ICML 2026PastMath & reasoningGenerative models
ICML 2026 Workshop on Foundations of Deep Generative Models: Understanding Memorization, Generalization, and Reasoning
ICML 2026 FoGen Workshop
- Submission deadline
- May 9, 2026, 11:59 UTCOpenReview-synced 2026-05-09 11:59 UTC (as of 2026-06-23) — extensions on OpenReview are applied automatically; verify on the website.
- Submission portal
- OpenReview
- Notes
- Topics were auto-suggested and may be imprecise — edits welcome.
Accepted papers (193)
Fetched from OpenReview (v2) on 2026-06-10.
A Theoretical Analysis of Curriculum Training in Diffusion Models
A Theoretical Analysis of Why Masked Diffusion Models Mitigate the Reversal Curse
A Unified Perspective on Task Retrieval and Learning in In-Context Learning on Markov Data
Accelerating Discrete Diffusion Models with Parallel-In-Time Sampling
Active Flow Expansion for Out-of-Distribution Discovery: from Theory to Molecules
Amortising Bayesian Experimental Design for Sequential Information Gathering in LLMs
An Isotropic Approach to Efficient Uncertainty Quantification with Gradient Norms
Attention as In-Context Empirical Bayes: A Two-Stage View via Particle Dynamics
Autoregressive Ranking: Bridging the Gap Between Dual and Cross Encoders
Benchmarking Multimodal Personalized Reasoning of Vision-Language Models in the Wild
Benign Overfitting Does Not Occur in Diffusion Models
Beyond Pixel Space: Frequency-Domain Uncertainty for Structure Aware Diffusion Guidance
Beyond Power Spectra: Cross-Frequency Interactions in Generative Dynamics
Beyond Raw Competence: Logical Equivariance in Diffusion Language Models
Bidirectional Trajectory Smoothing for Training-Free Image Generation with Rectified Flows
Blind denoising diffusion models and adaptive sampling algorithms
Boltz-Perturb: Probing Generalization in Co-Folding Models via Inference-Time Perturbation
Brain-Measurable Diffusion Decoding: Auditing Information Provenance in fMRI Reconstruction
Chain-of-Generation: Progressive Latent Diffusion for Text-Guided Molecular Design
Chain-of-Image Generation: Toward Monitorable and Controllable Image Generation
Chain-of-Thought Gradient Descent
Characterizing Memorization in Diffusion Language Models: Generalized Extraction and Sampling Effects
Complexity-Stratified Evaluation Reveals Shortcut Regimes in Rotational Novel View Synthesis
Compositional Flow Matching with Factored Velocity Fields
Context Over Content: Exposing Evaluation Faking in Automated Judges
Data-Forcing Distillation: Restoring Diversity and Fidelity in Few-Step Video Generation
Demystifying the Slash Pattern in Attention: The Role of RoPE
Diagnosing LLM Judge Reliability: Conformal Prediction Sets and Transitivity Violations
Diffusion Model's Generalization Can Be Characterized by Inductive Biases toward a Data-Dependent Ridge Manifold
Diffusion Models for Inverse Problems on Riemannian Manifolds
Distributional Biases in Post-Training: A Markovian Analysis of Reasoning Trajectories
Distributional Readout: A Memorization Regime in Autoregressive Generative Models
Do LLMs Really Memorize Personally Identifiable Information? Revisiting PII Leakage with a Cue-Controlled Memorization Framework
Do Thinking Tokens Help with Safety?
DPMI: A Principled Index for Neural Polysemanticity via Dirichlet Process Mixture Modeling
DPRM: A Plug-in Token-Ordering Module for Diffusion Language Models
Dream.exe: Can Video Generation Models Dream Executable Robot Manipulation?
DUEL: Exact Likelihood for Masked Diffusion via Deterministic Unmasking
DVD: Deterministic Video Depth Estimation with Generative Priors
Early Semantic Commitment in Diffusion Sampling
EMA Policy Gradient: Taming Reinforcement Learning for LLMs with EMA Anchor and Top-k KL
Emergent and Subliminal Misalignment Through the Lens of Data-Mediated Transfer
Enhancing Knowledge Injection with Surrounding Backgrounds in Continual Training LLMs
Evaluating Spatial World Modeling in Video \\ Generators via 3D Camera Trajectory Generation
Evaluating the Representation Space of Diffusion Models via Self-Supervised Principles
Evolutionary System Prompt Learning for Reinforcement Learning in LLMs
Extracting the Data Manifold from Diffusion Models via a Score-Based Non-Conformal Riemannian Metric
Feedforward Mixing is as Sharp as it is Slow in Reverse
FERMI: Feature-Mapping for Relational Membership Inference on Tabular Diffusion Models
Few-Shot Learning in Video Diffusion Models
Fine-Tuning Dynamics of In-Context Factual Recall in Transformers
Fixed-Point Reasoning: Stable and Adaptive Deep Looped Models
Flow Matching on General Manifolds via Pulling Back Geodesic Convex Latent Manifolds
ForcingDAS: Unified and Robust Data Assimilation via Diffusion Forcing
Forward-Chaining Temporal Point Process
Frontier Language Models Struggle to Copy: Text Can Be Better Viewed in 2D
Frontier Learning: Training LLM Reasoners at the Edge of Capability
Gating Enables Curvature: A Geometric Expressivity Gap in Attention
Generalization of Diffusion Models Arises with a Balanced Representation Space
Growing Images: Spatial Scheduling in Diffusion Inpainting
Hazard Compression: Catastrophic Forgetting in Diffusion-Based Generative Replay under Distribution Shift
How Cross-Entropy Shapes Representation Geometry: A Spectral Study on Cycle Graphs
How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization
How Deep Are Deep GPs, Really? A Sharp Threshold and a Non-Gaussian Limit for Compositional GPs
How Recursive Training Collapses and What Can Be Done About It
How to Train Your Latent Diffusion Language Model Jointly With the Latent Space
Imagined Memorisation: Training-Data Leakage in Model-Based RL World Models
In- and Out-of-Distribution Generalization of Reasoning in Multimodal LLMs for Simple Visual Planning Tasks
In-Place Feedback: Reliable Refinement for Multi-Turn Expert-LLM Collaboration
Interdomain Attention: Beyond Token-Level Key-Value Memory
Internal Data Repetition Destroys Language Models
Internal Tree Search Execution in Transformers
Interpreting Latent CoT Reasoning as Dynamical Systems
Intrinsic Wasserstein Rates for Score-Based Generative Models on Smooth Manifolds
Inverse-Confidence Sampling for Continuous Diffusion Language Models
Is your Flow Matching Model Really Generalising? A Path-Length Diagnostic
JUMP: Single-Pass Membership Inference on Fine-Tuned Diffusion Language Models
Just Add More Capacitors: Eliminating Flux Leakage in Electrostatic Field Matching
LangFlow: Continuous Diffusion Rivals Discrete in Language Modeling
Language Diffusion Models are Associative Memories Capable of Retrieving Unseen Data
Latent Phase-Shift Rollback: Inference-Time Error Correction via Residual Stream Monitoring and KV-Cache Steering
Learn from Your Mistakes: Self-Correcting Masked Diffusion Models
Learned Relay Representations for Forward-Thinking Discrete Diffusion Models
Learning Human Habits with Rule-Guided Active Inference
Learning Manifold Data with Flow Matching
Learning to Trade Like an Expert: Cognitive Fine-Tuning for Stable Financial Reasoning in Language Models
Leveraging Instruction Tuning and Merging for Reasoning Model Adaptation
LLM Generation Novelty Through the Lens of Semantic Similarity
LLM-WikiRace: A Benchmark for Planning and Reasoning over Real-World Knowledge Graphs
Local Coverage Governs Memorization in Diffusion Models
Local Manifold Identification with Latent Linear Models and OT Flows
Mamba as Measure-Valued Associative Memory: Infinite-Context Limits and Minimax-Optimal Learning
Manifold-Guided Attention Steering
Masked Distillation: Internalizing Chain-of-Thought in Small Language Models
MCLR: Improving Conditional Modeling via Inter-Class Likelihood-Ratio Maximization and Unifying Classifier-Free Guidance with Alignment Objectives
Measurement-Consistent Langevin Corrector for Stabilizing Latent Diffusion Inverse Problem Solvers
Memorization Detection in Diffusion Models via Text Embedding Interpolation
Memorization, Retrieval, and Reasoning in LLM-Driven EDA: A Case Study in FPGA Timing Closure
Memory-Efficient Looped Transformer: Decoupling Compute from Memory in Looped Language Models
Midpoint Generative Models
MINDGRAPH: Faithful Concept-Graph Memory for Long-Context Reasoning
Mixture-Greedy for Online Generative Model Selection: Is UCB Necessary in Diversity-Aware Multi-Armed Bandits?
Model Capacity Determines Grokking through Competing Memorisation and Generalisation Speeds
Neural Network-Based Diffusion Models Adapt to Low-Dimensional Multi-Modal Data Structure
Norm-Controlled Likelihood Guidance for Diffusion-based Inverse Solver
Not Every Time and Frequency Need to Be Forgotten in Diffusion Unlearning
On approximation and estimation of Schrödinger potentials without the curse of dimensionality
On the approximation of Schrödinger bridge potentials
On the Memorization of Consistency Distillation for Diffusion Models
On the Policy Gradient Foundations of Group Relative Policy Optimization: Credit Assignment, Gradient Sparsity, and Rank Collapse
On the Relationship between the Choice of Representation and In-context Learning
On-Policy Self-Distillation with Sampled Demonstrations Reduces Output Diversity
One Coupling to Rule Them All: Optimal Transport as the Unifying Geometry of Diffusion Models, Flow Matching, and Reasoning in Deep Generative Models
Pathwise Transported Memory Priors for Autoregressive Generative Models
Personalized Federated Training of Latent Diffusion Models with Privacy Guarantees
Personalized Privacy Control in LLMs via Attention Head Intervention
Physics in 2-Steps: Locking Motion Priors Before Visual Refinement Erases Them
Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation
POLYOMINOGEN: A Controlled Testbed for Understanding Memorization and Compositional Generalization in Conditional Diffusion Models
Position Augmentation: Reducing RoPE Extrapolation Cliffs via Random Position Scaling During Training
Prior Dominance in Audio-Visual LLMs: When Generative Models Memorize Over Reasoning Under Cross-modal Conflict
Probe Choice Changes Canary-Memorization Verdicts: Three Post-Hoc Disagreement Case Studies in a Text-Dominant LoRA-Tuned Autoregressive Testbed
Quantifying the Effect of Test Set Contamination on Generative Evaluations
Quantifying the Memorization-to-Generalization Transition: Scaling Laws and Phase Structure in Grokking
Reasoning Across Space: Tiny Recursive Models for Spatial Omics
Reasoning as State Transition: A Representational Analysis of Reasoning Evolution in Large Language Models
Reasoning Phases Are Continuous, Not Discrete: Evidence from Switching Linear Dynamical Systems Applied to Chain-of-Thought Residual Streams
ReCAST: Probing Sparse Reference Use in In-Context Image Generation
Reducing Diffusion Model Memorization with Higher Order Langevin Dynamics
Registers Matter for Pixel-space Diffusion Transformers
Reinforcement Learning with Promising Tokens for Large Language Models
Relative Score Policy Optimization for Diffusion Language Models
Rethinking "RL Generalizes, SFT Memorizes": The Role of SFT Data
Rethinking Generalization in Reasoning SFT: A Conditional Analysis on Optimization, Data, and Model Capability
Rethinking On-Policy Distillation of Large Language Models: Phenomenology, Mechanism, and Recipe
Rethinking On-Policy Self-Distillation for Thinking Models
Retrieval Dwelling: A Principled Sampling Strategy for Exploiting Spurious State Exploration
Revisiting Spectral Representations in Generative Diffusion Models
RLVR Training of LLMs Does Not Improve Thinking Ability for General QA: Evaluation Method and a Simple Solution
SALSA: State Augmentation via Learned Selective Attention
Sample Efficient Generative Model for Molecular Dynamics Trajectories via Twisted Sequential Monte Carlo
Scale Dependent Data Duplication
Scaling with Recursion in Masked Discrete Diffusion Models
SciReview: Diagnosing Compositional Scientific Reasoning in Frontier Models
Self-Distillation Zero: Self-Revision Turns Binary Rewards into Dense Supervision
Separating Intrinsic Ambiguity from Estimation Uncertainty in Deep Generative Models for Linear Inverse Problems
Setting-Matched and Semantics-Scaled Benchmarking of One-Step Generative Models Against Multistep Diffusion and Flow Models
Sobolev Regularized Score Difference Estimation in Diffusion Models
Solve the Loop: Attractor Models for Language and Reasoning
Sparse Memory Finetuning as a Low-Forgetting Alternative to LoRA and Full Finetuning
Spectral Signatures of Memorization in Diffusion Models: A Multi-Scale Diagnostic Study
SRM-LoRA: Sub-Riemannian-Style Updates for Mitigating LLM Hallucination in Low-Rank Adaptation
Steering Dynamical Regimes of Diffusion Models by Breaking Detailed Balance
Structure Over Scale: Rethinking Adaptation for Reinforcement Learning with Verifiable Rewards
Structuring The Future: Diffusion LLM Speculative Decoding via Calibrated Draft Graphs
Synthesizability-Aware Materials Generation with Target Properties via Reinforcement Learning
Temporal Backtracking Search for Test-time Generative Video Reasoning
Temporal Task Diversity: Inductive Biases Under Non-Stationarity in Synthetic Sequence Modelling
Test of Time: Rethinking Temporal Signal of Benchmark Contamination
The Confidence Shortcut: A Reasoning Failure Mode of Masked Diffusion Models
The Distillation Game: Adaptive Attacks & Efficient Defenses
The Surprising Effectiveness of Deleting Weights in LLM Reasoning and Adaptation
TIGER: Bridging the Multimodal Reasoning-Access Gap via Modality Counterfactuals
Tight PAC-Bayes Generalisation Guarantees for Large Language Model Safety Monitoring
Time-Correlated Video Bridge Matching
Towards \textit{Effective Theory} of LLMs: A Representation Learning Approach
Tracing Uncertainty in Language Model "Reasoning"
Training In-Context and In-Weights Mixtures Via Contrastive Context Sampling
Transformers Learn the Optimal DDPM Denoiser for Multi-Token GMMs
TUBE: Tangent Upper Bound on Evidence for Discrete Diffusion Language Models
Understanding Flatness in Generative Models: Its Role and Benefits
Understanding Generalization in Diffusion Distillation via Probability Flow Distance
Understanding LLM generalization through fine-tuning
Understanding Solver-Induced Variance Distortion in Conditional Diffusion Regression
Universality, Composition Generalization, and Algorithm Emulation All In-Context
Unlearning for One-Step Generative Models via Unbalanced Optimal Transport
Unlocking the Duality between Flow and Field Matching
Velocity Adaptation for Flow-Matching Models
ViGoR-Bench: How Far Are Visual Generative Models From Zero-Shot Visual Reasoners?
What a Small Autoregressive Transformer Briefly Learns and Then Forgets: Transient Structural Capabilities and Probe-Specific Head Repurposing
What Architectural Inductive Bias Makes Diffusion Models Succeed? A Perspective from the Implicit Regularization of Gradient Descent
What to Forget in Unlearning? Forget Set Curation for Language Models
What You Predict Shapes How You Memorize: Target-Parameterization and Memorization Dynamics in Flow Matching
When Does Diffusion Purification Amplify Perturbations?
Where Rollouts Begin: Low-Load, High-Leverage First-Token Diversification for RLVR
Where Should Diffusion Enter a Language Model? Geometry-Guided Hidden-State Replacement
Where’s the Plan? Locating Latent Planning in Language Models with Lightweight Mechanistic Interventions
Why Alignment Must Precede Distillation: A Minimal Working Explanation
Why Are Distribution-Matching Distilled Students Lazy? Understanding the Copying Behavior in Few-Step Distillation
Why Does Pruning during Training Work? A Signal-to-Noise Analysis of Sparse Neural Network Training
Why is A+B Better Than B? A Simple Graph Perspective on Task Transfer
Why Limit the Residual Stream to Layers and Not Tokens? Persistent Memory for Continuous Latent Reasoning
Within-Episode Failure Recovery in LLM Agents via Progress-Gated Dual-Process Routing