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Audio Flamingo Sound-CoT Technical Report: Improving Chain-of-Thought Reasoning in Sound Understanding
Zhifeng Kong, Arushi Goel, Joao Felipe Santos, Sreyan Ghosh, Rafael Valle, Wei Ping, Bryan Catanzaro
https://arxiv.org/abs/2508.11818

Audio Flamingo Sound-CoT Technical Report: Improving Chain-of-Thought Reasoning in Sound Understanding
Chain-of-thought reasoning has demonstrated significant improvements in large language models and vision language models, yet its potential for audio language models remains largely unexplored. In this technical report, we take a preliminary step towards closing this gap. For better assessment of sound reasoning, we propose AF-Reasoning-Eval, a benchmark targeting common-sense reasoning and the ability to discriminate among closely related choices. To prepare training corpus for sound reasoning…

Soeben eine Absage einer booking.com -Unterkunft erhalten, wegen des medizinischen Assistenzhunds 🐕‍🦺 meiner Frau, und weil angeblich Booking.com die Beschränkung "mindestens 4 Nächte" nicht übernommen hätte.
Habe die Buchung storniert (zum Glück kostenfrei weil genügend früh), aber die Gastgeber auf das Behindertengleichstellungsgesetz hingewiesen.

Besuch im #Balkongarten. Ich habe dieses Jahr drei Pak Choi stehen lassen, damit sie #Samen liefern. Mal sehen, ob die samenecht sind. Das #Tagpfauenauge hat sie entdeckt.
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Audio Flamingo 3: Advancing Audio Intelligence with Fully Open Large Audio Language Models
Arushi Goel, Sreyan Ghosh, Jaehyeon Kim, Sonal Kumar, Zhifeng Kong, Sang-gil Lee, Chao-Han Huck Yang, Ramani Duraiswami, Dinesh Manocha, Rafael Valle, Bryan Catanzaro
https://arxiv.org/abs/2507.08128…

Audio Flamingo 3: Advancing Audio Intelligence with Fully Open Large Audio Language Models
We present Audio Flamingo 3 (AF3), a fully open state-of-the-art (SOTA) large audio-language model that advances reasoning and understanding across speech, sound, and music. AF3 introduces: (i) AF-Whisper, a unified audio encoder trained using a novel strategy for joint representation learning across all 3 modalities of speech, sound, and music; (ii) flexible, on-demand thinking, allowing the model to do chain-of-thought-type reasoning before answering; (iii) multi-turn, multi-audio chat; (iv) …

Autonomous Learning From Success and Failure: Goal-Conditioned Supervised Learning with Negative Feedback
Zeqiang Zhang, Fabian Wurzberger, Gerrit Schmid, Sebastian Gottwald, Daniel A. Braun
https://arxiv.org/abs/2509.03206

Autonomous Learning From Success and Failure: Goal-Conditioned Supervised Learning with Negative Feedback
Reinforcement learning faces significant challenges when applied to tasks characterized by sparse reward structures. Although imitation learning, within the domain of supervised learning, offers faster convergence, it relies heavily on human-generated demonstrations. Recently, Goal-Conditioned Supervised Learning (GCSL) has emerged as a potential solution by enabling self-imitation learning for autonomous systems. By strategically relabelling goals, agents can derive policy insights from their …

DefFusionNet: Learning Multimodal Goal Shapes for Deformable Object Manipulation via a Diffusion-based Probabilistic Model
Bao Thach, Siyeon Kim, Britton Jordan, Mohanraj Shanthi, Tanner Watts, Shing-Hei Ho, James M. Ferguson, Tucker Hermans, Alan Kuntz
https://arxiv.org/abs/2506.18779

DefFusionNet: Learning Multimodal Goal Shapes for Deformable Object Manipulation via a Diffusion-based Probabilistic Model
Deformable object manipulation is critical to many real-world robotic applications, ranging from surgical robotics and soft material handling in manufacturing to household tasks like laundry folding. At the core of this important robotic field is shape servoing, a task focused on controlling deformable objects into desired shapes. The shape servoing formulation requires the specification of a goal shape. However, most prior works in shape servoing rely on impractical goal shape acquisition meth…