Search: information-theoretic - ai.jp.net

Research Paper #Thermodynamics, Information Theory, Statistical Physics 🔬 ResearchAnalyzed: Jan 3, 2026 08:50

Thermodynamics Reconstructed with Information Theory

Published:Dec 31, 2025 06:02

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1 min read

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ArXiv

Analysis

This paper offers a novel axiomatic approach to thermodynamics, building it from information-theoretic principles. It's significant because it provides a new perspective on fundamental thermodynamic concepts like temperature, pressure, and entropy production, potentially offering a more general and flexible framework. The use of information volume and path-space KL divergence is particularly interesting, as it moves away from traditional geometric volume and local detailed balance assumptions.

Key Takeaways

•Develops an axiomatic reconstruction of thermodynamics based on information theory.
•Introduces 'information volume' and uses it to define pressure.
•Characterizes entropy production through relative-entropy asymmetry.
•Defines heat without relying on local detailed balance or specific bath models.

Reference

“Temperature, chemical potential, and pressure arise as conjugate variables of a single information-theoretic functional.”

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Research Paper #Signal Processing, Radio Frequency, Information Theory 🔬 ResearchAnalyzed: Jan 3, 2026 06:32

Wigner-Ville Transform for RF Signal Analysis

Published:Dec 30, 2025 22:16

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1 min read

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ArXiv

Analysis

This paper explores the Wigner-Ville transform as an information-theoretic tool for radio-frequency (RF) signal analysis. It highlights the transform's ability to detect and localize signals in noisy environments and quantify their information content using Tsallis entropy. The key advantage is improved sensitivity, especially for weak or transient signals, offering potential benefits in resource-constrained applications.

Key Takeaways

•The paper introduces the Wigner-Ville transform as an information-theoretic tool for RF signal analysis.
•It uses Tsallis entropy to quantify information content within signals.
•Wigner-Ville-based methods offer improved sensitivity, especially for weak or transient signals.
•Significant performance gains (e.g., >15dB) are possible compared to energy-based methods.
•The approach avoids extensive training routines, making it suitable for resource-constrained applications.

Reference

“Wigner-Ville-based detection measures can be seen to provide significant sensitivity advantage, for some shown contexts greater than 15~dB advantage, over energy-based measures and without extensive training routines.”

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

Information-Theoretic Quality Metric of Low-Dimensional Embeddings

Published:Dec 30, 2025 04:34

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1 min read

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ArXiv

Analysis

The article's title suggests a focus on evaluating the quality of low-dimensional embeddings using information-theoretic principles. This implies a technical paper likely exploring novel methods for assessing the effectiveness of dimensionality reduction techniques, potentially in the context of machine learning or data analysis. The source, ArXiv, indicates it's a pre-print server, suggesting the work is recent and not yet peer-reviewed.

Key Takeaways

•Focus on evaluating low-dimensional embeddings.
•Utilizes information-theoretic principles.
•Likely explores novel methods for assessing dimensionality reduction.
•Published on ArXiv, indicating a pre-print.

Reference

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Paper #llm 🔬 ResearchAnalyzed: Jan 3, 2026 18:47

Information-Theoretic Debiasing for Reward Models

Published:Dec 29, 2025 13:39

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1 min read

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ArXiv

Analysis

This paper addresses a critical problem in Reinforcement Learning from Human Feedback (RLHF): the presence of inductive biases in reward models. These biases, stemming from low-quality training data, can lead to overfitting and reward hacking. The proposed method, DIR (Debiasing via Information optimization for RM), offers a novel information-theoretic approach to mitigate these biases, handling non-linear correlations and improving RLHF performance. The paper's significance lies in its potential to improve the reliability and generalization of RLHF systems.

Key Takeaways

•Addresses the problem of inductive biases in reward models, which can lead to overfitting and reward hacking.
•Proposes a novel information-theoretic debiasing method called DIR (Debiasing via Information optimization for RM).
•DIR maximizes the mutual information between RM scores and human preference pairs while minimizing the MI between RM outputs and biased attributes.
•Demonstrates effectiveness in mitigating biases related to response length, sycophancy, and format.
•Shows improved RLHF performance and better generalization abilities across diverse benchmarks.
•Provides code and training recipes for reproducibility.

Reference

“DIR not only effectively mitigates target inductive biases but also enhances RLHF performance across diverse benchmarks, yielding better generalization abilities.”

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Research Paper #Community Detection, Graph Theory, Machine Learning 🔬 ResearchAnalyzed: Jan 3, 2026 19:36

Exact Recovery in Geometric SBM Analyzed

Published:Dec 28, 2025 04:30

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1 min read

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ArXiv

Analysis

This paper addresses the problem of community detection in spatially-embedded networks, specifically focusing on the Geometric Stochastic Block Model (GSBM). It aims to determine the conditions under which the labels of nodes in the network can be perfectly recovered. The significance lies in understanding the limits of exact recovery in this model, which is relevant to social network analysis and other applications where spatial relationships and community structures are important.

Key Takeaways

•Focuses on exact label recovery in the Geometric Stochastic Block Model (GSBM).
•Characterizes the information-theoretic threshold for exact recovery.
•Generalizes previous work on the topic.

Reference

“The paper completely characterizes the information-theoretic threshold for exact recovery in the GSBM.”

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Research Paper #Physics, Numerical Simulation, Nonlinear Field Theory 🔬 ResearchAnalyzed: Jan 3, 2026 19:48

amangkurat: A Python Library for Simulating Nonlinear Klein-Gordon Equation

Published:Dec 27, 2025 16:15

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1 min read

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ArXiv

Analysis

This paper introduces a new open-source Python library, amangkurat, for simulating the nonlinear Klein-Gordon equation. The library uses a hybrid numerical method (Fourier pseudo-spectral spatial discretization and a symplectic Størmer-Verlet temporal integrator) to ensure accuracy and long-term stability. The paper validates the library's performance across various physical regimes and uses information-theoretic metrics to analyze the dynamics. This work is significant because it provides a readily available and efficient tool for researchers and educators in nonlinear field theory, enabling exploration of complex phenomena.

Key Takeaways

•Introduces amangkurat, a Python library for simulating the nonlinear Klein-Gordon equation.
•Employs a hybrid numerical method for accuracy and stability.
•Validates the library across various physical regimes.
•Uses information-theoretic metrics for dynamical analysis.
•Provides an efficient tool for research and education in nonlinear field theory.

Reference

“The library's capabilities are validated across four canonical physical regimes: dispersive linear wave propagation, static topological kink preservation in phi-fourth theory, integrable breather dynamics in the sine-Gordon model, and non-integrable kink-antikink collisions.”

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Research Paper #Quantum Physics, Computational Physics 🔬 ResearchAnalyzed: Jan 3, 2026 19:48

1D Quantum Tunneling Solver Library

Published:Dec 27, 2025 16:13

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1 min read

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ArXiv

Analysis

This paper introduces an open-source Python library for simulating 1D quantum tunneling. It's valuable for educational purposes and preliminary exploration of tunneling dynamics due to its accessibility and performance. The use of Numba for JIT compilation is a key aspect for achieving performance comparable to compiled languages. The validation through canonical test cases and the analysis using information-theoretic measures add to the paper's credibility. The limitations are clearly stated, emphasizing its focus on idealized conditions.

Key Takeaways

•Open-source Python library for 1D quantum tunneling simulations.
•Utilizes split-operator method and FFT-based spectral differentiation.
•Employs Numba for performance optimization.
•Validated with rectangular and Gaussian barrier test cases.
•Suitable for educational and preliminary research purposes.

Reference

“The library provides a deployable tool for teaching quantum mechanics and preliminary exploration of tunneling dynamics.”

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Research Paper #Biology, Information Theory, Decision-Making 🔬 ResearchAnalyzed: Jan 3, 2026 20:04

Hierarchical Preemption in Lambda Phage Decision-Making

Published:Dec 27, 2025 00:13

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1 min read

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ArXiv

Analysis

This paper introduces a novel information-theoretic framework for understanding hierarchical control in biological systems, using the Lambda phage as a model. The key finding is that higher-level signals don't block lower-level signals, but instead collapse the decision space, leading to more certain outcomes while still allowing for escape routes. This is a significant contribution to understanding how complex biological decisions are made.

Key Takeaways

•Hierarchical control in biological systems can be understood through the concept of hierarchical preemption.
•Higher-level signals collapse decision space rather than blocking lower-level signals.
•The Lambda phage lysis-lysogeny decision is a good model for studying this mechanism.
•The UV damage sensor (RecA) plays a key role in this preemption.
•This framework provides a quantitative understanding of cellular decision-making.

Reference

“The UV damage sensor (RecA) achieves 2.01x information advantage over environmental signals by preempting bistable outcomes into monostable attractors (98% lysogenic or 85% lytic).”

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Research Paper #Quantum Cryptography 🔬 ResearchAnalyzed: Jan 3, 2026 20:15

Quantum Secret Sharing Capacity Limits

Published:Dec 26, 2025 14:59

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1 min read

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ArXiv

Analysis

This paper investigates the fundamental limits of quantum secret sharing (QSS), a crucial area in quantum cryptography. It provides an information-theoretic framework for analyzing the rates at which quantum secrets can be shared securely among multiple parties. The work's significance lies in its contribution to understanding the capacity of QSS schemes, particularly in the presence of noise, which is essential for practical implementations. The paper's approach, drawing inspiration from classical secret sharing and connecting it to compound quantum channels, offers a valuable perspective on the problem.

Key Takeaways

•Introduces an information-theoretic model for analyzing QSS rates.
•Establishes a regularized characterization for QSS capacity.
•Determines the capacity for QSS with dephasing noise.

Reference

“The paper establishes a regularized characterization for the QSS capacity, and determines the capacity for QSS with dephasing noise.”

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Paper #LLM 🔬 ResearchAnalyzed: Jan 4, 2026 00:13

Information Theory Guides Agentic LM System Design

Published:Dec 25, 2025 15:45

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1 min read

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ArXiv

Analysis

This paper introduces an information-theoretic framework to analyze and optimize agentic language model (LM) systems, which are increasingly used in applications like Deep Research. It addresses the ad-hoc nature of designing compressor-predictor systems by quantifying compression quality using mutual information. The key contribution is demonstrating that mutual information strongly correlates with downstream performance, allowing for task-independent evaluation of compressor effectiveness. The findings suggest that scaling compressors is more beneficial than scaling predictors, leading to more efficient and cost-effective system designs.

Key Takeaways

•Introduces an information-theoretic framework for analyzing agentic LM systems.
•Uses mutual information to quantify compression quality in a task-independent manner.
•Demonstrates a strong correlation between mutual information and downstream performance.
•Suggests scaling compressors is more effective than scaling predictors.
•Enables more efficient and cost-effective system designs.

Reference

“Scaling compressors is substantially more effective than scaling predictors.”

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Research #Quantum Gravity 🔬 ResearchAnalyzed: Jan 10, 2026 17:51

Quantum Gravity Insights from Entanglement and Holography

Published:Dec 25, 2025 13:49

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1 min read

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ArXiv

Analysis

The article explores complex concepts in quantum gravity and holography, focusing on the interplay of entanglement and information. While the implications are highly theoretical, the research could contribute to a deeper understanding of spacetime.

Key Takeaways

•Investigates the relationship between entanglement and spacetime geometry.
•Applies holographic principles to study quantum gravity.
•Explores the information-theoretic aspects of quantum entanglement.

Reference

“The paper likely discusses aspects of the entanglement wedge cross section and its connection to holographic entanglement of assistance.”

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Research #quantum computing 🔬 ResearchAnalyzed: Jan 4, 2026 10:34

Security Boundaries of Quantum Key Reuse: A Quantitative Evaluation Method for QKD Key Rotation Interval and Security Benefits Combined with Block Ciphers

Published:Dec 25, 2025 08:13

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1 min read

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ArXiv

Analysis

This article presents a quantitative method for evaluating the security of Quantum Key Distribution (QKD) systems, specifically focusing on key reuse and its implications when combined with block ciphers. The research likely explores the optimal key rotation intervals to maintain security and quantifies the benefits of this approach. The use of ArXiv suggests this is a pre-print, indicating ongoing research.

Key Takeaways

•Focuses on the security implications of key reuse in QKD systems.
•Proposes a quantitative method for evaluating key rotation intervals.
•Examines the security benefits when QKD is combined with block ciphers.
•Likely involves mathematical and computational analysis of QKD security.

Reference

“The article likely delves into the mathematical and computational aspects of QKD security, potentially including discussions on information-theoretic security and practical implementation challenges.”

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Research #Schrödinger Bridge 🔬 ResearchAnalyzed: Jan 10, 2026 07:35

Novel Research Explores Non-Entropic Schrödinger Bridges

Published:Dec 24, 2025 16:10

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1 min read

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ArXiv

Analysis

The article's title suggests a highly specialized area of research within theoretical physics or applied mathematics, likely exploring connections between quantum mechanics and optimal transport. Without further context, the impact is difficult to gauge, but the topic's complexity indicates a focus on foundational theoretical understanding.

Key Takeaways

•The research likely delves into the mathematical properties of Schrödinger bridges.
•The 'non-entropic' aspect suggests a departure from standard information-theoretic constraints.
•The focus is probably on theoretical analysis rather than practical applications.

Reference

“The source is ArXiv, indicating a pre-print publication.”

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Research #llm 🔬 ResearchAnalyzed: Jan 4, 2026 09:31

Information-theoretic signatures of causality in Bayesian networks and hypergraphs

Published:Dec 23, 2025 17:46

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1 min read

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ArXiv

Analysis

This article likely presents research on identifying causal relationships within complex systems using information theory. The focus is on Bayesian networks and hypergraphs, which are mathematical frameworks for representing probabilistic relationships and higher-order interactions, respectively. The use of information-theoretic measures suggests an approach that quantifies the information flow and dependencies to infer causality. The ArXiv source indicates this is a pre-print, meaning it's likely undergoing peer review or has not yet been formally published.

Key Takeaways

•The research explores causal inference using information theory.
•It focuses on Bayesian networks and hypergraphs.
•The approach likely involves quantifying information flow to identify causal relationships.

Reference

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Research #Video compression 🔬 ResearchAnalyzed: Jan 10, 2026 09:56

InfoTok: Information-Theoretic Video Tokenization for Enhanced Compression

Published:Dec 18, 2025 17:13

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1 min read

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ArXiv

Analysis

This research paper introduces InfoTok, a novel approach to video tokenization using information-theoretic principles. The method aims to improve video compression efficiency, potentially leading to faster and more efficient video processing and storage.

Key Takeaways

•InfoTok leverages information-theoretic principles to improve video compression.
•The method uses an adaptive discrete video tokenizer.
•This research has the potential to enhance video processing and storage efficiency.

Reference

“InfoTok employs an adaptive discrete video tokenizer.”

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Research #LLM Editing 🔬 ResearchAnalyzed: Jan 10, 2026 10:09

Robust Editing Framework for Large Language Models Explored

Published:Dec 18, 2025 06:21

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1 min read

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ArXiv

Analysis

The ArXiv article introduces an information-theoretic approach to enhance the robustness of Large Language Model (LLM) editing. This work likely aims to improve the reliability and accuracy of LLMs by developing methods to modify their knowledge bases.

Key Takeaways

•Focuses on improving the resilience of LLM editing processes.
•Employs an information-theoretic framework.
•The research is published on ArXiv, indicating early-stage or ongoing work.

Reference

“The article is sourced from ArXiv.”

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Research #Model Discovery 🔬 ResearchAnalyzed: Jan 10, 2026 10:14

Unveiling Models: Information Theory and Discriminative Sampling

Published:Dec 17, 2025 22:08

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1 min read

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ArXiv

Analysis

This article likely explores a novel approach to model discovery, potentially combining information-theoretic principles with discriminative sampling techniques. The research area focuses on developing more efficient and effective methods for identifying and characterizing underlying models within datasets.

Key Takeaways

•The research likely leverages information theory to guide the model discovery process.
•Discriminative sampling is potentially used to enhance the efficiency of model identification.
•The focus is on developing improved methods for understanding and characterizing models.

Reference

“The context provides the title and source, indicating this is a research paper from ArXiv.”

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Research #llm 🔬 ResearchAnalyzed: Jan 4, 2026 07:21

Information-Theoretic Limits of Integrated Sensing and Communication with Finite Learning Capacity

Published:Dec 15, 2025 13:02

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1 min read

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ArXiv

Analysis

This article, sourced from ArXiv, likely presents a theoretical analysis of the information-theoretic limits of systems that combine sensing and communication capabilities, considering the constraints imposed by finite learning capacity. The research probably explores how much information can be reliably transmitted and sensed under these limitations. The focus is on the theoretical underpinnings rather than practical applications, given the source.

•Lena Voita is a prominent researcher in NLP.
•The article provides links to her papers and blog posts.
•The topics covered include source/target contributions to NMT, information-theoretic probing, and representation evolution in Transformers.

Reference

“Lena has been investigating many fascinating topics in machine learning and NLP.”

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Thermodynamics Reconstructed with Information Theory

Analysis

Key Takeaways

Wigner-Ville Transform for RF Signal Analysis

Analysis

Key Takeaways

Information-Theoretic Quality Metric of Low-Dimensional Embeddings

Analysis

Key Takeaways

Information-Theoretic Debiasing for Reward Models

Analysis

Key Takeaways

Exact Recovery in Geometric SBM Analyzed

Analysis

Key Takeaways

amangkurat: A Python Library for Simulating Nonlinear Klein-Gordon Equation

Analysis

Key Takeaways

1D Quantum Tunneling Solver Library

Analysis

Key Takeaways

Hierarchical Preemption in Lambda Phage Decision-Making

Analysis

Key Takeaways

Quantum Secret Sharing Capacity Limits

Analysis

Key Takeaways

Information Theory Guides Agentic LM System Design

Analysis

Key Takeaways

Quantum Gravity Insights from Entanglement and Holography

Analysis

Key Takeaways

Security Boundaries of Quantum Key Reuse: A Quantitative Evaluation Method for QKD Key Rotation Interval and Security Benefits Combined with Block Ciphers

Analysis

Key Takeaways

Novel Research Explores Non-Entropic Schrödinger Bridges

Analysis

Key Takeaways

Information-theoretic signatures of causality in Bayesian networks and hypergraphs

Analysis

Key Takeaways

InfoTok: Information-Theoretic Video Tokenization for Enhanced Compression

Analysis

Key Takeaways

Robust Editing Framework for Large Language Models Explored

Analysis

Key Takeaways

Unveiling Models: Information Theory and Discriminative Sampling

Analysis

Key Takeaways

Information-Theoretic Limits of Integrated Sensing and Communication with Finite Learning Capacity

Analysis

Key Takeaways

Bounding Hallucinations in RAG Systems with Information-Theoretic Guarantees

Analysis

Key Takeaways

Improving Shannon Entropy Estimation through Sample Space Partitioning

Analysis

Key Takeaways

Information-Theoretic Approach to Intentionality in Neural Networks

Analysis

Key Takeaways

Analyzing Chain-of-Thought for Code Generation: Empirical and Information-Theoretic Insights

Analysis

Key Takeaways

Data-Efficient AI: An Uncertainty-Aware Information-Theoretic Approach

Analysis

Key Takeaways

Information-Theoretic Constraints on Quantum Optimization: A Deep Dive

Analysis

Key Takeaways

New Method Slashes AI Hallucinations in Finance by 92%

Analysis

Key Takeaways

Lena Voita - NLP

Analysis

Key Takeaways

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