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Towards Event-Robust Acoustic Scene Classification
arXiv:2606.06921v1 Announce Type: new Abstract: This paper introduces the Event-Shifted Acoustic Scene (ESAS) dataset, a novel benchmark for evaluating the robustness of Acoustic Scene Classification (ASC) systems against unknown sound events. Existing ASC datasets typically contain recordings of clean and consistent audio, while real-world environments often include diverse and unexpected sound events. To bridge this gap, ESAS simulates real-world acoustic variability by injecting...
Answer Self-Consistency with Margin-Triggered Question Re-Arbitration for the CVPR 2026 VidLLMs Challenge
arXiv:2606.04323v1 Announce Type: new Abstract: In this report, we present our solution for Track 2 of the CVPR 2026 VidLLMs Challenge. This track evaluates visual relational reasoning in videos, where models must infer relations that are not always explicitly visible. We propose Answer Self-Consistency with Margin-Triggered Question Re-Arbitration (ASC-MQRA), a training-free test-time reasoning framework built on a multimodal reasoning model.
Space race: Why Portugal is reaching for the stars
Space race: Why Portugal is reaching for the stars May 31, 2026Imagine rockets being launched from the Azores, an archipelago out in the Atlantic Ocean, carrying Portuguese-built satellites into space — and then picture reusable space capsules returning to base. While this may sound like a rather futuristic scenario, elements of it could soon become reality. Portugal, after all, is working hard to become a spacefaring nation, with the help of its many highly skilled engineers and EU cooperation.
Small RL Controller, Large Language Model: RL-Guided Adaptive Sampling for Test-Time Scaling
Announce Type: new Abstract: Test-time scaling improves the reasoning performance of large language models but incurs substantial cost in both total computation and latency. Existing adaptive sampling methods partially mitigate this issue by dynamically deciding when to stop sampling, yet they typically rely on heuristic rules or rely on distribution assumptions. In this work, we formulate adaptive sampling as a Markov decision process (MDP).