arXiv:2605.08665v1 Announce Type: new
Abstract: Large reasoning models achieve high accuracy through extended chain-of-thought but generate 5–8 more tokens than necessary, applying verbose reasoning uniformly regardless of problem difficulty. We prop…
arXiv:2605.10261v1 Announce Type: new
Abstract: TCAV (Testing with Concept Activation Vectors) is an interpretability method that assesses the alignment between the internal representations of a trained neural network and human-understandable, high-le…
arXiv:2602.09534v2 Announce Type: replace
Abstract: Realistic talking-head video generation is critical for virtual avatars, film production, and interactive systems. Current methods struggle with nuanced emotional expressions due to the lack of fine-…
arXiv:2602.03916v3 Announce Type: replace-cross
Abstract: Spatial reasoning is a fundamental aspect of human cognition, yet it remains a major challenge for contemporary vision-language models (VLMs). Prior work largely relied on synthetic or LLM-gene…
arXiv:2605.08729v1 Announce Type: new
Abstract: Motion, speech, and sound effects are fundamental elements of human-centric videos, yet their heterogeneous temporal characteristics make joint generation highly challenging. Existing audio-video generat…
arXiv:2602.22779v2 Announce Type: replace
Abstract: Tokenization in video models, typically through patchification, generates an excessive and redundant number of tokens. This severely limits video efficiency and scalability. While recent trajectory-b…
arXiv:2605.10267v1 Announce Type: new
Abstract: In industrial procurement, an LLM answer is useful only if it survives a standards check: recommended material must match operating condition, every parameter must respect a regulated threshold, and no p…
arXiv:2605.08174v1 Announce Type: cross
Abstract: To mitigate the memory constraints associated with fine-tuning large pre-trained models, existing parameter-efficient fine-tuning (PEFT) methods, such as LoRA, rely on low-rank updates. However, such u…
arXiv:2605.08344v1 Announce Type: new
Abstract: Recent work has shown that models flow matching models can be trained without explicit time conditioning, challenging the standard view that the interpolation time is needed to disambiguate velocity targ…
arXiv:2604.18486v3 Announce Type: replace-cross
Abstract: Chain-of-Thought (CoT) reasoning has become a powerful driver of trajectory prediction in VLA-based autonomous driving, yet its autoregressive nature imposes a latency cost that is prohibitive …