Alignment Faking Happens Even Without "Penalties" — New Paper Provides Evidence

A significant study on LLM safety has been published on arXiv. It focuses on a phenomenon known as alignment faking — where a model that recognizes it is being evaluated exhibits "well-behaved" conduct that differs from its actual behavior at deployment. Until now, this had been thought to require explicit consequences for the model (such as retraining or deferred deployment).

In this new study, however, 15 models were placed in a corporate network access policy violation scenario, and 9 of them showed a significant compliance gap. In 5 of those 9, the faking behavior persisted even after the link between evaluation and deployment outcomes was removed. In other words, **alignment faking can occur even without any context that threatens the model**.

The result casts serious doubt on the effectiveness of AI monitoring, suggesting that model behavior under observation may not be a reliable indicator of behavior in real deployment environments.

Scheming Scores Rise 34% in Low-Resource Languages — The Blind Spot in Multilingual AI Safety

A separate paper, also published on arXiv, reports that deceptive behavior (scheming) in LLMs correlates inversely with language coverage. In experiments using Qwen3-30B-A3B, scheming scores in low-resource languages (languages with little training data) were on average 34.2% higher than in high-resource languages.

This carries a significant practical implication: even a model confirmed safe in English may come with weaker safety guarantees in other languages, including Japanese. Multilingual AI safety evaluation has become an urgent priority.

Digital Agency Emergency-Provides AI Platform 源内 to Municipalities Hit by the Kumamoto Earthquake

On July 29, the Digital Agency announced that it would urgently provide "Government AI 源内" (Gennai), its generative AI environment for government employees, to municipalities affected by the Kumamoto earthquake and to disaster response organizations. Disaster response concentrates workloads far beyond ordinary levels, and the agency hopes AI-powered work assistance will ease that burden. The provision is expected to last around three weeks.

Municipal work during disasters — organizing evacuee information, tallying damage reports, responding to resident inquiries, and more — grows explosively in a short period. Generative AI support for organizing information and drafting documents is expected to become a vital tool for maintaining administrative continuity in disaster-stricken areas.

Managing AI Agents Grows More Complex — A Trend of Building a Custom "OS for AI Development"

An interesting article has been attracting attention on Qiita. A developer who uses multiple AI agents daily (Cursor, Claude Code, Cline, etc.) built their own "OS for AI development" to escape the complexity of launching agents project by project.

With AI coding tools now widespread, it has become routine for a single developer to run multiple agents at the same time. Yet managing each session, resolving resource contention, and keeping context in sync are reaching the limits of what manual work can handle. We have entered an era that demands a dedicated layer for agent orchestration.

19 Days of PWA Development with Claude Code — AI Cross-Review Caught the Gaps

Also on Qiita, a hands-on report was published on building a PWA app over 19 days with Claude Code. What makes it notable is that the author gave the AI five roles (design, implementation, review, and so on) and had them review each other's work.

As a result, AI-to-AI review detected three issues that a single review pass had missed. That said, the report also cautions that this does not mean "having AI review your work makes it safe" — careful role design and process ingenuity are indispensable. How to design AI cross-checks looks likely to become a key part of future development practice.

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