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RAMP uncovers that agentic models can lose up to 80% of their effectiveness in complex, real-world workflows, a stark contrast to their performance in isolated benchmarks.
Forget specialized architectures: StepAudio 2.5 proves a single audio-language foundation, shaped by RLHF, can dominate ASR, TTS, and real-time dialogue simultaneously.
LLM agent skills are needlessly brittle and insecure: SkCC compiles them into a portable, hardened format that boosts performance by 50% and proactively blocks attacks.
