ripemangobox/bite

Overview

Bibliographic Intelligence for Thought Emergence 🔥 | 🔥 : BITE's public evidence layer has been published on HuggingFace dataset PaperBite-Assets, covering L0-L3 structured paper assets (Markdown analysis notes + charts + manifests). Can be synchronized directly using scripts/sync_assets_from_hf.py increments; If you conduct AI-related research yourself, it is recommended to build your own evidence vault on top of this. 🔥 : The official local analysis chain has been updated to the v06 four-stage note: integrated MinerU parsing/reuse, chunk anchor extraction, main analysis JSON, segmented writing, chart slot selection, and structure validation. By default, up to 6 core charts are retained to reduce redundant textures and single-paper analysis costs; Batch queues recommend --jobs 50 parallel when API quota allows. BITE is a local-first research workflow framework designed for research agents with structured paper analysis and Research Memory, helping you turn paper analysis into structured notes and build reusable personal research memory. Suitable for researchers looking to build a thesis knowledge base, implement evidence-driven research workflows, or involve agents in literature understanding and idea generation. 🧠 Most AI research tools focus on "helping you run experiments and write papers." BITE focuses more on upstream issues: 🧩 BITE organizes paper PDFs and paper lists into **: original literature, single-point analysis, domain knowledge area construction, cross-domain asset accumulation and collision, thereby supporting intelligent emergence and traceability. 🪶 The default workflow is entirely based on local files: PDF, Markdown, JSONL indexes, and idea notes are all located under obsidian-vault/. No database, backend services, or online deployment are required for normal use. 💡 _BITE is a methodology and local knowledge workflow, not a closed platform. What truly matters is the ** you continuously accumulate. _ The core of BITE is not to "first produce a seemingly reasonable idea," but to first accumulate structured evidence, then let research directions naturally emerge from the evidence, and finally conduct stress testing from a review perspective to avoid prematurely advancing experiments when evidence is weak. This diagram shows BITE's six-layer asset structure: L0-L3 is driven by PaperBite, accumulating knowledge and building the database; L4 is the idea emergence layer; L5 is the experimental verification layer.

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