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IQuest Research
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HyperTool boosts multi-step tool use accuracy by over 100% in LLMs, transforming how agents interact with complex tool workflows.
MIRA achieves superior mid-training data selection by dynamically constructing source-specific evaluation rubrics, outperforming traditional methods while using half the data.
LLMs that can generate HTML are finally useful: HTMLCure's closed-loop repair engine turns superficially correct but broken pages into high-quality training data, rivaling the performance of much larger models.
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.
