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This study exposes critical instability in LLM evaluation metrics, demonstrating that small-sample prompt sets fail to reliably distinguish top-performing models while consistently identifying the worst ones. The findings reveal that auditing evaluations can further weaken their conclusions due to inherent reproducibility issues and endpoint withdrawals, urging a shift toward reporting rank stability and per-cell provenance rather than definitive rankings.
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How Reproducible Are Evaluation Conclusions? A Self-Audit of LLM-Inferred Prompt Structure

arXiv10 min de cititArticolul și perspectiva sunt în engleză.

arXiv:2609.30074v1 Announce Type: cross Abstract: Evaluations of LLM systems routinely average over small prompt sets and report models as a ranked table. We ask how much confidence such a table deserves, using LLM-based prompt-structure inference as the case study: eight open model variants across five families and 8B to 675B parameters, caching disabled, 293 raw intermediate representations persisted. The measured phenomenon is unstable to begin with. Identical calls do not reliably recover identical structure, with mean node-set Jaccard from 0.39 to 0.96 and 72% of prompt-model cells never node-set-perfect. Auditing the evaluation weakens its conclusions further, and this is our main contribution. Under a joint cluster bootstrap over prompts, only the bottom of the ranking is firm: the two least reproducible models hold rank in 99% and 86% of replicates, the middle four in 27% to 48%, and the top two in 68% each, so the table identifies the worst model reliably but does not reliably identify the best. Two equally defensible rules for merging repeated campaigns change four of eight rows and move the study-wide headline by 7 percentage points. Checking the inferred structure against ground-truth annotations shows reproducibility cannot be read as accuracy. And four of the eight endpoints were withdrawn within ten weeks of measurement, so the study as specified can no longer be run. Small-sample LLM evaluations can therefore look far more definitive than their evidence supports. We recommend reporting rank stability, per-cell provenance, executed sensitivity comparisons, raw per-run outputs, and a measurement date alongside any ranking.

Title:How Reproducible Are Evaluation Conclusions? A Self-Audit of LLM-Inferred Prompt Structure

Abstract:Evaluations of LLM systems routinely average over small prompt sets and report models as a ranked table. We ask how much confidence such a table deserves, using LLM-based prompt-structure inference as the case study: eight open model variants across five families and 8B to 675B parameters, caching disabled, 293 raw intermediate representations persisted. The measured phenomenon is unstable to begin with. Identical calls do not reliably recover identical structure, with mean node-set Jaccard from 0.39 to 0.96 and 72% of prompt-model cells never node-set-perfect. Auditing the evaluation weakens its conclusions further, and this is our main contribution. Under a joint cluster bootstrap over prompts, only the bottom of the ranking is firm: the two least reproducible models hold rank in 99% and 86% of replicates, the middle four in 27% to 48%, and the top two in 68% each, so the table identifies the worst model reliably but does not reliably identify the best. Two equally defensible rules for merging repeated campaigns change four of eight rows and move the study-wide headline by 7 percentage points. Checking the inferred structure against ground-truth annotations shows reproducibility cannot be read as accuracy. And four of the eight endpoints were withdrawn within ten weeks of measurement, so the study as specified can no longer be run. Small-sample LLM evaluations can therefore look far more definitive than their evidence supports. We recommend reporting rank stability, per-cell provenance, executed sensitivity comparisons, raw per-run outputs, and a measurement date alongside any ranking.

13 pages. Previously submitted to TAE (Trust-AI-Eval), a NeurIPS 2026 workshop

Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

arXiv:2609.30074 [cs.CL]

(or arXiv:2609.30074v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.30074 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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results/_smoke_gemma.csv

results/ablation_ir_context.csv

results/ablation_oracle.csv

results/baseline_naive.csv

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results/behaviour_raw/gen_047_plan_a_travel_itinerary.prompt.rtpl.SwapRules_L19-31.json

results/behaviour_raw/gen_050_diagnose_build_failures.prompt.rtpl.NegateCondition_L25.json

results/behaviour_raw/gen_050_diagnose_build_failures.prompt.rtpl.SwapRules_L3-6.json

results/behaviour_raw/gen_054_compose_marketing_copy.prompt.rtpl.DropRule_L3.json

results/behaviour_raw/gen_054_compose_marketing_copy.prompt.rtpl.DropRule_L9.json

results/behaviour_raw/gen_054_compose_marketing_copy.prompt.rtpl.SwapRules_L9-12.json

results/behaviour_raw/gen_059_suggest_unit_tests.prompt.rtpl.DropRule_L6.json

results/behaviour_raw/gen_059_suggest_unit_tests.prompt.rtpl.SwapRules_L6-9.json

results/behaviour_raw/gen_061_review_sql_migrations.prompt.rtpl.NegateCondition_L28.json

results/behaviour_raw/gen_061_review_sql_migrations.prompt.rtpl.NegateCondition_L52.json

results/behaviour_raw/gen_061_review_sql_migrations.prompt.rtpl.SwapRules_L16-19.json

results/behaviour_raw/gen_062_moderate_forum_posts.prompt.rtpl.NegateCondition_L54.json

results/behaviour_raw/gen_063_summarize_legal_contracts.prompt.rtpl.DropRule_L3.json

results/behaviour_raw/gen_064_answer_medical_faqs_with_disclaimers.prompt.rtpl.DropRule_L39.json

results/behaviour_raw/gen_064_answer_medical_faqs_with_disclaimers.prompt.rtpl.SwapRules_L39-42.json

results/behaviour_raw/gen_065_grade_student_essays.prompt.rtpl.DropRule_L30.json

results/behaviour_raw/gen_065_grade_student_essays.prompt.rtpl.DropRule_L34.json

results/behaviour_raw/gen_066_route_sales_leads.prompt.rtpl.DropRule_L6.json

results/behaviour_raw/gen_067_generate_release_notes.prompt.rtpl.SwapRules_L16-32.json

results/behaviour_raw/gen_068_screen_job_applications.prompt.rtpl.DropRule_L15.json

results/behaviour_raw/gen_068_screen_job_applications.prompt.rtpl.NegateCondition_L51.json

results/behaviour_raw/gen_069_translate_with_tone_control.prompt.rtpl.NegateCondition_L39.json

results/behaviour_raw/gen_070_classify_incoming_emails.prompt.rtpl.DropRule_L19.json

results/behaviour_raw/gen_073_recommend_products.prompt.rtpl.DropRule_L3.json

results/behaviour_raw/gen_076_answer_hr_policy_questions.prompt.rtpl.DropRule_L19.json

results/behaviour_raw/gen_076_answer_hr_policy_questions.prompt.rtpl.DropRule_L22.json

results/behaviour_raw/gen_076_answer_hr_policy_questions.prompt.rtpl.NegateCondition_L13.json

results/behaviour_raw/gen_077_plan_a_travel_itinerary.prompt.rtpl.SwapRules_L16-19.json

results/behaviour_raw/gen_078_summarize_meetings.prompt.rtpl.NegateCondition_L23.json

results/behaviour_raw/gen_078_summarize_meetings.prompt.rtpl.NegateCondition_L39.json

results/behaviour_study.csv

results/bench.csv

results/bench_full.csv

results/bench_real_annotated.csv

results/bench_tailored.csv

results/coverage/code_review.json

results/coverage/content_moderation.json

results/coverage/customer_support.json

results/downstream_cost.csv

results/drift_decompose.csv

results/failure_attribution.csv

results/ir_correctness.csv

results/ir_stability.csv

results/ir_stability_combined.csv

results/ir_stability_crossed.csv

results/ir_stability_deepseek.csv

results/ir_stability_glm.csv

results/ir_stability_merged.csv

results/ir_stability_merged_cells.csv

results/ir_stability_minimax.csv

results/ir_stability_ministral.csv

results/ir_stability_ministral_v2.csv

results/ir_stability_paid_tierB.csv

results/ir_stability_paid_tierC.csv

results/ir_stability_paid_tierD.csv

results/ir_stability_qwen.csv

results/ir_stability_real.csv

results/ir_stability_real_v2.csv

results/ir_stability_scaled.csv

results/ir_stability_xmodel.csv

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results/irs_crossed/qwen3.5_397b/edb621956f8d_SKILL_r2.json

results/logs/tierA.log

results/logs/tierB.log

results/logs/tierC.log

results/logs/tierD.log

results/model_availability.csv

results/mutation/code_review.json

results/mutation/content_moderation.json

results/mutation/customer_support.json

results/op_realism.csv

results/operator_visibility.csv

results/pre_repair/README.md

results/pre_repair/ablation_oracle.csv

results/pre_repair/bench.csv

results/pre_repair/bench_original_committed.csv

results/pre_repair/operator_visibility.csv

results/pre_repair/operator_visibility_intermediate.csv

results/pre_repair/rq2_baseline.csv

results/regime_comparison.csv

results/rq2_baseline.csv

results/rq2_both_oracles.csv

results/rq3_detection.csv

results/stability_vs_correctness.csv

results/stats_analysis.json

results/unannotated_stability.csv

scripts/ablation.py

scripts/analyze_results.py

scripts/baseline_naive.py

scripts/behaviour_analysis.py

scripts/behaviour_study.py

scripts/collect_github.py

scripts/downstream_cost.py

scripts/drift_decompose.py

scripts/failure_attribution.py

scripts/ir_correctness.py

scripts/ir_stability.py

scripts/merge_ir_stability.py

scripts/model_availability.py

scripts/op_realism.py

scripts/operator_visibility.py

scripts/plots.py

scripts/regime_comparison.py

scripts/rq2_baseline.py

scripts/rq2_both_oracles.py

scripts/rq3_detection.py

scripts/rq3_detection_v2.py

scripts/run_experiments.sh

scripts/stability_vs_correctness.py

scripts/stats_analysis.py

scripts/synthesize.py

scripts/unannotated_stability.py

tool/crates/prompt_config/Cargo.toml

tool/crates/prompt_config/src/lib.rs

tool/crates/prompt_dap/Cargo.toml

tool/crates/prompt_dap/src/lib.rs

tool/crates/prompt_dap/src/protocol.rs

tool/crates/prompt_dap/src/server.rs

tool/crates/prompt_ir/Cargo.toml

tool/crates/prompt_ir/src/config.rs

tool/crates/prompt_ir/src/diagnostic.rs

tool/crates/prompt_ir/src/ir.rs

tool/crates/prompt_ir/src/lib.rs

tool/crates/prompt_ir/src/trace.rs

tool/crates/prompt_lsp/Cargo.toml

tool/crates/prompt_lsp/src/lib.rs

tool/crates/prompt_lsp/src/semantic_tokens.rs

tool/crates/prompt_runtime/Cargo.toml

tool/crates/prompt_runtime/src/cache.rs

tool/crates/prompt_runtime/src/coverage.rs

tool/crates/prompt_runtime/src/diff.rs

tool/crates/prompt_runtime/src/lib.rs

tool/crates/prompt_runtime/src/lint.rs

tool/crates/prompt_runtime/src/mutate.rs

tool/crates/prompt_runtime/src/provider.rs

tool/crates/prompt_runtime/src/skill_executor.rs

tool/crates/prompt_runtime/src/telemetry.rs

tool/crates/prompt_slm/Cargo.toml

tool/crates/prompt_slm/src/client.rs

tool/crates/prompt_slm/src/lib.rs

tool/crates/prompt_slm/src/prompts.rs

tool/crates/prompt_template/Cargo.toml

tool/crates/prompt_template/src/contextgen.rs

tool/crates/prompt_template/src/lib.rs

tool/crates/prompt_template/src/skill.rs

tool/crates/prompt_template/src/skill_directory.rs

tool/crates/prompt_tests/Cargo.toml

tool/crates/prompt_tests/src/fixtures.rs

tool/crates/prompt_tests/src/ir_stability.rs

tool/crates/prompt_tests/src/lib.rs

tool/crates/prompt_tests/src/runtime_trace.rs

tool/crates/prompt_tests/src/template_regression.rs

tool/crates/promptdbg/Cargo.toml

tool/crates/promptdbg/src/main.rs

tool/repairs.patch

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