# TAAF: A Trace Abstraction and Analysis Framework Synergizing Knowledge Graphs and LLMs

arXiv: https://arxiv.org/abs/2601.02632 · HTML: https://mlsanity.com/paper/2601.02632/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 36 references that are in the map.
- First posted on arXiv 6 Jan 2026.

## Builds on (most cited first, 15 of 15)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [REPLUG: Retrieval-Augmented Black-Box Language Models](https://mlsanity.com/paper/2301.12652/) (2023)
- [Unifying Large Language Models and Knowledge Graphs: A Roadmap](https://mlsanity.com/paper/2306.08302/) (2023)
- [ERNIE: Enhanced Language Representation with Informative Entities](https://mlsanity.com/paper/1905.07129/) (2019)
- [QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering](https://mlsanity.com/paper/2104.06378/) (2021)
- [LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention](https://mlsanity.com/paper/2010.01057/) (2020)
- [K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters](https://mlsanity.com/paper/2002.01808/) (2020)
- [Pretrained Language Models for Text Generation: A Survey](https://mlsanity.com/paper/2201.05273/) (2022)
- [A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multimodal](https://mlsanity.com/paper/2212.05767/) (2022)
- [Knowledge Solver: Teaching LLMs to Search for Domain Knowledge from Knowledge Graphs](https://mlsanity.com/paper/2309.03118/) (2023)
- [ReliableEval: A Recipe for Stochastic LLM Evaluation via Method of Moments](https://mlsanity.com/paper/2505.22169/) (2025)
- [Node Compass: Multilevel Tracing and Debugging of Request Executions in JavaScript-Based Web-Servers](https://mlsanity.com/paper/2401.08595/) (2023)

## Built on by (most cited first, 1 of 1)

- [Testing Storage-System Correctness: Challenges, Fuzzing Limitations, and AI-Augmented Opportunities](https://mlsanity.com/paper/2602.02614/) (2026)

## Abstract (arXiv)

Execution traces are a critical source of information for understanding, debugging, and optimizing complex software systems. However, traces from OS kernels or large-scale applications like Chrome or MySQL are massive and difficult to analyze. Existing tools rely on predefined analyses, and custom insights often require writing domain-specific scripts, which is an error-prone and time-consuming task. This paper introduces TAAF (Trace Abstraction and Analysis Framework), a novel approach that combines time-indexing, knowledge graphs (KGs), and large language models (LLMs) to transform raw trace data into actionable insights. TAAF constructs a time-indexed KG from trace events to capture relationships among entities such as threads, CPUs, and system resources. An LLM then interprets query-specific subgraphs to answer natural-language questions, reducing the need for manual inspection and deep system expertise. To evaluate TAAF, we introduce TraceQA-100, a benchmark of 100 questions grounded in real kernel traces. Experiments across three LLMs and multiple temporal settings show that TAAF improves answer accuracy by up to 31.2%, particularly in multi-hop and causal reasoning tasks. We further analyze where graph-grounded reasoning helps and where limitations remain, offering a foundation for next-generation trace analysis tools.