Stop running everything through LLMs: How I fixed my research agent (www.tinyfish.ai)

The launch of TinyResearch marks a significant advancement in AI-driven research systems, designed to provide detailed, reliable answers to complex questions by acting as a dependable intermediary for both human users and automated agents. Instead of merely processing input through large language models (LLMs), TinyResearch enhances the research process by utilizing a structured loop that evaluates factual correctness, credibility, readability, comprehensiveness, and instruction adherence. This approach allows it to dissect multifaceted questions into manageable components, gathering and verifying claims through live searches and dynamic reading of sources, ultimately generating comprehensive reports with citations only from trusted content. This system is significant for the AI/ML community as it addresses ongoing concerns about the reliability of AI-generated outputs, particularly in research contexts where accuracy and source credibility are paramount. By allowing users to tailor their research sources and establish specific filtering criteria, TinyResearch fosters a more rigorous assessment of information quality. Its performance in public benchmarks demonstrates its effectiveness in analyzing complex tasks—achieving scores of 51.74 and 46.42 out of 100 in the DRACO and DeepResearch Bench II evaluations, respectively. This reflects a shift towards prioritizing the strength of evidence over mere coverage, ultimately setting a new standard for research tools in AI.