This October 23, 2025 Xidian University academic survey systematically reviews the transformative impact of **Large Language Models (LLMs)** on the three core stages of **Knowledge Graph (KG) construction**: ontology engineering, knowledge extraction, and knowledge fusion. The text explains that LLMs are shifting the paradigm from rigid, rule-based systems to **unified, adaptive, and generative frameworks**. The paper is structured to first revisit traditional KG methodologies before examining emerging LLM-driven approaches, which are categorized into **schema-based** (emphasizing structure) and **schema-free** (emphasizing flexibility) paradigms across all stages. The authors outline how LLMs function as either **ontology assistants (top-down)** or as consumers of KGs for grounding and memory **(bottom-up)**, culminating in a discussion of future directions such as KG-based reasoning and dynamic knowledge memory for agentic systems. Ultimately, the work aims to clarify the evolving relationship between symbolic knowledge engineering and neural semantic understanding.

Source:

https://arxiv.org/pdf/2510.20345

The October 23, 2025 collaboration between UC San Diego , NVIDIA , META , UW-Madison , and UNC introduces **Real Deep Research (RDR)**, a systematic framework designed to analyze vast amounts of research literature in rapidly growing fields such as AI and robotics. The methodology uses **large language and multimodal models (LLMs/LMMs)** for content extraction, reasoning, and semantic embedding to map the research landscape. RDR’s capabilities include generating detailed surveys, analyzing **topic trends over time**, and identifying **cross-domain opportunities** across computer vision, NLP, machine learning, and robotics. Quantitative evaluations demonstrate that RDR produces higher-quality, more accurate surveys compared to existing commercial LLM-based tools, offering a valuable resource for researchers looking to track emerging areas and high-impact papers. The paper details the pipeline's components, including data collection from top conferences, perspective-guided content reasoning, and embedding analysis for clustering and knowledge discovery.

Source:

https://arxiv.org/pdf/2510.20809

The October 20, 2025 Meta FAIR paper introduces the **Free Transformer**, an innovative extension of the decoder-only Transformer architecture, which addresses the limitations of purely autoregressive language modeling by integrating **random latent variables** into the generative process. This new model is structured as a **conditional Variational Autoencoder (VAE)**, where an encoder learns the latent variables unsupervised, and a decoder conditions its token generation on these variables. The implementation requires only a minor computational overhead due to sharing half of the decoder's blocks with the encoder. Experimental results with 1.5B and 8B parameter models demonstrate that this conditioning leads to **substantial performance improvements** on reasoning and coding benchmarks like HumanEval+ and GSM8K. The authors conclude that the Free Transformer significantly **improves the inductive bias** of the vanilla Transformer.

Source:

https://arxiv.org/pdf/2510.17558v1

This 2025 CMU paper introduces **LithOS**, a novel operating system designed to improve the efficiency and utilization of Graphics Processing Units (GPUs) for machine learning (ML) workloads in data centers. The authors argue that current GPU management solutions, such as NVIDIA's MPS and MIG, are too coarse-grained, leading to low utilization and high latency in multi-tenant environments. LithOS proposes a transparent, OS-level approach featuring a **TPC Scheduler** for fine-grained resource control, a **Kernel Atomizer** that breaks up monolithic kernels to reduce head-of-line blocking, and mechanisms for **hardware right-sizing** and **transparent power management** (DVFS). Evaluation results demonstrate that LithOS significantly reduces tail latencies (up to 13× compared to MPS) and improves aggregate throughput in both inference-only and hybrid inference/training scenarios while achieving substantial capacity and energy savings. Overall, the work establishes a foundation for developing true operating systems for GPUs to address the growing efficiency crisis in ML infrastructure.

Source:

https://www.cs.cmu.edu/~dskarlat/publications/lithos_sosp25.pdf

This October 23, 2025 technical report from the Ling Team introduces the **Ring-linear model series**, specifically Ring-mini-linear-2.0 and Ring-flash-linear-2.0, which utilize a **hybrid attention architecture** combining linear and softmax attention mechanisms to enhance efficiency in long-context reasoning. The paper explains how this architecture, featuring **Mixture-of-Experts (MoE)** and advanced **FP8 training optimization** through kernels like LingHe, significantly reduces inference costs and improves training throughput. A major focus is on **systematic training-inference alignment** to achieve stable reinforcement learning (RL) training, addressing disparities in components like the KV Cache and RMSNorm that often lead to RL collapse in long-context models. Finally, the report presents **benchmark results** demonstrating that the Ring-linear models maintain state-of-the-art performance across various complex reasoning tasks compared to similar-scale counterparts.

Source:

https://arxiv.org/pdf/2510.19338

The October 22, 2025 GigaAI paper introduces **GigaBrain-0**, a novel Vision-Language-Action (VLA) model designed for general-purpose robotic systems, which is primarily trained using a combination of real-world robot data and synthetic data generated by a world model called **GigaWorld**. This approach aims to enhance generalization across various real-world conditions by leveraging diverse synthetic data streams like **Real2Real Transfer**, **Sim2Real Transfer**, and **View Transfer**. Architecturally, GigaBrain-0 incorporates **RGB-D input modeling** for better spatial reasoning and uses an **embodied Chain-of-Thought (CoT)** framework that generates intermediate reasoning steps such as manipulation trajectories and subgoal language. Experimental results across dexterous manipulation, long-horizon, and mobile manipulation tasks demonstrate that the model, particularly when augmented with world model-generated data, achieves superior performance and robustness compared to baseline models like $\pi0$. The paper also presents GigaBrain-0-Small, an optimized variant for efficient hardware deployment.

Source:

https://arxiv.org/pdf/2510.19430

The October 25, 2025 Bytedance paper introduces **Open-o3 Video**, a novel framework developed by researchers from **Peking University** and **ByteDance**, aimed at advancing video reasoning by incorporating explicit spatio-temporal evidence. Unlike prior models that only generate textual rationales, Open-o3 Video explicitly highlights key **timestamps** and **bounding boxes** to ground its answers in visual observations. To achieve this, the authors curate two new datasets, **STGR-CoT-30k** and **STGR-RL-36k**, and utilize a two-stage training strategy involving supervised fine-tuning and **Group Sequence Policy Optimization (GSPO)** with specialized rewards. This approach, which includes adaptive temporal proximity and temporal gating mechanisms, significantly improves performance on the **V-STAR benchmark** and other video understanding tasks, making video reasoning more accurate and verifiable.

Source:

https://arxiv.org/pdf/2510.20579

We review the Cattell-Horn-Carroll (CHC) used in recent AI papers on the definition of what AGI could be. The provided sources offer a comprehensive overview of the **Cattell–Horn–Carroll (CHC) theory of human cognitive abilities**, a widely accepted psychological model of intelligence. The first source, a Wikipedia excerpt, explains that the **CHC theory** synthesizes two prior models, Cattell and Horn's Gf–Gc model and Carroll's three-stratum theory, to structure cognitive abilities hierarchically into three strata: **narrow abilities**, **broad abilities**, and a single **general ability** or *g*. The second source, a visual tour and summary from the Institute for Applied Psychometrics, provides an extensive visual chart and detailed definitions of the numerous **broad and narrow abilities** within the **CHC framework**, including categories like Fluid Reasoning (*Gf*), Comprehension-Knowledge (*Gc*), and various sensory and psychomotor abilities. Both documents emphasize the empirical basis of the **CHC theory** and its relevance in modern psychoeducational assessment, particularly for the development and classification of IQ tests.

Sources:

http://www.iapsych.com/chcv2.pdf

https://en.wikipedia.org/wiki/Cattell%E2%80%93Horn%E2%80%93Carroll_theory

This March 27, 2025 Anthropic paper provides an overview and detailed excerpts from two related Anthropic papers concerning the **interpretability of large language models**, specifically focusing on Claude 3.5 Haiku. The core objective is to reverse engineer the **internal computational mechanisms**, or "circuits," that drive the model's behavior, analogous to studying biology or neuroscience. The research introduces a **circuit tracing methodology** that uses attribution graphs and feature analysis to examine how the model handles various tasks, including **multi-step reasoning**, **planning in poems**, **multilingual translation**, and **arithmetic**. Findings reveal sophisticated strategies like internal planning and the existence of "default" refusal circuits that must be **inhibited by "known answer" features** for the model to respond to questions, illuminating the mechanisms behind **hallucinations and jailbreaks**.

Sources:

https://transformer-circuits.pub/2025/attribution-graphs/biology.html

https://www.anthropic.com/research/tracing-thoughts-language-model

The provided text is an academic paper titled **"Active Use of Latent Constituency Representation in both Humans and Large Language Models,"** which explores how sentences are internally represented in both the human brain and large language models (**LLMs**) like ChatGPT. The authors introduce a novel **one-shot learning word deletion task** where participants infer a deletion rule from a single example; they found that both humans and LLMs tend to delete a **complete linguistic constituent** rather than a nonconstituent word string, suggesting that latent, hierarchical linguistic structures emerge in both. Furthermore, the study demonstrates that the **deletion behavior** can be used to reconstruct a **constituency tree representation** that is structurally consistent with linguistically defined trees. The research also investigates how **language-dependent rules** are inferred and finds that native speakers primarily rely on **syntactic structure** over semantic plausibility in this task.

Source:

https://arxiv.org/pdf/2405.18241