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By reallocating KV cache resources based on attention head specialization, SGD-KV cuts memory usage by 75% while handling contexts up to 1M tokens with state-of-the-art accuracy.
Truthfulness in NLP research has surged to 37% of papers by 2026, reflecting a critical shift in focus towards safety and alignment in generative systems.
Semantic watermarks, embedded via AMR, survive paraphrasing attacks that obliterate token-level watermarks.
Current red-teaming efforts miss the forest for the trees: ARES reveals that safety failures often stem from a systemic breakdown between the LLM *and* the reward model, not just the LLM itself.
