Symposium FAQs

Question 1

Can Symposium help fine-tune other LLMs?

Accepted Answer

Yes. Symposium's learning loop stores novel, high-quality discoveries. You can export this verified knowledge as DPO-format training data, which is ideal for fine-tuning LLMs to improve their accuracy and prevent hallucinations on similar complex issues in the future.

Question 2

How does Symposium discover missing knowledge?

Accepted Answer

It uses parallel agents, each researching from a different angle (e.g., documentation, GitHub repos, failure modes, codebase patterns, tests). Findings are then cross-validated and scored based on real signals like agent convergence and unique repository count, synthesizing a verified plan.

Question 3

What is Symposium and how does it improve Claude Code?

Accepted Answer

Symposium is a multi-agent reasoning engine that integrates with Claude Code via MCP. It spawns parallel research agents to find knowledge LLMs lack from live docs and code, significantly boosting Claude Code's performance on complex software engineering tasks by providing verified, real-time context.

Question 4

What kind of performance improvement can I expect with Symposium?

Accepted Answer

Symposium has demonstrated a +23.1% performance gain on challenging Terminal Bench 2.0 SWE tasks, increasing Claude Code's success rate from 46.2% to 69.2% when used together, with zero regressions on tasks Claude could already solve.

Question 5

What are the core features of Symposium?

Accepted Answer

Key features include multi-angle parallel research, dual-mode cross-validation, evidence scoring, a persistent learning loop for instant recall, export of DPO-format training data, and specialized tools for knowledge browsing and feedback.

Symposium

Symposium

Key Features

Use Cases

Key Features

Use Cases