👍 243
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,融合上下文学习与循环潜在推理。推理时输入持续更新模型循环记忆,在高维潜在空间迭代计算求解查询,无需显式思维链,提升推理效率。
👍 208
08/10 08:00
Macaron-V1 is an open agent-model family for experiential intelligence: learning from experience in real environments and continuing to learn after deployment. It is organized around two system goals. Adaptation is pursued through recursive improvement of versioned model-harness pairs, where experie
中文介绍 Macaron-V1 是一个面向体验智能的开放智能体-模型家族,能在真实环境中学习并在部署后持续进化。通过版本化模型-harness 对的递归改进实现自适应,并采用 Mixture-of-LoRA 缓解灾难性遗忘。
👍 116
08/10 08:00
As AI coding agents take on increasingly complex, long-horizon software engineering tasks, existing benchmarks are rapidly saturating and their evaluation quality has come under serious scrutiny: a recent audit found that nearly 60% of unsolved SWE-bench Verified instances contain flawed tests -- ei
中文介绍 提出 SWE-Bench ProMax 基准,用于评估大规模多语言代码重构任务。现有编码基准快速饱和,且近 60% 未解 SWE-bench Verified 实例含缺陷测试,该基准提供更严格的长期多语言评估,推动编码智能体能力测试。
👍 66
08/08 08:00
We present Ouroboros, a self-developing agent harness whose tools, prompts, context assembly, and core implementation improve through reviewed commits that become the runtime for later work. Core evolution proceeds in two modes. In recursive free evolution, improvement is itself a task, and completi
中文介绍 提出 Ouroboros,一种自我进化的编码智能体框架。其工具、提示、上下文组装与核心实现通过经审查的提交不断改进,并成为后续运行环境;核心进化包括递归自由进化等模式,使智能体具备自主提升能力。
👍 56
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 on-policy 自蒸馏依赖外部监督的问题,提出无需任何监督信号的自蒸馏方法,仅利用模型自身生成轨迹进行后训练,降低对 ground-truth、环境反馈和大模型指导的依赖,提升 LLM 后训练效率。
👍 33
08/10 08:00
We introduce Motif 3, a decoder-only Mixture-of-Experts language model with 314 billion total parameters and 13.2 billion activated per token. Each sparse MoE layer contains 384 routed experts, with eight selected per token. This fine-grained sparsity provides substantial expert capacity while limit
中文介绍 发布 Motif 3 技术报告:一个解码器专用 MoE 语言模型,总参数 314B,每 token 激活 13.2B。每个稀疏 MoE 层含 384 个路由专家,每 token 选择 8 个,以细粒度稀疏性在控制计算成本的同时提供大规模专家容量。
👍 32
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 揭示专有 LLM API 的一项安全漏洞:提供商虽隐藏思维链,却将加密的推理文本返回客户端。研究发现可从客户端侧提取这些加密痕迹,绕过知识产权保护,对 LLM 推理保密机制构成实际威胁。
👍 32
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 针对小型语言模型难以自行生成足够成功轨迹的问题,提出 Agent Memory Distillation(AMD),一种无训练框架。它将层次化教师记忆迁移到小模型智能体,使其无需额外训练即可利用经验记忆,提升任务表现。
👍 27
08/04 08:00
Human evaluation of AI systems and digital products is costly, slow, and difficult to scale. Offline evaluations are more scalable but often abstract away human diversity and interactive behavior. We therefore introduce MatrAIx, a population-scale simulated-user evaluation infrastructure for testing
中文介绍 提出 MatrAIx,一个人口规模级模拟用户评估基础设施,含 83 亿角色智能体。相比昂贵的人类评估和忽略多样性的离线评估,该设施可扩展地模拟真实用户多样性与交互行为,用于测试 AI 系统和数字产品。
👍 23
08/10 08:00
We introduce Sci-VBench, a comprehensive benchmark for evaluating knowledge- and reasoning-intensive video generation across scientific domains. It contains 1,253 expert-annotated examples spanning 60 subjects across four core disciplines: Natural Science, Healthcare, Humanities & Social Sciences, a
中文介绍 提出 Sci-VBench 基准,用于评估科学领域知识密集与推理密集型视频生成。包含 1,253 个专家标注示例,覆盖自然科学、医疗健康、人文社科等 60 个主题、四个核心学科。
👍 22
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 针对现有对话系统仅支持文本交互、图像创作对话未被充分探索的问题,研究图像编辑场景中的后续编辑建议。提出能反映用户偏好并视觉对齐的建议生成方法,提升对话式图像编辑体验。
👍 17
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 针对 LLM 推理服务受内存而非算力限制的问题,提出 OasisKV:通过 lookahead 稀疏预取将解码期 KV 缓存扩展到 HBM 之外,降低内存占用与访存流量,提升长上下文与长推理任务的生成性能。
👍 17
08/05 08:00
On-policy distillation (OPD) provides dense teacher supervision on student-generated trajectories, but standard reverse-KL training can assign insufficient probability to other plausible continuations. Teacher entropy alone does not reveal whether uncertainty is concentrated among a few plausible ne
中文介绍 针对 on-policy 蒸馏中标准反向 KL 对合理续接分配概率不足的问题,提出 SPOT(稀疏探测与结果校准)。通过稀疏探测识别教师不确定性来源,并校准学生输出分布,提升蒸馏质量。
👍 16
08/06 08:00
CLI-based software-engineering agents have matured rapidly, yet the open ecosystem has converged on a single training environment: trajectory datasets used to fine-tune open models are collected almost exclusively under OpenHands. Models fine-tuned on this data score well under OpenHands but degrade
中文介绍 指出 CLI 软件工程智能体的训练数据几乎全部来自 OpenHands 环境,导致跨脚手架泛化差。提出 DCAS 解耦智能体脚手架,将规划能力内化为跨环境可迁移的形式,提升模型在不同 CLI 环境下的表现。
👍 12
08/04 08:00
Small language models are often the only option for deployment under tight latency, cost, and on-premises constraints, but they are rarely trained from scratch: a compressed model is usually recovered through knowledge distillation (KD). This recovery step largely decides the final quality, yet it i
中文介绍 提出一种高效的 LLM 知识蒸馏方法,结合离线 Top-K logits 与融合分块 KL 损失。该方案在减少训练开销的同时提升压缩小模型的最终质量,适用于低延迟、低成本部署场景。
👍 10
08/10 08:00
Large Language Models (LLMs) have driven rapid progress in autonomous agents, yet standard evaluations remain confined to static task solving. An emerging frontier is harness evolution---the agent's capacity to autonomously optimize its own operating harness. However, systematically benchmarking thi
中文介绍 提出 Evo-Bench 基准,系统评估 LLM 智能体自主优化自身运行 harness(工具、提示等)的能力。该基准关注 harness evolution,突破静态任务求解的传统评测框架,填补智能体自我进化评估的空白。
👍 9
08/10 08:00
Learning-based memory systems for self-evolving LLM agents face two tightly coupled challenges. First, trajectory-indexed utilities grow with the interaction history, thereby dispersing limited feedback over an ever-expanding state space. Second, because trajectory-level rewards are jointly assigned
中文介绍 针对自进化 LLM 智能体记忆系统的两难:轨迹索引效用随历史增长分散反馈,轨迹级奖励联合分配导致记忆-奖励陷阱,提出 RoMeRL 降阶效用状态方法,在反馈覆盖与奖励利用间取得平衡。
👍 9
08/08 08:00
Vision-Language Models (VLMs) should answer from concrete image evidence rather than language priors, dataset shortcuts, or irrelevant visual context. Existing perception-aware post-training methods encourage image use through global perturbations or attention proxies, but they do not test whether a
中文介绍 针对视觉语言模型依赖语言先验或数据集捷径而非图像证据的问题,提出 Evidence-RL 强化学习方法,通过证据密集型训练促使模型依据具体图像证据推理,超越现有全局扰动或注意力代理方法。
👍 9
08/07 08:00
The malicious use of generative artificial intelligence to create highly realistic deepfake videos raises serious ethical concerns and poses substantial challenges to AI safety. However, existing deepfake video benchmarks provide limited coverage of recent synthesis methods and generally lack reliab
中文介绍 提出基于多智能体取证推理的深度伪造视频检测方法,应对逼真 deepfake 视频恶意使用带来的安全挑战。现有基准对最新合成方法覆盖不足、可靠性欠缺,该方法通过多智能体协同推理提升检测泛化能力与鲁棒性。
👍 9
08/05 08:00
Hard prompt compression reduces long-context inference cost by independently scoring tokens, sentences, or chunks and retaining the highest-scoring units under a budget. We identify a structural failure in this procedure: independent selection can split dependent evidence pairs, retaining one member
中文介绍 揭示硬提示压缩的一种范式级失败模式——引用悬空:独立评分并保留最高分单元会拆散相互依赖的证据对,导致保留内容相关但不完整。该发现对长上下文压缩方法的设计具有警示意义。