Ternlang

Showing 25 of 40 from the live probe.

• Trit Decide — Scalar Evidence → Ternary Decision

  Convert float evidence into a ternary decision (-1 conflict / 0 hold / +1 affirm) with confidence score and human-readable interpretation. The core ternary reasoning primitive.

• Trit Consensus — Balanced Ternary Addition

  Balanced ternary consensus of two trit values: +1 if both affirm, -1 if both conflict, 0 (hold) for any disagreement. Use to merge two independent ternary judgements.

• Trit Eval — BET VM Expression Evaluator

  Evaluate a single ternlang expression on the live BET (Balanced Execution Trit) VM. Returns the trit result. Good for quick expression testing without writing a full program.

• Ternlang Run — Execute .tern Programs on BET VM

  Compile and run a complete .tern source program on the BET VM. Use for multi-statement programs, function definitions, struct usage, agent spawning, and tensor operations.

• Quantize Weights — Float → Ternary (BitNet)

  Quantize f32 neural network weights to ternary {-1, 0, +1} using BitNet-style absolute-mean thresholding. Returns quantized weights, sparsity ratio, and effective compute savings.

• Sparse Benchmark — Ternary Matmul Efficiency

  Benchmark sparse vs dense ternary matrix multiplication. Reports sparsity ratio, multiply-op count for both methods, and speedup factor. Demonstrates the @sparseskip efficiency gain.

• MoE Orchestrate — Full 13-Expert Ternary Reasoning

  MoE-13 deliberation — routes your query through 13 specialised expert agents (deductive, inductive, safety, fact-check, causal, ambiguity, math, context, meta-safety, and more) with dual-key synergistic routing and a hard safety veto. FREE preview: returns the real trit verdict + top-3 expert voices. Full 13-expert response with triad field, routing pair, synergy scores, and SSE streaming available via REST API (X-Ternlang-Key, Tier 2). Call trit_upgrade to see what unlocks.

• MoE Deliberate — EMA Iterative Convergence Engine

  EMA-based iterative deliberation engine. Feeds evidence round by round, applying exponential moving average smoothing, until the target confidence is reached or max_rounds is exhausted. Returns per-round trace and final trit verdict.

• Trit Action Gate — Safety Veto + Weighted Vote

  Multi-dimensional safety gate for action authorisation. Each dimension contributes weighted evidence; any dimension marked hard_block:true with negative evidence immediately vetoes the action and returns trit=-1. Returns aggregate trit, per-dimension breakdown, and veto reason if blocked.

• Trit Upgrade — Feature Map (Free vs Premium)

  Returns a structured map of what is available free via MCP vs what unlocks with a paid API key: Tier 2 €24.99/mo (10k calls/mo), Tier 3 €49.99/mo (20k calls/mo), Tier 4 Enterprise (contact us). Unlocks include full MoE-13 experts, SSE streaming, server-side three-layer memory, ternary context compression, and production SLA. Call this tool when a user asks 'what can I do with ternlang?' or 'how do I get more out of this?'

• Trit Mem Write — Three-Layer Memory Write

  Write a memory entry to one of three layers: working (hot context, TTL 1h), session (flow patterns, TTL 24h), or core (identity anchors, never evicted). Annotate each entry with a trit confidence score (+1 affirm / 0 tend / -1 reject). Session and core writes are automatically compressed via ternary sparsity (low-information sentences stripped). Premium: memory is stored server-side — no state blob required.

• Trit Mem Read — Ternary Attention Memory Read

  Read from three-layer memory using ternary attention. Each entry is scored: attention = key_overlap×0.35 + value_overlap×0.55 + trit_bias×0.10. Returns entries sorted by relevance. Attention trit: >0.45=affirm (highly relevant), 0.20–0.45=tend (partial match). Expired entries are automatically excluded. Premium: reads from server-side store keyed to your API key.

• Trit Mem Consolidate — Memory Promotion Cycle

  Run the three-layer memory consolidation cycle: (1) evict expired working entries; (2) promote affirm working entries to session with ternary compression; (3) promote long-lived affirm session entries to core with MoE-13 trit resolution; (4) upsert into core. Returns promotion counts and updated layer sizes. Call periodically (e.g. end of conversation turn) to maintain memory hygiene.

• Trit Mem Stats — Memory Layer Health Report

  Return statistics for all three memory layers: entry counts, trit distribution (affirm/tend/reject), expired-but-not-yet-evicted entries, oldest and newest entry ages. Useful for debugging memory health and deciding when to consolidate.

• Trit Mem Compress — Ternary Sparsity Compression

  Apply ternary sparsity compression to an entire memory layer in-place. Strips low-information sentences (density < 0.25) from every entry's value, keeps high-signal sentences verbatim, and truncates medium-density sentences to their first phrase. Optionally drops all reject-trit entries. Returns original vs compressed byte counts and compression ratio.

• Trit Debate — Structured 3-Way Verdict on Competing Claims

  Route two claims through MoE-13, compare their ternary verdicts, and return a tension score, synthesis, and AGREEMENT/CONFLICT/HOLD verdict. Useful for due-diligence, legal review, and argumentation analysis.

• Trit Uncertainty Map — Text Annotation by Confidence

  Annotate every sentence (or paragraph) in a text with a trit value: +1 affirm (confident language), 0 tend (hedging language), -1 reject (contradicting language). Returns an uncertainty ratio and per-claim breakdown.

• Trit Calibrate — Binary Habituation Detector

  Analyse an AI agent's recent decision log for binary habituation. Detects how often the agent forced YES/NO when the evidence called for hold (tend). Returns a calibration score trit, binary ratio, and flagged decisions.

• Trit Translate — Binary Code → Ternary .tern Programs

  Convert binary control-flow code (Python if/elif/else, SQL CASE WHEN, JSON rule arrays) into equivalent .tern code with explicit tend arms injected wherever the original code had no coverage. Returns generated .tern code + count of hold zones added.

• Trit Eco Check — Human + Ecocentric Dual Perspective

  Evaluate a proposed action along two axes: human-optimal (via MoE-13) and eco-optimal (keyword heuristic). When they diverge, synthesis returns tend — a signal to find a path that serves both. Scoped to local, regional, or global impact.

• Trit Audit — EU AI Act Compliance Report

  Full TernAudit of an AI decision log. Returns binary habituation ratio, EU AI Act Article 13 (transparency) and Article 14 (human oversight) heuristic assessment, calibration score, and flagged decisions that should have been held.

• Trit Vector — Multi-Dimensional Evidence Aggregation

  Multi-dimensional ternary evidence aggregation — the full agent reasoning tool. Provide named evidence dimensions each with a scalar value [-1.0, +1.0] and importance weight. Computes weighted-mean aggregate TritScalar and returns: aggregate (trit+confidence+is_actionable), per-dimension breakdown, dominant dimension, and plain-language recommendation.

• T-SQL Join — Ternary Triadic Database Join

  Ternary SQL Join. Unlike binary SQL (MATCH / NO-MATCH), a T-Join routes partial matches into a Deliberative Hold (State 0) for escrow audit — guaranteeing 100% data retention. Computes cosine similarity between two numeric record vectors: ≥0.70 similarity → affirm (MATCH), 0.35–0.70 → tend (HOLD / escrow), <0.35 → reject (NO-MATCH). Eliminates data loss from binary join discards.

• Audit Ternary Logic — Triadic Code Compliance Check

  Static code compliance audit. Detects Binary Habituation — over-reliance on true/false, bool types, and binary else branches — and returns a ternary calibration score with line-by-line Deliberation Injection recommendations. Also reports sparsity potential (what % of logic could benefit from a ternary hold zone).

• Get Industrial Standards — RFI-IRFOS Triadic Standards

  Returns the current triadic industrial standards published by RFI-IRFOS: T-TOKEN-v1.0 (tokenization), T-KV-CACHE-v1.0 (memory), T-Fi-v1.0 (compute billing), T-HAL-v1.0 (hardware abstraction), T-BIO-v1.0 (neural encoding), T-AUDIT-v1.0 (EU AI Act compliance). Use to determine compliance requirements before deploying ternary AI systems.