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This work studies whether task-specialist models should be handled by separate task-specialist models or by a single model trained through multi-task training, sequential updates, or model merging, and concludes with practical guidelines for selecting fine-tuning strategies in real-world settings.
It is found that SFT consistently improves text-turn success, and that overall next-turn success increases for every model under gold-history evaluation, but these improvements do not transfer to autonomous workflow execution.
Limited controlled evidence exists on how training data, adaptation method, and model scale jointly affect tool-calling performance in language-model agents. We evaluate supervised fine-tuning (SFT) with LoRA, reinforcement learning (RL) via Group Relative Policy Optimization (GRPO), and SFT followed by GRPO across six...
This work introduces VisEditBench, a benchmark of 1,395 human-annotated visualization code-editing tasks grounded in realistic visualization workflows and failure cases, and proposes VisEditAgent, a render-grounded editing framework that iteratively generates, executes, validates, and refines candidate edits.
SpawnLoRA is evaluated on Phi-tiny-MoE-instruct and OLMoE-1B-7B across multiple mixture settings and finds that it effectively reduces negative transfer compared with standard and rank-adaptive LoRA, demonstrating that structural separation inside experts provides benefits beyond routing or rank expansion alone.
This work introduces MemeCULT-1K, a multilingual benchmark of 1,000 South Asian memes in Bengali, English, and Hindi, where each meme is paired with a cultural context note and three human-written explanations, along with a supplementary set of 54 Bengali regional dialect memes.
DSAgentBench is introduced, the first benchmark to evaluate whether agents can automate full data-science workflows inside real computer environments, and reveals a substantial capability gap between current agentic systems and real data-science workflows.
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