Dense IR primitives from focused cross-pollination

Question: Can we cross-pollinate primitive analogies and get anything that helps a token compression language — denser packing and better inference/thinking — without retraining?

Answer: Yes. Not as free magic, but as a small set of IR codecs that keep recurring when the pool is biased toward representation, schema, codes, diffs, and structured reasoning.

1. What we changed in the pool (relevance filter)

Old pool mixed tourism (tides, slime mold, soft_prompt…) that often produced metaphors without a codec hinge.

New pool (58 topics) is only things that can become a grammar, code, packet, or check:

Task pressure on every story: prefer schema/codes/diffs/tables over prose; cut tokens and/or structure thinking; no retrain.

Regenerate anytime:

python cross_pollinate_dense_ir.py --n 24 --seed 7 --mode all_buckets --jsonl memory/hybrids_dense_ir.jsonl

python cross_pollinate_dense_ir.py --list-pool

2. What fell out (auto IR primitives)

From 24 hybrids (auto labels still somewhat sticky — human re-map below):

Honesty: generator scores still cluster high (~0.98). Trust mechanisms, not scores. Below is the agent re-rationalization.

3. Primitive analogies that actually help the compression language

Each row: analogy → IR move → why tokens and thinking improve.

Tier A — build the language itself (implement first)

Tier B — runtime operators around the language

Tier C — weak / reject as primary codec

4. The compression language these primitives compose into

Name: STATE-IR v1 (mechanism name, not branding).

4.1 Alphabet (what may appear in model context)

PREFIX (cached, stable):

  schema_version

  LEGEND   entity/procedure codes

  TOOLS    short names + arg types

  RULES    emit only IR; no narrative memory



BODY (variable, dense):

  G:  <goal string|code>

  F:  [ fact | fact | ... ]      # facts, short

  D:  [ decision | ... ]

  O:  [ open_question | ... ]

  N:  [ next_op | ... ]          # plan bytecode preferred

  X:  [ constraint | ... ]

  Δ:  [ patch_op | ... ]         # since last commit

  R:  [ {id,title,sc} ... ]      # content refs, not bodies

  K:  keystone_ids               # always pinned

4.2 Operators (verbs of the language)

4.3 Why this helps inference / thinking (not only shorter prompts)

1. Attention has less junk — fewer filler tokens, more weight on constraints and open items.
2. Illegal states are visible — missing X or untyped tool args fail a checklist before “smart” prose hides the bug.
3. Plans are executable lists — multi-step work becomes N: ops you can verify, not a story that drifts.
4. Working memory split — F/D stable, Δ fast, R cold storage: classic dual-timescale without weight updates.
5. Tools ground in the same alphabet — no “tool returned a novel.”

This is not a new neural architecture. It is a representation swap (images-as-tokens class of idea): change the medium of thought, not the weights.

5. Strong hybrid examples (re-read stories, not auto name)

H1 — Chess CoT (IR0005-class)

Topics: chain_of_thought × chess_notation × bloom × enzyme

Primitive: Think in move notation, not paragraphs.

Language feature: N: e4 Nf3 d5 style steps or N: [ANALYZE, BRANCH, PICK, VERIFY].

Gate: bloom/hardness — deep variation only if needed.

Falsify: multi-step tasks; IR-CoT vs prose-CoT; tokens and error rate.

H2 — Schematic netlist memory (IR0001 / IR0020-class)

Topics: circuit_schematic × compression_dict × context_distill × mycorrhizal_trade

Primitive: System as symbols + wires, not description.

Language feature: entities as codes; relations as short edges T1->T2:calls.

Trade: tools exchange minimal goods (rows), not organisms (dumps).

H3 — Assembly + quanta + patch (IR0006)

Topics: assembly_line × memory_bank × diff_patch × synapse_quanta

Primitive: Typed stations write discrete packets into bank as deltas.

Language feature: stage tags ST:retrieve|reason|edit + Δ only between stages.

H4 — Sparse camouflage + router (IR0007)

Topics: camouflage_sparse × schematic × model_router × bloom

Primitive: Emit edges only (what changed / what differs from default).

Language feature: defaults in PREFIX; body is sparse Δ; route model size on hardness.

H5 — Legend + lattice + bytecode (IR0004)

Topics: map_legend × structured_output × crystal_lattice × bytecode

Primitive: Codes occupy fixed lattice slots (schema positions); plan as ops.

Language feature: every turn must fill/update known sites — no free-form memory paragraph.

H6 — Codon table as decode (IR0018-class)

Topics: genetic_code_table × chess × zipper × kv_cache metaphor

Primitive: Fixed lookup table maps short tokens → meaning (legend is the codon table).

User-space analog of kv_cache: reuse PREFIX decode table every turn without resending definitions.

6. Ranked: what to implement for compression language + thinking

Does NOT need train: all of the above (prompting + orchestrator + tools + files).

Needs train — skip: soft prompts, activation steering, learned compressors (LLMLingua-class) unless you add a separate cheap compressor model later (still not finetuning the main reasoner, but extra infra).

7. Minimal weekend codec (drop-in)

# === PREFIX (prompt-cache stable) ===

STATE-IR v1

Keys: G goal | F facts[] | D decisions[] | O open[] | N next[] | X constraints[] | R refs[] | Δ patches[]

LEGEND: (grow only)

  T1=...

Rules:

  - Never write narrative memory.

  - Prefer codes after definition.

  - Tools return table|json short keys ≤30 lines.

  - Plans in N as ops not essays.

  - On milestone: reencode G,F,D,O from verified facts only.



# === BODY ===

G: ship billing

F: [T1 uses stripe, webhook=/v1/hook]

D: [idempotency=hdr]

O: [PCI]

N: [CHK X, CALL verify_sig, IF fail FIX, COMMIT]

Δ: [F+ rate_limit=100rpm]

R: [{id:c91,title:pci-doc,sc:.7}]

A/B kill tests

1. Prose chat history vs STATE-IR — tokens_in ↓≥25%, success drop ≤10%.
2. Prose CoT vs N: bytecode — same quality bar, measure tokens + step errors.
3. Tool dump vs table IR — tokens ↓≥20%.

8. What this run did / did not show

Did show

• When the pool is relevant, cross-pollination repeatedly rediscovers the same codecs: legend, schema, diff, table, id, bytecode plan, gate, trace.
• Nature/analogy help when they force a discrete medium (codon, quanta, schematic, sparse camouflage, stigmergy traces).
• Token compression language and better thinking are aligned if the IR is typed and plans are lists — not if you only abbreviate randomly.

Did not show

• A brand-new primitive unknown to CS/AI practice.
• That auto score≈0.98 ranks quality (still inflated).
• That denser always means smarter without schema discipline (bad codes → confusion tax).

9. Link back to agent rationalization playbooks

10. Next actions

1. Freeze STATE-IR v1 schema in your agent system prompt (cached).
2. Implement orchestrator: validate IR each turn; reject prose memory.
3. Wire tools to table/json only.
4. Add plan-bytecode mode for hard tasks.
5. Optional: regenerate more hybrids

python cross_pollinate_dense_ir.py --n 40 --seed 99 --jsonl memory/hybrids_dense_ir_s99.jsonl

and only keep hybrids whose story forces a new op on the language (not another restatement of typed_schema).

Bottom line: cross-pollination with a representation-relevant pool supports building a token compression language. The useful “primitives” are codecs (legend, schema, patch, table, id, bytecode, gate, trace) — known mechanisms, sharp when used as the default medium of thought instead of English essays.