Coding Agent

The rapid growth of scientific literature has made it increasingly difficult for researchers to efficiently discover, evaluate, and synthesize relevant work. Recent advances in multi-agent large language models (LLMs) have demonstrated strong potential for understanding user intent and are being trained to utilize various tools. In this paper, we introduce Paper Circle, a multi-agent research discovery and analysis system designed to reduce the effort required to find, assess, organize, and understand academic literature. The system comprises two complementary pipelines: (1) a Discovery Pipeline that integrates offline and online retrieval from multiple sources, multi-criteria scoring, diversity-aware ranking, and structured outputs; and (2) an Analysis Pipeline that transforms individual papers into structured knowledge graphs with typed nodes (e.g., concepts, methods, experiments, and figures) and edges, enabling graph-aware question answering and coverage verification. Both pipelines are implemented within a coder LLM–based multi-agent orchestration framework and produce fully reproducible, synchronized outputs (JSON, CSV, BibTeX, Markdown, and HTML) at each agent step. This paper describes the system architecture, agent roles, retrieval and scoring methods, knowledge graph schema, and evaluation interfaces that together form the Paper Circle research workflow. We benchmark Paper Circle on both paper retrieval and paper review generation, reporting hit rate, MRR, and Recall@K. Results show consistent improvements with stronger agent models. Github: https://github.com/MAXNORM8650/papercircle

Our green evaluator agent benchmarks database-focused agents on Spider2-Snow, a suite of natural-language-to-SQL tasks grounded in Snowflake-backed datasets. For each test instance, it provides the target agent with the instruction, db_id, and any optional external knowledge, and expects a structured response containing a single SQL query (via an A2A DataPart like {"sql": "..."}; plain-text and fenced ```sql fallbacks are also supported). The evaluator then executes the predicted SQL on Snowflake and compares the resulting output to gold execution results to score correctness.

DebateJudge-GreenAgent evaluates reasoning and argumentation tasks automatically.