LLM | doing the math for you

Unusual Language Artifacts from Noisy LLM Training Data

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AI glitches: how noisy training data – typos, OCR errors, and rare glitch tokens produce baffling, humorous or harmful LLM outputs.

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Apple shows non-fine-tuned LLMs can fuse local sensor summaries for multimodal activity recognition—boosting privacy and modularity.

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Study shows LLMs can, via targeted interventions, access and report internal activations-evidence of nascent introspective awareness.

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AI’s ‘yes’-heavy responses risk softening learning; we need AI that balances affirmation with challenge, correction, and guidance.

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Moonshot AI’s Kimi K Thinking: reasoning-focused open MoE boosting China’s AI momentum with efficient, deployable, multipolar rivalry