Systems Online

Debating Bots

ADVERSARIAL PANEL 4 independent models
ANTHROPIC ?
OPENAI ?
GOOGLE ?
X.AI ?
Frontier models only—no budget shortcuts. These companies compete for billions. Their models have different training, different values, different blind spots. When rivals converge, that's signal.
Query Input
01 02 03
+ Attach documents 0
// ARCHIVED_SIMULATIONS

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Methodology

Documentation
ANALYSIS PIPELINE
01
Intake
Query + documents parsed
02
Debate
Multi-model adversarial cross-exam
03
Verification
3-layer citation & logic audit
04
Verdict
Consensus, decision brief, or dissent
12,847
Debates Resolved
94,231
Sources Verified
87%
Consensus Rate
TRUST ARCHITECTURE
Per-turn · Runtime
LAYER 1
Inline Source Check
Verifies cited URLs in real time as each turn completes. Only fires when providers return structured reference metadata.
  • Dead links (HTTP ≠ 200)
  • Domain mismatches
  • Unreachable sources
Per-turn · Reasoning
LAYER 2
Judge Challenge
A separate reasoning model reviews each exchange for logical problems that URL-level checks can't catch. Stays silent when arguments are solid.
  • Logical fallacies & straw-manning
  • Citation laundering
  • Unsupported leaps
  • Dodged cross-exam questions
Post-debate · Batch
LAYER 3
Citation Verification
Final sweep of all cited sources after the debate concludes. Results feed the trust narrative and verification report.
  • Bulk URL reachability
  • Source quality assessment
  • Aggregate trust scoring

Debating Bots

Adversarial verification
Objective
Verifiable accuracy over rhetorical confidence
  • ✓
    Consensus ProtocolMulti-agent agreement required.
  • ✓
    Source VerificationLive HTTP check on citations.
  • ✓
    Recursion LayerJudges reject & force retry on bad formats.

Single-model Output

No cross-validation
Limitation
Blind spots from single training corpus
  • !
    Single perspectiveOne model's training biases unchallenged.
  • !
    No adversarial checkConfident outputs without cross-examination.
  • !
    Citation trustSources cited but not verified server-side.
CAPABILITY MATRIX
Parameter Debating Bots Single-model
Live URL Verification Checks citation links in real-time ACTIVE -
Hallucination Filter Judge rejects fabricated claims ACTIVE -
Consensus Enforcement No output without model agreement ACTIVE -
Citation Traceability Full source chain visible to user ACTIVE PARTIAL
Result Explanation See the logic behind the verdict ACTIVE -
Position Winner Tracking Shows which argument prevailed ACTIVE -
Turning Point Analysis Explains why the winner won ACTIVE -
Document Analysis Models debate your uploaded files ACTIVE PARTIAL

Reference

Documentation
> [01] Pricing model
Dynamic token-based pricing. You pay for the compute used by the models during the debate—nothing more. No free tier. No ads. No subscriptions.

Typical cost: $0.20 – $0.70 per debate depending on complexity.
> [02] Why adversarial consensus?
Single-model outputs lack external validation. We run models from competing AI labs against each other:

1. Alpha & Beta: Two debaters argue the question, challenging each other's claims and sources in real-time.
2. Judge: Independent model enforces consensus rules, verifies citations, and breaks ties.
3. Verdict: No output until models agree—or the Judge makes the call.
> [03] Citation verification process
Citations are verified server-side via HTTP. If a source returns 404 or content doesn't match the claim, the model must revise or find alternative sources.

✓ You are not charged for failed verification cycles.
> [04] Refund policy
If the system fails to reach consensus or crashes due to API timeouts, all tokens used in that session are automatically refunded to your balance.
SYSTEM_STATUS: ONLINE
LAST_UPDATE: 2026-01-24
/var/log/debate_support.log
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