Great novels create immersive worlds with rich character arcs, well-structured plots, and nuanced writing styles. However, current novel generation methods often rely on brief, simplistic story outlines and generate details using plain, generic language.To bridge this gap, we introduce the task of Imitative Novel Generation, which requires the generated novels to imitate the distinctive features of the original work, including understanding character profiles and world views, predicting plausible plot developments, and writing concrete details using vivid, expressive language.To achieve this, we propose WriterAgent, a novel generation system designed to master the core aspects of literary imitative.WriterAgent is trained through a curriculum learning paradigm, progressing from low-level stylistic mastery to high-level narrative coherence. Its key tasks include language style learning, character modeling, plot planning, and stylish writing, ensuring comprehensive narrative control.To support this, WriterAgent leverages the WriterLoRA framework, an extension of LoRA with hierarchical and cumulative task-specific modules, each specializing in a different narrative aspect. We evaluate WriterAgent on multilingual classics like Harry Potter and Dream of the Red Chamber, demonstrating its superiority over baselines in capturing the target author’s settings, character dynamics, and writing style to produce coherent, faithful narratives.We hope this work inspires literary creativity in NLP: WriterAgent.

Recent work reports large gains for LLM agents on low-budget molecular optimization, with 2-3 times AUC improvements over reinforcement-learning and genetic-algorithm baselines on the PMO benchmark and similar gains on protein-targeted docking benchmarks. We investigate whether these gains reflect genuine optimization or benchmark artifacts, by re-evaluating a representative LLM agent under controlled blinding protocols and against a trivial random-walk baseline. We find that on PMO at the 50-call budget, the agent’s apparent advantage collapses below classical baselines once task names and curated reference compounds are removed from its prompt; a 2 times 2 ablation shows that retrieval from a small reference dictionary, not iterative optimization, is the dominant driver of the headline number. On a new protein-targeted lead-optimization benchmark with an AutoDock Vina composite oracle, a single-edit halogen/methyl random-walk mutator outperforms the LLM agent, a graph-based GA, and a non-iterative GPT baseline on every aggregate metric—a property of the oracle landscape under tight budgets, not of LLM agents per se. We release both protocols (name-blinded PMO scoring; random-walk lower bound for Vina-based protein benchmarks) as standard sanity checks for future LLM-agent papers in molecular optimization.

Automatic generation of graphical layouts is crucial for many real-world applications, including designing posters, flyers, advertisements, and graphical user interfaces. Given the incredible ability of Large language models (LLMs) in both natural language understanding and generation, we believe that we could customize an LLM to help people create compelling graphical layouts starting with only text instructions from the user. We call our method TextLap (text-based layout planning). It uses a curated instruction-based layout planning dataset (InsLap) to customize LLMs as a graphic designer. Human annotators are asked to build a benchmark to evaluate different layout planning models. We demonstrate the effectiveness of TextLap and show that it outperforms strong baselines, including GPT-4 based methods, for document generation and graphical design benchmarks.

Question decomposition has emerged as an effective strategy for prompting Large Language Models (LLMs) to answer complex questions. However, while existing methods primarily focus on unimodal language models, the question decomposition capability of Multimodal Large Language Models (MLLMs) has yet to be explored. To this end, this paper explores visual question decomposition on MLLMs. Specifically, we introduce a systematic evaluation framework including a dataset and several evaluation criteria to assess the quality of the decomposed sub-questions, revealing that existing MLLMs struggle to produce high-quality sub-questions. To address this limitation, we propose a specific finetuning dataset, DecoVQA+, for enhancing the model’s question decomposition capability. Aiming at enabling models to perform appropriate selective decomposition, we propose an efficient finetuning pipeline. The finetuning pipeline consists of our proposed dataset and a training objective for selective decomposition. Finetuned MLLMs demonstrate significant improvements in the quality of sub-questions and the policy of selective question decomposition. Additionally, the models also achieve higher accuracy with selective decomposition on VQA benchmark datasets.