Generative world models offer controllable and repeatable closed-loop simulation for end-to-end and vision-language-action driving policies, but production deployment exposes three unresolved requirements: faithfully reproducing a mixed fisheye-pinhole rig at native resolutions; reconciling causal, per-timestep interac...
Bo-Ni Hu, Xiong Wei, Hao-Ming Huang et al.· 0 citations
How much can test-time computation improve a language model, and at what cost? Test-time scaling is widely proposed as a substitute for larger models, but existing comparisons mostly evaluate one domain at a time and rarely charge selection to the budget. We introduce SELF-POT, a benchmark and evaluation framework that...
Bang Yang, Jing-Yuan Li, Jia-Jun Fan et al.· 0 citations
Scaling reasoning typically spends more compute on reinforcement learning (RL) or on inference. We show that a completed RL training history can yield policies stronger than the checkpoints visited by its optimizer. We call this policy-space scaling: expanding the deployable policy set accessible from a fixed RL histor...
Bang Yang, Jia-Jun Fan, Hong-Ba Ma et al.· 0 citations
Voice agents must call tools and hold multi-turn dialogue entirely through speech, yet the dominant paradigm trains them in text. Existing frameworks either cascade TTS and ASR around a proprietary voice API, where gradients cannot flow and per-call cost makes on-policy reinforcement learning prohibitive, or stay in te...
Reading a base Qwen3-Omni with a logit lens at the audio-token positions, it is found that the answer to a spoken question becomes legible - in words - in the model's middle layers, before it emits any token.
This work introduces Constraint-First Reasoning (CFR), a training-free two-stage prompting protocol that improves direct CoT on multiple backbones and positions CFR as a targeted test-time intervention whose benefit depends on recoverable constraints and reliable Stage 1 extraction.
Hongbo Ma, Bang Yang, Y. Cheng et al.· 0 citations
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