SyGra: The One-Stop Framework for Building Data for LLMs and SLMs

How To Build Your Own LLM Runtime From Scratch

If you have ever wanted to actually build an LLM inference runtime yourself — pack your own weights, own every barrier, capture your own CUDA graphs — this is what that journey looks like on an H100. A step-by-step tour of a small runtime called annotated-llm-runtime, and the three bugs that produced most of the annotations.

Can LLMs Reason Structurally? Benchmarking via the Lens of Data Structures

arXiv:2505. 24069v4 Announce Type: replace-cross Abstract: Large language models (LLMs) are deployed on increasingly complex tasks that require multi-step decision-making.

Beyond Quacking: Deep Integration of Language Models and RAG into DuckDB

The paper introduces FlockMTL, an extension for database management systems that deeply integrates large language models and retrieval‑augmented generation into DuckDB. It provides model‑driven scalar and aggregate functions, cost‑based optimizations like batching and caching, and new SQL DDL abstractions (PROMPT and MODEL) to treat LLMs as first‑class schema objects. These features aim to simplify the development of knowledge‑intensive analytical applications by reducing the effort required to orchestrate heterogeneous data systems and manage LLM context.

Introducing AutoRound: Intel’s Advanced Quantization for LLMs and VLMs

LLM4RTL: Tool-Assisted LLM for RTL Generation

arXiv:2606. 15500v1 Announce Type: cross Abstract: Large language models (LLMs) have facilitated impressive progress in software engineering, code generation, tooling, and systems.

StarCoder: A State-of-the-Art LLM for Code

Judge Arena: Benchmarking LLMs as Evaluators

Achieving Precise Text-To-Cypher Via Grounded Knowledge Graph Data Generation

arXiv:2606. 14325v1 Announce Type: cross Abstract: Property Graphs are rapidly being adopted as database frameworks for representing heterogeneous data sources.

Harmonia: End-to-End RAG Serving Optimization

arXiv:2505. 07833v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) improves the reliability of large language models by integrating external knowledge, but serving RAG pipelines efficiently is challenging because requests traverse heterogeneous components spanning LLM inference, databases, and CPU-side processing.

Scalable Synthesis of distributed LLM workloads through Symbolic Tensor Graphs

arXiv:2511. 10480v3 Announce Type: replace-cross Abstract: Optimizing the performance of large language models (LLMs) on large-scale AI training and inference systems requires a scalable and expressive mechanism to model distributed workload execution.

FusionFactory: Fusing LLM Capabilities with Multi-LLM Log Data

arXiv:2507. 10540v3 Announce Type: replace Abstract: The rapid advancement of large language models (LLMs) has created a diverse landscape of models, each excelling at different tasks.

Structured Outputs with LLMs: JSON Mode, Function Calling, and When to Use Each

Getting reliable, readable responses out of your LLM, and knowing which tool to reach for The post Structured Outputs with LLMs: JSON Mode, Function Calling, and When to Use Each appeared first on Towards Data Science .