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New study reveals optimal windows for clarifying instructions in long-horizon agents, with goal info losing value after 10% of execution.

When Should AI Agents Ask for Clarification? Timing Matters

A forced-injection framework across 6,000+ runs shows that the value of clarification depends sharply on information type and timing. Goal clarification loses nearly all value after 10% of execution, while input clarification retains value through 50%. Current frontier models fail to ask within optimal windows.

Combining hierarchical latent tokenization with block-wise discrete diffusion and self-speculation for faster byte-level language models

Fast Byte Latent Transformer: Efficient Byte-Level Generation via Diffusion and Speculation

This paper introduces BLT Diffusion (BLT-D), BLT Self-speculation (BLT-S), and BLT Diffusion+Verification (BLT-DV) to accelerate byte-level language models. By replacing autoregressive decoding with block-wise diffusion and verification, the methods achieve over 50% memory-bandwidth reduction and up to 92% with larger blocks, while maintaining competitive performance on translation and code generation tasks.