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ARXIV:2602.18230 · LLM EVALUATION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2602.18230LLM EVALUATIONSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
Evaluate LLM negotiation capabilities using a benchmark based on Scoreable Games with additional metrics for quality and fairness.
Opportunity summary
Pain Evaluate LLM negotiation capabilities using a benchmark based on Scoreable Games with additional metrics for quality and fairness.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
Evaluate LLM negotiation capabilities using a benchmark based on Scoreable Games with additional metrics for quality and fairness. Abdelnabi et al.
Large Language Models (LLMs) demonstrate significant potential in multi-agent negotiation tasks, yet evaluation in this domain remains challenging due to a lack of robust and generalizable benchmarks. Abdelnabi et al.
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Large Language Models (LLMs) demonstrate significant potential in multi-agent negotiation tasks, yet evaluation in this domain remains challenging due to a lack of robust…
LLM Evaluation moved forward this cycle; last verified April 2026. Public score 4.0/10.
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Evaluate LLM negotiation capabilities using a benchmark based on Scoreable Games with additional metrics for quality and fairness.
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10.48550/arXiv.2602.18230Evaluate LLM negotiation capabilities using a benchmark based on Scoreable Games with additional metrics for quality and fairness.
Abstract
Large Language Models (LLMs) demonstrate significant potential in multi-agent negotiation tasks, yet evaluation in this domain remains challenging due to a lack of robust and generalizable benchmarks. Abdelnabi et al. (2024) introduce a negotiation benchmark based on Scoreable Games, with the aim of developing a highly complex and realistic evaluation framework for LLMs. Our work investigates the reproducibility of claims in their benchmark, and provides a deeper understanding of its usability and generalizability. We replicate the original experiments on additional models, and introduce additional metrics to verify negotiation quality and evenness of evaluation. Our findings reveal that while the benchmark is indeed complex, model comparison is ambiguous, raising questions about its objectivity. Furthermore, we identify limitations in the experimental setup, particularly in information leakage detection and thoroughness of the ablation study. By examining and analyzing the behavior of a wider range of models on an extended version of the benchmark, we reveal insights that provide additional context to potential users. Our results highlight the importance of context in model-comparative evaluations.
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Derived fallbackRead summaries are estimated from adjacent metadata, not verified extraction rows.
Proof status
unverified0 refs; 0 sources; 17% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
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Preparing verified analysis
Dimensions overall score 4.0
PROBLEM
Evaluate LLM negotiation capabilities using a benchmark based on Scoreable Games with additional metrics for quality and fairness. Abdelnabi et al.
METHOD
Large Language Models (LLMs) demonstrate significant potential in multi-agent negotiation tasks, yet evaluation in this domain remains challenging due to a lack of robust and generalizable benchmarks. Abdelnabi et al.
RESULT
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Large Language Models (LLMs) demonstrate significant potential in multi-agent negotiation tasks, yet evaluation in this domain remains challenging due to a lack of robust and generalizable benchmarks.
WHY NOW
LLM Evaluation moved forward this cycle; last verified April 2026. Public score 4.0/10.
Abstract-backed public claims while anchored extraction refreshes.
Evaluate LLM negotiation capabilities using a benchmark based on Scoreable Games with additional metrics for quality and fairness. Abdelnabi et al.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Large Language Models (LLMs) demonstrate significant potential in multi-agent negotiation tasks, yet evaluation in this domain remains challenging due to a lack of robust and generalizable benchmarks. Abdelnabi et al.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Large Language Models (LLMs) demonstrate significant potential in multi-agent negotiation tasks, yet evaluation in this domain remains challenging due to a lack of robust and generalizable benchmarks.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
LLM Evaluation moved forward this cycle; last verified April 2026. Public score 4.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Evaluate LLM negotiation capabilities using a benchmark based on Scoreable Games with additional metrics for quality and fairness.
Segment
LLM Evaluation
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Commercial read
4.0/10 public viability
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CITED BY
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missing
reason
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proof status
unverified
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confidence low
next verification path
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Evidence coverage
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stale
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Build readiness
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passport absent
stale
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Artifact maturity
GitHub and Hugging Face maturity payloads
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stale
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Technical feasibility
partial
Current read
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Gaps
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missing
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Evidence
0 references, 0 sources, 17% evidence coverage.
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Buyer clarity
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Integration burden
missing
Current read
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Evidence
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Write integration checklist from prototype path and target workflow.
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Classify regulatory flags before commercialization planning.
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People
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ARTIFACTS
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DEFENSIBILITY
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