👍 553
08/10 08:00
We introduce BDH-CQ, a reasoning model that combines in-context learning with recurrent latent reasoning. Inputs presented at inference time continuously update the model's recurrent memory; the model then solves a query through iterative computation in a high-dimensional latent space, without verba
中文介绍 提出BDH-CQ推理模型,将上下文学习与循环潜在推理结合。推理时输入持续更新循环记忆,模型在高维潜在空间迭代计算求解查询,无需显式语言输出。核心贡献在于用潜在推理增强上下文学习能力。
👍 185
08/09 08:00
On-policy (Self-)Distillation (OPD / OPSD) has shown strong potential for post-training large language models (LLMs). However, existing methods still rely heavily on external supervision, including ground-truth signals, environmental feedback, or guidance from larger models, and therefore fall short
中文介绍 针对现有策略自蒸馏依赖外部监督的问题,提出无需任何监督的在线策略自蒸馏方法。利用模型自身输出作为训练信号,在策略分布上进行自我蒸馏,摆脱对真值标签、环境反馈或大模型指导的依赖,提升LLM后训练效果。
👍 173
08/11 08:00
After an older adult misses a medication dose, a software agent can send another reminder and an embodied agent can bring the medication. Yet neither explains whether the person forgot, is confused, has side effects, or deliberately refused, nor what support is appropriate. This reveals a structural
中文介绍 指出当前软件与具身智能体只能执行提醒、送药等动作,无法理解用户遗忘、困惑或拒绝的深层原因。提出以人为中心的ComBodied Agents范式,强调智能体应结构化理解用户状态与需求,实现真正贴合人的辅助。
👍 114
08/10 08:00
Agentic systems are increasingly expected to improve after deployment, yet single-entity self-evolution is often bounded by a static learning context, such as fixed tasks and feedback. This survey focuses on co-evolution in agentic systems, a multi-component form of self-evolution in which multiple
中文介绍 综述智能体系统中的共同进化(co-evolution)机制。指出单智能体自我进化受限于固定任务与反馈,而多组件协同进化可实现部署后的持续自我改进,超越人类预设设计。系统梳理了相关方法、挑战与未来方向。
👍 86
08/10 08:00
Leading large language model providers now conceal their models' step-by-step reasoning, or chain-of-thought, to protect intellectual property and limit information leakage. Rather than storing these traces server-side, providers return them to the client as blocks of encrypted text, which the clien
中文介绍 研究从专有LLM API窃取思维链推理痕迹的方法。供应商将逐步推理加密后返回客户端,攻击者可利用客户端可访问的加密文本块,通过侧信道或解密手段还原推理内容,揭示当前保护机制存在安全漏洞。
👍 46
08/07 08:00
Memory systems have shown promise for improving agent performance, but their potential remains largely unexplored for small language models, which struggle to generate sufficient successful trajectories on their own. We propose Agent Memory Distillation (AMD), a training-free framework that transfer
中文介绍 提出Agent Memory Distillation(AMD),一种无需训练的框架,将大模型教师的分层记忆迁移至小型LLM智能体。通过层级教师记忆传递成功轨迹经验,帮助小模型在没有足够自生成轨迹的情况下提升任务表现,拓展记忆系统在小模型上的应用。
👍 27
08/03 08:00
Conversational assistants increasingly recommend follow-up edits to help users continue a task. Existing systems primarily target text-only interactions, leaving image-creation conversations underexplored. In image-creation tasks, useful follow-up edit suggestions must reflect user preferences, offe
中文介绍 面向对话式图像编辑场景,研究如何生成视觉对齐的后续编辑建议。现有系统仅支持文本交互,而图像创作中建议需符合用户偏好并具视觉一致性。提出新方法生成与当前图像内容匹配的后续编辑推荐,填补该方向空白。
👍 24
08/08 08:00
Large language model (LLM) inference serving is increasingly constrained by memory rather than compute. As long-context and long-form reasoning workloads become more prevalent, the key-value (KV) cache dominates both memory footprint and memory traffic during LLM token generation, i.e., decode. In p
中文介绍 提出OasisKV,用前瞻稀疏预取(lookahead sparse prefetching)将解码期KV缓存扩展到HBM之外。针对长上下文与长推理场景中KV缓存主导内存占用和流量的问题,通过预测性稀疏预取降低内存瓶颈,提升LLM推理吞吐。
👍 22
08/07 08:00
Self-improving coding agents that iteratively rewrite their own source code have demonstrated impressive performance on coding tasks. However, existing solutions generally derive self-modification from a single failure trajectory at a time, overlooking rich comparative signals available in the agent
中文介绍 针对自改进代码智能体仅从单条失败轨迹学习、忽视对比信号的问题,提出Mendel Gödel Machine。通过比较进化机制,从多条轨迹中提取对照差异,递归重写自身源码,实现更稳健的自我改进,提升编码任务性能。
👍 16
08/09 08:00
Running a business is a challenging form of intelligent work. Operators must infer opportunities from partial signals, commit capital under uncertainty, adapt to delayed outcomes in a changing market, and satisfy regulatory obligations before trading legally. Frontier LLM agents can increasingly com
中文介绍 提出Business Arena基准,在逼真市场环境中评测LLM智能体经营企业的能力。任务要求智能体从不完整信号中推断机会、在不确定条件下配置资本、适应延迟的市场反馈并满足监管要求,全面评估其决策与适应能力。
👍 15
08/11 08:00
Large language model (LLM) agents are increasingly deployed as personal assistants. Existing evaluations, however, mostly use short, self-contained requests in static environments. Everyday life assistance is different. A task runs for weeks rather than minutes. The world keeps changing while the ag
中文介绍 提出VibeLifeBench基准,评测智能体在动态生活世界中的主动性与持久性。现有评测多基于静态环境中的短时任务,而日常任务持续数周且环境不断变化。该基准要求智能体长期跟踪目标、主动决策并适应变化,填补生活助手评估空白。
👍 12
08/10 08:00
Large language model evaluations typically focus on performance under nominal conditions, creating an illusion of capability where models comfortably walk a narrow, highly optimized generation corridor. In real-world deployments, however, complex system prompts, safety guardrails, and structural con
中文介绍 针对LLM评测仅关注标称条件、造成能力错觉的问题,提出解码层禁忌(Decoding-Level Taboo)诊断压力测试。在复杂系统提示、安全护栏等真实部署条件下,通过禁忌式解码扰动检验模型鲁棒性,暴露其在非优化生成通道上的脆弱点。
👍 11
08/09 08:00
Long-horizon research agents solve open-ended tasks through iterative retrieval, aggregation, and synthesis, but context grows rapidly while the marginal value of additional evidence often declines. This leads to unnecessary token cost, higher latency, and noisier inputs for final report generation.
中文介绍 针对长程研究智能体上下文快速增长而新增证据边际价值递减的问题,提出边际价值估计方法。在迭代检索与综合过程中动态评估额外token的收益,提前终止低价值检索,降低延迟与token成本,提升最终报告生成质量。
👍 11
08/11 08:00
Self-evolving agents accumulate reusable skills by appending successful procedures and failure fixes. Over time, the same requirement is often restated in several branches, examples, and warnings, while common action sequences are copied rather than reused. The resulting skill becomes expensive to i
中文介绍 提出SkillZip,无需评估的技能压缩方法,用于自进化智能体。通过发现技能库中可复用的结构化模式,消除重复分支、示例与警告,合并相同操作序列,压缩技能表示,在降低存储与推理开销的同时保留关键能力。
👍 11
08/10 08:00
General-purpose reward models are increasingly the bottleneck for scaling robot learning, yet the recipe for learning value-related capabilities from large-scale heterogeneous corpora remains underexplored. Existing approaches tie supervision to task-internal anchors such as preferences or normalize
中文介绍 提出RynnValue,利用时间距离(temporal distance)扩展机器人价值基础模型。现有方法将监督绑定到任务内部锚点,难以利用大规模异构语料。RynnValue以时间距离为通用监督信号,学习跨任务价值函数,缓解奖励模型瓶颈,提升机器人学习扩展性。
👍 10
08/11 08:00
Long-document understanding often requires reasoning over many visually rich pages, making inference costly and prone to context rot. In this work, we propose InSight-doc, an agentic visual perception framework that treats visual resolution as an adaptive reasoning-time resource. InSight-doc starts
中文介绍 提出InSight-doc,将视觉分辨率视为可自适应分配的推理时资源,用于长文档理解。框架以智能体方式动态选择页面区域与分辨率,避免对每页高分辨率处理,降低推理成本并缓解上下文腐化,提升多视觉页面文档的理解效率。
👍 10
08/11 08:00
Visual document retrieval (VDR) is dominated by multi-billion-parameter models that are slow to index at full corpus scale and expensive to serve. Prior compression routes either train a smaller multi-vector encoder from scratch or distil only the query side; neither yields a compact single-vector r
中文介绍 提出DistilVDR,通过双学生蒸馏(dual-student distillation)训练紧凑的端到端视觉文档检索器。针对现有数十亿参数模型索引慢、服务贵的问题,在压缩时同时蒸馏查询端与文档端,得到轻量单向量检索器,兼顾检索性能与部署效率。
👍 10
08/11 08:00
We study reference-free post-training for multilingual machine translation with open large language models. Starting from the supervised-finetuned MiLMMT-46-v0.1 models, we apply Group Relative Policy Optimization (GRPO) with a reward that averages two reference-free quality estimation models and is
中文介绍 研究开放大语言模型的无参考多语机器翻译后训练。在监督微调模型MiLMMT-46-v0.1基础上,采用GRPO强化学习,以两个无参考质量估计模型的平均分数为奖励,不依赖参考译文进行优化,提升多语翻译质量,扩展了后训练范式。
👍 10
08/11 08:00
Large language models (LLMs) are increasingly deployed as mobile assistants, where a key challenge is leveraging personal information scattered across multiple applications (apps) to complete user instructions. However, due to the lack of dedicated benchmarks, their capabilities remain poorly unders
中文介绍 提出SPIEval基准,评估LLM作为移动助手时利用跨应用分散个人信息的能力。现有评测缺乏专门基准,该工作构建多应用场景下的任务,检验模型聚合、推理并执行涉及散落信息指令的表现,填补移动助手评估空白。
👍 10
08/10 08:00
With the rapid advancement of large language models (LLMs), harnesses have become essential infrastructure for deploying agents across a wide range of domains. The fast-evolving harness ecosystem has also made rigorous capability evaluation increasingly important. However, efficiently building an en
中文介绍 提出A²E,一个端到端智能体审计引擎。针对LLM harness生态系统快速演进带来的能力评估难题,A²E提供高效的自动化审计流程,支持对智能体能力进行系统化构建与验证,降低评估成本,提升部署可靠性。