👍 157
06/10 08:00
Many moments in the real world do not wait for a user to ask. A fire starts on a security monitor, an expression flickers across a video call, or a product a viewer wants flashes by in a livestream. Yet today's large models remain mostly turn-based by design: they answer only when addressed, and eve
中文介绍 提出JoyAI-VL-Interaction,实现实时视觉-语言交互智能,无需用户主动发问即可响应监控起火、表情变化等连续事件,打破回合制延迟,提升交互即时性。
👍 100
06/09 08:00
Data tells stories that shape society; the data journalist's job is to turn raw information into stories non-experts can trust. A high-quality news feature takes a newsroom team weeks: hunting for context, running statistics, choosing an angle, and designing visuals. Recent agents handle individual
中文介绍 提出Data Journalist Agent,端到端将原始数据转化为可验证的多模态新闻故事,整合语境搜索、统计分析、角度选择和视觉设计,大幅缩短传统数周的制作周期。
👍 85
06/15 08:00
Generalist robot policies must follow user instructions while reasoning about how objects, cameras, and robot actions interact in the 3D physical world. Recent vision-language-action models (VLAs) and video world-action models (WAMs) inherit strong semantic or temporal priors from large-scale founda
中文介绍 针对通用机器人策略缺乏3D几何推理的问题,提出Geometric Action Model,显式建模物体、相机与动作的几何关系,提升指令遵循和物理交互能力。
👍 78
06/15 08:00
DreamX-World 1.0 is a general-purpose interactive text/image-to-video world model for controllable long-horizon generation. It supports camera navigation, revisits to previously observed regions, and promptable events across photorealistic, game-style, and stylized domains. Our data engine combines
中文介绍 DreamX-World 1.0是通用的交互式世界模型,支持文本/图像到视频的长时可控生成,涵盖相机导航、区域重访和提示事件,适用于逼真、游戏和风格化多种领域。
👍 76
06/12 08:00
Large Language Model (LLM) coding agents have achieved strong results on software engineering tasks, yet repository exploration remains a major bottleneck: locating relevant code consumes substantial token budget and pollutes the agent's context with irrelevant snippets. In most agents, the same mod
中文介绍 解决代码代理仓库探索消耗大量token的问题,提出FastContext,训练专门的仓库探索器高效定位相关代码,减少上下文污染和预算开销,提升代理效率。
👍 62
06/15 08:00
This technical report introduces VibeThinker-3B, a compact dense model with 3B parameters developed to investigate how far verifiable reasoning can be pushed within a strictly small-model regime. Building upon the Spectrum-to-Signal post-training paradigm, we systematically enhance the model through
中文介绍 VibeThinker-3B探索3B小模型的可验证推理极限,基于Spectrum-to-Signal后训练范式系统增强推理能力,在严格小模型框架下取得显著进步。
👍 23
06/15 08:00
Masked Diffusion Language Models (MDLMs) have emerged as a distinct paradigm for sequence generation. As MDLMs become diverse in capabilities and knowledge coverage, an important question is how to combine their knowledge. Toward this, we first investigate the unique decoding dynamics of MDLMs. We f
中文介绍 研究掩码扩散语言模型(MDLM)的集成策略,通过分析解码动态并追踪可靠轨迹,提升多MDLM组合生成的质量与一致性。
👍 21
06/15 08:00
Vision language models are serving as general-purpose interfaces for complex multimodal tasks. However, deployment still faces three gaps: VLMs typically incur high latency and cost when processing dense video frames and long prompts, the agent scaffold remains static after deployment, and standard
中文介绍 VisualClaw实现实时个性化物理世界代理,通过优化密集视频帧和长提示处理降低延迟,并支持动态更新scaffold,弥补VLM部署中的实时性、成本和灵活性差距。
👍 14
06/15 08:00
Multi-task learning (MTL) is essential in recommender systems to enable complementary learning among diverse user feedback. While modern industrial practices have shifted from DNNs to Transformer-centric architectures to strengthen sequence modeling and scaling capacity, they still decouple feature
中文介绍 提出OneRank统一Transformer原生的多任务推荐排序架构,端到端联合建模特征表示与排序,解决传统解耦导致的性能损失,提升互补学习效果。
👍 14
06/15 08:00
Visual world models (VWMs) synthesize interactive, action-conditioned rollouts from a single context image. However, it remains an open question how robust these models are to adversarial perturbations. Standard adversarial attacks fail to assess this vulnerability because attackers lack ground-trut
中文介绍 BadWorld对视觉世界模型(VWM)进行对抗攻击,设计适用于无真实值场景的攻击方法,揭示VWM在行动条件化 rollout 中的脆弱性。
👍 14
06/15 08:00
We introduce Qwen-RobotWorld, a language-conditioned video world model for embodied intelligence. With natural language as a unified action interface, it predicts physically grounded future visual trajectories from current observations across robotic manipulation, autonomous driving, indoor navigati
中文介绍 Qwen-RobotWorld以自然语言为统一动作接口,通过语言条件视频生成预测物理世界未来轨迹,统一机器人操控、自动驾驶和室内导航等多具身任务。
👍 13
06/15 08:00
As LLM agents are deployed in long-horizon sessions, context accumulation drives up inference costs. Existing approaches utilize text pruning or dynamic memory eviction to minimize token footprints; however, their unconstrained sequence mutations alter layouts, introducing prefix mismatches and cach
中文介绍 TokenPilot提出缓存高效的上下文管理方法,避免序列突变导致的前缀不匹配和缓存失效,降低LLM代理长期会话中的token开销和推理成本。
👍 13
06/10 08:00
Modern Lean theorem provers achieve strong performance only with substantial training and inference compute, driven in part by scarce verified proof data and the long reasoning traces of formal proof search, making both supervised fine-tuning (SFT) and sampling expensive. We introduce Pythagoras-Pro
中文介绍 Pythagoras-Prover通过增强Lean形式化(数据增强和高效搜索)加速形式证明,减少对大量核实数据的依赖,降低训练和推理计算开销。
👍 11
06/13 08:00
Advanced agents are increasingly demonstrating the potential to operate as autonomous engineers, creating a growing demand for evaluation benchmarks that capture the complexity of real-world development. Such environments typically involve both complex code and large-scale data (i.e., file system).
中文介绍 提出CODA-BENCH基准,评估代码代理在包含复杂代码和大规模数据的真实环境中的表现,揭示数据密集型任务中的能力瓶颈。
👍 10
04/08 08:00
Web agents act through long interaction sequences, yet existing benchmarks evaluate only terminal success, discarding all process information and offering little guidance on improvement. In this work, we conduct a process-level analysis of web agents. We introduce WebStep, a benchmark of 1,800 task
中文介绍 WebStep基准引入语义状态追踪进行过程级评估,包含1800任务,分析web代理每个步骤的正确性,弥补只评估最终成功率的不足。
👍 9
06/13 08:00
Efficient and scalable agentic intelligence requires models that can deliver both low-latency responses and strong reasoning capabilities while remaining practical to train, serve, and deploy. In this report, we present Ling-2.6 and Ring-2.6, a family of models designed to address this challenge at
中文介绍 Ling-2.6和Ring-2.6模型族在万亿参数规模实现低延迟与强推理的平衡,通过优化训练和服务流程提供即时代理性智能。
👍 9
06/15 08:00
As LLMs advance, post-training reinforcement learning (RL) increasingly relies on multi-dimensional rewards to cultivate comprehensive capabilities. This shift demands new algorithms capable of optimizing diverse and potentially competing objectives simultaneously. To address this, existing methods
中文介绍 GD^2PO提出群体动态奖励解耦策略优化,将多目标奖励分组解耦,缓解LLM后训练中多奖励冲突,综合提升模型能力。
👍 8
06/12 08:00
Phone agents are increasingly expected to complete real mobile workflows rather than merely predict the next screen action. However, much of the current mobile-agent literature still evaluates agents primarily as GUI controllers that observe a screen, emit taps and swipes, and are scored by target a
中文介绍 PhoneHarness融合GUI、CLI和工具动作,构建更真实的手机代理框架,超越纯GUI控制,提升复杂移动工作流的完成效率。
👍 8
06/15 08:00
Multi-turn LLM serving accumulates dialogue history whose Key-Value (KV) cache grows with every turn and every user, quickly exceeding the model weights themselves and making memory -- not compute -- the binding constraint on throughput. Non-uniform KV compression, which allocates heterogeneous budg
中文介绍 Tangram通过非均匀KV缓存压缩,根据重要性分配异质预算,显著减少多轮LLM服务中的内存占用,提高吞吐量。
👍 8
06/12 08:00
We introduce Nemotron 3 Ultra, a 550 billion total and 55 billion active parameter Mixture-of-Experts Hybrid Mamba-Attention language model. We pre-trained Nemotron 3 Ultra on 20 trillion text tokens, then extended the context length to 1M tokens, and post-trained using Supervised Fine Tuning (SFT),
中文介绍 Nemotron 3 Ultra是550B总参数/55B活跃参数的MoE混合Mamba-Transformer模型,预训练20T tokens并扩展至1M上下文,经SFT和RL后训练,专为代理推理优化。