DEDUPED Hot AI repos, turned into use cases Get 5 a day

How to test whether System 2 observers help a task with Cadence

Build a System 1 brain and a recursive brain with observers, then compare them on the same task.

  • Research
  • Advanced
  • 1 hour
  • From the README
  • Any agent

Who it is forA cognitive-architecture researcher who wants to know whether self-observing regions change acquisition or recovery on a fixed task.

The problemThe value of recursive feedback is unclear, so a fair test needs two brains with matched wiring except for the observer regions.

The outcomeA side-by-side record of reward over time for a plain System 1 brain and a System 2 brain with observers, on the same seed and task.

What you need first

  • Python 3.11 or newer
  • NumPy installed through pip
  • A toy environment or task that can supply an observation and a measured reward after each action

Steps

  1. python -m pip install cadence-net==0.84.3
  2. Compose the plain brain with brain = Brain.compose(inputs=4, actions=2, arousal=True).
  3. Compose the recursive brain with recursive = Brain.compose(inputs=4, actions=2, modules=(16, 8), observers=(8,), seed=7, arousal=True). (inferred, check the README)
  4. Run both brains through the same environment loop with live, recording the reward at each step.
  5. Compare reward curves across the run, including after a changed condition in the environment.

Prompt for your agent

Using the Python package cadence-net (repo muellerberndt/cadence), run Brain.compose with and without observers=(8,) on the environment at <task file> using live() for 500 steps each. Expected output: two reward-by-step series and a summary of their averages. Verify that both brains receive identical observations and rewards at each step.

Caveat. The README says whether recursive feedback helps on a task is a question for experience and evaluation, so a single run does not settle it.

Share: X LinkedIn Hacker News Email Copy link

Disclaimer

Deduped lists open-source repositories that belong to other people. We do not own, maintain, audit, or vouch for any of them, and a repo appearing here is not an endorsement or a safety check. The use cases, steps, and prompts are written by AI and may be incomplete, outdated, or just wrong. Where a use case is marked "Seen in the wild", we link to public posts where someone described something similar, but we have not verified those posts and the use case itself is still our AI's own write-up. Use all of this at your own risk. Only run code, commands, or agents on systems and accounts you are authorized to use, and read the code and every command before you run it, especially anything that touches credentials, files, or the network. Nothing here is professional, security, legal, or financial advice. All trademarks and project names belong to their respective owners, and their use here does not imply affiliation. Content is provided as is, without warranties of any kind. Found a mistake, a safety problem, or something you want removed? Email [email protected] and we will look at it promptly. Full disclaimer page