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Reviso is an agentic video captioning framework that uses hierarchical Context-Focus-Points planning and active perception to prioritize key visual evidence, trained with GRPO and specialized rewards, outperforming open-source baselines on four video captioning benchmarks.
SketchDirector 是一个用于复杂场景草图生成的智能体框架，通过 LLM 分解文本提示为矢量资产、基于自演化记忆的迭代布局推理以及扩散先验细节优化，并发布了 MCSketch8K 数据集以支持后续研究。
该论文提出了一种用于多模态情感推理的智能体框架，通过模态感知智能体、证据引用、验证阶段和目标化提示补丁实现无需更新权重的提示空间适配，在MER2025数据集上优于冻结静态流水线。
Scene-Designer 提出一个通过设计与批评双重经验学习设计智能的智能体框架，用于生成完整、连贯且可用的 3D 室内场景，并提出 Critic 引导的 Scene-GRPO 方法，在物理有效性、功能正确性和布局连贯性上取得更优性能。
CoWorld Reasoner 是一个利用视频世界模型作为灵活视觉推理引擎的智能体框架，通过引导视觉想象和工具协同进化，使视觉-语言模型在七个时空推理基准上取得显著提升（如 MMSI 相对提升 11.8%、ERQA 提升 13.0%）。
该论文提出了 RISEBench 及其扩展版本 RISEBench++，这是首个评估推理驱动视觉编辑（Reasoning-Informed Visual Editing）的基准，涵盖时序、因果、空间、逻辑、反事实及混合推理等六个维度，并提出训练无关的 RISE-Agent 框架，结果显示即使是 GPT-Image-2.5 Sunburst 也仅达 56.6% 准确率。
The paper introduces Recursive Game Creator, an experience-oriented agentic framework with Designer, Builder, Player, and Reviewer roles that recursively transforms game prototypes into polished games, achieving state-of-the-art results on GameCraft-Bench and GameASG-Bench via its Coding-Native Player for gameplay evaluation.
该论文提出 VepAgent，一个将因果转移推理与工具增强强化学习结合的智能体框架，用于视频事件预测，并构建了 futurebench-4K 思维链数据集，在 FutureBench 和 NEPBench 上达到 SOTA 性能。
Code2Games is an agentic framework that coordinates coding agents to build consistent gaming worlds from natural-language game intents, adapting them to Unreal Engine 5 via execution-guided reconstruction, and introduces the GameCode4D benchmark for evaluating game-world generation quality.
VTR-Bench introduces a systematic benchmark for evaluating visual text rendering in video generation models, covering 300 prompts across five scenario categories, an automated evaluation pipeline with human alignment, and a Keyframe-Guided Agentic Framework. Experiments on 11 state-of-the-art models reveal widespread difficulties in rendering scene text, with the best model achieving a word error rate of 0.250.
OmniSeek is an agentic framework that turns an Omni-LLM into an active multi-turn reasoning agent with native tool use, dynamically retrieving audio/visual evidence across long contexts. It uses a synthetic OmniTraj-170K trajectory corpus, supervised cold-starting, and two-stage verifiable-reward RL with an Audio-Visual Necessity objective to boost cross-modal reasoning.
StoryEngine proposes a state-grounded agentic framework for video storytelling that maintains structured state representations to ensure narrative coherence and visual consistency. It demonstrates superior performance over state-of-the-art methods in comprehensive evaluations.
ASIRF is an agentic framework that retrieves domain-specific definitions for sensitive information redaction at inference time, achieving higher recall than trained classifiers across various domains with minimal expert input.
SpecOpt introduces a molecular design task to improve drug binding specificity by making targeted modifications to existing compounds using an agentic framework with residue-aware contacts, showing improvement for 84.8% of compounds in a benchmark.
This paper proposes FairCompressAgent, an agentic framework that uses a language-model planner to optimize fairness-aware model compression for FPGA deployment, balancing accuracy, fairness, and efficiency.
The paper proposes CLEAR, an agentic framework for cross-source evidence adjudication to improve large language models in medicine by handling conflicts from multiple knowledge sources. It demonstrates competitive performance across benchmarks, with significant gains in settings where direct inference or retrieval is weak.
The paper introduces REALM, a framework for long-term memory in LLM agents that uses retrieval-driven reconsolidation to autonomously organize memories into a cognitive graph, achieving improved performance on long-term memory benchmarks.
CUDA-Harness is a framework that uses agentic techniques to generate and optimize CUDA kernels from natural language descriptions, addressing challenges in Text2CUDA by connecting high-level semantics with low-level implementation and verification.
FoldingAgent is an agentic framework that uses vision-language models and specialized tools to infer parametric origami folding programs from demonstration videos via sequential reasoning and physical verification.
The paper introduces Image Bundle Composition (IBC) to shift image retrieval from atomic matching to dynamic composition of cohesive image bundles, addressing limitations in traditional approaches. It proposes a benchmark dataset IBCBench and an agentic framework BundleWeaver that leverages LLMs and VLMs for relational composition.