The author curates a collection of 407 recent large‑model papers—264 frontier works across six innovation paths and 143 top‑conference papers—classifies them into 14 hot sub‑topics, and explains how labs can match these directions to their available compute, data, and time resources.
Current AI systems excel at perception and generation but rely on statistical association, limiting their ability to model causal mechanisms; this article examines the shortcomings of the Next Token Prediction paradigm, outlines the causal ladder framework, and argues that integrating causal reasoning is key to achieving robust, generalizable AI.
The article reveals that large‑model research has moved from sheer scale to deeper capabilities and multimodal integration, highlighting ten hot directions and summarizing 120 recent top‑conference papers—including Spec‑VLA, Mobile‑O, OccTENS, and latent‑CoT studies—while offering free access to the full collection.
DeepSeek-OCR 2 introduces a causal‑flow encoder‑decoder architecture that replaces fixed‑grid scanning with semantic token reordering, achieving higher accuracy and lower token budgets on diverse document benchmarks while being fully open‑source on GitHub and Hugging Face.
The article outlines DeepSeek's four‑level capability hierarchy—basic multimodal data fusion and dynamic governance, intermediate domain modeling with causal reasoning and multi‑objective optimization, advanced complex system modeling with digital twins and multi‑agent coordination, and ultimate autonomous evolution features such as concept‑space exploration and self‑programming.
This article introduces causal graphical models as a language for reasoning about cause‑and‑effect, explains key concepts such as conditional independence, colliders, back‑door paths, confounding and selection bias, and shows how to identify and adjust for bias using simple visual examples.
This article presents Baidu's large‑scale event graph technology, detailing its concepts, construction pipeline, detection, representation, extraction methods, causal relation mining, and real‑world applications such as hotspot discovery, event tracing, resource linking, and causal reasoning, while outlining future research directions.
This article explores why recommendation systems need reasoning, how recommendation reasoning connects to future strong AI, discusses explainability, causal inference, graph-based reasoning, and the philosophical underpinnings of AI, while also reflecting on practical examples from Hulu's recommendation platform.
