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20182026
most citedNEFTune: Noisy Embeddings Improve Instruction Finetuning

14 citations · 20 across the 17 of their papers we have counts for

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5 papers · 1 filter

cs.CY2026

Reducing Catastrophic Risk from AI with Systematic Monitoring and Evaluation of Rogue AI Progression

T. Bauer, W. P. Kegelmeyer, E. Begoli +15

This article presents a structured framework of behavioral indicators that may signal progression toward potentially catastrophic threats from artificial intelligence systems. We a…

cs.LG2026

Watermarking for Proprietary Dataset Protection

John Kirchenbauer, Brian R. Bartoldson, Bhavya Kailkhura +1

A growing body of literature suggests that training data membership inference problems are fundamentally hard tasks in modern language modeling settings. We argue that output water…

cs.CL2026

End-to-End Context Compression at Scale

Ang Li, Sean McLeish, Haozhe Chen +12

Long-context language model inference is bottlenecked by memory, as the KV cache grows with context length. Recent techniques to compress the KV cache fall short: they either degra…

cs.LG2026

LongCoT: Benchmarking Long-Horizon Chain-of-Thought Reasoning

Sumeet Ramesh Motwani, Daniel Nichols, Charles London +17

As language models are increasingly deployed for complex autonomous tasks, their ability to reason accurately over longer horizons becomes critical. An essential component of this…

cs.CL2026

Multi-Token Prediction via Self-Distillation

John Kirchenbauer, Abhimanyu Hans, Brian Bartoldson +3

Existing techniques for accelerating language model inference, such as speculative decoding, require training auxiliary speculator models and building and deploying complex inferen…