arXiv AI
Reusing Multimodal Agents: Efficient Evaluation of Sound Designs in Games
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv AI
Aug 5
AI-Based Sound Effect Generation: A Narrative Review of Generative Models Across Input Modalities
arXiv:2608. 03742v1 Announce Type: cross Abstract: Sound effects play a crucial role in conveying actions, events, and environmental cues across digital applications, often requiring a high degree of variation and contextual adaptability.
arXiv AI
Sep 21
Towards the Vision-Sound-Language-Action Paradigm: The HEAR Framework for Sound-Centric Manipulation
arXiv:2603.16086v2 Announce Type: replace-cross Abstract: While recent Vision-Language-Action (VLA) models have begun to incorporate audio, they typically treat sound as static pre-execution prompts...
arXiv Computer Vision
Oct 7
CueRator: Agentic Search for Symbolic Rules to Adapt Frozen Multimodal Encoders
arXiv:2610.07868v1 Announce Type: new Abstract: Large language model agents have been used to search over symbolic structures such as programs and equations. We propose CueRator, an agentic framework...
arXiv AI
Jul 2
GameDevBench: Evaluating Agentic Capabilities Through Game Development
arXiv:2602. 11103v2 Announce Type: replace Abstract: Despite rapid progress on coding agents, progress on their multimodal counterparts has lagged behind.
arXiv Machine Learning
Jun 2
Quality Audio Prototyping: a prototype system for unified sound retrieval and procedural generation
arXiv:2606. 00629v1 Announce Type: cross Abstract: Sound design workflows frequently oscillate between time-consuming library searches and the complexity of procedural synthesis, with practitioners typically relying on disconnected tools to address each challenge separately.
Hugging Face Trending Papers
Oct 6
CueRator: Agentic Search for Symbolic Rules to Adapt Frozen Multimodal Encoders
Large language model agents have been used to search over symbolic structures such as programs and equations. We propose CueRator, an agentic framework for policy-aware decision-rule discovery, which...