FTibSuite: A Comprehensive Resource Suite for Tibetan Vision–Language Modeling (2026)
Tibetan has been largely left out of recent progress in vision–language models. It lacks image–text data, multimodal instruction sets and standardized benchmarks, so Tibetan VLM results are hard to reproduce or compare. The authors address this with FTibSuite, which has three parts:
Reinforcement Learning with Semantic Rewards Enables Low-Resource Language Expansion without Alignment Tax (2026)
When LLMs are adapted to a poorly supported language, they often lose ground on skills they already had. This is known as the alignment tax. The authors blame supervised fine-tuning (SFT): its token-level likelihood objective forces the model to copy small, skewed datasets, which overwrites pretrained knowledge. They…
TingIS: Real-time Risk Event Discovery from Noisy Customer Incidents at Enterprise Scale (2026)
Large cloud-native and fintech services sometimes fail in ways that internal monitoring does not catch. Complaints from users can expose these failures early, but the complaints are colloquial, noisy, arrive in heavy streams and span many business lines. TingIS is an end-to-end system from Ant Group that turns this…

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Updated 2 November 2026