PaperMind

Abstract-only summarizers miss what a paper actually shows. PaperMind processes the full PDF, every section, table, and result, then grades its own output for faithfulness before returning it.

• Map-reduce summarization: prepare, map sections concurrently, synthesize, then grade
• Typed entity and quantitative-results extraction via Pydantic schemas, from prose and real PDF tables; no regex
• LLM-as-judge quality control: weak summaries loop back and get retried automatically
• RelationAgent labels paper-to-paper links (extends, replicates, contradicts, shares-method) into a pgvector knowledge graph
• Domain-agnostic across biomedicine, physics, and ML papers
• Benchmark harness runs the full pipeline against real arXiv papers: 100% success rate, up to 2.5x parallel speedup, and it caught a real figure-extraction bug
• Runs entirely on free LLM tiers (Gemini, Groq, local Ollama) with rate limits absorbed by concurrency caps and backoff retries

• LangGraph map-reduce pipeline covers the whole paper: every section is summarized concurrently, then synthesized into a 300 to 450 word summary with findings, contributions, and limitations.
• An LLM-as-judge grading node scores faithfulness and specificity and retries weak results automatically, with cascading failover across Gemini 2.0 Flash, Groq Llama 3.3, and local Ollama Qwen2.5.