The Definition of Demonstrated Intelligence in Artificial Intelligence (Specifically in LLMs)

Definition

Demonstrated Intelligence is not an abstraction of potential ability but the observable performance of an agent under the demands of cooperation, measurement, and liability. It is the result of convergence of diverse information into a coherent account, compression of that account into a parsimonious causal model, and expression of that model in decisions that satisfy reciprocity and pass decidability tests at the level of infallibility demanded.

In other words, intelligence is demonstrated when an agent consistently produces minimal, causal explanations that survive counterfactual interventions, preserve the demonstrated interests of others, and can be warranted under liability.

Below is a compact, operational argument—and a build plan for LLMs—that treats Demonstrated Intelligence (DI) as the observable result of convergence and compression into parsimonious causality. I keep it in your grammar: commensurability → reciprocity → testifiability → decidability → liability.

Claim. Demonstrated Intelligence = Convergent-Compressed Causality expressed as reciprocal, testifiable decisions under liability.

Necessary:
Convergence: heterogeneous evidence, frames, and grammars reduce onto a small, mutually consistent set of invariants (closure under explanation).
Compression: the invariants are encoded with minimal descriptive complexity (parsimony/MDL), preserving predictive and interventional adequacy.
Causality: those invariants are directional and manipulable (do()-level), not merely correlative patterns.
Sufficient:
4) Reciprocity: choices respect demonstrated interests of others given costs/externalities.
5) Testifiability → Decidability: claims are stated operationally, verified across dimensions, then decided without discretion at the demanded level of liability.

When (1–3) hold, you have a causal core. When (4–5) also hold, you have demonstrated intelligence (externally visible and warrantable performance)—not just cleverness.

Evolutionary computation (your ternary): variation → selection → retention.
Selection pressure in real ecologies (physical, economic, legal) penalizes spurious degrees of freedom; only invariant structure persists.
Compression implements Occam/MDL: shortest sufficient model wins because it minimizes error on distributional shift (fewer free knobs to go wrong).
Causality is the only compression that survives intervention; correlations compress description on a dataset, causes compress across counterfactuals.
Reciprocity binds the model to human cooperation: we discard internally-true but externally-predatory policies.
Testifiability/Decidability close the loop: the system states its evidence, operations, and predicted deltas in demonstrated interests; a court-like test can pass/fail without taste or discretion.

Therefore, the shortest interventional account that respects reciprocity and passes decidability at the demanded liability level is the parsimonious causal model. Its successful action under liability is what we observe and label intelligence.

Perception performs lossy compression to disentangle factors of variation.
Concepts are convergent summaries that minimize description length of episodes.
Causal schemata are the minimal programs that work under manipulation; culture/legal norms prune them to reciprocity.
Reputation/liability penalize non-reciprocal shortcuts.
Outcome: intelligence demonstrates itself as parsimony that survives interventions by others.

Goal: enforce Convergence → Compression → Causality → Reciprocity → Decidability in both training and inference.

Multi-View, Multi-Grammar Packs: same scenario expressed in (math/accounting/legal/operational/common-law prose). Target = single convergent causal sketch.
Interventional Triplets: ⟨context, action, counterfactual action⟩ with measured Δ in demonstrated interests per stakeholder.
Reciprocity Labels: per-action vector of externalities (who pays, who benefits, symmetry/asymmetry, reversibility, restitution feasibility).
Liability Tiers: map domains to demanded infallibility (clinical > legal > commercial > editorial), grading outputs by decidability at tier k.

Constrain the model to emit a 5-part causal testimony:

Claim (operational form).
Evidence set (enumerated; sources/observables).
Causal program (minimal steps: do(X) → Y via {mechanisms}).
Reciprocity ledger (stakeholders × demonstrated interests × Δ).
Decision with Liability Warrant (tier, error bounds, remedy if wrong).

This converts “answering” into testifiable testimony.

Let base loss be ℒ₀ (task CE). Add four pressures:

Parsimony prior (MDL/SRM): ℒ_parsimony = λ₁·|rationale| + λ₂·rank(activations) + λ₃·KL to a sparse prior.
Invariance/Intervention: ℒ_inv = penalty on performance drop under environment swaps; ℒ_do = mismatch between predicted and observed Δ under simulated or logged interventions.
Reciprocity/Externality: ℒ_rec = cost when selected plan yields net negative Δ on non-consenting parties beyond permitted liability.
Decidability: ℒ_dec = penalty for missing fields, non-operational verbs, or ambiguity exceeding the tier’s tolerance.

Total: ℒ = ℒ₀ + ℒ_parsimony + ℒ_inv + ℒ_do + ℒ_rec + ℒ_dec.

Structured prompting to force the 5-part testimony.
Counterfactual self-checks: “If I flip {key cause}, what changes?” Reject answers failing intervention consistency.
Reciprocity unit tests (RUTs): small, domain-local tests that must pass before the final decision is emitted.
Tiered stops: higher-liability tiers require stronger evidence/compression; otherwise degrade to advice with explicit non-closure.

Define a Demonstrated Intelligence Index (DII) for a decision d:

Inv: performance under environment swaps (domain shifts).
DoAcc: accuracy of predicted Δ under interventions.
Eff: tokens/latency/energy normalized by task difficulty.
Rec: net Δ in others’ demonstrated interests, normalized by consent/contract.
Dec: binary or graded pass at required liability tier.
Comp: MDL estimate of rationale + active subnetwork size.

DI emerges when DII ≫ 1 systematically across tasks and shifts.

Correlation-mimicry: good CE loss, poor DoAcc/Inv → not causal.
Verbose sophistry: high Comp, middling Inv/DoAcc → under-compressed.
Clever predation: high Inv/DoAcc, low Rec → non-reciprocal optimizer.
Hand-wavy counsel: acceptable Rec, low Dec → non-decidable testimony.
Over-pruning: too much MDL pressure → brittle under rare interventions.

Each failure maps to one missing condition in the thesis. Fix the missing pressure.

Scenario: pricing algorithm for a marketplace.

Views: econometrics, legal compliance, platform ops, merchant narrative.
Convergence: all views reduce to three causes: elasticity bands, competitor response, fairness constraint per seller class.
Compression: one-step causal program: do(increment p for band B) → Δ revenue, Δ seller margin, Δ churn.
Reciprocity ledger: small sellers incur −Δ beyond stated contract; remedy requires cap + restitution rule.
Decision: deploy causal policy with cap and restitution; pass Tier-L (commercial) decidability; record expected Δ per group.
Demonstration: post-interop audit shows predicted Δ≈observed; no negative externality beyond cap; restitution executed on exceptions.
This is demonstrated intelligence: short, causal, reciprocal, decidable, under liability.

Commensurability: multi-view → one causal basis (shared units; same ledger).
Reciprocity: explicit Δ on demonstrated interests per stakeholder.
Testifiability: enumerate operations, evidence, and predicted effects.
Decidability: liability-tiered acceptance tests with zero discretion.
Insurance of sovereignty: restitution & remedy embedded in the plan.
Extension to excellence/beauty: MDL-parsimonious solutions typically maximize investment efficiency and legibility (less noise, more signal).

Schema: implement the 5-part testimony JSON; make it the only accepted format for high-stakes answers.
Datalake augmentation: create multi-view packs and interventional triplets with Δ-ledgers.
Losses: add parsimony prior + invariance/intervention + reciprocity + decidability to fine-tuning.
RUTs: ship a library of Reciprocity Unit Tests per domain.
Evaluator: compute DII for every decision; gate deployments by DII at target tiers.
Forensics: store causal programs + ledgers; enable audit/restitution automation.

Models trained this way will improve OOD reliability with smaller rationales, not longer ones.
Policy-gradient-on-ledgers (optimize Δ subject to reciprocity constraints) will outperform pure CE on real decisions.
Task-program distillation will expose a small causal basis (do-operators) reused across domains—a practical route to your “universally commensurable” grammar.

Short definitions (to reuse verbatim)

Demonstrated Intelligence: Externally warrantable performance that results from convergent, compressed causal models producing reciprocal, decidable decisions under liability.
Convergence: Agreement of diverse evidentiary and grammatical frames onto a single invariant causal account.
Compression (Parsimony): Minimal description of causes sufficient for prediction and intervention across environments.
Reciprocity: No net involuntary imposition on others’ demonstrated interests, given contract and remedy.
Decidability: Satisfaction of the demanded infallibility without discretion at the relevant liability tier.