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Parallel LLM agents can finally achieve near-linear speedups by strategically parallelizing only when token-based cost estimates predict a net gain, eliminating redundant work and reconciliation overhead.
Achieving high-quality model performance with just 10% of the required labels could revolutionize the scalability of RLVR in large language models.
As human oversight wanes, LRMs could autonomously evolve, but this shift introduces significant risks like reward hacking and feedback drift.
Injecting physics-inspired spatial priors into video MLLMs drastically enhances temporal stability and segmentation accuracy without sacrificing image-level performance.