arXiv:2503.09820v2 Announce Type: replace-cross Abstract: We introduce ViLAM, a novel method for distilling vision-language reasoning from large Vision-Language Models (VLMs) into spatial attention maps for socially compliant robot navigation. Unlike traditional methods that rely on expert demonstrations or human-annotated datasets, ViLAM performs knowledge distillation and fine-tuning at the intermediate layer representation (attention) level by aligning attention maps from a pretrained vision-action model with socially guided attention maps derived from a large VLM. These distilled attention maps highlight key navigational regions in a scene and serve as socially informed spatial cost maps for motion planning. To achieve this, we introduce a novel attention-level distillation loss that fuses knowledge from both sources, generating augmented attention maps with enhanced social awareness. These refined attention maps are then used as a traversability costmap within a socially aware local planner for navigation. We validate our approach through real-world experiments on a Husky wheeled robot, and demonstrate 14.2% - 50% improvements in success rate over existing methods.

arXiv:2505.07365v2 Announce Type: replace-cross Abstract: We present Task 5 of the DCASE 2025 Challenge: an Audio Question Answering (AQA) benchmark spanning multiple domains of sound understanding. This task defines three QA subsets (Bioacoustics, Temporal Soundscapes, and Complex QA) to test audio-language models on interactive question-answering over diverse acoustic scenes. We describe the dataset composition (from marine mammal calls to soundscapes and complex real-world clips), the evaluation protocol (top-1 accuracy with answer-shuffling robustness), and baseline systems (Qwen2-Audio-7B, AudioFlamingo 2, Gemini-2-Flash). Preliminary results on the development set are compared, showing strong variation across models and subsets. This challenge aims to advance the audio understanding and reasoning capabilities of audio-language models toward human-level acuity, which are crucial for enabling AI agents to perceive and interact about the world effectively.