UltraEP is the first exact-load real-time expert balancer for large-EP MoE training and serving on rack-scale nodes, reaching 94.3% of ideal throughput and 1.49x over no-balancing.
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GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models
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We present GLM-4.5, an open-source Mixture-of-Experts (MoE) large language model with 355B total parameters and 32B activated parameters, featuring a hybrid reasoning method that supports both thinking and direct response modes. Through multi-stage training on 23T tokens and comprehensive post-training with expert model iteration and reinforcement learning, GLM-4.5 achieves strong performance across agentic, reasoning, and coding (ARC) tasks, scoring 70.1% on TAU-Bench, 91.0% on AIME 24, and 64.2% on SWE-bench Verified. With much fewer parameters than several competitors, GLM-4.5 ranks 3rd overall among all evaluated models and 2nd on agentic benchmarks. We release both GLM-4.5 (355B parameters) and a compact version, GLM-4.5-Air (106B parameters), to advance research in reasoning and agentic AI systems. Code, models, and more information are available at https://github.com/zai-org/GLM-4.5.
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- abstract We present GLM-4.5, an open-source Mixture-of-Experts (MoE) large language model with 355B total parameters and 32B activated parameters, featuring a hybrid reasoning method that supports both thinking and direct response modes. Through multi-stage training on 23T tokens and comprehensive post-training with expert model iteration and reinforcement learning, GLM-4.5 achieves strong performance across agentic, reasoning, and coding (ARC) tasks, scoring 70.1% on TAU-Bench, 91.0% on AIME 24, and 64.2% on SWE-bench Verified. With much fewer parameters than several competitors, GLM-4.5 ranks 3rd ove
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representative citing papers
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citing papers explorer
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User-turn generation reveals that LLMs' interaction awareness is largely decoupled from task accuracy, remaining near zero in deterministic settings even as accuracy scales to 96.8% on GSM8K.
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Can Agents Generalize to the Open World? Unveiling the Fragility of Static Training in Tool Use
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PRISM: A Benchmark for Programmatic Spatial-Temporal Reasoning
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SCI-PRM: A Tool Aware Process Reward Model for Scientific Reasoning Verification
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OpenClawBench: Benchmarking Process-side Anomalies in Real-world Agent Execution Trajectories
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Self-ReSET: Learning to Self-Recover from Unsafe Reasoning Trajectories
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Towards Knowledgeable Deep Research: Framework and Benchmark
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Token-Level LLM Collaboration via FusionRoute
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SCALER:Synthetic Scalable Adaptive Learning Environment for Reasoning
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SLAT: Segment-Level Adaptive Trimming for Efficient CoT Reasoning
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Agentic Reasoning for Large Language Models
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