The article presents a four‑phase, eight‑stage systematic analysis of AI‑driven auto‑research, exposing reliability gaps, bottlenecks, and best‑practice deployment through human‑governed collaboration, while detailing benchmarks, failure modes, and architectural families.
The article surveys AI‑assisted research across the full lifecycle—creation, writing, validation, and dissemination—detailing the capabilities of prompt engineering, retrieval‑augmented generation, training‑free agents and hybrid methods, reporting benchmark numbers, failure modes, and governance challenges that dictate when human oversight remains essential.
This survey reviews state‑of‑the‑art research up to Q1 2026 on integrating tactile sensing with vision and language for embodied AI, presenting a four‑stage fusion pipeline, a hierarchical taxonomy of datasets, methods, sensors, and highlighting current evaluation challenges and future directions.
The article provides an in‑depth analysis of the Skills mechanism that gives large language model agents reusable procedural memory, detailing its core components, seven design patterns, real‑world security threats, evaluation benchmarks, and the challenges of safely scaling autonomous AI systems.
This article surveys the emerging Deep Research (DR) paradigm that upgrades large language models into research agents capable of autonomous planning, multi‑source evidence gathering, memory management, and verifiable long‑form report generation, outlining its stages, core components, training pipeline, and evaluation benchmarks.
This article provides an in‑depth review of alignment algorithms for multimodal large language models, covering application scenarios, dataset construction methods, evaluation benchmarks, current challenges, and future research directions, while summarizing contributions from leading academic institutions.
This article provides a systematic review of recent advances in multimodal alignment for large language models, covering key contributions, application scenarios, dataset construction, evaluation benchmarks, future challenges, and insights from LLM alignment research to guide both academia and industry.
This article reviews recent advances in multimodal large language models, covering their background, architectural components, training strategies, application scenarios, evaluation benchmarks, team research on hallucination mitigation and long‑video understanding, and outlines promising future research directions.
