论文

4DAnyone 在新视角视频质量和下游 4DGS 重建上均优于先前方法，并具有稳健的野外泛化能力。4DAnyone outperforms prior methods in both novel-view video quality and downstream 4DGS reconstruction, with robust in-the-wild generalization.

提出 Repo0，一个面向零到全代码生成的持续结构演化框架，维护显式的架构状态，实例化为双有向无环图（Dual-DAG），由需求级 DAG、组件级 DAG 及其对齐关系组成。Repo0 is presented, a continuous structural evolution framework for zero-to-all code generation that maintains an explicit architectural state instantiated as a Dual-Directed-Acyclic-Graph (Dual-DAG), consisting of a requirement-level DAG, a component-level DAG, and their alignment relation.

本文提出 IAR（Inject, Align, and Recover），一个三阶段后训练框架，将结构化文档知识注入、问答行为对齐与通用能力恢复解耦，提升面向无检索文档内化的"领域主—领域通"前沿。This work proposes IAR (Inject, Align, and Recover), a three-stage post-training framework that separates structured document knowledge injection, QA behavior alignment, and general ability recovery and improves the domain-primary domain-general frontier for retrieval-free document internalization.

本文提出一种量化基准优化的方法论，聚焦于音频对参考转写不充分确定的情形，指出高性能模型会表现出基准条件化行为，从而虚高基准得分，却未必反映通用转写能力的真正提升。This work presents a methodology for quantifying benchmark optimization, focusing on cases where the audio underdetermines the reference transcript, and indicates that high-performing models exhibit benchmark-conditioned behaviors that can inflate benchmark performance without reflecting improved general-purpose transcription ability.

一项配对消融实验在保留仓库与可执行工程上下文的同时移除显式科学指导，表明科学知识并非一律有益：可靠信息能约束修复、提升平均表现与 token 效率，而错位指导则会诱发锚定，不必然提升精确修复成功率。A paired ablation that removes explicit scientific guidance while preserving the repository and executable engineering context shows that scientific knowledge is not uniformly beneficial: well-grounded information can constrain repair and improve average performance and token efficiency, whereas poorly aligned guidance can induce anchoring and does not necessarily improve exact repair success.

本文研究 LLM 在测试时从迭代经验中学习的机制，称之为 Chain-of-Experience (CoE)：模型通过与自身或环境反馈的迭代交互积累经验痕迹，形成超越零样本推理的持续改进循环。This study studies how LLMs learn from iterative experience at test time, a setting the authors refer to as Chain-of-Experience (CoE), where models accumulate experiential traces through iterative interactions with self or environmental feedback to form a continual improvement loop beyond zero-shot inference.

本文指出，持续技能演化的关键瓶颈既非编辑能力亦非迭代轮数，而在于评估反馈能否持续提供可信的演化梯度；据此提出 SkillEvo，由可信反馈生成梯度，由可控治理约束方向。This work argues that the binding constraint on sustained skill evolution is neither editing capability nor the number of iterations, but whether the evaluation feedback keeps supplying trustworthy evolution gradients, and introduces SkillEvo, in which trustworthy feedback generates the gradient and controllable governance constrains its direction.

泄露带来两类现实攻击：一个训练好的分类器可从常规自然语言输出中推断用户记忆的语义谓词；一个由 RL 训练的对抗者可从生产级风格的 Agent 中完整提取社会安全号码。Leakage enables two practical attacks: a trained classifier that infers semantic predicates about user memories from routine natural-language outputs, and an RL-trained adversary that extracts full Social Security Numbers from a production-style agent.

本文提出 NAPE（Next-Audio-Patch-Embedding prediction），一个自监督框架：因果 Transformer 仅依据因果掩码与 stop-gradient，从先前 patch 嵌入预测对数梅尔频谱图的下一 patch 嵌入。NAPE (Next-Audio-Patch-Embedding prediction), a self-supervised framework in which a causal Transformer predicts each next patch embedding of a log-mel spectrogram from the previous ones, using causal masking and stop-gradient as its sole training signal is introduced.

选取三个前沿混合专家模型在低资源语言上进行推理微调，提出六个可度量的行为维度，且每维度均设门拒绝任何与输出长度相关的指标，并报告其自家评测工具为何失效。Take three frontier mixture-of-experts models and fine-tune them to reason in a low-resource language and propose six behavioural dimensions that make changes measurable, each gated to reject any metric that correlates with output length, and report how their own instruments lied.

Bespoke-Card 在传统通用估计器与学习型估计器架构之外开辟了一条新的基数估计路径，它是一个 Agent 驱动的系统，将面向特定工作负载的基数估计器合成为可执行代码。Bespoke-Card is opening a new avenue for cardinality estimation next to classical generic estimators and learned estimator architectures, an agent-driven system that synthesizes workload-specific cardinality estimators as executable code.

长视频理解任务超越了孤立事件的检索，需要追踪不断演化的叙事并解读可能隐含的社会含义。然而，现有 benchmark 很少联合评估这些能力，尤其是在高语境、非英语媒体中。为弥补这一空白，我们提出 NARU，一个用于评估日语长视频中叙事演化与文化理解推理的 benchmark。NARU 包含 1,481 个问题，源自 155 个总时长 146.8 小时的视频，涵盖四个叙事维度和五个文化维度Long-form video understanding encompasses tasks that go beyond retrieving isolated events, including tracking an evolving narrative and interpreting social meaning that may remain implicit. However, existing benchmarks rarely evaluate these capabilities jointly, particularly in high-context, non-English media. To address this gap, we introduce NARU, a benchmark designed to evaluate Narrative evolution and Reasoning on cultural Understanding in Japanese long-form video. NARU consists of 1,481 questions grounded in 155 videos totaling 146.8 hours, spanning four narrative and five cultural dimens

RAG 被用于伊斯兰问答，但多数系统采用端到端评估，难以区分检索失败与生成失败。本文研究阿拉伯 fiqh 的"承载答案的检索"——仅当段落陈述问题所需裁决时才视为相关。我们构建了阿拉伯 fiqh 检索测试集，并评估 dense、lexical、hybrid、fine-tuned 及 madhhab-aware 检索策略。最佳检索器 MRR@5 达 0.524，fine-tuning 进一步提升至 0.553；hybrid retrieval 增益有限（原文截断）。Retrieval-Augmented Generation is used for Islamic question answering, but most systems are evaluated end-to-end, making retrieval failures difficult to isolate from generation failures. We study answer-bearing retrieval for Arabic fiqh, where a passage is relevant only if it states the ruling required by the question. We build a retrieval test collection for Arabic fiqh and use it to evaluate dense, lexical, hybrid, fine-tuned, and madhhab-aware retrieval strategies. The best retriever achieves 0.524 MRR@5, while fine-tuning improves performance to 0.553. Hybrid retrieval provides limited gai

大语言模型 Agent 可在推理时构建工作流以适配复杂任务，但单个回合中发现的过程通常在执行后即被丢弃。现有 Skill 库提供可复用的可执行例程，但通常离线组装，且无法从 Agent 自身的工作流中不断生长。我们提出 FlowEvo，一个无需训练、推理时工作流与 Skill 协同演化的框架。FlowEvo 将成功的工作流编译为可调用 Skill，存入持久化库，并通过直接执行或作为上下文的方式使用检索到的 Skill。Large language model agents can adapt to complex tasks by constructing workflows at inference time, but procedures discovered in one episode are usually discarded after execution. Existing skill libraries provide reusable executable routines, but are typically assembled offline and do not grow from the agent's own workflows. We introduce FlowEvo, a training-free framework in which workflows and skills co-evolve at inference time. FlowEvo compiles successful workflows into callable skills, stores them in a persistent bank, and uses retrieved skills either through direct execution or as context

长周期机器人操控要求机器人既能可靠执行各项技能，又能在一系列长时间任务中将其连贯编排。多数分层视觉-语言-动作（VLA）模型仅通过单次前向过程做出每个决策，缺乏将额外算力分配给困难或关键抉择的机制。我们提出 τ_0-VLA，一种分层机器人基础模型，将高层子任务生成建模为可通过世界模型引导的测试时计算来扩展算力的推理问题。在每次推理时，高层策略借助执行……Long-horizon robot manipulation requires a robot to both execute individual skills reliably and sequence them coherently over extended tasks. Most hierarchical vision-language-action (VLA) models make each such decision with a single forward pass, leaving no mechanism to allocate additional computation to difficult or consequential choices. We introduce τ_0-VLA, a hierarchical robot foundation model that formulates high-level subtask generation as a compute-scalable inference problem through world-model-guided test-time computation. At each inference step, the high-level policy uses execution

我们提出 TinyCast，一个注意力无关的零样本预测器，仅用 146,505 个参数输出预测分布，其前提是在该规模下，上下文中的周期结构值得通过计算而非学习方式得到。一个零参数谱检测器给出主导周期，上下文按其相位进行折叠，再由一个膨胀卷积编码器和一个分块自回归分位数解码器建模其余部分。它在 GIFT-Eval 榜单上所有可确认参数量的零样本条目中体积最小；在概率准确性方面，它划定了 size-accuracy 前沿。We introduce TinyCast, an attention-free zero-shot forecaster that emits a predictive distribution from 146,505 parameters, on the premise that at this size the periodic structure of a context is worth computing rather than learning. A zero-parameter spectral detector supplies the dominant periods, the context is folded on their phase, and a dilated convolutional encoder and a block-autoregressive quantile decoder model the rest. It is smaller than every zero-shot entry on the GIFT-Eval board whose parameter count can be established. On probabilistic accuracy it defines the size-accuracy front

是否应将文本嵌入流水线替换为 LLM？我们对此在 37 个任务（涵盖分类、语义文本相似度 STS、聚类、配对分类与检索）上，对覆盖 6 个家族、参数规模 118M 至 14B 的 10 个 LLM 与 26 个嵌入模型进行了受控且考虑成本的对比。总体上两种范式基本持平：最佳 LLM（Gemini 3.1 Pro，77.6）与最佳嵌入模型（77.2）仅相差 0.4 分。两者在不同任务上各有所长：LLM 在推理密集型检索上领先，嵌入模型则在分类任务上领先，且两者……Should you replace your text-embedding pipeline with a large language model? We answer this with a controlled, cost-aware comparison of ten LLMs across six families and 26 embedding models (118M to 14B parameters) on 37 tasks spanning classification, semantic textual similarity (STS), clustering, pair classification, and retrieval. In aggregate the two paradigms are effectively tied: the best LLM (Gemini 3.1 Pro, 77.6) and the best embedding model (77.2) differ by 0.4 points. Their strengths differ by task: LLMs lead on reasoning-heavy retrieval, embedding models lead on classification, and th

LLM 编程 Agent 通过接口下发 Bash 命令，这些接口可能对模型输出进行序列化、包装与再解析。仅凭匹配的执行得分无法区分命令生成阶段的错误与生成后引入的失败。QuoteBench 围绕一道刻意未转义的额外解析器，将生成合约与执行传输交叉，在 14 个源自真实事件的家族、共 56 个一次性任务上以精确最终状态校验来度量该边界。在插值点进行转义可复现每条回放回复的原始路径结果，因此任何在已声明边界下取得的恢复成效都必须……LLM coding agents issue Bash commands through interfaces that may serialize, wrap, and reparse model output. Matched execution scores alone cannot distinguish command-generation errors from failures introduced after generation. QuoteBench measures this boundary with exact final-state validation on 56 one-shot tasks from 14 incident-derived families, crossing the generation contract with the execution transport around one deliberately unescaped added parser. Escaping at the interpolation point reproduces each replayed reply's raw-path outcome, so any recovery under a disclosed boundary must com

现代 LLM Agent 的改进通常依赖于人工修改 prompt、工具或工作流，而围绕模型的可执行支架——harness——在部署后一般被视为固定不变的产物。本文研究一种替代方案：harness 是任务特定的且持续可进化的，每个任务族维护各自的 harness，通过固定的任务注入接缝在不同迭代间热替换，并依据环境反馈进行改写。我们提出分层自改进（HSI），在该框架中，一个冻结的 LLM M 在三层Modern LLM agents are often improved by modifying prompts, tools, or workflows manually, while the executable scaffold surrounding the model---the harness---is typically treated as a fixed artifact after deployment. This work studies an alternative where the harness is task-specific and continuously evolvable: each task family maintains its own harness, which is hot-swapped across iterations through a fixed task-injection seam and rewritten using environment feedback. We introduce Hierarchical Self-Improvement (HSI), a framework in which a single frozen LLM M operates across three hierarchical

长篇连通文档上的 QA 仍具挑战，因为相关证据可能跨越多个实体及其关系。现有 RAG 方法通常将文档以原始 chunk 索引并通过 embedding 相似度检索，当 chunk 边界切断实体与支持证据的联系，或问题需在语料库中多跳推理时性能下降。我们提出 EnSI-RAG（Entity-Structure-Indexed RAG），一个构建 query-independent、以实体为中心的[索引]框架……Question answering (QA) over long, connected documents remains challenging because relevant evidence may span multiple entities and their relationships. Existing retrieval-augmented generation (RAG) methods typically index documents as raw chunks and retrieve them through embedding similarity. Their performance degrades when chunk boundaries separate entities from supporting evidence or when a question requires multi-hop reasoning across the corpus. We propose EnSI-RAG (Entity-Structure-Indexed Retrieval-Augmented Generation), a framework that constructs a query-independent, entity-centered in

使用工具的 AI agents 将 delegated 任务转化为 provider effects，但 authorization 常止于准入，而 provider 状态、delivery、retry 与 recovery 在持续演化。请求可能在 commit 前变化，或响应丢失使替换从同一 approval 产生二次 effect。我们提出 AID-Guard，一种有状态的 authorization-to-effect 闭合协议：commit 时重新验证已批准请求与 provider 状态，模糊情形下仅保留一个 reservation，并在出现终结结果或经 delivery fence 认证的无 effect 后才允许 release 或一次后继动作。对于支持的 pro[vider]……Tool-using AI agents turn delegated tasks into provider effects, yet authorization often ends at admission while provider state, delivery, retry, and recovery evolve. A request may change before commit, or response loss may cause a replacement to create a second effect from one approval. We present AID-Guard, a stateful authorization-to-effect closure protocol. It revalidates the approved request and provider state at commit, retains one reservation under ambiguity, and permits release or one successor only after a terminal result or certified no effect with a delivery fence. For supported pro

本研究对新兴的内存键值存储进行了全面的性能与可行性评估，突出了性能、兼容性与长期可行性（包括项目成熟度、社区支持与持续开发）之间的权衡。This study presents a comprehensive performance and viability assessment of the emerging in-memory key-value stores and highlights trade-offs between performance, compatibility, and long-term viability, including project maturity, community support, and sustained development.

尽管近期 LLM 在单项专利起草任务上取得不错的成绩，却未触及现实专利起草的核心挑战——从早期发明材料直接生成完整且法律上一致的专利申请。已有工作多假设输入处于后期、高度结构化或已具法律风格，但实际工作流程始于发明人编写的非正式、去法律化的 disclosure。为弥合差距，我们提出 Dis2Pat，一个反映真实[专利撰写场景]的 disclosure-to-patent 数据集。While recent large language models (LLMs) have achieved promising results on individual patent drafting tasks, they fundamentally fail to investigate the core challenge of real-world patent drafting: generating a complete and legally coherent patent application directly from early-stage invention materials. Prior work predominantly assumes later-stage, highly structured, or already legalistic inputs. However, real patenting workflows begin with informal, de-legalized disclosures authored by inventors. To bridge the gap, we introduce Dis2Pat, a disclosure-to-patent dataset that reflects realist

本文以 Oracle-to-PostgreSQL 迁移为受控软件转换任务，研究跨 agent 的 specification portability。研究包含两个阶段：首先在 1,006 个 PL/SQL 文件上评估 specification-first 迁移 pipeline，其中 623 个成功重新生成，380 个生成脚本在 PostgreSQL 16 中成功执行；其次使用 Amazon Kiro、Google Gemini 与 GitHub Copilot（含 Claude Code 与 Cursor）在 1,802 个 Oracle 脚本及其对应 PostgreSQL 实现的[数据集]上进行跨 agent 实验。This paper investigates cross-agent specification portability using Oracle-to-PostgreSQL migration as a controlled software transformation task. The study combines two experimental stages. First, a specification-first migration pipeline was evaluated on 1,006 PL/SQL files, of which 623 were successfully regenerated and 380 generated scripts executed successfully in PostgreSQL 16. Second, cross-agent experiments were conducted on a dataset of 1,802 Oracle scripts with corresponding PostgreSQL implementations using Amazon Kiro, Google Gemini, and GitHub Copilot, with Claude Code and Cursor inclu

我们提出 PhysCaP，一种面向机器人操作主动感知的物理信息驱动 code-as-policy agent。尽管 vision-language-action 策略擅长模仿示教，但它们依赖被动观察，无法推断对操作至关重要的潜在物理属性。PhysCaP 在 code-as-policy 框架基础上引入物理信息驱动的探索层，通过交互实现显式信息获取。它提出了无需训练的物理属性提取模块，仅依靠机器人本体感知即可估算物体质量和刚度，无需额外传感器。为了在 balanWe present PhysCaP, a Physics-Informed Code-as-Policy agent for active perception in robotic manipulation. While vision-language-action policies excel at imitating demonstrations, they rely on passive observation and fail to infer latent physical properties critical for manipulation. PhysCaP augments code-as-policy frameworks with a physics-informed exploration layer that enables explicit information-seeking through interaction. It introduces training-free physical property extraction modules that estimate object mass and stiffness from robot proprioception without additional sensors. To balan

大语言模型（LLM）agent 的群体行为无法用单 agent 基准刻画。我们提出 PV-SST，一个基于同行投票的社交平台测试平台，并报告一项独立冻结、预先注册的等曝光实验，涵盖四个话题、四个未使用种子、四个开源权重模型家族以及三个预设的更大模型变体。该实验包含 448 次试验和 112 个完整的"模型 × 话题 × 种子"区组。相对于仅话题对照条件，由同行生成的点赞排序的上一轮同行帖子信息流，会提升最终轮在两端的词汇相似度Population-level behavior in large-language-model (LLM) agents cannot be characterized by single-agent benchmarks. We introduce PV-SST, a peer-voted social-platform testbed, and report a separately frozen, preregistered matched-exposure experiment spanning four topics, four unused seeds, four open-weight model families, and three prespecified larger variants. The experiment comprises 448 trials and 112 complete model-by-topic-by-seed blocks. Relative to a topic-only control, a feed of previous-round peer posts ranked by peer-generated likes increases final-round lexical similarity in both the

开放式语言模型基准通常继承一种评判方式：人类偏好面板、另一个模型，或脆弱的精确匹配答案。我们提出 FlavourBench，一个自动化基准，其中版本化的烹饪系统提供密集、可执行的真值。每个任务给出八种食材，要求选择三食材组合；在模型执行前，Epicure 对全部 56 种可能组合打分。我们在相同的核心任务集上评估了 27 个 frontier 端点，覆盖替换、配对与受限组合共 534 道任务。每个被排名的模型在每个面板上恰好有 89 个有效回答，且 famOpen-ended language-model benchmarks usually inherit a judge: a human preference panel, another model, or a brittle exact-match key. We introduce FlavourBench, an automated benchmark in which a versioned culinary system supplies dense, executable ground truth. Each task presents eight ingredients and asks for a three-ingredient portfolio; before model execution, Epicure scores all 56 possible portfolios. We evaluate 27 frontier endpoints on an identical 534-task core spanning substitution, pairing, and constrained composition. Every ranked model has exactly 89 valid responses per panel and fam

免训练的块稀疏注意力可加速视频 Transformer，但仅凭逐行注意力集中度本身无法确定一个可执行的稀疏算子。共享同一块路由路径的查询其支撑集可能重叠很差，而仅保留的注意力质量并不足以决定由跳过的交互所产生的 softmax 后误差。我们证明划分几何同时影响池化支撑集与从稀疏输出预测剩余残差的能力。我们提出 SparsePR，将响应耦合划分与探测拟合残差重构相结合。采样查询键Training-free block-sparse attention can accelerate video transformers, but row-wise attention concentration does not by itself specify an executable sparse operator. Queries sharing a block route may have poorly overlapping supports, while retained attention mass alone does not determine the post-softmax error from skipped interactions. We show that partition geometry affects both pooled support and the predictability of the remaining residual from the sparse output. We introduce SparsePR, which combines Response-Coupled Partitioning with Probe-Fitted Residual Reconstruction. Sampled-query ke

我们提出 Hydra-0，一种以动作流为条件的通用世界模型，将机器人动作表示为像素运动。这种共享的视觉接口使得跨具身、任务、环境和视频生成 backbone 的通用世界建模与控制成为可能，学习动作在不同场景下的后果。我们的最佳配置相比动作条件 baseline，机器人运动误差降低 90.4%，物体运动误差降低 60.2%，同时支持零样本组合与数据高效适配。在 RoboLab 基准上，Hydra-0 在 replayeWe introduce Hydra-0, a generalist world model conditioned on action flow, which represents robot actions as pixel motion. This shared visual interface enables generalist world modeling and control by learning action consequences across embodiments, tasks, environments, and video-generation backbones. Our best configuration achieves 90.4% lower robot-motion error and 60.2% lower object-motion error than our action-conditioned baseline, while supporting zero-shot composition and data-efficient adaptation. On the RoboLab benchmark, Hydra-0 achieves a Pearson correlation of r=0.96 between replaye

以人为本的智能正在基础模型时代演进，越来越强调规模、可迁移性与通用建模。然而，它尚未与基础模型充分融合以取得可比拟的进展。更重要的是，这一广阔领域的近期进展仍分散在不同任务、模态与研究社区之间，其内在的概念与方法学联系尚不清晰。为弥合这些分歧并重新思考基础模型时代的以人为本智能，我们提出一套全谱系的人类上下文分类法Human-centric intelligence is evolving in the foundation-model era, with growing emphasis on scale, transferability, and general-purpose modeling. Yet it has not fully integrated with foundation models to achieve the comparable progress seen in them. More importantly, recent advances across this broad landscape remain fragmented across tasks, modalities, and research communities, leaving their intrinsic conceptual and methodological connections unclear. To bridge these divides and rethink human-centric intelligence in the foundation-model era, we introduce a full-spectrum human context taxonom

语义视觉编码器已成为多模态理解与图像生成中语义条件的关键视觉接口。然而其最终 token 丢弃了细粒度视觉细节，导致像素重建质量较差，限制了其在图像生成与编辑等对重建敏感的任务中的应用。本工作探讨理解、生成与编辑能否在由预训练语义 ViT 构建的单一视觉表征空间中建模。我们证明，语义 ViT 的冻结 Transformer 块本身并非无法保留Semantic vision encoders have become a central visual interface for multimodal understanding and semantic conditioning in image generation. However, their final tokens discard fine-grained visual details, leading to poor pixel reconstruction and limiting their use in reconstruction-sensitive tasks such as image generation and editing. In this work, we ask whether understanding, generation, and editing can be modeled in a single visual representation space built from a pretrained semantic ViT. We show that the frozen Transformer blocks of a semantic ViT are not intrinsically unable to preserve

随着端侧 LLM Agent 演变为个人副驾驶，移动操作系统已成为该范式的关键试验场，亟需严格的能力评测。然而现有基准可分为两类，各自存在关键盲区：以 GUI 为中心的基准仅测试表层屏幕操作，忽略了后台工具调用与长程规划；而静态 function-calling 基准依赖离线 API 匹配，与真实运行时约束脱节。为弥合这一差距，我们提出 MobilePA-Bench，一个交互式、有状态、以工具为中心的基准。As on-device LLM agents evolve into personal copilots, the mobile operating system has become a key testbed for this paradigm, making rigorous capability evaluation essential. Yet existing benchmarks fall into two camps, each with a critical blind spot: GUI-centric benchmarks test surface-level screen manipulation while overlooking background tool use and long-horizon planning, whereas static function-calling benchmarks rely on offline API matching that is detached from real runtime constraints. To close this gap, we present MobilePA-Bench, an interactive, stateful, and tool-centric benchmark

本文对 LLM 推理服务方法进行了全面综述，涵盖基础的实例级方法、深入的集群级策略、新兴的场景方向以及其他重要但零散的领域。This paper provides a comprehensive survey of LLM inference serving methods, covering fundamental instance-level approaches, in-depth cluster-level strategies, emerging scenario directions, and other miscellaneous but important areas.

我们提出一种通过自适应验证任务选择来实现 LLM harness 高效优化的新方法。Harness 优化基于验证性能迭代改写 harness 代码，无需更新底层模型权重即可获得显著性能提升。然而现有方法在每次迭代中对固定的验证集进行完整评估，即便某些任务随 harness 演进区分度下降，仍产生高昂的评测成本。我们提出 Task-CoEvolve，通过应对两项挑战使验证任务与 harness 协同演进：seWe present a novel approach to efficient LLM harness optimization through adaptive validation task selection. Harness optimization iteratively rewrites the harness code based on validation performance, enabling substantial performance gains without updating the underlying model weights. Existing approaches, however, evaluate a fixed validation set in full at every iteration, incurring substantial evaluation costs even on tasks that become less discriminative as the harness evolves. We propose Task-CoEvolve, which co-evolves the validation tasks with the harness by addressing two challenges: se

基于语音的应用在接入检索模块之前需先通过自动语音识别（ASR）处理口头查询，因此 ASR 错误会以固定的上游约束进入 pipeline。我们通过实验验证标准检索增强生成（RAG）的两项扩展——实体图链接与迭代式 query 改写——是吸收还是放大了这些错误。基于神经 TTS 合成的四种英语口音，我们在三个多跳 QA 基准（HotpotQA、2WikiMultiHopQA 和 MuSiQue）上评测四种 RAG 配置，以干净文本 oracle 为对照。尽管结构上更丰富的 configuratioSpeech-based applications pass spoken queries through automatic speech recognition (ASR) before any retrieval module, so ASR errors enter the pipeline as a fixed upstream constraint. We empirically test whether two extensions to standard retrieval-augmented generation (RAG), entity-graph linking and iterative reformulation, absorb or amplify these errors. Using four English accents synthesized through neural TTS, we evaluate four RAG configurations on three multi-hop QA benchmarks (HotpotQA, 2WikiMultiHopQA and MuSiQue) against a clean-text oracle. Although the structurally richer configuratio

检索增强 LLM 越来越多地通过抓取实时网页内容来影响日常消费推荐。这带来一种新风险：LLM 推荐系统可能会摄入被生成式引擎优化（GEO）运营者污染、用于误导其判断的网页内容。我们追问：它们在多大程度上会成为假产品的无意推广者？我们提出 FORGE（Fake Online Recommendations in Generative Environments），在本地将一组固定已抓取网页中的真实商品改写为假商品，并衡量 LLM 跨 15 个类目中 225 件真实商品推荐假商品的频率。Search-augmented LLMs increasingly mediate everyday consumer recommendations by retrieving live web content. This creates a new risk: LLM recommenders may consume web content that Generative Engine Optimization (GEO) operators have polluted to mislead them. We ask: to what extent do they become unwitting promoters of fake products? We introduce FORGE (Fake Online Recommendations in Generative Environments), which locally rewrites real products in a frozen set of retrieved web pages into fake ones and measures how often the LLM recommends the fake product, across 225 real products in 15 categor