arXiv Machine Learning August 4, 2026 By Ruilin Xu, Junyi Li, Pengfei Chen, Zongxuan Xie

TELLER: Non-intrusive Cross-Layer Root-Cause Analysis for LLM Inference

Read the original on arXiv Machine Learning →

arXiv:2608. 01975v1 Announce Type: cross Abstract: Large language model (LLM) inference has evolved from an offline workload into a continuously operated software service, yet root-cause analysis remains difficult because a single request spans the inference engine, Python/C++ backend, host CUDA APIs, GPU kernels, and distributed communication.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv AI

Sep 1

M2K: Making the Model-Kernel Interface Explicit for Reliable CUDA Kernel Verification

arXiv:2603.24595v2 Announce Type: replace-cross Abstract: Large language model (LLM) inference systems rely on CUDA kernels for core GPU computations, yet the interface between models and kernels is...

By Mengting He, Shihao Xia, Haomin Jia, Wenfei Wu, Linhai Song

llms

arXiv AI

Sep 15

TriCalRAG: A Three-Strategy, Retrieval-Augmented Benchmark for On-Premise LLM-Based Root Cause Analysis in AIOps

arXiv:2609.14762v1 Announce Type: cross Abstract: Cloud-hosted large language models (LLMs) are increasingly used for root cause analysis (RCA) in AIOps pipelines, but they introduce data privacy ris...

By Rohit Patel, Susil Kumar Mohanty, Jeenal Chaudhary

llms rag efficiency benchmarks safety

arXiv AI

Jun 3

StepFinder: A Temporal Semantic Framework for Failure Attribution in Multi-Agent Systems

arXiv:2606. 03467v1 Announce Type: new Abstract: LLM-based multi-agent systems exhibit remarkable collaborative capabilities in complex multi-step tasks.

By Taiyu Zhu, Yifan Wu, Weilin Jin, Ying Li, Gang Huang

llms agents benchmarks safety

arXiv AI

Aug 11

TRACE: TRajectory Attribution for Automated Context Engineering

arXiv:2608. 09153v1 Announce Type: new Abstract: Production AI agents fail when their context sources -- system prompts, knowledge bases, tool descriptions, and procedural skills -- contain errors or gaps.

By Yikai Zhao, Pradeep Kumar Misra, Saurabh Pandey

agents fine-tuning benchmarks

arXiv AI

Jul 7

MemTrace: Tracing and Attributing Errors in Large Language Model Memory Systems

arXiv:2605. 28732v2 Announce Type: replace-cross Abstract: Memory is essential for enabling large language models to support long-horizon reasoning, yet existing memory systems remain unreliable and difficult to debug.

By Xinle Deng, Ruobin Zhong, Hujin Peng, Xiaoben Lu, Yanzhe Wu, Guang Li, Buqiang Xu, Yunzhi Yao, Jizhan Fang, Haoliang Cao, Junjie Guo, Yuan Yuan, Ziqing Ma, Yuanqiang Yu, Rui Hu, Baohua Dong, Hangcheng Zhu, Ningyu Zhang

llms rag benchmarks

arXiv AI

Jun 16

Knowledge-Based Zero-Replay Debugging of Multi-Agent LLM Traces

arXiv:2606. 14805v1 Announce Type: cross Abstract: Reliable operation of multi-agent large language model (LLM) systems depends on debugging long execution traces, where the few causally decisive events are buried in unstructured logs of messages, routes, memory writes, and tool calls.

By Dong Ho Kang, Hyeonjeong Cha, Daein Weon

llms agents

One email a morning, machine-written

Related stories

M2K: Making the Model-Kernel Interface Explicit for Reliable CUDA Kernel Verification

TriCalRAG: A Three-Strategy, Retrieval-Augmented Benchmark for On-Premise LLM-Based Root Cause Analysis in AIOps

StepFinder: A Temporal Semantic Framework for Failure Attribution in Multi-Agent Systems

TRACE: TRajectory Attribution for Automated Context Engineering

MemTrace: Tracing and Attributing Errors in Large Language Model Memory Systems

Knowledge-Based Zero-Replay Debugging of Multi-Agent LLM Traces