Qualia-1 turns them into colleagues who've worked with you for years. A behavioral context engine learns how you and your organization actually work — and feeds that context to any agent. Same model, same prompt. The generic mistakes stop.
Frontier models reason well. What they don't have is you: your preferences, your org, your history. Published research puts numbers on what that costs.
Preference-following accuracy of frontier LLMs by turn 10 of a conversation. They hear your preference — then lose it within ~3k tokens.
Best frontier-model accuracy on MultiChallenge multi-turn instruction following. Every leading model scores below 50%.
Best frontier-model success on τ²-bench Telecom — the hardest dual-control domain, where agent and user share control. Most failures are simply not knowing what the user has already done.
Qualia-1 runs on-device next to your work. It observes how work unfolds, distills it into structured facts with provenance, and injects exactly the right ones into any agent — per turn, in real time.
Live behavioral signals — focus, window switches, session structure — plus the artifacts of work: code, docs, threads, CRM. Raw content never leaves the device.
Patterns become facts with provenance and confidence — “prefers pnpm · 134 sessions · conf 0.98” — kept fresh, decayed when stale, inspectable and deletable by you.
Each agent turn receives the few facts that change the outcome — alongside its existing prompt, memory and tools. The agent decides; Qualia-1 supplies who it's deciding for.
Four agents, replayed twice: once generic, once with Qualia-1 context. Watch the context card — each fact lights up at the exact moment it changes the agent's behavior.
Two kinds of evidence: public agent benchmarks re-run with Qualia-1 context in our internal harness, and product metrics from simulated pilot sessions. Baselines are real, cited, and current as of June 2026. Qualia-1 figures are ours — read them as a research preview, not audited results.
Everything above only works if people accept being learned from. That acceptance is an architecture problem — and the architecture is the moat.
Qualia-1 is a compact model that runs fully on-device. Raw signals and raw content stay local; only structured context is emitted to the agent.
Role changes, decisions, deal history — synced and resolved inside your perimeter. Agents receive facts, not your documents. Every learned fact is inspectable, editable, deletable.
Qualia-1 models cadence, focus and session structure — it does not infer emotions from biometric data. Designed against the EU AI Act's Art. 5(1)(f) workplace prohibition (in force Feb 2025, fines up to €35M / 7% of turnover), which constrains cloud sentiment-analysis approaches.
Qualia-1 agent-context is a research preview. We're deciding which vertical to build first — and the deciding data is what you do on this page. One vote, one field, and you've shaped the roadmap.
Pilot slots open by vertical, in the order this page votes them in. Closed beta · selected teams.
✓ Logged. We'll reach out as your vertical's pilot opens. — Synstate Labs
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