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Slash LLM evaluation costs by over 270x without sacrificing accuracy, revealing that even top open-weight models struggle to outperform simple baselines on AML transaction fraud detection.
A deployable system for streaming Thai ASR that lets a user steer its vocabulary at decode time, without retraining is presented, which lifts keyword recall from 16.6% to 20.7% at no accuracy cost and negligible overhead.
It is found that synthetic-only training can be competitive, and the 0.9B-parameter PaddleOCR-VL-1.6 is adapted into Wayu-Paxa-OCR-Zero, a Thai OCR model adapted without OCR labels from real Thai document pages, showing that synthetic-only training can be competitive.
The resulting 82M-parameter model, Wayu-Paxa-TTS-Edge, enables on-device Thai TTS without reference audio and achieves the lowest pause-placement error and intra-word pause rates among the three systems.
Current LLM safety measures are critically vulnerable to attacks grounded in Thai cultural nuances, as demonstrated by a new benchmark showing higher attack success rates compared to general Thai-language attacks.
Forget fancy prompt optimization: carefully crafted rule-based role prompts with enforced function calls dramatically improve the performance of tool-augmented LLM dialogue agents.