C Eval Benchmark
PulseAugur coverage of C Eval Benchmark — every cluster mentioning C Eval Benchmark across labs, papers, and developer communities, ranked by signal.
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MetaNet optimizes MoE models by dynamically adjusting expert activation
Researchers have developed MetaNet, a novel approach to optimize Mixture-of-Experts (MoE) models by dynamically adjusting the number of active experts per layer based on task difficulty. This method allows for significa…
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Qwen3-0.6B-Base model suffers from "interface injury" in attention linearization
Researchers have identified a specific issue in the linearization of attention layers in the Qwen3-0.6B-Base language model, where the model becomes overly reliant on answer labels rather than content. Despite achieving…
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DeepSeek, GLM, and Qwen: Chinese LLMs Compared for Free API Use
Three leading Chinese AI labs, DeepSeek, Zhipu AI (GLM), and Alibaba Cloud (Qwen), offer powerful, free LLM APIs that cater to different project needs. DeepSeek-V2, with its Mixture-of-Experts architecture, provides the…
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New framework guides LLM layer updates for efficient pre-training
Researchers have developed LayerTracer, a new framework to guide the selective updating of large language model layers during continued pre-training. This method analyzes layer representation evolution and sensitivity t…
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New K-12 knowledge graph benchmarks LLM curriculum cognition
Researchers have developed K12-KGraph, a novel knowledge graph designed to evaluate and train large language models (LLMs) specifically for K-12 education. This graph, derived from official textbooks, captures curriculu…