SurveyLens: A Research Discipline-Aware Benchmark for Automatic Survey Generation

The exponential growth of scientific literature has driven the evolution of Automatic Survey Generation (ASG) from simple pipelines to multi-agent frameworks and commercial Deep Research agents. However, current ASG evaluation methods rely on generic metrics and are heavily biased toward Computer Science (CS), failing to assess whether ASG methods adhere to the distinct standards of various academic disciplines. Consequently, researchers, especially those outside CS, lack clear guidance on using ASG systems to yield high-quality surveys compliant with specific discipline standards. To bridge this gap, we introduce SurveyLens, the first discipline-aware benchmark evaluating ASG methods across diverse research disciplines. We construct SurveyLens-1k, a curated dataset of 1,000 high-quality human-written surveys spanning 10 disciplines. Subsequently, we propose a dual-lens evaluation framework: (1) Discipline-Aware Rubric Evaluation, which utilizes LLMs with human preference-aligned weights to assess adherence to domain-specific writing standards; and (2) Canonical Alignment Evaluation to rigorously measure content coverage and synthesis quality against human-written survey papers. We conduct extensive experiments by evaluating 11 state-of-the-art ASG methods on SurveyLens, including Vanilla LLMs, ASG systems, and Deep Research agents. Our analysis reveals the distinct strengths and weaknesses of each paradigm across fields, providing essential guidance for selecting tools tailored to specific disciplinary requirements.

Imagine-then-Plan: Agent Learning from Adaptive Lookahead with World Models

Recent advances in world models have shown promise for modeling future dynamics of environmental states, enabling agents to reason and act without accessing real environments. Current methods mainly perform single-step or fixed-horizon rollouts, leaving their potential for complex task planning under-exploited. We propose Imagine-then-Plan (\texttt{ITP}), a unified framework for agent learning via lookahead imagination, where an agent's policy model interacts with the learned world model, yielding multi-step ``imagined'' trajectories. Since the imagination horizon may vary by tasks and stages, we introduce a novel adaptive lookahead mechanism by trading off the ultimate goal and task progress. The resulting imagined trajectories provide rich signals about future consequences, such as achieved progress and potential conflicts, which are fused with current observations, formulating a partially \textit{observable} and \textit{imaginable} Markov decision process to guide policy learning. We instantiate \texttt{ITP} with both training-free and reinforcement-trained variants. Extensive experiments across representative agent benchmarks demonstrate that \texttt{ITP} significantly outperforms competitive baselines. Further analyses validate that our adaptive lookahead largely enhances agents' reasoning capability, providing valuable insights into addressing broader, complex tasks.

MegaFake: A Theory-Driven Dataset of Fake News Generated by Large Language Models

The advent of large language models (LLMs) has revolutionized online content creation, making it much easier to generate high-quality fake news. This misuse threatens the integrity of our digital environment and ethical standards. Therefore, understanding the motivations and mechanisms behind LLM-generated fake news is crucial. In this study, we analyze the creation of fake news from a social psychology perspective and develop a comprehensive LLM-based theoretical framework, LLM-Fake Theory. We introduce a novel pipeline that automates the generation of fake news using LLMs, thereby eliminating the need for manual annotation. Utilizing this pipeline, we create a theoretically informed Machine-generated Fake news dataset, MegaFake, derived from the GossipCop dataset. We conduct comprehensive analyses to evaluate our MegaFake dataset. We believe that our dataset and insights will provide valuable contributions to future research focused on the detection and governance of fake news in the era of LLMs.