Microsoft released the model Microsoft-Decision-1 for rapid decision scoring, now available on Microsoft Foundry and callable via OpenRouter. The model is post-trained from Qwen3.5-9B, outputs calibrated probability scores for fixed options, achieves the highest accuracy in comparisons across 36 benchmarks, and has a P50 latency about 35 times faster than GPT-6 Sol. Across 5,250 requests on 11 benchmarks, it refuses harmful behavior while maintaining utility, with an average decision flip rate of 1.3% under 8 perturbations.
ByteDance's Seed team found that the accuracy of the DeepSeek-V4 series in long-context retrieval fluctuates periodically with the position of the target information in the input, and attributed the cause to its chunked KV Cache compression. In a needle-in-a-haystack test with 128K tokens and about 16,000 key-value pairs, the maximum accuracy gap between different positions for DeepSeek-V4-Flash-Base was 40.2 percentage points, and for DeepSeek-V4-Pro-Base it was 34.8 percentage points; the gap narrowed after post-training.
Oct 7Wed
Wednesday · 3 items
TThe Asia Business DailyThe Asia Business Daily · Attention4848
Epoch AI released the InnovationEval benchmark, testing whether frontier AI can independently discover machine learning algorithm innovations that are new to it, benchmarked against the Self-Distillation Policy Optimization (SDPO) proposed in a human paper. The task requires an agent to start from GRPO and develop new post-training improvement methods, improving short-answer and coding task performance on Qwen3-8B, with a budget of 3,000 GPU hours. Results show that GPT-5.6 Sol and Claude Fable 5 both did not come close to SDPO: Sol's method, adjusted for the same wall-clock time, reached only 15% of SDPO's gain, or about 35% under lenient scoring; Fable 5 showed no measurable improvement. Both merely reused known techniques. Epoch AI also estimates that, based on AI chips shipped before 2027, if cheap models are run, the world could support nearly 2 billion concurrent AI agents, while running frontier models would support about 30 million to 170 million; the cost of reaching a fixed AI performance level has fallen about 47% per quarter over the past three years.
中国实验室今年夏天发布 Kimi K3、Qwen 3.8-Max、GLM-5.3 等开源模型,能力仅略落后于美国最强模型,引发开源模型是否会被滥用的争论。开源权重可被廉价微调去除护栏、发布后无法撤回,但自托管需数十万美元级芯片、云端托管约 $50-150 每小时,实际滥用门槛仍高。中国 TC260 已在 AI 风险框架中点名开源模型安全机制可能被移除或绕过,并着手起草开源 AI 安全标准。