NeurIPS 2025 Past Large language models
AI That Keeps Up: NeurIPS 2025 Workshop on Continual and Compatible Foundation Model Updates
CCFM
- Submission deadline
- Sep 3, 2025, 16:00 UTC imported from OpenReview — check the website for extensions
- Submission portal
- OpenReview
- Notes
- Auto-imported from the OpenReview venue record on 2026-06-10 — please verify and enrich (topics are keyword-guessed).
Accepted papers (34)
Fetched from OpenReview (v2) on 2026-06-10.
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Balancing Synthetic Data and Replay for Enhancing Task-Specific Capabilities
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Continual Learning of Domain Knowledge from Human Feedback in Text-to-SQL
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Continual Pre-training of MoEs: How robust is your router?
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Continuous Self-Improvement of Large Language Models by Test-time Training with Verifier-Driven Sample Selection
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CurLL: Curriculum Learning of Language Models
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Curriculum Learning as Transport: Training Along Wasserstein Geodesics
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Do Language Models Robustly Acquire New Knowledge?
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ELLA: Efficient Lifelong Learning for Adapters in Large Language Models
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Embedding‑to‑Prefix: Continual Personalization with Large Language Models
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EWC-Guided Diffusion Replay for Exemplar-Free Continual Learning in Medical Imaging
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Exploring Continual Distillation of Teachers from Different Domains
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Exploring The Effectiveness of Test Time Learning In LLMs for Long Contexts
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Harnessing Quantum Principles for Parameter-Efficient Continual Learning
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HyperAdapt: Simple High-Rank Adaptation
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Information-Geometric Perspectives on Merging Variational Foundation Models
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IPA: An Information-Preserving Input Projection Framework for Model Adaptation
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Mapping Post-Training Forgetting in Language Models at Scale
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Per-Axis Weight Deltas for Frequent Model Updates
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Pre-training Limited Memory Language Models with Internal and External Knowledge
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Probe-Rewrite-Evaluate: A Workflow for Reliable Benchmarks and Quantifying Evaluation Awareness
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PTPP-Aware Adaptation Scaling Laws: Predicting Domain-Adaptation Performance at Unseen Pre-Training Budgets
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Retrieval Capabilities of Large Language Models Scale with Pretraining FLOPs
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Revisiting Warm-Start Training: No Generalization Loss under Standard Training Schemes
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RL's Razor: Why On-Policy Reinforcement Learning Forgets Less
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Robust LLM Unlearning with MUDMAN: Meta-Unlearning with Disruption Masking And Normalization
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Sample-Efficient Parametric Learning from Natural Language
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Sculpting [CLS] Features for Foundation Model-Based Class-Incremental Learning
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Slim Adaptation Modules: A Simple yet Strong Baseline for Continual Foundation Models
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Specialization after Generalization: Towards Understanding Test-Time Training in Foundation Models
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TEMPiRL: Foundational Compounding Temporal Drift Theory for Temporal-Graph Adaptation in Large Language Models
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Unlearning That Lasts: Utility-Preserving, Robust, and almost Irreversible Forgetting in LLMs
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Vocabulary Customization for Efficient Domain‑Specific LLM Deployment
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When Data Falls Short: Grokking Below the Critical Threshold
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When Less is More: 8-bit Quantization Improves Continual Learning in Large Language Models