👍 54
05/27 08:00
As agent capabilities advance, existing benchmarks, such as τ^2-Bench, are becoming increasingly saturated. Yet constructing new benchmark tasks remains complex, costly, and labor-intensive. Moreover, the standard approach, in which scenarios are first written in natural language and then mapped to
中文介绍 提出 TASTE 方法,通过自动生成多样且难度递增的测试任务,提升智能体基准的覆盖率和难度,解决现有基准饱和及人工构建成本高的问题。
👍 31
06/01 08:00
Search agents are often trained as policies over growing transcripts: the model must decide how to search while also remembering what it has seen, which evidence is useful, which constraints remain open, and which claims have actually been checked. We argue that this formulation puts too much routin
中文介绍 提出 Harness-1,通过状态外部化(State-Externalizing Harnesses)将搜索过程与记忆解耦,使搜索智能体更高效地决定搜索策略并追踪已见证据,提升训练稳定性和性能。
👍 26
05/28 08:00
Speculative decoding accelerates LLM inference by drafting multiple tokens and verifying them in parallel with the target model. However, its practical speedup is constrained by the trade-off between draft quality and drafting cost: autoregressive drafters model causal dependencies among draft token
中文介绍 提出 Domino 推测解码方法,将候选草稿的因果建模与自回归生成解耦,在保证草稿质量的同时降低 drafting 成本,加速 LLM 推理。
👍 25
05/28 08:00
Watermarking embeds statistical signatures in AI-generated text for detection and attribution. We reveal a fundamental vulnerability: when users access multiple models (today's reality), watermarks trivially fail. Watermarks perturb output distributions away from the original, and in competitive mar
中文介绍 揭示 LLM 水印的根本漏洞:当用户可访问多个模型时,对输出分布进行线性集成即可轻易洗掉水印,使水印检测失效,挑战当前水印机制的鲁棒性。
👍 13
05/22 08:00
Multi-agent LLM workflows route inference through specialized roles to lift end-task accuracy, but jointly training those roles with reinforcement learning is unstable in ways that are poorly understood. We study when end-to-end RL training of multi-agent LLM workflows improves over their base model
中文介绍 研究多智能体 LLM 工作流使用强化学习联合训练的效果,分析工作流结构、规模与策略共享对性能的影响,揭示联合训练比单独微调基线提升的条件。
👍 12
06/01 08:00
The Model Context Protocol (MCP) has emerged as a transformative standard for connecting large language models (LLMs) with external data sources and tools, and has been rapidly adopted across personal applications and development platforms. However, existing benchmarks predominantly focus on generic
中文介绍 提出 MCP-Persona 基准,通过环境模拟评测 LLM 智能体在真实个人应用(遵循 MCP 协议)中的表现,填补现有基准缺乏真实场景的空白。
👍 11
06/01 08:00
Autoregressive (AR) video diffusion enables variable-length synthesis, but long-horizon generation often suffers from accumulated errors and identity drift. For efficiency, existing methods commonly adopt sliding-window attention during generation. This creates an irreversible generation trajectory:
中文介绍 提出 LongLive-RAG,一种面向长视频生成的通用检索增强框架,通过结合外部记忆与检索减少自回归累积误差和身份漂移,实现高质量长时间视频合成。
👍 11
06/01 08:00
In open-ended environments, exploration is fundamental for autonomous agents, yet current language model agents struggle with this. Effective exploration requires memory, but retaining raw interaction histories is computationally expensive over long trajectories. While latent memory offers a solutio
中文介绍 提出基于新奇信号的联合智能体记忆与探索学习方法,通过 latent memory 存储探索经验,利用新奇度引导探索策略,提升语言智能体在开放环境中的自主探索能力。
👍 9
06/01 08:00
Building capable visual web agents requires long-horizon reasoning, precise grounding, and robust interaction with dynamic real-world websites. Despite rapid progress, the strongest systems remain largely proprietary, while open agents still depend heavily on supervised post-training over large coll
中文介绍 开源 OpenWebRL,面向视觉网页智能体采用在线多轮强化学习训练,在真实动态网站上实现长程推理与精准交互,弥合开放系统与专有系统的性能差距。
👍 7
06/01 08:00
Reinforcement learning (RL) improves large language model (LLM) agents by teaching them which actions lead to high rewards, but provides little supervision on what those actions do to the environment. World modeling (WM) can fill this gap, yet existing approaches often require separate simulators, e
中文介绍 提出策略与世界模型联合训练方法,在强化学习基础上引入世界模型预测动作环境效果,无需额外模拟器即可提升语言智能体的规划与推理能力。
👍 6
05/31 08:00
Reusable skills are a key mechanism for extending agent capabilities, allowing agents to accumulate experience and solve increasingly complex tasks. Yet most existing skill-learning methods store reusable experience as text-only assets, such as instructions, reasoning traces, or summarized trajector
中文介绍 论证智能体技能不应仅限于文本,应包含视觉技能(如截图、视觉特征),提出视觉技能学习方法,扩展智能体在视觉复杂任务中的复用能力。
👍 6
05/29 08:00
Multimodal large language models (MLLMs) have shown strong capabilities in perception, reasoning, and action generation. However, their ability to sustain exploration in dynamic open worlds remains unclear. Existing embodied and game-based benchmarks often compress interaction into short-horizon tas
中文介绍 提出 MineExplorer 基准,在 Minecraft 开放世界中系统评估多模态大模型智能体的探索能力,包含长程探索、动态规划等任务,揭示当前 MLLM 在持续探索中的不足。
👍 6
05/28 08:00
Large Vision-Language Models (LVLMs) map visual inputs into dense token sequences, imposing a quadratic computational bottleneck for inference. Elastic visual-token compression addresses this by training a single model that can run at multiple visual-token budgets. However, existing approaches strug
中文介绍 提出 PARCEL 方法,通过锚定池重采样与条件弹性查询实现高效视觉语言理解,在单个模型上支持多种视觉 token 预算,缓解 LVLM 推理时的二次计算瓶颈。
👍 5
05/23 08:00
Large language model (LLM) unlearning has emerged as a crucial post-hoc mechanism for privacy protection and AI safety, yet auditing whether target knowledge is truly erased remains challenging. Existing output-level metrics fail to detect when this knowledge remains recoverable from internal repres
中文介绍 提出利用激活修补(Activation Patching)衡量 LLM 遗忘深度,发现输出级指标无法检测的可恢复知识,提供更可靠的遗忘审计方法。
👍 5
06/01 08:00
Selecting the best response from multiple small-model samples using a stronger scorer is a simple inference-time strategy, but fails when the small model has already committed to incorrect reasoning paths. PRM guided search avoids this by scoring candidate continuations during generation, but requir
中文介绍 提出使用现成 LLM 作为过程评分器,无需训练即可替代过程奖励模型(PRM),在数学推理中通过评分候选推理路径提升小模型采样质量。
👍 5
06/01 08:00
Computer use agents (CUAs) today are primarily deployed as single serial agents. This setup is suboptimal for complex long-horizon tasks that benefit from task decomposition, parallel execution, and consistent re-planning based on new information. In this paper, we argue that we should instead move
中文介绍 提出多智能体计算机使用框架,将一个复杂长程任务分解为多个子任务并行执行,并支持动态重规划,显著提升计算机使用智能体在复杂任务上的表现。
👍 4
05/29 08:00
True video intelligence demands more than recognizing what is visible: it requires reasoning about why events unfold, predicting what would change under different conditions, and deciding what to do next. We refer to this progression, from perception through causal reasoning and simulation to strate
中文介绍 提出 SVI-Bench 动态微世界基准,评测视频智能体从感知、因果推理到策略决策的全链条能力,推动视频理解从识别向战略推理进阶。
👍 4
05/30 08:00
Agentic search requires language model agents to explore many sources and answer complex information-seeking questions. Scaling test-time compute is a promising way to improve these agents, but current approaches can fail, because correct answers are often sparse and score-based selection depends on
中文介绍 提出 FineVerify,通过细粒度自验证机制扩展智能体搜索的测试时计算,对搜索结果进行逐步验证与选择,提升复杂信息查找的准确性。
👍 4
05/25 08:00
Reinforcement learning with verifiable rewards (RLVR) has become a core technique for post-training of Large Language Models (LLMs). While policy optimization is driven by all sampled tokens under a globally broadcast scalar reward, the heterogeneous policy behaviors exhibited along trajectories are
中文介绍 提出时序调度方法 RLVR,在强化学习后训练中根据轨迹不同阶段给 token 分配差异化权重,缓解全局标量奖励忽略策略异质性的问题,提升 LLM 后训练效果。
👍 3
05/20 08:00
Academic researchers need efficient and reliable methods for collecting high-quality information from trusted sources, but modern tools for AI-assisted research still suffer from the tendency of Large Language Models (LLMs) to produce factually inaccurate or nonsensical output, commonly referred to
中文介绍 提出 ACL-Verbatim,通过约束生成过程严格基于可信来源,为科研问答提供无幻觉的可靠答案,解决 LLM 在学术文献查询中的事实错误问题。