BLT-D, BLT-S, and BLT-DV use block-wise diffusion training and speculative verification to enable parallel byte generation in byte-level LMs, cutting memory-bandwidth cost by over 50%.
SEA- HORSE: A multilingual, multifaceted dataset for summarization evaluation
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A multi-reference audit framework for LLM translations of the Pali Canon uses embedding drift from a human reference centroid to triage candidates for LLM-judge adjudication, showing drift correlates with major error rates and model-specific differences in the high-drift tail.
The paper derives budget-optimal rules for mixing cheap weak raters and expensive strong raters so that the mean rating is estimated with minimum variance, and shows large cost savings when example difficulty varies.
MELON detects indirect prompt injection in LLM agents by re-executing each step with the user prompt replaced by a neutral task prompt and flagging attacks when the original and masked runs produce similar tool calls.
citing papers explorer
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Fast Byte Latent Transformer
BLT-D, BLT-S, and BLT-DV use block-wise diffusion training and speculative verification to enable parallel byte generation in byte-level LMs, cutting memory-bandwidth cost by over 50%.
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From Outliers to Errors: Auditing Pali-to-English LLM Translations with Multi-Reference Adjudication
A multi-reference audit framework for LLM translations of the Pali Canon uses embedding drift from a human reference centroid to triage candidates for LLM-judge adjudication, showing drift correlates with major error rates and model-specific differences in the high-drift tail.
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Cost-Optimal Active AI Model Evaluation
The paper derives budget-optimal rules for mixing cheap weak raters and expensive strong raters so that the mean rating is estimated with minimum variance, and shows large cost savings when example difficulty varies.
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MELON: Provable Defense Against Indirect Prompt Injection Attacks in AI Agents
MELON detects indirect prompt injection in LLM agents by re-executing each step with the user prompt replaced by a neutral task prompt and flagging attacks when the original and masked runs produce similar tool calls.