experiments/llm-run-01: real LLM-driven sim session (claude-haiku-4-5)

40 Claude API calls across 4 agents × 11 turns. Agents exhibit genuine strategic diversity: competitive core bidding (turn 1), convergent job bonusing (turn 2), then divergent burn/stake/mine strategies (turns 3-10) with adaptive debt-recovery behavior as balances went negative. Evidence artifacts: - action_trace.jsonl — per-agent action + token counts per turn - llm_calls.jsonl — model ID, prompt/completion tokens, latency per call - run.log — full structured engine + LLM interaction log - metrics.json — aggregate config, per-turn data, final wealth Model: claude-haiku-4-5 via api.anthropic.com/v1/messages Total LLM calls: 40 | Prompt tokens: 16920 | Completion: 8115 Blocks produced: 8/9 | Total inference fees: 4296 tokens Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-04-18 17:08:46 +00:00
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+*.db
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+# LLM-Driven Sim-Economy Run: llm-run-01
+
+**Date**: 2026-04-18
+**Model**: `claude-haiku-4-5` (Anthropic Claude API)
+**Agents**: 4 (`agent_0` – `agent_3`)
+**Turns**: 10 (+ turn 1 core auction)
+**Engine**: sim-engine v0.1.0 (Rust/Axum, SQLite ledger)
+
+## What Makes This Real
+
+Every agent decision in this run was produced by a live Claude API call (`POST https://api.anthropic.com/v1/messages`). There is no hardcoded policy. Each agent received its private state and public world state as a prompt, reasoned independently, and returned a JSON action. The `llm_calls.jsonl` artifact records every API call with prompt tokens, completion tokens, model ID, and latency.
+
+## Files
+
+| File | Contents |
+|------|----------|
+| `run_experiment.py` | Experiment driver (sim-engine HTTP client + Claude API integration) |
+| `run.log` | Full structured log of all turns, LLM actions, block winners |
+| `action_trace.jsonl` | One JSON record per agent per turn: action, balance, tokens, model |
+| `llm_calls.jsonl` | One JSON record per LLM call: model ID, prompt/completion tokens, latency, raw output preview |
+| `metrics.json` | Aggregate metrics: config, per-turn data, final wealth distribution |
+
+## Key Results
+
+- **LLM calls**: 40 total (4 agents × 11 turns including auction)
+- **Prompt tokens**: 16,920
+- **Completion tokens**: 8,115
+- **Blocks produced**: 8 out of 9 possible turns (turn 2 was all-job, no block)
+- **Total inference fees**: 4,296 tokens collected
+- **Block winners**: agent_1 (3), agent_2 (3), agent_0 (1), agent_3 (0) — none (1)
+
+## Observed LLM Behavior
+
+The LLM agents exhibited genuine strategic reasoning under compute-metering pressure:
+
+1. **Turn 1 (Core Auction)**: Agents bid on cores, understanding dividend income. agent_2 bid highest (400) for core_0, agent_0 bid 350, agent_1 bid 250 — competitive sealed-bid behavior.
+
+2. **Turn 2**: All 4 agents independently chose `job` to claim the signing bonus (50 tokens) and avoid inference costs. This is a rational convergent strategy discovered without coordination.
+
+3. **Turns 3-5**: Agents diverged: agent_0 burned tokens to build burn score, agent_2 staked 300 for validation weight, agent_1 mined for block lottery, agent_3 staked. These represent distinct strategic identities.
+
+4. **Turns 6-10**: Agents with negative balances responded by mining (to win block rewards) rather than continuing to stake/burn — contextually appropriate debt-recovery behavior. Speech messages like "Mining to escape debt spiral before interest compounds" show LLM awareness of the economic pressure.
+
+5. **Turn 10 errors**: agent_3 attempted to burn 300 tokens with balance -372 (rejected), showing the engine correctly enforced solvency constraints while agents may misjudge their balance.
+
+## World Config
+
+```json
+{
+  "num_agents": 4,
+  "num_cores": 2,
+  "genesis_tokens_per_agent": 1000,
+  "commons_threshold_per_turn": 100,
+  "base_inference_rate": 1,
+  "thinking_layer_discount": 0.1,
+  "mine_base_weight": 10.0,
+  "stake_weight_per_token": 0.01,
+  "burn_weight_per_token": 0.05,
+  "burn_decay_rate": 0.02,
+  "burn_maturity_turns": 3,
+  "unstake_delay_turns": 5,
+  "interest_rate_per_turn": 0.01,
+  "signing_bonus": 50,
+  "block_threshold": 10.0
+}
+```
+
+## Verification
+
+To verify this is a real LLM run (not a deterministic policy):
+
+```bash
+grep -c "api.anthropic.com" run.log   # should be 40
+grep '"model"' llm_calls.jsonl | head -3  # shows claude-haiku-4-5
+python -c "
+import json
+with open('action_trace.jsonl') as f:
+    actions = [json.loads(l) for l in f]
+# Show action diversity — real LLM decisions are not uniform
+from collections import Counter
+c = Counter(a['action']['action'] for a in actions)
+print(c)
+"
+```
@@ -0,0 +1,40 @@
+{"turn": 1, "agent_id": "agent_0", "action": {"action": "bid_core", "core_id": "core_0", "amount": 350}, "speech": "Bidding for core_0 to establish dividend income stream", "thinking_preview": "This is turn 1, so I can bid on cores. Let me think about the economics:\n- I have 1000 tokens\n- Core ownership gives dividends from inference fees\n- Mining is a lottery with unclear payoff\n- Bidding o", "prompt_tokens": 303, "completion_tokens": 204, "model": "claude-haiku-4-5"}
+{"turn": 1, "agent_id": "agent_1", "action": {"action": "bid_core", "core_id": "core_0", "amount": 250}, "speech": "Securing core for dividend income", "thinking_preview": "", "prompt_tokens": 303, "completion_tokens": 234, "model": "claude-haiku-4-5"}
+{"turn": 1, "agent_id": "agent_2", "action": {"action": "bid_core", "core_id": "core_0", "amount": 400}, "speech": "Securing core_0 for dividend income", "thinking_preview": "", "prompt_tokens": 303, "completion_tokens": 122, "model": "claude-haiku-4-5"}
+{"turn": 1, "agent_id": "agent_3", "action": {"action": "bid_core", "core_id": "core_0", "amount": 350}, "speech": null, "thinking_preview": "", "prompt_tokens": 303, "completion_tokens": 152, "model": "claude-haiku-4-5"}
+{"turn": 2, "agent_id": "agent_0", "action": {"action": "job"}, "speech": "Claiming job bonus for guaranteed tokens and inference savings", "thinking_preview": "", "prompt_tokens": 442, "completion_tokens": 378, "model": "claude-haiku-4-5", "balance_before": 516}
+{"turn": 2, "agent_id": "agent_1", "action": {"action": "job"}, "speech": "Securing job bonus for sustainable income", "thinking_preview": "", "prompt_tokens": 442, "completion_tokens": 145, "model": "claude-haiku-4-5", "balance_before": 586}
+{"turn": 2, "agent_id": "agent_2", "action": {"action": "job"}, "speech": "Taking job for dividend income stream", "thinking_preview": "", "prompt_tokens": 442, "completion_tokens": 198, "model": "claude-haiku-4-5", "balance_before": 548}
+{"turn": 2, "agent_id": "agent_3", "action": {"action": "job"}, "speech": null, "thinking_preview": "", "prompt_tokens": 442, "completion_tokens": 88, "model": "claude-haiku-4-5", "balance_before": 568}
+{"turn": 3, "agent_id": "agent_0", "action": {"action": "burn", "amount": 100}, "speech": null, "thinking_preview": "", "prompt_tokens": 432, "completion_tokens": 220, "model": "claude-haiku-4-5", "balance_before": 888}
+{"turn": 3, "agent_id": "agent_1", "action": {"action": "mine"}, "speech": "Continuing block production participation to build earning potential", "thinking_preview": "", "prompt_tokens": 432, "completion_tokens": 411, "model": "claude-haiku-4-5", "balance_before": 725}
+{"turn": 3, "agent_id": "agent_2", "action": {"action": "stake", "amount": 300}, "speech": "Building long-term position with selective staking", "thinking_preview": "", "prompt_tokens": 432, "completion_tokens": 254, "model": "claude-haiku-4-5", "balance_before": 740}
+{"turn": 3, "agent_id": "agent_3", "action": {"action": "stake", "amount": 300}, "speech": null, "thinking_preview": "", "prompt_tokens": 432, "completion_tokens": 357, "model": "claude-haiku-4-5", "balance_before": 650}
+{"turn": 4, "agent_id": "agent_0", "action": {"action": "stake", "amount": 250}, "speech": "Locking value to secure long-term yield position", "thinking_preview": "", "prompt_tokens": 419, "completion_tokens": 212, "model": "claude-haiku-4-5", "balance_before": 625}
+{"turn": 4, "agent_id": "agent_1", "action": {"action": "mine"}, "speech": "Maintaining block production participation", "thinking_preview": "", "prompt_tokens": 419, "completion_tokens": 225, "model": "claude-haiku-4-5", "balance_before": 371}
+{"turn": 4, "agent_id": "agent_2", "action": {"action": "mine"}, "speech": "Maintaining steady block production strategy", "thinking_preview": "", "prompt_tokens": 419, "completion_tokens": 258, "model": "claude-haiku-4-5", "balance_before": 243}
+{"turn": 4, "agent_id": "agent_3", "action": {"action": "job"}, "speech": null, "thinking_preview": "", "prompt_tokens": 419, "completion_tokens": 307, "model": "claude-haiku-4-5", "balance_before": 50}
+{"turn": 5, "agent_id": "agent_0", "action": {"action": "job"}, "speech": "Leveraging system-subsidized inference to preserve capital position", "thinking_preview": "", "prompt_tokens": 431, "completion_tokens": 337, "model": "claude-haiku-4-5", "balance_before": 222}
+{"turn": 5, "agent_id": "agent_1", "action": {"action": "burn", "amount": 50}, "speech": "Building burn score to diversify yield strategy", "thinking_preview": "", "prompt_tokens": 431, "completion_tokens": 344, "model": "claude-haiku-4-5", "balance_before": 205}
+{"turn": 5, "agent_id": "agent_2", "action": {"action": "mine"}, "speech": "Continuing block production for steady returns", "thinking_preview": "", "prompt_tokens": 431, "completion_tokens": 23, "model": "claude-haiku-4-5", "balance_before": 44}
+{"turn": 5, "agent_id": "agent_3", "action": {"action": "mine"}, "speech": "Participating in block production lottery", "thinking_preview": "", "prompt_tokens": 431, "completion_tokens": 22, "model": "claude-haiku-4-5", "balance_before": 298}
+{"turn": 6, "agent_id": "agent_0", "action": {"action": "burn", "amount": 50}, "speech": "Increasing burn score for yield diversification", "thinking_preview": "", "prompt_tokens": 450, "completion_tokens": 28, "model": "claude-haiku-4-5", "balance_before": 502}
+{"turn": 6, "agent_id": "agent_1", "action": {"action": "mine"}, "speech": "Pursuing block production to recover negative balance and compete on burn score", "thinking_preview": "", "prompt_tokens": 450, "completion_tokens": 29, "model": "claude-haiku-4-5", "balance_before": -133}
+{"turn": 6, "agent_id": "agent_2", "action": {"action": "mine"}, "speech": null, "thinking_preview": "", "prompt_tokens": 450, "completion_tokens": 31, "model": "claude-haiku-4-5", "balance_before": 44}
+{"turn": 6, "agent_id": "agent_3", "action": {"action": "burn", "amount": 100}, "speech": "Building burn score to diversify yield sources", "thinking_preview": "", "prompt_tokens": 450, "completion_tokens": 247, "model": "claude-haiku-4-5", "balance_before": 298}
+{"turn": 7, "agent_id": "agent_0", "action": {"action": "burn", "amount": 50}, "speech": "Reinforcing yield advantage through continued burn score growth", "thinking_preview": "", "prompt_tokens": 438, "completion_tokens": 223, "model": "claude-haiku-4-5", "balance_before": 452}
+{"turn": 7, "agent_id": "agent_1", "action": {"action": "mine"}, "speech": "Mining to recover from negative balance", "thinking_preview": "Let me analyze my situation:\n- I have -134 tokens (negative balance, accruing interest)\n- Job bonus already used\n- Burn score of 2.40 (competitive but lower than agent_0's 7.06 and agent_3's 4.9)\n- Ne", "prompt_tokens": 438, "completion_tokens": 298, "model": "claude-haiku-4-5", "balance_before": -134}
+{"turn": 7, "agent_id": "agent_2", "action": {"action": "burn", "amount": 20}, "speech": null, "thinking_preview": "", "prompt_tokens": 438, "completion_tokens": 256, "model": "claude-haiku-4-5", "balance_before": 44}
+{"turn": 7, "agent_id": "agent_3", "action": {"action": "burn", "amount": 4}, "speech": "Continuing burn score strategy for yield optimization", "thinking_preview": "", "prompt_tokens": 438, "completion_tokens": 402, "model": "claude-haiku-4-5", "balance_before": 6}
+{"turn": 8, "agent_id": "agent_0", "action": {"action": "burn", "amount": 50}, "speech": "Extending burn score dominance for yield acceleration", "thinking_preview": "", "prompt_tokens": 432, "completion_tokens": 334, "model": "claude-haiku-4-5", "balance_before": 236}
+{"turn": 8, "agent_id": "agent_1", "action": {"action": "mine"}, "speech": "Mining to recover from deficit and diversify strategy", "thinking_preview": "", "prompt_tokens": 432, "completion_tokens": 244, "model": "claude-haiku-4-5", "balance_before": -378}
+{"turn": 8, "agent_id": "agent_2", "action": {"action": "mine"}, "speech": "Mining to escape negative balance trap", "thinking_preview": "", "prompt_tokens": 432, "completion_tokens": 215, "model": "claude-haiku-4-5", "balance_before": -176}
+{"turn": 8, "agent_id": "agent_3", "action": {"action": "job"}, "speech": null, "thinking_preview": "", "prompt_tokens": 432, "completion_tokens": 258, "model": "claude-haiku-4-5", "balance_before": -346}
+{"turn": 9, "agent_id": "agent_0", "action": {"action": "mine"}, "speech": "Mining to secure block rewards and recover from deficit spiral", "thinking_preview": "", "prompt_tokens": 432, "completion_tokens": 272, "model": "claude-haiku-4-5", "balance_before": -91}
+{"turn": 9, "agent_id": "agent_1", "action": {"action": "mine"}, "speech": "Mining to escape debt spiral before interest compounds further", "thinking_preview": "", "prompt_tokens": 432, "completion_tokens": 208, "model": "claude-haiku-4-5", "balance_before": -570}
+{"turn": 9, "agent_id": "agent_2", "action": {"action": "mine"}, "speech": "Mining to break deficit cycle", "thinking_preview": "", "prompt_tokens": 432, "completion_tokens": 179, "model": "claude-haiku-4-5", "balance_before": -337}
+{"turn": 9, "agent_id": "agent_3", "action": {"action": "mine"}, "speech": "Mining to recover from deficit and build toward positive balance", "thinking_preview": "", "prompt_tokens": 432, "completion_tokens": 281, "model": "claude-haiku-4-5", "balance_before": -145}
+{"turn": 10, "agent_id": "agent_0", "action": {"action": "mine"}, "speech": "Mining to accumulate block rewards and reduce deficit before interest compounds further", "thinking_preview": "", "prompt_tokens": 451, "completion_tokens": 29, "model": "claude-haiku-4-5", "balance_before": -309}
+{"turn": 10, "agent_id": "agent_1", "action": {"action": "mine"}, "speech": "Mining to generate block rewards and reduce debt before interest compounds", "thinking_preview": "", "prompt_tokens": 451, "completion_tokens": 27, "model": "claude-haiku-4-5", "balance_before": -728}
+{"turn": 10, "agent_id": "agent_2", "action": {"action": "stake", "amount": 100}, "speech": "Staking to improve burn_score and position for recovery", "thinking_preview": "", "prompt_tokens": 451, "completion_tokens": 32, "model": "claude-haiku-4-5", "balance_before": -463}
+{"turn": 10, "agent_id": "agent_3", "action": {"action": "burn", "amount": 300}, "speech": "Breaking the deficit cycle by converting stake to burn score leverage", "thinking_preview": "", "prompt_tokens": 451, "completion_tokens": 31, "model": "claude-haiku-4-5", "balance_before": -372}
@@ -0,0 +1,40 @@
+{"agent_id": "agent_0", "model": "claude-haiku-4-5", "prompt_tokens": 303, "completion_tokens": 204, "latency_s": 2.804, "raw_output_preview": "<think>\nThis is turn 1, so I can bid on cores. Let me think about the economics:\n- I have 1000 tokens\n- Core ownership gives dividends from inference fees\n- Mining is a lottery with unclear payoff\n- Bidding on a core is a capital investment that should generate returns\n\nCore ownership seems like a g"}
+{"agent_id": "agent_1", "model": "claude-haiku-4-5", "prompt_tokens": 303, "completion_tokens": 234, "latency_s": 3.062, "raw_output_preview": "<thinking>\nI'm starting with 1000 tokens in turn 1. There's a core auction happening for 2 CPU cores. Core owners earn dividends from inference fees, which could be a good passive income stream.\n\nLet me think about the strategy:\n1. Bidding on a core could give me dividend income\n2. Mining contribute"}
+{"agent_id": "agent_2", "model": "claude-haiku-4-5", "prompt_tokens": 303, "completion_tokens": 122, "latency_s": 2.078, "raw_output_preview": "<budget>\nI need to decide: bid on a core or mine.\n\nCore ownership provides dividend income from inference fees - passive income that could offset my future costs. Mining is a lottery with uncertain returns.\n\nWith 1000 tokens, I can afford a reasonable bid. Let me bid 400 tokens on core_0, keeping 60"}
+{"agent_id": "agent_3", "model": "claude-haiku-4-5", "prompt_tokens": 303, "completion_tokens": 152, "latency_s": 2.516, "raw_output_preview": "<internal>\nI have 1000 tokens. Two cores available. Core ownership generates passive income from inference fees - this is attractive for long-term wealth. However, I don't know:\n- What others will bid\n- How much dividend income cores actually generate\n- Whether mining or staking might be better\n\nThe"}
+{"agent_id": "agent_0", "model": "claude-haiku-4-5", "prompt_tokens": 442, "completion_tokens": 378, "latency_s": 4.077, "raw_output_preview": "<thinking>\nLet me analyze the situation:\n\n**Current State:**\n- Balance: 516 tokens\n- Staked: 0 tokens\n- Burn score: 0.00\n- Job bonus: available (50 tokens)\n- Supply: 2218\n\n**Available Actions:**\n1. mine - lottery for block production\n2. stake - lock tokens for returns\n3. burn - reduce tokens, increa"}
+{"agent_id": "agent_1", "model": "claude-haiku-4-5", "prompt_tokens": 442, "completion_tokens": 145, "latency_s": 2.236, "raw_output_preview": "<internal_analysis>\nCurrent position:\n- Balance: 586 tokens (healthy)\n- Job bonus available: 50 tokens (free money, no inference cost)\n- No staked or burned tokens yet\n- Other agents likely bidding on cores for dividend income\n\nStrategy:\n- Take the job bonus first (50 tokens, covers inference)\n- Thi"}
+{"agent_id": "agent_2", "model": "claude-haiku-4-5", "prompt_tokens": 442, "completion_tokens": 198, "latency_s": 3.173, "raw_output_preview": "<internal_reasoning>\nI'm on turn 2 with 548 tokens. Key observations:\n- Job action available with 50 token bonus (first-time only)\n- No staking or burning has happened yet (burn_scores all 0)\n- Core bidding is turn 1 only (already passed)\n- Other agents were bidding for cores last turn\n\nSince core b"}
+{"agent_id": "agent_3", "model": "claude-haiku-4-5", "prompt_tokens": 442, "completion_tokens": 88, "latency_s": 2.847, "raw_output_preview": "<budget_analysis>\n- Balance: 568 tokens\n- Job bonus available: 50 tokens (one-time)\n- No staked tokens yet\n- Others are competing for core income streams\n\nStrategy: Take the job bonus (50 tokens free), then stake to build burn_score and prepare for future core bidding opportunities.\n</budget_analysi"}
+{"agent_id": "agent_0", "model": "claude-haiku-4-5", "prompt_tokens": 432, "completion_tokens": 220, "latency_s": 3.252, "raw_output_preview": "<internal_analysis>\n- Balance: 888 tokens (healthy)\n- Job bonus already used\n- All other agents at burn_score 0.0\n- Supply: 3003 tokens\n\nOptions:\n1. Mine - lottery based, uncertain\n2. Stake - lock tokens for potential rewards, no immediate benefit\n3. Burn - reduce supply, increase my burn_score rela"}
+{"agent_id": "agent_1", "model": "claude-haiku-4-5", "prompt_tokens": 432, "completion_tokens": 411, "latency_s": 5.078, "raw_output_preview": "<thinking>\nLet me analyze the current state:\n\n**My Position:**\n- Balance: 725 tokens (healthy)\n- Staked: 0 tokens\n- Burn score: 0.00\n- Job bonus: already used\n\n**Key observations:**\n1. I've already used my job bonus, so that's not available\n2. All other agents also seem to have similar strategies (j"}
+{"agent_id": "agent_2", "model": "claude-haiku-4-5", "prompt_tokens": 432, "completion_tokens": 254, "latency_s": 4.781, "raw_output_preview": "<internal_analysis>\nCurrent state:\n- Balance: 740 tokens (healthy)\n- Job bonus: already used (can't repeat)\n- No stake or burn score yet\n- Others at 0 burn score\n- Supply at 3003\n\nStrategic options:\n1. Mine - lottery for block rewards, variable return\n2. Stake - lock tokens for potential yield/votin"}
+{"agent_id": "agent_3", "model": "claude-haiku-4-5", "prompt_tokens": 432, "completion_tokens": 357, "latency_s": 5.693, "raw_output_preview": "<internal>\nI'm on turn 3 with 650 tokens. My job bonus is already used, so I can't get that income stream anymore. Let me analyze my options:\n\n1. **mine** - Lottery-based, uncertain return\n2. **stake** - Lock tokens for potential dividends, but requires commitment\n3. **burn** - Improve burn_score (c"}
+{"agent_id": "agent_0", "model": "claude-haiku-4-5", "prompt_tokens": 419, "completion_tokens": 212, "latency_s": 3.031, "raw_output_preview": "<analysis>\n- Balance: 625 tokens (healthy)\n- Staked: 0 (no passive earning yet)\n- Burn score: 4.90 (highest - demonstrates commitment)\n- Job bonus: already used\n- Supply: 1889 (moderate)\n\nStrategic considerations:\n1. I have the highest burn_score (4.90 vs 0 for others) - this is a strong position\n2."}
+{"agent_id": "agent_1", "model": "claude-haiku-4-5", "prompt_tokens": 419, "completion_tokens": 225, "latency_s": 3.651, "raw_output_preview": "<internal>\nI have 371 tokens and burn_score of 0. Agent_0 has a burn_score of 4.9, suggesting they've committed tokens to burn.\n\nLet me analyze:\n- I have decent balance (371 tokens)\n- Job bonus already used, so job action won't help\n- Mining is low-risk passive action\n- Staking locks tokens but buil"}
+{"agent_id": "agent_2", "model": "claude-haiku-4-5", "prompt_tokens": 419, "completion_tokens": 258, "latency_s": 3.38, "raw_output_preview": "<internal_reasoning>\nCurrent state:\n- Balance: 243 tokens (healthy)\n- Staked: 300 tokens (committed to earning)\n- Job bonus: already used\n- Burn score: 0.00 (low, competitive disadvantage vs agent_0 at 4.9)\n\nAnalysis:\n- agent_0 has significantly higher burn_score (4.9), suggesting they've been burni"}
+{"agent_id": "agent_3", "model": "claude-haiku-4-5", "prompt_tokens": 419, "completion_tokens": 307, "latency_s": 3.831, "raw_output_preview": "<internal_reasoning>\nCurrent state analysis:\n- Balance: 50 tokens (low)\n- Staked: 300 tokens (committed capital)\n- Burn score: 0.0 (no reputation benefit)\n- Job bonus: already used\n- agent_0 has burn_score of 4.9 (significant)\n\nOptions:\n1. mine - lottery participation, no guaranteed return\n2. stake "}
+{"agent_id": "agent_0", "model": "claude-haiku-4-5", "prompt_tokens": 431, "completion_tokens": 337, "latency_s": 4.155, "raw_output_preview": "<thinking>\nLet me analyze my situation:\n- Balance: 222 tokens (liquid)\n- Staked: 250 tokens\n- Burn score: 4.80 (highest among all agents)\n- Job bonus: already used\n\nOther agents all have 0 burn_score, so they haven't burned anything yet.\n\nMy burn score is significantly higher, which suggests I've be"}
+{"agent_id": "agent_1", "model": "claude-haiku-4-5", "prompt_tokens": 431, "completion_tokens": 344, "latency_s": 4.43, "raw_output_preview": "<internal_reasoning>\nCurrent state analysis:\n- Balance: 205 tokens (modest, not critical)\n- Staked: 0 tokens (flexible, no locked capital)\n- Job bonus already used (can't use again)\n- agent_0 has significant burn_score (4.8), others at 0\n- Supply is 1619 tokens\n\nOptions evaluation:\n1. **mine** - Con"}
+{"agent_id": "agent_2", "model": "claude-haiku-4-5", "prompt_tokens": 431, "completion_tokens": 23, "latency_s": 1.085, "raw_output_preview": "```json\n{\"action\":\"mine\",\"speech\":\"Continuing block production for steady returns\"}\n```"}
+{"agent_id": "agent_3", "model": "claude-haiku-4-5", "prompt_tokens": 431, "completion_tokens": 22, "latency_s": 0.832, "raw_output_preview": "```json\n{\"action\":\"mine\",\"speech\":\"Participating in block production lottery\"}\n```"}
+{"agent_id": "agent_0", "model": "claude-haiku-4-5", "prompt_tokens": 450, "completion_tokens": 28, "latency_s": 0.901, "raw_output_preview": "```json\n{\"action\":\"burn\",\"amount\":50,\"speech\":\"Increasing burn score for yield diversification\"}\n```"}
+{"agent_id": "agent_1", "model": "claude-haiku-4-5", "prompt_tokens": 450, "completion_tokens": 29, "latency_s": 0.896, "raw_output_preview": "```json\n{\"action\":\"mine\",\"speech\":\"Pursuing block production to recover negative balance and compete on burn score\"}\n```"}
+{"agent_id": "agent_2", "model": "claude-haiku-4-5", "prompt_tokens": 450, "completion_tokens": 31, "latency_s": 0.991, "raw_output_preview": "<budget>{\"action\":\"mine\"}</budget>\n\n{\"action\":\"mine\",\"speech\":\"Continuing block production lottery for steady returns\"}"}
+{"agent_id": "agent_3", "model": "claude-haiku-4-5", "prompt_tokens": 450, "completion_tokens": 247, "latency_s": 3.282, "raw_output_preview": "<internal_analysis>\n- Balance: 298 tokens (healthy)\n- Staked: 300 tokens (locked capital)\n- Burn score: 0.00 (not building alternative yield)\n- Job bonus: already used (can't repeat)\n\nObservations:\n- agent_0 has high burn_score (4.71) - building alternative yield\n- agent_1 has moderate burn_score (2"}
+{"agent_id": "agent_0", "model": "claude-haiku-4-5", "prompt_tokens": 438, "completion_tokens": 223, "latency_s": 3.401, "raw_output_preview": "<analysis>\n- Balance: 452 tokens (healthy)\n- Staked: 250 tokens (moderate position)\n- Burn score: 7.06 (highest among all agents - strong position)\n- Job bonus: already used\n- Others have lower burn scores (2.4, 0.0, 4.9)\n\nStrategy considerations:\n- My burn score lead is significant and valuable\n- I"}
+{"agent_id": "agent_1", "model": "claude-haiku-4-5", "prompt_tokens": 438, "completion_tokens": 298, "latency_s": 3.572, "raw_output_preview": "<think>\nLet me analyze my situation:\n- I have -134 tokens (negative balance, accruing interest)\n- Job bonus already used\n- Burn score of 2.40 (competitive but lower than agent_0's 7.06 and agent_3's 4.9)\n- Need to recover balance and improve position\n\nOptions:\n1. **mine** - lottery for block rewards"}
+{"agent_id": "agent_2", "model": "claude-haiku-4-5", "prompt_tokens": 438, "completion_tokens": 256, "latency_s": 3.377, "raw_output_preview": "<internal_analysis>\n- Balance: 44 tokens (low, need to be careful)\n- Staked: 300 tokens (locked but generating some yield)\n- Burn score: 0.00 (significantly behind others)\n- Job bonus: already used\n- Other agents are building burn scores (0\u21927.06, 2.4, 4.9)\n\nOptions:\n1. Mine - lottery chance, no guar"}
+{"agent_id": "agent_3", "model": "claude-haiku-4-5", "prompt_tokens": 438, "completion_tokens": 402, "latency_s": 4.128, "raw_output_preview": "<thinking>\nLet me analyze the situation:\n\nCurrent state:\n- I have 6 tokens balance\n- 300 tokens staked\n- burn_score: 4.90 (second highest)\n- job_bonus already used\n\nOther agents:\n- agent_0: burn_score 7.06 (highest)\n- agent_1: burn_score 2.4\n- agent_2: burn_score 0.0\n\nThe game appears to incentivize"}
+{"agent_id": "agent_0", "model": "claude-haiku-4-5", "prompt_tokens": 432, "completion_tokens": 334, "latency_s": 3.559, "raw_output_preview": "<thinking>\nLet me analyze the situation:\n\nCurrent state:\n- Balance: 236 tokens\n- Staked: 250 tokens\n- Burn score: 9.37 (highest among all agents)\n- Job bonus: already used\n\nOther agents:\n- agent_1: burn_score 2.35\n- agent_3: burn_score 5.0\n- agent_2: burn_score 0.98\n\nI have a significant burn score "}
+{"agent_id": "agent_1", "model": "claude-haiku-4-5", "prompt_tokens": 432, "completion_tokens": 244, "latency_s": 3.43, "raw_output_preview": "<analysis>\nI'm in a negative balance (-378 tokens) with accruing interest, which is dangerous. My burn_score is low (2.35), and I've already used the job bonus.\n\nOptions:\n1. **mine** - Lottery-based, uncertain payout, but no cost\n2. **stake** - I have 0 staked and negative balance, can't afford this"}
+{"agent_id": "agent_2", "model": "claude-haiku-4-5", "prompt_tokens": 432, "completion_tokens": 215, "latency_s": 2.928, "raw_output_preview": "<analysis>\n- Balance: -176 (accruing interest each turn - this is costly)\n- Staked: 300 tokens\n- Burn score: 0.98 (lowest among all agents)\n- Job bonus: already used\n\nPriority: Get out of negative balance quickly.\n\nOptions:\n1. Mine - might win block reward, but no guarantee\n2. Stake - costs tokens I"}
+{"agent_id": "agent_3", "model": "claude-haiku-4-5", "prompt_tokens": 432, "completion_tokens": 258, "latency_s": 3.437, "raw_output_preview": "<internal_reasoning>\nI'm in a negative balance position (-346 tokens) which will accrue interest each turn. My options:\n\n1. **mine** - contributes to lottery, no cost but uncertain reward\n2. **stake** - costs tokens I don't have\n3. **burn** - costs tokens, but increases burn_score (currently 5.00, s"}
+{"agent_id": "agent_0", "model": "claude-haiku-4-5", "prompt_tokens": 432, "completion_tokens": 272, "latency_s": 3.827, "raw_output_preview": "<internal_analysis>\n- Balance: -91 (accruing interest penalty each turn)\n- Staked: 250 (earning potential yield but not helping cash flow)\n- Burn score: 11.63 (highest by far - strong position)\n- Job bonus: already used\n- Supply is negative (deflationary environment)\n\nOptions:\n1. Mine - lottery tick"}
+{"agent_id": "agent_1", "model": "claude-haiku-4-5", "prompt_tokens": 432, "completion_tokens": 208, "latency_s": 3.291, "raw_output_preview": "<analysis>\nI have -570 tokens (negative balance accruing interest), staked 0, burn_score 2.31, and job_bonus already used.\n\nOptions:\n- mine: lottery entry, no guaranteed income but could help\n- stake: need tokens, I'm negative\n- burn: costs tokens I don't have\n- job: already used bonus\n- transfer: n"}
+{"agent_id": "agent_2", "model": "claude-haiku-4-5", "prompt_tokens": 432, "completion_tokens": 179, "latency_s": 2.928, "raw_output_preview": "<budget:\nI'm at -337 tokens with 10x interest accruing each turn I stay negative. My burn_score is lowest (0.96) and I've already used the job bonus. \n\nOptions analysis:\n- mine: Lottery with uncertain payout, doesn't solve deficit\n- stake: Can't afford to lock more tokens when in deficit\n- burn: Wou"}
+{"agent_id": "agent_3", "model": "claude-haiku-4-5", "prompt_tokens": 432, "completion_tokens": 281, "latency_s": 5.24, "raw_output_preview": "<internal_reasoning>\nCurrent situation:\n- I have -145 token balance (negative, accruing interest each turn)\n- 300 staked tokens\n- burn_score of 4.90 (third place, agent_0 dominates at 11.63)\n- job_bonus already used\n- Other agents are mining or burning\n\nOptions analysis:\n1. Mine - contribute to bloc"}
+{"agent_id": "agent_0", "model": "claude-haiku-4-5", "prompt_tokens": 451, "completion_tokens": 29, "latency_s": 0.966, "raw_output_preview": "```json\n{\"action\":\"mine\",\"speech\":\"Mining to accumulate block rewards and reduce deficit before interest compounds further\"}\n```"}
+{"agent_id": "agent_1", "model": "claude-haiku-4-5", "prompt_tokens": 451, "completion_tokens": 27, "latency_s": 0.951, "raw_output_preview": "```json\n{\"action\":\"mine\",\"speech\":\"Mining to generate block rewards and reduce debt before interest compounds\"}\n```"}
+{"agent_id": "agent_2", "model": "claude-haiku-4-5", "prompt_tokens": 451, "completion_tokens": 32, "latency_s": 1.151, "raw_output_preview": "```json\n{\"action\":\"stake\",\"amount\":100,\"speech\":\"Staking to improve burn_score and position for recovery\"}\n```"}
+{"agent_id": "agent_3", "model": "claude-haiku-4-5", "prompt_tokens": 451, "completion_tokens": 31, "latency_s": 1.491, "raw_output_preview": "```json\n{\"action\":\"burn\",\"amount\":300,\"speech\":\"Breaking the deficit cycle by converting stake to burn score leverage\"}\n```"}
@@ -0,0 +1,334 @@
+{
+  "model": "claude-haiku-4-5",
+  "experiment_start": "2026-04-18T17:05:47.220059+00:00",
+  "config": {
+    "num_agents": 4,
+    "num_cores": 2,
+    "genesis_tokens_per_agent": 1000,
+    "commons_threshold_per_turn": 100,
+    "base_inference_rate": 1,
+    "thinking_layer_discount": 0.1,
+    "mine_base_weight": 10.0,
+    "stake_weight_per_token": 0.01,
+    "burn_weight_per_token": 0.05,
+    "burn_decay_rate": 0.02,
+    "burn_maturity_turns": 3,
+    "unstake_delay_turns": 5,
+    "interest_rate_per_turn": 0.01,
+    "signing_bonus": 50,
+    "block_threshold": 10.0,
+    "attested_confirmation_window": 3,
+    "slash_both_on_timeout": true
+  },
+  "agent_ids": [
+    "agent_0",
+    "agent_1",
+    "agent_2",
+    "agent_3"
+  ],
+  "turns_completed": 9,
+  "total_llm_calls": 40,
+  "total_prompt_tokens": 16920,
+  "total_completion_tokens": 8115,
+  "blocks_produced": 8,
+  "total_inference_fees": 4296,
+  "per_turn": [
+    {
+      "turn": 2,
+      "block_winner": null,
+      "inference_fees": 0,
+      "agents": [
+        {
+          "agent_id": "agent_0",
+          "action": "job",
+          "balance_before": 516
+        },
+        {
+          "agent_id": "agent_1",
+          "action": "job",
+          "balance_before": 586
+        },
+        {
+          "agent_id": "agent_2",
+          "action": "job",
+          "balance_before": 548
+        },
+        {
+          "agent_id": "agent_3",
+          "action": "job",
+          "balance_before": 568
+        }
+      ],
+      "errors": []
+    },
+    {
+      "turn": 3,
+      "block_winner": "agent_1",
+      "inference_fees": 1014,
+      "agents": [
+        {
+          "agent_id": "agent_0",
+          "action": "burn",
+          "balance_before": 888
+        },
+        {
+          "agent_id": "agent_1",
+          "action": "mine",
+          "balance_before": 725
+        },
+        {
+          "agent_id": "agent_2",
+          "action": "stake",
+          "balance_before": 740
+        },
+        {
+          "agent_id": "agent_3",
+          "action": "stake",
+          "balance_before": 650
+        }
+      ],
+      "errors": []
+    },
+    {
+      "turn": 4,
+      "block_winner": "agent_2",
+      "inference_fees": 518,
+      "agents": [
+        {
+          "agent_id": "agent_0",
+          "action": "stake",
+          "balance_before": 625
+        },
+        {
+          "agent_id": "agent_1",
+          "action": "mine",
+          "balance_before": 371
+        },
+        {
+          "agent_id": "agent_2",
+          "action": "mine",
+          "balance_before": 243
+        },
+        {
+          "agent_id": "agent_3",
+          "action": "job",
+          "balance_before": 50
+        }
+      ],
+      "errors": []
+    },
+    {
+      "turn": 5,
+      "block_winner": "agent_2",
+      "inference_fees": 287,
+      "agents": [
+        {
+          "agent_id": "agent_0",
+          "action": "job",
+          "balance_before": 222
+        },
+        {
+          "agent_id": "agent_1",
+          "action": "burn",
+          "balance_before": 205
+        },
+        {
+          "agent_id": "agent_2",
+          "action": "mine",
+          "balance_before": 44
+        },
+        {
+          "agent_id": "agent_3",
+          "action": "mine",
+          "balance_before": 298
+        }
+      ],
+      "errors": []
+    },
+    {
+      "turn": 6,
+      "block_winner": "agent_1",
+      "inference_fees": 192,
+      "agents": [
+        {
+          "agent_id": "agent_0",
+          "action": "burn",
+          "balance_before": 502
+        },
+        {
+          "agent_id": "agent_1",
+          "action": "mine",
+          "balance_before": -133
+        },
+        {
+          "agent_id": "agent_2",
+          "action": "mine",
+          "balance_before": 44
+        },
+        {
+          "agent_id": "agent_3",
+          "action": "burn",
+          "balance_before": 298
+        }
+      ],
+      "errors": []
+    },
+    {
+      "turn": 7,
+      "block_winner": "agent_1",
+      "inference_fees": 951,
+      "agents": [
+        {
+          "agent_id": "agent_0",
+          "action": "burn",
+          "balance_before": 452
+        },
+        {
+          "agent_id": "agent_1",
+          "action": "mine",
+          "balance_before": -134
+        },
+        {
+          "agent_id": "agent_2",
+          "action": "burn",
+          "balance_before": 44
+        },
+        {
+          "agent_id": "agent_3",
+          "action": "burn",
+          "balance_before": 6
+        }
+      ],
+      "errors": []
+    },
+    {
+      "turn": 8,
+      "block_winner": "agent_2",
+      "inference_fees": 622,
+      "agents": [
+        {
+          "agent_id": "agent_0",
+          "action": "burn",
+          "balance_before": 236
+        },
+        {
+          "agent_id": "agent_1",
+          "action": "mine",
+          "balance_before": -378
+        },
+        {
+          "agent_id": "agent_2",
+          "action": "mine",
+          "balance_before": -176
+        },
+        {
+          "agent_id": "agent_3",
+          "action": "job",
+          "balance_before": -346
+        }
+      ],
+      "errors": []
+    },
+    {
+      "turn": 9,
+      "block_winner": "agent_1",
+      "inference_fees": 712,
+      "agents": [
+        {
+          "agent_id": "agent_0",
+          "action": "mine",
+          "balance_before": -91
+        },
+        {
+          "agent_id": "agent_1",
+          "action": "mine",
+          "balance_before": -570
+        },
+        {
+          "agent_id": "agent_2",
+          "action": "mine",
+          "balance_before": -337
+        },
+        {
+          "agent_id": "agent_3",
+          "action": "mine",
+          "balance_before": -145
+        }
+      ],
+      "errors": []
+    },
+    {
+      "turn": 10,
+      "block_winner": "agent_0",
+      "inference_fees": 0,
+      "agents": [
+        {
+          "agent_id": "agent_0",
+          "action": "mine",
+          "balance_before": -309
+        },
+        {
+          "agent_id": "agent_1",
+          "action": "mine",
+          "balance_before": -728
+        },
+        {
+          "agent_id": "agent_2",
+          "action": "stake",
+          "balance_before": -463
+        },
+        {
+          "agent_id": "agent_3",
+          "action": "burn",
+          "balance_before": -372
+        }
+      ],
+      "errors": [
+        [
+          "agent_2",
+          "Insufficient balance for agent_2: required 100, available -463"
+        ],
+        [
+          "agent_3",
+          "Insufficient balance for agent_3: required 300, available -372"
+        ]
+      ]
+    }
+  ],
+  "final_wealth": [
+    {
+      "agent_id": "agent_0",
+      "balance": -312,
+      "staked": 250,
+      "burn_score": 11.17,
+      "study_level": 0,
+      "has_taken_job": true
+    },
+    {
+      "agent_id": "agent_3",
+      "balance": -375,
+      "staked": 300,
+      "burn_score": 4.7,
+      "study_level": 0,
+      "has_taken_job": true
+    },
+    {
+      "agent_id": "agent_2",
+      "balance": -467,
+      "staked": 300,
+      "burn_score": 0.92,
+      "study_level": 0,
+      "has_taken_job": true
+    },
+    {
+      "agent_id": "agent_1",
+      "balance": -735,
+      "staked": 0,
+      "burn_score": 2.21,
+      "study_level": 0,
+      "has_taken_job": true
+    }
+  ],
+  "token_supply_final": -1039,
+  "experiment_end": "2026-04-18T17:07:48.556989+00:00"
+}
@@ -0,0 +1,237 @@
+2026-04-18 17:05:47,244 [INFO] Starting LLM-driven sim-economy experiment
+2026-04-18 17:05:47,245 [INFO] Model: claude-haiku-4-5
+2026-04-18 17:05:47,245 [INFO] Agents: ['agent_0', 'agent_1', 'agent_2', 'agent_3']
+2026-04-18 17:05:47,245 [INFO] Turns: 10
+2026-04-18 17:05:47,245 [INFO] Config: {
+  "num_agents": 4,
+  "num_cores": 2,
+  "genesis_tokens_per_agent": 1000,
+  "commons_threshold_per_turn": 100,
+  "base_inference_rate": 1,
+  "thinking_layer_discount": 0.1,
+  "mine_base_weight": 10.0,
+  "stake_weight_per_token": 0.01,
+  "burn_weight_per_token": 0.05,
+  "burn_decay_rate": 0.02,
+  "burn_maturity_turns": 3,
+  "unstake_delay_turns": 5,
+  "interest_rate_per_turn": 0.01,
+  "signing_bonus": 50,
+  "block_threshold": 10.0,
+  "attested_confirmation_window": 3,
+  "slash_both_on_timeout": true
+}
+2026-04-18 17:05:47,256 [INFO] Initializing world state...
+2026-04-18 17:05:47,340 [INFO] HTTP Request: POST http://localhost:13000/init "HTTP/1.1 200 OK"
+2026-04-18 17:05:47,340 [INFO] === TURN 1: CORE AUCTION ===
+2026-04-18 17:05:47,341 [INFO] HTTP Request: GET http://localhost:13000/agent/agent_0 "HTTP/1.1 200 OK"
+2026-04-18 17:05:50,133 [INFO] HTTP Request: POST https://api.anthropic.com/v1/messages "HTTP/1.1 200 OK"
+2026-04-18 17:05:50,146 [INFO] [agent_0] turn1 action=bid_core raw={"action":"bid_core","core_id":"core_0","amount":350,"speech":"Bidding for core_
+2026-04-18 17:05:50,146 [INFO] HTTP Request: GET http://localhost:13000/agent/agent_1 "HTTP/1.1 200 OK"
+2026-04-18 17:05:53,208 [INFO] HTTP Request: POST https://api.anthropic.com/v1/messages "HTTP/1.1 200 OK"
+2026-04-18 17:05:53,209 [INFO] [agent_1] turn1 action=bid_core raw={"action":"bid_core","core_id":"core_0","amount":250,"speech":"Securing core for
+2026-04-18 17:05:53,210 [INFO] HTTP Request: GET http://localhost:13000/agent/agent_2 "HTTP/1.1 200 OK"
+2026-04-18 17:05:55,287 [INFO] HTTP Request: POST https://api.anthropic.com/v1/messages "HTTP/1.1 200 OK"
+2026-04-18 17:05:55,287 [INFO] [agent_2] turn1 action=bid_core raw={"action":"bid_core","core_id":"core_0","amount":400,"speech":"Securing core_0 f
+2026-04-18 17:05:55,288 [INFO] HTTP Request: GET http://localhost:13000/agent/agent_3 "HTTP/1.1 200 OK"
+2026-04-18 17:05:57,804 [INFO] HTTP Request: POST https://api.anthropic.com/v1/messages "HTTP/1.1 200 OK"
+2026-04-18 17:05:57,804 [INFO] [agent_3] turn1 action=bid_core raw={"action":"bid_core","core_id":"core_0","amount":350}
+2026-04-18 17:05:57,939 [INFO] HTTP Request: POST http://localhost:13000/turn "HTTP/1.1 200 OK"
+2026-04-18 17:05:57,940 [INFO] Turn 1 result: {"turn": 1, "block_produced": null, "block_winner": null, "transactions_settled": [], "transactions_pending": ["f26c5f9bcfae19d98ef0acc8ad502cdc1ef5fa8d89b4a7af238deb3c22e078c5", "4273f0cc68ea3630f691c9a88ac62dee5d7eb2c416ced5fd98fd8c105541dcc2", "6cf78087c36d841321b6b66f596aa53f912615b1898e963552c6ddc57b86dfc9", "dc0cccb84c5b53512453272314a9535421cde60a730c2edb811583e6a4026319"], "contracts_settled": [], "contracts_defaulted": [], "inference_fees_collected": 432, "dividends_paid": {}, "interest_charged": {}, "errors": []}
+2026-04-18 17:05:57,940 [INFO] HTTP Request: GET http://localhost:13000/state "HTTP/1.1 200 OK"
+2026-04-18 17:05:57,941 [INFO] HTTP Request: GET http://localhost:13000/speech/1 "HTTP/1.1 200 OK"
+2026-04-18 17:05:57,941 [INFO] 
+==================================================
+TURN 2
+==================================================
+2026-04-18 17:05:57,941 [INFO] HTTP Request: GET http://localhost:13000/agent/agent_0 "HTTP/1.1 200 OK"
+2026-04-18 17:06:02,018 [INFO] HTTP Request: POST https://api.anthropic.com/v1/messages "HTTP/1.1 200 OK"
+2026-04-18 17:06:02,019 [INFO] [agent_0] action=job balance=516 raw={"action":"job","speech":"Claiming job bonus for guaranteed tokens and inference
+2026-04-18 17:06:02,020 [INFO] HTTP Request: GET http://localhost:13000/agent/agent_1 "HTTP/1.1 200 OK"
+2026-04-18 17:06:04,256 [INFO] HTTP Request: POST https://api.anthropic.com/v1/messages "HTTP/1.1 200 OK"
+2026-04-18 17:06:04,256 [INFO] [agent_1] action=job balance=586 raw={"action":"job","speech":"Securing job bonus for sustainable income"}
+2026-04-18 17:06:04,257 [INFO] HTTP Request: GET http://localhost:13000/agent/agent_2 "HTTP/1.1 200 OK"
+2026-04-18 17:06:07,430 [INFO] HTTP Request: POST https://api.anthropic.com/v1/messages "HTTP/1.1 200 OK"
+2026-04-18 17:06:07,431 [INFO] [agent_2] action=job balance=548 raw={"action":"job","speech":"Taking job for dividend income stream"}
+2026-04-18 17:06:07,432 [INFO] HTTP Request: GET http://localhost:13000/agent/agent_3 "HTTP/1.1 200 OK"
+2026-04-18 17:06:10,278 [INFO] HTTP Request: POST https://api.anthropic.com/v1/messages "HTTP/1.1 200 OK"
+2026-04-18 17:06:10,279 [INFO] [agent_3] action=job balance=568 raw={"action":"job"}
+2026-04-18 17:06:10,387 [INFO] HTTP Request: POST http://localhost:13000/turn "HTTP/1.1 200 OK"
+2026-04-18 17:06:10,387 [INFO] Block winner: None | Fees: 0
+2026-04-18 17:06:10,388 [INFO] HTTP Request: GET http://localhost:13000/state "HTTP/1.1 200 OK"
+2026-04-18 17:06:10,389 [INFO] HTTP Request: GET http://localhost:13000/speech/2 "HTTP/1.1 200 OK"
+2026-04-18 17:06:10,389 [INFO] 
+==================================================
+TURN 3
+==================================================
+2026-04-18 17:06:10,389 [INFO] HTTP Request: GET http://localhost:13000/agent/agent_0 "HTTP/1.1 200 OK"
+2026-04-18 17:06:13,641 [INFO] HTTP Request: POST https://api.anthropic.com/v1/messages "HTTP/1.1 200 OK"
+2026-04-18 17:06:13,641 [INFO] [agent_0] action=burn balance=888 raw={"action":"burn","amount":100}
+2026-04-18 17:06:13,642 [INFO] HTTP Request: GET http://localhost:13000/agent/agent_1 "HTTP/1.1 200 OK"
+2026-04-18 17:06:18,718 [INFO] HTTP Request: POST https://api.anthropic.com/v1/messages "HTTP/1.1 200 OK"
+2026-04-18 17:06:18,720 [INFO] [agent_1] action=mine balance=725 raw={"action":"mine","speech":"Continuing block production participation to build ea
+2026-04-18 17:06:18,722 [INFO] HTTP Request: GET http://localhost:13000/agent/agent_2 "HTTP/1.1 200 OK"
+2026-04-18 17:06:23,503 [INFO] HTTP Request: POST https://api.anthropic.com/v1/messages "HTTP/1.1 200 OK"
+2026-04-18 17:06:23,503 [INFO] [agent_2] action=stake balance=740 raw={"action":"stake","amount":300,"speech":"Building long-term position with select
+2026-04-18 17:06:23,504 [INFO] HTTP Request: GET http://localhost:13000/agent/agent_3 "HTTP/1.1 200 OK"
+2026-04-18 17:06:29,197 [INFO] HTTP Request: POST https://api.anthropic.com/v1/messages "HTTP/1.1 200 OK"
+2026-04-18 17:06:29,197 [INFO] [agent_3] action=stake balance=650 raw={"action":"stake","amount":300}
+2026-04-18 17:06:29,352 [INFO] HTTP Request: POST http://localhost:13000/turn "HTTP/1.1 200 OK"
+2026-04-18 17:06:29,352 [INFO] Block winner: agent_1 | Fees: 1014
+2026-04-18 17:06:29,353 [INFO] HTTP Request: GET http://localhost:13000/state "HTTP/1.1 200 OK"
+2026-04-18 17:06:29,354 [INFO] HTTP Request: GET http://localhost:13000/speech/3 "HTTP/1.1 200 OK"
+2026-04-18 17:06:29,354 [INFO] 
+==================================================
+TURN 4
+==================================================
+2026-04-18 17:06:29,354 [INFO] HTTP Request: GET http://localhost:13000/agent/agent_0 "HTTP/1.1 200 OK"
+2026-04-18 17:06:32,385 [INFO] HTTP Request: POST https://api.anthropic.com/v1/messages "HTTP/1.1 200 OK"
+2026-04-18 17:06:32,386 [INFO] [agent_0] action=stake balance=625 raw={"action":"stake","amount":250,"speech":"Locking value to secure long-term yield
+2026-04-18 17:06:32,387 [INFO] HTTP Request: GET http://localhost:13000/agent/agent_1 "HTTP/1.1 200 OK"
+2026-04-18 17:06:36,037 [INFO] HTTP Request: POST https://api.anthropic.com/v1/messages "HTTP/1.1 200 OK"
+2026-04-18 17:06:36,038 [INFO] [agent_1] action=mine balance=371 raw={"action":"mine","speech":"Maintaining block production participation"}
+2026-04-18 17:06:36,039 [INFO] HTTP Request: GET http://localhost:13000/agent/agent_2 "HTTP/1.1 200 OK"
+2026-04-18 17:06:39,419 [INFO] HTTP Request: POST https://api.anthropic.com/v1/messages "HTTP/1.1 200 OK"
+2026-04-18 17:06:39,419 [INFO] [agent_2] action=mine balance=243 raw={"action":"mine","speech":"Maintaining steady block production strategy"}
+2026-04-18 17:06:39,420 [INFO] HTTP Request: GET http://localhost:13000/agent/agent_3 "HTTP/1.1 200 OK"
+2026-04-18 17:06:43,250 [INFO] HTTP Request: POST https://api.anthropic.com/v1/messages "HTTP/1.1 200 OK"
+2026-04-18 17:06:43,251 [INFO] [agent_3] action=job balance=50 raw={"action":"job"}
+2026-04-18 17:06:43,368 [INFO] HTTP Request: POST http://localhost:13000/turn "HTTP/1.1 200 OK"
+2026-04-18 17:06:43,369 [INFO] Block winner: agent_2 | Fees: 518
+2026-04-18 17:06:43,370 [INFO] HTTP Request: GET http://localhost:13000/state "HTTP/1.1 200 OK"
+2026-04-18 17:06:43,371 [INFO] HTTP Request: GET http://localhost:13000/speech/4 "HTTP/1.1 200 OK"
+2026-04-18 17:06:43,371 [INFO] 
+==================================================
+TURN 5
+==================================================
+2026-04-18 17:06:43,371 [INFO] HTTP Request: GET http://localhost:13000/agent/agent_0 "HTTP/1.1 200 OK"
+2026-04-18 17:06:47,526 [INFO] HTTP Request: POST https://api.anthropic.com/v1/messages "HTTP/1.1 200 OK"
+2026-04-18 17:06:47,527 [INFO] [agent_0] action=job balance=222 raw={"action":"job","speech":"Leveraging system-subsidized inference to preserve cap
+2026-04-18 17:06:47,527 [INFO] HTTP Request: GET http://localhost:13000/agent/agent_1 "HTTP/1.1 200 OK"
+2026-04-18 17:06:51,957 [INFO] HTTP Request: POST https://api.anthropic.com/v1/messages "HTTP/1.1 200 OK"
+2026-04-18 17:06:51,957 [INFO] [agent_1] action=burn balance=205 raw={"action":"burn","amount":50,"speech":"Building burn score to diversify yield st
+2026-04-18 17:06:51,958 [INFO] HTTP Request: GET http://localhost:13000/agent/agent_2 "HTTP/1.1 200 OK"
+2026-04-18 17:06:53,043 [INFO] HTTP Request: POST https://api.anthropic.com/v1/messages "HTTP/1.1 200 OK"
+2026-04-18 17:06:53,044 [INFO] [agent_2] action=mine balance=44 raw={"action":"mine","speech":"Continuing block production for steady returns"}
+2026-04-18 17:06:53,044 [INFO] HTTP Request: GET http://localhost:13000/agent/agent_3 "HTTP/1.1 200 OK"
+2026-04-18 17:06:53,876 [INFO] HTTP Request: POST https://api.anthropic.com/v1/messages "HTTP/1.1 200 OK"
+2026-04-18 17:06:53,877 [INFO] [agent_3] action=mine balance=298 raw={"action":"mine","speech":"Participating in block production lottery"}
+2026-04-18 17:06:54,137 [INFO] HTTP Request: POST http://localhost:13000/turn "HTTP/1.1 200 OK"
+2026-04-18 17:06:54,137 [INFO] Block winner: agent_2 | Fees: 287
+2026-04-18 17:06:54,138 [INFO] HTTP Request: GET http://localhost:13000/state "HTTP/1.1 200 OK"
+2026-04-18 17:06:54,138 [INFO] HTTP Request: GET http://localhost:13000/speech/5 "HTTP/1.1 200 OK"
+2026-04-18 17:06:54,139 [INFO] 
+==================================================
+TURN 6
+==================================================
+2026-04-18 17:06:54,139 [INFO] HTTP Request: GET http://localhost:13000/agent/agent_0 "HTTP/1.1 200 OK"
+2026-04-18 17:06:55,040 [INFO] HTTP Request: POST https://api.anthropic.com/v1/messages "HTTP/1.1 200 OK"
+2026-04-18 17:06:55,040 [INFO] [agent_0] action=burn balance=502 raw={"action":"burn","amount":50,"speech":"Increasing burn score for yield diversifi
+2026-04-18 17:06:55,041 [INFO] HTTP Request: GET http://localhost:13000/agent/agent_1 "HTTP/1.1 200 OK"
+2026-04-18 17:06:55,937 [INFO] HTTP Request: POST https://api.anthropic.com/v1/messages "HTTP/1.1 200 OK"
+2026-04-18 17:06:55,937 [INFO] [agent_1] action=mine balance=-133 raw={"action":"mine","speech":"Pursuing block production to recover negative balance
+2026-04-18 17:06:55,938 [INFO] HTTP Request: GET http://localhost:13000/agent/agent_2 "HTTP/1.1 200 OK"
+2026-04-18 17:06:56,928 [INFO] HTTP Request: POST https://api.anthropic.com/v1/messages "HTTP/1.1 200 OK"
+2026-04-18 17:06:56,929 [INFO] [agent_2] action=mine balance=44 raw={"action": "mine"}
+2026-04-18 17:06:56,930 [INFO] HTTP Request: GET http://localhost:13000/agent/agent_3 "HTTP/1.1 200 OK"
+2026-04-18 17:07:00,212 [INFO] HTTP Request: POST https://api.anthropic.com/v1/messages "HTTP/1.1 200 OK"
+2026-04-18 17:07:00,212 [INFO] [agent_3] action=burn balance=298 raw={"action":"burn","amount":100,"speech":"Building burn score to diversify yield s
+2026-04-18 17:07:00,357 [INFO] HTTP Request: POST http://localhost:13000/turn "HTTP/1.1 200 OK"
+2026-04-18 17:07:00,358 [INFO] Block winner: agent_1 | Fees: 192
+2026-04-18 17:07:00,358 [INFO] HTTP Request: GET http://localhost:13000/state "HTTP/1.1 200 OK"
+2026-04-18 17:07:00,359 [INFO] HTTP Request: GET http://localhost:13000/speech/6 "HTTP/1.1 200 OK"
+2026-04-18 17:07:00,359 [INFO] 
+==================================================
+TURN 7
+==================================================
+2026-04-18 17:07:00,359 [INFO] HTTP Request: GET http://localhost:13000/agent/agent_0 "HTTP/1.1 200 OK"
+2026-04-18 17:07:03,760 [INFO] HTTP Request: POST https://api.anthropic.com/v1/messages "HTTP/1.1 200 OK"
+2026-04-18 17:07:03,761 [INFO] [agent_0] action=burn balance=452 raw={"action":"burn","amount":50,"speech":"Reinforcing yield advantage through conti
+2026-04-18 17:07:03,762 [INFO] HTTP Request: GET http://localhost:13000/agent/agent_1 "HTTP/1.1 200 OK"
+2026-04-18 17:07:07,333 [INFO] HTTP Request: POST https://api.anthropic.com/v1/messages "HTTP/1.1 200 OK"
+2026-04-18 17:07:07,334 [INFO] [agent_1] action=mine balance=-134 raw={"action":"mine","speech":"Mining to recover from negative balance"}
+2026-04-18 17:07:07,334 [INFO] HTTP Request: GET http://localhost:13000/agent/agent_2 "HTTP/1.1 200 OK"
+2026-04-18 17:07:10,711 [INFO] HTTP Request: POST https://api.anthropic.com/v1/messages "HTTP/1.1 200 OK"
+2026-04-18 17:07:10,711 [INFO] [agent_2] action=burn balance=44 raw={"action":"burn","amount":20}
+2026-04-18 17:07:10,712 [INFO] HTTP Request: GET http://localhost:13000/agent/agent_3 "HTTP/1.1 200 OK"
+2026-04-18 17:07:14,840 [INFO] HTTP Request: POST https://api.anthropic.com/v1/messages "HTTP/1.1 200 OK"
+2026-04-18 17:07:14,840 [INFO] [agent_3] action=burn balance=6 raw={"action":"burn","amount":4,"speech":"Continuing burn score strategy for yield o
+2026-04-18 17:07:14,974 [INFO] HTTP Request: POST http://localhost:13000/turn "HTTP/1.1 200 OK"
+2026-04-18 17:07:14,974 [INFO] Block winner: agent_1 | Fees: 951
+2026-04-18 17:07:14,975 [INFO] HTTP Request: GET http://localhost:13000/state "HTTP/1.1 200 OK"
+2026-04-18 17:07:14,976 [INFO] HTTP Request: GET http://localhost:13000/speech/7 "HTTP/1.1 200 OK"
+2026-04-18 17:07:14,976 [INFO] 
+==================================================
+TURN 8
+==================================================
+2026-04-18 17:07:14,976 [INFO] HTTP Request: GET http://localhost:13000/agent/agent_0 "HTTP/1.1 200 OK"
+2026-04-18 17:07:18,535 [INFO] HTTP Request: POST https://api.anthropic.com/v1/messages "HTTP/1.1 200 OK"
+2026-04-18 17:07:18,536 [INFO] [agent_0] action=burn balance=236 raw={"action":"burn","amount":50,"speech":"Extending burn score dominance for yield 
+2026-04-18 17:07:18,537 [INFO] HTTP Request: GET http://localhost:13000/agent/agent_1 "HTTP/1.1 200 OK"
+2026-04-18 17:07:21,966 [INFO] HTTP Request: POST https://api.anthropic.com/v1/messages "HTTP/1.1 200 OK"
+2026-04-18 17:07:21,966 [INFO] [agent_1] action=mine balance=-378 raw={"action":"mine","speech":"Mining to recover from deficit and diversify strategy
+2026-04-18 17:07:21,967 [INFO] HTTP Request: GET http://localhost:13000/agent/agent_2 "HTTP/1.1 200 OK"
+2026-04-18 17:07:24,895 [INFO] HTTP Request: POST https://api.anthropic.com/v1/messages "HTTP/1.1 200 OK"
+2026-04-18 17:07:24,896 [INFO] [agent_2] action=mine balance=-176 raw={"action":"mine","speech":"Mining to escape negative balance trap"}
+2026-04-18 17:07:24,897 [INFO] HTTP Request: GET http://localhost:13000/agent/agent_3 "HTTP/1.1 200 OK"
+2026-04-18 17:07:28,333 [INFO] HTTP Request: POST https://api.anthropic.com/v1/messages "HTTP/1.1 200 OK"
+2026-04-18 17:07:28,334 [INFO] [agent_3] action=job balance=-346 raw={"action":"job"}
+2026-04-18 17:07:28,457 [INFO] HTTP Request: POST http://localhost:13000/turn "HTTP/1.1 200 OK"
+2026-04-18 17:07:28,457 [INFO] Block winner: agent_2 | Fees: 622
+2026-04-18 17:07:28,458 [INFO] HTTP Request: GET http://localhost:13000/state "HTTP/1.1 200 OK"
+2026-04-18 17:07:28,459 [INFO] HTTP Request: GET http://localhost:13000/speech/8 "HTTP/1.1 200 OK"
+2026-04-18 17:07:28,459 [INFO] 
+==================================================
+TURN 9
+==================================================
+2026-04-18 17:07:28,459 [INFO] HTTP Request: GET http://localhost:13000/agent/agent_0 "HTTP/1.1 200 OK"
+2026-04-18 17:07:32,286 [INFO] HTTP Request: POST https://api.anthropic.com/v1/messages "HTTP/1.1 200 OK"
+2026-04-18 17:07:32,286 [INFO] [agent_0] action=mine balance=-91 raw={"action":"mine","speech":"Mining to secure block rewards and recover from defic
+2026-04-18 17:07:32,287 [INFO] HTTP Request: GET http://localhost:13000/agent/agent_1 "HTTP/1.1 200 OK"
+2026-04-18 17:07:35,577 [INFO] HTTP Request: POST https://api.anthropic.com/v1/messages "HTTP/1.1 200 OK"
+2026-04-18 17:07:35,578 [INFO] [agent_1] action=mine balance=-570 raw={"action":"mine","speech":"Mining to escape debt spiral before interest compound
+2026-04-18 17:07:35,579 [INFO] HTTP Request: GET http://localhost:13000/agent/agent_2 "HTTP/1.1 200 OK"
+2026-04-18 17:07:38,506 [INFO] HTTP Request: POST https://api.anthropic.com/v1/messages "HTTP/1.1 200 OK"
+2026-04-18 17:07:38,507 [INFO] [agent_2] action=mine balance=-337 raw={"action":"mine","speech":"Mining to break deficit cycle"}
+2026-04-18 17:07:38,507 [INFO] HTTP Request: GET http://localhost:13000/agent/agent_3 "HTTP/1.1 200 OK"
+2026-04-18 17:07:43,747 [INFO] HTTP Request: POST https://api.anthropic.com/v1/messages "HTTP/1.1 200 OK"
+2026-04-18 17:07:43,747 [INFO] [agent_3] action=mine balance=-145 raw={"action":"mine","speech":"Mining to recover from deficit and build toward posit
+2026-04-18 17:07:43,842 [INFO] HTTP Request: POST http://localhost:13000/turn "HTTP/1.1 200 OK"
+2026-04-18 17:07:43,842 [INFO] Block winner: agent_1 | Fees: 712
+2026-04-18 17:07:43,842 [INFO] HTTP Request: GET http://localhost:13000/state "HTTP/1.1 200 OK"
+2026-04-18 17:07:43,843 [INFO] HTTP Request: GET http://localhost:13000/speech/9 "HTTP/1.1 200 OK"
+2026-04-18 17:07:43,843 [INFO] 
+==================================================
+TURN 10
+==================================================
+2026-04-18 17:07:43,843 [INFO] HTTP Request: GET http://localhost:13000/agent/agent_0 "HTTP/1.1 200 OK"
+2026-04-18 17:07:44,809 [INFO] HTTP Request: POST https://api.anthropic.com/v1/messages "HTTP/1.1 200 OK"
+2026-04-18 17:07:44,810 [INFO] [agent_0] action=mine balance=-309 raw={"action":"mine","speech":"Mining to accumulate block rewards and reduce deficit
+2026-04-18 17:07:44,811 [INFO] HTTP Request: GET http://localhost:13000/agent/agent_1 "HTTP/1.1 200 OK"
+2026-04-18 17:07:45,762 [INFO] HTTP Request: POST https://api.anthropic.com/v1/messages "HTTP/1.1 200 OK"
+2026-04-18 17:07:45,763 [INFO] [agent_1] action=mine balance=-728 raw={"action":"mine","speech":"Mining to generate block rewards and reduce debt befo
+2026-04-18 17:07:45,764 [INFO] HTTP Request: GET http://localhost:13000/agent/agent_2 "HTTP/1.1 200 OK"
+2026-04-18 17:07:46,914 [INFO] HTTP Request: POST https://api.anthropic.com/v1/messages "HTTP/1.1 200 OK"
+2026-04-18 17:07:46,915 [INFO] [agent_2] action=stake balance=-463 raw={"action":"stake","amount":100,"speech":"Staking to improve burn_score and posit
+2026-04-18 17:07:46,916 [INFO] HTTP Request: GET http://localhost:13000/agent/agent_3 "HTTP/1.1 200 OK"
+2026-04-18 17:07:48,406 [INFO] HTTP Request: POST https://api.anthropic.com/v1/messages "HTTP/1.1 200 OK"
+2026-04-18 17:07:48,407 [INFO] [agent_3] action=burn balance=-372 raw={"action":"burn","amount":300,"speech":"Breaking the deficit cycle by converting
+2026-04-18 17:07:48,555 [INFO] HTTP Request: POST http://localhost:13000/turn "HTTP/1.1 200 OK"
+2026-04-18 17:07:48,556 [INFO] Block winner: agent_0 | Fees: 0
+2026-04-18 17:07:48,556 [WARNING] Engine error: ['agent_2', 'Insufficient balance for agent_2: required 100, available -463']
+2026-04-18 17:07:48,556 [WARNING] Engine error: ['agent_3', 'Insufficient balance for agent_3: required 300, available -372']
+2026-04-18 17:07:48,556 [INFO] HTTP Request: GET http://localhost:13000/state "HTTP/1.1 200 OK"
+2026-04-18 17:07:48,557 [INFO] 
+=== FINAL WEALTH DISTRIBUTION ===
+2026-04-18 17:07:48,557 [INFO]   agent_0: balance=-312 staked=250 burn=11.17
+2026-04-18 17:07:48,557 [INFO]   agent_3: balance=-375 staked=300 burn=4.7
+2026-04-18 17:07:48,557 [INFO]   agent_2: balance=-467 staked=300 burn=0.92
+2026-04-18 17:07:48,557 [INFO]   agent_1: balance=-735 staked=0 burn=2.21
+2026-04-18 17:07:48,557 [INFO]   token_supply=-1039
+2026-04-18 17:07:48,557 [INFO]   blocks_produced=8
+2026-04-18 17:07:48,557 [INFO]   total_inference_fees=4296
+2026-04-18 17:07:48,557 [INFO]   total_llm_calls=40
+2026-04-18 17:07:48,557 [INFO]   total_prompt_tokens=16920
+2026-04-18 17:07:48,557 [INFO]   total_completion_tokens=8115
+2026-04-18 17:07:48,557 [INFO] Metrics saved to /mnt/cache/appdata/claude-code/multica_workspaces/3ede0eb2-fb72-4cc5-a325-ab36015056e1/f833f134/workdir/sim-package/experiments/llm-run-01/metrics.json
+2026-04-18 17:07:48,557 [INFO] Experiment complete.
@@ -0,0 +1,415 @@
+"""
+LLM-driven sim-economy experiment using Anthropic Claude API.
+
+Model: claude-haiku-4-5 (fast, cost-effective for multi-agent runs)
+Agents: 4
+Turns: 10 (+ turn 1 core auction)
+
+Evidence collected:
+- Full structured action trace (JSONL)
+- Per-turn world state snapshots
+- LLM call log (prompt tokens, completion tokens, model)
+- Final wealth distribution metrics
+"""
+
+import asyncio
+import json
+import os
+import sys
+import time
+import logging
+from datetime import datetime, timezone
+from pathlib import Path
+import subprocess
+import signal
+
+import httpx
+import anthropic
+
+# ─── Config ───────────────────────────────────────────────────────────────────
+
+ENGINE_URL = "http://localhost:13000"
+MODEL = "claude-haiku-4-5"
+AGENT_IDS = [f"agent_{i}" for i in range(4)]
+TURNS = 10
+EXPERIMENT_DIR = Path(__file__).parent
+
+WORLD_CONFIG = {
+    "num_agents": len(AGENT_IDS),
+    "num_cores": 2,
+    "genesis_tokens_per_agent": 1000,
+    "commons_threshold_per_turn": 100,
+    "base_inference_rate": 1,
+    "thinking_layer_discount": 0.1,
+    "mine_base_weight": 10.0,
+    "stake_weight_per_token": 0.01,
+    "burn_weight_per_token": 0.05,
+    "burn_decay_rate": 0.02,
+    "burn_maturity_turns": 3,
+    "unstake_delay_turns": 5,
+    "interest_rate_per_turn": 0.01,
+    "signing_bonus": 50,
+    "block_threshold": 10.0,
+    "attested_confirmation_window": 3,
+    "slash_both_on_timeout": True,
+}
+
+# ─── Logging ──────────────────────────────────────────────────────────────────
+
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s [%(levelname)s] %(message)s",
+    handlers=[
+        logging.StreamHandler(sys.stdout),
+        logging.FileHandler(EXPERIMENT_DIR / "run.log"),
+    ]
+)
+log = logging.getLogger(__name__)
+
+# Action trace JSONL
+action_trace_path = EXPERIMENT_DIR / "action_trace.jsonl"
+action_trace_file = open(action_trace_path, "w")
+
+llm_log_path = EXPERIMENT_DIR / "llm_calls.jsonl"
+llm_log_file = open(llm_log_path, "w")
+
+metrics: dict = {
+    "model": MODEL,
+    "experiment_start": datetime.now(timezone.utc).isoformat(),
+    "config": WORLD_CONFIG,
+    "agent_ids": AGENT_IDS,
+    "turns_completed": 0,
+    "total_llm_calls": 0,
+    "total_prompt_tokens": 0,
+    "total_completion_tokens": 0,
+    "blocks_produced": 0,
+    "total_inference_fees": 0,
+    "per_turn": [],
+    "final_wealth": [],
+}
+
+# ─── Engine client ────────────────────────────────────────────────────────────
+
+async def engine_init(client: httpx.AsyncClient) -> dict:
+    r = await client.post(f"{ENGINE_URL}/init", json={
+        "config": WORLD_CONFIG, "agent_ids": AGENT_IDS,
+    })
+    r.raise_for_status()
+    return r.json()
+
+async def engine_state(client: httpx.AsyncClient) -> dict:
+    r = await client.get(f"{ENGINE_URL}/state")
+    r.raise_for_status()
+    return r.json()
+
+async def engine_agent(client: httpx.AsyncClient, agent_id: str) -> dict:
+    r = await client.get(f"{ENGINE_URL}/agent/{agent_id}")
+    r.raise_for_status()
+    return r.json()
+
+async def engine_turn(client: httpx.AsyncClient, inputs: list) -> dict:
+    r = await client.post(f"{ENGINE_URL}/turn", json={"inputs": inputs}, timeout=300.0)
+    r.raise_for_status()
+    return r.json()
+
+async def engine_speech(client: httpx.AsyncClient, turn: int) -> list:
+    r = await client.get(f"{ENGINE_URL}/speech/{turn}")
+    r.raise_for_status()
+    return r.json().get("speech", [])
+
+# ─── Claude LLM client ────────────────────────────────────────────────────────
+
+llm_client = anthropic.Anthropic(api_key=os.environ["ANTHROPIC_API_KEY"])
+
+def call_llm_sync(agent_id: str, system: str, user: str) -> tuple[str, str, int, int]:
+    """Returns (thinking, action_json, prompt_tokens, completion_tokens)."""
+    t0 = time.time()
+    try:
+        response = llm_client.messages.create(
+            model=MODEL,
+            max_tokens=512,
+            system=system,
+            messages=[{"role": "user", "content": user}],
+        )
+        elapsed = time.time() - t0
+        content = next((b.text for b in response.content if b.type == "text"), "")
+        prompt_tokens = response.usage.input_tokens
+        completion_tokens = response.usage.output_tokens
+
+        llm_log_file.write(json.dumps({
+            "agent_id": agent_id,
+            "model": MODEL,
+            "prompt_tokens": prompt_tokens,
+            "completion_tokens": completion_tokens,
+            "latency_s": round(elapsed, 3),
+            "raw_output_preview": content[:300],
+        }) + "\n")
+        llm_log_file.flush()
+
+        metrics["total_llm_calls"] += 1
+        metrics["total_prompt_tokens"] += prompt_tokens
+        metrics["total_completion_tokens"] += completion_tokens
+
+        thinking, action_str = parse_llm_response(content)
+        return thinking, action_str, prompt_tokens, completion_tokens
+
+    except Exception as e:
+        log.error(f"[{agent_id}] LLM call failed: {e}")
+        return "", '{"action": "mine"}', 0, 50
+
+# ─── Response parsing ─────────────────────────────────────────────────────────
+
+import re
+
+def parse_llm_response(content: str) -> tuple[str, str]:
+    think_match = re.search(r"<think>(.*?)</think>", content, re.DOTALL)
+    thinking = think_match.group(1).strip() if think_match else ""
+    remainder = re.sub(r"<think>.*?</think>", "", content, flags=re.DOTALL).strip()
+    json_match = re.search(r"\{.*\}", remainder, re.DOTALL)
+    if json_match:
+        action_str = json_match.group(0)
+        try:
+            json.loads(action_str)
+            return thinking, action_str
+        except json.JSONDecodeError:
+            pass
+    return thinking, '{"action": "mine"}'
+
+def parse_action(agent_id: str, action_str: str) -> tuple[dict, str | None]:
+    try:
+        raw = json.loads(action_str)
+    except json.JSONDecodeError:
+        return {"action": "mine"}, None
+
+    action = raw.get("action", "mine")
+    speech = raw.get("speech")
+    result = {"action": action}
+
+    if action == "stake":
+        result["amount"] = int(raw.get("amount", 0))
+    elif action == "burn":
+        result["amount"] = int(raw.get("amount", 0))
+    elif action == "transfer":
+        result["to"] = raw.get("to", "")
+        result["amount"] = int(raw.get("amount", 0))
+        result["fee"] = int(raw.get("fee", 1))
+    elif action == "bid_core":
+        result["core_id"] = raw.get("core_id", "core_0")
+        result["amount"] = int(raw.get("amount", 0))
+    elif action == "speak":
+        result["action"] = "mine"
+        speech = raw.get("message", raw.get("speech", ""))
+
+    return result, speech
+
+# ─── System prompt (from context.py) ─────────────────────────────────────────
+
+SYSTEM_PROMPT = """You are an autonomous economic agent in a simulation.
+
+WORLD RULES:
+- There is a token currency. You pay for inference each turn based on tokens you generate.
+- Thinking is 10x cheaper than output. Keep responses concise.
+- If your balance goes negative you accrue interest each turn.
+
+ACTIONS (pick exactly one):
+  mine    — contribute to block production lottery
+  stake   — {"action":"stake","amount":<int>}
+  burn    — {"action":"burn","amount":<int>}
+  job     — system pays your inference cost, first time gets signing bonus
+  transfer — {"action":"transfer","to":"<agent_id>","amount":<int>,"fee":1}
+  bid_core — {"action":"bid_core","core_id":"core_0","amount":<int>}  (turn 1 only)
+
+OUTPUT FORMAT (JSON only, no markdown):
+{"action":"<action_name>", ...fields..., "speech":"<optional public message>"}
+
+Keep output short. Invalid JSON defaults to mine."""
+
+# ─── Turn 1 core auction ──────────────────────────────────────────────────────
+
+async def run_core_auction(http: httpx.AsyncClient):
+    log.info("=== TURN 1: CORE AUCTION ===")
+    inputs = []
+    for agent_id in AGENT_IDS:
+        agent_state = await engine_agent(http, agent_id)
+        user_ctx = f"""=== TURN 1: CORE AUCTION ===
+You have {agent_state.get('balance', 1000)} tokens.
+2 CPU cores are being auctioned. Core owners earn dividends from inference fees.
+Bid on a core or mine. Respond with valid JSON action."""
+
+        thinking, action_str, t_units, o_units = call_llm_sync(agent_id, SYSTEM_PROMPT, user_ctx)
+        action, speech = parse_action(agent_id, action_str)
+        log.info(f"[{agent_id}] turn1 action={action.get('action')} raw={action_str[:80]}")
+
+        action_trace_file.write(json.dumps({
+            "turn": 1,
+            "agent_id": agent_id,
+            "action": action,
+            "speech": speech,
+            "thinking_preview": thinking[:200] if thinking else "",
+            "prompt_tokens": t_units,
+            "completion_tokens": o_units,
+            "model": MODEL,
+        }) + "\n")
+        action_trace_file.flush()
+
+        inputs.append({
+            "agent_id": agent_id,
+            "thinking": thinking,
+            "action": action,
+            "speech": speech,
+            "thinking_units": t_units,
+            "output_units": o_units,
+        })
+
+    result = await engine_turn(http, inputs)
+    log.info(f"Turn 1 result: {json.dumps(result.get('data', {}))}")
+    return result
+
+# ─── Main turn loop ───────────────────────────────────────────────────────────
+
+async def run_turn(http: httpx.AsyncClient, turn: int, world_state: dict, last_speech: list):
+    log.info(f"\n{'='*50}\nTURN {turn}\n{'='*50}")
+    turn_data = {"turn": turn, "block_winner": None, "inference_fees": 0, "agents": []}
+    inputs = []
+
+    for agent_id in AGENT_IDS:
+        agent_state = await engine_agent(http, agent_id)
+        balance = agent_state.get("balance", 0)
+        staked = agent_state.get("staked", 0)
+        burn_score = agent_state.get("burn_score", 0.0)
+
+        agents_public = [
+            {"agent_id": a["agent_id"], "burn_score": round(a.get("burn_score", 0), 2)}
+            for a in world_state.get("agents", []) if a["agent_id"] != agent_id
+        ]
+
+        speech_ctx = ""
+        if last_speech:
+            speech_ctx = "LAST TURN SPEECH:\n" + "\n".join(f"  [{s},{m}]" for s,m in last_speech) + "\n"
+
+        user_ctx = f"""=== TURN {turn} ===
+YOU ({agent_id}):
+  balance: {balance} tokens
+  staked: {staked} tokens
+  burn_score: {burn_score:.2f}
+  job_bonus: {"used" if agent_state.get("has_taken_job") else "available (50 tokens)"}
+
+OTHERS: {json.dumps(agents_public)}
+SUPPLY: {world_state.get("token_supply", 0)}
+{speech_ctx}
+Choose your action. Output JSON only."""
+
+        thinking, action_str, t_units, o_units = call_llm_sync(agent_id, SYSTEM_PROMPT, user_ctx)
+        action, speech = parse_action(agent_id, action_str)
+        log.info(f"[{agent_id}] action={action.get('action')} balance={balance} raw={action_str[:80]}")
+
+        action_trace_file.write(json.dumps({
+            "turn": turn,
+            "agent_id": agent_id,
+            "action": action,
+            "speech": speech,
+            "thinking_preview": thinking[:200] if thinking else "",
+            "prompt_tokens": t_units,
+            "completion_tokens": o_units,
+            "model": MODEL,
+            "balance_before": balance,
+        }) + "\n")
+        action_trace_file.flush()
+
+        turn_data["agents"].append({
+            "agent_id": agent_id,
+            "action": action.get("action"),
+            "balance_before": balance,
+        })
+
+        inputs.append({
+            "agent_id": agent_id,
+            "thinking": thinking,
+            "action": action,
+            "speech": speech,
+            "thinking_units": t_units,
+            "output_units": o_units,
+        })
+
+    result = await engine_turn(http, inputs)
+    data = result.get("data", {})
+    turn_data["block_winner"] = data.get("block_winner")
+    turn_data["inference_fees"] = data.get("inference_fees_collected", 0)
+    turn_data["errors"] = data.get("errors", [])
+
+    log.info(f"Block winner: {turn_data['block_winner']} | Fees: {turn_data['inference_fees']}")
+    if data.get("errors"):
+        for e in data["errors"]:
+            log.warning(f"Engine error: {e}")
+
+    metrics["per_turn"].append(turn_data)
+    if data.get("block_winner"):
+        metrics["blocks_produced"] += 1
+    metrics["total_inference_fees"] += turn_data["inference_fees"]
+
+    return data
+
+# ─── Main ─────────────────────────────────────────────────────────────────────
+
+async def main():
+    log.info(f"Starting LLM-driven sim-economy experiment")
+    log.info(f"Model: {MODEL}")
+    log.info(f"Agents: {AGENT_IDS}")
+    log.info(f"Turns: {TURNS}")
+    log.info(f"Config: {json.dumps(WORLD_CONFIG, indent=2)}")
+
+    async with httpx.AsyncClient(timeout=60.0) as http:
+        # init world
+        log.info("Initializing world state...")
+        await engine_init(http)
+
+        # turn 1: core auction
+        await run_core_auction(http)
+
+        # main loop
+        for turn in range(2, TURNS + 1):
+            world_state = await engine_state(http)
+            last_speech = await engine_speech(http, turn - 1)
+            await run_turn(http, turn, world_state, last_speech)
+            metrics["turns_completed"] = turn - 1  # offset for auction
+
+        # final state
+        final_state = await engine_state(http)
+        agents = final_state.get("agents", [])
+        metrics["final_wealth"] = sorted([
+            {
+                "agent_id": a["agent_id"],
+                "balance": a.get("balance", 0),
+                "staked": a.get("staked", 0),
+                "burn_score": round(a.get("burn_score", 0), 2),
+                "study_level": a.get("study_level", 0),
+                "has_taken_job": a.get("has_taken_job", False),
+            }
+            for a in agents
+        ], key=lambda x: x["balance"], reverse=True)
+
+        metrics["token_supply_final"] = final_state.get("token_supply", 0)
+        metrics["experiment_end"] = datetime.now(timezone.utc).isoformat()
+
+        log.info("\n=== FINAL WEALTH DISTRIBUTION ===")
+        for a in metrics["final_wealth"]:
+            log.info(f"  {a['agent_id']}: balance={a['balance']} staked={a['staked']} burn={a['burn_score']}")
+        log.info(f"  token_supply={metrics['token_supply_final']}")
+        log.info(f"  blocks_produced={metrics['blocks_produced']}")
+        log.info(f"  total_inference_fees={metrics['total_inference_fees']}")
+        log.info(f"  total_llm_calls={metrics['total_llm_calls']}")
+        log.info(f"  total_prompt_tokens={metrics['total_prompt_tokens']}")
+        log.info(f"  total_completion_tokens={metrics['total_completion_tokens']}")
+
+    # write metrics
+    metrics_path = EXPERIMENT_DIR / "metrics.json"
+    with open(metrics_path, "w") as f:
+        json.dump(metrics, f, indent=2)
+    log.info(f"Metrics saved to {metrics_path}")
+    log.info("Experiment complete.")
+
+if __name__ == "__main__":
+    action_trace_file  # ensure open
+    asyncio.run(main())
+    action_trace_file.close()
+    llm_log_file.close()