arXiv AI

VLMs for Videogame Data Annotation

arXiv:2608. 05949v1 Announce Type: new Abstract: Vision Language Models (VLMs) and Artificial Intelligence (AI) agents have revolutionized how engineers approach complex problems in real-world applications.

arXiv AI

Aug 7

Training a Conditioned Video Game Agent on a VLM Annotated Dataset

arXiv:2608. 05954v1 Announce Type: new Abstract: Reinforcement Learning (RL) is a powerful but far from easy-to-use technique for policy learning.

By Katrin Schmid, Iuri Frosio

llms agents reinforcement-learning multimodal

arXiv Machine Learning

Jun 2

Towards Sparse Video Understanding and Reasoning

arXiv:2602. 13602v2 Announce Type: replace-cross Abstract: We present \revise (\underline{Re}asoning with \underline{Vi}deo \underline{S}parsity), a multi-round agent for video question answering (VQA).

By Chenwei Xu, Zhen Ye, Shang Wu, Weijian Li, Zihan Wang, Zhuofan Xia, Lie Lu, Pranav Maneriker, Fan Du, Manling Li, Han Liu

llms agents nlp fine-tuning multimodal benchmarks

arXiv AI

Jul 29

Evaluating VLMs for Autonomous Agent-Driven Geometry Clipping Detection in Video Game QA

arXiv:2607. 25921v1 Announce Type: cross Abstract: In this work, we study the use of Vision-Language Models (VLMs) for anomaly detection in an agent-driven game Quality Assurance (QA) pipeline focusing on geometry clipping.

By Carlos Celemin, Benedict Wilkins, Adri\'an Barahona-R\'ios, Saman Zadtootaghaj, Nabajeet Barman

llms agents multimodal benchmarks

arXiv AI

Jun 19

Augmenting Game AI with Deep Reinforcement Learning

arXiv:2606. 20210v1 Announce Type: new Abstract: Immersion in video games depends not only on graphics, audio, and game mechanics, but also on the quality of in-game characters.

By Alessandro Sestini, Joakim Bergdahl, Amir Baghi, Jean-Philippe Barrette-LaPierre, Florian Fuchs, Linus Gissl\'en

agents reinforcement-learning

arXiv AI

Jun 2

From Demonstrations to Rewards: Test-Time Prompt Optimization for VLM Reward Models

arXiv:2606. 00083v1 Announce Type: cross Abstract: Reinforcement learning relies on accurate reward functions, which are often hand-crafted or even unavailable in real-world applications, such as robotics.

By Christian Gumbsch, Leonardo Barcellona, Lennard Sch\"unemann, Platon Karageorgis, Andrii Zadaianchuk, Zehao Wang, Sergey Zakharov, Fabien Despinoy, Rahaf Aljundi, Efstratios Gavves

llms reinforcement-learning robotics multimodal

arXiv Machine Learning

Jul 7

Incentivizing Vision Language Models to Search for Long Video Question Answering

arXiv:2607. 02959v1 Announce Type: cross Abstract: We introduce VSeek, an agentic framework that transforms long-video question answering (LVQA) from a passive, single-pass perception task into a multi-turn retrieval process.

By Harsh Goel, S P Sharan, Sahil Shah, Minkyu Choi, Joungbin An, Kristen Grauman, Sandeep P. Chinchali

llms agents nlp reinforcement-learning benchmarks

Hugging Face Trending Papers

Jul 28

Evaluating VLMs for Autonomous Agent-Driven Geometry Clipping Detection in Video Game QA

In this work, we study the use of Vision-Language Models (VLMs) for anomaly detection in an agent-driven game Quality Assurance (QA) pipeline focusing on geometry clipping. In this evaluation, a custom exploration agent navigates a game level to collect visual observations, while the automatic annotation pipeline provides frame-level clipping labels.

llms agents multimodal benchmarks

arXiv AI

Jun 4

From Segments to Scenes: Temporal Understanding for Agentic Autonomous Driving via Vision-Language Models

arXiv:2512. 05277v4 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) are increasingly deployed as the perception and reasoning backbone of autonomous agents acting in the wild, with autonomous driving (AD) being one of the most safety-critical instances.

By Kevin Cannons, Saeed Ranjbar Alvar, Mohammad Asiful Hossain, Ahmad Rezaei, Mohsen Gholami, Alireza Heidarikhazaei, Zhou Weimin, Yong Zhang, Mohammad Akbari

llms agents robotics multimodal benchmarks safety

arXiv AI

Jun 2

From Segments to Scenes: Temporal Understanding in Autonomous Driving via Vision-Language Model

arXiv:2512. 05277v3 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) are increasingly deployed as the perception and reasoning backbone of autonomous agents acting in the wild, with autonomous driving (AD) being one of the most safety-critical instances.

By Kevin Cannons, Saeed Ranjbar Alvar, Mohammad Asiful Hossain, Ahmad Rezaei, Mohsen Gholami, Alireza Heidarikhazaei, Zhou Weimin, Yong Zhang, Mohammad Akbari

llms agents robotics multimodal benchmarks safety

arXiv AI

Jul 10

Open-ended Multi-agent Autocurricula via Visual Inspection of Policies with Multi-modal LLMs

arXiv:2607. 08193v1 Announce Type: cross Abstract: Open-ended curricula in Reinforcement Learning (RL) aim to train generally-capable agents by identifying tasks that facilitate learning increasingly complex skills.

By Lorenzo Pant\`e, Andrea Fanti, Roberto Capobianco

llms agents reinforcement-learning multimodal benchmarks

arXiv AI

Jun 2

MindGames Arena Generalization Track: In2AI Solution with Delayed Per-Step Reward Attribution

arXiv:2606. 00017v1 Announce Type: new Abstract: Training language model agents for multi-agent strategic interaction presents a core difficulty: the quality of any action may depend on future events that never materialize, on moves that violate game rules, or on decisions made by other players.

By Aliaksei Korshuk, Alexander Buyantuev, Ilya Makarov

llms agents reinforcement-learning benchmarks

arXiv AI

Aug 5

Enhancing VLM Reward Models Through Structure-Aware Fine-Tuning

arXiv:2608. 03875v1 Announce Type: cross Abstract: Designing effective reward functions remains a major bottleneck in Reinforcement Learning (RL).

By Pyrros Koussios, Chenhao Li, Xin Chen, Andreas Krause

llms diffusion reinforcement-learning fine-tuning multimodal safety

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