DIALECTIC: A Multi-Agent System for Startup Evaluation

Abstract

Venture capital (VC) investors face a large number of investment opportunities but only invest in few of these, with even fewer ending up successful. Early-stage screening of opportunities is often limited by investor bandwidth, demanding tradeoffs between evaluation diligence and number of opportunities assessed. To ease this tradeoff, we introduce DIALECTIC, an LLM-based multi-agent system for startup evaluation. DIALECTIC first gathers factual knowledge about a startup and organizes these facts into a hierarchical question tree. It then synthesizes the facts into natural-language arguments for and against an investment and iteratively critiques and refines these arguments through a simulated debate, which surfaces only the most convincing arguments. Our system also produces numeric decision scores that allow investors to rank and thus efficiently prioritize opportunities. We evaluate DIALECTIC through backtesting on real investment opportunities aggregated from five VC funds, showing that DIALECTIC matches the precision of human VCs in predicting startup success.

Figures (4)

• Figure 1: Overview of the DIALECTIC method. The right side shows the flow of operations. Agents are shown in red, agent inputs/outputs are shown in blue, and loops are shown in green. The left side illustrates the key outputs of the agents.
• Figure 2: Results from the hyperparameter optimization, showing AUC-PR, raw argument scores, QA pair count, and argument length for different numbers of arguments $K_t$ and iterations $T$.
• Figure 3: Precision and recall of DIALECTIC across all possible values of the decision threshold $\tau$ in comparison to the human VCs and GPT IO prompting baseline.
• Figure 4: Distribution of decision scores.