A head full of rival guessers

Time to open the machine. You know what it eats (triplets), and you know the question it has to answer while eating: how many knobs does this world have? Here’s the design decision everything else hangs on.

PRA does not contain a model of the world. It contains a crowd of them.

One guesser

Let me introduce a single member of the crowd first. In PRA it’s called a frame, and a frame is a bet. Specifically: “this world can be described with D knobs.” Every frame picks its own D. A three-knob frame and a fourteen-knob frame can live in the same head at the same time, watching the same triplets, each insisting on its own answer to chapter 3’s question.

A frame backs its bet by doing two jobs, over and over, on every triplet.

First job: place. A sight arrives. That means one step’s worth of raw numbers from the senses; for the rover you’ll meet in Part 4 it’s ten numbers (five laser ranges, a compass, a position beacon, a bumper). The frame’s work is to re-express that sight as a setting of its own knobs. It helps to keep the two ideas separate here. The frame is the panel itself: the knobs, however many it bet on. The pose is where those knobs point right now. A three-knob frame answers every sight with three numbers, its best summary of the sight it was just handed. So a pose is not the frame, and not the world either. It’s the frame’s reading of one observation, expressed in the frame’s own coordinates. New sight, new pose, same frame.

Second job: predict. Told the action (motors forward!), the frame predicts where its knobs will land next. Then the real next sight arrives and does the grading itself, the free teacher chapter 4 promised. Surprise, measured, delivered, logged.

A frame with too few knobs can’t help but blur things together. A one-knob frame watching a boat can track it east-west or north-south, but never both, so its predictions keep being wrong in ways it can’t even express. Too many knobs and the frame has room to fit its recent experience like a glove, including the noise, which reads well today and predicts poorly tomorrow. The bet is real, and the triplet stream settles it.

What a frame keeps

The word “space” invites vague readings, so here is the complete inventory of one frame, the way a debugger would list it. Three small networks: the encoder (observation in, pose out), the decoder (pose in, reconstructed observation out, which is how the frame proves its knobs can still express what it’s looking at), and one transition model per action (pose in, predicted next pose out). Plus some bookkeeping: its dimension D, its age, whether it’s still a protected newcomer, and three running averages of its recent scores. That is everything a frame is.

Notice what’s not on the list. No observations. No triplets. No poses. A pose exists for exactly one step: computed from the current observation, used for prediction and learning, folded into the running averages, discarded. Over a frame’s life the stream of poses traces a path through its space, and the path is stored nowhere. What the path leaves behind is the shape it wore into the weights, the way a field keeps no record of footsteps and still ends up with a trail. When chapter 11 tells you the whole brain keeps no scrapbook, this is where that starts: a single frame is already scrapbook-free.

Under the hood: what a frame is made of. Three small tanh networks sharing one hidden width: an encoder (observation → pose, dim = D), a decoder (pose → reconstructed observation, which measures how well this frame’s knobs can even express the current sight), and a per-action transition model (pose + action → predicted next pose). Every frame runs the identical kernel, with no per-frame branching anywhere, so frames differ only in D and learned weights, and the whole population batches on one code path. A frame is a coordinate space, not a slot for one object: one frame holds many concepts at once. The set of poses from every frame that mapped the current observation is the global pose: the system’s full interpretation of the moment, handed to the motivation layer in chapter 9.

The crowd

Why keep a crowd instead of building one excellent model? Because of chapter 3. To build the one right model you’d have to already know the right size, and nobody knows, not even the builder. The honest position is ignorance, and the honest mechanism for ignorance is to let rival answers coexist and make the world’s own replies decide. If that sounds like evolution, it should. PRA runs survival-of-the-fittest on world- models, inside one head, with generations measured in minutes.

The life cycle has four rules, and by now you can guess the disease each one exists to prevent.

New guessers keep arriving. On a schedule, the system spawns a fresh candidate frame, usually betting a knob-count near the current best performer’s: a rival that says “close, but I think it’s more like eleven.” And if ever a sight arrives that no frame can express, a new frame is born on the spot, that instant, mid-life. The crowd can start from literal zero this way: the first sight of the first day creates the first frame.

Nobody ever gets edited. This one took me a while to appreciate. If the system suspects a frame’s size is wrong, it does not reach in and resize it; it spawns a new candidate at the new size and lets the two fight it out. The original keeps running, unharmed. Every structural change is therefore reversible by default: a bad idea simply loses and gets evicted, while what already worked was never touched. Chapter 2’s first cliff, destroying old competence while reaching for new, is fenced off by construction. The system never rewrites what it knows; it out-competes it.

Children are protected. A newborn frame is hopeless at first, like all newborns, so for a fixed window it cannot be evicted no matter how badly it scores. It gets a childhood: guaranteed cycles of real experience before judgment day. Without this, no new bet could ever survive long enough to be tested fairly against seasoned rivals.

The losers are deleted. Past childhood, every frame is judged continually on its record: the gap between what it predicted and what arrived, plus a rent I’ll come back to in chapter 8. Fall below the bar and you’re evicted. Permanently. And this bar is the one piece of v3 wreckage I most needed to get right the second time: the more crowded the head gets, the harsher the bar becomes. Growth itself raises the pressure to be worth keeping. In v3 the bar bent the other way: crowding made survival easier, and chapter 2 showed you the straight-line hoarding that bought. Same mechanism, one sign flipped, opposite fate.

Under the hood: the life cycle, precisely. Two timescales. The fast loop runs per-event: frames place, predict, and learn weights; the only structural event allowed is birth-on-demand when zero frames map an observation. The slow loop (consolidation) runs between episodes on a paused, consistent state, in fixed order: age everyone and mature candidates past min_age_cycles; apply pending anatomy changes; evict (soft eviction against the population-scaled threshold, then a hard cap, respecting protection and min_frames); spawn spawn_per_cycle candidates via the proposal policy (default: near best_dim ± 1, with occasional exploration jumps). The threshold divides by crowding: base / (1 + coeff · excess_population). Doc 04 marks the dividing direction as a MUST, with v3’s rising-bar failure documented as the reason. Copy-don’t-mutate is likewise a MUST: dimensionality change only ever happens by spawning a rival.

What the crowd looks like, alive

Run PRA on the little rover world from Part 4 and you can watch all of this happen on one screen. The population “breathes”: spawns push it up, evictions pull it down, and it hovers in the teens (fifteen, nineteen, thirteen frames on three different runs of the same world) while the crowd’s best answer for the world’s size settles and steadies. No line in the code says “keep about sixteen frames.” That number is negotiated, continuously, between the spawn rate and the bar, and it holds while individual frames come and go.

That sentence is the quiet heart of this book. The knowledge in PRA doesn’t live in any frame. Frames are disposable: hired, tested, fired. What persists is the population: a shifting collection of coordinate systems whose current consensus is the system’s understanding of the world. Learning forever stops being “one model, carefully revised forever,” which chapter 2 showed is a walk between two cliffs, and becomes something sturdier: a steady turnover of mortal guesses under an immortal selection rule.

Which puts enormous weight on one question: is the judging fair? A tournament is exactly as good as its scoring, and chapter 2 already showed you what happens when it isn’t: v3’s contestants found four separate ways to win without being right. The next chapter is about those four cheats, how each got sealed, and the constitution that came out of it. It’s the most important chapter in the book.