TriAgent: Divergence-Aware Multi-Agent Committees for Cost-Efficient Financial Sentiment Analysis
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Computer Science > Computation and Language
arXiv:2607.19794 (cs)
[Submitted on 22 Jul 2026]
Title:TriAgent: Divergence-Aware Multi-Agent Committees for Cost-Efficient Financial Sentiment Analysis
Authors:Isabel Xu (The Overlake School)Cynthia Xu (The Overlake School)Rachel Ren (Edwards Vacuum Inc.)Cong Guo (The University of Memphis)Jiacheng Ding (The University of Memphis)
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Abstract:Production LLM-based financial sentiment analysis faces a structural cost trap: most queries are trivially classifiable, yet expensive cloud reasoners process them all, and the bill scales linearly with user count. We present TriAgent, a multi-agent committee stratified by contextual granularity -- a word-level lexicon (VADER), a sentence-level domain transformer (FinBERT), and a cross-sentence reasoner (Qwen2.5, 0.5B-14B-4bit, with Mistral-7B and Phi-3.5-mini cross-family checks). A three-way Semantic Divergence Index (SDI) measures pairwise disagreement across granularities and routes each query accordingly. Our central finding is the critic plateau: when the LLM is re-tasked as a critic over the smaller agents' outputs, F1 plateaus at ~0.87 across 1.5B-7B Qwen (bootstrap 95% CIs overlap), while a same-size 3-persona vote drops to F1=0.66, which is driven by granularity-stratified diversity. Three corollaries follow from the same SDI signal: (i) a Shared Consensus Dictionary on multilingual sentence-BERT answers 95% of Chinese queries from an English cache at F1=0.99 -- cross-border canonicalization at zero marginal cost; (ii) SDI doubles as a post-hoc LLM-hallucination detector at AUC=0.90; (iii) the SDI single-stage strategy attains the best risk-adjusted return (Sharpe=3.50) on a 20-ticker back-test, dominating both always-FinBERT (1.36) and always-LLM (0.11). At 10M-user scale, TriAgent saves $9.3M/year vs. a GPT-4o-mini baseline. Code, lexicons, and the SCD are released.
| Subjects: | Computation and Language (cs.CL); Computational Engineering, Finance, and Science (cs.CE); Databases (cs.DB); Machine Learning (cs.LG) |
| Cite as: | arXiv:2607.19794 [cs.CL] |
| (or arXiv:2607.19794v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2607.19794
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled TriAgent: Divergence-Aware Multi-Agent Committees for Cost-Efficient Financial Sentiment Analysis, by Isabel Xu (The Overlake School) Cynthia Xu (The Overlake School) Rachel Ren (Edwards Vacuum Inc.) Cong Guo (The University of Memphis) Jiacheng Ding (The University of Memphis)
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