👍 172
08/24 08:00
General-purpose language models can reason and synthesize knowledge, but complex work also requires sustained interaction with files, information sources, and executable code, together with state maintenance, failure recovery, and verifiable delivery. We call this working capability: sustained, veri
中文介绍 提出Apodex 1.1,扩展语言模型能力以处理复杂工作,强调持续交互、状态维护、失败恢复和可验证交付。核心是增强模型的"工作能力",支持与文件、信息源和代码的交互,实现长期任务执行。
👍 52
08/21 08:00
E-commerce live streaming requires omni-modal understanding of noisy, temporally extended streams, where product facts are distributed across speech, video frames, product images, overlaid text, and user queries. We present TLive-Omni, an omni-modal understanding model tailored to live-commerce scen
中文介绍 提出TLive-Omni,面向电商直播的全模态理解模型,整合语音、视频帧、产品图像、叠加文本和用户查询。解决噪声、长时流中的产品事实分布问题,提升直播场景中跨模态理解和问答能力。
👍 33
08/24 08:00
As on-device LLM agents evolve into personal copilots, the mobile operating system has become a key testbed for this paradigm, making rigorous capability evaluation essential. Yet existing benchmarks fall into two camps, each with a critical blind spot: GUI-centric benchmarks test surface-level scre
中文介绍 提出MobilePA-Bench,用于评估移动端规划Agent在复杂真实任务上的能力。弥补现有基准的盲区,既评测GUI操作,也评测任务规划,覆盖屏幕交互和功能调用,推动移动端LLM Agent发展。
👍 32
08/24 08:00
Language models are sequential processors, but long-horizon agency requires external information and computation beyond model weights and active context. Prime Agent is an open-source harness for long-horizon evaluation and coding-agent workflows. A persistent IPython REPL follows the Recursive Lang
中文介绍 开源Prime Agent,一个自改进的RLM工具包,用于长视野评估和编码Agent工作流。利用持久IPython REPL和递归语言模型,支持复杂任务的推理和代码执行,实现自我提升。
👍 32
08/17 19:24
Parallel reasoning improves the accuracy and robustness of large reasoning models by exploring multiple solution paths, but its computational cost grows with reasoning depth and branch count. Existing methods for managing these parallel paths typically rely on final-answer consensus, local token con
中文介绍 提出ParaTempo,通过时间置信度实现高效并行推理,管理多条推理路径。相比基于最终答案共识或局部token置信度的方法,它在计算成本与推理准确性间取得平衡,提升大型推理模型的鲁棒性。
👍 28
08/21 08:00
Recent omni-modal large language models (Omni-LLMs) show great potential as real-time video assistants, which continuously perceive environments and guide users to achieve specific goals. Unlike traditional passive video understanding, interactive assistants should actively combine visual states, us
中文介绍 提出OmniAssistBench,评测全模态大语言模型作为实时视频助手的交互能力。强调主动结合视觉状态和用户目标,提供引导,超越被动视频理解,推动Omni-LLMs在实际交互场景的应用。
👍 27
08/20 08:00
While text-to-3D generation has advanced rapidly, achieving high geometric fidelity at low inference cost remains challenging. Existing text-to-3D methods either decode discrete shape tokens autoregressively or iteratively refine global 3D representations with diffusion or flow models. However, auto
中文介绍 提出Block3D,通过分块扩散实现高效文本到3D生成,降低推理成本同时保持几何保真度。不同于自回归或全局3D表示迭代优化,它分块处理,减少计算负担,提升生成效率。
👍 15
08/24 08:00
Policy optimization (PO) for Large Language Models faces a stability--exploration trade-off, currently mediated by an action-side Policy-KL regularizer. This puts practitioners in a double bind: keeping Policy-KL constrains response behavior and consumes the action-side exploration budget, while dro
中文介绍 提出环境正则化,解决LLM策略优化的稳定性与探索困境。相比动作侧Policy-KL约束,它不限制响应行为,保留探索预算,通过环境正则化平衡两者,改善策略学习效果。
👍 15
08/13 08:00
Open-ended real-world interaction admits multiple valid behaviors: an agent may answer directly, ask for clarification, provide progress updates, or confirm before acting. This flexibility breaks a core assumption behind group-based RL: rollouts compared within a group are no longer guaranteed to be
中文介绍 提出ARC,用于开放世界真实交互中的公平相对优势比较。针对多有效行为打破分组强化学习假设的问题,通过公平比较rollouts,改进基于组的策略优化,适应灵活交互场景。
👍 15
08/17 08:00
On-policy distillation (OPD) transfers teacher capabilities by supervising trajectories sampled from the student's own policy, yet its generalization behavior remains poorly understood, as most studies evaluate OPD on a single domain and on benchmarks close to the training data. We present a control
中文介绍 研究在线策略蒸馏的双重泛化性质,在多领域和基准上进行对照实验。不同于以往单域评估,它揭示OPD在训练分布外和分布内的泛化差异,为理解蒸馏行为提供新视角。
👍 13
08/18 08:00
Game world models have recently demonstrated promising capabilities in generating visually coherent and action-controllable gameplay videos. However, non-player character (NPC) behavior in existing models is either implicitly entangled with video generation or explicitly prescribed through external
中文介绍 提出WorldMind,解耦游戏世界模型与NPC行为,实现状态感知的NPC控制。不同于隐式纠缠或外部指定,它分离世界模型和NPC决策,提升游戏视频生成中NPC行为的可控性和一致性。
👍 12
08/22 08:00
Recent large language models (LLMs) can operate as coding agents that build complete games from natural language requests. Game development is especially demanding because program logic, visual and audio content, interfaces, interaction and playability must function together in one executable artifa
中文介绍 推出GameXpert-Bench,评测编码Agent从自然语言构建游戏的能力。游戏开发要求逻辑、视觉、音频、界面和互动一体化,该基准衡量Agent在完整可执行游戏生成上的熟练度。
👍 9
08/21 08:00
Serving large language models cheaply increasingly means shipping models that are both structurally compressed to a fraction of their parameters and quantized to 4 bits. Together these steps degrade reasoning, mathematics, coding, and long-context behavior enough to require a recovery, or healing, s
中文介绍 提出量化感知修复方法,恢复结构压缩和4位量化导致的LLM性能下降。通过训练后修复,弥补推理、数学、编码和长上下文能力的退化,提供实用配方恢复压缩模型。
👍 9
08/20 08:00
Recent agent benchmarks increasingly ground evaluation in executable environments, from code repair to web navigation, app APIs, and function calling. Yet completing consequential work beyond code requires more than producing a plausible response or valid tool call: agents must gather missing inform
中文介绍 推出Thinkingbox,一个沙盒和基准,评估Agent在状态化商业工作流中的可靠性。强调完成实际工作需收集缺失信息、维护状态和验证交付,而不仅产生响应,填补现有基准的空白。
👍 9
08/21 08:00
Agents learn to act through interaction with environments, yet the environments used for training are often manually constructed or synthesized around predefined tasks and benchmarks. This task-centric paradigm makes it difficult to scale environments that reflect realistic and evolving workflows wh
中文介绍 提出AgentMercury,让Agent合成可验证的商业场景环境,解决环境扩展问题。与任务中心范式不同,它生成反映真实工作流的可扩展环境,支持大规模业务场景下的Agent学习和评估。
👍 8
08/23 08:00
Autonomous research systems are increasingly capable of executing long research workflows, yet automation alone does not ensure that the resulting process remains scientifically grounded. We introduce AutoResearch, a two-stage system that connects Idea Generation with Idea Execution to address both
中文介绍 提出AutoResearch,两阶段系统连接想法生成与执行,确保自主研究过程科学严谨。首阶段产生合理想法,次阶段执行并验证,减少幻觉,提升研究结果的可靠性。
👍 8
08/19 08:00
On-policy distillation (OPD) has emerged as an effective framework for post-training language models by pairing student-generated trajectories with dense token-level supervision from a teacher. However, OPD implicitly assumes that teacher-derived rewards are an appropriate proxy for reasoning progre
中文介绍 提出基于推理进展过滤的在线策略蒸馏方法。不同于依赖教师奖励作为推理进度的代理,它依据学生推理进展筛选轨迹,更有效地利用教师监督,提升后训练效果。
👍 7
08/24 08:00
We present a novel approach to efficient LLM harness optimization through adaptive validation task selection. Harness optimization iteratively rewrites the harness code based on validation performance, enabling substantial performance gains without updating the underlying model weights. Existing app
中文介绍 提出Task-CoEvolve,通过自适应验证任务选择优化harness。迭代重写harness代码,基于验证性能选择任务,无需更新模型权重即可显著提升性能,降低优化成本。
👍 7
08/20 08:00
Small language models are usually built like large ones and then squeezed onto a CPU afterwards. We did the opposite: we fixed the target first, one user, one token at a time, 4-bit weights, ordinary CPU, and chose the architecture to suit it. The result keeps full attention in only 6 of its 18 bloc
中文介绍 设计Daedalus-150M,面向CPU推理的卷积-注意力混合小模型。针对单个用户、单token、4位权重和普通CPU优化,仅在18个块中的6个使用全注意力,提升推理效率,适合资源受限部署。
👍 6
08/24 08:00
Large language models are increasingly expected to execute complex workflows whose success depends on maintaining interdependent constraints and producing artifacts that satisfy strict end-to-end verification. Yet successful execution experience is typically lost after a single run, forcing subseque
中文介绍 推出LongWoF-Bench,评估EvoMap基因在可验证长任务工作流中的表现。关注维护复杂约束和产生满足端到端验证的成果,避免单次运行后经验丢失,推动工作流执行能力提升。