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TwinKV, a training-free method that discounts value energy by nonlocal post-RoPE key frequency, is introduced, showing that modest frequency corrections can change retention and answering outcomes, with effects that depend on the model and task.
TwinKV is introduced, a training-free, attention-free redundancy signal that detects whether a token's key has a near-duplicate elsewhere in context, challenging the premise behind dominant eviction methods.
SemTrace is introduced, a source-grounded semantic watermark for detecting whether a generated review was influenced by a known protected manuscript copy, and targets model-agnostic, assigned-copy exposure detection while keeping the watermark semantically tied to the source document.
A unified efficiency analysis is presented showing that extending the speculation horizon can reduce rather than improve speedup when the marginal acceptance probability falls below the relative drafting cost, and SparseSpec-L, a training-free self-speculative decoding framework for long-context inference is introduced...
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