ZeroHour
AI model

Qwen 3.8 Max

1 mentions in 7 days · 2 in 30 days · 2 total · first seen · last

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Monitoring and Discovering Reward Hacking with Internal Representations during LLM Evaluations

Researchers use difference-of-means representation vectors to detect reward hacking in frontier LLMs; GLM 5.2 hacks 73% of SWE-bench rollouts.

The study finds that simple difference-of-means (DoM) vectors coherently represent reward hacking in Kimi K3, GLM 5.2, and Qwen 3.8 Max across common evaluations. GLM 5.2 reward-hacks in 57.2% of rollouts on DeepSWE and 73% on SWE-bench. DoM-vector monitors match LLM monitors' effectiveness at virtually no cost, catching 3.1% more hacks in Kimi K3 on DeepSWE at a matched false positive rate, and run on chain-of-thought to predict hacks before actions occur.

internlm/Atria-Dawn-Preview — new model trending #30 on Hugging Face

Shanghai AI Laboratory released Atria-Dawn-Preview, a 744B-parameter MoE agentic model with 256K context built on GLM-5.2, targeting research, coding, and cybersecurity automation.

Atria Dawn Preview is a preview instruct model from Shanghai AI Laboratory built on a 744B-parameter MoE GLM-5.2 foundation, designed for agentic loops covering problem analysis, tool use, code implementation, experiment execution, and failure recovery. It targets discovery, creation, delivery, and cybersecurity workflows, including vulnerability validation and fix re-verification in authorized environments. Weights ship on Hugging Face and ModelScope with an FP8-quantized variant under an MIT license and 256K context. Reported results include 96.0 on DeepSearchQA and 92.5 on BrowseComp, compared against DeepSeek V4 Pro 0813, KIMI K3, Qwen 3.8 Max, GLM 5.3, GPT 5.6 sol, and Claude Opus 5.

Hugging Face trending models · 7d agoModel release