{"slug": "triagent-divergence-aware-multi-agent-committees-for-cost-efficient-financial", "title": "TriAgent: Divergence-Aware Multi-Agent Committees for Cost-Efficient Financial Sentiment Analysis", "summary": "A new multi-agent committee called TriAgent, described in arXiv:2607.19794v1, achieves cost-efficient financial sentiment analysis by routing queries through 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) using a three-way Semantic Divergence Index (SDI). The system saves $9.3M/year at 10M-user scale versus a GPT-4o-mini baseline, achieves F1=0.87 with the LLM as a critic, and SDI doubles as a hallucination detector (AUC=0.90) and achieves a Sharpe ratio of 3.50 in back-tests.", "body_md": "arXiv:2607.19794v1 Announce Type: new\nAbstract: 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.", "url": "https://wpnews.pro/news/triagent-divergence-aware-multi-agent-committees-for-cost-efficient-financial", "canonical_source": "https://www.machinebrief.com/news/triagent-divergence-aware-multi-agent-committees-for-cost-ef-iajn", "published_at": "2026-07-23 04:00:00+00:00", "updated_at": "2026-07-23 04:04:44.239509+00:00", "lang": "en", "topics": ["artificial-intelligence", "large-language-models", "ai-agents", "natural-language-processing", "ai-research"], "entities": ["TriAgent", "VADER", "FinBERT", "Qwen2.5", "Mistral-7B", "Phi-3.5-mini", "GPT-4o-mini", "arXiv"], "alternates": {"html": "https://wpnews.pro/news/triagent-divergence-aware-multi-agent-committees-for-cost-efficient-financial", "markdown": "https://wpnews.pro/news/triagent-divergence-aware-multi-agent-committees-for-cost-efficient-financial.md", "text": "https://wpnews.pro/news/triagent-divergence-aware-multi-agent-committees-for-cost-efficient-financial.txt", "jsonld": "https://wpnews.pro/news/triagent-divergence-aware-multi-agent-committees-for-cost-efficient-financial.jsonld"}}