BACR adaptively schedules token budgets for LLM reasoning via curriculum learning and a unified policy, improving accuracy by up to 8.3% under tight budgets while cutting token use by 34% on math benchmarks.
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UniMark enables reliable multi-bit watermarking across different autoregressive image generators via adaptive semantic grouping, block-wise encoding with error correction, and a unified token interface.
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Avoiding Overthinking and Underthinking: Curriculum-Aware Budget Scheduling for LLMs
BACR adaptively schedules token budgets for LLM reasoning via curriculum learning and a unified policy, improving accuracy by up to 8.3% under tight budgets while cutting token use by 34% on math benchmarks.
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UniMark: Unified Adaptive Multi-bit Watermarking for Autoregressive Image Generators
UniMark enables reliable multi-bit watermarking across different autoregressive image generators via adaptive semantic grouping, block-wise encoding with error correction, and a unified token interface.