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Yu Pei

Publications and source records attributed to Yu Pei.

At least 19 recordsLinked to original sources

Assessing Behavioral Validation in UI Component Test Suites Using Inferred Metamorphic Relations

UI component libraries are commonly assessed using execution-based metrics such as statement and branch coverage, yet these metrics provide limited insight into whether tests verify the behavioral relations implied by component APIs and documentation. This paper presents an MR-based framework that uses inferred metamorphic relations (MRs) as an empirical behavioral reference, rather than a complete specification, for assessing UI component test suites. Given a component's source, documentation, and tests, the framework infers component-specific MRs using a UI-specific taxonomy, aligns tests with the inferred relations through hybrid deterministic and semantic analysis, and computes relation-level MR coverage metrics. We manually validate both the inferred MR space and the test--MR alignment. Our evaluation shows that existing test suites exercise substantially more behavioral relations than they explicitly validate: MR Cover remains between 42.5% and 47.6% across three LLM configurations and consistently below MR Touch. Most uncovered relations are weak-oracle cases, where behaviors are exercised but lack explicit behavioral validation. MR coverage also complements execution-based coverage by revealing behavioral gaps not reflected by statement or branch coverage alone. We further assess practical relevance through issue-description mapping, oracle strengthening, and MR-relevant injected faults. Most reported issue descriptions can be mapped to inferred MR relation types; weak-oracle relations often expose missing validation evidence; and MR labels show a trend in MR-relevant fault detection. Overall, MR coverage provides a complementary relation-level perspective for assessing behavioral validation in modern UI component testing.

cs.SE

From Bug Reports to Browser-Executable Procedures: An LLM-Driven Agent for Web GUI Bug Reproduction

Reproducing web GUI bugs from natural-language bug reports is critical for software maintenance, but remains difficult because reports often lack prerequisites such as dependencies and input files. Existing bug reproduction techniques mainly target code units or mobile applications and lack end-to-end visual execution and validation for web GUIs. We present ReBug, a context-aware agent system that reconstructs, executes, and validates browser-level reproduction procedures from web GUI bug reports by driving a real browser. ReBug separates reproduction into two stages. In the preparation stage, ReBug reconstructs missing prerequisites from the report and available artifacts, and it produces a high-level reproduction plan. In the execution stage, it performs tool-mediated interactions in the browser, maintains structured summaries of page state and action history, and validates the final state against expectations derived from the report. We evaluate ReBug on 667 real-world bug reports from four open-source web applications. On controlled current deployments, ReBug outperforms both baselines, achieving an average RSR of 49.96%, a mean task completion rate of 74.96%, and a mean action execution success rate of 86.54%. Our results show that explicit context reconstruction and state-aware browser execution effectively support report-derived browser reproduction, while historical replay shows that successful procedures often expose the original bug-present behavior on restored buggy versions.

cs.SE

ConFoThinking: Consolidated Focused Attention Driven Thinking for Visual Question Answering

Thinking with Images improves fine-grained VQA for MLLMs by emphasizing visual cues. However, tool-augmented methods depend on the capacity of grounding, which remains unreliable for MLLMs. In parallel, attention-driven methods to crop the Region of Interest (ROIs) are proposed but they are constrained by (1) fragmented attention signals scattered across layers, leading to suboptimal localization and (2) relying on question- or redundant-text-conditioned attention extraction. Our analysis reveals three patterns: MLLMs may attend to the correct region yet generate incorrect coordinates, where-to-look attention is often fragmented across layers, and attention extraction is query-sensitive. Motivated by these, We propose ConFoThinking, a Consolidated-Focused-Attention-Driven Thinking framework that learns to aggregate attention into a designated intermediate layer, from which we mine and zoom in salient regions for downstream visual understanding. Moreover, we extract attention using concise semantic cues of what to look into, which mitigates the semantic noise introduced by question- or redundant-text-based attention extraction. Experiments across five VQA benchmarks demonstrate ConFoThinking significantly improves perception performance. The code, checkpoints, and dataset will be released after being accepted.

cs.CV

FLEX: Continuous Agent Evolution via Forward Learning from Experience

Autonomous agents driven by Large Language Models (LLMs) have revolutionized reasoning and problem-solving but remain static after training, unable to grow with experience as intelligent beings do during deployment. We introduce Forward Learning with EXperience (FLEX), a gradient-free learning paradigm that enables LLM agents to continuously evolve through accumulated experience. Specifically, FLEX cultivates scalable and inheritable evolution by constructing a structured experience library through continual reflection on successes and failures during interaction with the environment. FLEX delivers substantial improvements on mathematical reasoning, chemical retrosynthesis, and protein fitness prediction (up to 23% on AIME25, 10% on USPTO50k, and 14% on ProteinGym). We further identify a clear scaling law of experiential growth and the phenomenon of experience inheritance across agents, marking a step toward scalable and inheritable continuous agent evolution. Project Page: https://flex-gensi-thuair.github.io.

cs.LG

Who is Introducing the Failure? Automatically Attributing Failures of Multi-Agent Systems via Spectrum Analysis

Large Language Model Powered Multi-Agent Systems (MASs) are increasingly employed to automate complex real-world problems, such as programming and scientific discovery. Despite their promising, MASs are not without their flaws. However, failure attribution in MASs - pinpointing the specific agent actions responsible for failures - remains underexplored and labor-intensive, posing significant challenges for debugging and system improvement. To bridge this gap, we propose FAMAS, the first spectrum-based failure attribution approach for MASs, which operates through systematic trajectory replay and abstraction, followed by spectrum analysis.The core idea of FAMAS is to estimate, from variations across repeated MAS executions, the likelihood that each agent action is responsible for the failure. In particular, we propose a novel suspiciousness formula tailored to MASs, which integrates two key factor groups, namely the agent behavior group and the action behavior group, to account for the agent activation patterns and the action activation patterns within the execution trajectories of MASs. Through expensive evaluations against 12 baselines on the Who and When benchmark, FAMAS demonstrates superior performance by outperforming all the methods in comparison.

cs.SE

ShortListing Model: A Streamlined SimplexDiffusion for Discrete Variable Generation

Generative modeling of discrete variables is challenging yet crucial for applications in natural language processing and biological sequence design. We introduce the Shortlisting Model (SLM), a novel simplex-based diffusion model inspired by progressive candidate pruning. SLM operates on simplex centroids, reducing generation complexity and enhancing scalability. Additionally, SLM incorporates a flexible implementation of classifier-free guidance, enhancing unconditional generation performance. Extensive experiments on DNA promoter and enhancer design, protein design, character-level and large-vocabulary language modeling demonstrate the competitive performance and strong potential of SLM. Our code can be found at https://github.com/GenSI-THUAIR/SLM

cs.LG

Power Stabilization for AI Training Datacenters

Large Artificial Intelligence (AI) training workloads spanning several tens of thousands of GPUs present unique power management challenges. These arise due to the high variability in power consumption during the training. Given the synchronous nature of these jobs, during every iteration there is a computation-heavy phase, where each GPU works on the local data, and a communication-heavy phase where all the GPUs synchronize on the data. Because compute-heavy phases require much more power than communication phases, large power swings occur. The amplitude of these power swings is ever increasing with the increase in the size of training jobs. An even bigger challenge arises from the frequency spectrum of these power swings which, if harmonized with critical frequencies of utilities, can cause physical damage to the power grid infrastructure. Therefore, to continue scaling AI training workloads safely, we need to stabilize the power of such workloads. This paper introduces the challenge with production data and explores innovative solutions across the stack: software, GPU hardware, and datacenter infrastructure. We present the pros and cons of each of these approaches and finally present a multi-pronged approach to solving the challenge. The proposed solutions are rigorously tested using a combination of real hardware and Microsoft's in-house cloud power simulator, providing critical insights into the efficacy of these interventions under real-world conditions.

cs.AR

AMix-1: A Pathway to Test-Time Scalable Protein Foundation Model

We introduce AMix-1, a powerful protein foundation model built on Bayesian Flow Networks and empowered by a systematic training methodology, encompassing pretraining scaling laws, emergent capability analysis, in-context learning mechanism, and test-time scaling algorithm. To guarantee robust scalability, we establish a predictive scaling law and reveal the progressive emergence of structural understanding via loss perspective, culminating in a strong 1.7-billion model. Building on this foundation, we devise a multiple sequence alignment (MSA)-based in-context learning strategy to unify protein design into a general framework, where AMix-1 recognizes deep evolutionary signals among MSAs and consistently generates structurally and functionally coherent proteins. This framework enables the successful design of a dramatically improved AmeR variant with an up to $50\times$ activity increase over its wild type. Pushing the boundaries of protein engineering, we further empower AMix-1 with an evolutionary test-time scaling algorithm for in silico directed evolution that delivers substantial, scalable performance gains as verification budgets are intensified, laying the groundwork for next-generation lab-in-the-loop protein design.

q-bio.BM

MPFNet: A Multi-Prior Fusion Network with a Progressive Training Strategy for Micro-Expression Recognition

Micro-expression recognition (MER), a critical subfield of affective computing, presents greater challenges than macro-expression recognition due to its brief duration and low intensity. While incorporating prior knowledge has been shown to enhance MER performance, existing methods predominantly rely on simplistic, singular sources of prior knowledge, failing to fully exploit multi-source information. This paper introduces the Multi-Prior Fusion Network (MPFNet), leveraging a progressive training strategy to optimize MER tasks. We propose two complementary encoders: the Generic Feature Encoder (GFE) and the Advanced Feature Encoder (AFE), both based on Inflated 3D ConvNets (I3D) with Coordinate Attention (CA) mechanisms, to improve the model's ability to capture spatiotemporal and channel-specific features. Inspired by developmental psychology, we present two variants of MPFNet--MPFNet-P and MPFNet-C--corresponding to two fundamental modes of infant cognitive development: parallel and hierarchical processing. These variants enable the evaluation of different strategies for integrating prior knowledge. Extensive experiments demonstrate that MPFNet significantly improves MER accuracy while maintaining balanced performance across categories, achieving accuracies of 0.811, 0.924, and 0.857 on the SMIC, CASME II, and SAMM datasets, respectively. To the best of our knowledge, our approach achieves state-of-the-art performance on the SMIC and SAMM datasets.

cs.CV

MMME: A Spontaneous Multi-Modal Micro-Expression Dataset Enabling Visual-Physiological Fusion

Micro-expressions (MEs) are subtle, fleeting nonverbal cues that reveal an individual's genuine emotional state. Their analysis has attracted considerable interest due to its promising applications in fields such as healthcare, criminal investigation, and human-computer interaction. However, existing ME research is limited to single visual modality, overlooking the rich emotional information conveyed by other physiological modalities, resulting in ME recognition and spotting performance far below practical application needs. Therefore, exploring the cross-modal association mechanism between ME visual features and physiological signals (PS), and developing a multimodal fusion framework, represents a pivotal step toward advancing ME analysis. This study introduces a novel ME dataset, MMME, which, for the first time, enables synchronized collection of facial action signals (MEs), central nervous system signals (EEG), and peripheral PS (PPG, RSP, SKT, EDA, and ECG). By overcoming the constraints of existing ME corpora, MMME comprises 634 MEs, 2,841 macro-expressions (MaEs), and 2,890 trials of synchronized multimodal PS, establishing a robust foundation for investigating ME neural mechanisms and conducting multimodal fusion-based analyses. Extensive experiments validate the dataset's reliability and provide benchmarks for ME analysis, demonstrating that integrating MEs with PS significantly enhances recognition and spotting performance. To the best of our knowledge, MMME is the most comprehensive ME dataset to date in terms of modality diversity. It provides critical data support for exploring the neural mechanisms of MEs and uncovering the visual-physiological synergistic effects, driving a paradigm shift in ME research from single-modality visual analysis to multimodal fusion. The dataset will be publicly available upon acceptance of this paper.

cs.CV

Steering Protein Family Design through Profile Bayesian Flow

Protein family design emerges as a promising alternative by combining the advantages of de novo protein design and mutation-based directed evolution.In this paper, we propose ProfileBFN, the Profile Bayesian Flow Networks, for specifically generative modeling of protein families. ProfileBFN extends the discrete Bayesian Flow Network from an MSA profile perspective, which can be trained on single protein sequences by regarding it as a degenerate profile, thereby achieving efficient protein family design by avoiding large-scale MSA data construction and training. Empirical results show that ProfileBFN has a profound understanding of proteins. When generating diverse and novel family proteins, it can accurately capture the structural characteristics of the family. The enzyme produced by this method is more likely than the previous approach to have the corresponding function, offering better odds of generating diverse proteins with the desired functionality.

q-bio.BM

Thermoelectrically Elevated Hydrogel Evaporation for Personal Cooling under Extreme Heat Stress

Extreme heat events with wet-bulb temperatures (WBT) above 35{\deg}C pose serious risks to human survival, and conventional hydrogel evaporative cooling alone may not provide sufficient relief as it must be maintained at a sufficiently high temperature to achieve effective evaporation in hot, humid conditions. This study integrates thermoelectric devices (TEDs) with hydrogels to create an effective personal cooling solution. TEDs pump heat away from the skin to maintain comfort while simultaneously increasing the temperature of hydrogel to enhance evaporation. This hybrid system outperforms TEDs or hydrogel alone in extreme conditions (temperature up to 55{\deg}C and relative humidity up to 88%, with WBT > 35{\deg}C) and can operate for over six hours with a manageable hydrogel and battery weight. The active temperature control of TEDs allows adaptation to changing thermal loads and environments. These results demonstrate the potential of hybrid evaporative and thermoelectric cooling as an efficient, adaptable, and sustainable personal cooling solution to combat extreme heat.

physics.flu-dyn

Ranking Plausible Patches by Historic Feature Frequencies

Automated program repair (APR) techniques have achieved conspicuous progress, and are now capable of producing genuinely correct fixes in scenarios that were well beyond their capabilities only a few years ago. Nevertheless, even when an APR technique can find a correct fix for a bug, it still runs the risk of ranking the fix lower than other patches that are plausible (they pass all available tests) but incorrect. This can seriously hurt the technique's practical effectiveness, as the user will have to peruse a larger number of patches before finding the correct one. This paper presents PrevaRank, a technique that ranks plausible patches produced by any APR technique according to their feature similarity with historic programmer-written fixes for similar bugs. PrevaRank implements simple heuristics, which help make it scalable and applicable to any APR tool that produces plausible patches. In our experimental evaluation, after training PrevaRank on the fix history of 81 open-source Java projects, we used it to rank patches produced by 8 Java APR tools on 168 Defects4J bugs. PrevaRank consistently improved the ranking of correct fixes: for example, it ranked a correct fix within the top-3 positions in 27% more cases than the original tools did. Other experimental results indicate that PrevaRank works robustly with a variety of APR tools and bugs, with negligible overhead.

cs.SE

Fuzzy Logic Guided Reward Function Variation: An Oracle for Testing Reinforcement Learning Programs

Reinforcement Learning (RL) has gained significant attention across various domains. However, the increasing complexity of RL programs presents testing challenges, particularly the oracle problem: defining the correctness of the RL program. Conventional human oracles struggle to cope with the complexity, leading to inefficiencies and potential unreliability in RL testing. To alleviate this problem, we propose an automated oracle approach that leverages RL properties using fuzzy logic. Our oracle quantifies an agent's behavioral compliance with reward policies and analyzes its trend over training episodes. It labels an RL program as "Buggy" if the compliance trend violates expectations derived from RL characteristics. We evaluate our oracle on RL programs with varying complexities and compare it with human oracles. Results show that while human oracles perform well in simpler testing scenarios, our fuzzy oracle demonstrates superior performance in complex environments. The proposed approach shows promise in addressing the oracle problem for RL testing, particularly in complex cases where manual testing falls short. It offers a potential solution to improve the efficiency, reliability, and scalability of RL program testing. This research takes a step towards automated testing of RL programs and highlights the potential of fuzzy logic-based oracles in tackling the oracle problem.

cs.SE

Understanding and Detecting Annotation-Induced Faults of Static Analyzers

Static analyzers can reason about the properties and behaviors of programs and detect various issues without executing them. Hence, they should extract the necessary information to understand the analyzed program well. Annotation has been a widely used feature for different purposes in Java since the introduction of Java 5. Annotations can change program structures and convey semantics information without awareness of static analyzers, consequently leading to imprecise analysis results. This paper presents the first comprehensive study of annotation-induced faults (AIF) by analyzing 246 issues in six open-source and popular static analyzers (i.e., PMD, SpotBugs, CheckStyle, Infer, SonarQube, and Soot). We analyzed the issues' root causes, symptoms, and fix strategies and derived ten findings and some practical guidelines for detecting and repairing annotation-induced faults. Moreover, we developed an automated testing framework called AnnaTester based on three metamorphic relations originating from the findings. AnnaTester generated new tests based on the official test suites of static analyzers and unveiled 43 new faults, 20 of which have been fixed. The results confirm the value of our study and its findings.

cs.SE

Grain boundary-limited thermal transport in suspended thin graphite across an unexplored thickness regime

We present systematic thermal conductivity measurements of suspended thin graphite ribbons, 234-527 nm thick, using a four-probe 3-omega method. Unlike recent reports of phonon hydrodynamics and exceptionally high thermal conductivity in micrometer-thick graphite (Science, 2020),we observe significantly lower thermal conductivity and no signatures of collective phonon flow in this intermediate thickness regime. Instead, our measured thermal conductivity lies between few-layer graphene and bulk graphite.These results agree with a first-principles-informed Peierls-Boltzmann transport model with spatially resolved Monte Carlo sampling. Additionally, the temperature for the peak thermal conductivity shifts lower with increasing thickness, due to the interplay of phonon-boundary and phonon-isotope scattering. Incorporating grain boundary scattering into simulations is necessary to replicate the experimental trends. These findings delineate the boundary between ballistic, hydrodynamic, and diffusive transport regimes in graphite, and underscore the dominant role of disorder and geometry in phonon transport in quasi-two-dimensional materials, offering insights for nanoscale thermal management.

cond-mat.mes-hall

Time-based Repair for Asynchronous Wait Flaky Tests in Web Testing

Asynchronous waits are one of the most prevalent root causes of flaky tests and a major time-influential factor of web application testing. To investigate the characteristics of asynchronous wait flaky tests and their fixes in web testing, we build a dataset of 49 reproducible flaky tests, from 26 open-source projects, caused by asynchronous waits, along with their corresponding developer-written fixes. Our study of these flaky tests reveals that in approximately 63% of them (31 out of 49), developers addressed Asynchronous Wait flaky tests by adapting the wait time, even for cases where the root causes lie elsewhere. Based on this finding, we propose TRaf, an automated time-based repair method for asynchronous wait flaky tests in web applications. TRaf tackles the flakiness issues by suggesting a proper waiting time for each asynchronous call in a web application, using code similarity and past change history. The core insight is that as developers often make similar mistakes more than once, hints for the efficient wait time exist in the current or past codebase. Our analysis shows that TRaf can suggest a shorter wait time to resolve the test flakiness compared to developer-written fixes, reducing the test execution time by 11.1%. With additional dynamic tuning of the new wait time, TRaf further reduces the execution time by 20.2%.

cs.SE

Thermal Conductivity Modeling of Monodispersed Microspheres using Discrete Element Method

Particle beds are widely used in various systems and processes, such as particle heat exchangers, granular flow reactors, and additive manufacturing. Accurate modeling of thermal conductivity of particle beds and understanding of their heat transfer mechanisms are important. However, previous models were based on a simple cubic packing of particles which could not accurately represent the actual heat transfer processes under certain conditions. Here, we examine the effect of the packing structure on thermal conductivity of particle beds. We use monodispersed silica microspheres with average article sizes ranging from 23 to 330 um as a model material. We employ a transient hot-wire technique to measure the thermal conductivity of the particle beds with packing density of 43 to 57% within a temperature range of room temperature to 500 deg. C and under N2 gaseous pressures of 20 to 760 Torr. We then use a discrete element method (DEM) to obtain the realistic packing structure of the particles, which is then fed into a finite-element model (FEM) to calculate the thermal conductivity, with the consideration of solid conduction, gas conduction, and radiation heat transfer. Our results show that the thermal conductivity model based on the more realistic random packing structure derived from the DEM shows better agreement with the experimental data compared to that based on the simple cubic packing structure. The combined DEM and FEM methodology can serve as a useful tool to predict effective thermal conductivity of particle beds and to quantify different heat transfer mechanisms under various conditions.

cond-mat.mtrl-sci