Alibaba's Qwen team built HopChain to fix how AI vision models fall apart during multi-step reasoning
When AI models reason about images, small perceptual errors compound across multiple steps and produce wrong answers. Alibaba’s HopChain framework tackles this by generating multi-stage image questions that break complex problems into linked individual steps, forcing models to verify each visual detail before drawing conclusions. The approach improves 20 out of 24 benchmarks.
AI safety research firm Lyptus Research has published a new study on the offensive cybersecurity capabilities of AI models. The study is based on the METR time-horizon method and involved testing with ten professional security experts.
According to the findings, AI's offensive cyber capability has been doubling every 9.8 months since 2019, and since 2024, that pace has accelerated to every 5.7 months. Opus 4.6 and GPT-5.3 Codex can now solve tasks at a 50 percent success rate with a two-million-token budget that would take human experts roughly three hours to complete.
Offensive cyber capability of AI models over time: From GPT-2 (2019) to Opus 4.6 and GPT-5.3 Codex (2026), the time horizon grew from 30 seconds to roughly three hours. The doubling time accelerated from 9.8 months (since 2019) to 5.7 months (since 2024). | Image: Lyptus Research
Performance jumps significantly with higher token budgets: GPT-5.3 Codex goes from a 3.1-hour to a 10.5-hour time horizon when given ten million tokens instead of two million. The researchers say this suggests they're still underestimating the actual rate of progress. Open-source models trail their closed-source counterparts by about 5.7 months.
Alibaba's Qwen team makes AI models think deeper with new algorithm
Reinforcement learning hits a wall with reasoning models because every token gets the same reward. A new algorithm from Alibaba’s Qwen team fixes this by weighting each step based on how much it shapes what comes next, doubling the length of thought processes in the process.
