Scientific research is not merely a sequence of tasks to be automated, but a knowledge-production process in which claims must remain verifiable, criticizable, revisable, and accountable. Yet most AutoResearch systems remain single scaffolds that collapse generation, execution, interpretation, and review into one loop. This survey diagnoses three resulting epistemic limits of such architectures: collapsed skepticism, where a single scaffold produces not only errors but also the narratives that allow them to survive; unverified translation from discovery to application, where proposal generation outpaces independent verification; and invisible evidence trails, where final manuscripts hide the failed attempts and post-hoc decisions that produced them. To overcome these structural fragilities, we propose a shift from autonomous AI scientists to AI research communities, where specialized agents, shared memory, inspectable artifacts, and governance checkpoints distribute epistemic labor. We synthesize AutoResearch as a transition from metric-driven search loops to AI-scientist workflows, domain-mediated discovery, research infrastructure, and accountable human–AI co-evolution. Across these layers, we argue for a fundamental shift from productivity-centered evaluation to verifiability-centered evaluation.

Structure-based drug design (SBDD) requires ligands that satisfy both 3D target affinity and 1D chemical validity. Existing controllable generation methods often rely on task-specific fine-tuning or externally imposed sampling-time guidance, adding cost and potentially conflicting with evolving 3D geometric constraints. We propose LiFT, a language-informed cross-modal framework built on Flow Matching for trend-guided 3D molecular generation across both de novo design and scaffold hopping. LiFT uses a “Sense-Evolve-Assemble” agent to generate target-aware SMILES as intermediate chemical conditions, from which a pre-trained chemical foundation model extracts continuous semantic priors. These priors are integrated into geometric generation through a lightweight semantic projector with zero-initialized adaptive normalization for stable cross-modal conditioning. We further introduce a Self-Conditioned Decoupled Router (SCDR), which modulates the velocity field according to intermediate structural states during ODE integration. Experiments on CrossDocked2020 show that LiFT achieves competitive distribution matching while improving medicinal chemistry metrics and maintaining competitive structural validity under task-steering settings without additional generator fine-tuning. Our results suggest that language-derived chemical priors provide effective trend-level guidance for 3D molecular generation. Code and released artifacts are available at https://github.com/kasurl/LiFT.

This paper argues that evaluation of educational LLMs should move beyond decontextualized task-based benchmarks toward situated benchmarks centered on pedagogical action. Prior work in human-computer interaction and the learning sciences has long emphasized situated use, user context, and interaction processes; our focus is narrower and complementary: how these concerns should reshape benchmark design for educational LLMs. Existing evaluations already cover question generation, knowledge answering, error diagnosis, feedback generation, and teaching analysis, but their dominant unit remains the single input-output episode, making it difficult to compare whether a model genuinely supports learning progress, instructional judgment, or subsequent revision in context. Educational activity is processual, context-dependent, and development-oriented, so explanations, feedback, and hints can only be evaluated properly in relation to learner state, interaction history, and pedagogical goals. We review a landscape still dominated by task-based paradigms and argue, from educational conceptions, educational values, and research methodology, that next-generation educational benchmarks should make pedagogical function in context a core target. We further propose layered, scalable, and benchmarkable situated protocols built through simulation-first development and follow-up empirical calibration, extending evaluation beyond task quality to contextual fit and downstream consequences for learning and teaching decisions.

The escalating demand for comprehensive literature surveys in rapidly evolving research areas makes manual writing increasingly impractical, underscoring the necessity of automation. Large Language Models (LLMs) provide a promising foundation for this task, yet guiding them to generate accurate, reliable content remains a fundamental challenge, as issues such as hallucinations and vague organization often persist. To address this, we propose FIKSurvey, a feedback-driven framework grounded in the idea that “Feedback is the key for automatic survey generation.” Specifically, FIKSurvey systematically incorporates feedback across three dimensions: outline feedback for structural clarity, citation feedback for evidence validation, and content feedback for readability and analytical depth. The framework also supports optional human-in-the-loop intervention for user-specific needs. Experiments confirm that FIKSurvey substantially improves both citation and content quality, demonstrating feedback as the critical mechanism for automatic survey generation.

The rapid advance of automatic survey generation (ASG) has created a critical evaluation challenge. Existing evaluation methods suffer from both cognitive dimensional simplification and methodological unreliability, primarily due to the over-reliance on the ”LLM-as-a-Judge” approach. To bridge this gap, we establish Bloom-Eval, a six-tiered benchmark based on Bloom’s Taxonomy that reliably evaluates ASG systems by prioritizing deterministic algorithms and introducing our GRADE approach for abstract abilities. Furthermore, we construct a large-scale, cross-disciplinary dataset of over 3,000 high-quality papers. Our empirical study on this benchmark reveals that while leading ASG systems are proficient format organizers, they remain unqualified knowledge integrators. This work aims to redefine ASG evaluation standards, shifting the research focus from the formal mimicry of surface structure to the cognitive deepening of intellectual content. Our method provides the ASG field with a systematic, reproducible, and theoretically grounded benchmark to guide future research.