arXiv Machine Learning July 15, 2026 By Yuvraj Sehgal, Sneh Patel, Mahsa Panahandeh, Naser Ezzati-Jivan, Francois Tetreault

TraceSynth: Generating Production-Quality Kernel Traces with Constraint-Guided Diffusion Models

Read the original on arXiv Machine Learning →

arXiv:2607. 12104v1 Announce Type: cross Abstract: Machine learning models for system diagnostics rely on kernel execution traces to capture fine-grained system behavior, but collecting production traces in industrial systems is costly due to runtime overhead, storage demands, and privacy constraints.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Aug 4

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

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.

By Ruilin Xu, Junyi Li, Pengfei Chen, Zongxuan Xie

llms nlp multimodal

arXiv Machine Learning

Jun 19

Model soups need only one ingredient

arXiv:2602. 09689v2 Announce Type: replace Abstract: Fine-tuning large pre-trained models on a target distribution often improves in-distribution (ID) accuracy, but at the cost of out-of-distribution (OOD) robustness as representations specialize to the fine-tuning data.

By Alireza Abdollahpoorrostam, Nikolaos Dimitriadis, Adam Hazimeh, Pascal Frossard

llms fine-tuning benchmarks

arXiv Machine Learning

Jul 10

Closing the Null Space: Guidance-Aware Quantization for Classifier-Free Diffusion

arXiv:2607. 08241v1 Announce Type: cross Abstract: Deploying classifier-free guidance (CFG) diffusion models under real-world compute budgets requires quantization, yet existing post-training quantization (PTQ) methods treat CFG models as single-branch networks, ignoring the paired conditional/unconditional structure that CFG inference fundamentally relies on.

By Abdullah Al Shafi, Sumaiya Rahim Suma

diffusion efficiency

arXiv AI

Jul 29

Kernel Forge: An Agent Harness for LLM-based Generation and Optimization of CUDA Kernels

arXiv:2607. 24762v1 Announce Type: new Abstract: Machine learning models are increasingly embedded in everyday software, and most of their runtime is spent in a small set of compute kernels such as matrix multiplication, convolution, and normalization.

By Joshua Brodsky, Dhravid Kumar, Savini Kashmira, Jayanaka Danatanarayana, Jason Mars, Krisztian Flautner, Lingjia Tang

llms agents diffusion data-engineering

arXiv AI

Jun 8

Beyond the Black Box: Interpretability of Agentic AI Tool Use

arXiv:2605. 06890v3 Announce Type: replace Abstract: AI agents are promising for high-stakes enterprise workflows, but dependable deployment remains limited because tool-use failures are difficult to diagnose and control.

By Hariom Tatsat, Ariye Shater

llms agents benchmarks safety

arXiv AI

Jun 30

KernelSight-LM: A Kernel-Level LLM Inference Simulator

arXiv:2606. 28565v1 Announce Type: cross Abstract: As large language models (LLMs) move into production serving, practitioners must rapidly evaluate inference performance across diverse hardware, models, and serving parameters to meet cost and latency targets.

By Xiteng Yao, Taeho Kim, Hengzhi Pei, Xinle Liu, Kyle Ulrich, Leonard Lausen, Ashish Khetan, Xiang Song, George Karypis, Martin Herbordt

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Related stories

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

Model soups need only one ingredient

Closing the Null Space: Guidance-Aware Quantization for Classifier-Free Diffusion

Kernel Forge: An Agent Harness for LLM-based Generation and Optimization of CUDA Kernels

Beyond the Black Box: Interpretability of Agentic AI Tool Use

KernelSight-LM: A Kernel-Level LLM Inference Simulator