This survey addresses the crucial issue of factuality in Large Language Models (LLMs). As LLMs find applications across diverse domains, the reliability and accuracy of their outputs become vital. We define the “factuality issue” as the probability of LLMs to produce content inconsistent with established facts. We first delve into the implications of these inaccuracies. Subsequently, we analyze the mechanisms through which LLMs store and process facts, seeking the primary causes of factual errors. Our discussion then transitions to methodologies for evaluating LLM factuality, emphasizing key metrics, benchmarks, and studies. We further explore strategies for enhancing LLM factuality. Our survey offers a structured guide for researchers aiming to fortify the factual reliability of LLMs. We consistently maintain and update the related open-source materials at https://github.com/wangcunxiang/LLM-Factuality-Survey .

As AI systems engage in extended thinking processes for research tasks, interaction becomes increasingly important for effective long-horizon research. In these tasks, many current deep research systems still follow an “input–wait–output” paradigm: users submit a query and receive results only after a non-interruptible process with limited visibility into intermediate decisions. In multi-agent systems, delayed feedback can amplify early errors, limit query refinement during investigation and reduce opportunities for expertise integration. To address these limitations, we introduce Deep Cognition, a multi-agent deep research system that enables users to monitor and steer an ongoing research process through targeted interventions during execution. Deep Cognition provides two key capabilities: (1) Transparent, controllable, and interruptible interaction that reveals AI reasoning, exposing intermediate plans and evidence and allowing users to intervene at intermediate checkpoints; (2) Fine-grained bidirectional dialogue that enables per-subagent control during execution.