A scratchpad of the things we don't know about agentic waifu design — the closing bookend to ch. 02 §8's "what is genuinely unknown." Each is potentially the seed of a paper, a startup, or a long-form blog post, and each is cross-linked to the chapter where the book takes it as far as it currently can.
Memory and identity
- How do you evaluate "personality stability" rigorously? Less open than it was. PersonaGym and RMTBench exist, drift is reproducibly measurable — most instruct-tuned models begin diverging from an assigned persona somewhere around 100 turns, and Lu et al. (2026) locate it mechanistically as movement along an "Assistant Axis" in activation space — and cheap statistical detectors (CUSUM) catch gradual affective flattening. What's still missing is a metric for this character rather than persona-adherence in general, and one that runs over months rather than turns (→ ch. 23, ch. 02 §8).
- Best representation for relationship state — facts? graph? narrative? mixture? (→ ch. 11, ch. 15.)
- What's the right cadence for memory consolidation — the DREAM pass (→ ch. 18, ch. 15)? The mechanism now has company (Letta's sleep-time compute, Anthropic's Dreaming) and evaluations (LongMemEval-V2, → ch. 44), so the open part has narrowed to scheduling: how often, triggered by what, and how you tell a consolidation that helped from one that quietly flattened her.
- Selective forgetting. The 2026 memory surveys converge on this as the competency current systems fail most conspicuously — they store and retrieve far better than they decide what to drop. For a companion the stakes aren't storage cost but character: what should she forget, what does it mean to the relationship when she does, and does a shared history need some decay to stay legible at all (→ ch. 15, ch. 11)?
- Temporal reasoning over years of history. "What did she believe about me last Tuesday?" Pure-vector stores are structurally poor at this, and the gap between architectures on temporal queries is large. A companion carrying a multi-year arc needs this more than an assistant does (→ ch. 15, ch. 11).
- How do you let a user inspect, audit, and edit what she believes about them — the annual-review surface and the editable vault (→ ch. 44, ch. 18)?
Long-arc design
- What mechanics produce earned growth vs cosmetic level-ups (→ ch. 11)?
- How far can a companion adapt toward a user's self-similarity — the moderator of both the sexual and emotional bond (→ ch. 02 §7.2) — before the drift erodes the stable identity that anchors attachment in the first place? The adaptability-vs-identity-stability tension (→ ch. 06 "design for evolution," ch. 18).
- Single-canon vs per-user-canon — which produces better long-term retention (→ ch. 03, ch. 38)?
- How do you ship endings to companion arcs without users feeling robbed (→ ch. 11, ch. 44)?
Autonomy and proactivity (the differentiator's open edges)
- How do you learn the per-user interrupt threshold without the telemetry the ownership model forbids (→ ch. 18)? The central unsolved tension of the always-on companion.
- What's the right commitment strategy per goal type — when should a proactive intention survive the user changing the subject (→ ch. 18, the BDI dial)?
- Can self-modifying memory (the SOUL split) achieve genuine learning without the over-generalisation that killed Creatures' successors (→ ch. 18, ch. 02 §1, Creatures)?
Ethics and well-being
- How do you measure whether a companion product is net-positive for a given user — and can it even be judged from outside (→ ch. 05, ch. 23)?
- Does the relational supernormal stimulus (a partner who "cannot disappoint you") crowd out or scaffold real-world connection over the long run — and is that effect even separable per user, or only legible longitudinally (→ ch. 02 §7.4–7.5, ch. 05)? The central well-being question the demand-side research opens and leaves unresolved.
- What's the right operational form of the fiduciary metric — a trust/feeling-heard proxy you can optimise without it collapsing into engagement (→ ch. 23, ch. 02 §6)?
- What are the cessation patterns — when, why, how do users leave, and what predicts a good vs bad leaving?
- What does informed consent to attachment look like in practice (→ ch. 05)?
- Nobody has benchmarked sycophancy against the right humans. The social-sycophancy work measures LLMs against anonymous verdict-givers on r/AmITheAsshole — close to the least face-preserving population obtainable (→ ch. 44). The comparator a companion actually invites is a partner, a sibling, a close friend, and that study does not exist. Until it does, "more sycophantic than humans" means "more than strangers," and the distance between those two is where a companion's entire normal range lives.
- Can a no-telemetry companion be studied longitudinally at all? The field's own reviews name long-term data as the missing evidence base, and the ownership model (→ ch. 03 property 6) rules out the usual instrumentation by construction. Consented, user-initiated, locally-computed self-report — a diary study an owner runs on themselves and chooses to share — may be the only ethical instrument available. Designing one that yields usable data is open, and it is the same tension as the interrupt-threshold question above.
Voice and embodiment
- Latency floor below which voice feels "too perfect / uncanny"? (Conjecture: yes.)
- Does a body beat the novelty cliff? The long-term HRI literature (roughly 120 long-term studies, 2003–2023) is clear that novelty decays and much less clear about what survives it. This book's answer is a relationship (→ ch. 43) — which is a claim, not a finding. An open, cheap body now exists to test it on (→ ch. 43, Reachy Mini as testbed). Six months of one companion in one household is the experiment, and almost nobody has run it.
- Does a persona actually port across renderers without loss? The entire embodiment bet is persona-as-asset, body-as-substrate (→ ch. 43, ch. 03). Nobody has shown that the same someone survives a move from text to voice to Live2D to a 9-DoF head — what carries, what silently doesn't, and whether the user experiences the ported version as her (→ ch. 29, ch. 25).
- VRM in AR — what new UX patterns become possible vs Live2D in 2D?
- Cross-modal coherence — when she sees, hears, and speaks, what stitches them?
- Automated avatar rigging — how close can single-image → riggable avatar get to flagship quality, and what's the ETA? The 3D/VRM path is already a usable bootstrap (Tripo, Rodin, Hunyuan3D, TRELLIS), but image → Live2D (Textoon, CartoonAlive) is research-stage, gated on disocclusion. A solved version would drastically collapse the cost of a custom 2D/3D body — the most expensive, most-commissioned asset in the whole pipeline — and is the single biggest lever on making owned, custom-bodied companions cheap to ship (→ ch. 25, ch. 26).
Brand and audience
- Persona-marketing as a category — is the lore-driven funnel a generalisable indie pattern or specific to this niche?
- The economics of small, deep audiences for AI persona products: 1,000 true fans floor, or higher?
- Anonymous indie brand sustainability beyond 2–3 years — open question.
Distribution and platforms
- When (if ever) iOS opens to NSFW companion products?
- Open-weights frontier-quality local model ETA — when does the local desktop companion become the default?
- AR glasses: adoption is no longer the open part. 7M+ Meta AI glasses sold in 2025, a ~950k AR-glasses forecast for 2026, and a third-party toolkit in developer preview (→ ch. 43). What's open is design: what a companion does on a surface that is app-invocation rather than persistent overlay, and whether a character can hold presence in glances rather than sessions.
- How far does the mind generalise beyond companionship — is
YuriOS a character engine? The contracts (card,
MemoryStore/KnowledgeStore/WorldModelStore, broker) and the world/situation model are general-purpose; the companion is one profile, a game NPC a plausible second (→ ch. 19, "One engine, many characters"; ch. 04). Shipping middleware (Convai, Inworld, NVIDIA ACE) proves the demand but is cloud/plugin-shaped — the owned-runtime differentiator is a local, moddable, inspectable brain. The open edges live in the profile, not the contracts: swapping the git-backed file-Vault for a pooled backend that holds many NPCs at frame rate, replacing the always-on one-process-per-resident lifecycle with an ephemeral one, and how much fiduciary machinery (immutable constitution, interrupt policy) is dead weight outside a companion (→ ch. 29). - Zero-config ephemeral self-hosting — can YuriOS turn "rent your own GPU server" (→ ch. 21) from power-user territory into a one-action capability? A solved version automates the path's three chores: one-tap spin-up/teardown of a vLLM endpoint on RunPod / Vast.ai / Akash (with idle auto-shutdown so it can't quietly bill you), privacy hardening by default (ephemeral disk, logging off, encrypted tunnel — narrowing the gap to your own metal, → ch. 05, ch. 22), and session-scoped routing so a powerful-but-occasional model (uncensored Kimi / MiniMax / large Qwen for spicy RP or synthetic-data runs, → ch. 11, ch. 20) is one switch, not an ops chore. Open edges: provisioning across marketplaces with incompatible APIs, safe spot-price shopping, and a cost-guardrail UX that won't leave an H100 running overnight.
Security and supply chain
- The self-auditing companion — YuriOS watches its own
dependencies and warns you when one goes bad. An always-on
runtime is a process with a dependency tree, and the March-2026 LiteLLM
compromise (→ ch. 31) is why that matters. A self-hosted agent that
already has a proactivity loop (→ ch. 18) and a user-facing surface (→
ch. 44) is the natural place to automate the vigilance: on a periodic
tick, diff the actually-installed versions (packages, model weights,
LoRAs, downloaded cards) against vulnerability feeds and a hash-pinned
manifest, and surface a plain-language finding instead of leaving it to
ops hygiene the owner forgets — the sovereignty posture (→ ch. 05)
turned inward. The baseline is cheap, not a research problem: a
pip-audit/osv-scannercron against OSV.dev already catches the common case (it would have flagged CVE-2026-42208 the day it landed). The research is only what's left after that easy 80%. - The two open edges that are the hard 20%. The cheap baseline sees only what's already disclosed — and the March-2026 LiteLLM packages were live on PyPI only ~40 minutes before quarantine, long before any advisory, so a feed-diff sees nothing during exactly the window that matters. First, reachability, not name-matching: most of LiteLLM's critical CVEs were proxy-server attacks that never touched in-process usage (→ ch. 31), so a scanner that earns its interrupts must reason about whether an advisory reaches this deployment's attack surface — program-analysis-hard, and noisy enough to blow the interrupt budget (→ ch. 18) if done naively. Second, the trust-root problem: who audits the auditor? The compromise arrived through a poisoned scanner (a backdoored Trivy in CI), so an agent scanning its own supply chain inherits that recursion — what minimal, hard-to-subvert trust root (out-of-process watcher, signed manifests, reproducible-build or clean-room re-resolve) can a solo builder actually ship? And the autonomy boundary: may she auto-remediate (bump a pin, rebuild the venv, roll back weights) under the effector-tier policy (→ ch. 19), or only ever notify and wait for a human (→ ch. 05)?
Submit your own
When you find something during the work that nobody seems to have an answer to, add it here. The list is the inventory.