Causal Abstraction

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1 paper in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

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Trajectory Abstraction for the Science of Language Agent Behavior
Oct 6, 2026Tianqiang YanAgent Trajectory Analysis Causal Abstraction
Faculty of Information Technology, Monash University
Efficient Fairness Auditing Across Guidance Scales in Text-to-Image Diffusion Models via Causal Abstraction
Sep 8, 2026Nabila Tasfiha Rahman, Rajatsubhra Chakraborty, Depeng Xu +1Classifier-Free Guidance Algorithmic Fairness
University of Arkansas Fayetteville, AR, USA · University of North Carolina at Charlotte Charlotte, NC, USA
Operationalising Relative Causal Knowledge: Backbone Identifiability from Private Reports on a Shared Outcome
Aug 11, 2026Fabrizio Russo, Mark SomersStructural Causal Models Causal Abstraction
Department of Computing, Imperial College London · London, UK · Fifty One Degrees Ltd
Abstract Event Causal Rules: Induction and Application
Aug 5, 2026Ziwei Zheng, Peiqiong Chen, Bang WangCausal Abstraction Causal Reasoning
School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China
Hidden APIs in Language Models: Discovering Reusable Causal Interfaces from Forked Futures
Jul 30, 2026SiYuan Ma, Yiqin Luo, Zhangji +8LLM Interpretability Causal Abstraction
1Nanyang Technological University · 2Southern University of Science and Technology · 3Tianjin University +7
Property-driven Causal Abstractions for Markov Decision Processes
Jul 29, 2026Jule Schmidt, Maximilian Weininger, Clemens Dubslaff +2Markov Decision Processes Markov Models
Ruhr-University Bochum, Germany · Eindhoven University of Technology, The Netherlands · Oxford University, UK +1
Validating Causal Abstraction Metrics on Simulated Complex Systems
Jun 30, 2026Maxime Méloux, Tiago Pimentel, François Portet +1Causal Abstraction Benchmark Validity
Université Grenoble Alpes, CNRS, Grenoble INP, LIG · ETH Zürich
Unsupervised Causal Abstractions Discovery
Jun 17, 2026Théo Saulus, Simon Lacoste-Julien, Dhanya SridharStructural Causal Models Causal Discovery
Mila - Quebec AI Institute · Université de Montréal · Canada CIFAR AI Chair
A Simple Hierarchical Causality Primer
Jun 1, 2026Tim GebbieCausal Abstraction
Department of Statistical Science, University of Cape Town, Rondebosch 7701, South Africa
Formalizing and falsifying causal pathways of rare events
May 29, 2026Anahita Haghighat, Dominik JanzingStructural Causal Models Causal Abstraction
Independent · Amazon Research, Tübingen, Germany
Coarsening Linear Non-Gaussian Causal Models with Cycles
May 11, 2026Francisco Madaleno, Francisco C Pereira, Alex MarkhamStructural Causal Models Causal Discovery
Department of Technology, Management and Economics, Technical University of Denmark · Department of Mathematical Sciences,11 University of Copenhagen
PLOT: Progressive Localization via Optimal Transport in Neural Causal Abstraction
May 7, 2026Jonathn Chang, Arya Datla, Ziv GoldfeldMechanistic Interpretability Causal Abstraction
Cornell University
Bucketing the Good Apples: A Method for Diagnosing and Improving Causal Abstraction
May 4, 2026Li Puyin, Jiyuan Tan, Ahmad Jabbar +2Mechanistic Interpretability Causal Abstraction
♢Stanford University · ♠Goodfire
Arithmetic in the Wild: Llama uses Base-10 Addition to Reason About Cyclic Concepts
May 1, 2026Sheridan Feucht, Tal Haklay, Usha Bhalla +9Numerical Reasoning in Language Models LLM Interpretability
aNortheastern University · bTechnion IIT · cHarvard University +1
Causal Foundations of Collective Agency
Apr 30, 2026Frederik Hytting Jørgensen, Sebastian Weichwald, Lewis HammondAI Agent Safety Causal Abstraction
Copenhagen Causality Lab and Pioneer Centre for AI, University of Copenhagen, Denmark · Cooperative AI Foundation, United Kingdom
Grounding Before Generalizing: How AI Differs from Humans in Causal Transfer
Apr 27, 2026Liangru Xiang, Yuxi Ma, Zhihao Cao +2LLM Grounding Causal Structure Learning
Department of Automation, Tsinghua University · State Key Laboratory of General Artificial Intelligence · Institute for Artificial Intelligence, Peking University +2
From Local to Cluster: A Unified Framework for Causal Discovery with Latent Variables
Apr 24, 2026Zongyu LiCausal Effect Estimation Causal Discovery
Causal Mechanism Reduction: Mechanism Replacement for Neural Network Pruning and Abstraction
Feb 27, 2026Amir AsiaeeStructural Causal Models Causal Abstraction
Vanderbilt University Medical Center Nashville, TN
How Causal Abstraction Underpins Computational Explanation
Aug 15, 2025Atticus Geiger, Jacqueline Harding, Thomas IcardComputational Cognitive Modeling Causal Abstraction

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Trajectory Abstraction for the Science of Language Agent Behavior

Tianqiang Yan

Faculty of Information Technology, Monash University

Agent Trajectory Analysis Causal Abstraction

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Scientific studies of language agents need behavioral variables that support hypotheses across tasks and models. We formulate this research problem as learning and testing a hierarchy of trajectory abstractions. A concrete recursive procedure first measures role- and phase-indexed events, proposes temporally constrained relations, and tests their stability across conditions. It then constructs episode-level motif variables from selected relations and repeats the analysis on those variables. Explicit measurement functions connect every abstraction level to the original trajectories. Observations and randomized protocol experiments assess the resulting hypotheses, while comparisons between intervention realizations determine whether an abstraction should be retained, refined, or restricted. We derive a finite-depth bound for accepted reductions, identify protocol effects on fixed abstractions, and characterize realization disagreement and composition of abstraction error. A finite-sample test makes projected intervention consistency operational, and constructed examples illustrate motif construction and abstraction refinement. The formulation distinguishes this experimental approach from semantic taxonomies, qualitative theory induction, and behavior-model recovery. It specifies a proposed research procedure for discovering generalizable behavioral hypotheses, with literature-relative novelty assessed separately from model-relative surprise.

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