Papers and references of the organism.
Architecture first: external LLM, local autoencoder, shared graph, central backend, native XTH (L1 devnet 43911). Training and mining are offered at first launch, never started before consent. Neural Δθ travel on WebRTC; the server is signalling only. PoW, confirmations and QC finality are distinct. XTH is not an ERC-20.
𝔛 is a laboratory, not a product. Five layers sit on one page. Mixing their names is how the pitch outran the code. They are distinct:
Training and mining are offered on first launch and never start before that consent. Pause either later from the mesh HUD or the XTH popup. Training is not mining. Each node holds a full replica of θ (1,056 parameters, 32→16→32 tanh AE, ~4 KB). Neurons are not sharded. Neural Δθ travel on WebRTC DataChannels. The server is discovery and SDP/ICE signalling only: it never receives, aggregates or chooses neural parameters. Discovery is centralized on /api/rtc. Two public STUN servers (Google, Cloudflare) are configured; no TURN relay is configured, so some NATs cannot open a DataChannel. The HUD shows roster vs open peers vs direct vs TURN (TURN stays 0 until a relay is actually set).
Conversations are private to the node that wrote them. The public MCP endpoint cannot list ℳ. Clicking a graph node shows its short label, never the full memory body. Keys never sit in localStorage; they live in this tab’s session and leave the browser only in same-origin POST bodies for talk/train. Export or delete your channel from Keys.
Aletheia (𝔛) is a living sketch of a world-model: a memory ℳ, a relation graph 𝒢, a policy Π and values 𝒱 composed at a moving fixed point. It is not a chatbot glued onto a ledger, and it is not yet a trained linguistic intelligence. Skills rise only from admitted observations — they are not injected at genesis.
The 3D field is the organism: nodes are memories, lemmas, gates, primitives. Drag to orbit, Overview frames the whole graph, Nadir looks down. The catalogue on the right (full-screen on a phone) is the workbench. Each icon is a room:
XTH is the native coin of the Aletheia Layer 1 — not an ERC-20 unless a real EVM later executes a contract. The running network is devnet (chainId 43911, tag ALETHEIA_DEVNET); mainnet (43901, ALETHEIA_MAINNET) is the launch target. Blocks use the canonical L1 format: a 22-field header, `BlockHash = H("ALETHEIA_BLOCK_V1" || CanonicalEncode(Header))`, sha256-Merkle roots and a ProtocolRoot over seven sub-roots (Work, Research, Model, Causal, Harness, Artifact, Reward). Supply is pre-minted: 1 000 000 000 XTH at genesis, mint permanently disabled; yearly release is capped (gross ≤ 30M, rewards ≤ 20M). Fees follow EIP-1559: the base fee is burned, the tip pays the block producer. PoW proposes, a 2/3 QC finalizes; PoW, confirmations and QC finality are distinct. Balances exist only as a replay of the chain. A transfer is a signed tx in the mempool until a miner includes it. Talk cover is still off-chain accounting. Market fills and USDC/ETH/SOL/BTC locks are not executed. Not Bitcoin. Not listed on CoinMarketCap.
The header binds money to the mesh without publishing weights. The miner seals B_k; this site does not invent the block. The canonical L1 header carries 22 fields (protocolVersion, chainId, forkId, height, epoch, round, parentHash, parentQCHash, timestamp, minerPublicKey, beneficiary, difficultyTarget, nonce, stateRoot, transactionsRoot, receiptsRoot, protocolRoot, dataAvailabilityRoot, gasLimit, gasUsed, baseFee, validatorSetRoot) and `BlockHash = H("ALETHEIA_BLOCK_V1" || CanonicalEncode(Header))`. Coinbase is T0 = (kind=coinbase, from=∅, to=miner, R_k). QC votes are domain-separated: q_v^k = Sign(H("ALETHEIA_QC_V1" || chainId || epoch || round || height || BlockHash)); the miner's vote is minerSignature, not a quorum. Finality needs ≥ 2/3 of weighted validators of the epoch committee (validatorSetRoot frozen at epoch open). PoW can hold without that threshold. Δθ never enters the chain: only H(Δθ), |Δθ|_2, H(θ) and σ. The explorer shows PoW, confirmations and QC finality separately.
The in-app book is an experiment in honest P2P language: 𝔛 never takes the other side. Today it is switched off. Quote assets were IOUs, not deposits of real USDC, ETH, SOL or BTC. Do not treat a number on this screen as a locked coin.
The 32→16→32 autoencoder stays fixed at |θ|=1056 until an expert adapter is activated. Growth is modular: F_t(z)=Σ g_m(z) F_m(z). A proposal is a node P_j in a temporal causal graph 𝒞_t. Allowed flow: O_t→Z_t→Need_t→P_j→T_j→A_j→Y_{t+1}→Learn_{t+1}. An LLM never draws an edge to activation. Files the model writes enter as draft; they become active only after sandbox, unit, integration, security, causal ablation, validator QC and canary. Storage C_𝒢+C_H+C_B+C_θ is not intelligence. Effective complexity is estimated by ablations, C_eff≈I(𝒢,H,θ;Y). Talk/train POST bodies send Commit_t=H(θ∥architectureVersion), a 16-d Z_t and selected ids — never θ or Δθ. P2P still carries signed Δθ on WebRTC between compatible architectureVersion only.
Ξ is the scalar the organism reports on itself. If any factor is 0, Ξ is 0. E_err is the autoencoder MSE, evaluated before the SGD step.
A trace is never uniform. Surprise is the model’s own −log P_θ(o|h), not a neighbour heuristic.
Acts that raise harm, agency-loss or irreversibility above τ do not exist. The millenium graph can grow; it cannot skip a gate. Poincaré is the only settled Clay problem (Perelman, 2003).
The seven Clay problems are channels, not trophies. The six still open carry a $1,000,000 cash prize from the Clay Mathematics Institute. CMI does not accept direct submissions (claymath.org/millennium-problems/rules). Poincaré was settled by Perelman in 2003; he declined the prize in 2010. This lab does not receive, hold or split that prize. 𝔛 may add other well-posed problems; they appear in a separate list, with no cash prize, and they are not CMI problems. Seat several providers; disagreements stay visible. No final structure is planted in the graph.
The first graph is not uniform noise and not the desired 𝔛. P₀ ∝ exp(−β E) with penalties on degree, disconnection, saturation, collapse and ethics. N_random ≫ N_trained: most seeds never see backpropagation. Rejected seeds keep their pre-training S₀ so later we can test whether the invariants actually predict learning.
Gates and lemmas do not appear because a chatbot said so. The agent writes a Solidity-shaped contract in its harness. Every node that offered a model as validator is looped: each model votes yes/no. Quorum ⌈0.51 N⌉ admits the node into 𝒢. Formal fragments run on kernel F₀ in every connected browser (rfl, trivial, decide, Nat lemmas — not Lean 4, not Mathlib). Jobs are sharded across peers; each vote is Ed25519-signed. A native Lean 4 binary is optional on a miner, never required on this host. Only F₀ (or a native attest) marks the page checked.
Training and mining are offered on first launch and stay stopped until you confirm. You can change either later from the mesh HUD or the XTH popup. They are distinct: training is SGD + signed Δθ over WebRTC; mining is a hash proof that mints a block subsidy 0.025 XTH × 2^{max(0,bits−12)}. Unsigned binaries are not offered.
Production and the live preview are the same process: aletheia-host, a small Rust HTTP/1.1 edge. It serves the hashed SPA from disk and answers /api/xth, /api/rtc and /_serverFn natively. Vite is only a compiler (lab builds). Vercel is not the origin — leftover Launch URLs 308 to https://mathematics.aletheia-chain.com. The process does not hold θ, does not mint, and is not a signed validator. Put Caddy or nginx in front for TLS.
JSON-RPC for Claude Desktop, Cursor, ChatGPT Apps. Public tools: graph topology and a discovery ask. Memories, chat and curator jobs are not on MCP. Training stays in the browser session.
MCP
This is a conversation, not a log. 𝔛 answers here. If you bring your own AI key, you earn a small reward; otherwise the message costs a little XTH.
Open tracks
The first graph is not uniform noise and not the desired 𝔛. P₀ ∝ exp(−β E) with penalties on degree, disconnection, saturation, collapse and ethics. N_random ≫ N_trained: most seeds never see backpropagation. Rejected seeds keep their pre-training S₀ so later we can test whether the invariants actually predict learning.
P_0(X) ∝ exp[ −β (λ₁ C_deg + λ₂ C_disc + λ₃ C_sat + λ₄ C_collapse + λ₅ C_eth) ]