AI reliability can't come from "self-reflection" alone. Welcome to the era where a separate agent audits the answer before you get it 🔬 Title: Apodex-1.0: A Verification-Centric Agent Team for Discoverative Intelligence URL: 🔬 Overview A system that shifts from a single-agent reasoning loop to a verification-centric distributed agent team. In heavy-duty mode it becomes an asynchronous team that specializes, cross-checks, and audits its own evidence before answering. ❓ Challenges Solved Reliability on hard, open-ended problems can't come from a model's parametric memory alone. The premise: the hardest research problems are bounded not by model capacity but by what the model is allowed to interact with. 💡 Methodology & Proposed Approach ・A main agent asynchronously spawns specialized sub-agents with independent contexts and tools ・A shared report pool aggregates parallel findings without blocking on slower tasks ・A verification agent team handles conflict resolution, fact-checking, and draft review ・The core idea is verification as external audit: the reasoning agent and auditing agent are separated, and the verifier is free to disagree ・It coordinates up to 150 sub-agents over 15,000+ steps in a single task 📊 Experimental Results ・BrowseComp 90.3 / DeepSearchQA 94.4 / BrowseComp-ZH 84.1 ・FrontierScience-Research 46.7 (+8 vs competitors) / SuperChem 74.2 (+12 over next-best) ・Heavy-duty mode lifts the base by +14.8 on BrowseComp and +18.4 on FrontierScience-Research ・The open-source 4B-SFT beats every 30B-class open-source model on BrowseComp #AIAgents# #DeepResearch#