👍 24
10/03 08:00
LLM-based agents are increasingly capable of generating complex 3D structures, with the potential to reshape how objects are designed and realized in the physical world. Yet, producing elegant geometry is fundamentally different from producing objects that can be built and perform their intended fun
中文介绍 针对 LLM Agent 生成 3D 结构「好看但不可造」的问题,LMBuild 提出面向可建造性与功能性的评测基准,考察生成结构在物理约束下能否被实际构建并完成预期功能,从而将几何美学与工程可实现性区分开来。
👍 22
09/29 08:00
Proactive LLM agents can turn idle compute into useful support before users ask. Yet even correct work can misread user context, impose review costs, or undermine trust. This work proposes foundations for designing, realizing, and evaluating proactive LLM agents around three joint principles (3T): T
中文介绍 主动式 LLM Agent 可在用户提问前提供帮助,但错误预判会引发审视成本与信任损耗。本文以 3T 等联合原则为基础,提出主动式 Agent 的设计、实现与评估框架,并给出分层技术体系与 Proactivity-Gym 评测环境。
👍 17
09/30 08:00
Recent advances have enabled unified omni-modal models in understanding audio, vision, and language. However, existing benchmarks, training data, and learning methods largely treat the modalities independently, leaving the capability of audio-visual joint reasoning poorly evaluated and insufficientl
中文介绍 现有全模态模型虽能分别理解音频、视觉与语言,但基准、训练数据与方法多将模态割裂,音视频联合推理能力未被充分评测。OmniReasoning 面向该缺口构建评测与训练方案,推动音视频联合推理的能力边界。
👍 17
10/05 08:00
Geometry optimization is a major cost in many quantum-chemical workflows: each optimization step requires one force evaluation, and at the density-functional level that evaluation dominates the wall time. Research in this area has produced a broad range of optimization methods, and we ask whether a
中文介绍 几何优化中每步都需一次力评估,DFT 级别下评估耗时主导整体成本。本文让语言模型自动发现更快的分子弛豫优化器,以替代人工设计:由 LLM 搜索优化策略,减少达到收敛所需的力评估步数,从而加速量子化学工作流。
👍 14
10/04 08:00
Search agents repeatedly make short decisions about relevance, evidence sufficiency, and search actions. Using generative language models for these decisions introduces latency and unreliable confidence. We present SearchJev, a fast and calibrated System-1 model that separates search decisions from
中文介绍 搜索 Agent 需反复判断相关性、证据充分性与下一步动作,用生成式 LLM 决策会带来延迟且置信度不可靠。SearchJev 将搜索决策与文本生成解耦,训练快速且校准良好的 System-1 模型,直接输出分类决策,兼顾速度与置信可靠性。
👍 9
10/01 08:00
Interactive world models are increasingly capable of generating environments and acting within them, yet deliberately editing an existing executable world remains underexplored. We formulate world editing as intervening on an existing world while preserving properties that should remain unchanged, a
中文介绍 交互式世界模型已能生成环境并行动,但有意识地编辑既有可执行世界仍缺乏研究。本文将世界编辑形式化为对已有世界施加干预并保持应不变属性,提出随深度递增的干预方法,使编辑在可执行世界中被系统性度量与实现。
👍 8
09/28 08:00
Does conversational memory need LLM-extracted facts, or is selecting the right raw turns enough? Published results disagree. Extraction-based systems report gains from distilled facts. Recent studies find raw history with good ranking does as well, but disagree about whether ranking matters. We ran
中文介绍 对话记忆究竟需要 LLM 抽取事实,还是选对原始轮次即可,已有结论相互矛盾。本文以类型化决策模型开展预注册实验,系统比较抽取式与选择式记忆在何种条件下等价或优劣,厘清排序质量等因素的作用。
👍 8
09/29 08:00
Manipulation behaviors vary widely across objects and scenes, but they share a small set of reusable skills, and planning with these skills helps embodied agents generalize to new tasks. Yet an agent can only plan with skills it knows. Recovering skills from observed experience, the inverse of plann
中文介绍 操作行为随物体场景各异,却共享少量可复用技能,用技能规划有助于泛化,但 Agent 只能规划已知技能。Video2Skill 从流式观察经验中反向恢复可复用具身技能,形成技能库供规划调用,提升新任务上的泛化能力。
👍 8
09/29 08:00
Embodied agents now take on ever longer tasks. For long tasks, knowing only whether a task finally succeeds or fails says little; the steps along the way matter. Progress Reward Models (PRMs) score how far a task has come at every step, and serve as dense rewards, verifiers and monitors. Yet in long
中文介绍 长时程任务中仅看最终成败信息量不足,过程奖励模型 PRM 需逐步评估进展。本文指出具身 PRM 若缺乏恰当上下文便会失准,分析何种上下文信息对进展打分至关重要,并据此改进长任务中的密集奖励、验证与监控。
👍 8
10/04 08:00
Tool-using AI agents are increasingly deployed across enterprise software systems, yet widely used benchmarks primarily evaluate nominal task completion, conflating baseline planning competence with operational fault recovery. We introduce UndoBench, a benchmark spanning 36 base workflows and 36 fau
中文介绍 现有工具 Agent 基准多只考察任务完成,混淆了基础规划能力与故障恢复能力。UndoBench 构建 36 个基础工作流与 36 类故障,将任务胜任度与撤销、回滚等恢复能力分离评测,揭示 Agent 在操作纠错上的真实短板。
👍 7
10/01 08:00
Computer-use agents need to reliably ground action targets in complex desktop scenes, where multiple applications, overlapping windows, and visually similar controls compete for attention. Existing training data rarely pair such scenes with dense annotations or vary them in a controlled way. We intr
中文介绍 计算机操作 Agent 需在应用繁多、窗口重叠、控件相似的桌面场景中准确落点,而现有数据缺少密集标注。DeskForge 从桌面环境构造可控变化的大规模密集监督数据,为 GUI 定位与操作训练提供更可靠的标注来源。
👍 6
10/05 08:00
Latent reasoning lets a large language model (LLM) think in a continuous space and verbalize only the answer. We argue that an effective latent thought must meet five requirements: it should be useful, helping produce the correct answer rather than merely changing it, diverse, so that resampling yie
中文介绍 潜空间推理让 LLM 在连续空间思考、只输出答案。本文用 flow matching 研究其关键要素,提出有效潜思维应满足五个条件,如有用(助益正确答案而非仅改变输出)、多样(重采样可得不同解)等,并据此分析训练与采样设计。
👍 4
10/02 08:00
Real-world time series are frequently driven by exogenous events and structural shifts, rendering conventional forecasting based solely on historical numerical observations insufficient. While language models can retrieve external news, standard retrieval-augmented approaches struggle with high nois
中文介绍 真实时间序列常受外部事件与结构突变驱动,仅凭历史数值难以预测。SEER 面向时序预测提出自演化的事件推理与检索机制,缓解标准 RAG 在检索外部新闻时的高噪声问题,动态筛选有效事件信息以提升预测精度。
👍 4
10/05 08:00
Reinforcement learning (RL) has greatly advanced the capabilities of large language models (LLMs), but its memory demands remain a barrier to broader adoption. We introduce LoGRA, an approach to RL post-training that reduces memory by retaining useful learning signals in low-rank gradient sketches.
中文介绍 RL 后训练显著提升 LLM 能力,但显存开销制约推广。LoGRA 以低秩梯度草图保留有用学习信号,在训练中压缩存储优化器状态,从而降低 RL 后训练的内存占用,使更大规模的 LLM 强化学习更易落地。
👍 4
10/05 08:00
Modern information systems, including many agentic workflows, use dense retrieval to explore large amounts of unstructured data. However, dense retrieval relies on surface-level semantic similarity, which is insufficient for increasingly complex search applications. Here, we investigate agentic retr
中文介绍 密集检索依赖表层语义相似度,难以应对日益复杂的搜索任务。本文系统考察面向复杂任务的 Agent 式检索,比较其相对语义检索的性能与成本,分析多步检索在何时带来收益、何时开销不划算,给出选型依据。
👍 3
10/04 08:00
Jev-style decision models return categorical probability distributions over predefined options without generating free-form text, enabling software systems to act on their outputs directly. In this work, we investigate the extent to which general-purpose LLMs already possess this capability out of t
中文介绍 本文考察通用 LLM 在无需生成自由文本的情况下,直接输出预定义选项上类别概率分布的能力,即 Jev 式决策模型。研究其开箱即用的程度,并给出何时以及如何微调,使 LLM 输出可直接被软件系统使用。
👍 3
09/15 08:00
Face Image Quality Assessment determines the suitability of captured face images for automated face recognition (FR), a critical capability for reliable biometric systems. Existing state-of-the-art FR-integrated FIQA methods suffer from temporal instability: as the feature space evolves during train
中文介绍 人脸图像质量评估中,与识别模型集成的方法在训练时特征空间不断演化,导致评分时间不稳定。本文提出累积相对点间隔分数,通过学习相对排序式目标缓解该漂移,使 FIQA 评分在训练过程中保持稳定。
👍 2
10/05 08:00
Memory self-evolution uses task feedback to iteratively improve executable memory programs that store and retrieve information from past interactions. Existing approaches typically adopt holistic evolution, deriving revision directions from mixed feedback and judging progress by overall performance.
中文介绍 记忆自演化利用任务反馈迭代改进可执行记忆程序。现有做法多采用整体演化,混合反馈难辨改进方向。本文提出以能力为驱动的自演化方法,将反馈与具体能力单元对应,实现更精细的定位与修订,提升记忆程序效果。
👍 2
10/05 08:00
Real-world users often exhibit highly heterogeneous preferences over multiple objectives for LLM responses. A lightweight aligner can tailor these responses to individual preferences, but scarce user-specific feedback makes personalized training difficult. Learning shared initializations across user
中文介绍 真实用户偏好高度异质,个性化对齐又面临反馈稀缺。本文提出跨用户协同的个性化偏好对齐方法:在用户间学习共享初始化,再以少量用户专属反馈微调轻量对齐器,从而在数据不足时仍能拟合个体偏好。
👍 2
10/02 08:00
Autoregressive transformers remain comparatively weak for protein sequence and structure generation. We study the role of target representation: amino acid tokens encode residue identities without explicit contextual semantics, while backbone coordinates require a discrete representation in our fram
中文介绍 自回归 Transformer 在蛋白质序列与结构生成上相对薄弱。本文研究目标表示的作用:氨基酸 token 仅编码残基身份而缺少显式上下文语义,主链坐标在本框架中需离散化,据此学习潜在蛋白质语言以改进自回归生成。