arXiv:2609.01603v1 Announce Type: cross Abstract: Evaluating software engineering agents on realistic benchmarks is costly, since each task may require multi-step code exploration, modification, and...
arXiv:2607. 27023v1 Announce Type: new Abstract: Evaluating large generative models across benchmarks is time-consuming and computationally expensive.
arXiv:2607. 12338v1 Announce Type: new Abstract: Agent benchmarks often compare two agents after all tasks have run, but costly evaluations make partial runs tempting.
Agent evaluations increasingly benchmark LLMs, but rankings can be swayed by evaluation conditions such as scaffolds or tasks, making reliability claim‑dependent. A Bayesian variance‑decomposition framework applied to 22 benchmarks shows that reliability varies with the measurement goal: fixed model‑scaffold systems rank reliably, while underlying‑model rankings are less stable. Scaffold choice can alter conclusions, and adding more tasks only modestly improves reliability when scaffold coverage is limited; however, pooling diverse benchmarks can substantially raise cross‑task ranking reliability and reduce cost.
arXiv:2604. 00594v2 Announce Type: replace Abstract: As the focus in LLM-based coding shifts from static single-step code generation to multi-step agentic interaction with tools and environments, understanding which tasks will challenge agents and why becomes increasingly difficult.
The paper introduces Speculative Evaluation, a method to reduce variance in evaluating stochastic large language models (LLMs) under a fixed rollout budget. It employs a Hierarchical Bayesian Neyman (HBN) policy that first runs a short uniform pilot, then pools task-level success counts via a hierarchical Bayesian model to compute posterior expectations of task-level sampling variances. Using these expectations, the method applies exact positive-integer Neyman allocation to allocate rollouts, and an asynchronous variant (HBN-async) speculatively executes continuations from partial pilot feedback to mitigate synchronization overhead. Across six checkpoints and 18 benchmark groups, Speculative Evaluation achieves 12.8%-33.6% lower variance compared to uniform allocation, outperforming empirical and independent Bayesian baselines, and demonstrates practical benefits in real-generation experiments.
The paper introduces Strategy Accumulation and Guided Execution (SAGE), a two-stage framework that makes automated fine-tuning of large language models cumulative. In the first stage, a multi-agent pipeline uses Monte Carlo Tree Search to explore training strategies while a Distillation Agent records task-specific insights and cross-task confidence scores into a structured repository. In the second stage, SAGE retrieves relevant experience from this repository to guide training on new tasks, achieving a 12.4‑percentage‑point improvement over a baseline pipeline without accumulated experience on nine unseen tasks.
The paper introduces Balance of Benchmarks (BoB), a framework that improves task-conditioned model comparison by weighting benchmark evidence based on semantic density, equating scores across varying difficulty levels, and pooling task-relevant residuals. BoB retains all eligible benchmark data while adjusting its influence, outperforming uniform averaging on the WildScores dataset with higher Spearman correlation, lower MAE, and better shortlist hit rates. The method also reduces ranking instability when benchmarks are repeated or paraphrased, and lowers retrospective regret in model selection.
arXiv:2610.02563v1 Announce Type: cross Abstract: We present OpenGameEval, a benchmark and evaluation framework for agentic game development inside Roblox Studio. It runs language models as agents in...
The paper introduces prediction‑powered smoothing (PP‑S) and its taxonomy‑aware extension (PP‑TS) to improve point and interval estimates of domain‑specific AI performance when only a limited sample of labeled units is available. It also proposes a new design‑based cross‑validation score that is approximately unbiased for selecting between direct and smoothed estimators. Experiments on a curated benchmark and real‑world agent traffic show that the proposed methods outperform direct estimators in both accuracy and coverage, and that the new score matches the performance of an independent validation sample while providing more precise error estimates.
arXiv:2606. 22678v2 Announce Type: replace-cross Abstract: Agentic coding harnesses - such as Agent-Skills, Superpowers, and Agent-Rigor - are increasingly deployed to augment underlying LLMs for real-world software engineering tasks.
arXiv:2610.14146v1 Announce Type: new Abstract: Long-horizon terminal benchmarks measure whether an agent can sustain progress through extended workflows with process rewards, not only whether its fi...