论文

本文通过大规模超参数搜索，系统地重新评估了 Vanilla LoRA 以及九个代表性 LoRA 变体，发现不同 LoRA 方法偏好的学习率区间各异，并将最优学习率区间的差异归因于最大 Hessian 特征值的变化，与经典学习理论相吻合。This work systematically re-evaluate nine representative LoRA variants alongside vanilla LoRA through extensive hyperparameter searches, finding that different LoRA methods favor distinct learning rate ranges and attributes the differing optimal learning rate ranges to variations in the largest Hessian eigenvalue, aligning with classical learning theories.

本工作提出 AlphaEval，一个基于真实生产环境的基准，包含来自七家在其核心业务中部署 AI Agent 的公司的 94 个任务，覆盖六个 O*NET (Occupational Information Network) 领域；并贡献了一套从需求到基准的构建框架，将从需求到评估的完整流程标准化。This work presents AlphaEval, a production-grounded benchmark of 94 tasks sourced from seven companies deploying AI agents in their core business, spanning six O*NET (Occupational Information Network) domains, and contributes a requirement-to-benchmark construction framework that standardizes the entire pipeline from requirement to evaluation.

提出 RoboDojo，一个面向通用机器人操作策略综合评估的仿真-真机统一基准，将 30 种策略集成到 XPolicyLab 并在 RoboDojo 上进行评测，建立了公开的排行榜与系统性的策略性能分析。RoboDojo is introduced, a unified sim-and-real benchmark for comprehensive evaluation of generalist robot manipulation policies that integrates 30 policies into XPolicyLab and evaluates them on RoboDojo, establishing a public leaderboard and systematic analysis of current policy performance.

确认了丰富资源语言与代表性不足语言之间在数学推理性能上存在持续差距，性能更优主要与更强的指令遵循能力相关，并提出了完全开源的数据集、数据采集流程与评估框架。A persistent gap in mathematical reasoning performance between high-resource and underrepresented languages is confirmed, with stronger results largely associated with better instruction-following ability, and a fully open-source dataset, data acquisition pipeline, and evaluation framework is introduced.

该工作提出 HETERQA，一个包含 857 个 QA 对的综合性基准，涵盖五个异构来源的记录检索，并表明 HETERQA 为异构来源下的记录检索提供了有效的测试平台，为未来检索方法留下了显著空间。This work introduces HETERQA, a comprehensive benchmark with 857 QA pairs for record retrieval over five heterogeneous sources and indicates that HETERQA provides an effective testbed for record retrieval over heterogeneous sources and leaves substantial room for future retrieval methods.

结果表明，强大的 LLM 能够产出一系列合理思路，但其范围仍比人类研究品味更窄，并存在系统性偏移。It is suggested that strong LLMs can produce a range of reasonable ideas, but that range remains narrower than, and systematically shifted relative to, human research taste.

静脉识别是一种安全生物特征技术，常受限于标注数据稀缺与成像差异；而面向自然图像设计的增强策略可能破坏其关键的细粒度拓扑与纹理。本文提出 AGVBench，在 5 个公开掌/指静脉数据集、7 种骨干网络（含经典 CNN、视觉 Transformer 及静脉专用模型）上评测 30 种代表性增强策略。结果显示，多图混合类方法（如 MixUp、PuzzleMix……Vein recognition is a secure biometric technology often constrained by limited annotated data and imaging variations. While data augmentation mitigates this, strategies designed for natural images may disrupt the fine-grained topology and textures essential for identity discrimination. We present AGVBench, which evaluates 30 representative augmentation strategies on five public palm- and finger-vein datasets with seven backbone architectures, covering classic CNNs, vision transformers, and vein-specific recognition models. Our results show that multi-image mixing methods (e.g., MixUp, PuzzleMi

提出"自主策略演化"评估范式：在固定交互预算下，由 harness-model Agent 反复编辑可执行策略系统；并在 EvoPolicyGym 中实例化，该基准基于一组紧凑型交互式 RL 环境构建，用于评测 Agent 如何迭代改进已探索策略。This work introduces Autonomous Policy Evolution, a controlled evaluation setting in which a harness-model agent repeatedly edits an executable policy system under a fixed interaction budget, and instantiates this setting in EvoPolicyGym, a benchmark built from compact interactive RL environments that evaluates how agents iteratively improve explored policies.

应在真实类别分布下使用跨网络评估来判断部署就绪度，而非仅依赖域内准确率。Deployment readiness should be assessed using cross-network evaluation under realistic class distributions, rather than within-domain accuracy alone, to suggest deployment readiness should be assessed using cross-network evaluation under realistic class distributions, rather than within-domain accuracy alone.

BeyondArena 是首个面向表格数据的统一整体基准，支持多种任务类型（IID、时序、分组），覆盖样本量与特征维度的不同尺度，并涵盖来自广泛学科的多样化特征类型。BeyondArena is the first unified holistic benchmark for tabular data that supports diverse task types (IID, temporal, grouped), across sample size and feature dimensionality scales, with diverse feature types from a broad range of disciplines.

提出 PerceptionRubrics——一个基于评分量表的评估框架，旨在弥合饱和的基准分数与真实场景脆弱性之间的差距，并验证了严格的感知保真是可靠生成的前提。PerceptionRubrics is introduced, a rubric-based evaluation framework that addresses the gap between saturated benchmark scores and real-world brittleness, validating that strict perceptual fidelity is the prerequisite for reliable generation.

所得到的模型在大多数数据集-预测步长组合上优于先前的线性预测器，并在八个基准中的六个上超越 Transformer、MLP 和 CNN 基线；同时它还可作为对数据本身的诊断工具，揭示那些被更大模型默默吸收进其学习参数中的结构。The resulting models beat prior linear forecasters on most dataset-horizon entries and exceed Transformer, MLP, and CNN baselines on six of eight benchmarks, and serve as a diagnostic on the data itself, revealing structures that larger models absorb silently into their learned parameters.

提出 GauntletBench，一个用于评估 Agent 在挑战性场景中泛化能力的 Web 基准，聚焦于三种被低估的能力（时间感知、图形理解与 3D 推理），揭示了当前 Agent 能力与复杂真实场景所需能力之间的巨大差距。GauntletBench, a web-based benchmark for evaluating agent generalisation in challenging scenarios, focusing on three underexplored capabilities (temporal perception, graphical understanding, and 3D reasoning), is introduced, revealing the substantial gap between current agent capabilities and those required for complex real-world scenarios.

研究发现重建质量与表示质量是解耦的；在所考察的任务中，没有任何单一方法能够在所有任务上稳定取得最优表现。It is found that reconstruction and representation quality are decoupled, and no single method consistently performs best across the tasks considered here and no single method consistently performs best across the tasks considered here.

一个包含 382 个真实企业任务、覆盖 6 种专业角色和 22 项程序性技能的基准，用于评估技能在任务、角色和模型骨干间的迁移能力，发现部分技能可在任务和模型间广泛泛化，而另一些则专化为角色特定工作流，在迁移时失去效力。A benchmark of 382 realistic enterprise tasks spanning six professional roles and 22 procedural skills, designed to evaluate how skills transfer across tasks, roles, and model backbones finds that some skills generalize broadly across tasks and models, whereas others become specialized to role-specific workflows and lose effectiveness under transfer.

结果表明仅评估求解器或仅评估答案是不足的：Agent 的差异不仅体现在发现关键 contingency 上，还体现在验证预算的使用、显式提交、类型强制、重复验证、基于证据的报告以及缓解行为等方面。The results show why solver-only or answer-only evaluation is insufficient: agents are distinguished not only by top-contingency discovery, but also by validation-budget use, explicit submission, type coercions, duplicate validations, evidence-backed reporting, and mitigation behavior.

本文提出 AdvancedMathBench，一个用于评估高级数学推理能力的 benchmark 套件，并推出 VerifierBench，包含 888 条模型生成的证明轨迹及专家 ground truth，用于评估模型能否正确判断证明有效性并给出合理的验证理由。This work introduces AdvancedMathBench, a benchmark suite designed to evaluate advanced mathematical reasoning capabilities, and introduces VerifierBench, consisting of 888 model-generated proof trajectories paired with expert ground truth, to evaluate whether models can correctly judge proof validity and provide sound verification rationales.

本文提出 MCLASH，一个多语言道德决策 benchmark，用于捕捉跨语言的文化情境化道德直觉和社会规范；并提出 MET（Multilingual Ethics with Theory-grounded reasoning），一种基于心理学与哲学专家策划的理论依据的两步提示方法。This work introduces MCLASH, a multilingual moral decision-making benchmark to capture culturally situated moral intuitions and social norms across languages, and proposes MET (Multilingual Ethics with Theory-grounded reasoning), a two-step prompting method built on expert-curated, theory-based grounds drawn from psychology and philosophy.

开发了自动化评分流水线，用于评估多种模型，包括开源权重模型、闭源语言模型、视觉语言模型和图像生成模型，结果显示没有单一模型在所有任务类型上占优，且某些任务对所有评估模型仍具挑战性。An automated grading pipeline is developed to evaluate a wide range of models, including open-weight and closed-source language, vision-language, and image-generation models, and shows that no single model dominates across all task types, and that some tasks remain challenging for all evaluated models.

介绍其潜在原因的理论基础，阐明强化学习算法的渐近性能在性能排名与数据规模之间不存在单调关系。The theoretical foundations of the underlying causes outlining that the asymptotic performance of reinforcement learning algorithms does not have a monotone relationship between performance rankings and data-regimes are introduced.

提出 LakeQuest，一个经人工校验的基准，包含 9,846 个 QA 对，用于在真实数据湖场景下评估端到端检索与综合 pipeline，并揭示现代问答系统中的关键失效模式。LakeQuest is introduced, a human-validated benchmark of 9,846 QA pairs designed to evaluate the end-to-end retrieve-and-synthesize pipeline over realistic data lakes and exposes critical failure modes in modern QA systems.

提出 SDABench，一个围绕六项能力（描述性、探索性、推断性、推断性、预测性、因果性、机制性）跨五大领域（生物、化学、环境、地理、物理）重新组织评估的基准。SDABench is introduced, a benchmark that reorganizes evaluation around six capabilities (descriptive, exploratory, inferential, inferential, predictive, causal, and mechanistic) across five domains (Biology, Chemistry, Environment, Geography, Physics).

视觉语言模型（VLMs）在 DocVQA、ChartQA、MMLongBench-Doc 等视觉文档理解基准上表现强劲。但真实文档融合长度、布局复杂度、模态、问题难度等多因素，难以将模型失败归因于具体原因。我们提出 SynthDocBench，一个全合成的长上下文视觉文档理解基准，系统性控制文档长度、布局结构、模态构成与问题类型等因子。该基准的构建……Vision language models (VLMs) have achieved strong performance on visual document understanding benchmarks such as DocVQA, ChartQA, and MMLongBench-Doc. However, real-world documents combine multiple factors such as length, layout complexity, modality, and question difficulty, which makes it difficult to attribute model failures to specific causes. We introduce SynthDocBench, a fully synthetic benchmark for long-context visual document understanding that systematically controls factors including document length, layout structure, modality composition, and question type. The benchmark is constr

AgentCompass 被提出，它是一个开源、轻量且可扩展的面向 LLM-based Agent 的评估基础设施，将评估流程围绕三个独立组件组织，从而在不重新实现复杂执行逻辑的前提下支持灵活配置。AgentCompass is introduced, an open-source, lightweight, and extensible infrastructure for evaluating LLM-based agents that organizes the evaluation process around three independent components, thereby enabling flexible configurations without requiring the reimplementation of complex execution logic.

本文提出一种实用评估协议，将评估重点从任务完成转向经过验证的漏洞发现，可在涵盖多种攻击面与漏洞类型的足够复杂目标上开展评估，并结合结构化真值标注与基于 LLM 的语义匹配来识别漏洞。This paper presents a practical evaluation protocol that shifts assessment from task completion to validated vulnerability discovery, allowing evaluation in sufficiently complex targets spanning multiple attack surfaces and vulnerability classes, and combines structured ground-truth with LLM-based semantic matching to identify vulnerabilities.

本文提出 SIS-Bench，一个在统一 self-in-space 表述下评估 UAV 场景具身空间智能的基准，并探索了一种融合光流与视觉特征的运动感知表征，以纳入与自身相关的动态信息。SIS-Bench is introduced, a benchmark for evaluating embodied spatial intelligence in UAV scenarios under a unified self-in-space formulation, and a motion-aware representation that incorporates self-related dynamics through optical flow and visual feature fusion is explored.

压缩可减少推理 token 并保持大部分选择题准确率，同时提示影响接近基线——在一个前沿条件下，降低推理成本移除的证据比单纯缩短轨迹所预期的更多。Compression reduces reasoning tokens and preserves most multiple choice accuracy, while hint influence remains near baseline, in a frontier where reducing reasoning costs removes more evidence than shorter traces alone would predict.

本文提出 UniVR，这是首个从纯视觉演示中同时学习复杂推理、细粒度物理动力学和长期规划的研究，并配套首个在纯视觉协议下评估这些异质能力的综合评测套件。UniVR, the first investigation into simultaneously learning complex reasoning, fine-grained physical dynamics, and long-term planning from pure visual demonstrations, is introduced, the first comprehensive suite to assess these heterogeneous capabilities under a purely visual protocol.

对 LLM Agent 自动 harness 演化进行了广泛评估，在相当的反馈和推理预算下，将 harness 演化与简单的测试时扩展及发现类基线进行比较，并在留出任务上评估演化后的 harness 以判断其发现的改进是否具有泛化性。An extensive evaluation of automatic harness evolution for LLM agents is conducted, comparing harness evolution with simple test-time scaling and discovery baselines under comparable feedback and inference budgets, and also evaluating evolved harnesses on held-out tasks to assess whether the discovered improvements generalize.

认为（该早期版本的）GPT-4 属于新一代具备更通用智能的 LLM（与 ChatGPT、谷歌 PaLM 等并列），并讨论了这些模型不断增强的能力及其影响。It is argued that (this early version of) GPT-4 is part of a new cohort of LLMs (along with ChatGPT and Google's PaLM for example) that exhibit more general intelligence than previous AI models, and the rising capabilities and implications of these models are discussed.

发现对 GPT 语言模型进行重复采样，是为困难 prompt 生成可用解的一种出乎意料的有效策略，并讨论了部署强大代码生成技术在安全性、安全保障与经济等方面的潜在更广泛影响。It is found that repeated sampling from the GPT language model is a surprisingly effective strategy for producing working solutions to difficult prompts, and the potential broader impacts of deploying powerful code generation technologies, covering safety, security, and economics are discussed.

本文对 LLM 的评估方法进行了全面综述，围绕三个关键维度展开：评估什么、在何处评估、如何评估，并为 LLM 评估领域的研究者提供了宝贵洞见。This paper presents a comprehensive review of these evaluation methods for LLMs, focusing on three key dimensions: what to evaluate, where to evaluate, and how to evaluate, and offers invaluable insights to researchers in the realm of LLMs evaluation.

在 BIG-bench 上对 OpenAI 的 GPT 模型、Google 内部稠密 Transformer 架构及 Switch 风格稀疏 Transformer 进行评估，模型规模跨越百万至千亿参数，结果显示性能与校准均随规模提升而改善，但绝对水平仍然欠佳。Evaluation of OpenAI's GPT models, Google-internal dense transformer architectures, and Switch-style sparse transformers on BIG-bench, across model sizes spanning millions to hundreds of billions of parameters finds that model performance and calibration both improve with scale, but are poor in absolute terms.

对 SOTA LLM GPT-4 在医学能力考试与基准数据集上进行全面评估，并通过案例研究定性探索其行为，展示了 GPT-4 解释医学推理、为学生定制个性化讲解以及围绕病例交互式构造新反事实场景的能力。A comprehensive evaluation of GPT-4, a state-of-the-art LLM, on medical competency examinations and benchmark datasets and explores the behavior of the model qualitatively through a case study that shows the ability of G PT-4 to explain medical reasoning, personalize explanations to students, and interactively craft new counterfactual scenarios around a medical case.

分析表明，借助 LLM，美国约 15% 的工作任务可在保持同等质量的前提下显著提速完成，意味着 LLM 驱动的软件将对底层模型经济影响的规模化产生实质性作用。The analysis suggests that, with access to an LLM, about 15% of all worker tasks in the US could be completed significantly faster at the same level of quality, implying that LLM-powered software will have a substantial effect on scaling the economic impacts of the underlying models.

研究揭示了 LLM 空间能力中的清晰规律：尽管其仍落后于 SOTA 方法，但具有潜力并能同时处理多种空间约束，从而可扩展到异构场景。A clear pattern is revealed in LLM spatial capabilities: while they still lag behind state-of-the-art approaches, they are promising and can handle multiple spatial constraints simultaneously, enabling scaling to heterogeneous setups.