Shenzhen Research Institute of Big Data

Theory-of-mind benchmarks usually ask story questions about what others believe, and a correct answer does not show the model can use that judgment in a task. This evaluation extends household collaboration and calendar scheduling with asymmetric information and separates world understanding, information access, beliefs, decisions and execution into independent interfaces. On matched scheduling cases Claude recovers all agent beliefs yet produces subjectively optimal plans in only 67.9% of cases, while supplying beliefs raises Qwen-27B household task success from 55.0% to 75.0%.

Theory-of-mind benchmarks usually ask story questions about what others believe, and a correct answer does not show the model can use that judgment in a task. This evaluation extends household collaboration and calendar scheduling with asymmetric information and separates world understanding, information access, beliefs, decisions and execution into independent interfaces. On matched scheduling cases Claude recovers all agent beliefs yet produces subjectively optimal plans in only 67.9% of cases, while supplying beliefs raises Qwen-27B household task success from 55.0% to 75.0%.

TCM language models have been trained on under 1GB of text and handle text alone, while TCM diagnosis itself draws on looking, listening, smelling and pulse-taking. ShizhenGPT pairs more than 100GB of text from 3,256 TCM books and web sources with over 200GB of multimodal data, including 1.2M annotated images, 200+ hours of audio and pulse, smell and ECG signals, through two-stage pretraining and instruction tuning. On five 2024–2025 TCM exams ShizhenGPT-32B averages 78.1 against 84.0 for DeepSeek-R1-671B, and on a 7,204-question TCM vision benchmark it scores 63.6 against 61.4 for Gemini-3-Pro.

TCM language models have been trained on under 1GB of text and handle text alone, while TCM diagnosis itself draws on looking, listening, smelling and pulse-taking. ShizhenGPT pairs more than 100GB of text from 3,256 TCM books and web sources with over 200GB of multimodal data, including 1.2M annotated images, 200+ hours of audio and pulse, smell and ECG signals, through two-stage pretraining and instruction tuning. On five 2024–2025 TCM exams ShizhenGPT-32B averages 78.1 against 84.0 for DeepSeek-R1-671B, and on a 7,204-question TCM vision benchmark it scores 63.6 against 61.4 for Gemini-3-Pro.

The authors show that lower reconstruction loss on calibration data in weight-only PTQ does not guarantee better model performance and can even degrade it on the same calibration data; they propose DRQ, which minimizes worst-case reconstruction loss over a constrained set of input activation distributions by updating only the integer codes of quantized weights while keeping bit width, grouping, scales, and zero points fixed, improving perplexity and average task accuracy across six PTQ methods and both dense and MoE models without adding inference overhead.

The authors show that lower reconstruction loss on calibration data in weight-only PTQ does not guarantee better model performance and can even degrade it on the same calibration data; they propose DRQ, which minimizes worst-case reconstruction loss over a constrained set of input activation distributions by updating only the integer codes of quantized weights while keeping bit width, grouping, scales, and zero points fixed, improving perplexity and average task accuracy across six PTQ methods and both dense and MoE models without adding inference overhead.