Accuracy, Stability, and Repeated-Run Reliability of Large Language Models on Deterministic Programming Tasks
- URL: http://arxiv.org/abs/2606.00920v1
- Date: Sat, 30 May 2026 23:03:05 GMT
- Title: Accuracy, Stability, and Repeated-Run Reliability of Large Language Models on Deterministic Programming Tasks
- Abstract summary: Run-level pass rate overstates retry-free coverage by up to 17.8 percentage points.<n>We present a repeated-run evaluation protocol with metrics for run-level accuracy, retry-free coverage, and per-problem variability.
- Score: 0.8699677835130409
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Run-level pass rate overstates retry-free coverage by up to 17.8 percentage points -- and the gap is largest precisely for mid-performing systems. We investigate this accuracy--stability relationship in large language model (LLM) evaluation for deterministic text-conditioned generation, using programming tasks as a concrete testbed. Standard code-generation benchmarks emphasize single-run accuracy or eventual success under repeated sampling, but many deployment settings also require stability: consistent outcomes across repeated invocations under the same task description. We present a repeated-run evaluation protocol with metrics for run-level accuracy, retry-free coverage, and per-problem variability. On a recency-based benchmark of 100 LeetCode-style problems, we evaluate 16 models from five provider families under two prompt templates with five repeated runs per problem, yielding 16,000 evaluation instances. Although run-level pass rate and perfect stability rate are strongly correlated (r=0.985), pass rate consistently exceeds retry-free coverage -- a gap that reaches 17.8 percentage points and reverses model rankings even among closely matched systems. Prompt effects are model-dependent rather than uniformly beneficial. These results suggest that repeated-run stability analysis is a necessary complement to conventional accuracy reporting for deterministic text-conditioned generation tasks.
Related papers
- Recursive Agentic Reasoning [4.7490116928645065]
Test-time reasoning methods such as iterative refinement, decomposition, and repeated sampling are often evaluated in isolation.<n>We introduce a unified view of these methods as recursion operators over an agent's reasoning trace.<n>We evaluate all three operators against a single-pass chain-of-thought baseline under a shared harness with identical prompts, token budgets, and grading code.
arXiv Detail & Related papers (2026-08-25T01:34:08Z) - Analysis of Federated Aggregation under Model Poisoning and Backdoor Attacks: A Reconstructed Cross-Dataset and Cross-Architecture Benchmark [0.0]
A 500-cell seed-1 evaluation matrix was reconstructed across five aggregation methods.<n>Execution logs were identified for 454 original runs and 36 repaired or rerun executions.<n>Krum attained the highest recorded accuracy under both sign-flipping and Gaussian configurations.
arXiv Detail & Related papers (2026-08-11T20:42:56Z) - Conformal Reliability: A New Evaluation Metric for Conditional Generation [62.761166941396844]
Conditional generative models have recently remarkable success in various applications.<n>We propose a novel evaluation metric called reliability based on prediction set at a pre-specified confidence level.<n>We introduce Conformal ReLiability (CReL), a framework that can (i) construct a prediction set and (ii) accurately optimize the reliability score within the constructed prediction set.
arXiv Detail & Related papers (2026-05-29T03:52:44Z) - Learning to Assess the Reliability of Number-of-Runs Estimation in Stochastic Optimization [3.9407886544063384]
We study a learning-based extension of a recent empirical online that adaptively estimates the required number of runs using handling and skewness-based symmetry checks.<n>We train classifiers on 23 statistical, energy-free, and shape and stability features to predict whether a run-number estimate is reliable, prioritizing detection of incorrect estimates via minority-class recall.<n>The results show that run-number reliability can be learned in a within-configuration scenario, enabling detection of unreliable estimates with high minority-class recall, although performance remains limited by the restricted data diversity within fixed configurations.
arXiv Detail & Related papers (2026-05-27T11:08:19Z) - What Single-Prompt Accuracy Misses: A Multi-Variant Reliability Audit of Language Models [0.0]
Single-prompt accuracy is the dominant way to benchmark language models, but it can miss reliability failures that matter.<n>We evaluate a 15-model open-weight corpus, with the main reliability analyses focused on 10 instruct models across five classification and reasoning benchmarks.<n>We find three broad results. First, evaluation design can materially change the conclusion.<n>Second, confidence signals are fragile. On MMLU-Pro, every primary model verbally reports confidence substantially above both its accuracy and its token-probability confidence on the same rows, and verbal parse rate can collapse for a single model on a single prompt variant.
arXiv Detail & Related papers (2026-05-03T20:05:08Z) - Evaluating AI Meeting Summaries with a Reusable Cross-Domain Pipeline [2.4832413743954618]
We present a reusable evaluation pipeline for generative AI applications, instantiated for AI meeting summaries.<n>The system separates reusable orchestration from task-specific semantics across five stages: source intake, structured reference construction, candidate generation, structured scoring, and reporting.<n>We benchmark the offline loop on a typed dataset of 114 meetings spanning city_council, private_data, and whitehouse_press_briefings.
arXiv Detail & Related papers (2026-04-23T07:02:11Z) - Correct Answers from Sound Reasoning: Verifiable Process Supervision for Language Models [94.68358825189738]
We propose a post-training framework for verifiable domains that jointly optimize prediction accuracy and reasoning quality.<n>We evaluate VPS on chess, a controlled testbed where reasoning steps can be deterministically verified against engine signals.<n>VPS preserves accuracy while significantly improving reasoning quality, reducing win-rate error by up to 30% and restoring consistency to near saturation.
arXiv Detail & Related papers (2026-04-03T15:19:46Z) - Ensemble-Based Uncertainty Estimation for Code Correctness Estimation [36.53771380869671]
We propose Ensemble Semantic Entropy (ESE), which estimates uncertainty by evaluating the consistency of samples aggregated across an ensemble of models.<n>Experiments on LiveCodeBench demonstrate that ESE correlates more strongly with program correctness than single-model semantic entropy.<n>We also propose a cascading test-time scaling framework Cas, which maintains performance while reducing FLOPs by 64.9% compared to single-model scaling.
arXiv Detail & Related papers (2026-03-28T02:37:36Z) - Solver-in-the-Loop: MDP-Based Benchmarks for Self-Correction and Behavioral Rationality in Operations Research [19.31559944205485]
Operations Research practitioners routinely debug infeasible models through an iterative process.<n>We introduce two benchmarks that place the textbfsolver in the evaluation loop<n>We find that domain-specific RLVR training enables an 8B model to surpass frontier APIs.
arXiv Detail & Related papers (2026-01-28T20:02:44Z) - COIN: Uncertainty-Guarding Selective Question Answering for Foundation Models with Provable Risk Guarantees [51.5976496056012]
COIN is an uncertainty-guarding selection framework that calibrates statistically valid thresholds to filter a single generated answer per question.<n>COIN estimates the empirical error rate on a calibration set and applies confidence interval methods to establish a high-probability upper bound on the true error rate.<n>We demonstrate COIN's robustness in risk control, strong test-time power in retaining admissible answers, and predictive efficiency under limited calibration data.
arXiv Detail & Related papers (2025-06-25T07:04:49Z) - Accelerated Test-Time Scaling with Model-Free Speculative Sampling [58.69141724095398]
We introduce STAND (STochastic Adaptive N-gram Drafting), a novel model-free speculative decoding approach.<n>We show that STAND reduces inference latency by 60-65% compared to standard autoregressive decoding.<n>As a model-free approach, STAND can be applied to any existing language model without additional training.
arXiv Detail & Related papers (2025-06-05T07:31:18Z) - Self-Consistency Improves Chain of Thought Reasoning in Language Models [53.45015291520658]
We explore a simple ensemble strategy, self-consistency, that significantly improves the reasoning accuracy of large language models.
For arithmetic and commonsense reasoning benchmarks we find that self-consistency yields significant accuracy improvements.
arXiv Detail & Related papers (2022-03-21T17:48:52Z) - Investigating Crowdsourcing Protocols for Evaluating the Factual
Consistency of Summaries [59.27273928454995]
Current pre-trained models applied to summarization are prone to factual inconsistencies which misrepresent the source text or introduce extraneous information.
We create a crowdsourcing evaluation framework for factual consistency using the rating-based Likert scale and ranking-based Best-Worst Scaling protocols.
We find that ranking-based protocols offer a more reliable measure of summary quality across datasets, while the reliability of Likert ratings depends on the target dataset and the evaluation design.
arXiv Detail & Related papers (2021-09-19T19:05:00Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.