NeurIPS 2025PastInterpretabilityNeuroscience
First Workshop on CogInterp: Interpreting Cognition in Deep Learning Models
CogInterp @ NeurIPS 2025
- Submission deadline
- Aug 28, 2025, 11:59 UTCimported from OpenReview — check the website for extensions
- Submission portal
- OpenReview
- Notes
- Topics were auto-suggested and may be imprecise — edits welcome.
Accepted papers (112)
Fetched from OpenReview (v2) on 2026-06-10.
(How) Do LLMs Plan in One Forward Pass?
A Cognitive Architecture for Probing Hierarchical Processing and Predictive Coding in Deep Vision Models
A Computational Model for Binding by Enhanced Firing Rate: Implementing Smooth Power-law enhancement in Object-Centric Representations
A Control-Theoretic Account of Cognitive Effort in Language Models
A Few Bad Neurons: Isolating and Surgically Correcting Sycophancy
A Multi-Method Interpretability Framework for Probing Cognitive Processing in Deep Neural Networks across Vision and Biomedical Domains
A Neuroscience-Inspired Dual-Process Model of Compositional Generalization
Acoustic Degradation Reweights Cortical and ASR Processing: A Brain-Model Alignment Study
Actual or counterfactual? Asymmetric responsibility attributions in language models
Are Humans Evolved Instruction Followers? An Underlying Inductive Bias Enables Rapid Instructed Task Learning
Assessing Behavioral Effects of Reasoning (or the lack of) in LLMs
Bitter Lesson of the ARC-AGI Challenge: Intelligence may look very different in machines and humans
Bridging the Von Neuman Gap: Why LLMs Haven’t Made Novel Discoveries
Can You Spot the Virtual Patient? Expert Review, Turing Test, and Linguistic–Semantic Analysis
Causal Interventions on Continuous Features in LLMs: A Case Study in Verb Bias
Causality $\neq$ Decodability, and Vice Versa: Lessons from Interpreting Counting ViTs
Cognitive Behavior Modeling via Activation Steering
Cognitive Load Traces as Symbolic and Visual Accounts of Deep Model Cognition
Cognitive Machine Learning for Patient-First Modeling in Clinical Research
Cognitive Maps in Language Models: A Mechanistic Analysis of Spatial Planning
Conflict Adaptation in Vision-Language Models
Context informs pragmatic interpretation in vision–language models
CORE – Cognitive Observation of Reasoning Errors
Culturally transmitted color categories in LLMs reflect a learning bias toward efficient compression
CurLL: Curriculum Learning of Language Models
DecepBench: Benchmarking Multimodal Deception Detection
Decoding and Reconstructing Visual Experience from Brain Activity with Generative Latent Representations
Deconstructing the Reasoning Process of a Neuro-Fuzzy Agent: From Learned Concepts to Natural Language Narratives
Demystifying Emergent Exploration in Goal-conditioned RL
Detecting Motivated Reasoning in the Internal Representations of Language Models
Disaggregation Reveals Hidden Training Dynamics: The Case of Agreement Attraction
Discovering Functionally Sufficient Projections with Functional Component Analysis
Disentangling Interpretable Cognitive Variables That Support Human Generalization
Do Cognitively Interpretable Reasoning Traces Improve LLM Performance?
Do Large Language Models Show Biases in Causal Learning? Insights from Contingency Judgment
Do Sparse Subnetworks Exhibit Cognitively Aligned Attention? Effects of Pruning on Saliency Map Fidelity, Sparsity, and Concept Coherence
Does FLUX Know What It’s Writing?
Don’t Think of the White Bear: Ironic Negation in Transformer Models under Cognitive Load
Emergent World Beliefs: Exploring Transformers in Stochastic Games
Exact Learning Dynamics of In-Context Learning in Linear Transformers and Its Application to Non-Linear Transformers
Extracting Belief-Update Rules to Explain Theory-of-Mind Generalization Failures
Forgetting as a Lens into Model Cognition: Selective Unlearning Reveals Cognitive Biases in Deep Neural Networks
From Black Box to Bedside: Distilling Reinforcement Learning for Interpretable Sepsis Treatment
From Cephalopods to Large Language Models: Conceptions of Intelligence and Reasoning
From Comparison to Composition: Towards Understanding Machine Cognition of Unseen Categories
Fuzzy, Symbolic, and Contextual: Enhancing LLM Instruction via Cognitive Scaffolding
GBEval: A SHAP-based Interpretable Gender Bias Assessment Framework for LLMs
Generating Compromises Between Two Points of View
Gradual Forgetting: Logarithmic Compression for Extending Transformer Context Windows
How Do LLMs Ask Questions? A Pragmatic Comparison with Human Question-Asking
How Intrinsic Motivation Shapes Learned Representations in Decision Transformers: A Cognitive Interpretability Analysis
I Am Large, I Contain Multitudes: Persona Transmission via Contextual Inference in LLMs
Inside you are many wolves: Using cognitive models to interpret value trade-offs in LLMs
InterpDetect: Interpretable Signals for Detecting Hallucinations in Retrieval-Augmented Generation
Interpretable Hybrid Neural-Cognitive Models Discover Cognitive Strategies Underlying Flexible Reversal Learning
Interpretable Traces, Unexpected Outcomes: Investigating the Disconnect in Trace-Based Knowledge Distillation
Interpreting style–content parsing in vision–language models
Kindness or Sycophancy? Understanding and Shaping Model Personality via Synthetic Games
Language models can associate objects with their features without forming integrated representations
Language Models use Lookbacks to Track Beliefs
Language-Based Dementia Classification Should Consider Model Cognition for Interpretability
Learning to Look: Cognitive Attention Alignment with Vision-Language Models
Let's Think 一步一步: A Cognitive Framework for Characterizing Code-Switching in LLM Reasoning
LLM Agents Beyond Utility: An Open-Ended Perspective
LRP-CLIP: A Zero Shot Approach for the Explanation of the Cognitive Functions of Vision Models
Measuring LLM Generation Spaces with EigenScore
Mechanisms of Symbol Processing in Transformers
Mechanistic Interpretability of GPT-2: Lexical and Contextual Layers in Sentiment Analysis
Mechanistic Interpretability of Semantic Abstraction in Biomedical Text
MetaCD: A Meta Learning Framework for Cognitive Diagnosis based on Continual Learning
Metacognitive Sensitivity for Test-Time Dynamic Model Selection
Mind Games Machines Play: Contrastive Cognitive Bias Detection in LLMs and Distilled Models
Minimization of Boolean Complexity in In-Context Concept Learning
Misalignment Between Vision-Language Representations in Vision-Language Models
Modulation of temporal decision-making in a deep reinforcement learning agent under the dual-task paradigm
NiceWebRL: a Python library for human subject experiments with reinforcement learning environments
On the Role of Pretraining in Domain Adaptation in an Infant-Inspired Distribution Shift Task
Pedagogical Alignment of LLMs requires Diverse Cognitively-Inspired Student Proxies
Perceived vs. True Emergence: A Cognitive Account of Generalization in Clinical Time Series Models
Personality Manipulation as a Cognitive Probe in Large Language Models
PluriHarms: Benchmarking the Full Spectrum of Human Judgments on AI Harm
Post-hoc Stochastic Concept Bottleneck Models
Predicting the Formation of Induction Heads
Priors in Time: A Generative View of Sparse Autoencoders for Sequential Representations
Privileged Self-Access Matters for Introspection in AI
Reverse-Engineering Memory in DreamerV3: From Sparse Representations to Functional Circuits
RNNs reveal a new optimal stopping rule in sequential sampling for decision-making
Sarc7: Evaluating Sarcasm Detection and Generation with Seven Types and Emotion-Informed Techniques
Scratchpad Thinking: Alternation Between Storage and Computation in Latent Reasoning Models
Shared Parameter Subspaces and Cross-Task Linearity in Emergently Misaligned Behaviour
Signatures of human-like processing in Transformer forward passes
Sparse Feature Coactivation Reveals Composable Semantic Modules in Large Language Models
STAT: Skill-Targeted Adaptive Training
Stop Anthropomorphizing Intermediate Tokens as Reasoning/Thinking Traces!
Strategy and structure in Codenames: Comparing human and GPT-4 gameplay
The Mechanistic Emergence of Symbol Grounding in Language Models
The One Where They Brain-Tune for Social Cognition: Multi-Modal Brain-Tuning on Friends
Theoretical Linguistics Constrains Hypothesis-Driven Causal Abstraction in Mechanistic Interpretability
Towards Cognitively Plausible Concept Learning: Spatially Grounding Concepts with Anatomical Priors
Towards finding consensus about similarity of symbolic encodings associated with concepts between LLMs and human brain
Towards Visual Simulation in Multimodal Language Models
Tracing the Development of Syntax and Semantics in a Model trained on Child-Directed Speech and Visual Input
Understanding Pre-trained and Fine-tuned model behaviour using Model Diffing
Understanding the Thinking Process of Reasoning Models: A Perspective from Schoenfeld's Episode Theory
Unifying Gestalt Principles Through Inference-Time Prior Integration
Unraveling the cognitive patterns of Large Language Models through module communities
Value Entanglement: Conflation Between Moral and Grammatical Good In (Some) Large Language Models
Video Finetuning Improves Reasoning Between Frames
Visual symbolic mechanisms: Emergent symbol processing in vision language models
What Comes to Mind? Interpretable Dimensions in Embedding Space Predict Human Ad Hoc Category Construction
What is a Number, That a Large Language Model May Know It?
When Researchers Say Mental Model/Theory of Mind of AI, What Are They Really Talking About?