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Technology#AI Development📝 BlogAnalyzed: Jan 4, 2026 05:51

I got tired of Claude forgetting what it learned, so I built something to fix it

Published:Jan 3, 2026 21:23
1 min read
r/ClaudeAI

Analysis

This article describes a user's solution to Claude AI's memory limitations. The user created Empirica, an epistemic tracking system, to allow Claude to explicitly record its knowledge and reasoning. The system focuses on reconstructing Claude's thought process rather than just logging actions. The article highlights the benefits of this approach, such as improved productivity and the ability to reload a structured epistemic state after context compacting. The article is informative and provides a link to the project's GitHub repository.
Reference

“The key insight: It's not just logging. At any point - even after a compact - you can reconstruct what Claude was thinking, not just what it did.”

Permalinkr/ClaudeAI
Research Paper#Language Models (LLMs), Evaluation, Robustness🔬 ResearchAnalyzed: Jan 3, 2026 16:00

DDFT: A New Test for LLM Reliability

Published:Dec 29, 2025 20:29
1 min read
ArXiv

Analysis

This paper introduces a novel testing protocol, the Drill-Down and Fabricate Test (DDFT), to evaluate the epistemic robustness of language models. It addresses a critical gap in current evaluation methods by assessing how well models maintain factual accuracy under stress, such as semantic compression and adversarial attacks. The findings challenge common assumptions about the relationship between model size and reliability, highlighting the importance of verification mechanisms and training methodology. This work is significant because it provides a new framework for evaluating and improving the trustworthiness of LLMs, particularly for critical applications.
Reference

“Error detection capability strongly predicts overall robustness (rho=-0.817, p=0.007), indicating this is the critical bottleneck.”

PermalinkArXiv
Research Paper#Artificial Intelligence, Logic, Belief Representation🔬 ResearchAnalyzed: Jan 3, 2026 19:14

Hypergraph Semantics for Belief Logics

Published:Dec 28, 2025 21:49
1 min read
ArXiv

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.”

PermalinkArXiv
Research Paper#Knowledge Representation, Logic, AI Reasoning🔬 ResearchAnalyzed: Jan 3, 2026 20:00

Refining Explicit Knowledge with Awareness and Indistinguishability

Published:Dec 27, 2025 05:47
1 min read
ArXiv

Analysis

This paper addresses a known limitation in the logic of awareness, a framework designed to address logical omniscience. The original framework's definition of explicit knowledge can lead to undesirable logical consequences. This paper proposes a refined definition based on epistemic indistinguishability, aiming for a more accurate representation of explicit knowledge. The use of elementary geometry as an example provides a clear and relatable context for understanding the concepts. The paper's contributions include a new logic (AIL) with increased expressive power, a formal system, and proofs of soundness and completeness. This work is relevant to AI research because it improves the formalization of knowledge representation, which is crucial for building intelligent systems that can reason effectively.
Reference

“The paper refines the definition of explicit knowledge by focusing on indistinguishability among possible worlds, dependent on awareness.”

PermalinkArXiv
Research Paper#AI in Mental Health, LLMs, Psychotherapy, Marginalized Clients🔬 ResearchAnalyzed: Jan 3, 2026 20:01

LLM Chatbots as Relational Mediators in Psychotherapy

Published:Dec 27, 2025 04:35
1 min read
ArXiv

Analysis

This paper is significant because it moves beyond viewing LLMs in mental health as simple tools or autonomous systems. It highlights their potential to address relational challenges faced by marginalized clients in therapy, such as building trust and navigating power imbalances. The proposed Dynamic Boundary Mediation Framework offers a novel approach to designing AI systems that are more sensitive to the lived experiences of these clients.
Reference

“The paper proposes the Dynamic Boundary Mediation Framework, which reconceptualizes LLM-enhanced systems as adaptive boundary objects that shift mediating roles across therapeutic stages.”

PermalinkArXiv
Research#Education🔬 ResearchAnalyzed: Jan 10, 2026 07:43

AI's Impact on Undergraduate Mathematics Education Explored

Published:Dec 24, 2025 08:23
1 min read
ArXiv

Analysis

This ArXiv paper likely investigates how AI tools affect undergraduate math students' understanding and problem-solving abilities. It's a relevant topic, considering the increasing use of AI in education and the potential for both positive and negative impacts.
Reference

“The paper likely discusses the interplay of synthetic fluency (AI-generated solutions) and epistemic offloading (reliance on AI for knowledge) within the context of undergraduate mathematics.”

PermalinkArXiv
Research#llm🔬 ResearchAnalyzed: Jan 4, 2026 10:26

The Silent Scholar Problem: A Probabilistic Framework for Breaking Epistemic Asymmetry in LLM Agents

Published:Dec 24, 2025 02:02
1 min read
ArXiv

Analysis

This article likely presents a novel approach to address a specific challenge in the design and application of Large Language Model (LLM) agents. The title suggests a focus on epistemic asymmetry, meaning unequal access to knowledge or understanding between agents. The use of a "probabilistic framework" indicates a statistical or uncertainty-aware method for tackling this problem. The source, ArXiv, confirms this is a research paper.

Key Takeaways

    Reference

    “”

    PermalinkArXiv
    Research#Trust🔬 ResearchAnalyzed: Jan 10, 2026 09:05

    MEVIR 2 Framework: A Moral-Epistemic Model for Trust in AI

    Published:Dec 20, 2025 23:32
    1 min read
    ArXiv

    Analysis

    This research article from ArXiv introduces the MEVIR 2 framework, a model for understanding human trust decisions, particularly relevant in the context of AI. The framework's virtue-informed approach provides a unique perspective on trust dynamics, addressing both moral and epistemic aspects.
    Reference

    “The article discusses the MEVIR 2 Framework.”

    PermalinkArXiv
    Research#llm🔬 ResearchAnalyzed: Jan 4, 2026 09:15

    Plausibility as Failure: How LLMs and Humans Co-Construct Epistemic Error

    Published:Dec 18, 2025 16:45
    1 min read
    ArXiv

    Analysis

    This article, sourced from ArXiv, likely explores the ways in which Large Language Models (LLMs) and humans contribute to the creation and propagation of errors in knowledge. The title suggests a focus on how the 'plausibility' of information, rather than its truth, can lead to epistemic failures. The research likely examines the interaction between LLMs and human users, highlighting how both contribute to the spread of misinformation or incorrect beliefs.

    Key Takeaways

      Reference

      “”

      PermalinkArXiv
      Research#LLM🔬 ResearchAnalyzed: Jan 10, 2026 10:13

      New Benchmark Evaluates LLMs' Self-Awareness

      Published:Dec 17, 2025 23:23
      1 min read
      ArXiv

      Analysis

      This ArXiv article introduces a new benchmark, Kalshibench, focused on evaluating the epistemic calibration of Large Language Models (LLMs) using prediction markets. This is a crucial area of research, examining how well LLMs understand their own limitations and uncertainties.
      Reference

      “Kalshibench is a new benchmark for evaluating epistemic calibration via prediction markets.”

      PermalinkArXiv
      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.”

      PermalinkArXiv
      Research#LLM🔬 ResearchAnalyzed: Jan 10, 2026 10:35

      Diverse Language Models Prevent Knowledge Degradation

      Published:Dec 17, 2025 02:03
      1 min read
      ArXiv

      Analysis

      This research suggests a promising approach to improve the long-term reliability of AI models. The use of diverse language models could significantly enhance the robustness and trustworthiness of AI systems.
      Reference

      “Epistemic diversity across language models mitigates knowledge collapse.”

      PermalinkArXiv
      Research#GenAI🔬 ResearchAnalyzed: Jan 10, 2026 13:15

      Analyzing Student Inquiry in GenAI-Supported Clinical Practice

      Published:Dec 4, 2025 02:08
      1 min read
      ArXiv

      Analysis

      This research explores how students use GenAI in clinical practice. The integration of Epistemic Network Analysis and Sequential Pattern Mining offers a novel approach to understanding student learning behavior.
      Reference

      “The study uses Epistemic Network Analysis and Sequential Pattern Mining.”

      PermalinkArXiv
      Research#llm🔬 ResearchAnalyzed: Jan 4, 2026 07:09

      Epistemic Substitution: How Grokipedia's AI-Generated Encyclopedia Restructures Authority

      Published:Dec 3, 2025 01:05
      1 min read
      ArXiv

      Analysis

      This article likely analyzes the impact of AI-generated content, specifically an AI-generated encyclopedia called Grokipedia, on the established structures of authority and knowledge dissemination. It probably explores how the use of AI alters the way information is created, validated, and trusted, potentially challenging traditional sources of authority like human experts and established encyclopedias. The focus is on the epistemological implications of this shift.

      Key Takeaways

        Reference

        “”

        PermalinkArXiv
        Ethics#Trust🔬 ResearchAnalyzed: Jan 10, 2026 13:33

        MEVIR Framework: A Virtue-Based Model for Human Trust in AI

        Published:Dec 2, 2025 01:11
        1 min read
        ArXiv

        Analysis

        This research article from ArXiv proposes the MEVIR framework, a novel approach to understanding and modeling human trust in AI systems. The framework's virtue-informed approach provides a potentially valuable perspective on the ethical and epistemic considerations of AI adoption.
        Reference

        “The article introduces the MEVIR Framework.”

        PermalinkArXiv
        Research#Quantum🔬 ResearchAnalyzed: Jan 10, 2026 14:00

        Quantum Foundations: Einstein, Schrödinger, Popper, and the PBR Framework

        Published:Nov 28, 2025 12:15
        1 min read
        ArXiv

        Analysis

        This article likely delves into the philosophical implications of quantum mechanics, specifically examining the debate around the nature of the wave function and its relation to reality. The reference to Einstein, Schrödinger, and Popper suggests a historical analysis of the epistemic and ontological interpretations of quantum theory.
        Reference

        “The article's focus is on Einstein's 1935 letters to Schrödinger and Popper.”

        PermalinkArXiv
        Research#AI Reasoning🔬 ResearchAnalyzed: Jan 10, 2026 14:07

        Modal Logic's Role in AI Simulation, Refinement, and Knowledge Management

        Published:Nov 27, 2025 12:16
        1 min read
        ArXiv

        Analysis

        This ArXiv paper likely explores the application of modal logic in AI, focusing on simulation, refinement, and mutual ignorance within AI systems. The use of modal logic suggests an attempt to formally represent and reason about knowledge, belief, and uncertainty in these complex systems.
        Reference

        “The paper examines the utility of modal logic for simulation, refinement, and the handling of mutual ignorance in AI contexts.”

        PermalinkArXiv
        Research#LLM🔬 ResearchAnalyzed: Jan 10, 2026 14:46

        LLMs Demonstrate Community-Aligned Behavior in Uncertain Scenarios

        Published:Nov 14, 2025 20:04
        1 min read
        ArXiv

        Analysis

        This ArXiv paper explores the ability of Large Language Models (LLMs) to align their behavior with community norms, particularly under uncertain conditions. The research investigates how LLMs adapt their responses based on the context and implied epistemic stance of the provided data.
        Reference

        “The study provides evidence of 'Epistemic Stance Transfer' in LLMs.”

        PermalinkArXiv