ARES

Autonomous Research & Evidence System

1. Purpose

ARES (Autonomous Research & Evidence System) is the recursive intelligence layer of the ZAYAZ platform.

It enables controlled, auditable, multi-path exploration of knowledge, data, and evidence to improve the quality, completeness, and trustworthiness of ESG decisions.

ARES does not make decisions.

ARES ensures that decisions made by Modules are:

• more complete
• more validated
• more defensible
• more transparent

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2. Architectural Role

ARES operates as a cross-module recursive reasoning service within the Shared Intelligence Services (SIS) layer.

It is invoked by Modules such as:

• ZARA (primary)
• RIF
• NETZERO
• Verification & Assurance (VERA)

ARES enhances decision-making without owning decision authority.

Modules decide.
ARES challenges, expands, and validates those decisions.

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3. Core Concept

ARES implements a bounded autoresearch loop:

1. Hypothesis / Intent received
2. Knowledge exploration (SSSR + internal/external sources)
3. Multi-path reasoning
4. Validation and trust scoring
5. Comparative evaluation
6. Convergence or escalation

This loop is:

• recursive (within limits)
• traceable (ALTD)
• governed (AI Risk + trust thresholds)
• deterministic in structure, probabilistic in exploration

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4. Key Capabilities

4.1 Multi-Path Evidence Exploration

ARES generates multiple alternative:

• interpretations
• data completion strategies
• policy formulations
• estimation pathways

Each path is independently evaluated and scored.

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4.2 Trust-Weighted Convergence

All outputs are ranked using:

• DICE (completeness)
• DaVE (validation)
• VTE (trust score)

ARES selects:

• highest-confidence path, or
• escalates if no path meets threshold

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4.3 Gap Resolution Intelligence

ARES specializes in resolving:

• missing ESG signals
• incomplete disclosures
• weak trust scores
• inconsistent datasets

Typical domains:

• Scope 3 estimation
• policy generation
• supplier data inference

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4.4 Pre-Audit Simulation

ARES can simulate:

• verifier scrutiny
• audit rejection scenarios
• compliance weaknesses

Outputs:

• risk flags
• remediation suggestions
• confidence breakdowns

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4.5 Recursive Refinement (Bounded)

ARES may iterate over its own outputs to:

• refine assumptions
• test alternative hypotheses
• improve trust score

All recursion is:

• depth-limited
• governance-controlled
• fully logged

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5. Invocation Model

ARES is never user-facing.

It is triggered by:

• ZARA intent orchestration
• validation failures (DICE / DaVE)
• trust thresholds not met
• telemetry-based friction signals

Invocation is routed via:

• ZADIF dispatcher
• signal metadata (SSSR)
• agent profiles (ZAAM)

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6. Governance & AI Risk

ARES is classified as a High-Risk AI subsystem due to:

• probabilistic reasoning
• multi-path inference
• potential impact on compliance outputs

Mandatory Controls

• Trust threshold enforcement (≥ configurable)
• Recursion depth limits
• Full ALTD audit logging
• Human-in-the-loop for critical outputs
• Model versioning and replay support

ARES cannot:

• override Module decisions
• bypass validation engines
• produce unlogged outputs

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7. Relationship to Core Systems

System	Relationship
ZARA	Primary orchestrator invoking ARES
ZAAM	Executes task-specific agents within ARES loops
SSSR	Defines search space and signal constraints
DICE / DaVE / VTE	Validation and trust scoring
ALTD	Full audit trail and replay
MICE	Executes computations within research paths

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8. Architectural Constraints (Non-Negotiable)

ARES MUST:

• operate within defined signal scopes (SSSR)
• remain bounded in recursion
• route all actions through governed engines
• log every step in ALTD
• expose full reasoning trace for audit

ARES MUST NOT:

• act as a standalone decision system
• bypass Modules
• introduce uncontrolled learning loops
• access unverified external data without tagging

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9. Strategic Role in ZAYAZ

ARES transforms ZAYAZ from:

→ a compliance automation system

into:

→ a self-validating, evidence-driven ESG intelligence system

It enables:

• higher trust scores
• reduced verifier friction
• stronger audit defensibility
• continuous improvement of ESG data quality

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10. Design Principle

ARES follows the core ZAYAZ doctrine:

Precision before automation.
Integrity above velocity.

Recursive intelligence is only valuable if it is:

• explainable
• governed
• auditable

ARES enforces this at scale.

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Appendix A — ARES Execution Model

Purpose

This appendix defines the execution model of ARES (Autonomous Research & Evidence System) as a governed SIS subsystem within ZAYAZ.

It formalizes how ARES:

• receives an invocation
• decomposes a research problem
• explores multiple evidence paths
• validates and scores those paths
• converges on a recommended outcome or escalates

This appendix is technical and normative for implementation planning, governance design, and OMR-linked architecture documentation.

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A.1. Normative Position

ARES is a Shared Intelligence Service (SIS).

It therefore:

• does not own ESG domain authority
• does not emit final decision authority on its own
• does not bypass module governance

ARES exists to improve the quality of module decisions through bounded recursive evidence exploration.

Modules decide.
ARES investigates, tests, and strengthens.

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A.2. Execution Model Overview

ARES operates as a session-based, multi-path research orchestration service.

Each ARES execution is represented as an ARES Session that moves through a controlled lifecycle:

1. Invocation
2. Scope Resolution
3. Path Planning
4. Path Execution
5. Validation & Trust Scoring
6. Convergence
7. Recommendation / Escalation
8. Audit Finalization

This lifecycle is mandatory for all ARES runs.

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A.3. Core Design Principles

The ARES execution model is governed by the following principles:

A.3.1. Bounded Recursion

ARES may recurse, but recursion MUST be:

• explicitly depth-limited
• policy-controlled
• observable in ALTD

A.3.2. Multi-Path Exploration

ARES MUST explore more than one viable reasoning or evidence path when uncertainty, incompleteness, or trust deficiency is detected.

A.3.3. Trust Before Selection

ARES MUST NOT select a preferred path until:

• validation is complete
• trust scores are calculated
• governance thresholds are applied

A.3.4. Deterministic Control / Probabilistic Exploration

The orchestration structure MUST be deterministic. Exploration inside a path MAY be probabilistic or AI-assisted.

A.3.5. Full Replayability

All path inputs, decisions, scores, and convergence outcomes MUST be replayable or explainably reconstructable.

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A.4. ARES Session Model

An ARES Session is the canonical execution container for one bounded autoresearch process.

Each session MUST have:

• ares_session_id
• invoking_component
• invoking_module_id
• execution_purpose
• scope_definition
• recursion_policy
• governance_policy
• status
• opened_at
• closed_at

A.4.1. Session Purpose Types

Typical execution_purpose values include:

• gap_resolution
• policy_evidence_search
• trust_recovery
• pre_audit_simulation
• alternative_estimation
• disclosure_strengthening

A.4.2. Session Boundary Rule

An ARES session MUST remain bounded to one declared problem frame. If a new materially distinct problem emerges, a new session MUST be opened.

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A.5. Invocation Layer

ARES is invoked only by governed platform components.

A.5.1. Allowed Invokers

ARES MAY be invoked by:

• ZARA
• RIF
• NETZERO
• VERA
• other approved Modules registered in OMR
• SIS-triggered trust recovery flows

ARES MUST NOT be directly invoked by:

• end users
• unregistered services
• UI-only layers
• ad hoc scripts outside governed execution contexts

A.5.2. Invocation Triggers

Typical triggers:

• missing required signals
• low trust score
• conflicting evidence
• verifier readiness check
• unresolved policy/document gap
• repeated validation failure
• telemetry-detected friction or anomaly

A.5.3. Invocation Contract

At minimum, an invocation MUST supply:

• invoking_module_id
• problem_statement
• signal_scope
• entity_scope
• time_scope
• required_output_type
• governance_profile_id

Missing these fields is an execution contract violation.

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A.6. Scope Resolution Phase

After invocation, ARES resolves the executable scope.

This phase defines:

• signal boundaries
• permissible data sources
• applicable frameworks
• required trust thresholds
• allowed engines and agents
• escalation requirements

A.6.1. Scope Inputs

Scope resolution SHOULD use:

• SSSR metadata
• USO signal domains
• entity metadata
• framework mappings
• jurisdiction context
• role and verifier constraints
• OMR policy metadata

A.6.2. Scope Outputs

The output of this phase is a Research Scope Object containing:

• declared signal set
• data source allowlist
• path generation rules
• convergence policy
• escalation policy
• recursion depth cap

ARES MUST NOT execute path planning before the scope object is finalized.

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A.7. Path Planning Phase

ARES next constructs a set of candidate Research Paths.

A Research Path is a structured, testable route for answering the session problem.

A.7.1. Path Types

Typical path categories:

• Direct Evidence Path
  Uses directly available trusted signals or artifacts

• Cross-Signal Inference Path
  Uses related signals and known dependencies

• Model-Assisted Estimation Path
  Uses SEM, Bayesian, Monte Carlo, or other governed engines

• Policy/Template Synthesis Path
  Uses controlled AI generation plus framework alignment checks

• Verifier Simulation Path
  Tests the output against assurance expectations

A.7.2. Minimum Path Rule

If uncertainty exists, ARES SHOULD generate at least two viable paths. If trust is materially low, ARES SHOULD generate three or more.

A.7.3. Invalid Path Rule

A path MUST NOT be created if it:

• uses disallowed sources
• violates governance policy
• exceeds configured risk policy
• depends on non-replayable hidden logic
• cannot produce explainable intermediate outputs

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A.8. Research Path Object Model

Each candidate path SHOULD be represented by a canonical structure.

ares-research-path.example.jsonGitHub ↗

{
  "path_id": "arp-000145",
  "ares_session_id": "ares-2026-00021",
  "path_type": "model_assisted_estimation",
  "goal": "Estimate missing Scope 3 Category 4 value",
  "inputs": {
    "signals": ["ghg.s3.cat4.activity", "supplier.transport.mode"],
    "artifacts": ["invoice_batch_2025_q2"],
    "entity_scope": "eco196123456789"
  },
  "engines": ["SEM", "DICE", "DaVE"],
  "micro_engines": ["MEID_MC02", "MEID_CFIL01"],
  "agents": ["Emissions Modeling Analyst"],
  "assumptions": [
    "supplier mode mix stable across reporting quarter",
    "validated invoice sample representative"
  ],
  "status": "planned"
}

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A.9. Path Execution Phase

Each approved path is then executed through governed orchestration.

A.9.1. Execution Steps

A path typically runs in this order:

1. Resolve context
2. Load allowed inputs
3. Execute engines / agents
4. Capture intermediate artifacts
5. Validate intermediate outputs
6. Compute trust and quality metrics
7. Store path result

A.9.2. Execution Isolation

Each path MUST be independently executable and auditable.

Path execution MUST preserve:

• path-local assumptions
• source lineage
• intermediate scores
• model or engine versions used

A.9.3. Cross-Path Contamination Rule

One path MUST NOT silently overwrite or contaminate another path’s results. Cross-path comparison is allowed only in the convergence phase.

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A.10. Validation Layer

ARES does not trust raw path output by default.

Every path MUST pass through a validation layer before becoming eligible for convergence.

A.10.1. Required Validators

Depending on path type, validation SHOULD include:

• DICE
• DaVE
• VTE
• schema validation
• unit normalization checks
• provenance integrity checks
• framework consistency checks

A.10.2. Validation Dimensions

Validation SHOULD assess:

• completeness
• consistency
• traceability
• methodological defensibility
• source quality
• policy alignment
• verifier plausibility

A.10.3. Validation Outcome States

A path MAY be labeled:

• validated
• validated_with_warnings
• weak
• rejected

Rejected paths MUST remain in audit history and MUST NOT be silently deleted.

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A.11. Trust Scoring Model

After validation, ARES computes trust-weighted path scores.

A.11.1. Trust Score Components

Each path SHOULD receive component scores for:

• source quality
• signal completeness
• methodological fitness
• validation pass quality
• framework alignment
• audit trace strength
• uncertainty burden
• verifier acceptability likelihood

A.11.2. Composite Trust Score

ARES SHOULD compute a composite path trust score:

PathTrustScore = weighted(
  source_quality,
  completeness,
  methodological_fitness,
  validation_strength,
  audit_trace_strength,
  verifier_acceptability
) - uncertainty_penalties

Exact weights are implementation-specific but MUST be versioned and governed.

A.11.3. Trust Threshold Enforcement

A path is convergence-eligible only if:

• it meets the minimum trust threshold for the invoking governance profile, and
• no hard validation blockers remain

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A.12. Convergence Phase

Convergence is the controlled process through which ARES compares candidate paths and determines the session outcome.

A.12.1. Convergence Modes

ARES SHOULD support the following modes:

• Best Path Selection Select the highest-trust validated path
• Hybrid Merge Merge compatible path fragments into a composite result
• Escalation by Insufficiency Escalate because no path meets threshold
• Escalation by Conflict Escalate because top paths materially disagree

A.12.2. Best Path Rule

A best path MAY be selected only if:

• it is validated
• its trust score exceeds policy threshold
• it materially outperforms alternatives or is otherwise justifiably preferred

A.12.3. Conflict Rule

If two or more top paths disagree materially and trust scores are too close, ARES MUST either:

• run an additional tie-breaker path, or
• escalate to human review

A.12.4. Hybrid Merge Rule

A hybrid result MAY be created only if the merge logic is:

• explicitly defined
• reproducible
• traceable back to source paths

Opaque merges are prohibited.

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A.13. Recommendation Output Model

ARES produces a Recommendation Package for the invoking Module.

ARES does not emit final module decisions. It emits a governed recommendation with full trace context.

A.13.1. Recommendation Package Must Include

• ares_session_id
• recommended_path_id or merge_strategy_id
• recommendation_type
• proposed_output
• trust_score
• warnings
• escalation_required
• supporting_path_summary
• audit_trace_ref

A.13.2. Recommendation Types

Typical values:

• preferred_path
• preferred_estimate
• preferred_policy_variant
• evidence_gap_unresolved
• manual_review_required
• verifier_attention_required

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A.14. Escalation Model

ARES MUST escalate when confidence is not good enough.

A.14.1. Mandatory Escalation Conditions

Escalation is required when:

• no path meets trust threshold
• top paths materially conflict
• high-risk output affects compliance or filing
• evidence provenance is insufficient
• required source data is missing and no defensible estimation path exists
• verifier simulation indicates likely rejection

A.14.2. Escalation Targets

Escalation MAY route to:

• invoking module owner workflow
• human reviewer
• verifier workflow
• governance officer
• board / executive task queue
• ZARA follow-up task assignment

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A.15. Recursion Policy

ARES supports recursion, but only under strict policy control.

A.15.1. Recursion Triggers

ARES MAY recurse when:

• a promising path is incomplete
• tie-break analysis is needed
• a validation warning suggests recoverable weakness
• one additional evidence search could materially raise trust

A.15.2. Recursion Limits

Every session MUST define:

• max_depth
• max_paths_total
• max_retry_per_path
• max_execution_time_budget

A.15.3. Forbidden Recursion

ARES MUST NOT recurse:

• indefinitely
• after policy stop conditions are reached
• to “search until convenient”
• to manufacture certainty from weak evidence

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A.16. ARES Session State Machine

Each session MUST follow a governed state model.

A.16.1. Session States

State	Meaning
invoked	Invocation accepted
scoping	Scope resolution underway
planned	Research paths planned
executing	Paths actively executing
validating	Path outputs under validation
converging	Paths compared and ranked
recommended	Recommendation package emitted
escalated	Human or verifier escalation required
closed	Session finalized and logged
failed	Execution failed under governed error handling

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A.16.2. State Transition Rule

A session MUST NOT skip directly from invoked to recommended. Validation and convergence are mandatory.

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A.17. Audit and Lineage Requirements

Every ARES session is part of the platform’s audit spine.

A.17.1. Mandatory Logging

ARES MUST log:

• invocation metadata
• scope object
• path plans
• path executions
• engine/agent versions
• assumptions
• validation outcomes
• trust scores
• convergence rationale
• escalation events
• final recommendation package

A.17.2. Lineage Integrity

Every output MUST support lineage back to:

• input signals
• source artifacts
• engines and micro-engines used
• prompt/template version where applicable
• model version where applicable

A.17.3. Replay Requirement

High-risk sessions MUST support:

• deterministic replay where possible, or
• explainable bounded replay with version-pinned dependencies

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A.18. Error Handling

ARES failures MUST be governed, not silent.

A.18.1. Failure Types

Typical failures:

• scope resolution failure
• source access failure
• validation pipeline failure
• convergence deadlock
• policy violation
• time budget exhaustion

A.18.2. Failure Handling Rule

On failure, ARES MUST:

• preserve partial execution history
• mark the session state appropriately
• notify the invoking component
• trigger escalation if needed

Silent fallback is prohibited for high-risk sessions.

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A.19. Example Execution Flow

Example: Missing Scope 3 Disclosure Support

Invocation:

• ZARA detects incomplete Scope 3 Category 4 disclosure

ARES session:

1. Scope resolved to relevant signals, suppliers, invoices, transport metadata
2. Three paths generated:

• direct supplier evidence path
• invoice-derived estimation path
• SEM-assisted benchmark path

3. Paths executed independently
4. DICE / DaVE / VTE applied
5. Supplier evidence path too incomplete
6. Invoice-derived path validated with warnings
7. SEM-assisted path validated but lower source quality
8. Convergence selects invoice-derived path with explicit warning note
9. Recommendation package returned to ZARA
10. ZARA either uses result or routes for human review based on trust threshold

This is the canonical ARES operating style: bounded, comparative, and audit-first.

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A.20. Canonical Pseudocode (Normative Structure)

ares-execution-flow.pseudo.tsGitHub ↗

function executeAresSession(invocation) {
  const session = openSession(invocation);

  const scope = resolveScope(session);
  assertScopeValid(scope);

  const paths = planResearchPaths(scope);
  assertMinimumPathQuality(paths);

  const executedPaths = [];
  for (const path of paths) {
    const result = executePath(path, scope);
    const validated = validatePathResult(result, scope);
    const scored = scoreTrust(validated, scope);
    executedPaths.push(scored);
  }

  const convergence = convergePaths(executedPaths, scope);

  if (convergence.requiresEscalation) {
    escalateSession(session, convergence);
    return finalizeSession(session, convergence, "escalated");
  }

  const recommendation = buildRecommendationPackage(convergence, session);
  return finalizeSession(session, recommendation, "recommended");
}

This pseudocode is illustrative of required flow order. Production implementation may vary, but not in governance sequence.

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A.21. Strategic Outcome

The ARES execution model gives ZAYAZ a governed mechanism for:

• evidence-seeking instead of assumption-locking
• trust-weighted decision support
• verifier-aware intelligence refinement
• recursive improvement without uncontrolled autonomy

It is the difference between:

• an AI that answers quickly, and
• an ESG intelligence system that can justify why its answer deserves trust

Precision before automation.

Integrity above velocity.

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Appendix B — OMR Draft Entry (Deferred)

(To be defined)

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