u/LevyTateLabs

This is from the Is This A Simulation Or Real Life research. Colab notebook free for anyone who wants it so you can run it without affecting your computer. 

The last clip I posted showed the exact cells that hold a hint for which world it's in. I uploaded a newer version of this video yesterday showing the exact memory cells to represent the information more accurately.

This new video shows the actions, meaning what the digital organism decides to do next and what it actually expects to happen. I list the actions in the purple cells and the guesses in yellow cells. 

If ya'll want the correct watch order to see exactly how the creature's brain learns and how I conducted the research, you should watch senses, then process, then memory, then actions. I ended up posting memory and actions first because I hadn't quite decided how detailed I wanted this series to get yet. I will post the process video later. 

The only goal it had was to find food and survive, unprompted to look for "glitches in the matrix." The teal rings aren't the absolute truth, just the clue. 73% is how often you can read real vs fake out of its memory once food is on the line. In the fake world, those actions miss a little bit because the messed-up physics make food hard to get. That is how it starts to find the glitch.

I updated the animation from yesterday to show the exact locations where the memory "cells" show up from the actual study's results. This is from the "Is This A Simulation Or Real Life" research, where I wanted to see what it would take for a digital organism to tell if it was in a fake world, unprompted, meaning it was never explicitly told to look for "glitches in the matrix." The only goal it had was to find food and survive. When physics messed with the creature's ability to get food, it started to create the idea that something was not right in the environment it was in. If ya'll wanna' run the sim yourself I built a Colab notebook you can run in the cloud without worrying about hardware restrictions.

Quick refresher: we gave a little creature a world to live in, then made a fake copy with one rule wrong (how well the ground grips). An outsider can spot the fake almost every time. The creature's own mind? A coin flip, until the fake starts costing it food. Then it begins to tell real from fake.

This clip is the receipt inside its head.

Same mind, shown twice: real world on the left, fake on the right. Zoom into memory. The teal rings are the cells that hold the clue.

Detectable was always there. Noticing shows up in memory when survival needs it.

ItaSoRL · ItaoRL

Clip 4 of 4. Closing the arc.

https://i.redd.it/rclohi0jy1ih1.gif

The setup: Previously, when there was no reward for noticing the world was fake, the creature was basically guessing at 50%. An outside observer could easily spot the fake 99% of the time.

The change: We made the fake physics actually matter for the goal. We made food harder to catch in the fake world.

The result (10 tests): The creature's ability to detect the fake world jumped to 73%. We never trained it to look for fakes! It learned this entirely on its own because it needed the information to get food.

The takeaway for reinforcement learning: Just because a detail is obvious does not mean a creature will automatically care about it. The creature only started paying attention when the fake physics affected its main task.

Research:

https://ilevytate.github.io/ItaSoRL/

Clip 3 of 4.

https://i.redd.it/ja1wbbg7sshh1.gif

Same near-copy world (one dynamics rule changed: ground grip). Outside watcher was ~99%.

New probe: readout from the agent's own internal state while it is just living in the fake.

Result (real runs, n=10): ~50% (chance). Oracle-detectable seam, no free encoding in the policy network.

Takeaway: detectability of a sim mismatch is not evidence the agent represented it.

Mute-friendly clip

Research:

https://ilevytate.github.io/ItaSoRL/

Clip 2 of 4. Follow-up to the spot-the-fake setup.

https://i.redd.it/9yjuw9fmklhh1.gif

Same near-copy environment: one dynamics rule changed (ground grip / step slip). Everything else identical.

Probe: an outside watcher / oracle-style discriminator that knows the true rules and replays every step.

Result (real runs, n=10): ~99%. The fake is in-band detectable from outside.

So detectability is not the open question. The next clips ask whether the agent's own representation encodes that seam.

Mute-friendly clip.

Research:

https://ilevytate.github.io/ItaSoRL/

Short animation from a sim. I have been running (ItaSoRL). Is This a Simulation or Real Life.

Clip 1 of 4. Spot the Fake.

Two copies of the same little world. Same start, same plan. We changed exactly one rule: how well the creature's feet grip the ground. The slip is tiny. By eye, most people cannot tell which is the copy.

Clip asks you to look first, then reveals which side is fake.

That is only the setup. Later results ask a harder question: if the difference is real and catchable from the outside, does the creature's own mind notice it? (Spoiler for a follow-up: often no, until survival makes it matter.)

Mute-friendly, text on screen.