👍 232
10/18 20:00
Brain encoding models aim to predict neural responses to stimuli. The most widely used approach fits readouts from frozen pretrained backbones. Using neural data to train large models from scratch or fully fine-tune them after task-pretraining has proven difficult because neural data is costly and l
中文介绍 论文聚焦于脑编码模型,通过对冻结的预训练模型进行微调来预测神经反应。受限于神经数据的获取成本,训练大型模型或完全微调任务后预训练的做法存在难度。该研究提出了一种参数高效的微调策略,以提升模型在脑编码任务上的表现,推动脑机接口等应用的发展。
👍 146
10/18 20:00
Memory-native evolution requires making memory an intrinsic capability of foundation models, enabling them to continuously accumulate and utilize information. To this end, we introduce MemT-1.0, a new architecture of memory foundation models that internalize memory as part of model computation. Buil
中文介绍 提出了MemT-1.0架构,将内存作为基础模型的内在能力,使模型能够持续积累和利用信息。通过将内存内化为模型计算的一部分,该研究推动了记忆相关模型的发展,促进其在持续学习和人机交互中的应用潜力。
👍 109
10/18 20:00
Human egocentric experience offers a rich foundation for learning how the world is organized and how it changes through interaction. Realizing this potential at scale requires turning growing volumes of recordings into learning resources that connect scene structure, object semantics, and interactio
中文介绍 研究人类自我中心体验作为学习世界组织和变化的基础,提出将大量录音转换为学习资源的方法。该研究旨在整合场景结构、物体语义和交互,提高人工智能在理解复杂世界动态中的能力,影响人机智能和自动化系统的应用。
👍 81
10/18 20:00
Matryoshka Representation Learning (MRL) is designed to support flexible representation sizes by jointly optimizing nested prefixes. While effective, this design lacks an explicit constraint to ensure that the leading portion provides a suitable compact encoding. To address this, we propose SpecMRL,
中文介绍 提出了SpecMRL方法,以优化嵌套前缀并确保前导部分提供合适的紧凑编码。此方法解决了Matryoshka表示学习中的灵活表示大小问题,推动了更有效的表示学习技术,为多任务学习和模型压缩等方向提供了新的思路。
👍 27
10/18 20:00
Large Language Model (LLM) agents are increasingly deployed in unfamiliar and evolving environments, requiring them to proactively explore, understand, and adapt to environmental dynamics. However, existing benchmarks rarely test whether agents can proactively discover how an environment works and a
中文介绍 AdaExploreBench为大型语言模型(LLM)代理提供了一种新的基准,测试其在不断变化的环境中主动探索和适应的能力。研究强调填补现有基准的空白,以支持代理在复杂环境中的自主学习,促进人工智能的适应性与智能决策能力的提升。
👍 26
10/04 20:00
Memory has become integral to the LLM agent ecosystem, supporting information retention and reuse across interactions. However, most existing agent memory systems construct memory in a query-agnostic manner, which can incur unnecessary preprocessing cost and discard details that later prove essentia
中文介绍 MemPilot提出了一种按需的多模态记忆管理方法,改善LLM代理的信息保留和重用。通过针对性地构建记忆,该方法减少了不必要的预处理开销,确保了更有效的记忆系统,推动了智能代理在复杂任务中的表现与适应能力。
👍 25
10/18 20:00
Procedural generation plays a key role in synthetic data creation and game development, offering greater controllability, editability, and diversity than static mesh representations. However, VLM-based code generation often produces relatively simple 3D assets, while sophisticated photorealistic pro
中文介绍 InfiniDream通过程序生成技术实现开放式的真实感3D世界,解决了静态网格表示的局限性。研究显示,该方法提升了3D资产生成的复杂性与多样性,推动了游戏开发和虚拟现实等领域的应用潜力。
👍 23
10/18 20:00
Robotic value models assess task execution from visual observations and instructions, providing feedback for policy learning and execution monitoring. However, existing evaluations cover only selected aspects of execution understanding, leaving the reliability of value feedback insufficiently tested
中文介绍 RoboValue提出了一种细粒度的模拟与真实环境基准,用于统一评估机器人价值模型。此工作填补了现有评估在执行理解方面的不足,为机器人学习和执行监控提供了透明的反馈机制,有助于改进人工智能在复杂任务中的表现。
👍 23
10/18 20:00
Adapting existing manga into coherent animation that stays faithful to the source is a valuable yet challenging task. Existing methods either access the manga mainly through text, losing its characters and art style, or feed manga pages directly into video generation models, which misinterpret the m
中文介绍 Manga2Animate提供了一种框架,将漫画转化为准确的动画,该方法关注于剧本和分镜的规划。通过理解漫画的角色和艺术风格,该研究在动画制作领域开辟了新路径,推动了内容创作的创新。
👍 21
10/13 20:00
Recent advances in multimodal large language models (MLLMs) have enabled them to act as autonomous agents, creating and editing 3D models via code or direct software manipulation. In practical workflows, these models must infer object shapes, materials, spatial layouts, and lighting from diverse ref
中文介绍 BlenderWorld为视觉代理在3D创作中的表现提供了基准,强调多模态大语言模型(MLLM)在自动化3D模型创建中的新能力。研究目标是提升模型在真实世界参考下的推理能力,推动其在计算机视觉和图形学中的应用前景。
👍 17
10/17 20:00
Feed-forward 3D Gaussian Splatting (3DGS) promises instant scene reconstruction with high-quality novel-view synthesis. Yet, fine-grained geometry and appearance remain difficult to recover, motivating us to revisit how feed-forward models represent, aggregate, and refine detailed visual information
中文介绍 FineSplat提出了一种前馈3D高斯喷溅技术,旨在实现即时的场景重建和高质量的新视图合成。研究强调了细粒度几何和外观重建的挑战,推动了视觉信息处理的创新,提升了计算机视觉技术的应用价值。
👍 16
10/18 20:00
In this paper, we systematically investigate the mechanisms, scaling behavior, and generalization of physical Recursive self-improvement. We introduce PhysRSI-eval to quantify physical RSI through a five-axis metric: First-Attempt Performance, Efficiency, Success, Budget and Improvement from failure
中文介绍 论文系统性地研究了物理递归自我改进的机制、规模行为和泛化能力,提出了PhysRSI-eval指标来量化其表现。研究深化了我们对智能体在物理能力评估中的理解,推动了强化学习和自我改进体系的研究。
👍 16
10/12 20:00
Long-horizon, semi-verifiable agentic tasks often rely on rubric-based evaluation, but the evidence needed to assess these tasks can be spread across the environment state and the execution trace. A judge may overlook this evidence or accept unsupported claims, including an agent's own reports of su
中文介绍 论文探讨了长周期、半可验证的智能体任务的证据评估问题,提出改进评价机制的方法,以便更好地收集和分析与环境状态和执行轨迹相关的证据。这一贡献有助于提高智能体系统的可验证性,助力安全性和可信性研究。
👍 11
10/14 20:00
Image editing models produce impressive results for individual edits, but their quality often deteriorates over multiple editing turns. Each editing turn introduces small distribution shifts that accumulate over successive edits, creating a gap between the input distributions encountered during the
中文介绍 ACEdit设计了一种不对称条件的图像编辑方法,旨在稳定多轮编辑过程,以减少每轮编辑时的质量下降。研究提出的方法通过改进输入分布的匹配,增强了图像编辑系统的可靠性,推动多媒体内容创作技术的发展。
👍 11
10/15 20:00
Safety in Multimodal Large Language Models (MLLMs) depends on unsafe multimodal data for post-training alignment or activation steering. However, such data is hard to collect, must be gathered anew for each modality, and its collection and distribution raise ethical and legal concerns. In this work,
中文介绍 论文聚焦于多模态大型语言模型的安全性,探讨了文本拒绝方向在后期对齐或激活引导中的作用。研究解决了安全性数据的收集与伦理问题,为多模态系统的安全性提升提供了新思路,推动技术的伦理设计。
👍 8
10/16 20:00
Existing Multi-token prediction (MTP) methods are auxiliary by design: the multi-horizon loss mostly trains modules attached to the backbone, and each added horizon costs another module and another pass. We propose Efficient Causal Self-Recursion (ECHO), which removes both costs. The backbone's own
中文介绍 ECHO通过高效因果自我递归方法,解决了多项预测中的附加模块成本问题。研究表明,ECHO能够提升模型性能并降低计算开销,为序列建模与时间序列预测的任务提供了更高效的解决方案。
👍 7
10/18 20:00
Many modern language models have adopted hybrid architectures that incorporate both linear-time recurrence and softmax attention. Modern recurrence layers provide a highly expressive mixing mechanism while ensuring high throughput by compressing contextual information into a fixed-size state, but un
中文介绍 文章探讨了混合架构中竞争学习的应用,重点研究了现代语言模型如何集成线性时间递归和softmax注意力。研究揭示了此类模型在高吞吐量下的表达能力,对自然语言处理模型的设计和优化具有重要启示。
👍 6
10/17 20:00
Coding agents keep working on runs that will fail verification, and asking a frontier model to watch for this is expensive in its own right. We introduce StopLoss, a small external monitor that learns failure recognition from completed trajectories and their verifier outcomes. The name is inspired b
中文介绍 StopLoss提出了一种外部监控机制,学习如何识别失败并提前停止不成功的代码运行。该方法优化了智能体的效率,减少了不必要的计算资源消耗,对智能体的学习和监控策略的改进具有重要意义。
👍 6
10/16 20:00
While LLM-based agent systems have demonstrated remarkable proficiency in reasoning and solving complex real-world tasks, they also come with substantial token consumption and inference latency, limiting their practical deployment. To enhance the efficiency of agent systems, recent efforts have focu
中文介绍 研究针对小型语言模型智能体系统在推理和复杂任务解决中的效率问题,提出了进度感知蒸馏技术来减轻停滞现象。此方法旨在改善模型的推理速度与有效性,推动高效智能体的研究与实际应用。
👍 6
10/15 20:00
Conditional memory expands language-model capacity through selective lookup, but the full embedding table must remain available for every possible input. We introduce Conditional Program (CP), a compressed representation that reconstructs only the rows an input requests. CP represents each row by a
中文介绍 论文介绍了条件记忆在大语言模型中的扩展,提出了一种量化查找表的方法,通过选择性查找来提升语言模型的容量和效率。研究对记忆能力的优化可能对增强模型的智能应用产生良好影响。