Retrieval-augmented generation (RAG) improves large language models (LLMs) with external knowledge, but this access path creates security risks distinct from inherent prompt-only or parametric-model flaws. We frame secure RAG as securing external knowledge access. We conducted a systematic search and curated 135 works on attacks, defenses, and security evaluation, and organized them with SLOT: a taxonomy along the attack Surface (S) and the corresponding defense Layer (L), with cross-cutting axes Objective (O) and attack-target scope (T). Mapping these studies onto an external knowledge-access pipeline, we expose three mismatches: target mismatch (T2 attacks outpace T2 defenses and evaluation), stage mismatch (S1 attacks outnumber L1 defenses), and signal mismatch (fluent, retrievable, corpus-fitting S1 attacks challenge anomaly-based L2/L3 defenses). Finally, we discuss directions for more realistic targets, surface-complete defense stacks, standardized evaluation, confidentiality, and multimodal and agentic systems, and release the screening process, selected-paper metadata, and machine-readable SLOT labels at https://github.com/TreeAI-Lab/Awesome-RAG-Security.

Automatic Survey Generation (ASG) aims to produce comprehensive literature surveys by retrieving, organizing, and synthesizing academic papers. Despite rapid progress in specialized ASG frameworks and Deep Research agents, existing evaluations largely center on Computer Science or rely on generic criteria, leaving it unclear whether current systems satisfy the survey standards of diverse disciplines. We introduce SurveyLens, the first discipline-aware ASG benchmark. SurveyLens comprises SurveyLens-1k, a curated dataset of 1,000 human-written surveys across 10 disciplines, and a dual-lens framework that combines discipline-aware rubric scoring with reference-based alignment to human-written surveys. Evaluating 11 state-of-the-art systems spanning vanilla LLMs, specialized ASG systems, and Deep Research agents, we find that Deep Research agents perform most consistently across the 10 disciplines, some specialized ASG systems show particular strength in outline generation, and reference coverage and relevance remain a shared bottleneck across paradigms. These findings further provide practical guidance for discipline-specific system selection and future ASG design.

Tabular data derives its value from inter-feature dependencies, yet preserving them during synthesis is fragile when samples are scarce. Existing approaches either learn dependencies implicitly through distribution fitting, rely on statistical graph learning that becomes unstable with few samples, or encode structure through flat text serialization. Recent graph-aware methods use dependency graphs as attention biases or as backbones for non-LLM samplers, but they do not use the graph as a prompt-level plan for organizing the LLM’s own generation process. We introduce StructSynth, a framework that treats a dependency graph as a generation plan—determining the generation order, conditioning context, and scope of each black-box LLM call. In Evidence-Grounded Graph Induction, LLM reasoning and statistical association cues jointly construct a Directed Acyclic Graph (DAG) from limited samples. In Graph-Planned Conditional Synthesis, this DAG drives autoregressive synthesis in topological order, conditioning each feature on previously generated values with graph-specified parent structure guiding each step, making the conditioning schedule explicit throughout synthesis. Experiments show that StructSynth achieves state-of-the-art downstream utility and the best privacy-risk ranking among fourteen compared generators in low-data settings.

Cementitious materials teams need machine-readable databases of compressive strength records to support mix-design optimization. However, manual curation of the literature is too slow and hard to scale. We present CemLit-IE, a five-stage zero-shot pipeline, converting raw PDFs into fully attributed records with provenance. The core challenge is that the relevant information is distributed across tables, text, and figures. Even though table-only extraction produces plausible numbers, the results are not auditable. CemLit-IE addresses this through a series of methods, including keyword-weighted page scoring, structured table injection, value-provenance schema, and review queue. On a benchmark of 20 papers with 655 expert-annotated records, CemLit-IE achieves a recall of 76.3% with 99.2% precision and 100% exact-match accuracy in a text-only model. The recall can be raised to 83.5% when figure reading is enabled. The ablation study shows that each design choice contributes to improving precision. As for the cost, a separate 270-paper journal-batch run extracted 2,039 strength records at $31.28 total API cost. All data and code are publicly released.

Cultural reasoning lies at the core of many emerging AI applications, yet existing benchmarks provide limited insight into how well models understand and contextualize cultural artifacts. We introduce ExhibitionBench, the first benchmark that frames exhibition curation as a high-level evaluation testbed, comprising 300 institution-derived exhibition and thematic collection records, 23,658 artifacts, and three interconnected tasks that evaluate complementary aspects of cultural understanding and reasoning, translating them into user-perceivable utilities. Extensive experiments across 29 systems reveal a significant gap between frontier LLMs and expert-level competence, particularly on tasks that require collection-level coherence and thematic understanding. Performance gains from scaling, prompting, and multimodal inputs are limited and highly model-dependent, whereas tailored model optimizations can equip open-weight models with better capabilities that support on-premise deployment. These findings highlight the difficulty of reasoning over cultural collections and provide a practical benchmark for developing more culturally grounded and reliable AI systems. The full project is released at https://github.com/mengze-hong/ExhibitionBench.

Large Language Models (LLMs) have demonstrated impressive capabilities in natural language processing tasks, such as text generation and semantic understanding. However, their performance on numerical reasoning tasks, such as basic arithmetic, numerical retrieval, and magnitude comparison, remains surprisingly poor. This gap arises from their reliance on surface-level statistical patterns rather than understanding numbers as continuous magnitudes. Existing benchmarks primarily focus on either linguistic competence or structured mathematical problem-solving, neglecting fundamental numerical reasoning required in real-world scenarios. To bridge this gap, we propose NumericBench, a comprehensive benchmark to evaluate six fundamental numerical capabilities: number recognition, arithmetic operations, contextual retrieval, comparison, summary, and multi-step reasoning. NumericBench includes datasets ranging from synthetic number lists to crawled real-world data, addressing challenges like long contexts, noise, and multi-step reasoning. Extensive experiments on state-of-the-art LLMs, including GPT-4 and DeepSeek, reveal persistent weaknesses in numerical reasoning, highlighting the urgent need to improve numerically-aware language modeling. The benchmark is released in: https://github.com/TreeAI-Lab/NumericBench.