👍 54
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 论文提出了 ABSeeker 方法，通过追溯回答的信用分配来训练长时间搜索代理。与现有训练方法不同，该方法不再将轨迹内所有步骤视为均匀，从而提高了代理在执行多步动作时的有效性。关键结果表明，该方法能够更好地搜索、验证和整合证据，显著增强了回答的准确性。这项研究对于长时间决策和复杂环境中的智能代理具有重要意义。
👍 45
08/04 20:00
Text-to-image (T2I) models can produce visually compelling images, yet they remain limited on open-world tasks that require complex semantic understanding, multi-step reasoning, and the integration of external world knowledge. Existing efforts introduce agent capabilities into image generation, but
中文介绍 ToolArtist 提出了一个统一的多模态模型，用于改进工具使用的图像生成。该模型增强了文本到图像转换（T2I）的能力，支持复杂的语义理解和多步骤推理。通过整合外部知识，模型在开放世界任务中表现优异，显示出在生成具有更高语义复杂性的图像方面的潜力。这对推动艺术创作和设计领域的智能化应用具有重要意义。
👍 37
08/04 20:00
Personalized LLMs with persistent memory are increasingly deployed, yet the faithfulness of their user models remains unexamined. We study over-inference (OI): the phenomenon where LLMs fabricate user attributes beyond what evidence supports. We introduce MirageBench, comprising 150 personas balance
中文介绍 本研究分析了个性化大型语言模型（LLM）中用户画像的可靠性，提出了 MirageBench 基准来评估 LLM 的用户属性推断准确性。研究显示，LLMs 常常超出证据范围虚构用户特征，导致过度推断现象。研究结果为个性化应用提供了重要的警示，促使研究者重视用户模型的真实可信性，影响用户体验和信任度。
👍 28
08/03 20:00
LLM agents are increasingly applied to open-ended everyday requests that span work, study, and life. These tasks are long-horizon, cross-environment, and multimodal, forcing the agent to preserve goals and constraints across many steps while navigating heterogeneous tools and attachments. While prio
中文介绍 OneDayAgent 针对自主代理向开放式日常任务的需求，提出了一种能够在多步任务中保持目标与约束的长时间框架。该框架整合了异构工具，增强了代理在不同环境中的适应能力。研究结果表明，该方法提升了在复杂场景下的任务完成率，推动了在智能助手及日常生活应用中的发展。
👍 27
08/03 20:00
Diffusion language models (dLLMs) offer an alternative to autoregressive (AR) language modeling, yet the scaling behavior of Mixture-of-Experts (MoE) dLLMs remains poorly understood. We systematically characterize how optimization hyperparameters, compute allocation, and architecture scale for MoE d
中文介绍 本文探讨了 Mixture-of-Experts (MoE) 扩散语言模型 (dLLMs) 的扩展行为。通过系统分析优化超参数、计算分配以及架构规模，深入理解了 MoE dLLMs 的性能特性。研究结果为未来在更大规模下训练 MoE 模型提供了基础，使其在处理复杂语言任务时更具效率。这一研究将推动大规模语言模型的发展，提高响应能力和准确性。
👍 21
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 研究了智能体自我进化在实际商业任务中的效果。通过更新智能体的持久状态，增强其解决相关任务的能力，研究者为现有评估基准提供了新的视角。研究结果表明现有基准在经济任务领域的覆盖面有限，这突显了自我进化的重要性，对业务流程自动化具有重要贡献。
👍 20
08/04 20:00
Long-horizon reasoning in recent LLMs demands that the model switch between distinct skills inside a reasoning chain, such as first doing a math derivation, then using the result to plan a schedule. We call such problems cross-skill long-horizon tasks: multi-step tasks whose steps require different
中文介绍 该研究定义了跨技能长时间推理任务，探讨了 LLM 在推理链中切换多个技能的能力。通过提出技能熵来评估 LLM 在复杂推理中的表现，结果显示大多数模型在处理多步骤任务时的表现不足，提示更有效的训练方法的必要性。这一探索对提高 LLms 在推理和决策任务中的应用具有重要意义。
👍 19
08/03 20:00
On-Policy distillation (OPD) transfers teacher capabilities by supervising student-sampled trajectories with dense token-level teacher signals. Recent selective OPD methods improve this process by prioritizing signals that are confident, informative, or learnable. However, the assumptions overlook a
中文介绍 本文提出了一种新的 On-Policy 稀疏信号感知蒸馏方法，旨在改进教师模型引导下的学生学习。通过优先选择高置信和信息量的信号，弥补了现有方法的不足。该研究为如何提升模型在稀疏奖励下的学习效果提供了新的思路，具有较强的应用潜力，尤其在复杂决策系统中。
👍 15
07/16 20:00
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 AVE-Compass 为音频-视频编辑能力提供了整体评估框架，强调音频与视觉信号的紧密结合。通过分析现有基准在此领域的局限性，研究呼吁更全面的评估方法以提高多模态编辑的有效性。这对未来音频-视频处理领域的创新具有重要推动作用。
👍 14
08/01 20:00
Video world models predict future observations conditioned on historical observations and control signals, enabling long-horizon generation through autoregressive state transitions. Unlike conventional video generation models that primarily capture visual appearance and motion, video world models le
中文介绍 MiniWorld 研究了一种从头开始训练视频世界模型的方法，通过预测历史观察与控制信号来实现长时间生成。这些模型针对长时序生成具有显著优势，有助于改善视频生成器在捕捉动态变化方面的表现。该方法的开发为视频理解和生成提供了新思路，推动智能视频分析技术的发展。
👍 13
07/31 20:00
We introduce a new problem domain for human action recognition: the fine-grained analysis of children's gait behaviors from standard RGB video. We specifically target the ambulatory patterns of children aged 3-17 years. Such behaviors arise naturally in the diagnosis and treatment of several critica
中文介绍 本文提出了一种新的人类行为识别任务，针对儿童步态行为进行细粒度分析，着重于3-17岁儿童的步态模式。该研究为儿童发展相关的诊断和治疗提供了新的工具，探讨了行为识别技术在健康监测和心理评估中的潜在应用，具有重要的社会意义。
👍 12
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 技术报告介绍了一种开放权重的多语言基础模型，旨在提升全球范围内的基础模型开发效率。通过对 K-EXAONE 的架构扩展，该模型利用 Mixture-of-Experts 方法提高了性能及可扩展性，推动多语言处理和应用的进一步发展，具有重要的行业意义。
👍 12
07/31 20:00
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 论文提出了 'Distill Where You Fail' 方法，通过适应性教师指导从负面强化学习组中恢复学习信号。这种方法应对了传统强化学习在面对稀疏奖励时的效果问题，为模型提供了更稳健的反馈机制。研究成果对于未来强化学习算法的改进和实际应用具有重要意义。
👍 12
08/04 20:00
Memory-augmented VLM agents act on persistent spatial knowledge, yet that knowledge silently goes stale as the environment changes. We ask what happens when an agent must reconcile a confident memory claim with a contradicting observation, and whether current models can catch the conflict before it
中文介绍 本研究探讨了内存增强的视觉语言模型（VLM）代理如何处理持久性空间知识的过时问题。研究发现，代理在面临不符的记忆和观察时，现有模型往往难以及时捕捉冲突。该研究对于提高智能代理在动态环境中的表现和及时决策能力具有重要推动作用。
👍 11
07/21 20:00
Do Large Language Models (LLMs) possess genuine structural reasoning, or merely rely on surface-level pattern matching? The financial domain, demanding numerical precision and multi-step logic over long contexts, is an ideal testbed. Existing benchmarks fail to capture real-world industrial complexi
中文介绍 该论文探讨了大型语言模型（LLMs）在处理金融推理时的可信度，通过长时间声明的真实世界测试，评估其结构性推理能力。现有基准无法捕捉到实际工业的复杂性，研究结果的提出为未来 LLM 在金融领域的应用与改进提供了一个关键参考，具有实际经济意义。
👍 11
08/03 20:00
Modern agent frameworks equip large language models with external skill libraries to solve complex tasks. However, it remains unclear whether these systems can effectively evolve their skills and whether the resulting skills improve task-solving capabilities. To bridge this gap, we introduce Continu
中文介绍 ContinualSkillBench 研究了现代代理框架如何利用外部技能库来解决复杂任务，但尚不清楚这些技能是否能够有效进化。该研究提出了一种评估框架，以量化代理技能发展的效果，结果显示技能进化对任务解决能力有显著提升，推动智能代理系统的演进。
👍 10
08/04 20:00
GUI agents must remember both useful experience from earlier tasks and unfinished progress in the current interaction. Latent memory offers a compact solution by compressing multimodal trajectories into a few continuous tokens. Existing methods, however, usually map each trajectory to one fixed memo
中文介绍 FocusMem 提出了一个因子化的潜在 GUI 记忆框架，旨在帮助 GUI 代理有效地记忆早期任务经历及当前交互的进展。该方法通过压缩多模态轨迹，将信息高效保存。研究为未来 GUI 代理的设计和优化提供了重要思路，对人机交互领域的应用具有深远影响。
👍 10
08/01 20:00
Query-agnostic KV cache eviction compresses a context once and reuses the resulting cache for arbitrary future queries, but performance can collapse under tight budgets. Existing methods primarily improve which original KV pairs are retained. We introduce RestoreKV, which complements this selection-
中文介绍 RestoreKV 提出了在激进的查询无关 KV 缓存驱逐下恢复全缓存行为的方法。研究针对现有方法在紧张预算下的性能下降问题，提供了补充选择机制以提升 KV 对的保留率。该研究为模型在动态环境下的缓存管理优化提供了新思路，对提升模型的响应速度和效率具有重要意义。
👍 9
08/03 20:00
Skill-based prompting has become a practical mechanism for improving large language model (LLM) agents, yet existing skill acquisition methods often treat skills as experience summaries, memory entries, or direct summaries of successful demonstrations. This creates a mismatch for weaker student agen
中文介绍 SKILL-KD 提出了对 LLM 代理的对比技能蒸馏方法，旨在改善技能获取机制。研究显示，现有方法对技能的处理存在不匹配现象，导致学习效果不佳。该研究为提升 LLM 的学习效率及任务完成率提供了重要参考，对智能系统的发展具有积极影响。
👍 8
08/02 20:00
Model checkpoints are growing in both number and size, which makes archival, transfer, and deployment increasingly costly. General-purpose compressors can reduce storage requirements but ignore tensor structure, whereas existing tensor-specific compressors rely on fixed and format-specific pipelines
中文介绍 本文探讨了无损张量压缩作为程序合成的方案，针对模型检查点的存储需求提出了一种新的优化方法。相较于通用压缩器，该方法能够更好地保留张量结构，提高存储和传输效率，研究为模型部署和迁移提供了技术上的支持，具有广泛的应用意义。