👍 211
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 针对终端智能体长时程任务训练数据人工合成成本高(每任务数百至上千美元)且难以扩展的问题,提出递归合成方法,自动生成指令、环境、参考解与验证器相互一致的高质量长时程任务数据,实现低成本规模化数据生产。
👍 66
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 针对可验证奖励强化学习在长时程多轮智能体任务中难以对少数关键决策进行信用分配的问题,提出AgentOPSD,采用递归自蒸馏方法结合特权信息进行信用分配,提升智能体强化学习的样本效率与任务表现。
👍 60
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 针对长时程搜索智能体训练中所有步骤被同等对待、关键决策难以被有效学习的问题,提出ABSeeker,通过答案回溯进行信用分配,在SFT和强化学习阶段区分关键步骤,提升搜索智能体在检索、验证与证据整合上的整体性能。
👍 51
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 针对计算机使用智能体(CUA)轨迹验证缺乏统一标准的问题,提出OSReward,建立跨平台计算机使用奖励模型的标准化评估体系,统一衡量轨迹是否完成指令任务,支持智能体评估、数据筛选与强化学习。
👍 46
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 针对脑磁图(MEG)语音解码深度模型权重难以映射到电生理量、缺乏可解释性的问题,研究利用CLIP式目标与wav2vec 2.0嵌入训练的深度网络从MEG信号检索语音,解析驱动检索的大脑皮层来源与刺激特征,提升模型可解释性。
👍 46
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 针对从开放文本生成大规模可自由探索3D世界时全局空间一致性、丰富局部内容与可复用显式资产难以兼顾的问题,提出WorldClaw全智能体从粗到细生成框架,实现大规模3D开放世界的高质量自动化生成。
👍 30
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 针对现有空间智能基准只评估单/少视角局部感知、忽略连续长时程视觉流中全局空间意识的问题,提出全局-空间-时间基准GST-Bench,用于评测视频理解模型是否具备全局空间感知能力,推动具身智能空间智能研究。
👍 28
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 针对长时程工具使用智能体训练依赖真实或合成可执行环境、成本高且外部模拟器难以接地的问题,提出EnvACE,通过世界预演(World Rehearsal)将环境动态内化,实现无需昂贵环境构建的智能体强化学习。
👍 26
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 针对统一多模态检索中直接编码原始多模态输入易丢失细粒度判别信息的问题,提出基于难负样本的检索中心思维链(Retrieval-Centric CoT)方法,使LVLM检索器从失败样本中学习,增强复杂用户意图下的候选匹配能力。
👍 24
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 本文提出经济世界模型(EWM)的系统蓝图,通过建模异质智能体的信念与行动以及市场制度机制,从内部生成经济演化,实现从个体经济智能体到智能体经济体的构建路径。
👍 24
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 针对多模态大模型在多步时间推理中表现差、基于语言的推理难以精确刻画连续视觉动态的问题,提出ChronoVision,通过潜在状态重建将视觉状态内化为潜在表征,从而增强多步时间推理能力,提升复杂视觉认知任务的性能。
👍 23
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 针对智能体自我进化评估困难、现有基准缺乏经济价值任务且训练/测试设计不合理的问题,提出GDPevo基准,在真实商业任务上系统评估智能体利用历史经验更新自身状态并解决相关任务的能力,推动自我进化实用化评测。
👍 20
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 针对在线策略蒸馏(OPD)在多语言数学推理场景下的有效性尚不明确的问题,系统研究OPD及其变体在线策略增量蒸馏(OPD²),在英语等多语言数学推理任务上验证其可作为强化学习的有效替代,提升多语言推理性能。
👍 20
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 针对LLM在智能体系统中的性能受提示、工具、控制流等外围harness影响的问题,提出HarnessOpt-Bench基准,评估LLM能否根据评估反馈迭代优化harness,实现自动化harness优化,提升智能体系统整体能力。
👍 19
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 针对数据智能体在跨数据库、文件、文档和多媒体的异构工作区中进行可验证分析缺乏基准的问题,提出DataSpace基准,评测智能体整合分散证据、执行自然语言分析并产出可验证结果的能力。
👍 17
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NVIDIA Nemotron检索模型和主流多语基准中缺失的问题,提出端到端适配方案:挖掘领域语料、适配检索模型,并基于RAG在专业领域法律、能源、金融、医疗中实现接地生成,补齐现代希腊语检索与生成能力。
👍 16
08/05 08:00
This technical report presents K-EXAONE 2.0, an open-weight multilingual foundation model developed by LG AI Research as a step in our effort toward global frontier-scale foundation models. Rather than training from scratch, we upcycle K-EXAONE and expand its architecture, yielding a Mixture-of-Expe
中文介绍 本技术报告介绍LG AI Research开发的K-EXAONE 2.0开源多语言基础模型。采用upcycling方式扩展现有架构,形成Mixture-of-Experts(MoE)模型,以较低成本迈向全球前沿规模基础模型,并开放权重。
👍 15
07/17 08:00
While instruction-based video editing has advanced rapidly, real-world videos contain tightly coupled audio and visual signals, and editing one modality often requires coordinated changes in the other. Existing benchmarks primarily evaluate visual transformations on silent clips or isolated audio ed
中文介绍 针对现有视频编辑基准主要评估视觉变换、忽视音视频联合编辑的问题,提出AVE-Compass基准,全面评测指令驱动的音频-视频编辑能力,关注编辑一个模态时对另一模态的协同调整,为真实世界音视频编辑提供整体评价。
👍 14
08/01 08:00
Reinforcement learning with verifiable rewards (RLVR) has become a standard paradigm for post-training large language models (LLMs). While Group Relative Policy Optimization (GRPO) is widely adopted, it suffers from sparse reward signals and loses gradients entirely when all responses within a group
中文介绍 针对GRPO在组内全部响应均错误时梯度消失、奖励信号稀疏的问题,提出“在失败处蒸馏”方法,利用自适应教师指导从负向RL组中恢复学习信号,提升可验证奖励强化学习后训练的样本效率与稳定性。
👍 12
08/06 08:00
Video object removal must eliminate not only the target object but also its induced effects while maintaining high-fidelity and spatiotemporally coherent restoration. Existing methods mainly learn object-effect correspondences implicitly from predefined effect categories and fixed data distributions
中文介绍 针对视频目标移除需同时消除目标对象及其引发效应、现有方法依赖预定义效应类别与固定数据分布的问题,提出EffectLearner,通过世界感知的目标-效应推理,实现真实世界视频中的物体移除与高保真时空一致恢复。