PokeeAI/pokee_research_7b

Hugging Face
TEXT GENERATIONConcurrency Cost:1Model Size:7.6BQuant:FP8Ctx Length:32kPublished:Oct 17, 2025License:apache-2.0Architecture:Transformer0.1K Open Weights Warm

PokeeResearch-7B by Pokee AI is a 7.6 billion parameter tool-augmented LLM research agent, fine-tuned from Qwen2.5-7B-Instruct with a 131072 token context length. It integrates Reinforcement Learning from AI Feedback (RLAIF) and a robust reasoning scaffold to conduct complex, multi-step research workflows including self-correction and synthesis. This model is optimized for deep research automation, autonomously decomposing queries, retrieving external sources, and synthesizing factual, verifiable answers.

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PokeeResearch-7B: Deep Research Agent

PokeeResearch-7B, developed by Pokee AI, is a 7.6 billion parameter tool-augmented LLM research agent built upon the Qwen2.5-7B-Instruct backbone. It is specifically designed to advance reliable, aligned, and scalable research-grade reasoning, integrating Reinforcement Learning from AI Feedback (RLAIF) with a robust reasoning scaffold.

Key Capabilities

  • Autonomous Deep Research: Decomposes complex queries, retrieves and reads from external sources, and synthesizes factual, verifiable, and grounded answers.
  • Multi-step Workflows: Capable of self-correction, verification, and synthesis across multiple independent research threads.
  • Performance: Achieves state-of-the-art performance among 7B-scale open deep research agents across 10 benchmarks, including HLE, GAIA, and BrowseComp, validating its RLAIF and reasoning scaffold design.
  • Multilingual Support: Supports English, Chinese, and many other languages.

Good for

  • Standalone Research Assistant: Automating deep research tasks.
  • Multi-agent Systems: Supporting academic, enterprise, or product-level research.
  • Fine-tuning: Domain-specific scientific discovery, autonomous document retrieval, multi-source verification, and integration into RLHF/RLAIF frameworks.

Limitations

  • Dependence on external data quality and retrieval accuracy.
  • Potential semantic bias from AI-based feedback signals.
  • Limited coverage for non-English or multi-modal reasoning tasks.
  • Risk of hallucinated synthesis when sources conflict or lack clarity.