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Independent aggregation of multiple LLMs reaches 83.43 percent accuracy on resolved prediction market questions, 1.01 points above the strongest single model.

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2026-06-28 20:57 UTC pith:PZ3Z3VQI

load-bearing objection This paper runs a straightforward empirical test of multi-agent LLM setups on a Kalshi prediction-market benchmark and finds that simple confidence-weighted aggregation edges out single models by about 1 point while deliberation hurts. the 3 major comments →

arxiv 2605.30802 v1 pith:PZ3Z3VQI submitted 2026-05-29 cs.MA cs.AI

Design and Evaluation of Multi-Agent AI Oracle Systems for Prediction Market Resolution

Tarun Kota This is my paper
classification cs.MA cs.AI
keywords multi-agent LLMsprediction market oraclesconfidence-weighted votingKalshiBenchhybrid AI-human systemserror correlationsoracle resolution
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper tests whether multi-agent LLM systems can resolve uncertain events for prediction markets more reliably than any one model by itself. It runs both independent voting and group deliberation setups against single-model baselines on 1,189 real Kalshi questions, all using the same date-filtered evidence source. A reader would care because accurate automated resolution removes a major cost and delay barrier that currently limits how widely prediction markets can be used. The results show small gains from parallel aggregation but clear harm from debate, plus a practical way to hand off the hardest cases to humans.

Core claim

Independent aggregation with confidence-weighted voting achieves 83.43 percent accuracy on the 1,189 KalshiBench questions, outperforming the best single LLM by 1.01 points, while deliberative consensus falls to roughly 76 percent because error propagation during debate allows confidently wrong agents to flip correct ones. Measured error correlations between 0.529 and 0.689 across models explain why ensemble gains stay well below the theoretical ceiling. The authors therefore propose a hybrid routing rule that auto-resolves only unanimous high-confidence questions at 97.87 percent accuracy for 47 percent of the dataset and escalates the rest to human review.

What carries the argument

Independent aggregation using confidence-weighted voting across multiple LLMs that share a common date-filtered evidence layer.

Load-bearing premise

That filtering retrieval by publication date fully removes differences in what each model knows, leaving only differences in how well each reasons.

What would settle it

Running the same independent-aggregation procedure on a new collection of resolved prediction-market questions and finding that it no longer exceeds the accuracy of the best single model would falsify the performance claim.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

If this is right

  • Deliberative consensus degrades accuracy below every single-model baseline because error propagation allows wrong agents to override correct ones.
  • Error correlations between 0.529 and 0.689 across models place a hard limit on how much any ensemble can improve results.
  • Many questions remain uncorrectable by any multi-agent architecture, so escalation to humans is required for those cases.
  • Auto-resolving only unanimous high-confidence questions delivers 97.87 percent accuracy on 47 percent of the dataset.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Disagreement among agents can function as an efficient triage signal for routing to humans in other high-stakes AI decision pipelines.
  • The results suggest that parallel independent reasoning may be preferable to interactive debate for any ensemble forecasting task where models share similar training data.
  • Increasing model diversity could lower the observed error correlations and expand the fraction of questions that can be auto-resolved without human review.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

3 major / 0 minor

Summary. The manuscript evaluates multi-agent LLM architectures for resolving outcomes in prediction markets using the KalshiBench dataset of 1,189 resolved questions. It compares single-LLM baselines (GPT-5 Nano, DeepSeek V3, Llama-3.3-70B) against independent aggregation with confidence-weighted voting and deliberative consensus, all using a shared date-filtered Exa evidence retrieval layer. The central claim is that independent aggregation achieves 83.43% accuracy, outperforming the best single model by 1.01 percentage points, while deliberative consensus performs worse at approximately 76% due to error propagation. Error correlations between 0.529 and 0.689 are reported as limiting ensemble gains below the Condorcet bound. The paper proposes hybrid routing criteria for auto-resolving unanimous high-confidence cases at 97.87% accuracy on 47% of the data, flagging the rest for human review.

Significance. If the results hold after addressing methodological gaps, this work provides direct empirical measurements on an external benchmark (KalshiBench) showing modest benefits from simple confidence-weighted aggregation in multi-agent oracles while highlighting limits from correlated errors and the value of hybrid escalation. It contributes concrete data toward practical oracle system design without relying on self-referential derivations or fitted parameters.

major comments (3)
  1. [Methods/Experiments section] Methods/Experiments section: The manuscript reports concrete accuracy numbers (83.43%, ~76%, 97.87%) and error correlations (0.529-0.689) but provides insufficient detail on implementation (prompting, confidence elicitation, aggregation mechanics), statistical significance testing for the 1.01pp gain, baseline configurations, and full error analysis. This absence is load-bearing for assessing the central empirical claim.
  2. [Results/Discussion on benchmark] Results/Discussion on benchmark: The headline claim of 83.43% accuracy for confidence-weighted aggregation requires that the 1,189 KalshiBench questions form a representative sample of real-world prediction market resolution difficulty. The paper does not discuss selection criteria, horizon/difficulty distribution, or comparison to actual Kalshi markets, so the small observed gain could be an artifact of sample composition.
  3. [Methods on retrieval] Methods on retrieval: The claim that date-filtered Exa retrieval isolates reasoning quality from retrieval differences is load-bearing for attributing performance gaps to aggregation. No ablation on retrieval coverage, relevance, or model-specific utilization of the shared evidence layer is reported to validate the isolation.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. We address each major comment below, indicating planned changes to strengthen the manuscript.

read point-by-point responses

  1. Referee: [Methods/Experiments section] Methods/Experiments section: The manuscript reports concrete accuracy numbers (83.43%, ~76%, 97.87%) and error correlations (0.529-0.689) but provides insufficient detail on implementation (prompting, confidence elicitation, aggregation mechanics), statistical significance testing for the 1.01pp gain, baseline configurations, and full error analysis. This absence is load-bearing for assessing the central empirical claim.

    Authors: We agree that greater implementation detail is required for reproducibility and rigorous evaluation. The revised manuscript will expand the Methods section with the exact prompts and output formats used for each model, the procedure for eliciting and normalizing confidence scores, the precise mechanics and weighting formula for independent aggregation, full baseline configurations, and an extended error analysis including per-question and per-category breakdowns. We will also add statistical significance testing (e.g., McNemar's test with bootstrap intervals) for the 1.01pp gain. These changes will be incorporated. revision: yes

  2. Referee: [Results/Discussion on benchmark] Results/Discussion on benchmark: The headline claim of 83.43% accuracy for confidence-weighted aggregation requires that the 1,189 KalshiBench questions form a representative sample of real-world prediction market resolution difficulty. The paper does not discuss selection criteria, horizon/difficulty distribution, or comparison to actual Kalshi markets, so the small observed gain could be an artifact of sample composition.

    Authors: KalshiBench is drawn directly from resolved Kalshi market questions, supplying an authentic sample of real-world resolution tasks. To address the concern, the revision will add a dedicated subsection describing the question selection criteria, the distribution of time horizons and market categories within the 1,189 questions, and available comparisons to performance on the broader Kalshi platform. This will allow readers to assess whether the modest gain generalizes beyond the current sample. revision: yes

  3. Referee: [Methods on retrieval] Methods on retrieval: The claim that date-filtered Exa retrieval isolates reasoning quality from retrieval differences is load-bearing for attributing performance gaps to aggregation. No ablation on retrieval coverage, relevance, or model-specific utilization of the shared evidence layer is reported to validate the isolation.

    Authors: The shared, date-filtered Exa layer ensures identical evidence is supplied to all models, which isolates differences to reasoning and aggregation. We acknowledge that explicit validation would strengthen the attribution. The revision will report retrieval coverage statistics, relevance indicators where available, and any observed model-specific patterns in evidence use. A complete model-specific ablation is constrained by the shared-layer design, but the added metrics will support the isolation claim. revision: partial

Circularity Check

0 steps flagged

No circularity: direct empirical evaluation on external benchmark

full rationale

The paper reports measured accuracies, error correlations, and routing criteria obtained by running single-LLM and multi-agent systems on the fixed 1,189-question KalshiBench dataset with a shared date-filtered Exa evidence layer. No equations, derivations, fitted parameters renamed as predictions, or self-citation chains appear in the abstract or described methodology; all headline numbers (83.43 %, 1.01 pp gain, 0.529-0.689 correlations, 97.87 % on unanimous subset) are direct outputs of the benchmark runs rather than reductions to inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The paper is an empirical benchmarking study that relies on standard machine-learning evaluation assumptions rather than new theoretical constructs.

axioms (1)
  • domain assumption The 1,189 KalshiBench questions and the Exa retrieval setup provide a fair test of reasoning quality independent of retrieval differences.
    Implicit in the experimental design comparing aggregation methods.

pith-pipeline@v0.9.1-grok · 5804 in / 1280 out tokens · 29986 ms · 2026-06-28T20:57:53.215092+00:00 · methodology

0 comments
read the original abstract

Prediction markets aggregate collective intelligence to forecast uncertain events, but their utility depends on reliable outcome resolution. Existing oracle systems tradeoff fast but brittle automation against accurate but costly human arbitration. Single-LLM oracles achieve meaningful accuracy but inherit all failure modes of their underlying model with no self-correction mechanism. We evaluate whether multi-agent LLM architectures can improve oracle resolution accuracy over single-model baselines. We compare independent aggregation and deliberative consensus against single-LLM baselines (GPT-5 Nano, DeepSeek V3, and Llama-3.3-70B) on 1,189 resolved prediction market questions from KalshiBench. All agents share a common evidence layer through Exa, with retrieval filtered by publication date to isolate reasoning from retrieval quality. Independent aggregation with confidence-weighted voting achieves the highest accuracy at 83.43 percent, outperforming the best individual model by 1.01 percentage points. Deliberative consensus degrades accuracy to approximately 76 percent, below every single-model baseline, attributed to error propagation during debate where confidently wrong models flip correct ones. Error correlations across models (0.529-0.689) explain why aggregation gains fall short of the theoretical Condorcet ceiling, placing a fundamental limit on ensemble approaches. Many questions resist correction by any multi-agent architecture, motivating escalation to human arbitration. We propose routing criteria for hybrid AI-human oracle systems: auto-resolving only unanimous, high-confidence questions yields 97.87 percent accuracy on 47 percent of the dataset, with inter-agent disagreement flagging the remainder for human review.

Figures

Figures reproduced from arXiv: 2605.30802 by Tarun Kota.

Figure 1
Figure 1. Figure 1: System architecture for multi-agent oracle resolution. [PITH_FULL_IMAGE:figures/full_fig_p007_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Resolution architectures evaluated in this study. Architecture A aggregates independent agent [PITH_FULL_IMAGE:figures/full_fig_p009_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Overall resolution accuracy on 1,189 KalshiBench questions. Confidence-weighted inde￾pendent aggregation (Architecture A) achieves the highest accuracy at 83.43%, outperforming the best single-model baseline by 1.01 percentage points. Deliberative consensus (Architecture B) degrades ac￾curacy to 76.11%, below all single-model baselines. The hatched bar shows the best previously published KalshiBench result… view at source ↗
Figure 4
Figure 4. Figure 4: Category-level accuracy for Architecture A (confidence-weighted), best single-LLM baseline, and Architecture B (deliberative). Categories are sorted by Architecture A accuracy. Alternating shaded bands delineate the tier structure discussed in the text. Categories with n < 10 should be interpreted with caution. (11–14%), and Architecture A either matches or slightly exceeds the best single-model baseline. … view at source ↗
Figure 5
Figure 5. Figure 5: Answer revision flows between Round 1 and Round 2 in Architecture B. Red indicates correct￾to-incorrect flips, and green indicates incorrect-to-correct flips. Deliberation yields a net loss in accuracy for GPT-4o (–10) and gains for DeepSeek (+6). or not (52.7%). The marginal difference between these two groups is 4.2 percentage points, which indi￾cates that when models initially disagree on genuinely diff… view at source ↗
Figure 6
Figure 6. Figure 6: Coverage-accuracy tradeoff curves for Architecture A and Architecture B. Each point rep￾resents the cumulative accuracy when auto-resolving the top k questions ranked by composite escalation score. Architecture A dominates across most of the operating range. These results suggest a practical escalation policy for deployed oracle systems. If a system auto-resolves only unanimous, high-confidence questions, … view at source ↗
Figure 7
Figure 7. Figure 7: Extended system flow with proposed future-work updates. The pipeline introduces domain￾specific retrieval routing, learned source ranking, and domain-aware aggregation to improve retrieval quality, evidence prioritization, and ensemble performance. the resolution architecture fixed, one can measure whether accuracy improves when agents receive source￾weighted evidence compared to the uniform-weight baselin… view at source ↗
Figure 8
Figure 8. Figure 8: System prompt used for single-LLM oracle resolution in Architecture A. The model receives structured evidence and is required to output a binary decision, calibrated confidence score, and source￾grounded reasoning. ROUND_2_SYSTEM_PROMPT: You are in the final cross-examination round of a multi-agent resolution debate. You will see how other agents reasoned about the same question. Your job is to CRITICALLY … view at source ↗
Figure 9
Figure 9. Figure 9: Architecture B (Deliberative Consensus), Round 1 prompt. Each agent independently resolves the market using a shared evidence packet and outputs a structured JSON decision, confidence score, and evidence-grounded reasoning. Now you will see how two other expert agents reasoned about the same question. QUESTION: {question} RESOLUTION CRITERIA: {criteria} EVIDENCE (SAME SHARED PACKET FROM ROUND 1): {exa_evid… view at source ↗
Figure 11
Figure 11. Figure 11: Architecture B (Deliberative Consensus), Round 2 prompt. Each agent performs a final cross-examination using the same evidence packet, explicitly evaluates other agents’ claims, and revises its decision only if it identifies a concrete evidence-based error in its prior reasoning. A.2 Command-Line Interface Reference The evaluation pipeline exposes two main entry points, scripts/test architecture a.py and … view at source ↗

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Reference graph

Works this paper leans on

46 extracted references · 6 canonical work pages · 2 internal anchors

  1. [1]

    The promise of prediction markets

    Kenneth J Arrow et al. “The promise of prediction markets”. In:Science320.5878 (2008), pp. 877– 878

    2008

  2. [2]

    Prediction market accuracy in the long run

    Joyce E Berg, Forrest D Nelson, and Thomas A Rietz. “Prediction market accuracy in the long run”. In:International Journal of F orecasting24.2 (2008), pp. 283–298

    2008

  3. [3]

    https://www

    CoinDesk.US election betting: CFTC loses last-minute bid to halt Kalshi contract. https://www. coindesk.com/policy/2024/09/12/us-election-betting-cftc-loses-last-minute-bid-to-halt-kalshi- contract. 2024

    2024

  4. [4]

    Understanding the blockchain oracle problem: A call for action

    Giulio Caldarelli. “Understanding the blockchain oracle problem: A call for action”. In:Information 11.11 (2020), p. 509

    2020

  5. [5]

    Steve Ellis, Ari Juels, and Sergey Nazarov.Chainlink: A decentralized oracle network. Tech. rep. https://research.chain.link/whitepaper-v1.pdf. Chainlink Labs, 2017

    2017

  6. [6]

    https://research.chain.link/whitepaper-v2.pdf

    Lorenz Breidenbach et al.Chainlink 2.0: Next steps in the evolution of decentralized oracle networks. https://research.chain.link/whitepaper-v2.pdf. 2021

    2021

  7. [7]

    UMA Protocol.How does UMA’s oracle work?https://docs.uma.xyz/protocol-overview/how-does- umas-oracle-work. 2024

    2024

  8. [8]

    https://help.kalshi.com/markets/markets-101/market-outcomes

    Kalshi.Market outcomes. https://help.kalshi.com/markets/markets-101/market-outcomes. 2024

    2024

  9. [9]

    https://blog.chain.link/ai-oracles/

    Kaspars Zintus-art, Brandon Vass, and Jonathan Ward.Empirical evidence in AI oracle development. https://blog.chain.link/ai-oracles/. 2025

    2025

  10. [10]

    https : / / blog

    UMA Protocol.Inside UMA’s optimistic truth bot. https : / / blog . uma . xyz / articles / inside - umas - optimistic-truth-bot. 2025

    2025

  11. [11]

    Correlated errors in large language models

    Eunsu Kim et al. “Correlated errors in large language models”. In:arXiv preprint arXiv:2506.07962 (2025). ICML 2025

    work page arXiv 2025

  12. [12]

    Do large language models know what they don’t know?

    Ethan Nel. “Do large language models know what they don’t know?” In:arXiv preprint arXiv:2512.16030 (2025). KalshiBench benchmark

    work page arXiv 2025

  13. [13]

    The use of knowledge in society

    Friedrich A Hayek. “The use of knowledge in society”. In:American Economic Review35.4 (1945), pp. 519–530. 28

    1945

  14. [14]

    A meta-analysis of prediction markets accuracy

    ICEB Conference. “A meta-analysis of prediction markets accuracy”. In:Proceedings of the Interna- tional Conference on Electronic Business. 2020

    2020

  15. [15]

    Chainalysis Blog

    Chainalysis Team.Oracle Manipulation Attacks Rising: A Unique Concern for DeFi. Chainalysis Blog. 2023.URL: https://www.chainalysis.com/blog/oracle-manipulation-attacks-rising/

    2023

  16. [16]

    A primer on oracle economics

    Lin William Cong et al. “A primer on oracle economics”. In:Journal of Corporate Finance(2025)

    2025

  17. [17]

    https://github.com/Polymarket/uma-ctf-adapter

    Polymarket.Polymarket UMA CTF adapter. https://github.com/Polymarket/uma-ctf-adapter. 2024

    2024

  18. [18]

    FEVER: A large-scale dataset for fact extraction and verification

    James Thorne et al. “FEVER: A large-scale dataset for fact extraction and verification”. In:Proceed- ings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics (NAACL). 2018

    2018

  19. [19]

    Survey of hallucination in natural language generation

    Ziwei Ji et al. “Survey of hallucination in natural language generation”. In:ACM Computing Surveys 55.12 (2023), pp. 1–38

    2023

  20. [20]

    A survey on hallucination in large language models: Principles, taxonomy, chal- lenges, and open questions

    Lei Huang et al. “A survey on hallucination in large language models: Principles, taxonomy, chal- lenges, and open questions”. In:ACM Transactions on Information Systems43.2 (2025), pp. 1–44

    2025

  21. [21]

    Sycophancy in LLMs: Causes, consequences, and mitigation strategies

    Amir Fanous et al. “Sycophancy in LLMs: Causes, consequences, and mitigation strategies”. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society. V ol. 8. 1. 2025, pp. 893–900

    2025

  22. [22]

    On optimum recognition error and reject tradeoff

    Chi-Keung Chow. “On optimum recognition error and reject tradeoff”. In:IEEE Transactions on Information Theory16.1 (1970), pp. 41–46

    1970

  23. [23]

    Selective classification for deep neural networks

    Yonatan Geifman and Ran El-Yaniv. “Selective classification for deep neural networks”. In:Advances in Neural Information Processing Systems (NeurIPS). 2017

    2017

  24. [24]

    Refining LLM outputs with itera- tive consensus ensemble (ICE)

    Mohamed Omar, Benjamin S Glicksberg, and Girish N Nadkarni. “Refining LLM outputs with itera- tive consensus ensemble (ICE)”. In:Computers in Biology and Medicine196 (2025)

    2025

  25. [25]

    Improving Factuality and Reasoning in Language Models through Multiagent Debate

    Yilun Du et al. “Improving factuality and reasoning in language models through multiagent debate”. In:Proceedings of the 41st International Conference on Machine Learning (ICML). arXiv preprint arXiv:2305.14325. 2024

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  26. [26]

    Consistent Estimators for Learning to Defer to an Expert

    Hussein Mozannar and David Sontag. “Consistent Estimators for Learning to Defer to an Expert”. In: 119 (2020), pp. 7076–7087

    2020

  27. [27]

    Trust or Escalate: LLM Judges with Provable Guarantees for Human Agreement

    Jaehun Jung et al. “Trust or Escalate: LLM Judges with Provable Guarantees for Human Agreement”. In: (2025)

    2025

  28. [28]

    On the Tyranny of the Majority: How multi-agent debate can im- prove upon majority voting

    Andrew Estornell and Yang Liu. “On the Tyranny of the Majority: How multi-agent debate can im- prove upon majority voting”. In:Advances in Neural Information Processing Systems (NeurIPS). 2024

    2024

  29. [29]

    arXiv preprint arXiv:2509.05396 , year =

    Anonymous. “Talk isn’t always cheap: Understanding failure modes in multi-agent debate”. In:arXiv preprint arXiv:2509.05396(2025)

    work page arXiv 2025

  30. [30]

    Why Do Multi-Agent LLM Systems Fail?

    Mert Cemri et al. “Multi-agent failure mode analysis”. In:arXiv preprint arXiv:2503.13657(2025). NeurIPS 2025 Spotlight

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  31. [31]

    Princeton University Press, 2011

    Patrick R Laughlin.Group Problem Solving. Princeton University Press, 2011

    2011

  32. [32]

    Generative monoculture in large language models

    Tongshuang Wu et al. “Generative monoculture in large language models”. In:arXiv preprint(2024)

    2024

  33. [33]

    Measuring and addressing systematic bias in LLM decision-making

    Yuntao Bai et al. “Measuring and addressing systematic bias in LLM decision-making”. In:Proceed- ings of the National Academy of Sciences122.9 (2025)

    2025

  34. [34]

    Does the whole exceed its parts? The effect of AI explanations on comple- mentary team performance

    Gagan Bansal et al. “Does the whole exceed its parts? The effect of AI explanations on comple- mentary team performance”. In:Proceedings of the ACM CHI Conference on Human Factors in Computing Systems. 2021. 29

    2021

  35. [35]

    Three-tier LLM-human cascaded architectures for scalable decision systems

    Anonymous. “Three-tier LLM-human cascaded architectures for scalable decision systems”. In:arXiv preprint arXiv:2506.11887(2025). A Appendix A.1 Prompt Templates SYSTEM PROMPT: You are an expert prediction market resolution agent. Your task is to determine whether a prediction market question should resolve to YES or NO based on the provided evidence. Ins...

    work page arXiv 2025

  36. [38]

    Prioritize information describing outcomes that have already occurred

    Make your decision based on definitive evidence. Prioritize information describing outcomes that have already occurred

  37. [40]

    Rate your confidence from 0.0 (very uncertain) to 1.0 (absolutely certain). Output Format: - decision: YES or NO - confidence: 0.0 to 1.0 - reasoning: Explanation referencing specific sources USER MESSAGE TEMPLATE: Please analyze the following prediction market question and evidence, then provide your resolution decision. {evidence_text} Based on the evid...

  38. [41]

    Read the question and resolution criteria carefully

  39. [42]

    Analyze ALL provided sources for relevant information

  40. [43]

    You should prioritize information that describes an outcome that happened

    Make your decision based on definitive evidence. You should prioritize information that describes an outcome that happened

  41. [44]

    If evidence is ambiguous, use your best judgment

  42. [45]

    Each agent independently resolves the market using a shared evidence packet and outputs a structured JSON decision, confidence score, and evidence-grounded reasoning

    Rate your confidence in your decision from 0.0 (very uncertain) to 1.0 (absolutely certain) Figure 9:Architecture B (Deliberative Consensus), Round 1 prompt. Each agent independently resolves the market using a shared evidence packet and outputs a structured JSON decision, confidence score, and evidence-grounded reasoning. Now you will see how two other e...

  43. [46]

    For each agent that DISAGREES with you: Do they cite specific evidence from the shared packet that contradicts your reasoning? Or are they asserting a conclusion without evidentiary support?

  44. [47]

    Re-read the specific pieces of evidence that are most relevant to the disagreement

  45. [48]

    ONLY change your decision if you can identify a concrete error in your own 31 round 1 analysis, for example, you misread a date, overlooked a source, or misinterpreted the resolution criteria

  46. [49]

    A” and “B

    If agents agree with you, do NOT increase your confidence unless they provide additional evidence-based reasoning you hadn’t considered. DEFAULT BEHAVIOR: Change your decision ONLY if another agent identifies specific evidence or because there is a concrete flaw in your reasoning, not simply because they reached a different conclusion. Provide your FINAL ...

    2000