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ContextOS is an open-source token-budget-aware context orchestration layer for long-horizon LLM agents, using hybrid retrieval and deterministic planning to select memories for context with execution traces for inspection.
This paper proposes HybridRAG-BN, a retrieval-augmented framework for Bangla knowledge-base question answering that combines hybrid retrieval, Gemma-based generation, and LoRA fine-tuned verification, achieving first place with F1 scores of 0.71654 and 0.72912.
This paper presents VLD-RAG, an agentic multimodal retrieval-augmented generation framework for question answering over long, visually-rich documents. It uses a page-preserving index and a verifier-guided agent workflow to improve cross-page evidence retrieval and reasoning, outperforming prior vision-based baselines on benchmarks like LongDocURL and MMLongBench-Doc.
Mem0 is an open-source memory layer that helps AI agents retain and retrieve user context across conversations using hybrid search (semantic, BM25, entity), with SDKs for Python and npm.
A developer introduces Watch Skill, an open-source MCP server that gives AI agents persistent memory for videos by creating local indexes of transcripts, OCR, and visual observations for efficient retrieval.
Hybrid-IR introduces a dual-path retrieval framework combining graph-based and dense retrieval with iterative reasoning to improve complex medical QA, addressing limitations in existing RAG methods. Experiments on three benchmarks show effectiveness.
Archex is a new open-source code RAG tool that improves retrieval by combining hybrid search (BM25F + dense embeddings), cross-encoder reranking, and dependency-graph expansion, achieving much higher recall and token efficiency than pure embeddings-based approaches.
An open-source full-stack pipeline that constructs a Knowledge Graph from raw text, uses hybrid search (dense + sparse + graph traversal) to solve multi-hop reasoning problems in LLMs, and re-ranks results with Reciprocal Rank Fusion and a Cross-Encoder.