ICML 2025 Past Other
Second Workshop on Test-Time Adaptation: Putting Updates to the Test! at ICML 2025
PUT at ICML 2025
- Submission deadline
- May 24, 2025, 12:00 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 (55)
Fetched from OpenReview (v2) on 2026-06-10.
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Accurate Parameter-Efficient Test-Time Adaptation for Time Series Forecasting
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Adaptive Diffusion Denoised Smoothing : Certified Robustness via Randomized Smoothing with Differentially Private Guided Denoising Diffusion
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Adaptive Monocular Depth Estimation with Masked Image Consistency
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AdaptMI: Adaptive Skill-based In-context Math Instructions for Small Language Models
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Agentic Adversarial QA for Improving Domain-Specific LLMs
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An Evidence-Based Post-Hoc Adjustment Framework for Anomaly Detection Under Data Contamination
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Beyond Markovian: Reflective Exploration via Bayes-Adaptive RL for LLM Reasoning
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Beyond Multiple Choice: Evaluating Steering Vectors for Adaptive Free-Form Summarization
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Causal Fine-Tuning of Pre-trained Language Models for Robust Test Time Adaptation
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CCC: Enhancing Video Generation via Structured MLLM Feedback
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Context Tuning for In-Context Optimization
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Diffusion Tree Sampling: Scalable inference‑time alignment of diffusion models
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Distilling Prompts at Test-Time for Multimodal Few-Shot Learning
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DPCore: Dynamic Prompt Coreset for Continual Test-Time Adaptation
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e3: Learning to Explore Enables Extrapolation of Test-Time Compute for LLMs
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GRIP: In-Parameter Graph Reasoning through Fine-Tuning Large Language Models
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Inference-Time Alignment via Hypothesis Reweighting
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JEDI: The Force of Jensen-Shannon Divergence in Disentangling Diffusion Models
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Keep the Alignment, Skip the Overhead: Lightweight Instruction Alignment for Continually Trained LLMs
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Language Model Personalization via Reward Factorization
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Language System: A Lightweight Ranking Framework for Language Models
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Learning to Self-Correct through Chain-of-Thought Verification
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Leto: Modeling Multivariate Time Series with Memorizing at Test Time
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LIFT: Improving Long Context Understanding of Large Language Models through Long Input Fine-Tuning
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Lightweight Online Adaption for Time Series Foundation Model Forecasts
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LoRA-TTT: Low-Rank Test-Time Training for Vision-Language Models
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MADCAT: Combating Malware Detection Under Concept Drift with Test-Time Adaptation
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Mitigating Forgetting in Low Rank Adaptation
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Monitoring Risks in Test-Time Adaptation
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N-Gram Induction Heads for In-Context RL: Improving Stability and Reducing Data Needs
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On Distributional Robustness of In-Context Learning for Text Classification
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On Training-Test (Mis)alignment in Unsupervised Combinatorial Optimization: Observation, Empirical Exploration, and Analysis
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Prefix-Tuning+: Modernizing Prefix-Tuning by Decoupling the Prefix from Attention
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Prune ’n Predict: Optimizing LLM Decision-making with Conformal Prediction
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Reasoning as an Adaptive Defense for Safety
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Rejection Sampling Based Fine Tuning Secretly Performs PPO
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Replacing thinking with tool usage enables reasoning in small language models
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Right Question is Already Half the Answer: Fully Unsupervisedd LLM Reasoning Incentization
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Scalable Defense against In-the-wild Jailbreaking Attacks with Safety Context Retrieval
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Scalable Temporal Domain Generalization via Prompting
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Scaling Textual Gradients via Sampling-Based Momentum
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Self-Generated In-Context Examples Improve LLM Agents for Sequential Decision-Making Tasks
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Shift-Aware Test Time Adaptation and Benchmarking for Time-Series Forecasting
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SteeringTTA: Guiding Diffusion Trajectories for Robust Test-Time-Adaptation
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SwiTTA: Switching Domain Experts and Aggregating Contextual Features Towards Realistic Test-Time Adaptation
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Temporal Sampling for Forgotten Reasoning in LLMs
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Test Time Adaptation Using Adaptive Quantile Recalibration
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Test-Time Adaptation for Generalizable Task Progress Estimation
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Test-Time Alignment of Discrete Diffusion Models with Sequential Monte Carlo
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Test-time Offline Reinforcement Learning on Goal-related Experience
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The Curious Language Model: Strategic Test-Time Information Acquisition
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UniTTA: Unified Benchmark and Versatile Framework Towards Realistic Test-Time Adaptation
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Value Conditioned Policy Fine Tuning for Test Time Domain Adaptation
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When and How Unlabeled Data Provably Improve In-Context Learning
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Zero-Shot Adaptation of Behavioral Foundation Models to Unseen Dynamics