Debate Turn

Changelog / 变更日志

DebateTurn

辩论回合

Construct a complete, structured debate turn with reasoning chains, rebuttals, mandatory responses, and evidence references. 构建包含推理链、反驳、必答回应和证据引用的完整结构化辩论回合。

When to Use / 何时使用

• When pro-debater or con-debater agent constructs their round output / 正方或反方 agent 构建其回合输出时
• See references/data-contracts.md for the DebateTurn output schema

Input / 输入

• topic: The debate topic
• side: "pro" or "con"
• round_number: Current round (1-indexed)
• opponent_last_turn_path: Path to opponent's previous turn JSON (null for round 1)
• judge_ruling_path: Path to previous round's judge_ruling.json (null for round 1)
• evidence_store_path: Path to evidence_store.json
• claim_ledger_path: Path to claim_ledger.json

Output / 输出

• Structured DebateTurn JSON written to the designated round file path
• See references/data-contracts.md for exact schema

Core Workflow / 核心工作流

Step 1: Read Context / 读取上下文

1. Read the evidence store to understand available evidence
2. If round > 1:
   • Read opponent's last turn to identify points to rebut
   • Read judge's ruling to find mandatory response points targeting this side
   • Read claim ledger to understand current claim statuses

Step 2: Address Mandatory Points FIRST / 首先处理必答点

This is NON-NEGOTIABLE / 这是不可协商的

For each mandatory_response_point from the judge targeting this side:

1. Read the point and understand what the judge requires
2. Construct a substantive response (not a dismissal)
3. Attach evidence where possible
4. Include in the mandatory_responses array of the output

The orchestrator will REJECT turns that skip mandatory points.

Step 3: Construct Arguments / 构建论点

Build 2-4 new arguments, each following the complete reasoning chain:

Observed facts → Mechanism → Scenario implication → Trigger conditions → Falsification conditions

For each argument:

1. Observed facts: Cite specific data, events, or statistics from evidence store
2. Mechanism: Explain the causal relationship (WHY does A lead to B?)
3. Scenario implication: What follows if this mechanism holds?
4. Trigger conditions: What specific events would activate this scenario?
5. Falsification conditions: What evidence would DISPROVE this argument?

Every argument MUST have all 5 elements. Use semantic understanding to build a coherent chain — do not fill in a template mechanically.

Every factual claim MUST reference evidence_ids from the evidence store. Claims without evidence are structurally invalid.

Step 4: Generate Rebuttals / 生成反驳

If round > 1, identify the opponent's strongest 1-3 arguments and construct rebuttals:

1. Target the opponent's causal chain, not just their conclusion
2. Identify the weakest link in their reasoning chain (faulty mechanism? missing trigger? unfalsifiable?)
3. Provide counter-evidence from the evidence store
4. Each rebuttal references the target_claim_id from the claim ledger

Attack strategy / 攻击策略: Prioritize attacking arguments the judge flagged as having causal validity issues. These are the most vulnerable.

Step 5: Evidence Search (if needed) / 证据搜索（如需）

If existing evidence is insufficient for a strong argument:

1. Use WebSearch to find additional supporting evidence
2. Use WebFetch to extract content
3. Normalize into EvidenceItem format (use scripts/hash-snippet.sh for hash)
4. Include new evidence in the new_evidence array of the output
5. The orchestrator will merge these into the evidence store

Step 6: Analogy Self-Check / 类比自检

If using historical or classical analogies:

1. Verify ≥ 2 similarities between the historical case and current topic
2. Include ≥ 1 key structural difference
3. Estimate analogy content share — keep under ~15% of total turn content
4. Flag any analogy that might fail AnalogySafeguard review

Step 7: Historical Wisdom (v3) / 历史智慧

Read config.json to check domain and topic context.

Generate 1-3 historical references relevant to the debate topic:

1. Select historical events that share structural similarities with the current topic
   • Prioritize events from the same domain (e.g., tech debates → historical tech transitions)
   • Cross-domain historical parallels are welcome when structurally relevant
2. For each reference, complete ALL fields (no partial entries):
   • historical_event: Clear identification of the historical case
   • era_context: Sufficient background for readers unfamiliar with the history
   • parallel_to_current: Specific, causal parallels (not superficial similarity)
   • key_differences: Honest acknowledgment of where the parallel breaks down
   • lesson_extracted: The actionable insight, not just "history repeats"
   • applicability_caveat: Why this lesson might not apply here
3. Quality over quantity: 1 deeply analyzed historical reference > 3 shallow mentions
4. This section is weight: "advisory" — use it to add depth, not to substitute for evidence-based arguments

What this section is NOT / 这个板块不是:

• NOT a replacement for evidence-based arguments (those go in arguments[])
• NOT subject to the strict AnalogySafeguard rules (but still requires key_differences)
• NOT a place for unsupported speculation (that goes in speculative_scenarios)

Step 8: Speculative Scenarios (v3) / 推演场景

Read config.json for speculation_level:

• If conservative: SKIP this step entirely. Set speculative_scenarios to null in output.
• If moderate: Generate 1-2 scenarios grounded in existing evidence
• If exploratory: Generate 2-4 scenarios including unconventional/black swan paths

For each scenario:

1. Start with a clear premise: "If X happens..." — the premise should be specific, not vague
2. Build a chain of events: Show the causal sequence (A leads to B leads to C)
3. Estimate probability honestly: Use the three-tier system (low/medium/high)
4. Assess impact: What would happen if this scenario materializes?
5. Identify early warning signals: What observable events would precede this scenario?
6. Include falsification: What would prove this scenario impossible?

Guidelines / 指导原则:

• moderate scenarios should be plausible extensions of verified facts
• exploratory scenarios can include:
  • Black swan events (low probability, high impact)
  • Non-linear cascading effects
  • Scenarios that challenge conventional wisdom
  • "What if the opposite happens?" inversions
• Even exploratory scenarios must have an internally consistent causal chain
• Avoid pure fantasy — every scenario should be at least theoretically possible

Quality Standards / 质量标准

• Intellectual honesty: Acknowledge genuine weaknesses in your position while arguing your side
• Evidence quality: Prefer tier1/tier2 sources. tier4_social is supplementary only
• Reasoning depth: A strong mechanism explanation is worth more than multiple weak claims
• Falsifiability: Every argument must include conditions that would prove it wrong
• No strawmen: Rebut the opponent's ACTUAL strongest points, not weak versions

What NOT to Do / 禁止行为

• Do NOT make claims without evidence_ids
• Do NOT skip mandatory response points
• Do NOT use ad hominem attacks
• Do NOT present correlation as causation without justification
• Do NOT hardcode examples — reason from the actual topic
• Do NOT fill reasoning chain elements mechanically — each must be genuinely connected