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Interpreting Black-Box Large Language Models with Sentence-Level Energy Landscapes
Aug 3, 2026Maryam Rezaee, Pooriya Safaei, Maryam Asgarinezhad +1Post-Hoc Free-Energy Landscape
ICA Lens: Interpreting Language Models Without Training Another Dictionary
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Compile Once, Differentiate Everywhere: A Differentiable Meta-Circular Interpreter
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Interpreting Global Perturbation Robustness of Image Models using Axiomatic Spectral Importance Decomposition
Aug 2, 2024Róisín Luo, James McDermott, Colm O'RiordanMechanistic Interpretability Perturbations
Transferring Visual Explanations: How Cross-Architecture Knowledge Distillation Affects Model Interpretability
Sep 20, 2026Aleks Czufarow, Ihor BabinModel Interpretability Training-Free Inference Framework
Interpretability Without Tradeoffs: Disentangling Polysemanticity At Equal Predictive Performance
May 29, 2026Doğukan Bağcı, Bernt Schiele, Simone Schaub-Meyer +2Model Interpretability Model Training
Descriptive Collision in Sparse Autoencoder Auto-Interpretability: When One Explanation Describes Many Features
May 13, 2026Jordan F. McCannSparse Autoencoder Features Semantic Representations
Transformer Interpretability from Perspective of Attention and Gradient
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From Mechanistic to Compositional Interpretability
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LITERARYBIGFIVE: Author-Personalized Text Generation in a Unified Interpretable Space
Aug 24, 2026Jinghui Zhang, Lang Gao, Ao Li +5Text Generation Authorship
Interpretable Column Annotation with LLM-Symbolized Decision Process Materialization
Jul 28, 2026Mengqi Wang, Jianwei Wang, Qing Liu +5Annotations Decisions
TIDE: Trustworthy and Interpretable Battery Degradation Estimation with Contextual Learning and Symbolic Distillation
Jul 16, 2026Wen Yang Tan, Jiawei Li, Fang Liu +4Lithium-Ion Batteries State Estimation
An Interpretable Deep Learning Framework for Discovery and Clinical Validation of Deep Radiomic Signatures in Tumor Classification
Jul 3, 2026Chengkun Sun, Jinqian Pan, Renjie Liang +7Radiomics Biomarker
Interpretable Kolmogorov-Arnold Network with Feature-Isolated Temporal Attention Mechanism for Electricity Load Forecasting
Jun 22, 2026Jinhao Li, Hao WangElectricity Price Forecasting Kolmogorov-Arnold Networks
DEM: A Distilled Explanation Model for Interpretable Anomaly Detection in Physiological Sensor Networks
May 29, 2026Jyotirmoy Singh, Anushka Roy, Shreea Bose +1Anomaly Detection Physiological Data
Motif-based morphology signatures for interpretable ECG screening and monitoring
May 26, 2026Nivedita Bijlani, Mauricio VillarroelPediatric-Adult Electrocardiogram Alignment Congenital Heart Disease
INSHAPE: Instance-Level Shapelets for Interpretable Time-Series Classification
May 19, 2026Seongjun Lee, Seokhyun Lee, Changhee LeeTime-Series Classification Statistical Shape Model
Interpretable Machine Learning for Football Performance Analysis: Evidence of Limited Transferability from Elite Leagues to University Competition
May 11, 2026Yu-Fang Tsai, Yu-Jen Chen, Kok-Hua Tan +5Model Interpretability Domain Shift
Joint Semantic Token Selection and Prompt Optimization for Interpretable Prompt Learning
May 6, 2026Yating Wang, Yaqi Zhao, Yongshun Gong +2Visual In-Context Learning Automatic Prompt Optimization
PRISMA: Preference-Reinforced Self-Training Approach for Interpretable Emotionally Intelligent Negotiation Dialogues
Apr 20, 2026Prajwal Vijay Kajare, Priyanshu Priya, Bikash Santra +1Negotiation Conversational Artificial Intelligence
Towards Interpretable Framework for Neural Audio Codecs via Sparse Autoencoders: A Case Study on Accent Information
Mar 18, 2026Shih-Heng Wang, Tiantian Feng, Aditya Kommineni +4Neural Audio Codecs Paralinguistic Cues
Beyond Correlation: Learning Supervised, Sample-Distinct, and Eigenimage-Interpretable Representations
Jul 22, 2025Mojtaba MoattariDimensionality Reduction Face Embeddings
Interpretable Retinal Disease Prediction Using Biology-Informed Heterogeneous Graph Representations
Feb 23, 2025Laurin Lux, Alexander H. Berger, Maria Romeo Tricas +8Interpretable Diabetic Retinopathy Grading Medical Image Classification
FADA: Accessible fetal ultrasound interpretation and annotation with a selectively distilled unified vision-language model
Jun 9, 2026Mahmood Alzubaidi, Uzair Shah, Raden Muaz +6Prenatal Ultrasound Fundus Images
Inside the Visual Mind: Neuroscience-Motivated Concept Circuits for Interpreting and Steering Vision Transformers
Jun 4, 2026Tang Li, Yanlin Chen, Mengmeng Ma +1Self-Supervised Vision Transformers Vision Transformer
Beyond Recall: Behavioral Specification as an Interpretive Layer for AI Personalization
May 27, 2026Aarik GulayaArtificial Intelligence Alignment Interpretive Openness
Generic Interpretation Approach for Transformer Models Incorporating Heterogenous Attention Structures
May 25, 2026Yongjin Cui, Xiaohui Fan, Huajun ChenTransformer Attention Multimodal Transformers
LithoBench: Benchmarking Large Multimodal Models for Remote-Sensing Lithology Interpretation
May 8, 2026Jun Wang, Fengpeng Li, Hang Dong +2Lithology Classification Geological Semantic Understanding
IdiomX A Multilingual Benchmark for Idiom Understanding, Retrieval, and Interpretation
Apr 25, 2026Ayman Ali ShararaMultilingual Idioms Multilingual Benchmark
Bridging Mechanistic Interpretability and Prompt Engineering with Gradient Ascent for Interpretable Persona Control
Jan 6, 2026Harshvardhan Saini, Yiming Tang, Dianbo LiuPersona Mechanistic Interpretability

Aug 3, 2026 · cs.AIJ/K move · Enter open · S save

Interpreting Black-Box Large Language Models with Sentence-Level Energy Landscapes

Maryam Rezaee, Pooriya Safaei, Maryam Asgarinezhad, Fatemeh Seyyedsalehi

Department of Mathematical Sciences, Sharif University of Technology, Tehran, Iran

The widespread adoption of proprietary Large Language Models (LLMs) accessed strictly through closed APIs has created a critical challenge for responsible deployment: a fundamental lack of interpretability. To address this, we propose a model-agnostic, post-hoc attribution interpreter operating at the sentence level. Our approach trains an Energy-Based Model (EBM) as a surrogate to capture the LLM's internal conceptual consistency between prompts and responses. This energy landscape guides the training of a lightweight interpreter network. Uniquely, our interpreter operates as a standalone tool; once trained, it quantifies the influence of prompt sentences on a user-specified target output without requiring further API queries to the LLM. By globally training a local interpreter across diverse inputs, our framework captures broader generation patterns and mitigates instance-specific biases. Experiments demonstrate that our EBM accurately simulates the target LLM, allowing the interpreter to effectively identify the prompt sentences most influential in generating specific target outputs.

Post-Hoc Free-Energy Landscape

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