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Paper2LLM++ introduces a framework that turns the growing literature on LLM failure modes into a stream of supervision for continual self-improvement, verifying whether reported limitations still exist and integrating updates via a try-evaluate-commit procedure that preserves prior gains across sequential research papers.
DeskForge introduces a controllable desktop environment that composes real applications to generate 1.2M densely annotated desktop observations (DeskForge-1M) for training computer-use agents. Fine-tuning four VLMs on this data yields significant gains on GUI grounding benchmarks (e.g., Qwen3.5-4B improves 11.51 points on ScreenSpot-Pro) and long-horizon task completion.
This paper introduces techniques for the second MLC-SLM Challenge, including random leading-silence cropping and synthetic data generation to enhance multilingual conversational speech tasks, achieving improved accuracy and reduced error rates.