$ on average.

astro-ph.GA↗

arXiv2024

A privacy-preserving distributed credible evidence fusion algorithm for collective decision-making

The theory of evidence reasoning has been applied to collective decision-making in recent years. However, existing distributed evidence fusion methods lead to participants' preference leakage and fusion failures as they directly exchange raw evidence and do not assess evidence credibility like centralized credible evidence fusion (CCEF) does. To do so, a privacy-preserving distributed credible evidence fusion method with three-level consensus (PCEF) is proposed in this paper. In evidence difference measure (EDM) neighbor consensus, an evidence-free equivalent expression of EDM among neighbored agents is derived with the shared dot product protocol for pignistic probability and the identical judgment of two events with maximal subjective probabilities, so that evidence privacy is guaranteed due to such irreversible evidence transformation. In EDM network consensus, the non-neighbored EDMs are inferred and neighbored EDMs reach uniformity via interaction between linear average consensus (LAC) and low-rank matrix completion with rank adaptation to guarantee EDM consensus convergence and no solution of inferring raw evidence in numerical iteration style. In fusion network consensus, a privacy-preserving LAC with a self-cancelling differential privacy term is proposed, where each agent adds its randomness to the sharing content and step-by-step cancels such randomness in consensus iterations. Besides, the sufficient condition of the convergence to the CCEF is explored, and it is proven that raw evidence is impossibly inferred in such an iterative consensus. The simulations show that PCEF is close to CCEF both in credibility and fusion results and obtains higher decision accuracy with less time-comsuming than existing methods.

cs.AI↗