:: Library Catalog

Copertina

Salvato in:

Dettagli Bibliografici
Autori principali:	Pan, Haolin, Lin, Hongyu, Luo, Haoran, Liu, Yang, Yao, Kaichun, Zhang, Libo, Xing, Mingjie, Wu, Yanjun
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Machine Learning Artificial Intelligence
Accesso online:	https://arxiv.org/abs/2506.15701
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

AwareCompiler: Agentic Context-Aware Compiler Optimization via a Synergistic Knowledge-Data Driven Framework
di: Lin, Hongyu, et al.
Pubblicazione: (2025)

KGCompiler: Deep Learning Compilation Optimization for Knowledge Graph Complex Logical Query Answering
di: Lin, Hongyu, et al.
Pubblicazione: (2025)

Synergy-Guided Compiler Auto-Tuning of Nested LLVM Pass Pipelines
di: Pan, Haolin, et al.
Pubblicazione: (2025)

A Hybrid, Knowledge-Guided Evolutionary Framework for Personalized Compiler Auto-Tuning
di: Pan, Haolin, et al.
Pubblicazione: (2025)

ECCO: Evidence-Driven Causal Reasoning for Compiler Optimization
di: Pan, Haolin, et al.
Pubblicazione: (2026)

GRACE: Globally-Seeded Representation-Aware Cluster-Specific Evolution for Compiler Auto-Tuning
di: Pan, Haolin, et al.
Pubblicazione: (2025)

BYOS: Knowledge-driven Large Language Models Bring Your Own Operating System More Excellent
di: Lin, Hongyu, et al.
Pubblicazione: (2025)

Behavioral Embeddings of Programs: A Quasi-Dynamic Approach for Optimization Prediction
di: Pan, Haolin, et al.
Pubblicazione: (2025)

A Method for Efficient Heterogeneous Parallel Compilation: A Cryptography Case Study
di: Tan, Zhiyuan, et al.
Pubblicazione: (2024)

Exploring the Feasibility of End-to-End Large Language Model as a Compiler
di: Zhang, Hongbin, et al.
Pubblicazione: (2025)

CrypTorch: PyTorch-based Auto-tuning Compiler for Machine Learning with Multi-party Computation
di: Liu, Jinyu, et al.
Pubblicazione: (2025)

Agentic Harness for Real-World Compilers
di: Zheng, Yingwei, et al.
Pubblicazione: (2026)

Your Compiler is Backdooring Your Model: Understanding and Exploiting Compilation Inconsistency Vulnerabilities in Deep Learning Compilers
di: Chen, Simin, et al.
Pubblicazione: (2025)

DeepRefine: Agent-Compiled Knowledge Refinement via Reinforcement Learning
di: Huang, Haoyu, et al.
Pubblicazione: (2026)

CCrepairBench: A High-Fidelity Benchmark and Reinforcement Learning Framework for C++ Compilation Repair
di: Sun, Weixuan, et al.
Pubblicazione: (2025)

CompilerKV: Risk-Adaptive KV Compression via Offline Experience Compilation
di: Yang, Ning, et al.
Pubblicazione: (2026)

Can Large Language Models Understand Intermediate Representations in Compilers?
di: Jiang, Hailong, et al.
Pubblicazione: (2025)

Reinforcement Learning from Compiler and Language Server Feedback
di: Zhang, Yifan, et al.
Pubblicazione: (2025)

VisRefiner: Learning from Visual Differences for Screenshot-to-Code Generation
di: Deng, Jie, et al.
Pubblicazione: (2026)

VecTrans: Enhancing Compiler Auto-Vectorization through LLM-Assisted Code Transformations
di: Zheng, Zhongchun, et al.
Pubblicazione: (2025)

Towards Revealing the Effectiveness of Small-Scale Fine-tuning in R1-style Reinforcement Learning
di: Chen, Yutong, et al.
Pubblicazione: (2025)

AutoForge: Automated Environment Synthesis for Agentic Reinforcement Learning
di: Cai, Shihao, et al.
Pubblicazione: (2025)

TSCG: Deterministic Tool-Schema Compilation for Agentic LLM Deployments
di: Sakizli, Furkan
Pubblicazione: (2026)

LEGO-Compiler: Enhancing Neural Compilation Through Translation Composability
di: Zhang, Shuoming, et al.
Pubblicazione: (2025)

The New Compiler Stack: A Survey on the Synergy of LLMs and Compilers
di: Zhang, Shuoming, et al.
Pubblicazione: (2026)

Learning to Compile Programs to Neural Networks
di: Weber, Logan, et al.
Pubblicazione: (2024)

Doctor-R1: Mastering Clinical Inquiry with Experiential Agentic Reinforcement Learning
di: Lai, Yunghwei, et al.
Pubblicazione: (2025)

Meta Large Language Model Compiler: Foundation Models of Compiler Optimization
di: Cummins, Chris, et al.
Pubblicazione: (2024)

AutoMedBench: Towards Medical AutoResearch with Agentic AI Models
di: Liu, Junqi, et al.
Pubblicazione: (2026)

HPCTransCompile: An AI Compiler Generated Dataset for High-Performance CUDA Transpilation and LLM Preliminary Exploration
di: Lv, Jiaqi, et al.
Pubblicazione: (2025)

Compile to Compress: Boosting Formal Theorem Provers by Compiler Outputs
di: Li, Guchan, et al.
Pubblicazione: (2026)

FlowSteer: Towards Agents Designing Agentic Workflows via Reinforced Progressive Canvas Editing
di: Zhang, Mingda, et al.
Pubblicazione: (2026)

AutoLoop: Fast Visual SLAM Fine-tuning through Agentic Curriculum Learning
di: Lahiany, Assaf, et al.
Pubblicazione: (2025)

GroupTuner: Efficient Group-Aware Compiler Auto-Tuning
di: Gao, Bingyu, et al.
Pubblicazione: (2025)

AutoSearch: Adaptive Search Depth for Efficient Agentic RAG via Reinforcement Learning
di: Sun, Jingbo, et al.
Pubblicazione: (2026)

Towards Agentic Self-Learning LLMs in Search Environment
di: Sun, Wangtao, et al.
Pubblicazione: (2025)

ACC: Compiling Agent Trajectories for Long-Context Training
di: Su, Qisheng, et al.
Pubblicazione: (2026)

Learning When Not to Act: Mitigating Tool Abuse in Agentic Reinforcement Learning
di: Chen, Liuji, et al.
Pubblicazione: (2026)

Compile Scene Graphs with Reinforcement Learning
di: Chen, Zuyao, et al.
Pubblicazione: (2025)

Compiling Deterministic Structure into SLM Harnesses
di: Chong, Zan Kai, et al.
Pubblicazione: (2026)