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From the 1 of 14 linked papers with an AI index.

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20242026
most citedERNIE 5.0 Technical Report

2 citations · 2 across the 7 of their papers we have counts for

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cs.CL2026

Memory Augmentation Unlocks Efficient Chain-of-Thought Reasoning

Simeng Zhang, Yilong Chen, Wenyuan Zhang +4

Large language models often rely on Chain-of-Thought (CoT) reasoning to solve complex tasks, but verbose reasoning traces introduce substantial inference overhead. CoT compression…

cs.CL2026

Autonomy-of-Heads: Data-Free Sparse Attention from Frozen Query-Key Geometry

Yehan Yang, Junyuan Shang, Yang Li +3

Long-context LLM inference is bottlenecked by quadratic attention computation and growing KV-cache costs. Existing sparse attention and KV-compression methods typically decide whic…

cs.CL2026

Revisiting Lossy Verification in Speculative Decoding: Mechanisms, Trade-offs, and Failure Modes

Tianyu Wang, Yuxuan Zhou, Wenbin Wang +3

The paper analyzes lossy verification techniques used in speculative decoding for large language models, categorizing them into truncation‑based and collaborative methods, identify…

cs.CL2026

ConSA: Controllable Sparsity in Hybrid Attention via Learnable Allocation

Yao Chen, Yinqi Yang, Junyuan Shang +6

Hybrid architectures combining full attention (FA) and sliding-window attention (SWA) are a promising paradigm for efficient LLM inference. However, existing methods typically rely…

cs.CL2026

Sparse Growing Transformer: Training-Time Sparse Depth Allocation via Progressive Attention Looping

Yao Chen, Yilong Chen, Yinqi Yang +9

Existing approaches to increasing the effective depth of Transformers predominantly rely on parameter reuse, extending computation through recursive execution. Under this paradigm,…

cs.CL20262 cited

ERNIE 5.0 Technical Report

Haifeng Wang, Hua Wu, Tian Wu +432

In this report, we introduce ERNIE 5.0, a natively autoregressive foundation model desinged for unified multimodal understanding and generation across text, image, video, and audio…