👍 216
08/04 20:00
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 论文提出了一种递归合成方法,用于生成高质量的长时间任务数据,这些数据通常难以获得且成本高昂。采用该方法,系统能够在保持指令、环境、参考解及验证器一致性的同时,自动创建训练数据。此方法的关键在于通过递归机制降低人力成本,从而提升生成效率。意义:为长时间任务的智能体训练提供了一种可扩展解决方案。
👍 75
08/05 20:00
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 本研究提出AgentOPSD,通过递归自蒸馏机制改进强化学习中对关键决策的归因。此方法能够提升长期、多回合智能体任务中的奖励信号,对少数重要决策进行有效信贷分配。实验结果显示,相比传统方法,性能得到显著提升,推动了智能体在复杂决策环境下的应用。意义:为强化学习中的信贷分配提供了新的思路,影响长期决策任务的研究方向。
👍 63
08/04 20:00
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 论文提出ABSeeker,旨在训练长时间搜索智能体,使用回溯信用分配方法提高效能。此方法在多步行动中,将每个步骤对最终答案的影响进行差异化处理,最终显著提高了模型的任务成绩和准确率。研究为智能体在复杂信息检索场景中的应用提供了新模型。意义:推进了实用搜索智能体的研发,为信息检索领域的重要应用奠定基础。
👍 60
07/29 20:00
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 OSReward提出了一种标准化评估模型,用于计算机使用代理的奖励模型。通过记录代理的动作及状态,该方法提升了任务指令的完成度评估,为数据整理及强化学习奠定了基础。研究填补了现有验证工具在任务验证上的不足,有助于提升智能体的评估准确性和可靠性。意义:为推进跨平台计算机智能体的评价标准化提供了参考。
👍 56
08/01 20:00
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 研究提出了一种可解释的磁脑电图(MEG)解码框架,能够从MEG记录中提取感知语音的短段信号。通过与wav2vec 2.0音频嵌入对比,该方法展现出强大的泛化能力,揭示了神经网络权重与电生理特征之间的关系,为语音识别领域的深入研究提供了重要的工具。意义:在神经解码及生物信号研究中具有重要应用潜力。
👍 50
08/04 20:00
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 WorldClaw是一种全新的方法,致力于从开放式文本生成大规模、可自由探索的3D世界。研究通过提供全球空间一致性和丰富的本地内容来解决文本生成中的各种挑战。此系统具备高度的灵活性,以便后续编辑和重用,显著提升了3D环境生成的质量。意义:推动了开创性虚拟世界的生成技术,为游戏及虚拟现实应用提供支持。
👍 37
08/05 20:00
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 GST-Bench提出了一种新的评估框架,旨在提升具身智能体在长时间视觉流中的全球空间意识。通过构建全局空间时间基准,填补了现有评估中对局部空间感知的局限,推动了深度学习模型在复杂视觉场景下的应用。研究成果显示,该基准有效提升了智能体在多视角情境中的表现。意义:促进了视觉智能体在动态环境中的发展。
👍 33
08/05 20:00
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 EnvACE为智能代理的长期工具使用引入了一种新的训练框架。通过内部化环境动态,研制出有效的环境重演策略,以降低真实或合成环境构建及验证的成本。这一方法在提升代理的操作效能方面展现出良好的效果,为未来的智能体应用提供了可行方案。意义:推动了强化学习领域在工具使用中的智能代理研究。
👍 32
08/05 20:00
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 ChronoVision提出了一种新的时序推理方法,通过潜在状态重建提升多模态大语言模型在复杂视觉任务中的表现。研究指出,传统的语言推理往往无法有效处理视觉信息的连续性,因此本研究通过合成重建减轻了这一不足,展现了较好的推理能力。意义:为多模态智能体在理性推理和动态场景理解中提供洞察。
👍 30
08/05 20:00
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 论文研究了统一多模态检索中的基于检索的链式推理,通过硬负例提高检索准确性。虽然大型视觉语言模型(LVLM)在处理复杂用户意图时高效且可扩展,但直接编码原始输入常会丧失细粒度区分能力。研究展示出对比学习在推进检索能力方面的潜力。意义:增强了多模态检索技术在实际应用中的影响力。
👍 27
08/05 20:00
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 本研究开发了一套经济世界模型(EWM),通过模拟异质代理及其市场互动,展示经济如何自我演化。该模型不仅具有生成性,还为经济预测提供了系统化的方法,有助于理解代理行为如何影响整体经济表现。研究成果可能对经济建模和政策制定产生深远影响。意义:在智能经济体制和市场行为分析方面具有重要应用价值。
👍 27
08/05 20:00
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 HarnessOpt-Bench聚焦于评估LLM在获取优化中的表现,强调模型的能力不仅依赖于权重,也受到提示、工具及代码控制流的影响。该方法通过自动化优化与评估提升了其在智能体系统中的有效性,帮助实现更高效的模型使用。意义:为智能体系统中LLM的优化和推广提供了新视角。
👍 24
08/03 20:00
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 DataSpace为异构工作空间的可验证分析提供了基准评估,支持自然语言分析在结构化数据库及多媒体中的应用。现有基准往往无法有效整合不同数据源,论文的创新在于提升了对异质数据的处理能力,有助于未来统合数据分析工具的研究。意义:推动了数据驱动决策和分析的智能化进程。
👍 24
08/03 20:00
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 GDPevo探讨了代理自我演化在真实商业任务中的评估挑战,针对现有基准方法的局限性进行了创新设计。研究结果表明,该方法在经济价值任务领域具有更好的覆盖率,推动了评估智能体自我学习能力的发展。意义:为自我进化智能体的实践应用提供了新的范例,对商业智能决策产生影响。
👍 23
08/04 20:00
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 论文针对现代希腊在法律、能源及医疗等领域的检索增强生成(RAG)应用,开发了NVIDIA的Nemotron检索模型的适配方案。通过挖掘语料库、调整检索策略及生成环境,该研究为现代希腊的多领域应用提供了实用的解决方案,填补了现有模型的空白。意义:增强了对小语种及专业领域的支持,推动信息检索技术的发展。
👍 23
08/05 20:00
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 本研究探讨了On-Policy蒸馏(OPD)在多语言数学推理中的应用,提出了一种新变体·On-Policy Delta Distillation(OPD²)。通过对比实验,验证了该方法在提高多语言数学推理能力方面的有效性,为后续的多语种模型训练提供了研究基础。意义:推动了多语言处理及数学推理领域的研究进展。
👍 18
08/04 20:00
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 SmartMage提出了一种动态模态协调的方法,以提升3D场景理解能力。通过对多种模态信息的联合推理,研究解决了在不同查询间模态相关性不一致的问题,从而提高了在二维与三维信息融合任务中的表现。意义:增强了对3D场景的理解能力,对虚拟现实等应用具有重要影响。
👍 18
08/04 20:00
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 K-EXAONE 2.0技术报告阐述了一款由LG AI研究开发的开放权重多语言基础模型,旨在推动全球范围内的基础模型研究。该模型通过对K-EXAONE的架构进行调整和扩展,展示了其在多语言处理上的潜力,为未来的研究与应用奠定了基础。意义:推动了开源多语言模型的发展,有助于全球多样化的数据应用。
👍 16
08/05 20:00
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 论文提出了一种将屏幕活动编译为代理记忆的方法,以提高计算机使用代理的记忆效果。通过无模型的确定性管道处理历史屏幕活动,显著提升了用户行为的重现率。这一研究为提升代理记忆的准确性和重放能力提供了重要途径。意义:在计算机代理的自我学习和任务执行能力方面具有重要实际应用价值。
👍 16
08/05 20:00
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 EffectLearner旨在解决视频对象移除任务中的目标对象及其引发效果的消除。研究提出了一种新方式,通过训练模型理解对象效果的关联,达到高保真度的修复效果。其方法在保持时序一致性的同时,提升了视频处理质量,为实际视觉处理应用提供支持。意义:拓展了视频编辑领域的应用潜力,促进计算机视觉技术的发展。