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LlmReply carried a bare `microjoules`, and charge_llm_step tagged every model step Estimated. A backend that ran a model in this process and read it on a live counter (an eoc-local backend's MeterSpan, now backed by the per-process counter) had no way to say so. Its measured figure was recorded as an estimate. - LlmReply carries a JouleCost. LlmReply::new is the provider's estimate, as before, and LlmReply::with_cost gives a figure with its grade. - charge_llm_step records the provider's cost as graded and charges the budget that figure. - NeuralProvider wraps any in-process NeuralBackend as an LlmProvider. The reply's energy is the backend's own JouleCost. Tokens are counted from the text (approx_tokens, or the model's tokenizer through with_token_counter) and feed only the token budget. A model served over the network still reports an estimate: it ran on hardware no counter here reads. Tests: an MlcBackend metered by an advancing counter gives a measured span, and a held, failed or stub counter gives the estimate. A whole ReAct run records the model step measured. Reverting charge_llm_step to always-estimated fails both end-to-end tests. Co-Authored-By: Claude Opus 5.5 <[email protected]> |
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| THREE_FACES.md | ||
EOC — Energy-Optimized Compute
A free, always-available, energy-honest AI substrate. AI that costs joules, not tokens.
AI is not free yet. The internet is. EOC closes the gap: a substrate that resolves work at the lowest sufficient cost and refuses to spend unbounded energy chasing benchmark deltas. Every query flows through a four-stage pipeline — state-construct → retrieve → refine → check — and a large language model is one of six refine operators, ordered last. The Fourth Truth: refinement before generation. The substrate runs in a browser, runs on commodity CPUs, denominates everything in joules per task against an open evaluation corpus, and cannot be turned off because no one entity runs it.
The substrate is intentionally ownerless: no protocol-level token, permissive licensing, conformance test vectors and open eval corpora so any implementation can verify itself without a trust relationship with the authors. Transaction Science is one steward — it publishes the spec, ships a reference implementation, and operates commercial services around the substrate (managed nodes, certification, registry hosting) — not its proprietor.
Contents
spec/eoc1_spec.docx,spec/eoc1_v0_1_1_patch.docx,spec/eoc1_v0_2.docx,spec/eoc1_v0_2_addendum_A.docx— EOC-1: the substrate architecture (v0.1 tiers → v0.2 four-stage pipeline; Addendum A: non-goals — token economics)spec/eoc2_wire.docx— EOC-2: the wire protocol (capability exchange, gossip, signed envelopes)spec/eoc3.docx— EOC-3: artifact distribution (content-addressed fetch for models, state machines, corpora)spec/eoc4_dcy.docx— EOC-4: DCY (Deterministic Cypher — the query language for the knowledge-graph retrieve operator)spec/eoc5_registry.docx— EOC-5: the Operator Family Registry (stable identifiers + conformance tests)spec/eoc1_eval001*.docx,spec/eval001_worked_instances.docx,spec/eval002.docx— Eval-001 (schema-conformant generation proficiency standard) and Eval-002 (retrieval/provenance)spec/eoc_architecture.docx— the architecture working draftspec/tokens_concealment.docx— Tokens Are the Concealment (the rhetorical companion to Addendum A)os/THEOREM.md,os/Q1_W_DEFINITION.md— the energy-first OS theorem the substrate is designed to run on, and the resolution of its open question on defining "useful work" in joules-per-work
Status
EOC-1 at v0.2; EOC-2..5 and Eval-001/002 at working-draft 0.1; OS theorem at v0.2. The reference implementation referenced across the specs as "v0.3" is to be brought into this repository.
Related
- Smart Byte (
../smart-byte/) — the value substrate; uses EOC's energy attribution. - TX Science AI (
ais.transaction.science) — the AI PaaS; its memoizing cascade is an instance of the EOC four-stage pipeline.