👍 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(In-Context World Modeling)方法,让VLA模型在推理时通过上下文学习适应新相机视角、机器人形态等系统配置变量,无需额外训练,显著提升泛化能力。
👍 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(On-Policy Skill Distillation)框架,结合结果导向RL的稳定性和on-policy自蒸馏的密集监督,为语言智能体提供token级奖励信号,缓解稀疏奖励难题。
👍 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 发现coding agent中“验证比产生容易”的传统直觉不再成立,生成解越来越强而验证成为瓶颈,提出验证地平线概念,揭示当前奖励设计的根本局限。
👍 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 ViQ:一种文本对齐的可视量化表示方法,支持任意分辨率输入。通过离散量化与文本空间对齐,同时保留视觉细节,在理解与生成任务中取得更好平衡。
👍 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 提出LLM潜在思维表示的公理化评估框架,包含四条独立于下游基准的度量,可揭示掩藏在准确率背后的表示缺陷,为思维表示研究提供理论工具。
👍 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 JetSpec:通过并行树草稿(Parallel Tree Drafting)打破投机解码的扩展天花板,显著提升大语言模型在增加草稿预算时的加速比,降低起草开销。
👍 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,解耦GUI与CLI交互模态,系统比较屏幕操作与程序化指令两种计算机使用智能体的执行瓶颈,发现不同任务下各有优劣。
👍 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 Qwen-Image-2.0-RL:在扩散模型上应用RLHF和On-Policy蒸馏(OPD)进行后训练,结合可靠奖励信号,有效提升图像生成质量与指令跟随能力。
👍 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 Fast LeWorldModel:改进LeWorldModel的全局潜在过渡模型,实现多步并行预测和快速规划,在视觉规划任务上显著提速,同时保持世界模型重建精度。
👍 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评估套件,覆盖远超现有基准的多样化环境和复杂任务,重新评估智能体在真实场景中的能力,揭示模型在熟悉环境外的显著性能下降。
👍 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 分析多步骤工具使用RL训练中常见的不稳定和崩溃现象,诊断原因为奖励稀疏性和探索不足,并证明引入监督信号(如工具返回结果)能有效稳定训练。
👍 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 LISA:通过似然分数对齐(Likelihood Score Alignment)训练侧网络与主模型,提升双分支可控生成中视觉条件特征的利用效率,无需额外假设。
👍 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 SingGuard:策略自适应的多模态LLM护栏,具备动态推理能力,可识别跨模态组合风险,并根据不同应用政策灵活调整审查标准。
👍 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缓存压缩方法,超越仅依赖注意力权重的传统方式,利用信息度量识别关键token,在长推理任务中高效压缩缓存而保持生成质量。
👍 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 Tatoxa:针对低资源鞑靼语的文本去毒系统,结合数据增强与多语言模型微调,在检测和转化毒性内容上达到与高资源语言接近的性能。
👍 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 提出Progress Advantage方法,利用过程监督信号作为免费后训练信号,为LLM智能体提供细粒度进度奖励,缓解长程任务中人工标注和MC估算困难。
👍 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 CoffeeBench:面向异构多智能体经济系统的长视界LLM智能体评估基准,模拟复杂协作与竞争场景,全面评估策略规划与资源管理能力。
👍 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 提出置信度感知工具编排框架,视频推理模型根据帧间不确定性动态选择工具或模态,在运动模糊等现实扰动下将准确率从70-85%提升至90%以上。
👍 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 GBC:基于梯度连接的多智能体系统优化方法,为LLM智能体提供细粒度信用分配,通过计算贡献梯度直接优化交互策略,减少协调失误。
👍 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 证明生成式世界模型中的幻觉集中在状态-动作空间的低覆盖区域,并提出通过不确定性估计检测和基于重加权训练的预防方法,有效减少不真实滚动。