[2511.14043] AISAC: An Integrated multi-agent System for Transparent, Retrieval-Grounded Scientific Assistance

Computer Science > Artificial Intelligence arXiv:2511.14043 (cs) [Submitted on 18 Nov 2025 (v1), last revised 27 Mar 2026 (this version, v3)] Title:AISAC: An Integrated multi-agent System for Transparent, Retrieval-Grounded Scientific Assistance Authors:Chandrachur Bhattacharya, Sibendu Som View a PDF of the paper titled AISAC: An Integrated multi-agent System for Transparent, Retrieval-Grounded Scientific Assistance, by Chandrachur Bhattacharya and Sibendu Som View PDF HTML (experimental) Abstract:AI Scientific Assistant Core (AISAC) is a transparent, modular multi-agent runtime developed at Argonne National Laboratory to support long-horizon, evidence-grounded scientific reasoning. Rather than proposing new agent algorithms or claiming autonomous scientific discovery, AISAC contributes a governed execution substrate that operationalizes key requirements for deploying agentic AI in scientific practice, including explicit role semantics, budgeted context management, traceable execution, and reproducible interaction with tools and knowledge. AISAC enforces four structural guarantees for scientific reasoning: (1) declarative agent registration with runtime-enforced role semantics and automatic system prompt generation; (2) budgeted orchestration via explicit per-turn context and delegation depth limits; (3) role-aligned memory access across episodic, dialogue, and evidence layers; and (4) trace-driven transparency through persistent execution records and a live event-stream ...