arXiv subjects

Bin Yin

Publications and source records attributed to Bin Yin.

16 recordsLinked to original sources

arXiv2026

Thinking More, Harnessing Better: State Machine Guided Harness Automatic Generation with Project Digestion and Workflow Decomposition

High-quality fuzz harnesses are essential for effective gray-box fuzzing. While Large Language Models (LLMs) offer promise for automating this task, existing one-turn generation methods suffer from hallucinations and inadequate coverage due to coarse-grained function targeting and misaligned generation workflows. We present SynapseFlow, an automatic harness generator that addresses these limitations through two key innovations: dataflow-aware function aggregation and a staged, rollback-enabled generation workflow decomposition. SynapseFlow first analyzes source code to construct Structural Flow Graphs and extract coherent Function Triplets. It then synthesizes harnesses via a decomposed four-stage process governed by a staged rollback algorithm to ensure correctness. We evaluated SynapseFlow on 25 real-world open-source software projects. The experimental results indicate that SynapseFlow outperforms state-of-the-art tools (OSS-Fuzz-Gen, CKGFuzzer, PromeFuzz), achieving 3.07$\times$, 1.71$\times$, and 4.26$\times$ higher branch coverage, and 1.77$\times$, 1.51$\times$, and 1.36$\times$ higher bug detection rates, respectively. Most importantly, SynapseFlow discovered 7 previously unreported bugs (5 assigned CVEs), demonstrating its practical effectiveness in real-world bug discovery.

cs.CR↗

arXiv2026

Molecular-optomechanical phonon laser

Molecular cavity optomechanics (COM) leverages ultrastrong interactions between confined optical fields and high-frequency molecular vibration, providing a unique platform for exploring high-frequency phonon dynamics. In this work, we theoretically propose the use of a hybrid molecular COM system for realizing an ultra-low-threshold mid-infrared (MIR) phonon laser. Despite an optical quality factor of only $Q_a=100$, an ultra-low threshold power of $\mathrm{P}_{\mathrm{th}} = 17.5~\mathrm{nW}$ is achieved, enabled by giant single-photon optomechanical coupling and molecular collective effect. Moreover, the mechanical gain and threshold power can be further tuned by adjusting the distance between mirrors of the Fabry-P\'{e}rot cavity. Our findings establish the first direct connection between molecular COM and MIR phonon lasers, with potential applications in MIR acoustics and biomedical imaging.

physics.optics↗

arXiv2025

Robust photon blockade with hybrid molecular optomechanics

Molecular cavity optomechanical systems, featuring ultrahigh vibrational frequencies and strong light-matter interactions, hold significant promise for advancing applications in quantum science and technology. Specifically, by introducing metallic nanoparticles into microcavities, hybrid molecular cavity optomechanical systems can further enhance optical quality factors and system tunabilities, which enables scalable and controllable quantum platforms. In this study, we propose how to realize robust photon blockade, i.e., strong photon antibunching with arbitrary detuning conditions, by combining degenerate optical parametric amplification with a hybrid molecular cavity optomechanical system. More interesting, we find near-perfect optomechanical photon blockade at room temperature, which is robust against temperature and optical dissipation. In addition, our approach can release the strict condition of high temporal resolution by combining features of conventional and unconventional photon blockade. Our approach offers a feasible route to study intriguing quantum effects in hybrid molecular cavity optomechanical systems, and holds promise for applications in nonclassical state engineering, quantum sensing, and photonic precision measurements.

physics.optics↗

arXiv2025

Strong Molecule-Light Entanglement with Molecular Cavity Optomechanics

We propose a molecular optomechanical platform to generate robust entanglement among bosonic modes-photons, phonons, and plasmons-under ambient conditions. The system integrates an ultrahigh-Q whispering-gallery-mode (WGM) optical resonator with a plasmonic nanocavity formed by a metallic nanoparticle and a single molecule. This hybrid architecture offers two critical advantages over standalone plasmonic systems: (i) Efficient redirection of Stokes photons from the lossy plasmonic mode into the long-lived WGM resonator, and (ii) Suppression of molecular absorption and approaching vibrational ground states via plasmon-WGM interactions. These features enable entanglement to transfer from the fragile plasmon-phonon subsystem to a photon-phonon bipartition in the blue-detuned regime, yielding robust stationary entanglement resilient to environmental noise. Remarkably, the achieved entanglement surpasses the theoretical bound for conventional two-mode squeezing in certain parameter regimes. Our scheme establishes a universal approach to safeguard entanglement in open quantum systems and opens avenues for noise-resilient quantum information technologies.

quant-ph↗

arXiv2025

MTGR: Industrial-Scale Generative Recommendation Framework in Meituan

Scaling law has been extensively validated in many domains such as natural language processing and computer vision. In the recommendation system, recent work has adopted generative recommendations to achieve scalability, but their generative approaches require abandoning the carefully constructed cross features of traditional recommendation models. We found that this approach significantly degrades model performance, and scaling up cannot compensate for it at all. In this paper, we propose MTGR (Meituan Generative Recommendation) to address this issue. MTGR is modeling based on the HSTU architecture and can retain the original deep learning recommendation model (DLRM) features, including cross features. Additionally, MTGR achieves training and inference acceleration through user-level compression to ensure efficient scaling. We also propose Group-Layer Normalization (GLN) to enhance the performance of encoding within different semantic spaces and the dynamic masking strategy to avoid information leakage. We further optimize the training frameworks, enabling support for our models with 10 to 100 times computational complexity compared to the DLRM, without significant cost increases. MTGR achieved 65x FLOPs for single-sample forward inference compared to the DLRM model, resulting in the largest gain in nearly two years both offline and online. This breakthrough was successfully deployed on Meituan, the world's largest food delivery platform, where it has been handling the main traffic.

cs.IR↗

arXiv2025

ReCopilot: Reverse Engineering Copilot in Binary Analysis

Binary analysis plays a pivotal role in security domains such as malware detection and vulnerability discovery, yet it remains labor-intensive and heavily reliant on expert knowledge. General-purpose large language models (LLMs) perform well in programming analysis on source code, while binaryspecific LLMs are underexplored. In this work, we present ReCopilot, an expert LLM designed for binary analysis tasks. ReCopilot integrates binary code knowledge through a meticulously constructed dataset, encompassing continue pretraining (CPT), supervised fine-tuning (SFT), and direct preference optimization (DPO) stages. It leverages variable data flow and call graph to enhance context awareness and employs test-time scaling to improve reasoning capabilities. Evaluations on a comprehensive binary analysis benchmark demonstrate that ReCopilot achieves state-of-the-art performance in tasks such as function name recovery and variable type inference on the decompiled pseudo code, outperforming both existing tools and LLMs by 13%. Our findings highlight the effectiveness of domain-specific training and context enhancement, while also revealing challenges in building super long chain-of-thought. ReCopilot represents a significant step toward automating binary analysis with interpretable and scalable AI assistance in this domain.

cs.CR↗

arXiv2025

MTGenRec: An Efficient Distributed Training System for Generative Recommendation Models in Meituan

Recommendation is crucial for both user experience and company revenue in Meituan as a leading lifestyle company, and generative recommendation models (GRMs) are shown to produce quality recommendations recently. However, existing systems are limited by insufficient functionality support and inefficient implementations for training GRMs in industrial scenarios. As such, we introduce MTGenRec as an efficient and scalable system for GRM training. Specifically, to handle real-time insertions/deletions of sparse embeddings, MTGenRec employs dynamic hash tables to replace static ones. To improve training efficiency, MTGenRec conducts dynamic sequence balancing to address the computation load imbalances among GPUs and adopts feature ID deduplication alongside automatic table merging to accelerate embedding lookup. Extensive experiments show that MTGenRec improves training throughput by $1.6\times -- 2.4\times$ while achieving good scalability when running over 100 GPUs. MTGenRec has been deployed for many applications in Meituan and is now handling hundreds of millions of requests on a daily basis. On the delivery platform, we observe a 1.22% growth in user order volume and a 1.31% enhancement in online PV_CTR.

cs.DC↗

arXiv2025

Teleology-Driven Affective Computing: A Causal Framework for Sustained Well-Being

Affective computing has made significant strides in emotion recognition and generation, yet current approaches mainly focus on short-term pattern recognition and lack a comprehensive framework to guide affective agents toward long-term human well-being. To address this, we propose a teleology-driven affective computing framework that unifies major emotion theories (basic emotion, appraisal, and constructivist approaches) under the premise that affect is an adaptive, goal-directed process that facilitates survival and development. Our framework emphasizes aligning agent responses with both personal/individual and group/collective well-being over extended timescales. We advocate for creating a "dataverse" of personal affective events, capturing the interplay between beliefs, goals, actions, and outcomes through real-world experience sampling and immersive virtual reality. By leveraging causal modeling, this "dataverse" enables AI systems to infer individuals' unique affective concerns and provide tailored interventions for sustained well-being. Additionally, we introduce a meta-reinforcement learning paradigm to train agents in simulated environments, allowing them to adapt to evolving affective concerns and balance hierarchical goals - from immediate emotional needs to long-term self-actualization. This framework shifts the focus from statistical correlations to causal reasoning, enhancing agents' ability to predict and respond proactively to emotional challenges, and offers a foundation for developing personalized, ethically aligned affective systems that promote meaningful human-AI interactions and societal well-being.

cs.HC↗

arXiv2025

Molecular optomechanically-induced transparency

Molecular cavity optomechanics (COM), characterized by remarkably efficient optomechanical coupling enabled by a highly localized light field and ultra-small effective mode volume, holds significant promise for advancing applications in quantum science and technology. Here, we study optomechanically induced transparency and the associated group delay in a hybrid molecular COM system. We find that even with an extremely low optical quality factor, an obvious transparency window can appear, which is otherwise unattainable in a conventional COM system. Furthermore, by varying the ports of the probe light, the optomechanically induced transparency or absorption can be achieved, along with corresponding slowing or advancing of optical signals. These results indicate that our scheme provides a new method for adjusting the storage and retrieval of optical signals in such a molecular COM device.

physics.optics↗

arXiv2025

Phase-controlled robust tripartite quantum entanglement in cavity-magnon optomechanics

The preparation of highly entangled states involving multiparticle systems is of crucial importance in quantum physics, playing a fundamental role in exploring the nature of quantum mechanics and offering essential quantum resources for nascent quantum technologies that surpass classical limits. Here we present how to generate and manipulate tripartite entangled state of photons, phonons, and magnons within a hybrid cavity magnomechanical system. It is shown that by simultaneously applying two coherent driving fields to this system in opposite input directions, it enables a coherent and effective way to regulate the magnomechanical interaction by tuning the phase difference of the driving fields. Based on this feature, it is found that the tripartite entanglement also becomes phase-dependent and can be enhanced for certain phase difference. More interestingly, it is shown that the robustness of tripartite entanglement against environmental thermal noises can also be improved by choosing proper phase difference of the driving fields. Our findings open up a promising way to manipulate and protect fragile tripartite entanglement, which is applicable to a wide range of quantum protocols that require multipartite entangled resources such as quantum communication and quantum metrology.

quant-ph↗

arXiv2024

LegiLM: A Fine-Tuned Legal Language Model for Data Compliance

Ensuring compliance with international data protection standards for privacy and data security is a crucial but complex task, often requiring substantial legal expertise. This paper introduces LegiLM, a novel legal language model specifically tailored for consulting on data or information compliance. LegiLM leverages a pre-trained GDPR Fines dataset and has been fine-tuned to automatically assess whether particular actions or events breach data security and privacy regulations. By incorporating a specialized dataset that includes global data protection laws, meticulously annotated policy documents, and relevant privacy policies, LegiLM is optimized for addressing data compliance challenges. The model integrates advanced legal reasoning methods and information retrieval enhancements to enhance accuracy and reliability in practical legal consulting scenarios. Our evaluation using a custom benchmark dataset demonstrates that LegiLM excels in detecting data regulation breaches, offering sound legal justifications, and recommending necessary compliance modifications, setting a new benchmark for AI-driven legal compliance solutions. Our resources are publicly available at https://github.com/DAOLegalAI/LegiLM

cs.CL↗

arXiv2024

LARR: Large Language Model Aided Real-time Scene Recommendation with Semantic Understanding

Click-Through Rate (CTR) prediction is crucial for Recommendation System(RS), aiming to provide personalized recommendation services for users in many aspects such as food delivery, e-commerce and so on. However, traditional RS relies on collaborative signals, which lacks semantic understanding to real-time scenes. We also noticed that a major challenge in utilizing Large Language Models (LLMs) for practical recommendation purposes is their efficiency in dealing with long text input. To break through the problems above, we propose Large Language Model Aided Real-time Scene Recommendation(LARR), adopt LLMs for semantic understanding, utilizing real-time scene information in RS without requiring LLM to process the entire real-time scene text directly, thereby enhancing the efficiency of LLM-based CTR modeling. Specifically, recommendation domain-specific knowledge is injected into LLM and then RS employs an aggregation encoder to build real-time scene information from separate LLM's outputs. Firstly, a LLM is continual pretrained on corpus built from recommendation data with the aid of special tokens. Subsequently, the LLM is fine-tuned via contrastive learning on three kinds of sample construction strategies. Through this step, LLM is transformed into a text embedding model. Finally, LLM's separate outputs for different scene features are aggregated by an encoder, aligning to collaborative signals in RS, enhancing the performance of recommendation model.

cs.IR↗

arXiv2024

Context-based Fast Recommendation Strategy for Long User Behavior Sequence in Meituan Waimai

In the recommender system of Meituan Waimai, we are dealing with ever-lengthening user behavior sequences, which pose an increasing challenge to modeling user preference effectively. Existing sequential recommendation models often fail to capture long-term dependencies or are too complex, complicating the fulfillment of Meituan Waimai's unique business needs. To better model user interests, we consider selecting relevant sub-sequences from users' extensive historical behaviors based on their preferences. In this specific scenario, we've noticed that the contexts in which users interact have a significant impact on their preferences. For this purpose, we introduce a novel method called Context-based Fast Recommendation Strategy to tackle the issue of long sequences. We first identify contexts that share similar user preferences with the target context and then locate the corresponding PoIs based on these identified contexts. This approach eliminates the necessity to select a sub-sequence for every candidate PoI, thereby avoiding high time complexity. Specifically, we implement a prototype-based approach to pinpoint contexts that mirror similar user preferences. To amplify accuracy and interpretability, we employ JS divergence of PoI attributes such as categories and prices as a measure of similarity between contexts. A temporal graph integrating both prototype and context nodes helps incorporate temporal information. We then identify appropriate prototypes considering both target contexts and short-term user preferences. Following this, we utilize contexts aligned with these prototypes to generate a sub-sequence, aimed at predicting CTR and CTCVR scores with target attention. Since its inception in 2023, this strategy has been adopted in Meituan Waimai's display recommender system, leading to a 4.6% surge in CTR and a 4.2% boost in GMV.

cs.IR↗

arXiv2023

Heterogeneous Knowledge Fusion: A Novel Approach for Personalized Recommendation via LLM

The analysis and mining of user heterogeneous behavior are of paramount importance in recommendation systems. However, the conventional approach of incorporating various types of heterogeneous behavior into recommendation models leads to feature sparsity and knowledge fragmentation issues. To address this challenge, we propose a novel approach for personalized recommendation via Large Language Model (LLM), by extracting and fusing heterogeneous knowledge from user heterogeneous behavior information. In addition, by combining heterogeneous knowledge and recommendation tasks, instruction tuning is performed on LLM for personalized recommendations. The experimental results demonstrate that our method can effectively integrate user heterogeneous behavior and significantly improve recommendation performance.

cs.IR↗

arXiv2023

PerCoNet: News Recommendation with Explicit Persona and Contrastive Learning

Personalized news recommender systems help users quickly find content of their interests from the sea of information. Today, the mainstream technology for personalized news recommendation is based on deep neural networks that can accurately model the semantic match between news items and users' interests. In this paper, we present \textbf{PerCoNet}, a novel deep learning approach to personalized news recommendation which features two new findings: (i) representing users through \emph{explicit persona analysis} based on the prominent entities in their recent news reading history could be more effective than latent persona analysis employed by most existing work, with a side benefit of enhanced explainability; (ii) utilizing the title and abstract of each news item via cross-view \emph{contrastive learning} would work better than just combining them directly. Extensive experiments on two real-world news datasets clearly show the superior performance of our proposed approach in comparison with current state-of-the-art techniques.

cs.IR↗

arXiv2023

HomoDistil: Homotopic Task-Agnostic Distillation of Pre-trained Transformers

Knowledge distillation has been shown to be a powerful model compression approach to facilitate the deployment of pre-trained language models in practice. This paper focuses on task-agnostic distillation. It produces a compact pre-trained model that can be easily fine-tuned on various tasks with small computational costs and memory footprints. Despite the practical benefits, task-agnostic distillation is challenging. Since the teacher model has a significantly larger capacity and stronger representation power than the student model, it is very difficult for the student to produce predictions that match the teacher's over a massive amount of open-domain training data. Such a large prediction discrepancy often diminishes the benefits of knowledge distillation. To address this challenge, we propose Homotopic Distillation (HomoDistil), a novel task-agnostic distillation approach equipped with iterative pruning. Specifically, we initialize the student model from the teacher model, and iteratively prune the student's neurons until the target width is reached. Such an approach maintains a small discrepancy between the teacher's and student's predictions throughout the distillation process, which ensures the effectiveness of knowledge transfer. Extensive experiments demonstrate that HomoDistil achieves significant improvements on existing baselines.

cs.CL↗