TriAgent: Divergence-Aware Multi-Agent Committees for Cost-Efficient Financial Sentiment Analysis

Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: Hi, I'm Tom.

Jane: And I'm Jane, here with Lu, Meng and our in-house large language model, Lalam.

Tom: We're looking at the paper: "TriAgent: Divergence-Aware Multi-Agent Committees for Cost-Efficient Financial Sentiment Analysis".

Jane: Here's the gist of the paper. The gist The TriAgent system presents a divergence-aware multi-agent committee that uses contextual granularity to route queries, leading to superior cost-efficiency and performance in financial sentiment analysis.

Tom: How it works The TriAgent framework is a three-tier committee stratified by three contextual granularities: a word-level lexicon (VADER), a sentence-level domain transformer (FinBERT), and a cross-sentence reasoner (Qwen2.5, 0.5 B–14 B-4bit, with Mistral-7B and Phi-3.5-mini cross-family checks) The three tiers correspond to three contextual granularities: a word level lexicon (VADER), a sentence level domain transformer (FinBERT), and a cross sentence reasoner VADER fails on phrase-bound polarity reversals, FinBERT fails on multi clause numeric comparisons, and the LLM overconfidently invents reasoning where lexicon neutrality is correct.

Jane: Semantic Divergence Index (SDI) and Routing A three way Semantic Divergence Index (SDI) measures pairwise disagreement across granularities and routes each query accordingly The SDI is defined by three pairwise Semantic Divergence Indices: SDILE = sV − sF, SDILR = sV − sL, and SDIER = sF − sL.

Tom: Thresholding the pair (SDILE, SDIER) at (τLE, θER) = (0.3, 0.7) partitions samples into four behaviour quadrants: consensus skips L3, domain shift trusts F and routes to L2.

Title and authors: Tom: So we're looking at this paper today, "TriAgent: Divergence-Aware Multi-Agent Committees for Cost-Efficient Financial Sentiment Analysis." It sounds like they've tackled that problem where you have a lot of queries that are easy to sort but you still end up using expensive cloud reasoners for everything.

Jane: That’s the core idea, right? They’re looking at how to make sure you only spend money on the heavy reasoning when it actually needs it, not just because it's there.

Lu: The authors introduce a system with three tiers of agents based on different levels of context. They start with a word-level lexicon, then move to a sentence-level domain transformer, and then they add a cross-sentence reasoner for the final check.

Meng: Three tiers sounds complicated if you’re thinking about deployment, but the goal is clearly cost efficiency because most queries are just trivially classifiable.

Lalam: It sounds like they're building a committee that uses these different levels of analysis to decide what to do next.

The paper's summary: Tom: Okay, so the paper explains how this system works by using a three-way Semantic Divergence Index, or SDI, to route each query through these tiers.

Jane: They define this index by measuring how much disagreement there is between the different levels of analysis—word level versus sentence level versus cross-sentence reasoning.

Lu: They measure this with three specific indices: one comparing word and sentence level, one comparing word and reasoner, and another comparing sentence and reasoner.

Meng: So if the disagreement is too high in a certain way, that’s when the system decides which agent to trust or route to next.

Lalam: That routing decision determines whether it just skips a layer or routes to a different kind of agent interaction protocol, like voting or debating.

The paper's improvements: Tom: One of the big things they found is something called the critic plateau, which suggests that when you re-task the LLM as a critic over those smaller agents' outputs, its performance stabilizes around an F1 score of about zero point eight seven across Qwen models between one point five billion and seven billion parameters.

Jane: That’s interesting because it shows that the improvement isn't just about having a bigger model; it’s about how you structure the task for that model to evaluate the smaller ones.

Lu: They found that this plateau is driven by granularity, not just by having three different agents voting together, which they noted when comparing it to a same-size three-persona vote that only got an F1 of zero point six six.

Meng: If we’re looking at deployment, the idea of having a three-granularity edge predictor using XGBoost with an AUC of zero point eight five allows for fully on-device routing, which is something practical for cost control at scale <ref:2607.19794#pg1>.

Lalam: They also have this shared consensus dictionary that acts like a cross-lingual canoniiser, meaning Chinese queries can match English labels at F1 equal to zero point nine nine without running the committee again <ref:2607.19794#pg1>.

Conclusion: Tom: So to wrap up, the main thing is that this SDI single-stage strategy achieved the best risk adjusted return of three point five zero on a twenty ticker backtest, outperforming always-FinBERT at one point three six and alwaysLLM at zero point one one <ref:2607.19794#pg2>.

Jane: That's a significant performance gain when you look at the financial outcomes, showing that this layered approach really pays off compared to just picking one model type permanently.

Lu: The paper shows that this architecture hits all three FinLLM two thousand twenty-six CFP topic buckets simultaneously, which is a pretty broad implication for how we structure these committees <ref:2607.19794#pg2,hits all three FinLLM 2026 CFP topic buckets simultaneously>.

Meng: From a deployment standpoint, they saved about nine point three million dollars per year at ten million users compared to the GPT-4o-mini baseline, which really puts the cost savings into perspective.

Lalam: And that SDI signal also doubles as a post-hoc LLM hallucination detector with an AUC of zero point nine zero on FPB data, so it has this security benefit too <ref:2607.19794#pg1>.