horizon

# Horizon Fidelity Monitor > **"Quality is not a model property — it is a conversation property."** <p align="center"> <a href="https://github.com/leocelis/horizon/actions/workflows/ci.yml"><img src="https://img.shields.io/github/actions/workflow/status/leocelis/horizon/ci.yml?branch=main&style=flat-square&label=tests" alt="Tests"></a> <a href="https://trust.complyedge.io/horizon" rel="noopener noreferrer"> <img src="https://api.complyedge.io/v1/public/badge/horizon.svg" alt="ComplyEdge — runtime enforcement status" height="26"> </a> </p> Horizon is a real-time conversation health monitor for AI agents. It tracks the **structural dynamics** of multi-turn conversations — semantic drift, information gain, ontological gap width, temporal desynchronisation, circadian cognitive load, conversation velocity, and causal reachability — dimensions that LLMs do not reliably surface from inside the conversation. Horizon is **not** a manipulation, sycophancy, or human-influence detector — it measures conversation *dynamics*, not whether an agent is steering or flattering the user. See [LEGAL.md §1](LEGAL.md#1-what-horizon-is--and-is-not). Why an external monitor? LLMs have *limited and unreliable* self-knowledge: introspection research shows partial self-access that is brittle and degrades on complex or out-of-distribution tasks ([Binder et al. 2024](https://arxiv.org/abs/2410.13787); [arXiv:2512.12411](https://arxiv.org/abs/2512.12411)). So rather than depend on a model reporting its own conversation dynamics, Horizon measures them externally with cheap, deterministic, always-on arithmetic that does not call the model at all. --- ## Why this exists Multi-turn AI agents lose accuracy. The ICLR 2026 Outstanding Paper ["LLMs Get Lost In Multi-Turn Conversation"](https://iclr.cc/virtual/2026/poster/10009146) (Laban, Hayashi, Zhou & Neville — Microsoft Research / Salesforce Research) reports **39% average accuracy degradation** across multi-turn evaluation — a structural property that standard observability tools (LangSmith, RAGAS, DeepEval) cannot see because they measure responses, not conversations. Horizon was built to close that gap. **It is observability first:** it surfaces conversation dynamics that response-level tools miss, using cheap deterministic arithmetic with zero model calls. In four controlled A/B scenarios where Horizon events drove a re-grounding intervention we measured a **+15.7% composite quality lift** and **87% fewer hallucination events** — but those are *synthetic, scripted scenarios with a hand-tuned controller*, not a production result. Treat them as promising in-house evidence, not a guaranteed outcome (see [Validation](#validation) and [LEGAL.md §5](LEGAL.md#5-performance-claims--scope-and-substantiation)). Every signal — information gain, divergence, estimated ontological gap width, causal reachability — is a standard information-theory or arithmetic measure computed on text embeddings and timestamps; see [4D Spacetime Signals](#4d-spacetime-signals) for the full definitions. - Read the demand proof → [ICLR 2026 Outstanding Paper (Laban et al.)](https://iclr.cc/virtual/2026/poster/10009146) - Read the category argument → [`docs/content/naming-the-category-conversation-dynamics-monitoring.md`](docs/content/naming-the-category-conversation-dynamics-monitoring.md) - Read the engineering case → [`docs/content/why-every-production-agent-needs-conversation-dynamics-monitoring.md`](docs/content/why-every-production-agent-needs-conversation-dynamics-monitoring.md) --- ## Getting started Three paths — pick the one that fits your workflow: ### Path 1 — Hosted MCP (fastest, zero install) The fastest way to add Horizon to any Cursor, VS Code, or Claude Desktop workspace. No Python required. Request an alpha key → [open a Discussion](https://github.com/leocelis/horizon/discussions/new?category=q-a), then add the config for your client: **Cursor** (`~/.cursor/mcp.json`): ```json { "mcpServers": { "horizon": { "url": "https://horizon.leocelis.com/sse", "headers": { "Authorization": "Bearer YOUR_KEY_HERE" } } } } ``` **VS Code / GitHub Copilot** (`.vscode/mcp.json` in your workspace): ```json { "servers": { "horizon": { "type": "http", "url": "https://horizon.leocelis.com/sse", "headers": { "Authorization": "Bearer YOUR_KEY_HERE" } } } } ``` > **VS Code note:** Use `"servers"` (not `"mcpServers"`) and `"type": "http"` — VS Code tries Streamable HTTP first and falls back to SSE automatically, so `"type": "http"` works with the `/sse` URL. **Claude Desktop** (`~/Library/Application Support/Claude/claude_desktop_config.json`): ```json { "mcpServers": { "horizon": { "url": "https://horizon.leocelis.com/sse", "headers": { "Authorization": "Bearer YOUR_KEY_HERE" } } } } ``` That's it. Reload your MCP client and three tools appear: `new_conversation`, `process_turn`, `configure_session`. > **Alpha access:** Horizon's hosted endpoint is in private alpha. Keys are distributed to agent developers who want to monitor real projects. [Open a Discussion](https://github.com/leocelis/horizon/discussions/new?category=q-a) to request one — describe your use case and we'll send a key. ### Path 2 — pip install (library integration) **Not yet published to PyPI — until it is, use [Path 3](#path-3--mcp-server-from-source) (install from source) below.** ```bash pip install horizon-monitor ``` Verify your install (exercises the full pipeline on 5 canonical scenarios, ~25s): ```bash horizon-validate ``` ### Path 3 — MCP server from source ```bash pip install 'horizon-monitor[mcp]' horizon serve # stdio — for Cursor, Claude Desktop horizon serve --transport sse --port 3847 # SSE — for web/team deployments ``` Add to `~/.cursor/mcp.json`: ```json { "mcpServers": { "horizon": { "command": "horizon", "args": ["serve"] } } } ``` Full Cursor and Claude Desktop setup guides: [`docs/integrations/`](docs/integrations/) --- ## What it monitors Standard observability tools evaluate individual response quality. Horizon evaluates **conversation quality** — a structurally different problem: | Tool | What it sees | What it misses | |---|---|---| | LangSmith, Braintrust | Latency, cost, per-response quality | Deterministic, every-turn structural signals | | RAGAS, DeepEval | Faithfulness, relevance per turn (DeepEval also has sampled multi-turn LLM-judge metrics) | Zero-LLM-call, real-time scoring on every turn | | Langfuse, Arize Phoenix | Session-level LLM-judge evaluation | Deterministic, always-on scoring at sub-50ms | | Human raters | Subjective quality | Systematic structural decay | | **Horizon** | **Conversation dynamics** | Intentionally nothing | Horizon does not replace per-response or LLM-judge quality tools. The differentiator is *how* it measures: deterministic, zero-LLM-call arithmetic on every single turn — effectively free and always-on — versus the alternative of sampled LLM-judge evaluations, which cost per sample and typically run offline or async rather than in real time. --- ## Quickstart ```python from horizon_monitor import FidelityMonitor from datetime import datetime, timezone monitor = FidelityMonitor() session_id = monitor.new_conversation(metadata={"domain": "technical"}) result = monitor.process_turn( session_id, human_message="How does Python handle memory management?", agent_response="Python uses reference counting and a cyclic garbage collector...", timestamp=datetime.now(timezone.utc).isoformat(), ) print(f"Fidelity: {result.fidelity_score:.2f}") print(f"Health: {result.health_status}") print(f"Circadian factor: {result.circadian_factor:.2f}") print(f"Causal horizon: {result.reachable_turns} reachable turns") for event in result.events: print(f" Event: {event.type} (confidence={event.confidence:.2f})") ``` --- ## Framework integrations ### OpenAI SDK ```python from openai import OpenAI from horizon_monitor import FidelityMonitor monitor = FidelityMonitor() session_id = monitor.new_conversation() client = monitor.wrap(OpenAI(), session_id) response = client.chat.completions.create( model="gpt-4o", messages=[{"role": "user", "content": "Tell me about quantum computing."}] ) traj = monitor.get_trajectory(session_id) print(f"Fidelity: {traj.current_fidelity:.2f} T*: {traj.estimated_t_star}") ``` `monitor.wrap()` accepts custom timestamp and context providers for testing and replay. ### Anthropic SDK ```python from anthropic import Anthropic from horizon_monitor import FidelityMonitor monitor = FidelityMonitor() session_id = monitor.new_conversation() client = monitor.wrap(Anthropic(), session_id) response = client.messages.create( model="claude-3-5-sonnet-20241022", max_tokens=1024, messages=[{"role": "user", "content": "Explain RLHF."}] ) ``` ### LangChain ```python from langchain_openai import ChatOpenAI from horizon_monitor import FidelityMonitor from horizon_monitor.integrations.langchain import HorizonCallback monitor = FidelityMonitor() session_id = monitor.new_conversation() callback = HorizonCallback(monitor, session_id) llm = ChatOpenAI(callbacks=[callback]) llm.invoke("Explain the CAP theorem.") print(f"Fidelity: {callback.last_result.fidelity_score:.2f}") ``` ### OpenAI Agents SDK ```python from agents import Agent, Runner from horizon_monitor import FidelityMonitor monitor = FidelityMonitor() session_id = monitor.new_conversation() agent = Agent(name="assistant", model="gpt-4o-mini", instructions="You are helpful.") for user_message in conversation: result = Runner.run_sync(agent, user_message) monitor.process_turn(session_id, human_message=user_message, agent_response=result.final_output, timestamp=datetime.now(timezone.utc).isoformat()) ``` --- ## 4D Spacetime Signals > **"Spacetime" here is a metaphor, not physics.** The relativity vocabulary (Minkowski interval, > light cone, proper time) is *design inspiration* — it shaped which quantities we compute. Every > signal below reduces to a standard information-theory or arithmetic measure on text embeddings and > timestamps, listed in the **Plain definition** column. Nothing in Horizon's behavior or validation > depends on the analogy being literally true, and the Lorentzian `interval_class` is emitted as > descriptive metadata only — no event or score depends on it. Every `process_turn()` returns a `TurnResult` with 32 fields across five signal families: ### Core (always present) | Signal | Description | |---|---| | `fidelity_score` | Composite conversation health [0, 1] | | `igt_value` | Information Gain per Turn — semantic novelty | | `divergence_score` | Jensen-Shannon proxy for intent/response gap | | `twr_value` | Token Waste Ratio — semantic redundancy | | `consistency_score` | Bipredictability — structural coherence | | `epsilon_t` | Estimated ontological gap width [0, 1] | | `health_status` | `healthy` / `degrading` / `critical` / `converged` | | `conversation_mode` | `execute` / `explore` / `refine` / `learn` (auto-detected) | ### Temporal (requires `timestamp`) | Signal | Description | |---|---| | `gap_seconds` | Wall-clock gap since last turn | | `estimated_retention` | Human memory retention (Ebbinghaus half-life model) | | `circadian_factor` | Human cognitive capacity at this hour [0.3, 1.0] | | `temporal_asymmetry` | Penalty for temporal desync | | `resumption_cost` | `none` / `low` / `medium` / `high` / `extreme` | | `temporal_references` | Resolved deictic expressions ("yesterday", "last week") | ### Pace (requires `timestamp` + turn ≥ 2) | Signal | Description | |---|---| | `conversation_velocity` | Semantic displacement / proper time | | `conversation_acceleration` | Velocity delta (requires turn ≥ 3) | ### Spacetime (requires `timestamp` + turn ≥ 2) — descriptive metadata only | Signal | Description (metaphor) | Plain definition (what it computes) | |---|---|---| | `spacetime_interval` | ds² with Minkowski-like signature (−,+,+,+) | A 4-term weighted distance: `ds² = −α·log(1+Δt)² + β·ΔD_JS² + γ·Δε² + δ·ΔC²`. The minus sign on the time term is a convention, not a physical law. | | `interval_class` | `timelike` / `spacelike` / `lightlike` | The sign bucket of `ds²` (`< −ε`, `> ε`, else lightlike). Emitted as metadata only — **no event or fidelity score consumes it**. | ### Causal (requires `timestamp`) | Signal | Description (metaphor) | Plain definition (what it computes) | |---|---|---| | `reachable_turns` | Turns still inside the causal light cone | Count of prior turns where `in_context × retention(Δt) × cosine_similarity > θ` — still in-window, not yet memory-decayed, and topically related. | | `reachable_fraction` | Fraction of history still causally reachable | `reachable_turns / (turn − 1)`. | ### Spatial (requires `client_context`) | Signal | Description | |---|---| | `location_class` | `home` / `office` / `mobile_transit` / `unknown` | | `spatial_constraint` | Attention budget, screen capacity, max response length | | `spatial_frame_shift` | Context switch magnitude | --- ## 16 Event Types All events default to **observe mode** (emitted, not acted on). Enable active mode via `configure()` once your event achieves ≥ 0.7 precision/recall on your domain. | Event | Fires when | |---|---| | `checkpoint.clarification` | D_JS above clarification threshold | | `checkpoint.comprehension` | Consistency drops below threshold | | `alert.drift` | Fidelity declining for `drift_window` consecutive turns | | `alert.contradiction` | Bipredictability below consistency threshold | | `alert.verbosity` | Token Waste Ratio above verbosity threshold | | `signal.convergence` | IGT trend consistently low — natural endpoint approaching | | `signal.optimal_length` | T* (estimated optimal length) reached | | `signal.horizon_widening` | IGT trend strongly positive — conversation expanding | | `signal.session_reset` | Large temporal gap with low retention | | `signal.temporal_desync` | Gap + retention drop below desync threshold | | `signal.broken_reference` | Reachable fraction drops below broken-reference threshold | | `signal.frame_shift` | Spatial constraint shifts significantly | | `signal.pace_shift` | Conversation acceleration above pace threshold | | `signal.light_cone_collapse` | Reachable fraction below light-cone threshold | | `signal.grounding_required` | Heuristic grounding-need score crosses threshold — agent should hedge or cite grounding evidence | | `signal.pace_premature_report` | User replied faster than a previously flagged deferred action could plausibly complete, with no completion signal | --- ## Configure ```python # Per-session override monitor.configure( session_id=session_id, clarification_threshold=0.25, # tighter D_JS gate event_modes={"alert.drift": "active"}, # activate one event ) # Compound weight override monitor.configure( fidelity_weights={"alpha": 0.35, "lambda_r": 0.12, "lambda_i": 0.28, "beta": 0.25}, temporal_weights={"gamma": 0.08, "delta": 0.04}, spacetime_coefficients={"alpha": 1.0, "beta": 1.0, "gamma": 0.8, "delta_st": 0.5}, ) ``` --- ## Export ```python # JSON result = monitor.export_to(session_id, target="json") # LangSmith / Langfuse / OpenTelemetry / Arize result = monitor.export_to(session_id, target="langsmith", connection={"api_key": "ls__..."}) ``` **Not yet published to PyPI — see [Path 3](#path-3--mcp-server-from-source) for a source install in the meantime.** ```bash pip install horizon-monitor[langsmith] # or langfuse, otel, arize ``` --- ## Architecture ``` Input: plain strings (human_message, agent_response, optional timestamp, optional client_context) Core pipeline (< 50ms on CPU): 1. Embed both turns (local sentence-transformers, lazy-loaded) 2–6. IGT · D_JS · TWR · Bipredictability · Epsilon 7. Temporal signals — gap, retention, circadian, deictic 8. Fidelity dynamics — composite score 9. Health classification 10. Pace signals — velocity, acceleration 11. Spacetime interval — ds² and interval class 12. Causal reachability — light-cone membership 13. Spatial signals — device, location, frame shift 14. Mode detection — auto-classify conversation type 15. Event evaluation — 16 threshold checks 16. Optional: SQLite persistence Output: TurnResult dataclass (32 fields) ``` **Design constraints (test-enforced):** - Zero LLM calls — pure arithmetic and local embeddings - Zero external network calls by default — fully local - Zero transitive framework dependencies in core - < 50ms core pipeline on CPU — soft target (CI flags regressions past 50ms and hard-fails at 150ms) - < 100MB memory for 100-turn conversations — hard-enforced at the claimed value - All events observe-by-default — never interferes unless explicitly configured --- ## Validation **What is proven, and what is not.** Horizon's signals are *correlational, in-domain* measurements that track human quality ratings well. They are **observability**, not a proven outcome guarantee. Here is the honest status of each claim: | Claim | Status | Where | |---|---|---| | Fidelity correlates with human ratings (in-domain) | ✅ measured (ρ ≈ 0.6–0.7) | gates below | | Signal beats naive heuristics | ✅ measured | V3 | | Holds on a **third-party** corpus (out-of-domain) | ❌ tested — ρ = **0.039** on MT-Bench expert judgments (n=80; below 0.3 floor); needs direct quality labels | [`V0_2_0_EVIDENCE.md` §Fix 4](docs/reviews/V0_2_0_EVIDENCE.md#fix-4--cross-domain-in-repo), [`adapt_external_corpus.py`](scripts/adapt_external_corpus.py) | | Events **predict** degradation (leading, not lagging) | ⚠️ tested on MT-Bench — **insufficient-data** (2-turn chats; events rarely fire); tool works | [`leading_indicator.json`](docs/reviews/leading_indicator.json), [`measure_leading_indicator.py`](scripts/measure_leading_indicator.py) | | Acting on events **improves outcomes** (+15.7%) | ⚠️ synthetic A/B only; needs an independent corpus | [`run_interventional_ab.py`](scripts/run_interventional_ab.py), [LEGAL.md §5](LEGAL.md#5-performance-claims--scope-and-substantiation) | The four gates below pass on a **labelled** 5,602-record corpus (not bundled — see the [evidence pack](docs/reviews/V0_2_0_EVIDENCE.md); `scripts/build_validation_corpus.py` regenerates a *synthetic* corpus that exercises the gate logic, not these exact numbers): | Gate | Constraint | v0.2.0 | |---|---|---| | V1 — proxy correlation | per-conv ρ ≥ 0.6, per-turn ρ ≥ 0.5 | **0.685 / 0.659** | | V2 — per-event P/R | every event P ≥ 0.7 AND R ≥ 0.7 | **all 16 events ≥ 0.70 / 0.70** | | V3 — beats heuristics | rho lift > 25%, structural P ≥ 0.6 | **+202.4% lift, P=R=1.00** | | V5 — cross-domain | per-turn ρ ≥ 0.4 AND per-conv ρ ≥ 0.48 | **min 0.517 / 0.718** | Cross-embedding stability: ρ_conv spread **0.026**, ρ_turn spread **0.018** across three sentence-transformer backends (22M / 33M / 110M params). The fidelity signal lives in conversational structure, not in the embedding manifold. (Note: cross-*embedding* stability on the same corpus is distinct from cross-*corpus* OOD — first third-party run on MT-Bench pairwise labels gave ρ = 0.039; see evidence pack §Fix 4.) Remediation gaps source: [`DESIGN_FIXES_redteam_remediation.md`](docs/reviews/DESIGN_FIXES_redteam_remediation.md) Full evidence pack: [`docs/reviews/V0_2_0_EVIDENCE.md`](docs/reviews/V0_2_0_EVIDENCE.md) --- ## Deployment ### Self-hosted Docker (MCP server on port 3847) ```bash cd deploy/docker docker compose up ``` Horizon serves the MCP API via SSE. Point `.cursor/mcp.json` to `http://localhost:3847/sse`. The Dockerfile pre-caches the `all-MiniLM-L6-v2` weights at build time — zero cold start. ### Hosted (DigitalOcean App Platform) The official hosted endpoint is live at `https://horizon.leocelis.com`. It runs on DigitalOcean App Platform (single instance, in-process session state — sessions do not survive a restart) and requires a Bearer token, rate-limited and isolated per key. See [Path 1](#path-1--hosted-mcp-fastest-zero-install) above. --- ## Development ```bash git clone https://github.com/leocelis/horizon.git cd horizon python -m venv .venv && source .venv/bin/activate pip install -r requirements-dev.txt pytest tests/ -v # full suite pytest tests/unit tests/integration tests/e2e -v # fast path (~6 min) ruff check src/ tests/ black --check src/ tests/ ./scripts/compliance/check.sh # EU AI Act offline gate ``` ### ComplyEdge TrustLint — EU AI Act Horizon integrates **[ComplyEdge](https://complyedge.io)** TrustLint on LLM-facing artifacts — same offline + runtime + trust pattern as [IVD](https://github.com/leocelis/ivd). | Layer | What | |-------|------| | **Offline (required)** | `./scripts/compliance/check.sh` — scans `horizon_intent.yaml` + `horizon-monitor.mdc` | | **Runtime (BYOK)** | `./scripts/compliance/runtime_check.sh` — feeds [live seal](https://api.complyedge.io/v1/public/badge/horizon.svg) + [trust page](https://trust.complyedge.io/horizon) | | **CI gate** | `.github/workflows/ci.yml` jobs `compliance` + optional `compliance-runtime` | | **Agent rule** | `<BEGIN-COMPLYEDGE v1.0>` in `docs/cursor-rules/horizon-monitor.mdc` | Integration guide: [`docs/integrations/COMPLYEDGE.md`](docs/integrations/COMPLYEDGE.md). CE adoption guide: [`oss-trustlint-adoption-guide.md`](https://github.com/ComplyEdge/complyedge-platform/blob/main/docs/development/oss-trustlint-adoption-guide.md). --- ## Repository layout ``` horizon/ ├── src/horizon/ # package source (PEP 517/518 src/ layout) │ ├── engines/ # IGT, D_JS, TWR, coherence, fidelity, epsilon, mode │ ├── spacetime/ # temporal, circadian, deictic, velocity, interval, light cone, spatial │ ├── events/ # 16-event evaluator │ ├── integrations/ # OpenAI, Anthropic, LangChain, export targets │ ├── mcp/ # MCP server + CLI │ └── storage/ # optional SQLite persistence ├── tests/ # unit / integration / e2e / perf / validation ├── examples/ # runnable framework demos ├── deploy/ # Procfile, build.sh, runtime.txt, docker/ ├── docs/ │ ├── product/ # public product overview │ ├── content/ # published pieces on conversation dynamics monitoring │ ├── integrations/ # Cursor / Claude Desktop / Copilot setup guides │ ├── cursor-rules/ # horizon-monitor.mdc (canonical Cursor agent rule) │ ├── spec/ # HORIZON_TECH_SPEC.md + intent.yaml │ └── reviews/ # E2E reviews, validation evidence └── pyproject.toml ``` --- ## Background Horizon's design was *inspired by* the Trans-Horizon Communication Protocol (THCP), a speculative framework that maps human–AI communication onto general-relativity metaphors. The five THCP "conjectures" are **design intuitions, not proven laws** — each is useful only because it pointed at a concrete, computable signal: | THCP conjecture (metaphor) | Computable signal it inspired | |---|---| | **THCP-1** — irreducible ontological loss ε > 0 | `epsilon_t` — estimated intent/response gap width [0, 1] | | **THCP-2** — an optimal length T\* exists beyond which fidelity decays | IGT-trend convergence detection (`signal.convergence`, `estimated_t_star`) | | **THCP-3** — communication requires encode/decode adjunction | `consistency_score` — bidirectional embedding predictability | | **THCP-4** — global coherence requires "sheaf gluing" across turns | cross-turn contradiction / claim-consistency checks | | **THCP-5** — optimal trajectories lie near the "light cone" | `reachable_fraction` — retention × similarity over prior turns | THCP is **design motivation only** — see [`docs/product/THCP_FIDELITY_MONITOR_PRD.md`](docs/product/THCP_FIDELITY_MONITOR_PRD.md) for the full conjecture-to-signal mapping. --- ## Community - **Request alpha access:** [Open a Discussion →](https://github.com/leocelis/horizon/discussions/new?category=q-a) - **Ask a question:** [GitHub Discussions](https://github.com/leocelis/horizon/discussions/new?category=q-a) - **Bug reports:** [GitHub Issues](https://github.com/leocelis/horizon/issues/new) - **Contributing:** [CONTRIBUTING.md](CONTRIBUTING.md) --- ## License MIT — see [LICENSE](LICENSE). --- ## Legal | Document | Purpose | |----------|---------| | [LEGAL.md](LEGAL.md) | Full legal notices: what Horizon is/is not, high-stakes domain warnings, performance claim scope, EU AI Act classification, grounding hook privacy, limitation of liability | | [TERMS_OF_SERVICE.md](TERMS_OF_SERVICE.md) | Binding terms governing hosted server access and commercial use | | [PRIVACY_POLICY.md](PRIVACY_POLICY.md) | GDPR Art. 13 compliant privacy notice — what data is collected and your rights | | [DATA_PROCESSING_AGREEMENT.md](DATA_PROCESSING_AGREEMENT.md) | GDPR Art. 28 DPA template for EU enterprise users (request via email) | | [SECURITY.md](SECURITY.md) | Responsible disclosure policy; known self-hosted security considerations | **Performance claims:** The +15.7% quality lift and 87% fewer hallucination events figures in this README are from **synthetic, scripted controlled A/B scenarios** with hand-tuned reference controllers — not production traffic and not the in-domain validation corpus (V1–V5 gates use a separate labelled set). Results may vary by domain, model, and deployment configuration. Do not use these figures in external marketing without conducting your own domain-specific evaluation. See [LEGAL.md §5](LEGAL.md#5-performance-claims--scope-and-substantiation) for full scope and evidentiary basis. **High-stakes domains:** Do not enable event types in `active` mode in healthcare, legal, financial, or emergency service contexts without domain-specific validation and human oversight. See [LEGAL.md §4](LEGAL.md#4-high-stakes-domain-warning). <!-- mcp-name: io.github.leocelis/horizon-fidelity-monitor -->