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Building agents that can reliably automate complex, multi-step workflows over local files and tools just got a whole lot easier.
By dynamically injecting frequency-aware n-gram features, X-GRAM achieves state-of-the-art accuracy with smaller embedding tables, offering a practical path to scaling memory-augmented architectures.
Noise-robust visual prompts can improve model performance by over 11% without increasing inference costs.
Industrial code generation gets a reasoning boost: InCoder-32B-Thinking leverages error-driven feedback and a code world model to achieve top-tier performance on complex hardware-aware tasks.
Code LLMs can achieve SOTA performance in agentic tasks by explicitly modeling the dynamic evolution of software logic across different training stages.
A new 32B code LLM trained specifically for industrial tasks crushes existing models on specialized domains like chip design and GPU kernel optimization, while remaining competitive on general coding benchmarks.
