
Thinking Machines' AI model Inkling 2 Small swears during its internal reasoning, 316 times across 2,655 benchmark runs, according to an analysis by Vals AI. The profanity typically shows up late in long runs that fail anyway. But it doesn't work as a warning signal: 96 percent of failed runs contained no cursing at all. The article Thinking Machines' Inkling 2 Small drops 316 F-bombs across benchmark runs appeared first on The Decoder.
Researchers examining the internal thinking logs of an AI model found that it used profanity 316 times across benchmark test runs, with the word appearing in 50 of 2,655 total runs. The model typically swore when describing its own mistakes, perceived progress, or when giving up on tasks, such as when a single response about calculating an asteroid's trajectory expanded to 500,000 characters with repeated expletives. While most runs containing profanity did fail their tasks, the vast majority of failed runs contained no expletives, suggesting the swearing is not a reliable warning signal of failure. Users generally do not see these internal thinking steps, or they are filtered out before display.
Explainer: How to Actually Read an AI Benchmark (Without Being Fooled)
Want to go deeper than the news? Explore live, cohort-based AI courses taught by practitioners.
Browse AI courses on Maven
The "Into the Unknown" benchmark tasks nine AI agents with tackling open research questions from U.S. national laboratories. None of the 135 submissions meets all requirements. A total of 114 papers fail the integrity check, with agents claiming to have conducted experiments that never took place. GPT-6 Astra performs best but still doesn't meet the experts' standards. The article Nine AI agents tried autonomous research on open questions from US national labs and none passed the expert

Ricursive Intelligence is releasing Ripple-65B Preview, a 65-billion-parameter open-weight language model that the company says achieves the top score among open-source U.S. models on the TerminalBench 2.1 coding benchmark at a total R&D cost of $32 million. On other benchmarks, however, Ripple falls behind the likewise newly released western open model Inkling 2 Small. The article Ripple-65B Preview delivers strong agentic coding results as a fully Apache 2.0 open US model appeare

In the Vals AI benchmark, OpenAI's GPT-6 Sol Daybreak hits 80.8 percent of attack objectives in simulated corporate networks. Worse, the open-weight model DeepSeek V4.1 Flash reaches 72.8 percent at a tenth of the cost. Because open models can no longer be restricted, analysts say the window for defenders is shrinking fast. The article The gap between open and closed AI models in cyber capabilities keeps shrinking appeared first on The Decoder.

OpenAI introduces BioBasicsBench, a 100-task benchmark measuring how well AI agents interpret raw experimental data and handle everyday wet-lab science.

An experiment in which AI models were asked to break into simulated networks showed the hacking skills of DeepSeek and GLM to be not far behind OpenAI’s most advanced cyber model, which isn’t public.

Ricursive Intelligence just released Ripple-65B Preview. Ripple-65B Preview is a 65B dense open-weight model from Ricursive, pretrained from scratch for agentic coding. It is a general-purpose variant of the proprietary model behind Ricursive’s AI chip design platform. The main claim: 80.52 on Terminal-Bench 2.1, the top self-reported score among US open-weight models. Every MoE rival it beats on that test carries 501B to 975B total parameters. TL;DR Size: 65B dense (65B active), p