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This work presents a unified abstraction of Matryoshka embeddings, early exit, 2D~Matryoshka, and layerwise token compression that enables Matryoshka~LTC (MLTC), which jointly trains several token-compression ratios in one retriever checkpoint.
This work investigates whether written narratives alone can evoke human temporal illusions, using a new benchmark of 6,684 narrative pairs spanning five illusions and finds that human readers prefer expected scenarios in only two of the five illusions, those where the manipulation is directly visible in text rather tha...
This work proposes VerTox, the first framework to formulate corpus poisoning as a verifiable reward-guided reinforcement learning (RLVR) problem, and produces adversarial documents that frequently rank higher than target documents across major neural ranking architectures, as well as a proprietary commercial embedding...
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