ICML 2026PastLarge language modelsSafety & alignment
The Second Workshop on the Impact of Memorization on Trustworthy Foundation Models at ICML
ICML MemFM 2026 Workshop
- Submission deadline
- May 9, 2026, 12:00 UTCOpenReview-synced 2026-05-09 12:00 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 (45)
Fetched from OpenReview (v2) on 2026-06-10.
\textsc{ContinuousBench}: Can Differentially Private Synthetic Text Improve Capabilities?
Alignment Whack-a-Mole : Finetuning Activates Verbatim Recall of Copyrighted Books in Large Language Models
Alignment-aware Data Selection for Unlearning in Contrastive Vision-Language Models
Amplifying Membership Signal Through Iterative Regeneration
An Explicit Memory-Driven Agentic Framework for Power System Simulation
Auditing Reasoning-Trace Memorization Claims after Unlearning with Head-Conditioned Canaries
Bayes-Optimal Coexistence via Fact Localizability in Trainable-Feature Decoder-Only Transformers
Break the Output Geometry for Large Language Model Unlearning
Cheap Forgetting: Linear Adapter Interpolation as a Post-Hoc Memorization Mitigation
Deployment-Time Memorization in Foundation-Model Agents
Detecting Functional Memorization in Code Language Models
Do Text Anonymizers Generalize Across Contexts? Extending RAT-Bench to Malaysian Microdata and PII
Estimating Model-Level Membership Inference Vulnerability Without Reference Models
Estimating near-verbatim extraction risk in language models with decoding-constrained beam search
Evidence-bearing Insights under Differential Privacy: Beyond the Limits of Private Text Generation
Internal Data Repetition Destroys Language Models
KVEraser: Learning to Steer KV Cache for Efficient Localized Context Erasing
Local Coverage Governs Memorization in Diffusion Models
Machine Text Detectors are Membership Inference Attacks
MemBoost: A Memory-Boosted Framework for Cost-Aware LLM Inference
Memorization Dynamics of Fill-in-the-Middle Pretraining
Memorization Removal as a Two-Player Game: The Adversarial Work Criterion as a Test for Foundation-Model Defenses
Memory Adapters Enable Fast, Flexible Knowledge Unlearning in LLMs
Mitigating Unintended Memory Use in LLMs via Structured Memory
NumLeak: Public Numeric Benchmarks as Latent Label in Foundation Models
On Optimization Complexity of Second-Order Certified Unlearning
On the Geometry of Memorization: Interpolation and Second-Order Representation Irregularity
On the Learning Dynamics of Label-Noise Memorization in ReLU MLPs
Position: The Term “Machine Unlearning” Is Overused in LLMs
Probing Memorization of Tabular In-Context Learning
Probing Policy-Level Memorization in Reasoning LLMs via Atomic Chess
Prune to Protect: Faster Training and Enhanced Privacy by Dynamic Data Pruning
Rare, Distinctive, Memorized: Auditing Memorization in Fine-Tuned Medical Foundation Models
Reconstructing Training Images from Foundation Model Parameters in the Healthcare Domain: Privacy Risks and Defences
Scale Dependent Data Duplication
Semantic Gravity: When Parametric Memory Overpowers Visual Thermodynamics in Video-LLMs
Structural Memorization in AlphaFold: Adversarial Mutations Reveal Template Reliance, Confidence Failures, and Implications for Protein Design
Suppression is not Deletion: Adversarial Probes Recover Unlearned Knowledge in Code LLMs
SYMBOLICDRIFT: Measuring Reasoning Drift on Unverifiable Questions
Synthetic Data and the Rise of Spiky Intelligence
The Distillation Game: Adaptive Attacks & Efficient Defenses
The Source of Competence Shapes Metacognition in Language Models
Watermarking for Proprietary Dataset Protection
What to Forget in Unlearning? Forget Set Curation for Language Models
Why Forget-Only Unlearning Needs Memorization