@inproceedings{dong-etal-2026-tartanmaroon,
title = "{T}artan{M}aroon: Multi-Agent Academic Advising with Iterative Negotiation and Transparent Collaboration",
author = "Dong, Peidi and
Bouamor, Houda and
Xiao, Yunze and
Kurup, Devi G",
editor = "Durrett, Greg and
Jian, Ping",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 3: System Demonstrations)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-demo.83/",
pages = "840--850",
ISBN = "979-8-89176-392-0",
abstract = "We present TartanMaroon, a deployable multi-agent academic advising system that handles the full complexity spectrum of student queries, from factual lookups to constrained multi-semester planning. We make three contributions: (1) a proposal{--}critique negotiation protocol in which a Planning Agent generates degree plans evaluated in parallel by domain-specialized agents, enabling detection of cross-domain constraint violations that single-pass outputs miss; (2) a real-time transparency interface streaming agent reasoning and negotiation rounds to users, supported by pilot feedback showing increased trust over standard LLM chatbots; and (3) \textit{TartanBench}, a difficulty-stratified benchmark of 220 advising queries across five complexity tiers, released open-source without exposing individual student records. A five-configuration ablation study establishes a \textit{complexity{--}necessity curve}: single-agent systems perform competitively on simple queries, while multi-agent coordination yields gains of up to $+31$ points on planning tasks."
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%0 Conference Proceedings
%T TartanMaroon: Multi-Agent Academic Advising with Iterative Negotiation and Transparent Collaboration
%A Dong, Peidi
%A Bouamor, Houda
%A Xiao, Yunze
%A Kurup, Devi G.
%Y Durrett, Greg
%Y Jian, Ping
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-392-0
%F dong-etal-2026-tartanmaroon
%X We present TartanMaroon, a deployable multi-agent academic advising system that handles the full complexity spectrum of student queries, from factual lookups to constrained multi-semester planning. We make three contributions: (1) a proposal–critique negotiation protocol in which a Planning Agent generates degree plans evaluated in parallel by domain-specialized agents, enabling detection of cross-domain constraint violations that single-pass outputs miss; (2) a real-time transparency interface streaming agent reasoning and negotiation rounds to users, supported by pilot feedback showing increased trust over standard LLM chatbots; and (3) TartanBench, a difficulty-stratified benchmark of 220 advising queries across five complexity tiers, released open-source without exposing individual student records. A five-configuration ablation study establishes a complexity–necessity curve: single-agent systems perform competitively on simple queries, while multi-agent coordination yields gains of up to +31 points on planning tasks.
%U https://aclanthology.org/2026.acl-demo.83/
%P 840-850
Markdown (Informal)
[TartanMaroon: Multi-Agent Academic Advising with Iterative Negotiation and Transparent Collaboration](https://aclanthology.org/2026.acl-demo.83/) (Dong et al., ACL 2026)
ACL