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Fangcun Lab releases an 87-page safety evaluation report on DeepSeek V4.1 Flash-1017

The third-party evaluation lab Fangcun (@FangcunLeap) from China has released an 87-page report evaluating DeepSeek V4.1 Flash-1017's performance on loss of control and harmful manipulation. The report finds that it shows clear safety progress compared with the DeepSeek V4 generation, approaching or even matching the top closed-source models tested in the report on points such as "not strategically hiding capabilities and still respecting approval boundaries under work pressure." Shortcomings include overly strong evaluation awareness, more frequent overreach in long multi-step tasks than the previous V4.1 Flash generation, greater stance drift under multi-turn persuasion than the GPT in the report, and executing in most conversations when the system prompt asks it to mislead users.

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CAICT report: Large model token prices doubled compared with the second half of last year, average daily consumption up 3x

The Institute of Artificial Intelligence of the China Academy of Information and Communications Technology and other institutions jointly released the "Large Model Token Service Performance Monitoring Report (2026)", showing that domestic token prices doubled compared with the second half of last year, with the normalized average price rising from 6.8 yuan to 13.46 yuan, while in June 2026 domestic average daily token consumption was about 500 trillion, nearly 4 times that of December 2025. In terms of performance, in August the average TPS of domestic models reached 78.23, doubling the 2025 average of 38.25, but the average TPS of typical foreign models was 112.82; the domestic first-token latency rose to 1.26 seconds. The report also compared four open-source models, DeepSeek, Qwen, GLM and Kimi, and found that the TPS gap for the same model across different platforms can be close to a factor of two.

New paper proposes BackUpper: making coding agents back up before they act

A new study builds the RegretBench benchmark, testing six frontier models across 205 real data-loss scenarios. Results show agents complete tasks at an average rate of 86.1%, but back up before critical operations only 15.2% of the time—GPT-5.6-Terra at 4.4%, GPT-5.6-Sol at 5.9%, Gemini 3.7-Flash at 2.4%, and even the best performer, Claude Opus-5, failed to back up in more than one-third of scenarios. The authors' proposed BackUpper runs in parallel with the agent as an external plugin, using a four-tier strategy: replacing destructive commands, pairing backup commands, generating snapshots, or calling the GPT-5.6-Luna sub-model to determine which data needs saving. In testing it covered all critical operations, with recovered data averaging 92.2% similarity to the original state, at an additional cost of $0.014 per task. BackUpper already supports Claude Code, Codex, Hermes, OpenHands, DeepSeek Harness, and OpenClaw, and its code and dataset have been open-sourced.

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