---
type: analysis
title: "Pokémon TCG AI Battle — would-KO, RoPE-ND, and Prospective Planning"
description: "Current would-KO mechanism (implemented, at build + inference) and historical RoPE-ND + prospective planner design (deferred backlog, not in current runtime)."
tags: [pokemon-tcg, would-ko, rope-nd, grpo, search, counterfactuals, backlog]
timestamp: "2026-08-15T18:12:00-03:00"
---

# Pokémon TCG AI Battle — would-KO, RoPE-ND, and Prospective Planning

## Status

- **would-KO** — implemented and shipping. The trio (`P(KO)`, `expected prizes taken`, `P(ending the game)`) is a per-option input feature computed by the offline dataset builder from the search agent and packed into `opt_attr`. At inference the same search agent recomputes it deterministically. Toggle via `--bc-would-ko` at build time and honored end-to-end.
- **RoPE-ND** — not implemented. Positional information in the current model is carried by zone-typed token embeddings and explicit `opt_src_pos`/`opt_tgt_pos` gather from state tokens (see [[pokemon_tcg_agent_architecture]]). RoPE-ND remains research backlog in [[pokemon_tcg_ladder_and_research]].
- **Prospective V2 planner (sidecar)** — deprecated. No sidecar `.npy` files exist in the current dataset layout and no code path in `scripts/`/`rl/` reads them. See [[pokemon_tcg_prospective_v2]] for the historical design and the reason it was rejected. If the prospective signal returns it will be integrated (aux head or offline GRPO), not a second separate model.



## Current would-KO

`would_ko` is a prospective option feature computed before attack decisions.
It estimates:

```text
P(any KO)
expected prizes
P(ending the game)
```

The repaired builder passes the resolved configuration to spawned workers,
uses deterministic decision seeds, distinguishes valid zero from failure,
rejects non-finite values, records an audit sidecar, and fails an enabled build
when no computation succeeds. It does not use the replay's hidden opponent deck
as a feature.

The real smoke corpus computed 78 eligible attack options, including 44 valid
zeros, with no failures.

## Relationship to TBPTT and GRPO

TBPTT carries causal past state through recurrent registers. would-KO queries
the simulator about a candidate present action. The current would-KO feature is
not GRPO because it does not compute group-relative advantages or update policy
probabilities.

It is, however, the first bounded instance of the computation needed by a
group-relative planner:

\[
G(s)=\{\tau_1,\ldots,\tau_K\},
\qquad
A_i=\frac{r_i-\operatorname{mean}(r_G)}
{\operatorname{std}(r_G)+\epsilon}.
\]

When behavior/reference log-probabilities are retained, this signal can enter a
GRPO-style clipped policy objective. Without those probabilities, the same
groups support relative ranking or policy distillation and should not be
described as strict GRPO.

All branches in a group must use the same hidden-state determinizations and
random streams. This common-random-number contract prevents an action from
appearing better merely because it received a more favorable sampled world.

## Historical additive planner design

The broader Prospective V2 implementation is now recorded separately in
[[pokemon_tcg_prospective_v2]]. The exact legal-action coverage contract is in
[[pokemon_tcg_action_coverage]]. At the historical planner snapshot, the
planner was a separate module layered over the existing Transformer. That
design is preserved here for lineage; it is not a current runtime statement.
This page keeps the conceptual boundary between the older would-KO feature and
the newer branch planner.

The planner is a separate module. It does not replace the existing Transformer,
TBPTT, BC loss, would-KO feature, or recurrent registers.

```text
current Transformer + TBPTT memory
             |
             v
prospective branch tokens + RoPE-ND
             |
             v
branch score, return, KO, prizes, terminality, uncertainty
             |
             v
gated/cross-attention contribution to the existing policy
```

RoPE-ND supplies the planner's relational geometry:

1. real match time;
2. future rollout depth;
3. branch/action identity;
4. entity, zone, and relation coordinate.

RoPE-ND is therefore part of the planning solution, but rollout generation,
reward construction, tree masks, and policy supervision provide the remaining
mechanism.

## Learning from own defeats

Daily metadata recognizes `FitaLabs` and `Alef Oliveira`. For each real decision
from an own-agent defeat, the simulator can compare the chosen action with
legal alternatives under matched determinizations. The relative signal answers
which branch was better at that state; it does not clone the losing action or
infer that every action in a lost game was wrong.

The 2026-07-28 archive contains no confirmed replay under either alias, so own
defeat training requires a source day where those names occur.

## Hidden-information boundary

Exact solving is available only in fully specified, tractable subspaces.
Pokémon TCG includes hidden cards, stochastic effects, and a large branching
factor, so general planning is belief-state search over sampled
determinizations. Claims of exactness or soundness must be restricted to the
bounded subspace actually enumerated.

Stockfish provides a useful boundary: ordinary play uses selective search and
position evaluation, while exact Syzygy tablebase results apply only to bounded
endgames.

## Sources

- [RoFormer: Rotary Position Embedding](https://arxiv.org/abs/2104.09864)
- [LieRE: generalized N-dimensional rotary encodings](https://arxiv.org/abs/2406.10322)
- [Qwen2-VL and multimodal RoPE](https://arxiv.org/abs/2409.12191)
- [Stockfish search implementation](https://github.com/official-stockfish/Stockfish/blob/master/src/search.cpp)
- [Stockfish advanced topics and Syzygy tablebases](https://official-stockfish.github.io/docs/stockfish-wiki/Advanced-topics.html)

## Status (audit 2026-08-07)

Reconciling the design described on this page against the live codebase at commit `290d6f9`:

- **would-KO trio** — shipped and stable. Computed at build time by the search agent, packed into `opt_attr[OPT_WK : OPT_WK+3]` per option, consumed as a pure input feature by the primary Transformer at both training and inference. Toggled by `--bc-would-ko` at build time and honored end-to-end.
- **would-KO variance count** — `--bc-wk-nvar` (default 10) drives the number of variable-attack determinizations. Persisted in the checkpoint's `inference_config` so inference reruns match training-time semantics.
- **`--zero-wouldko`** — flag on `bc_train_mlx.py` to zero the trio at load time (ablation tool).
- **RoPE-ND positional encoding** — not implemented on the primary model. Positional information is carried by zone-typed token embeddings and explicit `opt_src_pos`/`opt_tgt_pos` gather. RoPE-ND remains research backlog in [[pokemon_tcg_ladder_and_research]].
- **MLX-side prospective planner and PyTorch runtime planner as separate modules** — **removed** at commit `a942373` (2026-08-03). No sidecar `.npy` files exist under the current dataset layout and no code path in `scripts/` or `rl/` reads them. The prospective signal, if it returns, will be an integrated aux head or an offline GRPO objective on the primary model, not a second module. See [[pokemon_tcg_prospective_v2]] for the full design record.
- **Autoregressive multi-select fallback (from the planner era)** — the fallback is now the only inference path; there is no planner to fall back FROM. See [[pokemon_tcg_torch_inference]].

## Provenance / anchor commits

- `2996583` (2026-07-29) — complete compact would-ko prospective pipeline — first shipping of the would-KO builder + planner.
- `10c3750` (2026-07-29) — fix prospective reward balancing and action coverage.
- `770364b` (2026-07-29) — add real prospective RoPE-ND planner foundation (design; later reverted).
- `a942373` (2026-08-03) — sidecar removed, aux heads + meta features + parquet pipeline — kept would-KO; removed the RoPE-ND planner and sidecar file layout.

Full timeline in [[pokemon_tcg_repository_timeline]].