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research#agent🏛️ OfficialAnalyzed: Jan 5, 2026 09:06

Replicating Claude Code's Plan Mode with Codex Skills: A Feasibility Study

Published:Jan 1, 2026 09:27
1 min read
Zenn OpenAI

Analysis

This article explores the challenges of replicating Claude Code's sophisticated planning capabilities using OpenAI's Codex CLI Skills. The core issue lies in the lack of autonomous skill chaining within Codex, requiring user intervention at each step, which hinders the creation of a truly self-directed 'investigate-plan-reinvestigate' loop. This highlights a key difference in the agentic capabilities of the two platforms.
Reference

Claude Code の plan mode は、計画フェーズ中に Plan subagent へ調査を委任し、探索を差し込む仕組みを持つ。

Analysis

This paper addresses a critical limitation in robotic scene understanding: the lack of functional information about articulated objects. Existing methods struggle with visual ambiguity and often miss fine-grained functional elements. ArtiSG offers a novel solution by incorporating human demonstrations to build functional 3D scene graphs, enabling robots to perform language-directed manipulation tasks. The use of a portable setup for data collection and the integration of kinematic priors are key strengths.
Reference

ArtiSG significantly outperforms baselines in functional element recall and articulation estimation precision.

Analysis

This paper addresses the challenge of achieving average consensus in distributed systems with limited communication bandwidth, a common constraint in real-world applications. The proposed algorithm, PP-ACDC, offers a communication-efficient solution by using dynamic quantization and a finite-time termination mechanism. This is significant because it allows for precise consensus with a fixed number of bits, making it suitable for resource-constrained environments.
Reference

PP-ACDC achieves asymptotic (exact) average consensus on any strongly connected digraph under appropriately chosen quantization parameters.

Research#Algorithms🔬 ResearchAnalyzed: Jan 10, 2026 07:07

New Algorithms Advance Global Minimum Vertex-Cut in Directed Graphs

Published:Dec 30, 2025 17:06
1 min read
ArXiv

Analysis

This ArXiv article presents advancements in algorithms for the global minimum vertex-cut problem within directed graphs. The research likely explores computational complexity and efficiency improvements for network flow and related graph theory applications.
Reference

The context is from ArXiv, indicating a research paper.

Paper#LLM🔬 ResearchAnalyzed: Jan 3, 2026 15:55

LoongFlow: Self-Evolving Agent for Efficient Algorithmic Discovery

Published:Dec 30, 2025 08:39
1 min read
ArXiv

Analysis

This paper introduces LoongFlow, a novel self-evolving agent framework that leverages LLMs within a 'Plan-Execute-Summarize' paradigm to improve evolutionary search efficiency. It addresses limitations of existing methods like premature convergence and inefficient exploration. The framework's hybrid memory system and integration of Multi-Island models with MAP-Elites and adaptive Boltzmann selection are key to balancing exploration and exploitation. The paper's significance lies in its potential to advance autonomous scientific discovery by generating expert-level solutions with reduced computational overhead, as demonstrated by its superior performance on benchmarks and competitions.
Reference

LoongFlow outperforms leading baselines (e.g., OpenEvolve, ShinkaEvolve) by up to 60% in evolutionary efficiency while discovering superior solutions.

Analysis

This paper explores how public goods can be provided in decentralized networks. It uses graph theory kernels to analyze specialized equilibria where individuals either contribute a fixed amount or free-ride. The research provides conditions for equilibrium existence and uniqueness, analyzes the impact of network structure (reciprocity), and proposes an algorithm for simplification. The focus on specialized equilibria is justified by their stability.
Reference

The paper establishes a correspondence between kernels in graph theory and specialized equilibria.

Analysis

This paper introduces a novel semantics for doxastic logics (logics of belief) using directed hypergraphs. It addresses a limitation of existing simplicial models, which primarily focus on knowledge. The use of hypergraphs allows for modeling belief, including consistent and introspective belief, and provides a bridge between Kripke models and the new hypergraph models. This is significant because it offers a new mathematical framework for representing and reasoning about belief in distributed systems, potentially improving the modeling of agent behavior.
Reference

Directed hypergraph models preserve the characteristic features of simplicial models for epistemic logic, while also being able to account for the beliefs of agents.

Research#llm📝 BlogAnalyzed: Dec 28, 2025 21:00

Force-Directed Graph Visualization Recommendation Engine: ML or Physics Simulation?

Published:Dec 28, 2025 19:39
1 min read
r/MachineLearning

Analysis

This post describes a novel recommendation engine that blends machine learning techniques with a physics simulation. The core idea involves representing images as nodes in a force-directed graph, where computer vision models provide image labels and face embeddings for clustering. An LLM acts as a scoring oracle to rerank nearest-neighbor candidates based on user likes/dislikes, influencing the "mass" and movement of nodes within the simulation. The system's real-time nature and integration of multiple ML components raise the question of whether it should be classified as machine learning or a physics-based data visualization tool. The author seeks clarity on how to accurately describe and categorize their creation, highlighting the interdisciplinary nature of the project.
Reference

Would you call this “machine learning,” or a physics data visualization that uses ML pieces?

Analysis

This paper introduces Reinforcement Networks, a novel framework for collaborative Multi-Agent Reinforcement Learning (MARL). It addresses the challenge of end-to-end training of complex multi-agent systems by organizing agents as vertices in a directed acyclic graph (DAG). This approach offers flexibility in credit assignment and scalable coordination, avoiding limitations of existing MARL methods. The paper's significance lies in its potential to unify hierarchical, modular, and graph-structured views of MARL, paving the way for designing and training more complex multi-agent systems.
Reference

Reinforcement Networks unify hierarchical, modular, and graph-structured views of MARL, opening a principled path toward designing and training complex multi-agent systems.

Analysis

This paper introduces a novel, positive approximation method for the parabolic Anderson model, leveraging the Feynman-Kac representation and random walks. The key contribution is an error analysis for the approximation, demonstrating a convergence rate that is nearly optimal, matching the Hölder continuity of the solution. This work is significant because it provides a quantitative framework for understanding the convergence of directed polymers to the parabolic Anderson model, a crucial connection in statistical physics.
Reference

The error in $L^p (Ω)$ norm is of order \[ O ig(h^{ rac{1}{2}[(2H + H_* - 1) \wedge 1] - ε}ig), \] where $h > 0$ is the step size in time (resp. $\sqrt{h}$ in space), and $ε> 0$ can be chosen arbitrarily small.

Analysis

This paper investigates the Parallel Minority Game (PMG), a multi-agent model, and analyzes its phase transitions under different decision rules. It's significant because it explores how simple cognitive features at the agent level can drastically impact the large-scale critical behavior of the system, relevant to socio-economic and active systems. The study compares instantaneous and threshold-based decision rules, revealing distinct universality classes and highlighting the impact of thresholding as a relevant perturbation.
Reference

Threshold rules produce a distinct non-mean-field universality class with β≈0.75 and a systematic failure of MF-DP dynamical scaling. We show that thresholding acts as a relevant perturbation to DP.

Analysis

This paper introduces a simplified model of neural network dynamics, focusing on inhibition and its impact on stability and critical behavior. It's significant because it provides a theoretical framework for understanding how brain networks might operate near a critical point, potentially explaining phenomena like maximal susceptibility and information processing efficiency. The connection to directed percolation and chaotic dynamics (epileptic seizures) adds further interest.
Reference

The model is consistent with the quasi-criticality hypothesis in that it displays regions of maximal dynamical susceptibility and maximal mutual information predicated on the strength of the external stimuli.

Research#llm📝 BlogAnalyzed: Dec 26, 2025 11:59

How to Use Chat AI "Correctly" for Learning ~With Prompt Examples~

Published:Dec 26, 2025 11:57
1 min read
Qiita ChatGPT

Analysis

This article, originating from Qiita, focuses on effectively utilizing chat AI like ChatGPT, Claude, and Gemini for learning purposes. It acknowledges the widespread adoption of these tools and emphasizes the importance of using them correctly. The article likely provides practical advice and prompt examples to guide users in maximizing the learning potential of chat AI. The promise of prompt examples is a key draw, suggesting actionable strategies rather than just theoretical discussion. The article caters to individuals already familiar with chat AI but seeking to refine their approach for educational gains. It's a practical guide for leveraging AI in self-directed learning.
Reference

Are you using chat AI (ChatGPT, Claude, Gemini, etc.) when learning new technologies?

Research#graph theory🔬 ResearchAnalyzed: Jan 4, 2026 10:47

Acyclic subgraphs of digraphs with high chromatic number

Published:Dec 26, 2025 09:55
1 min read
ArXiv

Analysis

This article likely presents research on graph theory, specifically focusing on the properties of directed graphs (digraphs) and their chromatic number. The research explores the relationship between the chromatic number of a digraph and the existence of acyclic subgraphs. The title suggests a focus on digraphs with a high chromatic number, implying an investigation into how the structure of these graphs influences the size or properties of their acyclic subgraphs. The source, ArXiv, indicates this is a pre-print or research paper.

Key Takeaways

    Reference

    Analysis

    This paper addresses the challenges of analyzing diffusion processes on directed networks, where the standard tools of spectral graph theory (which rely on symmetry) are not directly applicable. It introduces a Biorthogonal Graph Fourier Transform (BGFT) using biorthogonal eigenvectors to handle the non-self-adjoint nature of the Markov transition operator in directed graphs. The paper's significance lies in providing a framework for understanding stability and signal processing in these complex systems, going beyond the limitations of traditional methods.
    Reference

    The paper introduces a Biorthogonal Graph Fourier Transform (BGFT) adapted to directed diffusion.

    Analysis

    This paper addresses the problem of releasing directed graphs while preserving privacy. It focuses on the $p_0$ model and uses edge-flipping mechanisms under local differential privacy. The core contribution is a private estimator for the model parameters, shown to be consistent and normally distributed. The paper also compares input and output perturbation methods and applies the method to a real-world network.
    Reference

    The paper introduces a private estimator for the $p_0$ model parameters and demonstrates its asymptotic properties.

    Research#Graph Theory🔬 ResearchAnalyzed: Jan 10, 2026 07:19

    Dynamic Spectral Sparsification for Directed Hypergraphs Explored

    Published:Dec 25, 2025 13:31
    1 min read
    ArXiv

    Analysis

    This ArXiv paper explores a complex topic in graph theory with potential applications in various AI domains. The focus on dynamic spectral sparsification suggests a contribution to efficient processing of evolving graph structures.
    Reference

    The article's source is ArXiv, indicating a pre-print research paper.

    Research#llm🔬 ResearchAnalyzed: Jan 4, 2026 07:34

    Enhanced geometry prediction in laser directed energy deposition using meta-learning

    Published:Dec 23, 2025 18:44
    1 min read
    ArXiv

    Analysis

    The article focuses on using meta-learning to improve geometry prediction in laser directed energy deposition. This suggests an application of AI in manufacturing, specifically in optimizing the additive manufacturing process. The use of meta-learning implies an attempt to create a model that can quickly adapt to new data and improve its predictive capabilities, which is a significant advancement in this field.
    Reference

    Research#llm🔬 ResearchAnalyzed: Jan 4, 2026 08:05

    Information-directed sampling for bandits: a primer

    Published:Dec 23, 2025 06:49
    1 min read
    ArXiv

    Analysis

    This article is a primer on information-directed sampling for bandit problems. It likely introduces the concept and provides a basic understanding of the technique. The source being ArXiv suggests it's a research paper, focusing on a specific area within reinforcement learning.

    Key Takeaways

      Reference

      Research#Attention🔬 ResearchAnalyzed: Jan 10, 2026 08:44

      Analyzing Secondary Attention Sinks in AI Systems

      Published:Dec 22, 2025 09:06
      1 min read
      ArXiv

      Analysis

      The ArXiv source indicates this is likely a research paper exploring how attention mechanisms function in AI, possibly discussing unexpected behaviors or inefficiencies. Further analysis of the paper is needed to fully understand its specific findings and contributions to the field.
      Reference

      The context provides no specific key fact, requiring examination of the actual ArXiv paper.

      Research#Fuzzing🔬 ResearchAnalyzed: Jan 10, 2026 09:27

      Novel Metric 'Attention Distance' Enhances Fuzzing with LLMs

      Published:Dec 19, 2025 17:03
      1 min read
      ArXiv

      Analysis

      The article proposes a new metric, 'Attention Distance', to improve directed fuzzing techniques leveraging Large Language Models. This innovation could potentially lead to more effective vulnerability detection in software systems.
      Reference

      The context mentions the article originates from ArXiv, indicating a research paper.

      Research#GenAI🔬 ResearchAnalyzed: Jan 10, 2026 10:04

      K12 Education's Future: GenAI's Role and the Shifting Skillset

      Published:Dec 18, 2025 11:29
      1 min read
      ArXiv

      Analysis

      This ArXiv article likely explores the impact of Generative AI (GenAI) on K12 education, analyzing how it reshapes necessary skills and guides EdTech innovation. The article's focus on future readiness suggests a proactive stance toward integrating AI in the educational landscape.
      Reference

      The article likely discusses the skills students will need to succeed in the future, given the rise of GenAI.

      Research#llm🔬 ResearchAnalyzed: Jan 4, 2026 08:48

      DAG Learning from Zero-Inflated Count Data Using Continuous Optimization

      Published:Dec 18, 2025 06:26
      1 min read
      ArXiv

      Analysis

      This article likely presents a novel approach to learning Directed Acyclic Graphs (DAGs) from count data that has an excess of zero values (zero-inflated). The use of continuous optimization suggests a computational method for estimating the DAG structure. The source, ArXiv, indicates this is a pre-print or research paper.

      Key Takeaways

        Reference

        Research#llm🔬 ResearchAnalyzed: Jan 4, 2026 07:57

        BUILD with Precision: Bottom-Up Inference of Linear DAGs

        Published:Dec 18, 2025 03:06
        1 min read
        ArXiv

        Analysis

        This article likely presents a novel approach to inferring Directed Acyclic Graphs (DAGs) with linear relationships, focusing on a bottom-up inference strategy. The title suggests a focus on precision and efficiency in the inference process. The use of 'BUILD' might indicate a construction or generative aspect of the method.

        Key Takeaways

          Reference

          Research#LLM Reasoning🔬 ResearchAnalyzed: Jan 10, 2026 10:18

          Self-Directed LLM Exploration: A New Approach to Reasoning

          Published:Dec 17, 2025 18:44
          1 min read
          ArXiv

          Analysis

          This research explores a novel method for improving LLM reasoning capabilities using gradient-guided reinforcement learning, suggesting potential advancements in LLM performance. The ArXiv source indicates a focus on self-directed exploration, which could significantly impact how LLMs approach problem-solving.
          Reference

          The research focuses on using gradient-guided reinforcement learning for LLM reasoning.

          Research#RL🔬 ResearchAnalyzed: Jan 10, 2026 10:25

          EUBRL: Bayesian Reinforcement Learning for Uncertain Environments

          Published:Dec 17, 2025 12:55
          1 min read
          ArXiv

          Analysis

          The EUBRL paper, focusing on Epistemic Uncertainty Directed Bayesian Reinforcement Learning, likely presents a novel approach to improving the robustness and adaptability of RL agents. It suggests potential advancements in handling uncertainty, crucial for real-world applications where data is noisy and incomplete.
          Reference

          The paper focuses on Epistemic Uncertainty Directed Bayesian Reinforcement Learning.

          Research#llm🔬 ResearchAnalyzed: Jan 4, 2026 09:52

          Graph Contextual Reinforcement Learning for Efficient Directed Controller Synthesis

          Published:Dec 17, 2025 10:45
          1 min read
          ArXiv

          Analysis

          This article likely presents a novel approach to controller synthesis using graph-based reinforcement learning. The focus is on efficiency, suggesting improvements over existing methods. The use of 'directed' implies a specific type of control problem, and 'contextual' suggests the model considers environmental factors. The source being ArXiv indicates this is a research paper, likely detailing the methodology, experiments, and results.

          Key Takeaways

            Reference

            Research#Networks🔬 ResearchAnalyzed: Jan 10, 2026 11:05

            Harmonic Analysis Framework for Directed Networks: A New Approach

            Published:Dec 15, 2025 16:41
            1 min read
            ArXiv

            Analysis

            This research explores a novel framework for analyzing directed networks, a significant area in graph theory and network science. The biorthogonal Laplacian framework offers a potentially powerful new tool for understanding complex network structures and dynamics.
            Reference

            The article proposes a 'Biorthogonal Laplacian Framework for Non-Normal Graphs'.

            Analysis

            This research explores a novel application of graph neural networks in traffic management, specifically estimating traffic volume using speed profiles. The use of a directed spatial attention mechanism suggests an attempt to capture complex spatial dependencies within traffic networks.
            Reference

            The study uses a Spatio-Temporal Graph Neural Network with Directed Spatial Attention.

            Research#Bioinformatics🔬 ResearchAnalyzed: Jan 10, 2026 11:52

            AI Algorithm Advances Gene Regulatory Network Inference

            Published:Dec 12, 2025 00:54
            1 min read
            ArXiv

            Analysis

            This research explores a novel AI approach to understanding gene regulation, which is a significant area in bioinformatics. The use of spectral signed directed graph convolution presents a potentially innovative method for modeling complex biological systems.
            Reference

            The article is sourced from ArXiv, suggesting it is a pre-print of a scientific paper.

            Research#VLM🔬 ResearchAnalyzed: Jan 10, 2026 11:54

            VDAWorld: New Approach to World Modeling Using VLMs

            Published:Dec 11, 2025 19:21
            1 min read
            ArXiv

            Analysis

            The ArXiv source suggests that this is a research paper introducing a new methodology. The use of VLM (Vision-Language Models) for world modeling is an active area with potential for creating more robust and generalizable AI systems.
            Reference

            The context indicates the paper focuses on VLM-directed abstraction and simulation.

            Research#Fuzzing🔬 ResearchAnalyzed: Jan 10, 2026 13:13

            PBFuzz: AI-Driven Fuzzing for Proof-of-Concept Vulnerability Exploitation

            Published:Dec 4, 2025 09:34
            1 min read
            ArXiv

            Analysis

            The article introduces PBFuzz, a novel approach utilizing agentic directed fuzzing to automate the generation of Proof-of-Concept (PoC) exploits. This is a significant advancement in vulnerability research, potentially accelerating the discovery of critical security flaws.
            Reference

            The article likely discusses the use of agentic directed fuzzing.

            Research#Graph Theory🔬 ResearchAnalyzed: Jan 10, 2026 13:39

            Research Advances on Feedback Vertex Sets in Digraphs

            Published:Dec 1, 2025 13:44
            1 min read
            ArXiv

            Analysis

            The article's focus on feedback vertex sets within digraphs with bounded maximum degree suggests a niche area of graph theory research. The subject matter is highly technical and likely geared toward specialists in algorithms and discrete mathematics.
            Reference

            The article explores feedback vertex sets of digraphs with bounded maximum degree.

            Research#LLM🔬 ResearchAnalyzed: Jan 10, 2026 14:30

            Goal-Directed Search Improves Memory in Long-Context AI Tasks

            Published:Nov 20, 2025 22:45
            1 min read
            ArXiv

            Analysis

            This ArXiv article highlights a significant advance in long-context AI. The research suggests that goal-directed search strategies are superior to traditional memory compression techniques.
            Reference

            Goal-directed search outperforms goal-agnostic memory compression in long-context memory tasks.

            OpenAI Statement Analysis

            Published:Sep 11, 2025 14:00
            1 min read
            OpenAI News

            Analysis

            The article highlights OpenAI's commitment to its nonprofit roots while leveraging a Public Benefit Corporation (PBC) structure. The key takeaway is the allocation of significant resources ($100B+) towards safe and beneficial AI development. The brevity of the statement leaves room for further scrutiny regarding the specifics of resource allocation and the definition of 'safe and beneficial AI'.
            Reference

            OpenAI reaffirms its nonprofit leadership with a new structure granting equity in its PBC, enabling over $100B in resources to advance safe, beneficial AI for humanity.

            Meta Invests $14.3B in Scale AI

            Published:Jun 13, 2025 13:09
            1 min read
            Hacker News

            Analysis

            This news highlights a significant financial commitment by Meta towards AI development, specifically focusing on superintelligence. The investment in Scale AI suggests a strategic move to leverage their expertise in data labeling and AI infrastructure for Meta's own research and development efforts. The large sum indicates Meta's ambition and the high stakes involved in the race for advanced AI capabilities.
            Reference

            N/A - No direct quote provided in the summary.

            Technology#AI Adoption👥 CommunityAnalyzed: Jan 3, 2026 08:50

            AI is stifling new tech adoption?

            Published:Feb 14, 2025 12:45
            1 min read
            Hacker News

            Analysis

            The article poses a question about the impact of AI on the adoption of other new technologies. It suggests a potential negative correlation, implying that the focus and resources directed towards AI might be hindering the development and implementation of other innovative advancements. Further investigation would be needed to determine the specific mechanisms and extent of this potential stifling effect.

            Key Takeaways

            Reference

            MM15 - Save Your Servants!: Barker, Blatty & Writers In Hell

            Published:Oct 23, 2024 18:03
            1 min read
            NVIDIA AI Podcast

            Analysis

            This NVIDIA AI Podcast episode, part of the Movie Mindset Horrortober Season 1, analyzes two films directed by their writers: Clive Barker's "Hellraiser" (1987) and William Peter Blatty's "The Exorcist III" (1990). The discussion, led by Brendan James, explores the contrasting visions of evil presented in these films, one from a British gay man and the other from a devout American Catholic. The podcast highlights the practical effects of "Hellraiser" and dissects a famous jump scare from "Exorcist III". The episode is available on the public feed after being previously released on Patreon.
            Reference

            Both films feature visions of Hell’s intrusion onto earth; two competing and complementary visions of evil, one from a gay British man and the second from a devout American Catholic.

            Business#AI Hardware👥 CommunityAnalyzed: Jan 3, 2026 16:07

            OpenAI Commits to Buying $51M of AI Chips from a Startup Backed by Sam Altman

            Published:Dec 3, 2023 12:42
            1 min read
            Hacker News

            Analysis

            This news highlights the ongoing race for AI hardware and the strategic investments being made by major players like OpenAI. The commitment to purchase chips from a startup, especially one backed by a key figure like Sam Altman, suggests a potential shift in the AI chip market and a desire for specialized hardware. The amount, $51M, is significant and indicates a substantial bet on the startup's technology.
            Reference

            The article itself doesn't contain a direct quote, but the core information is the commitment to purchase AI chips.

            Research#Self-taught👥 CommunityAnalyzed: Jan 10, 2026 16:43

            Self-Taught AI Researcher's Journey: A Personal Narrative

            Published:Jan 20, 2020 18:39
            1 min read
            Hacker News

            Analysis

            This Hacker News article likely offers a firsthand account of someone navigating the AI research landscape without formal training. The piece's value lies in providing insights into alternative learning paths and the challenges faced by self-taught individuals.
            Reference

            The article's core is the personal journey of an individual.

            Research#Machine Learning👥 CommunityAnalyzed: Jan 10, 2026 17:26

            Accidental AI: A Developer's Elixir Journey into Machine Learning

            Published:Jul 23, 2016 18:36
            1 min read
            Hacker News

            Analysis

            The article likely chronicles a developer's unexpected foray into machine learning using the Elixir programming language. The focus is on a personal learning journey, potentially highlighting the ease (or difficulty) of applying ML principles within a functional programming context.
            Reference

            The article's primary focus is on a month-long exploration of machine learning.

            Analysis

            The article summarizes the week's key developments in machine learning and AI, highlighting several interesting topics. These include research on intrinsic motivation for AI, which aims to make AI systems more self-directed, and the development of a kill-switch for intelligent agents, addressing safety concerns. Other topics mentioned are "knu" chips for machine learning, a screenplay written by a neural network, and more. The article provides a concise overview of diverse advancements in the field, indicating a dynamic and rapidly evolving landscape. The inclusion of a podcast link suggests a focus on accessibility and dissemination of information.
            Reference

            This Week in Machine Learning & AI brings you the week’s most interesting and important stories from the world of machine learning and artificial intelligence.