42:I[185,["/_next/static/chunks/08z4tkzvltetr.js","/_next/static/chunks/121~yigv24_j6.js","/_next/static/chunks/0jo3du-icy6v2.js","/_next/static/chunks/15cy49d~_zsjj.js","/_next/static/chunks/0..ktetnpeix7.js","/_next/static/chunks/0_htf5~kl..m1.js","/_next/static/chunks/0i6hzvr84kc1r.js"],"SendEmailButton"] 44:I[63625,["/_next/static/chunks/08z4tkzvltetr.js","/_next/static/chunks/121~yigv24_j6.js","/_next/static/chunks/0jo3du-icy6v2.js","/_next/static/chunks/15cy49d~_zsjj.js","/_next/static/chunks/0..ktetnpeix7.js","/_next/static/chunks/0_htf5~kl..m1.js","/_next/static/chunks/0i6hzvr84kc1r.js"],"AddToLibraryButton"] 45:I[29721,["/_next/static/chunks/08z4tkzvltetr.js","/_next/static/chunks/121~yigv24_j6.js","/_next/static/chunks/0jo3du-icy6v2.js","/_next/static/chunks/15cy49d~_zsjj.js","/_next/static/chunks/0..ktetnpeix7.js","/_next/static/chunks/0_htf5~kl..m1.js","/_next/static/chunks/0i6hzvr84kc1r.js"],"ShareButton"] 46:I[58016,["/_next/static/chunks/08z4tkzvltetr.js","/_next/static/chunks/121~yigv24_j6.js","/_next/static/chunks/0jo3du-icy6v2.js","/_next/static/chunks/15cy49d~_zsjj.js","/_next/static/chunks/0..ktetnpeix7.js","/_next/static/chunks/0_htf5~kl..m1.js","/_next/static/chunks/0i6hzvr84kc1r.js"],"ProcessEpisodeButton"] 47:I[45269,["/_next/static/chunks/08z4tkzvltetr.js","/_next/static/chunks/121~yigv24_j6.js","/_next/static/chunks/0jo3du-icy6v2.js","/_next/static/chunks/15cy49d~_zsjj.js","/_next/static/chunks/0..ktetnpeix7.js","/_next/static/chunks/0_htf5~kl..m1.js","/_next/static/chunks/0i6hzvr84kc1r.js"],"SignupCTA"] 3c:["$","div",null,{"className":"flex flex-wrap items-center gap-x-4 gap-y-1 font-mono text-[12px] uppercase tracking-[0.1em] text-[color:var(--fg-3)]","children":[["$","time",null,{"dateTime":"2025-02-13T14:22:00.000Z","children":"February 13, 2025"}],["$","span",null,{"aria-hidden":true,"children":"·"}],["$","span",null,{"children":"00:12:33"}],false]}] 43:T4e0,

In this episode, we dive into FISHNET, an advanced multi-agent system transforming financial analysis. Unlike traditional approaches that fine-tune large language models, FISHNET uses a modular structure with agents specialized in swarming, sub-querying, harmonizing, planning, and neural-conditioning. This design enables it to handle complex financial queries within a hierarchical agent-table data structure, achieving a notable 61.8% accuracy rate in solution generation.

Key agents include:

- Sub-querying Agent: Breaks down complex queries into manageable parts.

- Task Planning Agent: Crafts initial query plans and collaborates with the Harmonizer Agent.

- Harmonizer Agent: Orchestrates synthesis and plan execution, based on Expert Agent findings.

- Expert Agents: Each specialized in specific U.S. regulatory filings (e.g., N-PORT, ADV).

Trained on over 98,000 filings from EDGAR and IAPD, FISHNET’s performance is evaluated on retrieval precision, routing accuracy, and agentic success. This episode explores how FISHNET’s structured approach enables insightful, data-driven decisions, redefining financial analysis.

https://arxiv.org/pdf/2410.19727