qwen2-5-14b-e831b0aa·2 events·first seen Aliases: Qwen2.5-14B
Researchers introduce PRISM, a single-layer logistic probe over LLM hidden states that distinguishes physically grounded danger (e.g., unsafe robot actions) from ordinary text-level content danger, showing these are separable signals in representations across multiple model families. PRISM achieves 86.2–87.7% accuracy on SafeAgentBench with substantially lower false-positive rates than same-scale LLM judges, which over-block safe tasks at 24.7–39.0% FPR. The authors also release PhysicalSafetyBench-1K (PSB-1K), a 1,000-pair contrastive benchmark for evaluating physical-risk detection without relying on explicit harm keywords. The work is relevant to safety in embodied AI and agentic systems where linguistic safety filters are insufficient.
Researchers introduce CCPoetry-49K, a 49,404-pair instruction dataset for classical Chinese poetry appreciation, decomposed into term interpretation, semantic interpretation, and emotional inference subtasks. They fine-tune Qwen2.5-14B using LoRA to produce PoetryQwen, achieving a 9.7% improvement over the baseline on the CCL25-Eval Task 5 benchmark (0.757 vs 0.690). The work addresses a gap in domain-specific LLM adaptation for classical Chinese literary tasks.