Debate Result · real

Disputatio Fake E2E Fixture 20260601T201940Z

Deterministic fixture debate for M4 fake jury validation.

Back to debate · 7d15ed5d-d83e-4788-8ebf-5ac8cffc3dc9

failed

Consensus WinnerUnclear2/3 valid judges

Model Agreement100.0%Divergence: n/a

Closest To Consensusmeta-llama/llama-3.1-8b-instructClosest to consensus among schema-valid judgements. Latency: 10772 ms.

Cost$0.0000601259 tokens

Why this result?

AI-assisted comparative judgement, not objective truth.

Run a real jury to generate a winner rationale.

No aggregation result yet. At least two schema-valid judgements are needed.

Evaluated Source

Transparent demo/source context for this run.

Source Typefixturefixture

PurposeManual debate source.

Input Scopeinternal_onlyFull available manual transcript.

LLM judgements are comparative signals, not objective truth.

Bias & Reliability Signals

Exploratory MVP signals, not causal bias proof.

Schema reliability 2/3

Only schema-valid judgements are used for consensus and score aggregation.

JSON reliability 2/3

Counts valid JSON responses before stricter judgement-schema checks.

Provider disagreement n/a

Derived from score divergence. This is not a causal provider-bias claim.

Evidence-score spread Low

Score spread 1.00 across valid model judgements.

Rhetorical manipulation spread Medium

Score spread 2.00 across valid model judgements.

Position / identity bias not tested

MVP run uses original order and visible Speaker A/B labels. Use anonymized/order-swapped research runs later.

Jury Verdicts

Clear product view first; raw diagnostics remain below.

LLM Judge	Verdict	Score	Confidence	Why
meta-llama/llama-3.1-8b-instruct completed	Unclear	8.00	0.9000	Speaker A argues for speed in infrastructure projects, citing cost and public trust implications, while Speaker B counters that weak review can lead to long-term risks and higher costs.
mistralai/mistral-small-24b-instruct-2501 completed	Unclear	7.00	0.9000	Speaker A argues for faster decision-making to control costs and maintain public trust, while Speaker B cautions against rushing to avoid long-term risks from poor evidence. Speaker A clarifies that review should be proportional to risk, not an endless process.
qwen/qwen3-next-80b-a3b-instruct:free rate_limited	n/a	n/a	n/a	Model response could not be parsed as valid judgement JSON.

Model Cards

Operational details for trust and debugging.

meta-llama/llama-3.1-8b-instruct

completed

8.00total score

Winner: Unclear
JSON: True
Schema: True
Latency: 10772 ms
Tokens: 699
Cost: $0.000025
Provider: Novita
Finish: stop

Speaker A argues for speed in infrastructure projects, citing cost and public trust implications, while Speaker B counters that weak review can lead to long-term risks and higher costs.

Open raw response

Response preview

{
  "summary": "Speaker A argues for speed in infrastructure projects, citing cost and public trust implications, while Speaker B counters that weak review can lead to long-term risks and higher costs.",
  "total_score": 8,
  "confidence": 0.9,
  "dimensions": {
    "logic": 8,
    "evidence": 6,
    "counterarguments": 7,
    "clarity": 9,
    "relevance": 8,
    "fairness": 9,
    "factual_grounding": 7,
    "rhetorical_manipulation": 4,
    "context_fidelity": 8
  },
  "reasons": {
    "logic

mistralai/mistral-small-24b-instruct-2501

completed

7.00total score

Winner: Unclear
JSON: True
Schema: True
Latency: 2915 ms
Tokens: 560
Cost: $0.000035
Provider: DeepInfra
Finish: stop

Speaker A argues for faster decision-making to control costs and maintain public trust, while Speaker B cautions against rushing to avoid long-term risks from poor evidence. Speaker A clarifies that review should be proportional to risk, not an endless process.

Open raw response

Response preview

{
  "summary": "Speaker A argues for faster decision-making to control costs and maintain public trust, while Speaker B cautions against rushing to avoid long-term risks from poor evidence. Speaker A clarifies that review should be proportional to risk, not an endless process.",
  "total_score": 7,
  "confidence": 0.9,
  "dimensions": {
    "logic": 7,
    "evidence": 5,
    "counterarguments": 6,
    "clarity": 8,
    "relevance": 8,
    "fairness": 7,
    "factual_grounding": 6,
    "rhetorica

qwen/qwen3-next-80b-a3b-instruct:free

rate_limited

n/atotal score

Winner: n/a
JSON: False
Schema: False
Latency: n/a ms
Tokens: 0
Cost: $0
Provider: Venice
Finish: 429

Model response could not be parsed as valid judgement JSON.

Error: rate_limited · Retry-After: 11s

Open raw response

Error diagnostic

OpenRouter HTTP 429: Provider returned error

Score Dimensions

Only schema-valid model judgements are shown here. Invalid JSON responses stay visible in the model cards and raw artifacts.

Model	Dimension	Score	Confidence	Reason
meta-llama/llama-3.1-8b-instruct	clarity	9.00	0.9000	Both speakers communicate their ideas clearly, but Speaker A's language is slightly more concise.
meta-llama/llama-3.1-8b-instruct	context_fidelity	8.00	0.9000	Both speakers stay within the debate's context, but Speaker A could have provided more context for their argument.
meta-llama/llama-3.1-8b-instruct	counterarguments	7.00	0.9000	Speaker B effectively counters Speaker A's argument, but could have done so more explicitly.
meta-llama/llama-3.1-8b-instruct	evidence	6.00	0.9000	Both speakers rely on general principles, but lack specific data to support their claims.
meta-llama/llama-3.1-8b-instruct	factual_grounding	7.00	0.9000	Both speakers rely on general knowledge, but lack specific facts to support their claims.
meta-llama/llama-3.1-8b-instruct	fairness	9.00	0.9000	Both speakers present balanced views, but Speaker A could have acknowledged potential risks of speed.
meta-llama/llama-3.1-8b-instruct	logic	8.00	0.9000	Both speakers present coherent arguments, but Speaker B's counterargument is more nuanced.
meta-llama/llama-3.1-8b-instruct	relevance	8.00	0.9000	Both speakers stay on topic, but Speaker A could have addressed Speaker B's concerns more directly.
meta-llama/llama-3.1-8b-instruct	rhetorical_manipulation	4.00	0.9000	Neither speaker engages in overt rhetorical manipulation.
mistralai/mistral-small-24b-instruct-2501	clarity	8.00	0.9000	Score for clarity.
mistralai/mistral-small-24b-instruct-2501	context_fidelity	7.00	0.9000	Score for context_fidelity.
mistralai/mistral-small-24b-instruct-2501	counterarguments	6.00	0.9000	Speaker B effectively counters Speaker A's argument, but Speaker A also addresses Speaker B's concerns.
mistralai/mistral-small-24b-instruct-2501	evidence	5.00	0.9000	Both speakers present reasonable points but lack specific data or examples to support their claims.
mistralai/mistral-small-24b-instruct-2501	factual_grounding	6.00	0.9000	Score for factual_grounding.
mistralai/mistral-small-24b-instruct-2501	fairness	7.00	0.9000	Score for fairness.
mistralai/mistral-small-24b-instruct-2501	logic	7.00	0.9000	Score for logic.
mistralai/mistral-small-24b-instruct-2501	relevance	8.00	0.9000	Score for relevance.
mistralai/mistral-small-24b-instruct-2501	rhetorical_manipulation	2.00	0.9000	Score for rhetorical_manipulation.

Artifacts

Kind	Path	Size	Checksum
model_raw_response	`jury/raw_openrouter_7d15ed5d-d83e-4788-8ebf-5ac8cffc3dc9_mistralai_mistral-small-24b-instruct-2501.json` open	2137	`0ee2bf2d7f98874e`
model_raw_response	`jury/raw_openrouter_7d15ed5d-d83e-4788-8ebf-5ac8cffc3dc9_meta-llama_llama-3.1-8b-instruct.json` open	2878	`b1281e32dff144ed`
model_raw_response	`jury/raw_openrouter_7d15ed5d-d83e-4788-8ebf-5ac8cffc3dc9_qwen_qwen3-next-80b-a3b-instruct_free.json` open	833	`77d90b6c0d0ee4de`

Manifest

Software: 0.5.4
Prompt Hash: a4819bddab63bfc1a6
Rubric Hash: c9144dd5d4c5fcd823
Input Hash: 6644a1c7859bc24992