LLM News Digest

Tag: rag

Contexts are Never Long Enough: Structured Reasoning for Scalable Question Answering over Long Document Sets
Intermediate

Contexts are Never Long Enough: Structured Reasoning for Scalable Question Answering over Long Document Sets

Harshit Joshi, Priyank Shethia, Jadelynn Dao, Monica S. Lam
ragreasoningllmssoftware-engineering

SLIDERS challenges the conventional chunk-and-aggregate approach to document QA by extracting information into a relational database and reasoning with SQL instead of concatenated text. This architectural approach sidesteps the fundamental limitation that any fixed context window will eventually be exceeded, making it essential reading for engineers building document analysis systems that need to scale beyond typical RAG limitations.

Takeaways
  • Traditional chunk-and-aggregate approaches hit an aggregation bottleneck as document collections grow, even with infinite context windows.
  • Extracting information into structured databases and reasoning with SQL scales better than reasoning over concatenated text.
  • Data reconciliation using provenance and extraction rationales is crucial for maintaining coherence in locally extracted information.
from Apr 27, 2026 · via api-hf · arXiv:2604.22294
Don't Retrieve, Navigate: Distilling Enterprise Knowledge into Navigable Agent Skills for QA and RAG
Intermediate

Don't Retrieve, Navigate: Distilling Enterprise Knowledge into Navigable Agent Skills for QA and RAG

Yiqun Sun, Pengfei Wei, Lawrence B. Hsieh
ragagentssoftware-engineering

Corpus2Skill fundamentally reimagines RAG by giving AI agents a navigable map of your knowledge base instead of treating them as passive consumers of search results. Rather than hoping retrieval finds the right documents, agents can see the corpus structure, drill down through hierarchical summaries, and strategically combine evidence across different branches—solving the core limitation that RAG systems can't reason about what they haven't seen.

Takeaways
  • Traditional RAG limits AI agents to passive consumption of search results without visibility into corpus structure or unexplored areas.
  • Hierarchical skill directories enable agents to navigate knowledge strategically and combine evidence across different topic branches.
  • Offline corpus compilation into navigable structures provides better performance than runtime retrieval-only approaches.
from Apr 20, 2026 · via api-hf · arXiv:2604.14572
The Design of AI Memory Systems
Intermediate

The Design of AI Memory Systems

agentsragfoundational

Unable to provide detailed description due to missing content, but AI memory systems design is crucial for building production agents and RAG applications that need to maintain context and learn from interactions.

from Apr 6, 2026 · 7 points on Lobsters · via api-lobsters
Show HN: Robust LLM extractor for websites in TypeScript
Intermediate

Show HN: Robust LLM extractor for websites in TypeScript

andrew_zhong
software-engineeringhow-we-workrag

A practical TypeScript library that solves the common problem of extracting structured data from websites using LLMs, addressing real pain points like HTML noise, token budget management, and brittleness of traditional CSS selectors. This represents the kind of focused tooling that makes AI-powered data extraction reliable enough for production use.

Takeaways
  • LLM-based extraction needs preprocessing to remove HTML noise and stay within token budgets for reliable results.
  • Focused tools that solve specific AI integration problems are more valuable than general-purpose solutions for production teams.
  • AI extraction can replace brittle CSS selectors but requires thoughtful engineering to handle edge cases and failures.
from Mar 29, 2026 · 72 points on HN · via api-hn