Tesla-loop codec discovery report

seed=11 n=36 design_space_cells=16,669,800

seed_signatures=20 axis_coverage={'carriers': 0.8, 'coupling': 1.0, 'memory': 1.0, 'addressing': 0.8571428571428571, 'time': 1.0, 'conservation': 1.0, 'credit': 0.8333333333333334, 'update': 0.8571428571428571, 'granularity': 1.0, 'locality': 1.0}

How this differs from keyword cross-pollination

1. Mechanism first — 10-axis signatures, not topic name mashup.
2. Transfer only disagreeing axes — Tesla move: keep skeleton, import 1–3 properties.
3. Compile to codec algebra — axes force ops (intern/patch/gate/plan…).
4. Measure — token proxy + fidelity proxy on fixed prose corpus.
5. Void + taboo — sample unoccupied cells; down-rank pure rediscovery.

There is no free magical codec. There is systematic search for

high utility (saving × fidelity) operators under no-retrain constraints.

Top discoveries

1. V0006 rank=1.055 void_cell save=84% fid=0.83

• transfer: void <- rotating_field_tesla axes[coupling,memory] align=0.40
• pair: void × rotating_field_tesla moved_axes=['coupling', 'memory']
• ops: set_field, emit, intern, ref_by_hash, fetch_on_miss, patch, compress_path, freeze_prefix, sample_paths, vote_merge, phase_gate, batch_table
• encode: name every bulky blob by content-hash; body out-of-line; send edit scripts only; collapse chains of renames; one global frozen prefix (schema+legend) broadcast cheaply; phase: skim↔deep on control parameter (uncertainty); many entities as tables not paragraphs
• thinking: reason about Δ not full world; gated multi-path sample; merge into IR fields; allow non-local ref jumps via ids (not linear chat order)
• metrics: ratio=0.159 utility=0.785

2. C0009 rank=0.968 exotic save=84% fid=0.83

• transfer: content_addressed_store <- tool_use axes[conservation,locality,update] align=0.50
• pair: content_addressed_store × tool_use moved_axes=['conservation', 'locality', 'update']
• ops: set_field, emit, intern, ref_by_hash, fetch_on_miss, write_bank, recall_topk, gate, winner_take, publish_id, subscribe_id, batch_table
• encode: name every bulky blob by content-hash; body out-of-line; durable external IR bank; prompt holds summary+ids only; compete candidates; admit only winners under budget; many entities as tables not paragraphs
• thinking: cross-session continuity without chat dump; hardness/relevance competition before deep CoT
• metrics: ratio=0.159 utility=0.743

3. C0007 rank=0.926 exotic save=84% fid=0.83

• transfer: tool_use <- symbol_intern_pool axes[credit,update,conservation] align=0.50
• pair: tool_use × symbol_intern_pool moved_axes=['credit', 'update', 'conservation']
• ops: set_field, emit, intern, ref_by_hash, fetch_on_miss, write_bank, recall_topk, reencode, collapse, write_medium, read_medium, checksum, invertible_decode, batch_table
• encode: name every bulky blob by content-hash; body out-of-line; durable external IR bank; prompt holds summary+ids only; milestone reencode: larval notes → adult dense STATE; agents/tools communicate only via shared IR medium; prefer invertible codecs; measure fact-retention; many entities as tables not paragraphs
• thinking: cross-session continuity without chat dump; stigmergic coordination: no full state broadcast; no silent drop of X constraints
• metrics: ratio=0.159 utility=0.701

4. C0019 rank=0.926 exotic save=84% fid=0.83

• transfer: content_addressed_store <- bytecode_vm axes[coupling,locality] align=0.50
• pair: content_addressed_store × bytecode_vm moved_axes=['coupling', 'locality']
• ops: set_field, emit, intern, ref_by_hash, fetch_on_miss, write_bank, recall_topk, checksum, invertible_decode, batch_table
• encode: name every bulky blob by content-hash; body out-of-line; durable external IR bank; prompt holds summary+ids only; prefer invertible codecs; measure fact-retention; many entities as tables not paragraphs
• thinking: cross-session continuity without chat dump; no silent drop of X constraints
• metrics: ratio=0.159 utility=0.701

5. C0013 rank=0.896 remix save=84% fid=0.83

• transfer: symbol_intern_pool <- content_addressed_store axes[update] align=0.90
• pair: symbol_intern_pool × content_addressed_store moved_axes=['update']
• ops: set_field, emit, intern, ref_by_hash, fetch_on_miss, write_bank, recall_topk, publish_id, subscribe_id, checksum, invertible_decode, batch_table
• encode: name every bulky blob by content-hash; body out-of-line; durable external IR bank; prompt holds summary+ids only; prefer invertible codecs; measure fact-retention; many entities as tables not paragraphs
• thinking: cross-session continuity without chat dump; no silent drop of X constraints; allow non-local ref jumps via ids (not linear chat order)
• metrics: ratio=0.159 utility=0.701

6. C0024 rank=0.896 remix save=84% fid=0.83

• transfer: content_addressed_store <- symbol_intern_pool axes[update] align=0.90
• pair: content_addressed_store × symbol_intern_pool moved_axes=['update']
• ops: set_field, emit, intern, ref_by_hash, fetch_on_miss, write_bank, recall_topk, reencode, collapse, publish_id, subscribe_id, checksum, invertible_decode, batch_table
• encode: name every bulky blob by content-hash; body out-of-line; durable external IR bank; prompt holds summary+ids only; milestone reencode: larval notes → adult dense STATE; prefer invertible codecs; measure fact-retention; many entities as tables not paragraphs
• thinking: cross-session continuity without chat dump; no silent drop of X constraints; allow non-local ref jumps via ids (not linear chat order)
• metrics: ratio=0.159 utility=0.701

7. V0002 rank=0.871 void_cell save=66% fid=0.83

• transfer: void <- stigmergy_trails axes[coupling,memory] align=0.50
• pair: void × stigmergy_trails moved_axes=['coupling', 'memory']
• ops: set_field, emit, deposit_trace, decay_uncited, gate, winner_take, undo, batch_table
• encode: traces decay if uncited for K steps (evaporating context); compete candidates; admit only winners under budget; reversible patches for safe metamorphic reencode; many entities as tables not paragraphs
• thinking: hardness/relevance competition before deep CoT; allow non-local ref jumps via ids (not linear chat order)
• metrics: ratio=0.335 utility=0.596

8. C0018 rank=0.863 exotic save=66% fid=0.83

• transfer: chain_of_thought <- s_expression_ast axes[time] align=0.50
• pair: chain_of_thought × s_expression_ast moved_axes=['time']
• ops: set_field, emit, path_get, path_set, share_prefix, level_skip, express_lane, plan, blame_upstream, seq_tag
• encode: address nested facts by path; share common prefixes; multi-level IR: coarse summary lanes + fine pages; N: as reverse-creditable bytecode steps; preserve order tags on plans and facts
• thinking: plan then attribute failure to earlier N: step
• metrics: ratio=0.335 utility=0.638

9. V0001 rank=0.856 void_cell save=66% fid=0.83

• transfer: void <- radix_trie axes[carriers,coupling] align=0.20
• pair: void × radix_trie moved_axes=['carriers', 'coupling']
• ops: set_field, emit, scatter_shards, reconstruct_partial, scale_weight, level_skip, express_lane, checksum, invertible_decode, branch, merge_crdt, batch_table
• encode: facts as overlapping shards; partial context still reconstructs gist; multiplicative weights on trace strength / priority; multi-level IR: coarse summary lanes + fine pages; prefer invertible codecs; measure fact-retention; many entities as tables not paragraphs
• thinking: graceful degradation under budget cuts; no silent drop of X constraints; explicit branch/merge in N: for hard search
• metrics: ratio=0.335 utility=0.596

10. C0021 rank=0.853 exotic save=66% fid=0.83

• transfer: content_addressed_store <- genetic_codon_table axes[update,addressing,memory] align=0.30
• pair: content_addressed_store × genetic_codon_table moved_axes=['update', 'addressing', 'memory']
• ops: set_field, emit, match_mix, topk, working_set, forget, gate, winner_take, publish_id, subscribe_id, checksum, invertible_decode, batch_table
• encode: store keys not full neighbors; retrieve by similarity gate; small working set; age-out non-keystone; compete candidates; admit only winners under budget; prefer invertible codecs; measure fact-retention; many entities as tables not paragraphs
• thinking: attention-like select: only matched slots enter working set; hardness/relevance competition before deep CoT; no silent drop of X constraints; allow non-local ref jumps via ids (not linear chat order)
• metrics: ratio=0.335 utility=0.638

11. V0010 rank=0.851 void_cell save=66% fid=0.83

• transfer: void <- chess_notation axes[carriers,coupling] align=0.10
• pair: void × chess_notation moved_axes=['carriers', 'coupling']
• ops: set_field, emit, path_get, path_set, share_prefix, min_plus, viterbi_path
• encode: address nested facts by path; share common prefixes; tropical semiring plan scoring (min-plus paths)
• thinking: optimal path plans without enumerating prose trees
• metrics: ratio=0.335 utility=0.596

12. V0007 rank=0.845 void_cell save=65% fid=0.83

• transfer: void <- attention_kv axes[carriers,coupling] align=0.20
• pair: void × attention_kv moved_axes=['carriers', 'coupling']
• ops: set_field, emit, path_get, path_set, share_prefix, patch, compress_path, publish_id, subscribe_id, iterate_to_fixed, checksum, invertible_decode, batch_table
• encode: address nested facts by path; share common prefixes; send edit scripts only; collapse chains of renames; prefer invertible codecs; measure fact-retention; many entities as tables not paragraphs
• thinking: reason about Δ not full world; iterate critique until IR fixed-point / checklist pass; no silent drop of X constraints; allow non-local ref jumps via ids (not linear chat order)
• metrics: ratio=0.348 utility=0.585

Ops enriched in top-20 (build these into STATE-IR)

• set_field ×20
• emit ×20
• batch_table ×15
• checksum ×9
• invertible_decode ×9
• write_bank ×8
• recall_topk ×8
• intern ×6
• ref_by_hash ×6
• fetch_on_miss ×6
• publish_id ×6
• subscribe_id ×6
• level_skip ×6
• express_lane ×6
• path_get ×5

System upgrades this loop encodes (do more of this)

Hard truth

A 'magical free codec' would mean: arbitrary English → far fewer tokens,

perfect recall, better reasoning, zero schema design. **Information theory

and model interfaces forbid the free part.** What you can invent is a

new operator in the codec algebra (new addressing×update×credit combo)

that wins measured utility — the induction-motor analogue is a new relation,

not zero cost.