👍 223
08/05 08:00
High-quality long-horizon training data for terminal agents is expensive to produce, often costing hundreds to thousands of dollars per task, because each task must keep the instruction, environment, reference solution, and verifier mutually consistent. Human authoring does not scale, and direct gen
中文介绍 针对终端代理长时程任务训练数据昂贵、难以保证指令/环境/参考答案/验证器一致的问题,提出递归合成方法,从种子任务迭代生成高质量可验证任务,降低人工编写成本,扩展长时程任务数据规模。
👍 85
08/06 08:00
Reinforcement learning (RL) with verifiable rewards constructs trajectory-level advantage estimates, yet it often fails to credit the few pivotal decisions that determine outcomes in long-horizon, multi-turn agentic tasks. Recent work introduces privileged self-distillation for credit assignment, pr
中文介绍 提出 AgentOPSD,用递归自蒸馏改进智能体强化学习的信用分配。长时程多轮任务中只有少数关键决策决定结果,该方法通过特权自蒸馏迭代放大关键步骤的贡献,缓解轨迹级优势估计难以精确定位关键动作的问题,提升策略学习效率。
👍 67
07/30 08:00
Computer-using agents (CUAs) are advancing rapidly across the digital world. A CUA trajectory records the agent's actions, states, and reasoning. Verifying whether it fulfilled the task instruction is central to CUA evaluation, data curation, and reinforcement learning. Neither human-written verifie
中文介绍 提出 OSReward,为跨平台计算机使用智能体建立标准化的轨迹奖励模型评估体系。针对 CUA 轨迹验证缺乏统一标准、人工验证器难以扩展的问题,覆盖多平台任务评估,支撑智能体评测、数据筛选与强化学习。
👍 65
08/02 08:00
Short segments of perceived speech can be retrieved from non-invasive magnetoencephalographic (MEG) recordings by deep networks trained with a CLIP-style objective against wav2vec 2.0 audio embeddings. Yet their weights do not map onto electrophysiological quantities, and it remains unclear which sp
中文介绍 研究可解释的 MEG 语音解码:CLIP 式深度模型能从脑磁图检索感知语音,但权重难以对应电生理量。本文揭示驱动检索的皮层来源与刺激特征,厘清模型依赖的神经和声学线索,提升解码可解释性。
👍 63
08/05 08:00
Long-horizon search agents must make multiple sequential actions (steps) to search, retrieve, verify, and integrate evidence to reach a final answer. However, existing methods for training these agents typically treat all steps within a trajectory uniformly during both supervised fine-tuning (SFT) a
中文介绍 提出 ABSeeker,用答案回溯的信用分配训练长时程搜索智能体。现有方法在 SFT 和 RL 中对轨迹内所有步骤一视同仁,导致关键检索/验证步骤难以被强化;ABSeeker 根据最终答案回溯,为决定性步骤分配合适权重。
👍 58
08/05 08:00
Generating large-scale, freely explorable 3D worlds from open-ended text remains challenging because a system must jointly maintain global spatial coherence, rich local content, and explicit assets suitable for downstream editing and reuse. We present WorldClaw, a fully agentic, coarse-to-fine frame
中文介绍 提出 WorldClaw,一种全智能体、由粗到精的 3D 开放世界生成框架,从开放式文本生成大规模可自由探索场景;在保持全局空间一致性和丰富局部内容的同时,输出可编辑、可复用的显式资产,支持下游编辑与重用。
👍 42
08/06 08:00
Spatial intelligence is fundamental to embodied agents, yet existing benchmarks focus on local spatial perception from single or few viewpoints, overlooking global spatial awareness over continuous, long-horizon visual streams. To address this limitation, we introduce the Global-Spatial-Temporal Ben
中文介绍 提出 GST-Bench,用于评估 VLM 从连续长时程视频中发展全局空间感知能力。现有基准多局限于单/少视角的局部空间推理,GST-Bench 聚焦时空连续的全局空间意识,为具身智能体的空间智能提供更全面的评测。
👍 38
08/06 08:00
Training large language model agents for long-horizon tool use typically relies on interactions with real or synthesized executable environments, whose construction and verification are costly, or on external simulators that are difficult to ground. We introduce EnvACE, an agentic reinforcement lear
中文介绍 提出 EnvACE,通过世界预演将环境动态内化到智能体强化学习中。相比依赖真实/合成可执行环境或难接地气的模拟器,该方法让长时程工具使用智能体在内部世界模型中训练,降低环境构建与验证成本。
👍 37
08/06 08:00
Unified multimodal retrieval aims to identify candidates that satisfy complex user intent expressed through heterogeneous inputs. Although Large Vision-Language Model (LVLM)-based retrievers are efficient and scalable, directly encoding raw multimodal inputs often misses fine-grained discriminative
中文介绍 面向统一多模态检索,提出基于困难负样本的检索中心思维链(Retrieval-Centric CoT)。直接编码原始多模态输入常遗漏细粒度判别信息,该方法让 LVLM 从失败案例中学习逐步推理,提升复杂跨模态意图下的检索性能。
👍 37
08/06 08:00
Multimodal large language models excel at passive perception but struggle with complex visual cognitive tasks requiring multi-step temporal reasoning. This degradation largely stems from the inherent ambiguity of language-based reasoning, which often fails to accurately articulate continuous visual
中文介绍 提出 ChronoVision,通过潜状态重建增强多模态大模型的时间推理能力。语言化推理难以准确表达连续视觉变化,该方法在潜在空间重建状态序列,缓解多步时间认知任务中的歧义,提升复杂视觉推理表现。
👍 33
08/06 08:00
As LLMs are increasingly deployed within agentic systems, their capabilities depend not only on the model weights but also on the harness: the prompts, tools, control flow, memory, and orchestration code surrounding them. This makes automated harness optimization -- the iterative and evaluation-guid
中文介绍 提出 HarnessOpt-Bench,评估 LLM 对智能体外围编排(harness)进行自动优化的能力。该基准覆盖提示、工具、控制流、记忆与编排代码,以迭代和评估驱动的方式衡量模型能否改进自身运行框架,拓展对智能体能力的评测。
👍 32
08/06 08:00
Economic World Models (EWMs) are generative economic models that simulate how economies evolve from within by modeling heterogeneous agents, their beliefs and actions, and the market and institutional mechanisms through which their interactions produce aggregate outcomes. This paper develops an impl
中文介绍 提出经济世界模型(EWM)的系统蓝图:从异质智能体的信念与行动出发,模拟市场与制度机制如何聚合产生宏观结果。该框架支持生成式经济建模,使经济体从内部演化,为“智能体经济体”提供可实现的设计方案。
👍 30
08/04 08:00
Data agents enable natural-language analytics over organizational workspaces, where relevant evidence may be scattered across databases, structured files, long documents, and multimedia. Existing benchmarks largely isolate structured querying, retrieval, or open-ended analysis, leaving heterogeneous
中文介绍 提出 DataSpace 基准,评估数据智能体在异构工作空间中进行可验证分析的能力。任务需跨数据库、结构化文件、长文档与多媒体整合证据,弥补现有基准将结构化查询、检索和开放式分析割裂的不足。
👍 29
08/05 08:00
Modern Greek is absent from NVIDIA's Nemotron retrieval models and from major multilingual retrieval benchmarks, despite being important for retrieval-augmented generation (RAG) in legal, energy, financial, and medical applications. We present an end-to-end adaptation of the Nemotron retrieval stack
中文介绍 针对现代希腊语在 Nemotron 检索模型与主流多语基准中缺失的问题,端到端适配检索栈:挖掘专业语料、调节检索策略、约束生成,覆盖法律、能源、金融、医疗等领域的 RAG,提升低资源语言专业检索能力。
👍 28
08/06 08:00
On-Policy Distillation (OPD) is emerging as a promising alternative to reinforcement learning for LLM post-training, yet its effectiveness in multilingual settings remains underexplored. We study OPD and its advanced variant, On-Policy Delta Distillation (OPD^2), for mathematical reasoning in Englis
中文介绍 研究在线策略蒸馏(OPD)及其变体 OPD^2 在多语言数学推理中的效果。OPD 正成为 RL 后训练的替代方案,本文探索其在英语等多语言场景下的蒸馏表现,检验 delta 蒸馏能否提升多语言数学推理能力。
👍 24
08/04 08:00
Agent self-evolution updates an agent's persistent state from prior experience and reuses it to solve related tasks more effectively. Evaluating self-evolution is difficult: existing benchmarks provide limited coverage of economically valuable task domains, do not always design training and test tas
中文介绍 提出 GDPevo,在真实商业任务上评估智能体自我进化能力。现有基准缺乏经济价值任务覆盖且训练/测试设计不足;GDPevo 要求智能体从既往经验更新持久状态并迁移到相关新任务,更贴近实际业务部署。
👍 23
08/06 08:00
Computer-use agents pay full frontier inference to re-derive routines their user has already performed, because an agent's memory today records what the user said, not what the user did. We compile passively captured screen activity into agent memory with a deterministic, zero-model pipeline: it seg
中文介绍 提出 Activity Frames,用确定性零模型流水线将被动捕获的屏幕操作编译为智能体记忆。与记录用户说了什么不同,它记录用户做了什么,让计算机使用智能体无需重新推理即可复现日常操作,支持高效记忆与回放。
👍 23
08/05 08:00
Understanding 3D scenes is fundamental to embodied intelligence, requiring joint reasoning over heterogeneous information from multiple modalities, including visual and geometric cues. However, the relevance of these modalities often varies across queries. Existing Multimodal Large Language Models (
中文介绍 提出 SmartMage,通过动态模态编排提升 3D 场景理解。视觉、几何等模态的相关性随查询变化,该方法按查询动态调度模态融合,避免固定融合引入噪声,提升具身智能在复杂场景中的联合推理能力。
👍 21
08/06 08:00
End-to-end document parsers provide a unified interface, but serialize page layouts and regional contents into one autoregressive sequence. This formulation forces independent regions onto a decoding path whose length grows with the total content, whereas crop-based two-stage parsers expose region-l
中文介绍 提出 PaDoc,利用版面信息实现文档解析的并行解码。端到端解析器将版面与区域内容串行化成自回归序列,长度随内容增长;PaDoc 以版面为锚并行解码各区域,在保持统一接口的同时提升解析效率。
👍 21
08/06 08:00
Training terminal agents requires executable and verifiable tasks that are not merely solvable, but appropriately challenging for learning. Executable validation establishes feasibility, yet does not reveal how a task behaves relative to a given solver setting. In this paper, we present CalibForge,
中文介绍 提出 CalibForge,通过对抗式求解器校准为终端智能体生成可学习任务。可执行验证只能说明任务可行,无法反映相对当前求解器的难度;该方法迭代校准任务难度,兼顾可验证性与适当挑战性,支撑大规模智能体训练。