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cloud.google.com·7h ago
AI Research
arXiv CS.AI·Sep 2·5 min read
Long-Horizon State Tracking in LLMs: Executing MD5 through a Deep Sequence of Dependent Tool Calls
TL;DR
A large language model (LLM) successfully executed the MD5 cryptographic hash function through 196 dependent tool calls, maintaining accurate state across all steps. This demonstrates the model's capability for long-horizon state tracking in complex tasks.
✦ Why It Matters
Consider testing LLMs for long-horizon tasks in your projects to assess their state-tracking capabilities.
Key Takeaways
- Test LLMs on long-horizon tasks to evaluate state tracking.
- Implement complex functions like MD5 to assess model capabilities.
- Use multiple LLMs for error correction in dependent tasks.
- Monitor and localize errors to improve model performance.
How It Works
The model computes the MD5 hash by maintaining four 32-bit words across 196 dependent calls. Each call's output is used as input for the next, ensuring that the model's internal state is preserved.
By implementing MD5 from scratch, the researchers could directly compare the model's outputs to the expected results, isolating bookkeeping errors from computational inaccuracies.