arXiv subjects

Daixuan Li

Publications and source records attributed to Daixuan Li.

9 recordsLinked to original sources

arXiv2026

Benchmarking LLMs on File System Design and Implementation

Large Language Models (LLMs) are fundamentally transforming computer system research and development. As we employ LLMs in file system (fs) development, it is essential to understand their capabilities, limitations, and operational efficiency for domain-specific tasks. We present \phi-Bench, an LLM benchmarking framework for fs-specific tasks. To facilitate benchmarking, we develop six types of tasks in \phi-Bench: basic understanding, basic implementation, performance modeling, debugging, optimization, and new feature development. Each type emphasizes different LLM capabilities: instruction following, knowledge recall, reasoning, or coding. To create high-quality tasks while achieving broad coverage with minimal human effort, we develop a new AI-assisted task generation pipeline in addition to expert-written and textbook-adapted tasks. With 505 tasks in \phi-Bench, we conduct an empirical study with both open source (DeepSeek-V4-Flash, GLM-5.1, and MiniMax-M2.7) and proprietary (Claude-Opus-4.7, GPT-5.2, and Gemini-3.1-Pro) LLMs. Our study discloses the model efficiency for different tasks, causes of failed fs tasks, and techniques for mitigating LLM failures. We will open source \phi-Bench to facilitate public research on using LLMs for fs development.

cs.OS↗

arXiv2026

QChunker: Learning Question-Aware Text Chunking for Domain RAG via Multi-Agent Debate

The effectiveness upper bound of retrieval-augmented generation (RAG) is fundamentally constrained by the semantic integrity and information granularity of text chunks in its knowledge base. To address these challenges, this paper proposes QChunker, which restructures the RAG paradigm from retrieval-augmentation to understanding-retrieval-augmentation. Firstly, QChunker models the text chunking as a composite task of text segmentation and knowledge completion to ensure the logical coherence and integrity of text chunks. Drawing inspiration from Hal Gregersen's "Questions Are the Answer" theory, we design a multi-agent debate framework comprising four specialized components: a question outline generator, text segmenter, integrity reviewer, and knowledge completer. This framework operates on the principle that questions serve as catalysts for profound insights. Through this pipeline, we successfully construct a high-quality dataset of 45K entries and transfer this capability to small language models. Additionally, to handle long evaluation chains and low efficiency in existing chunking evaluation methods, which overly rely on downstream QA tasks, we introduce a novel direct evaluation metric, ChunkScore. Both theoretical and experimental validations demonstrate that ChunkScore can directly and efficiently discriminate the quality of text chunks. Furthermore, during the text segmentation phase, we utilize document outlines for multi-path sampling to generate multiple candidate chunks and select the optimal solution employing ChunkScore. Extensive experimental results across four heterogeneous domains exhibit that QChunker effectively resolves aforementioned issues by providing RAG with more logically coherent and information-rich text chunks.

cs.CL↗

arXiv2025

Toward Engineering AGI: Benchmarking the Engineering Design Capabilities of LLMs

Modern engineering, spanning electrical, mechanical, aerospace, civil, and computer disciplines, stands as a cornerstone of human civilization and the foundation of our society. However, engineering design poses a fundamentally different challenge for large language models (LLMs) compared with traditional textbook-style problem solving or factual question answering. Although existing benchmarks have driven progress in areas such as language understanding, code synthesis, and scientific problem solving, real-world engineering design demands the synthesis of domain knowledge, navigation of complex trade-offs, and management of the tedious processes that consume much of practicing engineers' time. Despite these shared challenges across engineering disciplines, no benchmark currently captures the unique demands of engineering design work. In this work, we introduce EngDesign, an Engineering Design benchmark that evaluates LLMs' abilities to perform practical design tasks across nine engineering domains. Unlike existing benchmarks that focus on factual recall or question answering, EngDesign uniquely emphasizes LLMs' ability to synthesize domain knowledge, reason under constraints, and generate functional, objective-oriented engineering designs. Each task in EngDesign represents a real-world engineering design problem, accompanied by a detailed task description specifying design goals, constraints, and performance requirements. EngDesign pioneers a simulation-based evaluation paradigm that moves beyond textbook knowledge to assess genuine engineering design capabilities and shifts evaluation from static answer checking to dynamic, simulation-driven functional verification, marking a crucial step toward realizing the vision of engineering Artificial General Intelligence (AGI).

cs.CE↗

arXiv2025

Invoke Interfaces Only When Needed: Adaptive Invocation for Large Language Models in Question Answering

The collaborative paradigm of large and small language models (LMs) effectively balances performance and cost, yet its pivotal challenge lies in precisely pinpointing the moment of invocation when hallucinations arise in small LMs. Previous optimization efforts primarily focused on post-processing techniques, which were separate from the reasoning process of LMs, resulting in high computational costs and limited effectiveness. In this paper, we propose a practical invocation evaluation metric called AttenHScore, which calculates the accumulation and propagation of hallucinations during the generation process of small LMs, continuously amplifying potential reasoning errors. By dynamically adjusting the detection threshold, we achieve more accurate real-time invocation of large LMs. Additionally, considering the limited reasoning capacity of small LMs, we leverage uncertainty-aware knowledge reorganization to assist them better capture critical information from different text chunks. Extensive experiments reveal that our AttenHScore outperforms most baselines in enhancing real-time hallucination detection capabilities across multiple QA datasets, especially when addressing complex queries. Moreover, our strategies eliminate the need for additional model training and display flexibility in adapting to various transformer-based LMs.

cs.CL↗

arXiv2023

RackBlox: A Software-Defined Rack-Scale Storage System with Network-Storage Co-Design

Software-defined networking (SDN) and software-defined flash (SDF) have been serving as the backbone of modern data centers. They are managed separately to handle I/O requests. At first glance, this is a reasonable design by following the rack-scale hierarchical design principles. However, it suffers from suboptimal end-to-end performance, due to the lack of coordination between SDN and SDF. In this paper, we co-design the SDN and SDF stack by redefining the functions of their control plane and data plane, and splitting up them within a new architecture named RackBlox. RackBlox decouples the storage management functions of flash-based solid-state drives (SSDs), and allow the SDN to track and manage the states of SSDs in a rack. Therefore, we can enable the state sharing between SDN and SDF, and facilitate global storage resource management. RackBlox has three major components: (1) coordinated I/O scheduling, in which it dynamically adjusts the I/O scheduling in the storage stack with the measured and predicted network latency, such that it can coordinate the effort of I/O scheduling across the network and storage stack for achieving predictable end-to-end performance; (2) coordinated garbage collection (GC), in which it will coordinate the GC activities across the SSDs in a rack to minimize their impact on incoming I/O requests; (3) rack-scale wear leveling, in which it enables global wear leveling among SSDs in a rack by periodically swapping data, for achieving improved device lifetime for the entire rack. We implement RackBlox using programmable SSDs and switch. Our experiments demonstrate that RackBlox can reduce the tail latency of I/O requests by up to 5.8x over state-of-the-art rack-scale storage systems.

cs.OS↗

arXiv2022

UniHeap: Managing Persistent Objects Across Managed Runtimes for Non-Volatile Memory

Byte-addressable, non-volatile memory (NVM) is emerging as a promising technology. To facilitate its wide adoption, employing NVM in managed runtimes like JVM has proven to be an effective approach (i.e., managed NVM). However, such an approach is runtime specific, which lacks a generic abstraction across different managed languages. Similar to the well-known filesystem primitives that allow diverse programs to access same files via the block I/O interface, managed NVM deserves the same system-wide property for persistent objects across managed runtimes with low overhead. In this paper, we present UniHeap, a new NVM framework for managing persistent objects. It proposes a unified persistent object model that supports various managed languages, and manages NVM within a shared heap that enables cross-language persistent object sharing. UniHeap reduces the object persistence overhead by managing the shared heap in a log-structured manner and coalescing object updates during the garbage collection. We implement UniHeap as a generic framework and extend it to different managed runtimes that include HotSpot JVM, cPython, and JavaScript engine SpiderMonkey. We evaluate UniHeap with a variety of applications, such as key-value store and transactional database. Our evaluation shows that UniHeap significantly outperforms state-of-the-art object sharing approaches, while introducing negligible overhead to the managed runtimes.

cs.PL↗

arXiv2021

A Learning-based Approach Towards Automated Tuning of SSD Configurations

Thanks to the mature manufacturing techniques, solid-state drives (SSDs) are highly customizable for applications today, which brings opportunities to further improve their storage performance and resource utilization. However, the SSD efficiency is usually determined by many hardware parameters, making it hard for developers to manually tune them and determine the optimal SSD configurations. In this paper, we present an automated learning-based framework, named LearnedSSD, that utilizes both supervised and unsupervised machine learning (ML) techniques to drive the tuning of hardware configurations for SSDs. LearnedSSD automatically extracts the unique access patterns of a new workload using its block I/O traces, maps the workload to previously workloads for utilizing the learned experiences, and recommends an optimal SSD configuration based on the validated storage performance. LearnedSSD accelerates the development of new SSD devices by automating the hard-ware parameter configurations and reducing the manual efforts. We develop LearnedSSD with simple yet effective learning algorithms that can run efficiently on multi-core CPUs. Given a target storage workload, our evaluation shows that LearnedSSD can always deliver an optimal SSD configuration for the target workload, and this configuration will not hurt the performance of non-target workloads.

cs.AR↗

arXiv2021

RisGraph: A Real-Time Streaming System for Evolving Graphs to Support Sub-millisecond Per-update Analysis at Millions Ops/s

Evolving graphs in the real world are large-scale and constantly changing, as hundreds of thousands of updates may come every second. Monotonic algorithms such as Reachability and Shortest Path are widely used in real-time analytics to gain both static and temporal insights and can be accelerated by incremental computing. Existing streaming systems adopt the incremental computing model and achieve either low latency or high throughput, but not both. However, both high throughput and low latency are required in real scenarios such as financial fraud detection. This paper presents RisGraph, a real-time streaming system that provides low-latency analysis for each update with high throughput. RisGraph addresses the challenge with localized data access and inter-update parallelism. We propose a data structure named Indexed Adjacency Lists and use sparse arrays and Hybrid Parallel Mode to enable localized data access. To achieve inter-update parallelism, we propose a domain-specific concurrency control mechanism based on the classification of safe and unsafe updates. Experiments show that RisGraph can ingest millions of updates per second for graphs with several hundred million vertices and billions of edges, and the P999 processing time latency is within 20 milliseconds. RisGraph achieves orders-of-magnitude improvement on throughput when analyses are executed for each update without batching and performs better than existing systems with batches of up to 20 million updates.

cs.DB↗

arXiv2020

CPM: A Large-scale Generative Chinese Pre-trained Language Model

Pre-trained Language Models (PLMs) have proven to be beneficial for various downstream NLP tasks. Recently, GPT-3, with 175 billion parameters and 570GB training data, drew a lot of attention due to the capacity of few-shot (even zero-shot) learning. However, applying GPT-3 to address Chinese NLP tasks is still challenging, as the training corpus of GPT-3 is primarily English, and the parameters are not publicly available. In this technical report, we release the Chinese Pre-trained Language Model (CPM) with generative pre-training on large-scale Chinese training data. To the best of our knowledge, CPM, with 2.6 billion parameters and 100GB Chinese training data, is the largest Chinese pre-trained language model, which could facilitate several downstream Chinese NLP tasks, such as conversation, essay generation, cloze test, and language understanding. Extensive experiments demonstrate that CPM achieves strong performance on many NLP tasks in the settings of few-shot (even zero-shot) learning. The code and parameters are available at https://github.com/TsinghuaAI/CPM-Generate.

cs.CL↗