Retrieval
Research papers, repositories, and articles about retrieval
Showing 8 of 8 items
Learning to Reason by Analogy via Retrieval-Augmented Reinforcement Fine-Tuning
This paper trains a retriever to select past reasoning traces that actually help solve a new problem, then uses those traces during reinforcement-based customization. On hard math benchmarks like AIME, their analogy-aware method beats standard reinforcement setups by several points, showing that reasoning-aware retrieval is a real lever.
google/langextract
Langextract turns messy text into structured records using LLMs with grounded citations. It targets production use cases where you need both high recall and traceable sources.
Panniantong/Agent-Reach
Agent-Reach gives agents "eyes" on social and developer platforms without expensive APIs. It can read and search across Twitter, Reddit, YouTube, GitHub, Bilibili, and more from a single CLI.
GRIP: Feedback-Guided Prompt Retrieval for Large Multimodal Models
GRIP trains a retriever to pick in-context examples that actually improve a multimodal model’s answers, instead of just being visually similar. The retriever learns from model feedback and then transfers across different vision-language models, boosting accuracy on classification, captioning and VQA.
yichuan-w/LEANN
LEANN is a compact retrieval system for "RAG on everything" with big storage savings. It compresses document representations while keeping accuracy high, making private, on-device retrieval far cheaper.
RyanCodrai/turbovec
Turbovec is a vector index built on TurboQuant with Rust internals and Python bindings. It targets high-speed similarity search for embeddings. Drop it into your stack if your current vector store is the bottleneck.
Optimizing RAG Rerankers with LLM Feedback via Reinforcement Learning
Uses a language model’s own feedback as a training signal for retrieval rerankers in RAG pipelines. Aims to pick more useful documents for question answering.
WeKnora
Tencent’s LLM-powered framework for deep document understanding, semantic retrieval, and context-aware answering with a RAG paradigm. Essentially a production-grade answer engine stack rather than a toy demo. ([github.com](https://github.com/trending?since=daily))