Solving the 1+N Query Problem
Curated retrieval versus open web search in public AI information...
Public institutions increasingly use large language models (LLMs) to answer citizens' questions, often pairing a curated knowledge base with live web search, yet whether the sources behind these...
Relational lenses | Proceedings of the twenty-fifth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
As part of the Digital Library's transition to Open Access, new features for researchers are available in the Premium Edition. Click here to learn more.
danabra.mov/resolve-lexicon
Resolve AT Lexicons over the network https://resolve-lexicon.pages.dev/
Bidirectionalization for free! (Pearl) | Proceedings of the 36th annual ACM SIGPLAN-SIGACT symposium on Principles of programming languages
A bidirectional transformation consists of a function get that takes a source (document or value) to a view and a function put that takes an updated view and the original source back to an updated source, governed by certain consistency conditions ...
Functional Query Languages with Categorical Types
We study three category-theoretic types in the context of functional query languages (typed lambda-calculi extended with additional operations for bulk data processing). The types we study are:
Stanford CS336 | Language Modeling from Scratch (Spring 2025 Archive)
Archived course website for Stanford CS336: Language Modeling from Scratch (Spring 2025), including schedule, assignments, logistics, and materials.
Resugaring | Proceedings of the 35th ACM SIGPLAN Conference on Programming Language Design and Implementation
Syntactic sugar plays a crucial role in engineering programming languages. It offers convenient syntax and higher-level of abstractions, as witnessed by its pervasive use in both general-purpose and domain-specific contexts. Unfortunately, the ...
Retrieval as Reasoning: Self-Evolving Agent-Native Retrieval via LLM-Wiki
LLM agents require retrieval to behave less like one-shot context fetching and more like reasoning: searching, reading, traversing, and deciding when evidence is sufficient. Yet current Retrieval-Augmented Generation (RAG) systems organize external knowledge as flat chunks retrieved by embedding similarity, exposing a retrieval-as-lookup interface ill-suited to iterative reasoning agents. We propose LLM-Wiki, an agent-native retrieval system that operationalizes the Retrieval-as-Reasoning paradigm by treating external knowledge as a compilable, composable, and self-evolving structure rather than a static retrieval index. LLM-Wiki compiles documents into structured Wiki pages with bidirectional links, exposes search, read, and link-following operations through standard tool-calling interfaces, and introduces an Error Book for persistent structural and semantic self-correction. LLM-Wiki achieves state-of-the-art results on HotpotQA, MuSiQue, and 2WikiMultiHopQA, outperforming HippoRAG 2, LightRAG, and GraphRAG by 2.0-8.1 F1 points. On AuthTrace, LLM-Wiki achieves the best overall accuracy, with especially strong gains on multi-document structured queries, confirming that compilation-based retrieval generalizes beyond chain-style multi-hop reasoning.
QUE - This is a test question
![[../../presentations/sl4/ScholOnto.png]] ![[QUE - How to do interop.md]] 
Informatics of the Oppressed
logicmag.io