example: add Information Cascade model to benchmark STLTMemory latency

[ZhehaoZhao423]

Pre-PR Checklist

• I have opened a corresponding issue (feat(examples): Add Information Cascade model to benchmark memory latency (Relates to #200) #246) and am submitting this PR as a proposed implementation for community review.

Approval Link

None yet. This PR is a proactive submission following the creation of Issue #246. I'm opening this now to provide a concrete working implementation for the maintainers to evaluate alongside the feature request.

Summary

This PR introduces a new agent-based model example: Information Cascade (Financial Rumor Mill). It simulates a volatile stock market where LLM-powered quantitative traders make decisions (BUY/SELL/HOLD) based on peer-to-peer rumors and their internal STLTMemory.

Motive

This feature addresses the ongoing performance concerns regarding API latency and event-loop blocking (discussed in #200).

• Latency Stress Testing: By constraining short_term_capacity=1, this model forces continuous STLTMemory consolidation, providing a reproducible environment to measure synchronous API blocking.
• Cognitive Dynamics: It serves as a demonstration of "Echo Chamber" effects and information cascading in multi-agent systems.

Implementation

• Model Architecture: Implemented MarketPanicModel and TraderAgent with custom rumor-broadcasting logic. Used strict system prompting to prevent agents from hallucinating spatial interactions (move_one_step).
• Performance Profiling: Included run_benchmark.py, a headless script to profile step-execution time.
• UI Stability: Developed a custom app.py using raw solara reactive components with strict CSS formatting. This approach bypasses the current SolaraViz NoneType initialization conflict, ensuring a robust demonstration even with high-latency LLM calls.
• Documentation: Integrated the model into the official registry via docs/examples.md.

Usage Examples

To run the performance benchmark (headless):

cd examples/information_cascade
python run_benchmark.py

To launch the visualization:

cd examples/information_cascade
solara run app.py

(Feel free to run the app to see the complete terminal-style CoT logs rendered in the browser!)

Additional Notes

• Tested locally with pre-commit (Ruff/Black) to ensure strict compliance with project standards.
• Requires a .env file with a valid DEEPSEEK_API_KEY (or other LiteLLM-compatible keys).

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