arXiv subjects

Qinghua Zhao

Publications and source records attributed to Qinghua Zhao.

At least 19 recordsLinked to original sources

arXiv2026

Modular and Cost-effective Scanning Photocurrent Microscopy System for Sub-micron characterization of 2D optoelectronic devices

Scanning photocurrent microscopy (SPCM) is a powerful technique for probing local optoelectronic phenomena in 2D semiconducting devices. However, commercial setups remain costly, complex and often lack flexibility and adaptability. In this work, we present a home-built SPCM platform built around the retrofitting of a conventional metallographic microscope by coupling it with different light sources (single-mode fiber-coupled lasers and multimode fiber-coupled high-power LEDs), a motorized XY stage, a digital camera and an electronic readout module. This system enables simultaneous acquisition of photocurrent and reflection intensity maps, requiring minimal modifications of the microscope. We reached sub-micron spatial resolution and high imaging fidelity by correlating photocurrent maps with reflection maps, optical micrographs and AFM topography data on different devices fabricated with different materials (InSe, MoS2, WSe2, Gr), on different substrates (Si/SiO2, compact disk). This work provides a reliable, accessible and reproducible high-performance SPCM platform that can be easily implemented in most laboratories for microscale optoelectronic characterization of 2D devices.

cond-mat.mtrl-sci↗

arXiv2026

Who Pays for Open Review? Visible Author Reputation and Its Effect on Ratings

An OpenReview bug in November 2025 broke anonymity at several conferences and prompted calls for open review, which motivate us to ask what shifting from blind to open would mean for authors. Analyzing over 18,000 reviewed submissions to ICLR 2026, split into de facto open and blind groups by arXiv preprint timing, we find that ratings rise with author reputation under both mechanisms, with a steeper slope under open review that is statistically significant, and that the open-blind difference is concentrated at the borderline ratings. The pattern holds across five reputation proxies (including institution, h-index, and citation count), three author-aggregation rules, and five definitions of the open window. A controlled simulation with five AI models as reviewers, holding the manuscript fixed and varying the author reputation, reproduces the effect. With claude-opus-5 as the reviewer, for example, rating rises by 0.5 points as the author moves from low to high reputation.

cs.DL↗

arXiv2026

KESA: A Knowledge Enhanced Approach For Sentiment Analysis

Though some recent works focus on injecting sentiment knowledge into pre-trained language models, they usually design mask and reconstruction tasks in the post-training phase. In this paper, we aim to benefit from sentiment knowledge in a lighter way. To achieve this goal, we study sentence-level sentiment analysis and, correspondingly, propose two sentiment-aware auxiliary tasks named sentiment word cloze and conditional sentiment prediction. The first task learns to select the correct sentiment words within the input, given the overall sentiment polarity as prior knowledge. On the contrary, the second task predicts the overall sentiment polarity given the sentiment polarity of the word as prior knowledge. In addition, two kinds of label combination methods are investigated to unify multiple types of labels in each task. We argue that more information can promote the models to learn more profound semantic representation. We implement it in a straightforward way to verify this hypothesis. The experimental results demonstrate that our approach consistently outperforms pre-trained models and is additive to existing knowledge-enhanced post-trained models. The code and data are released at https://github.com/lshowway/KESA.

cs.CL↗

arXiv2026

How Chain-of-Thought Works? Tracing Information Flow from Decoding, Projection, and Activation

Chain-of-Thought (CoT) prompting significantly enhances model reasoning, yet its internal mechanisms remain poorly understood. We analyze CoT's operational principles by reversely tracing information flow across decoding, projection, and activation phases. Our quantitative analysis suggests that CoT may serve as a decoding space pruner, leveraging answer templates to guide output generation, with higher template adherence strongly correlating with improved performance. Furthermore, we surprisingly find that CoT modulates neuron engagement in a task-dependent manner: reducing neuron activation in open-domain tasks, yet increasing it in closed-domain scenarios. These findings offer a novel mechanistic interpretability framework and critical insights for enabling targeted CoT interventions to design more efficient and robust prompts. We released our code and data at https://anonymous.4open.science/r/cot-D247.

cs.AI↗

arXiv2026

A Layer-wise Analysis of Supervised Fine-Tuning

While critical for alignment, Supervised Fine-Tuning (SFT) incurs the risk of catastrophic forgetting, yet the layer-wise emergence of instruction-following capabilities remains elusive. We investigate this mechanism via a comprehensive analysis utilizing information-theoretic, geometric, and optimization metrics across model scales (1B-32B). Our experiments reveal a distinct depth-dependent pattern: middle layers (20\%-80\%) are stable, whereas final layers exhibit high sensitivity. Leveraging this insight, we propose Mid-Block Efficient Tuning, which selectively updates these critical intermediate layers. Empirically, our method outperforms standard LoRA up to 10.2\% on GSM8K (OLMo2-7B) with reduced parameter overhead, demonstrating that effective alignment is architecturally localized rather than distributed. The code is publicly available at https://anonymous.4open.science/r/base_sft.

cs.LG↗

arXiv2026

What Is a Skill Worth? Structure-Aware Shapley Valuation of Agent Skills

Agent skills are increasingly optimized by automated feedback loops, producing long structured artifacts whose internal value remains unclear. We study skill valuation: assigning credit to the internal units of a fixed skill, such as rules, examples, scripts, and heuristics, under a fixed agent and held-out task distribution. Skill valuation differs from data or prompt-span valuation because skill units are structured: they may depend on other units, belong to a document hierarchy, trigger agent behavior, and consume limited prompt context. We introduce SkillSV, a structure-aware Shapley-style framework for skill valuation. SkillSV compiles a skill into units, dependencies, and hierarchy, so that only valid counterfactual skills are evaluated. It uses paired deletion and length-neutral padding to separate content value from context cost, and estimates the resulting values with a rollout-budgeted estimator for noisy agent evaluations. On four agentic benchmarks, we assess the faithfulness, actionability, and explanation of SkillSV: it recovers unit interactions, preserves aggregate skill lift, and guides safe pruning and compression.

cs.AI↗

arXiv2025

Bounded Dynamic Level Maintenance for Efficient Logic Optimization

Logic optimization constitutes a critical phase within the Electronic Design Automation (EDA) flow, essential for achieving desired circuit power, performance, and area (PPA) targets. These logic circuits are typically represented as Directed Acyclic Graphs (DAGs), where the structural depth, quantified by node level, critically correlates with timing performance. Modern optimization strategies frequently employ iterative, local transformation heuristics (\emph{e.g.,} \emph{rewrite}, \emph{refactor}) directly on this DAG structure. As optimization continuously modifies the graph locally, node levels require frequent dynamic updates to guide subsequent decisions. However, a significant gap exists: existing algorithms for incrementally updating node levels are unbounded to small changes. This leads to a total of worst complexity in $O(|V|^2)$ for given local subgraphs $\{ΔG_i\}_{i=1}^{|V|}$ updates on DAG $G(V,E)$. This unbounded nature poses a severe efficiency bottleneck, hindering the scalability of optimization flows, particularly when applied to large circuit designs prevalent today. In this paper, we analyze the dynamic level maintenance problem endemic to iterative logic optimization, framing it through the lens of partial topological order. Building upon the analysis, we present the first bounded algorithm for maintaining level constraints, with $O(|V| Δ\log Δ)$ time for a sequence $|V|$ of updates $\{ΔG_i\}$, where $Δ= \max_i \|ΔG_i\|$ denotes the maximum extended size of $ΔG_i$. Experiments on comprehensive benchmarks show our algorithm enables an average 6.4$\times$ overall speedup relative to \rw and \rf, driven by a 1074.8$\times$ speedup in the level maintenance, all without any quality sacrifice.

cs.CC↗

arXiv2025

REMA: A Unified Reasoning Manifold Framework for Interpreting Large Language Model

Understanding how Large Language Models (LLMs) perform complex reasoning and their failure mechanisms is a challenge in interpretability research. To provide a measurable geometric analysis perspective, we define the concept of the Reasoning Manifold, a latent low-dimensional geometric structure formed by the internal representations corresponding to all correctly reasoned generations. This structure can be conceptualized as the embodiment of the effective thinking paths that the model has learned to successfully solve a given task. Based on this concept, we build REMA, a framework that explains the origins of failures by quantitatively comparing the spatial relationships of internal model representations corresponding to both erroneous and correct reasoning samples. Specifically, REMA first quantifies the geometric deviation of each erroneous representation by calculating its k-nearest neighbors distance to the approximated manifold formed by correct representations, thereby providing a unified failure signal. It then localizes the divergence points where these deviations first become significant by tracking this deviation metric across the model's layers and comparing it against a baseline of internal fluctuations from correct representations, thus identifying where the reasoning chain begins to go off-track. Our extensive experiments on diverse language and multimodal models and tasks demonstrate the low-dimensional nature of the reasoning manifold and the high separability between erroneous and correct reasoning representations. The results also validate the effectiveness of the REMA framework in analyzing the origins of reasoning failures. This research connects abstract reasoning failures to measurable geometric deviations in representations, providing new avenues for in-depth understanding and diagnosis of the internal computational processes of black-box models.

cs.AI↗

arXiv2025

Beyond Frequency: The Role of Redundancy in Large Language Model Memorization

Memorization in large language models poses critical risks for privacy and fairness as these systems scale to billions of parameters. While previous studies established correlations between memorization and factors like token frequency and repetition patterns, we revealed distinct response patterns: frequency increases minimally impact memorized samples (e.g. 0.09) while substantially affecting non-memorized samples (e.g., 0.25), with consistency observed across model scales. Through counterfactual analysis by perturbing sample prefixes and quantifying perturbation strength through token positional changes, we demonstrate that redundancy correlates with memorization patterns. Our findings establish that: about 79% of memorized samples are low-redundancy, these low-redundancy samples exhibit 2-fold higher vulnerability than high-redundancy ones, and consequently memorized samples drop by 0.6 under perturbation while non-memorized samples drop by only 0.01, indicating that more redundant content becomes both more memorable and more fragile. These findings suggest potential redundancy-guided approaches for data preprocessing, thereby reducing privacy risks and mitigating bias to ensure fairness in model deployments.

cs.LG↗

arXiv2025

SynapticRAG: Enhancing Temporal Memory Retrieval in Large Language Models through Synaptic Mechanisms

Existing retrieval methods in Large Language Models show degradation in accuracy when handling temporally distributed conversations, primarily due to their reliance on simple similarity-based retrieval. Unlike existing memory retrieval methods that rely solely on semantic similarity, we propose SynapticRAG, which uniquely combines temporal association triggers with biologically-inspired synaptic propagation mechanisms. Our approach uses temporal association triggers and synaptic-like stimulus propagation to identify relevant dialogue histories. A dynamic leaky integrate-and-fire mechanism then selects the most contextually appropriate memories. Experiments on four datasets of English, Chinese and Japanese show that compared to state-of-the-art memory retrieval methods, SynapticRAG achieves consistent improvements across multiple metrics up to 14.66% points. This work bridges the gap between cognitive science and language model development, providing a new framework for memory management in conversational systems.

cs.CL↗

arXiv2025

Reassessing the Role of Chain-of-Thought in Sentiment Analysis: Insights and Limitations

The relationship between language and thought remains an unresolved philosophical issue. Existing viewpoints can be broadly categorized into two schools: one asserting their independence, and another arguing that language constrains thought. In the context of large language models, this debate raises a crucial question: Does a language model's grasp of semantic meaning depend on thought processes? To explore this issue, we investigate whether reasoning techniques can facilitate semantic understanding. Specifically, we conceptualize thought as reasoning, employ chain-of-thought prompting as a reasoning technique, and examine its impact on sentiment analysis tasks. The experiments show that chain-of-thought has a minimal impact on sentiment analysis tasks. Both the standard and chain-of-thought prompts focus on aspect terms rather than sentiment in the generated content. Furthermore, counterfactual experiments reveal that the model's handling of sentiment tasks primarily depends on information from demonstrations. The experimental results support the first viewpoint.

cs.CL↗

arXiv2024

ROME: Memorization Insights from Text, Logits and Representation

Previous works have evaluated memorization by comparing model outputs with training corpora, examining how factors such as data duplication, model size, and prompt length influence memorization. However, analyzing these extensive training corpora is highly time-consuming. To address this challenge, this paper proposes an innovative approach named ROME that bypasses direct processing of the training data. Specifically, we select datasets categorized into three distinct types -- context-independent, conventional, and factual -- and redefine memorization as the ability to produce correct answers under these conditions. Our analysis then focuses on disparities between memorized and non-memorized samples by examining the logits and representations of generated texts. Experimental findings reveal that longer words are less likely to be memorized, higher confidence correlates with greater memorization, and representations of the same concepts are more similar across different contexts. Our code and data will be publicly available when the paper is accepted.

cs.CL↗

arXiv2024

Word Order's Impacts: Insights from Reordering and Generation Analysis

Existing works have studied the impacts of the order of words within natural text. They usually analyze it by destroying the original order of words to create a scrambled sequence, and then comparing the models' performance between the original and scrambled sequences. The experimental results demonstrate marginal drops. Considering this findings, different hypothesis about word order is proposed, including ``the order of words is redundant with lexical semantics'', and ``models do not rely on word order''. In this paper, we revisit the aforementioned hypotheses by adding a order reconstruction perspective, and selecting datasets of different spectrum. Specifically, we first select four different datasets, and then design order reconstruction and continuing generation tasks. Empirical findings support that ChatGPT relies on word order to infer, but cannot support or negate the redundancy relations between word order lexical semantics.

cs.CL↗

arXiv2024

Word Order and World Knowledge

Word order is an important concept in natural language, and in this work, we study how word order affects the induction of world knowledge from raw text using language models. We use word analogies to probe for such knowledge. Specifically, in addition to the natural word order, we first respectively extract texts of six fixed word orders from five languages and then pretrain the language models on these texts. Finally, we analyze the experimental results of the fixed word orders on word analogies and show that i) certain fixed word orders consistently outperform or underperform others, though the specifics vary across languages, and ii) the Wov2Lex hypothesis is not hold in pre-trained language models, and the natural word order typically yields mediocre results. The source code will be made publicly available at https://github.com/lshowway/probing_by_analogy.

cs.CL↗

arXiv2023

D-HAN: Dynamic News Recommendation with Hierarchical Attention Network

News recommendation models often fall short in capturing users' preferences due to their static approach to user-news interactions. To address this limitation, we present a novel dynamic news recommender model that seamlessly integrates continuous time information to a hierarchical attention network that effectively represents news information at the sentence, element, and sequence levels. Moreover, we introduce a dynamic negative sampling method to optimize users' implicit feedback. To validate our model's effectiveness, we conduct extensive experiments on three real-world datasets. The results demonstrate the effectiveness of our proposed approach.

cs.IR↗

arXiv2023

Effects of Tonal Coarticulation and Prosodic Positions on Tonal Contours of Low Rising Tones: In the Case of Xiamen Dialect

Few studies have worked on the effects of tonal coarticulation and prosodic positions on the low rising tone in Xiamen Dialect. This study addressed such an issue. To do so, a new method, the Tonal Contour Analysis in Tonal Triangle, was proposed to measure the subtle curvature of the tonal contour. Findings are as follows: (1) The low rising tone in Xiamen Dialect has a tendency towards the falling-rising tone, which is significantly affected by the tonal coarticulation and prosodic positions. (2) The low rising tone presents as a falling-rising tone when preceded by a tone with a high offset, and as a low rising tone when preceded by a tone that ends up low. (3) The curvature of the low rising tone is greatest in the sentence-initial position, and is positively correlated to its own duration.

cs.SD↗

arXiv2022

Ered: Enhanced Text Representations with Entities and Descriptions

External knowledge,e.g., entities and entity descriptions, can help humans understand texts. Many works have been explored to include external knowledge in the pre-trained models. These methods, generally, design pre-training tasks and implicitly introduce knowledge by updating model weights, alternatively, use it straightforwardly together with the original text. Though effective, there are some limitations. On the one hand, it is implicit and only model weights are paid attention to, the pre-trained entity embeddings are ignored. On the other hand, entity descriptions may be lengthy, and inputting into the model together with the original text may distract the model's attention. This paper aims to explicitly include both entities and entity descriptions in the fine-tuning stage. First, the pre-trained entity embeddings are fused with the original text representation and updated by the backbone model layer by layer. Second, descriptions are represented by the knowledge module outside the backbone model, and each knowledge layer is selectively connected to one backbone layer for fusing. Third, two knowledge-related auxiliary tasks, i.e., entity/description enhancement and entity enhancement/pollution task, are designed to smooth the semantic gaps among evolved representations. We conducted experiments on four knowledge-oriented tasks and two common tasks, and the results achieved new state-of-the-art on several datasets. Besides, we conduct an ablation study to show that each module in our method is necessary. The code is available at https://github.com/lshowway/Ered.

cs.CL↗

arXiv2022

Investigation of Deformation and Fracture Mechanisms in Two-dimensional Gallium Telluride Multilayers Using Nanoindentation

Two-dimensional (2D) materials possess great potential for flexible devices, ascribing to their outstanding electrical, optical, and mechanical properties. However, their mechanical deformation property and fracture mechanism, which are inescapable in many applications like flexible optoelectronics, are still unclear or not thoroughly investigated due methodology limitations. In light of this, such mechanical properties and mechanisms are explored on example of gallium telluride (GaTe), a promising optoelectronic candidate with an ultrahigh photo-responsibility and a high plasticity within 2D family. Considering the driving force insufficient in atomic force microscopy (AFM)-based nanoindentation method, here the mechanical properties of both substrate-supported and suspended GaTe multilayers were systematically investigated through full-scale Berkovich-tip nanoindentation, micro-Raman spectroscopy, AFM, and scanning electron microscopy. An unusual concurrence of multiple pop-in and load-drop events in loading curve was observed. By further correlating to molecular dynamics calculations, this concurrence was unveiled originating from the interlayer sliding mediated layers-by-layers fracture mechanism within GaTe multilayers. The van der Waals force between GaTe multilayers and substrates was revealed much stronger than that between GaTe interlayers, resulting in the easy sliding and fracture of multilayers within GaTe. This work provides new insights into the deformation and fracture mechanisms of GaTe and other similar 2D multilayers in flexible applications.

cond-mat.mtrl-sci↗