👍 54
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 针对长时程搜索代理训练中轨迹内所有步骤被统一对待的问题,提出ABSeeker,通过答案回溯的信用分配机制区分各步骤贡献,提升搜索、检索、验证与整合证据的多步决策能力。
👍 45
08/05 08:00
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 针对文生图模型在开放世界任务中缺乏复杂语义理解、多步推理和外部知识整合的问题,提出ToolArtist,将工具使用能力引入统一多模态模型,实现代理式图像生成,通过调用外部工具增强生成质量与任务完成能力,弥补现有方法的不足。
👍 37
08/05 08:00
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 研究个性化LLM的过度推断现象:模型会捏造超出证据支持的用户属性。引入MirageBench基准,包含150个平衡人格,揭示自监控机制在识别此类捏造时可能产生误导,并对记忆增强个性化系统的可靠性提出警示。
👍 28
08/04 08:00
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 面向跨环境、多模态的长期日常任务,提出OneDayAgent,一种长时程自治代理框架,帮助LLM代理在多步交互中保持目标与约束,并处理异构工具和附件,推进自主代理在真实开放场景中的应用。
👍 21
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,用于评估代理自我进化在真实商业任务上的表现。现有基准在经济价值任务覆盖和训练/测试任务设计上存在不足,GDPevo通过更贴合真实业务的设置衡量代理从经验中更新状态并复用知识的能力。
👍 20
08/05 08:00
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 提出技能熵度量,用于基准测试和训练跨技能长时程推理。这类任务要求模型在推理链中切换不同技能,如先数学推导再制定计划,该指标可衡量技能切换复杂度并指导训练,为技能原生LLM的开发提供新视角。
👍 19
08/04 08:00
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 针对在线策略蒸馏中教师信号可能包含虚假相关而误导学生的问题,提出虚假信号感知的OPD方法。现有选择性蒸馏只关注置信度、信息量或可学习性,忽略了信号相关性,新方法通过识别并去伪提升蒸馏效果。
👍 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 提出AVE-Compass,面向音视频联合编辑能力的整体评估基准。现有基准多针对静音视频或孤立音频,忽略真实视频中音视频信号的强耦合;AVE-Compass衡量指令引导下跨模态协调编辑能力,推动更全面的音视频编辑评测。
👍 14
08/02 08:00
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 提出MiniWorld,降低从零训练视频世界模型的成本,使其更普及。视频世界模型根据历史观测与控制信号预测未来,支持自回归状态转换的长期生成。MiniWorld在保证建模能力的同时简化训练流程,赋能更多研究者。
👍 13
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 LG AI Research发布K-EXAONE 2.0,一个开源权重的多语言基础模型。采用升级循环方式而非从零训练,扩展架构得到Mixture-of-Experts模型,目标是迈向全球前沿规模基础模型。
👍 13
08/01 08:00
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 提出人类动作识别新任务:从标准RGB视频中对3-17岁儿童步态行为进行细粒度分析。该任务可服务于多种疾病的诊断与治疗,为医疗场景下的儿童步态识别提供新的研究方向。
👍 12
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在组内所有响应均错误时梯度完全消失、奖励信号稀疏的问题,提出「在失败处蒸馏」方法,利用自适应教师引导恢复负RL组的可学习信号,改善可验证奖励强化学习(RLVR)中LLM的后训练效果。
👍 12
08/05 08:00
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 实证研究记忆增强的视觉语言模型代理中空间记忆过时问题:当环境变化后,代理需要调和自信记忆与矛盾观察,考察现有模型能否在冲突导致失败前察觉,揭示记忆不可靠时的行为模式与局限。
👍 11
07/22 08:00
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 检验LLM在金融领域长时程陈述上是否具备真正的结构化推理,而非表面模式匹配。现有基准未覆盖真实工业复杂度,本文设计贴近实际的长上下文金融推理测试,评估模型数值精度与多步逻辑能力。
👍 11
08/04 08:00
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 提出ContinualSkillBench,评估LLM代理能否真正进化技能库并提升任务解决能力。现代代理框架引入外部技能库,但技能进化机制及其效果尚不明确,该基准填补了这一评估空白。
👍 10
08/05 08:00
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 面向GUI代理的潜在记忆,提出FocusMem,将内容、读出与信任三个因素解耦。现有方法通常将每个轨迹映射为固定记忆,FocusMem通过分解设计更灵活地压缩多模态轨迹,兼顾历史经验与当前交互进度。
👍 10
08/02 08:00
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 提出RestoreKV,用于在激进的查询无关KV缓存驱逐下恢复完整缓存行为。现有方法仅改进保留哪些原始KV对;RestoreKV补充选择策略,通过补偿被驱逐信息减轻性能坍塌,在紧预算下提升缓存压缩质量。
👍 9
08/04 08:00
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 提出SKILL-KD,一种用于LLM代理的对比技能蒸馏方法。现有技能获取方式常将技能视为经验摘要或成功示范的直接概括,导致弱学生代理不匹配;SKILL-KD通过对比学习从教师模型中提炼可迁移技能,提升学生代理的技能应用能力。
👍 8
08/03 08:00
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 将无损张量压缩形式化为程序合成问题,提出新方法:通过生成专门针对张量结构的压缩程序,兼顾通用压缩器的灵活性与张量压缩器的结构性,降低模型检查点的存储与传输成本。
👍 8
08/05 08:00
On-Policy Self-Distillation (OPSD) has become a standard post-training approach for improving visual reasoning in multimodal large language models (MLLMs). Existing methods draw privileged information from diverse input sources to guide self-distillation. Yet these designs overlook Modality Imbalanc
中文介绍 提出OPD-V,一种带模态平衡的视觉在线策略自蒸馏方法。现有OPSD方法从多种输入源提取特权信息,但忽略模态不平衡问题;OPD-V通过平衡多模态信号,改善多模态大语言模型的视觉推理后训练效果。