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The paper presents TourSynbio-Search, an LLM-driven agent framework for unified protein engineering search across literature and biological databases, powered by the TourSynbio-7B multimodal model with dual PaperSearch and ProteinSearch components.
Yu Su's team trained a frontier Deep Research Agent on an academic budget using 8K synthetic samples and RL, releasing fully open training infrastructure and models from 2B to 35B parameters.
The author tests the Apodex 4B-SFT and 35B mini models, finding the 4B-SFT surpasses other 4B models in multi-hop search tasks without hallucination, and notes the design philosophy of separating answer checking from generation.
LongTraceRL introduces tiered distractor construction and rubric reward design to improve long-context reasoning in language models using reinforcement learning. The method generates multi-hop questions via knowledge graph random walks and uses search agent trajectories to build challenging distractors, with a rubric reward providing entity-level process supervision.