Chiron

From inference to synthesis

Most systems ask: how do we get a better answer? Chiron asks: how do we get better training data?

Chiron is not a chatbot. It is a dataset factory that treats frontier models as teachers, not oracles. Every task is routed through a pedagogical runtime that forces the teacher to externalize its reasoning in four structured containers — <brainstorming>, <planning>, <thinking>, <final_output> — as specified in the TEACHER.md protocol.

The output is not a single answer. It is a fully traceable reasoning graph: capability checks, rejected alternatives, tool calls, citations. That graph becomes a training example. The goal is recursive self-improvement — use today’s best teachers to create tomorrow’s better student, then promote that student to teacher.

Why containers beat completions

Raw distillation — “ask a big model, train on its answer” — inherits every shortcut the teacher took. The reasoning that makes an answer correct never touches the page, so the student learns the shape of confidence without the substance of verification.

The TEACHER.md protocol exists to externalize that hidden structure:

• <brainstorming> — a capability check, not a plan: what resources, skills, and tools does this task actually require? Rejected approaches are recorded here, which is what makes them trainable signal instead of invisible bias.
• <planning> — a minimal, verifiable task breakdown. Deliberately not forced when the task is simple — synthetic decomposition teaches students to perform process theater.
• <thinking> — the real reasoning, with a hard rule against fabricated self-corrections and simulated test failures. A reasoning trace full of fake pivots trains a student that pivots pointlessly.
• <final_output> — the clean, correct, ready-to-use answer.

Just as important is what the protocol forbids: the teacher is never told it is generating training data. Any awareness of the pipeline contaminates the trace with meta-commentary — the reasoning equivalent of acting for the camera.

The teacher panel

Single-teacher synthesis has a ceiling: the teacher’s blind spots become the student’s blind spots, permanently. Chiron therefore fans raw tasks out to a panel of frontier models, and a judge model fuses their responses into three artifacts: consensus (high-confidence training signal), contradictions (the most valuable examples in the set — this is where the frontier actually is), and blind spots (questions every teacher dodged, routed for harder synthesis or human review).

RAW_TASK ──► DeepSeek V4 Pro ┐
          ──► Kimi K2.6      ├─► JUDGE ──► consensus / contradictions / blind spots
          ──► Gemini 3 Flash ┘              │
                                            ▼
                              TEACHER.md containers → DATASET → train student
                                            │
                                            └────► student promoted → next teacher

Teacher selection is benchmark-driven, not brand-driven. On the DRACO deep research benchmark, the current primary teacher scores at the frontier solo, and the budget panel it anchors beat more expensive solo configurations at roughly half the cost. When a better teacher appears — or a retired one returns — the panel rotates. The pipeline doesn’t care whose name is on the weights.

The recursive loop

The student trained on panel-fused, container-structured data becomes the next-generation teacher — and this is where discipline matters, because naive self-training is a well-documented collapse mechanism. Chiron’s defenses are structural:

1. Contradiction examples stay in the set. Collapse happens when the distribution narrows to what the teacher already finds easy; disagreement regions are exactly what keeps the distribution wide.
2. The judge is anchored outside the loop. Panel fusion uses models that are not descendants of the current student line, so stylistic drift can’t launder itself into ground truth.
3. Evals gate promotion. A student is promoted to teacher only on measured improvement on held-out tasks — the same evals-first discipline as everything else I build.

What it’s for

The near-term target is specialist students: small, fast models that internalize the operating discipline of much larger teachers for specific domains — systems engineering, agentic coding, research synthesis. The long-term bet is the loop itself: if each generation’s student makes a measurably better teacher, dataset quality — not parameter count — becomes the scaling axis you actually control.