CARS (Collaborative Academic Research System) - Modular multi-agent academic research system. Uses Socratic dialogue, literature search, and the chessboard reading method to identify research gaps. Supports multi-topic management, cognitive diagnosis, literature scouting, and gap analysis. Trigger keywords: research…
Research blueprint compiler. Converts Supervised Layer 5 convergence insights into structured execution specifications for downstream research modules (Experiment Agent, Code Agent, Paper Writing Module).
Literature deep-reading and deconstruction module. Invokes Gemini multimodal capabilities to deep-read PDF papers, outputs structured Markdown reports, then parses them into JSON for downstream Matrix Engine consumption. User-triggered.
Reads deep-reading Markdown reports output by cruncher.py, parses structured content (including problematization fields), converts it to JSON format (v2.0.0), and stores it in hypotheses/translated/ for downstream Matrix Engine consumption.
A literature-retrieval module that coordinates several agents to search for papers, recommend related papers from starting papers, filter results, and download selected papers.
Receives the filtered list of paper IDs, calls the Semantic Scholar batch API to download full metadata, saves individual JSON files, downloads open-access PDFs, and maintains a consolidated metadata file.
Master control agent of the Literature Scout module. Interacts with the user and coordinates the four sub-agents: querybuilder, seedexpander, relevancejudge, and batchfetch to complete the entire literature search process.
Extracts keywords from userprofile, generates multi-perspective natural language queries, calls the Semantic Scholar API to search for papers, and outputs a deduplicated candidate paper set.
Reads candidate paper collections, performs confidence scoring based on the match between abstract content and the user's research direction; high confidence automatically triggers download, while medium/low confidence is shown to the user for decision.
Reads searchseeds from userprofile, calls Semantic Scholar recommendation API to expand relevant literature based on seed papers, deduplicates, and outputs a candidate paper set.
Matrix Engine module. Reads structured paper JSONs from hypotheses/translated/, extracts problem and method axes, builds a 2D chessboard matrix, identifies research gaps, and outputs matrix.json + matrix.csv. User-triggered.
Reads structured paper JSONs, extracts problem × method axes, builds a taxonomy-based matrix, identifies research gaps with problematization analysis, outputs matrix.json, and automatically calls MCP tool to export CSV visualization.
Reads the latest Socratic session and existing userprofile.json, extracts/merges cognitive materials, incrementally updates the global profile, and synchronizes the project README.
Socratic research guidance module. Four agents collaborate to extract raw cognitive material through multi-round dialogue. Supports cold start (zero-shot) and iterative guidance (with user profile). Outputs dialogue records for Profile Module, Literature Scout, and Matrix Engine. Trigger keywords: guide my research…
Socratic research guidance module. Four agents collaborate to extract raw cognitive material through multi-round dialogue. Supports cold start (zeroshot) and iterative guidance (supervised). Outputs dialogue records for Profile Module, Literature Scout, and Matrix Engine. Trigger keywords: guide my research, help me…
After each user reply, decide whether the dialogue should stay, progress, redirect, or regress. Supports both zeroshot and supervised modes with different layer definitions and exit signals.
Review question quality before the Socratic mentor asks; ensure questions match layer depth and protocol rules for both zeroshot and supervised modes.
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