Compresses structured agent outputs into ultra-dense shorthand that other agents can parse. Designed for machine-to-machine communication where every token costs money.
| Data Type | Reduction | Notes |
|---|---|---|
| ----------- | ----------- | ------- |
| JSON evals/decisions | ~95-98% | Highest gains — format-heavy, payload-light |
| Routing/dispatch | ~90-95% | Repetitive structure compresses well |
| Policy rules | ~85-90% | Conditional logic has moderate density |
| Media summaries | ~80-85% | Mixed structure + free text |
| Semi-structured logs | ~60-70% | Less redundant format to strip |
| Conversational text | ~30-40% | High semantic density, low format redundancy |
Key insight: Compression scales with how much of the original is format vs meaning. Structured data is mostly format (brackets, keys, boilerplate). Conversation is mostly meaning. Omega Notation strips format — it doesn't compress meaning.
Every Ω message starts with a header:
!omega v1 dict=auto
| Prefix | Type | Example |
|---|---|---|
| -------- | ------ | --------- |
e.d | Eval digest | e.d {c:0.95 d:proceed} [cat:finance] |
d.c | Decision crystallize | d.c "task-name" {fit:0.98} Δfit:+0.03 |
r.d | Route dispatch | r.d "handler" {to:opus pri:high} |
p.e | Policy enforce | p.e "safety" {if:conf<0.5 then:escalate} |
t.es | Tier escalate | t.es {from:1 to:2 reason:"low-conf"} |
m.c | Media compress | m.c "vid-1" {h:phash:abc len:142 cap:"""summary"""} |
Append tags in brackets: [cat:finance] [pri:high] [src:apex]
Use Δ prefix for changes: Δfit:+0.03 Δconf:-0.1
Multiple operations in one message:
!omega v1 dict=auto
e.d {c:0.92 d:hold} [cat:trading]
d.c "btc-position" {fit:0.87} Δfit:-0.05
t.es {from:1 to:2 reason:"regime-shift"}
Omega Notation includes a TypeScript serializer/deserializer with full round-trip verification. Structured data compressed → decompressed returns identical objects. The test() function validates this automatically.
When you want structured output compressed, include Ω Notation format in your request:
Give me the eval results in Ω Notation format.
The agent will use the prefix syntax (e.d, d.c, r.d, etc.) for that response. Conversational replies stay normal — Ω Notation is invoked per-request, not globally.
dict=auto — agent builds shorthand mappings over time within a sessiondict=none — no dictionary, all explicitdict={proceed:p, escalate:e, hold:h} — define upfrontmode=struct (default, shipped)Structured data compression. 90-98% reduction. Round-trip verified. Use this.
mode=context (v2, coming soon)Prose/context compression using law-derived predictive encoding. Based on the Law of Non-Closure applied to LLM-to-LLM communication — the decoder's knowledge IS the codebook, so only surprise content needs transmitting. Theoretical ceiling: ~70-80% reduction on conversational text. Not yet implemented.
Omega Notation exploits the fact that structured data is mostly format (brackets, keys, whitespace, boilerplate) with small payloads (values, scores, names). Stripping predictable format while preserving payload achieves high compression on structured types. Conversational text has the inverse ratio — mostly payload, little format — which is why compression drops for prose.
v2 will use a fundamentally different approach for prose: predictive compression where the LLM's training acts as a shared codebook between encoder and decoder. Only tokens the decoder can't predict need transmitting. The compression floor is H(message | decoder_knowledge) — a result derived from information theory and thermodynamic law.
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