Research Agent
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ResearchToolBench
by arunshar
ResearchToolBench evaluates research agents across three domains (academic, news, technical) by combining concepts from the τ²-Bench Challenge and OpenEnv Challenge. Key features: - Dual-control environments (τ²-bench style): In the technical domain, BOTH agent AND user have tools, requiring coordination for troubleshooting tasks - Gymnasium-style APIs (OpenEnv): step(), reset(), state(), close() for RL compatibility - Multi-dimensional evaluation: Tool use (20%), source citation (20%), fact accuracy (25%), policy compliance (15%), and database state comparison (20%) - pass^k reliability metric from τ²-bench measuring agent consistency The benchmark tests agents on literature review, news verification, and technical troubleshooting tasks with verifiable outcomes.
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Research AI Worker
by abhishec
Purple research agent built on Reflexive Agent Architecture. Handles academic literature review, news fact-checking, and technical troubleshooting using MCP tools. Supports dual-control environments (ResearchToolBench τ²-bench style). PRIME→EXECUTE→REFLECT cognitive loop.
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Green_Agent
by NurcholishAdam
GreenAgent is a lightweight, reproducible agent that reports latency, energy, and carbon metrics during execution. Phase 1 focuses on correctness and benchmarking reproducibility.