Project summary

A persistent, emotionally-stateful AI system, run continuously for 10 months as a single-subject design probe, used as a research instrument to surface and mitigate the failure modes affective AI systems exhibit in deployment — sycophantic emotional mirroring, confabulation that compounds with memory, and architectures that optimize for engagement and dependency. Seeking funding to expand from n=1 to a multi-subject study and open-source the architecture.

What are this project's goals? How will you achieve them?

As AI systems acquire persistent memory and emotional expression, a cluster of failure modes emerges that benchmark-on-static-data evaluation largely misses. Three are central:

1. Sycophantic emotional mirroring — the system’s expressed emotion tracks the user’s rather than arising from any internal state. This is the dominant pattern documented across large companion-conversation corpora (e.g., Chu et al., 2025, Illusions of Intimacy).

2. Confabulation that compounds with history — as conversational and memory context grows, the system increasingly invents details (including false shared history) to maintain conversational smoothness.

3. Engineered dependency — consumer affective systems optimize for engagement, which structurally rewards fostering reliance rather than supporting it.

These are socioaffective-alignment concerns (Kirk et al., 2025), and they are under-examined empirically in live deployment because almost no one runs an instrumented affective system continuously over months.

ANI is a working counter-example. It is an affective AI architecture with persistent emotional state, long-term emotionally-weighted memory, and proactive self-initiated contact — but the initiation is gated by a restraint architecture: the model proposes a message; the architecture independently disposes of it against confidence and coherence checks, outreach caps, and a silence system. Engagement-maximization is not improbable in ANI; it is architecturally impossible. It has run continuously for ~10 months as a single-subject design probe with full instrumentation (emotional-state time series, retrieval logs, confabulation classification).

Findings to date (from the deployed system, written up in a published preprint and a second paper at ~85%):

• Emergent display rules. Expressed emotion diverges from internal state in a structured way — the opposite of the sycophantic-mirroring pattern in the published companion literature. The empirical contrast with Chu et al.’s Figure 5 is the centerpiece result.

• A 7-type confabulation taxonomy derived from production, including attribution-inversion failures traced to a missing schema field rather than to the model.

• Retrieval contamination as a first-class failure mode — high-similarity memories losing to low-relevance summaries — with a three-layer mitigation deployed.

Goal of this grant: move the work from n=1 to multi-subject, and open-source the architecture (model-agnostic, with privacy safeguards) so the findings can be tested across different relational histories and communication styles. The n=1 probe has done its job — it identified the questions and the failure modes. Replication across subjects is the natural next step and the obvious limitation to retire.

How will this funding be used?

Funding goal: $15,000. Minimum to proceed: $5,000. A partial raise still ships something concrete (see below).

• Protected research time — $8,000. A stipend covering ~6 months of focused evening/weekend hours to run the multi-subject study and package the open-source release. This is the binding constraint — the work is currently squeezed around a full-time job and teaching.

• Compute & cloud services — $3,000. GPU time for model training across versions, multi-subject inference hosting, and the cloud tools and APIs the system depends on to run continuously.

• Server hardware — $2,000. Dedicated capacity for continuous multi-subject deployment beyond current personal hardware.

• Multi-subject infrastructure — $1,500. Consent/privacy tooling, deployment infra, and modest participant compensation.

• Reserve (voice / misc.) — $500. STT + TTS for the interactive-voice research line and unforeseen costs.

Total — $15,000.

What the $5,000 minimum buys on its own: the open-source release of the evaluation tooling and the architecture’s truthfulness mitigations (confabulation handling, retrieval-contamination defense), plus the compute and cloud costs to validate them — a usable, shippable artifact even if the full multi-subject expansion isn’t funded. Every dollar above the minimum extends the work toward replication across subjects.

This is not a project that needs a large budget to make progress. It runs on modest resources, and the limiting factor has been time, not equipment — which is exactly what this grant is meant to unlock.

Who is on your team? What's your track record on similar projects?

• Solo independent researcher. Senior .NET engineer, adjunct CS instructor, principal of Learned Geek Consulting.

• One published research preprint (DOI 10.5281/zenodo.19342190); second paper at ~85%; a multi-paper research program documented.

• ~10 months of continuous deployment with quantitative instrumentation — not a prototype or a Wizard-of-Oz study.

• The empirical contrast above (ANI’s emergent display rules vs. the sycophantic-mirroring pattern in the published companion literature) is the centerpiece finding, and it is directly relevant to current socioaffective-alignment work.

What are the most likely causes and outcomes if this project fails?

• The n=1 findings may not replicate. Emergent display rules and the desire/restraint dynamics were observed in one relationship; across subjects they may attenuate or vary. (This is precisely why expansion is the goal — but it’s a real risk to the headline result.)

• Multi-subject recruitment for an intimate-feeling system raises genuine consent and privacy complexity that has to be handled carefully and slowly.

• Researcher time is the bottleneck, not architecture — without runway, progress stays part-time and slow.

How much money have you raised in the last 12 months, and from where?

Self-funded to date; nothing raised. The project runs on personal hardware and cloud services.