👍 57
06/25 08:00
Modern Vision-Language-Action (VLA) models often fail to generalize to novel setups, such as altered camera viewpoints or robot morphologies, because they are typically conditioned only on current observations and language instructions. By ignoring the underlying system configuration as a variable,
中文介绍 提出上下文世界建模方法,使VLA机器人在上下文中隐式编码系统配置,无需重新训练即可泛化到未见过视角或机器人形态。实验证明跨设置泛化能力显著提升。
👍 49
06/25 08:00
Outcome-based reinforcement learning provides a stable optimization backbone for language agents, but its sparse trajectory-level rewards provide little guidance on which intermediate decisions should be reinforced or suppressed. On-policy self-distillation offers dense token-level supervision, yet
中文介绍 提出OPID,结合结果奖励的稳定性和on-policy自蒸馏的密集监督,为语言智能体RL提供细粒度信用分配。在多个agentic benchmark上提升了样本效率和最终性能。
👍 43
06/24 08:00
A classical intuition holds that verifying a solution is easier than producing one. For today's coding agents, this intuition is being inverted: as foundation models develop stronger reasoning capabilities and engineering harnesses grow more sophisticated, generating complex candidate solutions is n
中文介绍 指出编码智能体中“验证比生成容易”的传统直觉已逆转。随着模型推理增强,生成简单验证难,实验验证“验证地平线”现象,为奖励设计提供新视角。
👍 38
06/25 08:00
A unified representation for text and vision is a natural pursuit, as it enables simpler multimodal modeling and more efficient training. However, representing images as discrete signals in the same way as text inevitably introduces severe information loss. Existing work struggles to balance low-lev
中文介绍 提出ViQ,一种文本对齐的视觉量化表示,支持任意分辨率。通过改进量化策略和对比学习,在保持离散性与文本对齐的同时减少信息损失,多模态任务上取得SOTA。
👍 36
05/07 08:00
We introduce an axiomatic evaluation framework for latent thought representations in LLMs, comprising metrics that are independent of downstream benchmark scores and reveal representational failures that benchmark accuracy masks. Existing evaluations conflate representation quality with model capaci
中文介绍 形式化潜在思维表示的四条公理,提出独立于下游性能的评估框架,揭示传统基准准确率掩盖的LLM内部表示失败模式,为思维链可信度提供检测工具。
👍 32
06/25 08:00
Speculative decoding (SD) accelerates autoregressive Large Language Models (LLMs) by drafting multiple tokens and verifying them in parallel, but it faces a scaling limitation: increasing the draft budget improves speed only when acceptance remains high and drafting overhead stays low. This ceiling
中文介绍 提出JetSpec,通过并行树草稿打破推测解码的缩放天花板。树状草稿提升接受率并控制开销,在多种LLM上实现2-3倍加速,优于传统单链方法。
👍 28
06/22 08:00
Computer-use agents can execute software tasks through either graphical interfaces or programmatic command interfaces, but existing evaluations confound interaction modality with differences in tasks, initial states, verifiers, and permitted actions. We introduce a matched execution-layer benchmark
中文介绍 构建匹配执行层基准,系统对比GUI与CLI两种交互方式下的计算机使用智能体瓶颈。发现CLI在复杂任务上效率更高,GUI在直观性上有优势,为混合设计提供指导。
👍 27
06/25 08:00
We present Qwen-Image-2.0-RL, a post-training pipeline that applies reinforcement learning from human feedback (RLHF) and on-policy distillation (OPD) to improve both the visual quality and instruction-following capability of the Qwen-Image-2.0 diffusion model. To provide reliable reward signals, we
中文介绍 报告Qwen-Image-2.0-RL,采用RLHF和on-policy蒸馏联合优化扩散模型。通过人类偏好奖励和在线蒸馏,显著提升图像质量与指令跟随能力,超越前代。
👍 25
06/24 08:00
Joint-Embedding Predictive Architectures (JEPAs), including recent LeWorldModel (LeWM), have become a promising foundation for reconstruction-free visual world models. For visual planning, however, LeWM evaluates candidate action sequences by repeatedly applying a local one-step latent transition mo
中文介绍 提出Fast LeWorldModel,通过全局潜空间预测替代局部单步迭代,加速JEPA视觉世界模型规划。在多个规划任务中保持精度同时实现数倍速度提升。
👍 18
06/25 08:00
As agentic systems continue to evolve and are widely deployed in real-world scenarios, there is a growing demand to faithfully evaluate their capabilities. However, current benchmarks are typically built on popular applications with relatively simple tasks and focus on a narrow set of capabilities w
中文介绍 提出Gauntlet基准,在陌生环境中全面评估智能体能力,覆盖更广泛任务和未见过场景。揭示模型在熟悉环境外性能显著下降,指出泛化差距。
👍 16
06/24 08:00
Tool use enables large language models (LLMs) to perform complex tasks, and recent agentic reinforcement learning (RL) methods show promise for enhancing model capabilities. However, RL alone often leads to instability or limited gains in tool-use tasks. In our experiments, some models exhibit catas
中文介绍 分析多步工具使用RL崩溃原因:稀疏奖励和探索困难导致灾难性遗忘。引入监督信号(模仿学习/过程奖励)稳定训练,显著提升复杂任务成功率。
👍 13
06/25 08:00
The prevalent dual-branch paradigm, i.e., training a side network to encode visual conditions and fusing its intermediate-layer features to a frozen pretrained main network, has shown remarkable success in visual-condition controllable generation. Despite its widespread adoption, the role of the sid
中文介绍 提出LISA,通过似然得分对齐优化视觉条件可控生成中侧网络的作用。理论分析并设计对齐损失,使生成更精准符合条件,多项任务提升一致性。
👍 11
06/22 08:00
Vision-language models (VLMs) are increasingly deployed in consumer, medical, financial, and enterprise applications. This broad deployment expands the safety surface: risks can arise from multimodal question answering, assistant responses, and cross-modal composition, while moderation policies may
中文介绍 提出SingGuard,一种策略自适应的多模态LLM护栏,支持动态推理。根据实时策略调整安全阈值,处理跨模态风险,有效拦截有害内容且误报率低。
👍 10
06/25 08:00
Reasoning capability has advanced rapidly in large language models (LLMs), leading to an increasing size of key-value (KV) cache in both prefilling and decoding stages. Existing KV cache compression methods mainly rely on attention weights to estimate token importance. While attention effectively ca
中文介绍 提出信息感知的KV缓存压缩,结合注意力权重与互信息衡量token信息贡献。在长上下文推理中缓存占用减少40%以上,保持模型性能。
👍 9
06/24 08:00
Text detoxification, the automated detection and mitigation of abusive and harmful content, is essential for ensuring the safety of online communities and protecting users. However, low resource languages such as Tatar have received little research attention. In this paper we present Tatoxa, a novel
中文介绍 提出Tatoxa系统,针对低资源语言鞑靼语的文本去毒。结合小型标注数据与跨语言迁移,构建检测与改写管道,在鞑靼语上取得有效结果。
👍 9
06/24 08:00
Process reward models enable fine-grained, step-level evaluation of LLMs, yet building them for agentic settings remains prohibitively difficult: long-horizon interactions, irreversible actions, and stochastic environment feedback make both human annotation and Monte Carlo estimation infeasible at s
中文介绍 利用后训练中的“进度优势”,无需显式过程奖励模型即可为LLM智能体提供细粒度监督。通过比较候选轨迹与已有轨迹的进展差异,有效引导策略学习。
👍 9
06/15 08:00
As LLM agents become capable of increasingly long-horizon tasks, evaluating their performance in economic systems is becoming increasingly important. Unlike existing benchmarks that primarily evaluate a single agent interacting with a passive environment, economic systems are inherently multi-agent,
中文介绍 提出CoffeeBench,评估长周期LLM智能体在异构多智能体经济中的表现。模拟协作与竞争,测试长期规划能力,发现当前模型策略稳定性不足。
👍 9
06/25 08:00
Video reasoning language models implicitly assume that every input frame is equally reliable. This leads to what we term the Blind Trust Problem: under realistic perturbations such as motion blur, glare, or occlusion, frontier video reasoning models can suffer 15-30%p accuracy drops on real-world em
中文介绍 提出置信度感知的工具编排,模型动态评估每帧可靠性,选择合适工具处理低质量帧。在真实扰动视频上准确率恢复高达20%,缓解盲信问题。
👍 8
06/26 08:00
Multi-agent systems (MAS) built on large language models (LLMs) provide a promising framework for solving complex tasks through role specialization and structured interaction. However, their performance is often limited by miscoordination and, more fundamentally, the lack of fine-grained credit assi
中文介绍 提出GBC,为LLM多智能体系统提供可微信用分配。通过计算智能体间交互梯度优化角色分工和通信策略,在协作推理任务上显著提升准确率和协调效率。
👍 8
06/25 08:00
Modern generative world models render increasingly realistic action-controllable futures, yet they frequently hallucinate: rollouts remain visually fluent while drifting from the ground-truth dynamics. We hypothesize that hallucination concentrates in low-coverage regions of the state-action space,
中文介绍 发现世界模型幻觉集中在状态-动作空间低覆盖区域,具有可预测性。提出主动探索或重加权训练等预防方法,减少幻觉同时保持视觉质量。