Abstract. Frontier coding performance is typically bought with larger proprietary models at high cost. We introduce ledger-based zero-shot self-orchestration, a training-free method in which fresh instances of one model decompose problems and coordinate through a shared filesystem holding a plan, notes and current solution. Across nine open and closed-weight models on the 100 latest hard LiveCodeBench problems, the method yields gains of up to 23.2 percentage points on pinned backends and offers two routes to frontier-level accuracy. Orchestrated GPT-5.6-Terra reaches 88.0% pass@1 against Fable 5's 90.4% at 19% of the cost, and locally served, open-weight Qwen3.8-27B rises from 69.2% to 92.4%, slightly exceeding Fable 5. Gains are not universal: some models are unchanged or worse. Transcript analysis attributes the gain to decomposition and persistent context. Inference-time organization can approach frontier coding accuracy at a fraction of the cost, or slightly exceed it on self-hostable weights.

— the paper

Needs uv and an API key for the model you want to test.

cd codebase/v2-current
export OPENAI_API_KEY=...

LCB_RELEASE=release_v6 \
ESCALATION_CLOUD_MAX_TOKENS=128000 \
ESCALATION_CLOUD_TIMEOUT=7200 \
MULTIAGENT_MODEL=openai:gpt-5.6-terra \
uv run --no-project --python 3.12 --with 'datasets<4' --with numpy --with anthropic \
  python escalation/run_bench.py --engine multiagent --only lcb --lcb 100 --parallel 8

• --engine multiagent runs the manager; --engine single is the one-call baseline.
• Other models: anthropic:<model>, dashscope:<model>, openrouter:<model>, each with its own *_API_KEY.
• The pass@1 score prints at the end. Results are written to runs/results.json, workspaces to runs/ws/.