AI Text Detectors Miss Up to 48% of Style-Imitated Writing

Epoch AI ran style-imitation tests against three major AI text detectors (Pangram, GPTZero, and Originality.ai), and up to 18% of AI-generated text slipped past detection. The miss rate climbed to 48% for formal writing such as scientific papers, underscoring that detectors are least reliable precisely in the domain where they are supposed to matter most.

Style imitation is a technique that, instead of outputting AI-generated text as-is, learns a human author's style, vocabulary, and rhythm and then regenerates the text. Because detectors rely on 'AI-like statistical features' as their cues, shifting the output distribution toward human-like text makes discrimination structurally difficult. In high-stakes settings such as academic publishing and hiring, this serves as yet another reminder of the risk of treating detection results as the sole basis for decisions.

X-ray AI Is 'Confident Even When Wrong,' RadLE 2.0 Reveals

A new benchmark for measuring the safety of radiology AI, RadLE 2.0, has been released. The benchmark tests whether AI can recognize situations where it should defer the diagnosis to a human, and it also evaluates confidence calibration on incorrect answers.

Many of the tested models showed a strong tendency to express high confidence even while producing incorrect findings, revealing that they lack the basic ability to dial down confidence when wrong. Human radiologists still lead on both misdiagnosis and overconfidence rates, and AI will need to acquire the ability to 'know when to stay quiet' before moving toward autonomous diagnosis. In debates over deploying medical AI in practice, designing how uncertainty is expressed looks set to become the next challenge, beyond raw performance.

Shikigami Runs Multiple AI Coding Agents in Parallel with git worktrees

A desktop app called 'Shikigami' was featured on Hacker News. It runs multiple AI coding agents (Claude Code, Codex, etc.) simultaneously on the same repository, assigning each to its own independent git worktree to avoid file-editing conflicts. Each session is PTY-based and resumable, so progress is preserved even during long-running agent executions.

It also integrates a Monaco editor for diff viewing, git history, and per-line blame, letting you review code while agents are running. It is currently available free of charge for macOS (signed dmg, Apple Silicon / Intel) and Linux (AppImage), with the source code kept closed. The developer is soliciting feedback from people who run multiple agents as part of their daily workflow.

Signs of Qwen3.8 Approaching, Local LLM Crowd Calls for 'VRAM Preparation'

On Reddit's r/LocalLLaMA, word spread that Alibaba's Qwen team had made a post to the effect that they would be 'running something soon.' Posts hinting at the release of Qwen3.8, following the recent Qwen3.5-397B, kept coming, and the community saw semi-joking calls to secure VRAM for inference.

Specs, size, and license remain unconfirmed at this point, so we will have to wait for the official model card and technical report. Given that the recent Qwen3.5 was released as a massive 397B MoE, the speed at which quantized versions and derivative models roll out will likely be key for local operation as well.

Thinking Machines' Strong Tinker Results Were 'Based on Qwen3-235B'

Also on r/LocalLLaMA, attention has focused on the fact that the strong results recorded by Thinking Machines' reinforcement learning platform 'Tinker' on Bridgewater's financial document classification task used Qwen3-235B, not Inkling, as the base model.

Summarizing the posts: the Inkling model itself scores 97.1% on AIME 2026, but trails GLM 5.2, DeepSeek V4 Pro, and the Kimi family on HLE and SWEBench Pro. On IFBench (instruction following), however, it hits 79.8%, second only to Nemotron 3 Ultra. The Bridgewater case study was produced with Qwen3-235B two weeks before Inkling's debut, so the analysis goes, the claim that 'Tinker can shape open models to expert level' is well supported, but the claim that 'Inkling is superior as a base' currently lacks public backing.

Within the community, those with hands-on experience fine-tuning large-scale MoEs are split between voices that praise Inkling's IFBench score and 41B active-parameter configuration, and voices that favor the more thoroughly documented Qwen and Kimi models.

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