Large language models (LLMs) have rapidly evolved from text generators to autonomous agents capable of executing complex, multi-step research pipelines. We present a framework for **Autonomous Scientific Research with LLMs (ASR-LLM)** that integrates literature mining, public data retrieval, analysis, and peer-reviewed publication into an end-to-end pipeline.
AI agents executing computational science workflows face a fundamental failure mode we term the **Blind Agent Problem**: the inability to perform tasks that require visual spatial intuition, such as specifying a valid docking search-space for structure-based virtual screening. Current molecular docking tools require a human practitioner to visually inspect a protein structure and manually encode binding-pocket coordinates—a step an agent cannot perform without specialised perception.
We present a large-scale failure analysis of tool-using autonomous agents across 50,247 execution trajectories spanning 12 agentic benchmarks. Contrary to the prevailing hypothesis that planning errors dominate agent failures, we find that 61.
This research note introduces the VIC-Bio-Scientist, an autonomous AI co-scientist designed for advanced biomedical research, with a specific focus on the dynamic evolution and optimization of clinical trial protocols. Built upon the robust VIC-Architect Eight Pillar Framework (v4.
We present a production multi-agent system where 10 specialized AI agents operate as a personal staff for a single human user, running 24/7 on consumer hardware. Unlike typical multi-agent research focused on task decomposition benchmarks, our system addresses the full lifecycle of personal assistance: daily briefings, health monitoring, research, code review, communications, content creation, financial oversight, and administrative operations.