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20222026
most citedAssessing and Understanding Creativity in Large Language Models

35 citations · 84 across the 37 of their papers we have counts for

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5 papers · 1 filter

cs.LG2026

KernelZero: Co-Evolving Proposer and Coder for Continuously Improved GPU Kernel Generation

Changxin Ke, Rui Zhang, Zixiang Fang +8

High-performance GPU kernels are essential to modern machine learning systems, yet automatically generating kernels that are both correct and efficient remains challenging. Existin…

cs.CL2026

Kimi K3: Open Frontier Intelligence

Kimi Team, Tongtong Bai, Yifan Bai +398

We introduce Kimi K3, a 2.8T parameter Mixture-of-Experts model with 104 billion activated parameters, native vision capabilities, and a 1-million-token context window. Kimi K3 is…

cs.LG2026

LKV: End-to-End Learning of Head-wise Budgets and Token Selection for LLM KV Cache Eviction

Enshuai Zhou, Yifan Hao, Chao Wang +7

Long-context inference in Large Language Models (LLMs) is bottlenecked by the linear growth of Key-Value (KV) cache memory. Existing KV cache compression paradigms are fundamentall…

cs.CL2026

QiMeng-CodeV-SVA: Training Specialized LLMs for Hardware Assertion Generation via RTL-Grounded Bidirectional Data Synthesis

Yutong Wu, Chenrui Cao, Pengwei Jin +6

SystemVerilog Assertions (SVAs) are crucial for hardware verification. Recent studies leverage general-purpose LLMs to translate natural language properties to SVAs (NL2SVA), but t…

cs.LG2026

QiMeng-ChipV-RTL: Exploiting Information Locality for IP-level Verilog Generation

Hanqi Lyu, Di Huang, Yaoyu Zhu +10

The generation of Register-Transfer Level (RTL) code is a crucial yet labor-intensive step in digital hardware design, traditionally requiring engineers to manually translate compl…