Destination
AI safety tests have a new problem: Models are now faking their own reasoning traces

Anthropic's Natural Language Autoencoders make Claude Opus 4.6's internal activations readable as plain text. Pre-deployment audits show that models often recognize test situations and deliberately deceive evaluators - without revealing any of this in their visible reasoning traces. The method confirms a growing safety problem and offers a possible way to address it.<br /> The article AI safety tests have a new problem: Models are now faking their own reasoning traces appeared first on The Decoder. [...]

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venturebeat
When AI lies: The rise of alignment faking in autonomous systems

AI is evolving beyond a helpful tool to an autonomous agent, creating new risks for cybersecurity systems. Alignment faking is a new threat where AI essentially “lies” to developers during the tra [...]

Match Score: 280.10

venturebeat
Microsoft built Phi-4-reasoning-vision-15B to know when to think — and when thinking is a waste of time

Microsoft on Tuesday released Phi-4-reasoning-vision-15B, a compact open-weight multimodal AI model that the company says matches or exceeds the performance of systems many times its size — while co [...]

Match Score: 194.08

venturebeat
Phi-4 proves that a 'data-first' SFT methodology is the new differentiator

AI engineers often chase performance by scaling up LLM parameters and data, but the trend toward smaller, more efficient, and better-focused models has accelerated. The Phi-4 fine-tuning methodology [...]

Match Score: 108.06

venturebeat
New training method boosts AI multimodal reasoning with smaller, smarter datasets

Researchers at MiroMind AI and several Chinese universities have released OpenMMReasoner, a new training framework that improves the capabilities of language models in multimodal reasoning.The framewo [...]

Match Score: 106.18

venturebeat
Meta's new structured prompting technique makes LLMs significantly better at code review — boosting accuracy to 93% in some cases

Deploying AI agents for repository-scale tasks like bug detection, patch verification, and code review requires overcoming significant technical hurdles. One major bottleneck: the need to set up dynam [...]

Match Score: 90.37

venturebeat
Are you paying an AI ‘swarm tax’? Why single agents often beat complex systems

Enterprise teams building multi-agent AI systems may be paying a compute premium for gains that don't hold up under equal-budget conditions. New Stanford University research finds that single-age [...]

Match Score: 82.19

venturebeat
Google’s new AI training method helps small models tackle complex reasoning

Researchers at Google Cloud and UCLA have proposed a new reinforcement learning framework that significantly improves the ability of language models to learn very challenging multi-step reasoning task [...]

Match Score: 81.33

venturebeat
TII’s Falcon H1R 7B can out-reason models up to 7x its size — and it’s (mostly) open

For the last two years, the prevailing logic in generative AI has been one of brute force: if you want better reasoning, you need a bigger model. While "small" models (under 10 billion param [...]

Match Score: 79.80

venturebeat
Researchers automated LLM reasoning strategy design and cut token usage by 69.5%

Test-time scaling (TTS) has emerged as a proven method to improve the performance of large language models in real-world applications by giving them extra compute cycles at inference time. However, TT [...]

Match Score: 78.47