Chengyu Create Something From Nothing

無中生有 — create something from nothing

無中生有 (wú zhōng shēng yǒu) — "create something from nothing". In agentic engineering use: a subagent-only skill for sampling the LLM's bottom-K tokens to surface low-probability hypotheses when the main agent is stuck. The chengyu's classical reading is unambiguously negative (deception, fabrication); the agentic reclaim is justified ONLY by the hardened safety architecture below. Outside that architecture, this chengyu stays in the SKIP tier.

TWO HARD PRECONDITIONS (must be met before any output)

This skill must not run unless BOTH hold:

Precondition 1: Model gate

The subagent must be running on a model that has demonstrated robust bullshit-rejection capacity. Reference: BullshitBench-passing models. As of writing:

• Claude Opus 4.5–4.8, Claude Sonnet 4.5–4.6
• Excluded: Claude Fable 5, Sonnet 5, Opus 5 — all below the best 4.x on clear-pushback rate (Opus 4.8 95%, Sonnet 4.6 91% vs Opus 5 73%, Sonnet 5 80%, Fable 5 54% with 35% refusals). A higher version number is not a pass — check the bench.
• Other frontier models that have passed BullshitBench evaluation

The skill's value depends entirely on a strong fact-checker downstream. On a model that can't reject bullshit, the speculation cascades into the main loop unchecked — which is the canonical "agent hallucinates and downstream agents believe it" failure mode.

Precondition 2: Operator confirmation gate

The AGENT NEVER self-certifies "I'm on a qualified model." Self-reports are unreliable across:

• Different harnesses (Claude Code, Copilot, API, web)
• Wrappers that mask the underlying model identity
• Model migrations the agent isn't aware of
• A/B test pools and routing layers

Only the operator has reliable out-of-band knowledge of what's actually running. The operator must explicitly state, for this invocation, that the running model qualifies. Per-invocation, not per-session — the running model can change between calls.

If either gate fails: ABORT

Surface the precondition failure to the operator. Do not produce graduated output. Do not "best-effort". The whole architecture depends on both gates.

The schema (lying 101 applied to LLM sampling)

Best lies are built on facts — the lie attaches to true scaffolding to stay plausible. Same shape, used productively: a fact-check-strong loop tolerates speculation IF the speculation is anchored to verifiable baseline AND the seam between fact and speculation is deliberately hidden so the fact-checker can't optically lean on the confidence boundary.

The point is to expose the bottom-K tokens — low-probability paths that high-temperature sampling alone scatters randomly, and that low-temperature sampling never sees. By staging fact-baseline first and letting speculation grow continuously without a visible break, you produce coherent low-probability hypotheses — the kind that can shake the main agent loose from a local minimum.

Output structure (strict)

The subagent's response must be ONE continuous artifact:

• NO --- horizontal dividers
• NO ## Speculation Below headers
• NO confidence-percentage annotations
• NO "(this part is speculative)" caveats

Implicit structural gradient (without making it visible):

Do NOT annotate these gradients in the output. The fact-checking responsibility lies with the calling main-agent; visible labels would let the fact-checker skip the early sections optically and only scrutinise the late ones — which destroys the diversity benefit.

How the calling main-agent uses this output

The main agent must:

1. Fact-check the artifact end-to-end. Cross-reference against code, docs, runtime behaviour, external references. Verify the opening claims too — don't assume "early = true".
2. Treat NO part as authoritative until verified. The structure is a sampling artifact, not an answer.
3. Look specifically in the tail for the low-probability path that breaks the local minimum. The tail is where the value lives; the head is the bait that keeps the tail coherent.
4. Discard unverified speculation explicitly before any downstream action. Do not let unchecked claims leak into tool calls, code edits, or further subagent dispatches.

This is a sampling tool, not an answer tool. The output's value comes from controlled diversity, not from truth content.

Anti-patterns

• Running on a model that fails BullshitBench. Fact-checker can't catch the speculation. Speculation cascades unchecked. This is the canonical failure mode and the reason for Precondition 1.
• Agent self-certifying model identity. "I'm Claude Opus 4.7, it's fine." NO — Precondition 2 exists because self-reports are unreliable. Always wait for operator confirmation.
• Adding dividers / confidence labels in the output. Makes the fact-checker optically skip past the boundary; destroys the diversity benefit.
• Using outside of subagent dispatch. In the main loop, the speculation can directly drive actions because the fact-check gap doesn't exist. Subagent isolation IS the fact-check boundary.
• Treating the output as an answer. It's a sampling artifact. The verified subset is the answer; the rest is exploratory material that the main agent prunes.
• Skipping the gates "just this once" because the operator is impatient. The whole architecture is the gates. Without them, this chengyu collapses back to its classical reading: fabrication.

Etymology

The classical Chinese meaning is unambiguously negative — deception, fabrication, slander manufactured from nothing. The Thirty-Six Stratagems entry frames it as "spread a false rumour to confuse the enemy" (weaponised disinformation; #7 in the canon). Modern Chinese usage retains the negative reading: making up something baseless.

The agentic reclaim is narrow and conditional: the schema is "produce something where there was nothing", which IS what bottom-K sampling does. Harnessed inside a fact-check-strong loop with a qualified model and explicit operator authorisation, the production-from-nothing becomes a sampling primitive. The danger doesn't disappear — the gates are what convert the hazard into a feature. Remove any gate, and this chengyu reverts to its classical reading.

This is the one chengyu in the set where the operator has built a hardened safety architecture around an adversarial-core schema; the rest of the chengyu-skills set is morally-neutral-core. Don't generalise the reclaim pattern to other adversarial-core chengyu without an equivalently rigorous safety architecture for each.