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Achieve detailed tunnel defect inspection without any training by visually recalibrating foundation model proposals to overcome tunnel-specific interference.
RLVR, the dominant training paradigm for audio language models, may be turning them into unfeeling "answering machines" that excel on benchmarks but fail the vibe check.
Forget noisy pseudo-labels: SpatialEvo unlocks self-supervised 3D spatial reasoning by generating perfectly accurate training data directly from scene geometry.
VLMs can achieve superior visual reasoning by dynamically decomposing queries, extracting premise-conditioned visual latents, and reasoning through grounded rationales, outperforming even multimodal CoT methods.
Even reward models that get the right answer can be dangerously wrong in their reasoning, leading to worse RLHF outcomes, but R-Align fixes this by explicitly aligning rationales with gold standard judgments.
