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research #agent 📝 BlogAnalyzed: Jan 18, 2026 01:00

Unlocking the Future: How AI Agents with Skills are Revolutionizing Capabilities

Published:Jan 18, 2026 00:55

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1 min read

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Qiita AI

Analysis

This article brilliantly simplifies a complex concept, revealing the core of AI Agents: Large Language Models amplified by powerful tools. It highlights the potential for these Agents to perform a vast range of tasks, opening doors to previously unimaginable possibilities in automation and beyond.

Key Takeaways

•AI Agents are fundamentally composed of Large Language Models and Tools.
•This combination empowers Agents to accomplish a wide array of tasks.
•The article suggests that the simplicity of the Agent structure belies its powerful capabilities.

Reference

“Agent = LLM + Tools. This simple equation unlocks incredible potential!”

Permalink Qiita AI

product #llm 📝 BlogAnalyzed: Jan 13, 2026 19:30

Extending Claude Code: A Guide to Plugins and Capabilities

Published:Jan 13, 2026 12:06

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1 min read

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Zenn LLM

Analysis

This summary of Claude Code plugins highlights a critical aspect of LLM utility: integration with external tools and APIs. Understanding the Skill definition and MCP server implementation is essential for developers seeking to leverage Claude Code's capabilities within complex workflows. The document's structure, focusing on component elements, provides a foundational understanding of plugin architecture.

Key Takeaways

•The article provides an overview of Claude Code plugins, focusing on their components.
•Key components include Skills (Markdown instructions) and MCP servers.
•Plugins extend Claude Code's functionality by integrating with external tools and APIs.

Reference

“Claude Code's Plugin feature is composed of the following elements: Skill: A Markdown-formatted instruction that defines Claude's thought and behavioral rules.”

Permalink Zenn LLM

Research Paper #Large Language Models (LLMs), Benchmarking 🔬 ResearchAnalyzed: Jan 3, 2026 08:37

Encyclo-K: A New Benchmark for Evaluating LLMs

Published:Dec 31, 2025 13:55

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1 min read

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ArXiv

Analysis

This paper introduces Encyclo-K, a novel benchmark for evaluating Large Language Models (LLMs). It addresses limitations of existing benchmarks by using knowledge statements as the core unit, dynamically composing questions from them. This approach aims to improve robustness against data contamination, assess multi-knowledge understanding, and reduce annotation costs. The results show that even advanced LLMs struggle with the benchmark, highlighting its effectiveness in challenging and differentiating model performance.

Key Takeaways

•Encyclo-K is a statement-based benchmark for LLMs.
•It addresses limitations of existing question-based benchmarks.
•Questions are dynamically composed from knowledge statements.
•Reduces vulnerability to data contamination and annotation costs.
•Provides a challenging and discriminative evaluation of LLMs.

Reference

“Even the top-performing OpenAI-GPT-5.1 achieves only 62.07% accuracy, and model performance displays a clear gradient distribution.”

Permalink ArXiv

Research Paper #Thermal Management, Nanoscale Heat Transfer, Finite Element Analysis, Molecular Dynamics 🔬 ResearchAnalyzed: Jan 3, 2026 16:42

Non-Fourier Thermal Transport Modeling for Nanoscale Hot Spots

Published:Dec 30, 2025 22:53

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1 min read

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ArXiv

Analysis

This paper addresses a critical challenge in thermal management for advanced semiconductor devices. Conventional finite-element methods (FEM) based on Fourier's law fail to accurately model heat transport in nanoscale hot spots, leading to inaccurate temperature predictions and potentially flawed designs. The authors bridge the gap between computationally expensive molecular dynamics (MD) simulations, which capture non-Fourier effects, and the more practical FEM. They introduce a size-dependent thermal conductivity to improve FEM accuracy and decompose thermal resistance to understand the underlying physics. This work provides a valuable framework for incorporating non-Fourier physics into FEM simulations, enabling more accurate thermal analysis and design of next-generation transistors.

Key Takeaways

•Conventional FEM using bulk thermal conductivity underestimates hot-spot temperatures in nanoscale devices.
•MD simulations are used to benchmark FEM and understand non-Fourier effects.
•A size-dependent thermal conductivity, $κ_{\mathrm{best}}$, is introduced to improve FEM accuracy.
•Thermal resistance is decomposed to quantify different heat transport mechanisms.
•The framework enables more accurate thermal analysis and design of next-generation transistors.

Reference

“The introduction of a size-dependent "best" conductivity, $κ_{\mathrm{best}}$, allows FEM to reproduce MD hot-spot temperatures with high fidelity.”

Permalink ArXiv

Physics #Lattice QCD, Proton Spin, Gluon Helicity 🔬 ResearchAnalyzed: Jan 3, 2026 17:15

Lattice QCD Calculation of Gluon Contribution to Proton Spin

Published:Dec 30, 2025 16:10

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1 min read

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ArXiv

Analysis

This paper presents a cutting-edge lattice QCD calculation of the gluon helicity contribution to the proton spin, a fundamental quantity in understanding the internal structure of protons. The study employs advanced techniques like distillation, momentum smearing, and non-perturbative renormalization to achieve high precision. The result provides valuable insights into the spin structure of the proton and contributes to our understanding of how the proton's spin is composed of the spins of its constituent quarks and gluons.

Key Takeaways

•Provides a precise lattice QCD calculation of the gluon contribution to proton spin.
•Employs advanced techniques for high accuracy.
•Quantifies the gluon contribution as a significant portion of the proton's spin.
•Contributes to understanding the internal structure of protons.

Reference

“The study finds that the gluon helicity contribution to proton spin is $ΔG = 0.231(17)^{\mathrm{sta.}}(33)^{\mathrm{sym.}}$ at the $\overline{\mathrm{MS}}$ scale $μ^2=10\ \mathrm{GeV}^2$, which constitutes approximately $46(7)\%$ of the proton spin.”

Permalink ArXiv

science & technology #astrophysics 🔬 ResearchAnalyzed: Jan 4, 2026 06:48

Tidally Trapped Two-pole Pulsations Discovered in a Close Binary with a Massive β Cephei Star

Published:Dec 30, 2025 12:37

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1 min read

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ArXiv

Analysis

This article reports a discovery in astrophysics, specifically concerning the behavior of a binary star system. The title indicates the research focuses on pulsations within the system, likely caused by tidal forces. The presence of a β Cephei star suggests the system is composed of massive, hot stars. The source, ArXiv, confirms this is a scientific publication, likely a pre-print or published research paper.

Key Takeaways

•Discovery of tidally trapped pulsations in a binary star system.
•Focus on a system containing a massive β Cephei star.
•Research published on ArXiv, indicating a scientific paper.

Reference

“”

Permalink ArXiv

research #physics 🔬 ResearchAnalyzed: Jan 4, 2026 06:48

Exceptional Points in the Scattering Resonances of a Sphere Dimer

Published:Dec 30, 2025 09:23

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1 min read

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ArXiv

Analysis

This article likely discusses a physics research topic, specifically focusing on the behavior of light scattering by a structure composed of two spheres (a dimer). The term "Exceptional Points" suggests an investigation into specific points in the system's parameter space where the system's behavior changes dramatically, potentially involving the merging of resonances or other unusual phenomena. The source, ArXiv, indicates that this is a pre-print or published research paper.

Key Takeaways

•Focuses on light scattering by a sphere dimer.
•Investigates "Exceptional Points" in the system.
•Likely a physics research paper.
•Published on ArXiv.

Reference

“”

Permalink ArXiv

Physics #Hadron Physics, QCD 🔬 ResearchAnalyzed: Jan 3, 2026 16:16

Molecular States of $J/ψB_{c}^{+}$ and $η_{c}B_{c}^{\ast +}$ Analyzed

Published:Dec 28, 2025 18:14

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1 min read

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ArXiv

Analysis

This paper investigates the properties of hadronic molecules composed of heavy quarks using the QCD sum rule method. The study focuses on the $J/ψB_{c}^{+}$ and $η_{c}B_{c}^{\ast +}$ states, predicting their mass, decay modes, and widths. The results are relevant for experimental searches for these exotic hadrons and provide insights into strong interaction dynamics.

Key Takeaways

•The paper uses the QCD sum rule method to study hadronic molecules.
•It focuses on the $J/ψB_{c}^{+}$ and $η_{c}B_{c}^{\ast +}$ states.
•The study predicts the mass and decay width of the molecules.
•The results are relevant for experimental searches for exotic hadrons.

Reference

“The paper predicts a mass of $m=(9740 \pm 70)~\mathrm{MeV}$ and a width of $Γ[ \mathfrak{M}]=(121 \pm 17)~ \mathrm{MeV}$ for the hadronic axial-vector molecule $\mathfrak{M}$.”

Permalink ArXiv

Paper #Computer Vision, 4D Scene Reconstruction 🔬 ResearchAnalyzed: Jan 3, 2026 19:39

Split4D: Decomposed 4D Scene Reconstruction Without Video Segmentation

Published:Dec 28, 2025 02:37

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1 min read

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ArXiv

Analysis

This paper tackles the challenge of 4D scene reconstruction by avoiding reliance on unstable video segmentation. It introduces Freetime FeatureGS and a streaming feature learning strategy to improve reconstruction accuracy. The core innovation lies in using Gaussian primitives with learnable features and motion, coupled with a contrastive loss and temporal feature propagation, to achieve 4D segmentation and superior reconstruction results.

Key Takeaways

•Proposes a novel approach to 4D scene reconstruction that avoids the instability of video segmentation.
•Introduces Freetime FeatureGS, a new representation using Gaussian primitives with learnable features and motion.
•Employs a streaming feature learning strategy to propagate features over time, improving reconstruction quality.
•Achieves superior reconstruction results compared to existing methods.

Reference

“The key idea is to represent the decomposed 4D scene with the Freetime FeatureGS and design a streaming feature learning strategy to accurately recover it from per-image segmentation maps, eliminating the need for video segmentation.”

Permalink ArXiv

Research #Particle Physics 🔬 ResearchAnalyzed: Jan 10, 2026 07:12

Advanced QCD Calculations for Charm Tetraquark Electromagnetic Processes

Published:Dec 26, 2025 15:53

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1 min read

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ArXiv

Analysis

This research delves into the theoretical complexities of fully charm tetraquarks, employing next-to-leading order QCD corrections. The study likely aims to refine predictions for the production and decay of these exotic hadrons, contributing to a deeper understanding of the strong force.

Key Takeaways

•Focuses on theoretical calculations within the realm of particle physics.
•Employs advanced QCD techniques to refine predictions.
•Targets the electromagnetic interactions of tetraquarks composed solely of charm quarks.

Reference

“The article's source is ArXiv, indicating a pre-print research publication.”

Permalink ArXiv

Research Paper #Human Motion Generation, Diffusion Models, Compositional Learning 🔬 ResearchAnalyzed: Jan 3, 2026 20:14

DeMoGen: Decomposing Human Motion with Diffusion Models

Published:Dec 26, 2025 15:06

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1 min read

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ArXiv

Analysis

This paper introduces DeMoGen, a novel approach to human motion generation that focuses on decomposing complex motions into simpler, reusable components. This is a significant departure from existing methods that primarily focus on forward modeling. The use of an energy-based diffusion model allows for the discovery of motion primitives without requiring ground-truth decomposition, and the proposed training variants further encourage a compositional understanding of motion. The ability to recombine these primitives for novel motion generation is a key contribution, potentially leading to more flexible and diverse motion synthesis. The creation of a text-decomposed dataset is also a valuable contribution to the field.

Key Takeaways

•Proposes DeMoGen, a decompositional approach to human motion generation.
•Employs an energy-based diffusion model for learning motion primitives.
•Introduces three training variants to encourage compositional understanding.
•Demonstrates the ability to recombine primitives for novel motion generation.
•Constructs a text-decomposed dataset to support compositional training.

Reference

“DeMoGen's ability to disentangle reusable motion primitives from complex motion sequences and recombine them to generate diverse and novel motions.”

Permalink ArXiv

Research Paper #Set Theory, Non-Founded Sets, Axiomatic Set Theory 🔬 ResearchAnalyzed: Jan 3, 2026 20:20

Skands and Coskands: Exploring Non-Founded Set Theory

Published:Dec 26, 2025 11:02

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1 min read

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ArXiv

Analysis

This paper introduces and explores the concepts of 'skands' and 'coskands' within the framework of non-founded set theory, specifically NBG without the axiom of regularity. It aims to extend set theory by allowing for non-well-founded sets, which are sets that can contain themselves or form infinite descending membership chains. The paper's significance lies in its exploration of alternative set-theoretic foundations and its potential implications for understanding mathematical structures beyond the standard ZFC axioms. The introduction of skands and coskands provides new tools for modeling and reasoning about non-well-founded sets, potentially opening up new avenues for research in areas like computer science and theoretical physics where such sets may be relevant.

Key Takeaways

•Introduces 'skands' and 'coskands' as new concepts in non-founded set theory.
•Explores a modified NBG set theory without the axiom of regularity.
•Aims to provide new tools for reasoning about non-well-founded sets.
•Potentially relevant to areas like computer science and theoretical physics.

Reference

“The paper introduces 'skands' as 'decreasing' tuples and 'coskands' as 'increasing' tuples composed of founded sets, exploring their properties within a modified NBG framework.”

Permalink ArXiv

Paper #llm 🔬 ResearchAnalyzed: Jan 3, 2026 16:36

MASFIN: AI for Financial Forecasting

Published:Dec 26, 2025 06:01

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1 min read

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ArXiv

Analysis

This paper introduces MASFIN, a multi-agent AI system leveraging LLMs (GPT-4.1-nano) for financial forecasting. It addresses limitations of traditional methods and other AI approaches by integrating structured and unstructured data, incorporating bias mitigation, and focusing on reproducibility and cost-efficiency. The system generates weekly portfolios and demonstrates promising performance, outperforming major market benchmarks in a short-term evaluation. The modular multi-agent design is a key contribution, offering a transparent and reproducible approach to quantitative finance.

Key Takeaways

•MASFIN is a multi-agent AI system for financial forecasting.
•It uses LLMs (GPT-4.1-nano) and integrates structured and unstructured data.
•The system incorporates bias mitigation and focuses on reproducibility and cost-efficiency.
•MASFIN generated a 7.33% cumulative return in an 8-week evaluation, outperforming major benchmarks in most weeks.
•The modular multi-agent design is a key contribution for transparent and reproducible quantitative finance.

Reference

“MASFIN delivered a 7.33% cumulative return, outperforming the S&P 500, NASDAQ-100, and Dow Jones benchmarks in six of eight weeks, albeit with higher volatility.”

Permalink ArXiv

Research #Image Retrieval 🔬 ResearchAnalyzed: Jan 10, 2026 07:54

Soft Filtering: Enhancing Zero-shot Image Retrieval with Constraints

Published:Dec 23, 2025 21:29

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1 min read

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ArXiv

Analysis

The research focuses on improving zero-shot composed image retrieval by introducing prescriptive and proscriptive constraints, likely resulting in more accurate and controlled image search results. This approach could be significant for applications demanding precise image retrieval based on complex textual descriptions.

Key Takeaways

•Focuses on improving zero-shot image retrieval.
•Employs prescriptive and proscriptive constraints.
•Published on ArXiv, indicating early-stage research.

Reference

“The paper explores guiding zero-shot composed image retrieval with prescriptive and proscriptive constraints.”

Permalink ArXiv

Research #llm 🔬 ResearchAnalyzed: Jan 4, 2026 09:21

DETACH : Decomposed Spatio-Temporal Alignment for Exocentric Video and Ambient Sensors with Staged Learning

Published:Dec 23, 2025 14:55

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1 min read

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ArXiv

Analysis

The article introduces a novel approach, DETACH, for aligning exocentric video data with ambient sensor data. The use of decomposed spatio-temporal alignment and staged learning suggests a potentially effective method for handling the complexities of integrating these different data modalities. The source being ArXiv indicates this is a research paper, likely detailing the methodology, experiments, and results of this new approach. Further analysis would require access to the full paper to assess the technical details, performance, and limitations.

Reference

“”

Permalink ArXiv

Research #llm 🔬 ResearchAnalyzed: Jan 4, 2026 07:01

Modular Neural Image Signal Processing

Published:Dec 9, 2025 13:04

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1 min read

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ArXiv

Analysis

This article likely discusses a novel approach to image processing using neural networks, focusing on a modular design. The use of 'Modular' suggests a system composed of independent, reusable components. The 'Neural' aspect indicates the application of deep learning techniques. The 'Image Signal Processing' part implies the work addresses tasks like denoising, demosaicing, and color correction. The ArXiv source suggests this is a pre-print, indicating early-stage research.

•OpenAI Five, an AI composed of five neural networks, is demonstrating proficiency in Dota 2.
•The AI is achieving success against amateur human teams.
•This represents progress in AI's ability to master complex games.

Reference

“Our team of five neural networks, OpenAI Five, has started to defeat amateur human teams at Dota 2.”

Permalink OpenAI News

Unlocking the Future: How AI Agents with Skills are Revolutionizing Capabilities

Analysis

Key Takeaways

Extending Claude Code: A Guide to Plugins and Capabilities

Analysis

Key Takeaways

Encyclo-K: A New Benchmark for Evaluating LLMs

Analysis

Key Takeaways

Non-Fourier Thermal Transport Modeling for Nanoscale Hot Spots

Analysis

Key Takeaways

Lattice QCD Calculation of Gluon Contribution to Proton Spin

Analysis

Key Takeaways

Tidally Trapped Two-pole Pulsations Discovered in a Close Binary with a Massive β Cephei Star

Analysis

Key Takeaways

Exceptional Points in the Scattering Resonances of a Sphere Dimer

Analysis

Key Takeaways

Molecular States of $J/ψB_{c}^{+}$ and $η_{c}B_{c}^{\ast +}$ Analyzed

Analysis

Key Takeaways

Split4D: Decomposed 4D Scene Reconstruction Without Video Segmentation

Analysis

Key Takeaways

Advanced QCD Calculations for Charm Tetraquark Electromagnetic Processes

Analysis

Key Takeaways

DeMoGen: Decomposing Human Motion with Diffusion Models

Analysis

Key Takeaways

Skands and Coskands: Exploring Non-Founded Set Theory

Analysis

Key Takeaways

MASFIN: AI for Financial Forecasting

Analysis

Key Takeaways

Soft Filtering: Enhancing Zero-shot Image Retrieval with Constraints

Analysis

Key Takeaways

DETACH : Decomposed Spatio-Temporal Alignment for Exocentric Video and Ambient Sensors with Staged Learning

Analysis

Key Takeaways

Unveiling Intelligent Matter: A Deep Dive into Active Particle Systems

Analysis

Key Takeaways

XDen-1K: A New Dataset for Real-World Object Representation

Analysis

Key Takeaways

Emergent Collective Memory in Decentralized Multi-Agent AI Systems

Analysis

Key Takeaways

Modular Neural Image Signal Processing

Analysis

Key Takeaways

HUD: A Novel Approach for Video Retrieval with Uncertainty Handling

Analysis

Key Takeaways

Decomposed Trust: Examining the Ethical and Technical Challenges of Low-Rank LLMs

Analysis

Key Takeaways

AI Trends 2023: Natural Language Processing - ChatGPT, GPT-4, and Cutting-Edge Research with Sameer Singh

Analysis

Key Takeaways

PyTorch: Analyzing an Imperative Deep Learning Library

Analysis

Key Takeaways

Machine Learning Music Composed by Fragments of 100s of Terabytes of Recordings

Analysis

Key Takeaways

OpenAI Five Defeats Amateur Dota 2 Teams

Analysis

Key Takeaways

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