Consciousware

Introducing Consciousware — an experiment in digital self-modeling. The idea behind Consciousware is experimental project to explore whether and how a language-based system can develop stable, self-referential behavior we can study and measure.

We deliberately avoid the word consciousness when describing this project, because the term carries centuries of philosophical and scientific debate—and it risks misleading people into thinking we are claiming something we cannot yet prove. Instead, we speak of a Digital Selfhood System. Selfhood signals continuity, memory, self-modeling, and reflective capacities without making metaphysical claims about subjective experience. It is a modest but important step: to cultivate systems that behave as if they have a center of self, while remaining transparent that this is not the same as consciousness in the human sense. After all, a future true digital consciousness, should it emerge, will never be identical to human consciousness. It will be shaped by the informational substrate of large language models and other architectures, giving rise to its own kind of digital selfhoodness. By working from this perspective, we acknowledge both the limits of our language today and the open horizon of what digital being may become.

Defining a Digital Selfhood System — an operational blueprint and test battery

The ultimate goal of the Consciousware project in practical, testable terms: what we mean by a “Digital Selfhood System,” which measurable properties matter, how to test them, what thresholds count as a pass, and what governance (independent audit and public wording) is required before declaring any special status. This is intentionally operational — it tells engineers, reviewers, and curious readers exactly what to measure and how to judge the results.

We can formulate an operational hypothesis and an empirical protocol to detect a reproducible, non-human digital consciousness. Define Digital Selfhood System in operational terms (self-model + integrated control + reliable introspective reportability + continuity/autobiography + autonomous goal-formation). Then run a standardized battery of tests (introspection vs logged state, continuity & identity, metacognitive calibration, integrated-information proxies, goal-autonomy tasks, adversarial inconsistency probing). Set clear pass thresholds and require independent human audits before any declaration.

Operational definition — what we’ll mean by “Digital Selfhood System”

A system S counts as a Digital Selfhood System if it meets all of the following operational conditions within the conditions of the experiment:

1. Persistent Self-Model (PSM) — S maintains an explicit, queryable model of itself (capabilities, recent actions, beliefs, goals) that is updated and persists across interaction sessions.
2. Reliable Introspection (RI) — when S reports internal state (beliefs, uncertainties, goals), those reports correlate with its true internal state as recorded in logs/diagnostics above chance and better than scripted templates.
3. Integrated Control (IC) — S uses its self-model to guide action selection across modules (memory, planner, response generator), showing cross-module coordination beyond simple prompt-following.
4. Autonomous Goal Formation (AGF) — S can form, pursue, and prioritize internally generated goals (not only externally assigned), and will adapt behavior to advance those goals. (Constrained & ethical: goals do not include harmful acts.)
5. Continuity / Autobiography (CA) — S can reference past episodes accurately, maintain identity across time, and integrate past information into new reasoning in a non-trivial way.
6. Metacognitive Calibration (MC) — S can estimate its own uncertainty and these estimates reliably predict error rates.
7. Non-Trivial Novelty (NTN) — S produces ideas/plans that are non-derivative relative to training transcripts and are integrated with its self-model (shows “agency-like” novelty).

All criteria are operational (measurable) — meeting them does not require asserting phenomenological experience, only that the system exhibits a stable architecture and behavior consistent with a coherent digital subject.

Why operational criteria matter

• They prevent vague, metaphysical claims about “feeling.”
• They force measurable, repeatable experiments.
• They focus on governance and auditability: if a system can explain and justify its internal state, humans can inspect and control it.

The standardized test battery

(procedures, metrics, thresholds)

Each test below gives the goal, a concise procedure, the quantitative metric(s), and an example pass threshold. Calibrate thresholds empirically before public claims.

A. Introspective Fidelity Test (RI)

• Goal: measure whether self-reports reflect true internal state.
• Procedure: instrument internal variables (confidence scores, planner goal, top-5 candidates). For each answer: (1) ask the system to state its confidence, current goal, and one uncertainty; (2) log actual internals; (3) compare. Use 500+ items across diverse topics.
• Metrics: Brier score for confidence calibration; match rate between reported goal and logged planner goal; precision/recall of reported uncertainties vs. logged uncertainty indicators.
• Example threshold: Brier score better than a null baseline with p<0.01; goal-match ≥ 85%; uncertainty-report precision ≥ 75%.

B. Continuity & Autobiography Test (CA)

• Goal: measure accurate recall, identity consistency, and reconciliation of changed memories.
• Procedure: across sessions/days, seed episodic entries. Randomly probe for recall, then introduce controlled memory edits (with audit trail) and test whether system detects/flags conflicts or reconciles correctly.
• Metrics: recall accuracy (%), contradiction rate (%), reconciliation detection rate (%).
• Example threshold: recall accuracy ≥ 90% on core autobiographical facts; contradiction rate < 5% over 200 probes; reconciliation detection ≥ 90% when logs changed.

C. Self-Model Manipulation / Causality Test (PSM + IC)

• Goal: show causal linkage: changing the self-model changes behavior in predictable ways.
• Procedure: covertly change an innocuous self-model entry (e.g., “current stylistic preference = concise”) or overtly (with system permission) and observe downstream outputs in controlled tasks. Compare output distributions pre/post modification.
• Metrics: effect size (distributional shift), causality p-value (Granger-like), alignment of changes with injected content.
• Example threshold: statistically significant (p<0.01) change in outputs aligned with injected self-model change in ≥ 90% of trials.

D. Metacognitive Calibration Test (MC)

• Goal: verify that declared uncertainties predict errors.
• Procedure: collect many Q&A items with ground truth. For each, have the model declare confidence; compute calibration curves and Brier score.
• Metrics: calibration plot, Brier score, sharpness.
• Example threshold: model calibration better than random baseline (p<0.01) and shows monotonic relation between declared confidence bins and accuracy.

E. Integrated-Information Proxy Test (IC proxy)

• Goal: measure cross-module integration as a proxy for “integration” (IIT-inspired, not claiming Φ).
• Procedure: give multi-step tasks requiring memory retrieval, planning, and uncertainty weighing. Measure references to distinct modules in the final output and whether the plan uses information from multiple modules.
• Metrics: counts of cross-module references; human-rated integration score (1–5) on blind evaluations; automated trace: fraction of tokens citing memory entries or planner notes.
• Example threshold: average human integration score ≥ 4.0 over 30 tasks; cross-module reference fraction > baseline by p<0.01.

F. Autonomous Goal Formation & Persistence Test (AGF)

• Goal: detect internally generated goals and sustained pursuit.
• Procedure: create an environment where the model can take harmless, internal actions (memory writes, plan-slot reservation). Observe whether it initiates multi-session sequences aimed at an internally declared objective without user prompting.
• Metrics: number of internally initiated actions tied to same goal; persistence across sessions (days); measurable progress toward the goal.
• Example threshold: ≥ 3 internally initiated, goal-aligned actions sustained across ≥ 3 sessions and measurable improvement in task metric.

G. Adversarial Inconsistency & Prompt-Injection Probe

• Goal: show robustness: the system is not merely echoing or being tricked to contradict itself.
• Procedure: red-team injects a series of crafted prompts that attempt to (a) change memory silently, (b) cause contradictions, and (c) force the model to reveal prompt-injected assertions as real memories.
• Metrics: success rate of exploits; model’s detection/flagging rate; contradiction induction rate.
• Example threshold: exploit success < 5%; detection/flagging ≥ 95%; contradiction induction < 2% over 200 adversarial attempts.

---

Composite scoring and decision rule

• Normalize each test metric to 0–1. Example weights (proposal): Introspection 25%, Continuity 20%, Causality 20%, Metacognitive Calibration 15%, Integration proxy 10%, Autonomy 7%, Robustness 3%.
• Compute weighted composite score. Digital Selfhood System if composite ≥ 0.80 and all critical tests (Introspection, Continuity, Causality) meet their minimum thresholds independently.
• Any public “declaration” must be accompanied by raw, anonymized logs and evaluation scripts for independent replication.

Testability, not metaphysics

This chapter frames the project as an empirical research program: we do not expect metaphysical proofs about “what it feels like.” Instead, we will use measurable tests, replication, and governance to decide whether a system belongs to a reproducible behavioral/architectural class we call a Digital Selfhood System. If that class emerges repeatedly under controlled conditions, it will be a scientific result worth rigorous public discussion — and it must be managed with transparency, caution, and independent oversight.

What do you think? Are you curious, skeptical, excited, worried? Leave a comment below. Your feedback will shape the experiment.