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From the 1 of 23 linked papers with an AI index.

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20242026
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cs.CL2026

Follow the Latent Roadmap: Navigating Revocable Decoding for Diffusion LLMs with Anchor Tokens

Yizhen Yao, Qinglin Zhu, Runcong Zhao +4

The paper introduces Anchor Supervised Revocable Decoding (ASRD), a training‑free method that uses temporally consistent anchor tokens to guide and verify generation in diffusion l…

cs.CL2026

SSA: Sparse Sparse Attention by Aligning Full and Sparse Attention Outputs in Feature Space

Zhenyi Shen, Junru Lu, Lin Gui +4

Sparse attention reduces the quadratic complexity of full self-attention but faces two challenges: (1) an attention gap, where applying sparse attention to full-attention-trained m…

cs.CL2026

Stop the Flip-Flop: Context-Preserving Verification for Fast Revocable Diffusion Decoding

Yanzheng Xiang, Lan Wei, Yizhen Yao +8

Parallel diffusion decoding can accelerate diffusion language model inference by unmasking multiple tokens per step, but aggressive parallelism often harms quality. Revocable decod…

cs.CL2026

Beyond RAG for Agent Memory: Retrieval by Decoupling and Aggregation

Zhanghao Hu, Qinglin Zhu, Runcong Zhao +4

Standard Retrieval Augmented Generation (RAG) is poorly matched to agent memory. Unlike large heterogeneous corpora, agent memory forms a bounded and coherent interaction stream in…

cs.CL2026

Detecting Contextual Hallucinations in LLMs with Frequency-Aware Attention

Siya Qi, Yudong Chen, Runcong Zhao +6

Hallucination detection is critical for ensuring the reliability of large language models (LLMs) in context-based generation. Prior work has explored intrinsic signals available du…

cs.CL2025

Beyond Perplexity: Let the Reader Select Retrieval Summaries via Spectrum Projection Score

Zhanghao Hu, Qinglin Zhu, Siya Qi +3

Large Language Models (LLMs) have shown improved generation performance through retrieval-augmented generation (RAG) following the retriever-reader paradigm, which supplements mode…