arXiv subjects

Yucheng Li

Publications and source records attributed to Yucheng Li.

At least 37 records · Page 2Linked to original sources

arXiv2025

LogiNumSynth: Synthesizing Joint Logical-Numerical Reasoning Problems for Language Models

Joint logical-numerical reasoning remains a major challenge for language models, yet existing datasets rely on fixed rule sets and offer limited control over task complexity, constraining their generalizability for evaluation and training. We present LogiNumSynth, a flexible natural language problem synthesizer that synthesizes tasks requiring proficiency in joint logical reasoning (e.g., rule-based reasoning) and numerical reasoning (e.g., arithmetic computation). LogiNumSynth supports fine-grained control over reasoning world richness, logical reasoning depth, and the complexity of numerical computations, enabling flexible data synthesis across difficulty levels. We demonstrate three key contributions: (1) Synthesizer -- synthesizing fully controllable joint reasoning tasks over natural language; (2) Evaluation & Process Analysis -- evaluating both process accuracy and answer accuracy; (3) Targeted Training -- using synthesized data to enhance LLMs' reasoning performance. Experiments with multiple LLMs highlight persistent weaknesses in logical-numerical reasoning, showing that LogiNumSynth can serve as both a diagnostic tool and a source of targeted supervision for advancing integrated reasoning skills.

cs.CL↗

arXiv2025

Strongly correlated electronic superconductivity in the noncentrosymmetric Re-Os-based high/medium-entropy alloys

The class of unconventional superconductors, particularly noncentrosymmetric superconductors, has been highly considered as potential materials for understanding the complex properties of quantum materials. Here, five previously unreported Re3.5Os3.5Ta0.5Hf0.5Nb3, Re3Os3Ta0.5Hf0.5Nb3, Re3.5Os3.5Mo0.5Hf0.5Nb3, Re3.5Os3.5Mo0.5W0.5Nb3, and Re3Os3Mo0.5Hf0.5Nb3 Re-Os-based high/medium-entropy alloys (MEAs-HEAs) with valence electron count ranging from 6.45 to 6.81 were synthesized and investigated using x-ray diffraction, transport, magnetization, and specific heat measurements. Our analyses confirm that all five compounds crystallize in a noncentrosymmetric α-Mn-type structure and exhibit type-II superconductivity with Tc values from 4.20 K to 5.11 K, respectively. Unexpectedly, despite being immersed in an acidic environment for one month, the structures and superconducting properties of HEAs remain stable. Our findings indicate that the Tc increases with an increasing valence electron count in MEAs-HEAs. Furthermore, these noncentrosymmetric α-Mn-type HEA superconductors have large Kadowaki-Woods ratios (KWR), implying the presence of strong electronic correlations.

cond-mat.supr-con↗

arXiv2025

Efficient Byzantine Consensus MechanismBased on Reputation in IoT Blockchain

Blockchain technology has advanced rapidly in recent years and is now widely used in a variety of fields. Blockchain appears to be one of the best solutions for managing massive heterogeneous devices while achieving advanced data security and data reputation, particularly in the field of large-scale IoT (Internet of Things) networks. Despite the numerous advantages, there are still challenges while deploying IoT applications on blockchain systems due to the limited storage, power, and computing capability of IoT devices, and some of these problems are caused by the consensus algorithm, which plays a significant role in blockchain systems by ensuring overall system reliability and robustness. Nonetheless, most existing consensus algorithms are prone to poor node reliability, low transaction per second (TPS) rates, and scalability issues. Aiming at some critical problems in the existing consensus algorithms, this paper proposes the Efficient Byzantine Reputation-based Consensus (EBRC) mechanism to resolve the issues raised above. In comparison to traditional algorithms, we reinvented ways to evaluate node reliability and robustness and manage active nodes. Our experiments show that the EBRC algorithm has lower consensus delay, higher throughput, improved security, and lower verification costs. It offers new reference ideas for solving the Internet of Things+blockchain+Internet court construction problem.

cs.DC↗

arXiv2025

Strong correlation behavior and Strong coupling superconductivity in (Ti1/4Hf1/4Nb1/4Ta1/4)1-xNix with the rich magnetic element Ni

Searching for new superconductors, especially unconventional superconductors, has been studied extensively for decades but remains one of the major outstanding challenges in condensed matter physics. Medium/high-entropy alloys (MEAs-HEAs) are new fertile soils of unconventional superconductors and generate widespread interest and questions on the existence of superconductivity in highly disordered materials. Here, we report on the effect of Ni-doped on the crystal structure and superconductivity properties of strongly coupled TiHfNbTa MEA. XRD results indicate that the maximum solid solution of (Ti1/4Hf1/4Nb1/4Ta1/4)1-xNix is about 7.7%. Resistivity, magnetic susceptibility, and specific heat measurements demonstrated that (Ti1/4Hf1/4Nb1/4Ta1/4)1-xNix HEAs are all bulk type-II superconductors and follow the trend of the increase of Tc with the increase of Ni-doped contents. The specific heat jump of all (Ti1/4Hf1/4Nb1/4Ta1/4)1-xNix are much larger than the BCS value of 1.43, suggesting all these HEAs are strongly coupled superconductors. Additionally, large Kadawaki-Woods ratio values suggest that there is a strong electron correlation effect in this system. The (Ti1/4Hf1/4Nb1/4Ta1/4)1-xNix HEA system is a new ideal material platform for the study of strong correlation behavior and strongly coupled superconductivity, which provides an insight into the physics of high-temperature superconductors or other unconventional superconductors.

cond-mat.supr-con↗

arXiv2025

SecurityLingua: Efficient Defense of LLM Jailbreak Attacks via Security-Aware Prompt Compression

Large language models (LLMs) have achieved widespread adoption across numerous applications. However, many LLMs are vulnerable to malicious attacks even after safety alignment. These attacks typically bypass LLMs' safety guardrails by wrapping the original malicious instructions inside adversarial jailbreaks prompts. Previous research has proposed methods such as adversarial training and prompt rephrasing to mitigate these safety vulnerabilities, but these methods often reduce the utility of LLMs or lead to significant computational overhead and online latency. In this paper, we propose SecurityLingua, an effective and efficient approach to defend LLMs against jailbreak attacks via security-oriented prompt compression. Specifically, we train a prompt compressor designed to discern the "true intention" of the input prompt, with a particular focus on detecting the malicious intentions of adversarial prompts. Then, in addition to the original prompt, the intention is passed via the system prompt to the target LLM to help it identify the true intention of the request. SecurityLingua ensures a consistent user experience by leaving the original input prompt intact while revealing the user's potentially malicious intention and stimulating the built-in safety guardrails of the LLM. Moreover, thanks to prompt compression, SecurityLingua incurs only a negligible overhead and extra token cost compared to all existing defense methods, making it an especially practical solution for LLM defense. Experimental results demonstrate that SecurityLingua can effectively defend against malicious attacks and maintain utility of the LLM with negligible compute and latency overhead. Our code is available at https://aka.ms/SecurityLingua.

cs.CR↗

arXiv2025

Compressive Fourier-Domain Intensity Coupling (C-FOCUS) enables near-millimeter deep imaging in the intact mouse brain in vivo

Two-photon microscopy is a powerful tool for in vivo imaging, but its imaging depth is typically limited to a few hundred microns due to tissue scattering, even with existing scattering correction techniques. Moreover, most active scattering correction methods are restricted to small regions by the optical memory effect. Here, we introduce compressive Fourier-domain intensity coupling for scattering correction (C-FOCUS), an active scattering correction approach that integrates Fourier-domain intensity modulation with compressive sensing for two-photon microscopy. Using C-FOCUS, we demonstrate high-resolution imaging of YFP-labeled neurons and FITC-labeled blood vessels at depths exceeding 900 um in the intact mouse brain in vivo. Furthermore, we achieve transcranial imaging of YFP-labeled dendritic structures through the intact adult mouse skull. C-FOCUS enables high-contrast fluorescence imaging at depths previously inaccessible using two-photon microscopy with 1035 nm excitation, enhancing fluorescence intensity by over 20-fold compared to uncorrected imaging. C-FOCUS provides a broadly applicable strategy for rapid, deep-tissue optical imaging in vivo.

physics.optics↗

arXiv2025

MMInference: Accelerating Pre-filling for Long-Context VLMs via Modality-Aware Permutation Sparse Attention

The integration of long-context capabilities with visual understanding unlocks unprecedented potential for Vision Language Models (VLMs). However, the quadratic attention complexity during the pre-filling phase remains a significant obstacle to real-world deployment. To overcome this limitation, we introduce MMInference (Multimodality Million tokens Inference), a dynamic sparse attention method that accelerates the prefilling stage for long-context multi-modal inputs. First, our analysis reveals that the temporal and spatial locality of video input leads to a unique sparse pattern, the Grid pattern. Simultaneously, VLMs exhibit markedly different sparse distributions across different modalities. We introduce a permutation-based method to leverage the unique Grid pattern and handle modality boundary issues. By offline search the optimal sparse patterns for each head, MMInference constructs the sparse distribution dynamically based on the input. We also provide optimized GPU kernels for efficient sparse computations. Notably, MMInference integrates seamlessly into existing VLM pipelines without any model modifications or fine-tuning. Experiments on multi-modal benchmarks-including Video QA, Captioning, VisionNIAH, and Mixed-Modality NIAH-with state-of-the-art long-context VLMs (LongVila, LlavaVideo, VideoChat-Flash, Qwen2.5-VL) show that MMInference accelerates the pre-filling stage by up to 8.3x at 1M tokens while maintaining accuracy. Our code is available at https://aka.ms/MMInference.

cs.CV↗

arXiv2025

Ambient-pressure superconductivity onset at 10 K and robust Tc under high pressure in TiNbTaN3 medium-entropy nitride

Superconductivity has been one of the focal points in medium and high-entropy alloys (MEAs-HEAs) since the first discovery of the HEA superconductor in 2014. Until now, most HEAs' superconducting transition temperature (Tc) has not exceeded 10 K. Here we report the first observation of superconductivity in a bulk medium-entropy nitride (MEN), TiNbTaN3, which shows a Tc of 10 K at ambient pressure. Notably, the electronic specific heat coefficient γ(H) exhibits nonlinear H-dependence behavior, which is similar to other well-studied multigap superconductors. Furthermore, TiNbTaN3 exhibits extraordinary pressure resilience, maintaining robust superconductivity under high-pressure conditions. Density functional theory (DFT) calculations indicate that pressure exerts a negligible impact on the electronic structures of TiNbTaN3, thereby corroborating the experimental observations. These findings not only advance our understanding of emergent phenomena in entropy-stabilized nitrides but also establish a new material platform for finding more high-Tc superconductors with combinations of 4d/5d transition metal elements and light elements, motivating further investigations into high-entropy functional ceramics for extreme environment applications.

cond-mat.supr-con↗

arXiv2025

PyramidKV: Dynamic KV Cache Compression based on Pyramidal Information Funneling

In this study, we investigate whether attention-based information flow inside large language models (LLMs) is aggregated through noticeable patterns for long context processing. Our observations reveal that LLMs aggregate information through Pyramidal Information Funneling where attention is scattering widely in lower layers, progressively consolidating within specific contexts, and ultimately focusing on critical tokens (a.k.a massive activation or attention sink) in higher layers. Motivated by these insights, we developed PyramidKV, a novel and effective KV cache compression method. This approach dynamically adjusts the KV cache size across different layers, allocating more cache in lower layers and less in higher ones, diverging from traditional methods that maintain a uniform KV cache size. Our experimental evaluations, utilizing the LongBench benchmark, show that PyramidKV matches the performance of models with a full KV cache while retaining only 12% of the KV cache, thus significantly reducing memory usage. In scenarios emphasizing memory efficiency, where only 0.7% of the KV cache is maintained, PyramidKV surpasses other KV cache compression techniques, achieving up to a 20.5 absolute accuracy improvement on TREC dataset. In the Needle-in-a-Haystack experiment, PyramidKV outperforms competing methods in maintaining long-context comprehension in LLMs; notably, retaining just 128 KV cache entries enables the LLAMA-3-70B model to achieve 100.0 Acc. performance.

cs.CL↗

arXiv2025

Superconductivity in the Medium-Entropy/High-Entropy Re-based Alloys with a Non-Centrosymmetric $α$-Mn Lattice

Medium or high-entropy alloys (MEAs-HEAs) and rhenium-based compounds with a non-centrosymmetric (NC) structure have received a lot of attention for offering a fertile soil in search for unconventional superconductivity. Here, five previously unreported NC Re-based MEA-HEA superconductors with an $α$-Mn lattice are successfully synthesized, with their superconducting transition temperatures (Tcs) ranging from 4 to 5 K. An increase in the superconducting transition temperature (Tc) can be achieved by modulating the valence electron count (VEC) through compositional adjustments. Magnetization measurements confirm that all the synthesized Re-based MEA-HEAs are bulk type-II superconductors. Specific heat analysis reveals that the superconducting state of these HEAs can be well described by a single-gap s-wave model. Our results show that the Kadowaki-Woods ratio of these $α$-Mn MEA/HEA superconductors are close to the typical value of heavy fermion compounds, suggesting the existence of strong electronic correlation. These findings provide promising material platforms to study the role of high disorder in the origin of superconductivity in the NC MEAs-HEAs.

cond-mat.supr-con↗

arXiv2025

SCBench: A KV Cache-Centric Analysis of Long-Context Methods

Long-context LLMs have enabled numerous downstream applications but also introduced significant challenges related to computational and memory efficiency. To address these challenges, optimizations for long-context inference have been developed, centered around the KV cache. However, existing benchmarks often evaluate in single-request, neglecting the full lifecycle of the KV cache in real-world use. This oversight is particularly critical, as KV cache reuse has become widely adopted in LLMs inference frameworks, such as vLLM and SGLang, as well as by LLM providers, including OpenAI, Microsoft, Google, and Anthropic. To address this gap, we introduce SCBench(SharedContextBench), a comprehensive benchmark for evaluating long-context methods from a KV cachecentric perspective: 1) KV cache generation, 2) KV cache compression, 3) KV cache retrieval, 4) KV cache loading. Specifically, SCBench uses test examples with shared context, ranging 12 tasks with two shared context modes, covering four categories of long-context capabilities: string retrieval, semantic retrieval, global information, and multi-task. With it, we provide an extensive KV cache-centric analysis of eight categories long-context solutions, including Gated Linear RNNs, Mamba-Attention hybrids, and efficient methods such as sparse attention, KV cache dropping, quantization, retrieval, loading, and prompt compression. The evaluation is conducted on 8 long-context LLMs. Our findings show that sub-O(n) memory methods suffer in multi-turn scenarios, while sparse encoding with O(n) memory and sub-O(n^2) pre-filling computation perform robustly. Dynamic sparsity yields more expressive KV caches than static patterns, and layer-level sparsity in hybrid architectures reduces memory usage with strong performance. Additionally, we identify attention distribution shift issues in long-generation scenarios. https://aka.ms/SCBench.

cs.CL↗

arXiv2025

LongEval: A Comprehensive Analysis of Long-Text Generation Through a Plan-based Paradigm

Large Language Models (LLMs) have achieved remarkable success in various natural language processing tasks, yet their ability to generate long-form content remains poorly understood and evaluated. Our analysis reveals that current LLMs struggle with length requirements and information density in long-text generation, with performance deteriorating as text length increases. To quantitively locate such a performance degradation and provide further insights on model development, we present LongEval, a benchmark that evaluates long-text generation through both direct and plan-based generation paradigms, inspired by cognitive and linguistic writing models. The comprehensive experiments in this work reveal interesting findings such as that while model size correlates with generation ability, the small-scale model (e.g., LongWriter), well-trained on long texts, has comparable performance. All code and datasets are released in https://github.com/Wusiwei0410/LongEval.

cs.CL↗

arXiv2025

Scattering compensation through Fourier-domain intensity coupling in two-photon microscopy

Light penetration depth in biological tissue is limited by tissue scattering. Correcting scattering becomes particularly challenging in scenarios with limited photon availability and when access to the transmission side of the scattering tissue is not possible. Here, we introduce a new two-photon microscopy system with Fourier-domain intensity coupling for scattering correction (2P-FOCUS). 2P-FOCUS corrects scattering by intensity modulation in the Fourier domain, leveraging the nonlinearity of multiple-beam interference and two-photon excitation, eliminating the need for a guide star, iterative optimization, or measuring transmission or reflection matrices. 2P-FOCUS uses random patterns to probe scattering properties, combined with a single-shot algorithm to rapidly generate the correction mask. 2P-FOCUS can also correct scattering beyond the limitation of the memory effect by automatically customizing correction masks for each subregion in a large field-of-view. We provide several proof-of-principle demonstrations here, including focusing and imaging through a bone sample, and imaging neurons and cerebral blood vessels in the mouse brain ex vivo. 2P-FOCUS significantly enhances two-photon fluorescence signals by several tens of folds compared to cases without scattering correction at the same excitation power. 2P-FOCUS can also correct tissue scattering over a 230x230x500 um\textsuperscript{3} volume, which is beyond the memory effect range. 2P-FOCUS is able to measure, calculate, and correct scattering within a few seconds, effectively delivering more light deep into the scattering tissue. 2P-FOCUS could be broadly adopted for deep tissue imaging owing to its powerful combination of effectiveness, speed, and cost.

physics.optics↗

arXiv2024

On the Rigour of Scientific Writing: Criteria, Analysis, and Insights

Rigour is crucial for scientific research as it ensures the reproducibility and validity of results and findings. Despite its importance, little work exists on modelling rigour computationally, and there is a lack of analysis on whether these criteria can effectively signal or measure the rigour of scientific papers in practice. In this paper, we introduce a bottom-up, data-driven framework to automatically identify and define rigour criteria and assess their relevance in scientific writing. Our framework includes rigour keyword extraction, detailed rigour definition generation, and salient criteria identification. Furthermore, our framework is domain-agnostic and can be tailored to the evaluation of scientific rigour for different areas, accommodating the distinct salient criteria across fields. We conducted comprehensive experiments based on datasets collected from two high impact venues for Machine Learning and NLP (i.e., ICLR and ACL) to demonstrate the effectiveness of our framework in modelling rigour. In addition, we analyse linguistic patterns of rigour, revealing that framing certainty is crucial for enhancing the perception of scientific rigour, while suggestion certainty and probability uncertainty diminish it.

cs.CL↗

arXiv2024

MInference 1.0: Accelerating Pre-filling for Long-Context LLMs via Dynamic Sparse Attention

The computational challenges of Large Language Model (LLM) inference remain a significant barrier to their widespread deployment, especially as prompt lengths continue to increase. Due to the quadratic complexity of the attention computation, it takes 30 minutes for an 8B LLM to process a prompt of 1M tokens (i.e., the pre-filling stage) on a single A100 GPU. Existing methods for speeding up prefilling often fail to maintain acceptable accuracy or efficiency when applied to long-context LLMs. To address this gap, we introduce MInference (Milliontokens Inference), a sparse calculation method designed to accelerate pre-filling of long-sequence processing. Specifically, we identify three unique patterns in long-context attention matrices-the A-shape, Vertical-Slash, and Block-Sparsethat can be leveraged for efficient sparse computation on GPUs. We determine the optimal pattern for each attention head offline and dynamically build sparse indices based on the assigned pattern during inference. With the pattern and sparse indices, we perform efficient sparse attention calculations via our optimized GPU kernels to significantly reduce the latency in the pre-filling stage of long-context LLMs. Our proposed technique can be directly applied to existing LLMs without any modifications to the pre-training setup or additional fine-tuning. By evaluating on a wide range of downstream tasks, including InfiniteBench, RULER, PG-19, and Needle In A Haystack, and models including LLaMA-3-1M, GLM4-1M, Yi-200K, Phi-3-128K, and Qwen2-128K, we demonstrate that MInference effectively reduces inference latency by up to 10x for pre-filling on an A100, while maintaining accuracy. Our code is available at https://aka.ms/MInference.

cs.CL↗

arXiv2024

With Ears to See and Eyes to Hear: Sound Symbolism Experiments with Multimodal Large Language Models

Recently, Large Language Models (LLMs) and Vision Language Models (VLMs) have demonstrated aptitude as potential substitutes for human participants in experiments testing psycholinguistic phenomena. However, an understudied question is to what extent models that only have access to vision and text modalities are able to implicitly understand sound-based phenomena via abstract reasoning from orthography and imagery alone. To investigate this, we analyse the ability of VLMs and LLMs to demonstrate sound symbolism (i.e., to recognise a non-arbitrary link between sounds and concepts) as well as their ability to "hear" via the interplay of the language and vision modules of open and closed-source multimodal models. We perform multiple experiments, including replicating the classic Kiki-Bouba and Mil-Mal shape and magnitude symbolism tasks, and comparing human judgements of linguistic iconicity with that of LLMs. Our results show that VLMs demonstrate varying levels of agreement with human labels, and more task information may be required for VLMs versus their human counterparts for in silico experimentation. We additionally see through higher maximum agreement levels that Magnitude Symbolism is an easier pattern for VLMs to identify than Shape Symbolism, and that an understanding of linguistic iconicity is highly dependent on model size.

cs.CL↗

arXiv2024

On unitary equivalence and reducing subspaces of analytic Toeplitz operator on vector-valued Hardy space

In this paper, we proved that $T_{z^n}$ acting on the $\mathbb{C}^m$-valued Hardy space $H_{\mathbb{C}^m}^2(\mathbb{D})$, is unitarily equivalent to $\bigoplus_1^{mn}T_z$, where $T_z$ is acting on the scalar-valued Hardy space $H_{\mathbb{C}}^2(\mathbb{D})$. And using the matrix manipulations combined with operator theory methods, we completely describe the reducing subspaces of $T_{z^n}$ on $H_{\mathbb{C}^m}^2(\mathbb{D})$.

math.FA↗

arXiv2024

Fluorescence Diffraction Tomography using Explicit Neural Fields

Simultaneous imaging of fluorescence-labeled and label-free phase objects in the same sample provides distinct and complementary information. Most multimodal fluorescence-phase imaging operates in transmission mode, capturing fluorescence images and phase images separately or sequentially, which limits their practical application in vivo. Here, we develop fluorescence diffraction tomography (FDT) with explicit neural fields to reconstruct the 3D refractive index (RI) of phase objects from diffracted fluorescence images captured in reflection mode. The successful reconstruction of 3D RI using FDT relies on four key components: a coarse-to-fine structure, self-calibration, a differential multi-slice rendering model, and partially coherent masks. The explicit representation integrates with the coarse-to-fine structure for high-speed, high-resolution reconstruction, while the differential multi-slice rendering model enables self-calibration of fluorescence illumination, ensuring accurate forward image prediction and RI reconstruction. Partially coherent masks efficiently resolve discrepancies between the coherent light model and partially coherent light data. FDT successfully reconstructs the RI of 3D cultured label-free bovine myotubes in a 530 $\times$ 530 $\times$ 300 $μm^3$ volume at 1024 $\times$ 1024 pixels across 24 $z$-layers from fluorescence images, demonstrating high resolution and high accuracy 3D RI reconstruction of bulky and heterogeneous biological samples in vitro.

physics.optics↗