ICLR 2026 Past Other
Catch, Adapt, and Operate: Monitoring ML Models Under Drift Workshop
CAO
- Submission deadline
- Feb 11, 2026, 13:01 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 (74)
Fetched from OpenReview (v2) on 2026-06-10.
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A Credal-Set Perspective on Task-Induced Distributional Drift in Text Generation
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A Geometry-Based View of Mahalanobis OOD Detection
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Adaptive Quasimetric Mapping : Principled Topological Abstraction for Robust Offline Goal-Conditioned Navigation
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Approximating Function Space Distance for Continual Learning in Transformers
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Beyond Accuracy: Evaluating Visual Grounding in Multimodal Medical Reasoning
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CAdam: Confidence-Based Optimization for Online Learning
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Can Linear Probes Effectively Measure LLM Uncertainty ?
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CAO-LLM: Catching, Adapting and Operating Under Distribution Drift for Large Language Models
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Capacity and Redundancy Trade-offs in Multi-Task Learning
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CATS: Conformalized Adaptive Test-Time Scaling
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Compress to Impress: Efficient LLM Adaptation Using a Single Gradient Step on 100 Samples
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CROSS-LINGUAL FAIRNESS DRIFT IN LLM MORAL REASONING
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Detecting Distributional Drift in Transformers Through Representation Dynamics
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DISCO: Diversifying Sample Condensation for Efficient Model Evaluation
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Drift ≠ Error: Reliability Analysis of Agricultural Foundation Models Under Distribution Shift
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Drift-Aware Uncertainty Quantification via a Functional Spectral-Newton Method
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Drift-to-Action Controllers: Budgeted Interventions with Online Risk Certificates
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Duration Aware Scheduling for ASR Serving Under Workload Drift
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Efficient Dataset Selection for Continual Adaptation of Generative Recommenders
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Emergent Misalignment: Tracking the Emergence and Evolution of Misaligned traits throughout Model Training
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Evaluating Domain-Shift Generalization of Liquid Neural Networks in Autonomous Driving
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Evaluating Performance Drift from Model Switching in Multi-Turn LLM Systems
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Evi-BALD: Bayesian Active Learning by Disagreement via Evidential Deep Learning
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Explainability of predictive uncertainty models under drift in the telecom domain
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FedAgree: Leveraging Federated Checkpoints for Label-Free OOD Evaluation via Agreement
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Hidden-Layer Self-Distillation Yields Drift-Resilient Visual Representations
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Hindsight-Anchored Policy Optimization: Turning Failure into Feedback in Sparse Reward Settings
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Hyperspherical Filtering for Online Classification under Drift
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In-Context Adaptation
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Layer by layer, module by module: Choose both for optimal OOD probing of ViT
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Lifting the Veil of Non-Stationarity in Financial Market
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Localized Dynamics-Aware Domain Adaption for Off-Dynamics Offline Reinforcement Learning
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Locally Adaptive Multi-Objective Learning
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LogitScope: A Framework for Analyzing LLM Uncertainty Through Information Metrics
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LookSharp: Attention Entropy Minimization for Test-Time Adaptation
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Loss Smoothing for Continual Adaptation
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Manifold-Aware Temporal Domain Generalization for Large Language Models
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Measuring Control Intervention Awareness Across Frontier LLMs
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Network System Forecasting Despite Topology Shift
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Noise-Response Calibration: A Causal Intervention Protocol for LLM-Judges
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Not All Clients Are Equal: Collaborative Model Personalization on Heterogeneous Multi-Modal Clients
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Not All Queries Need Rewriting: When Prompt-Only LLM Refinement Helps and Hurts Dense Retrieval
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Noticing the Watcher: LLM Agents Can Infer CoT Monitoring from Blocking Feedback
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OASIS: Online Sample Selection for Continual Instruction Tuning
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On the Identifiability of Steering Vectors in Large Language Models
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Online Fine-Tuning of Pretrained Controllers for Autonomous Driving via Real-Time Recurrent RL
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Out-of-Support Generalisation via Weight-Space Sequence Modelling
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Paranoid Monitors: How Long Context Breaks LLM Agent Supervision
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PEFT-Arena: Understanding Parameter-Efficient Finetuning from a Stability-Plasticity Perspective
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Pitfalls of Unlabeled Disagreement-Based Drift Detection in Streaming Tree Ensembles
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Prior Distribution and Model Confidence
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Prompt-Level Drift as an Operational Monitoring Problem: Schema Failure Cliffs and Judge-Version Risk in Artifact-Grounded Evaluation
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Q-Sched: Pushing the Boundaries of Few-Step Diffusion Models with Quantization-Aware Scheduling
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QueST: Persistent Queries as Semantic Monitors for Drift Suppression in Long-Horizon Tracking
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RDUMB++: DRIFT-AWARE CONTINUAL TEST-TIME ADAPTATION
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Reasoning Is Not Free: Robust Adaptive Cost-Efficient Router for LLM-as-a-Judge
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Reliability-Aware Environment Discovery: Leveraging Feature Entanglement for Subpopulation Robustness
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Rethinking Layer Relevance in Large Language Models Beyond Cosine Similarity
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Right Regions, Wrong Labels: Semantic Label Flips in Segmentation under Correlation Shift
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Risk-Averse Learning with Nonstationary Distribution
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Robust LLM Performance Certification via Constrained Maximum Likelihood Estimation
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Structured Event Logging for Tracking Model Behavior Under Distributional Drift
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SymTorch: A Framework for Symbolic Distillation of Deep Neural Networks
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TamperBench: A Systematic Framework to Stress-Test LLM Safety Under Fine-Tuning and Tampering
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TamperTest: A Framework for Testing Tamper Resistance in Open-Weight LLMs
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Test-Time Adaptation for Event Prediction via Lightweight Adapters
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The Magic Correlations: Understanding Knowledge Transfer from Pretraining to Supervised Fine-Tuning
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TRUST: Trajectory-guided State-Space Temporal Test-Time Adaptation
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Understanding Reasoning Collapse in Multi-Turn Agent Reinforcement Learning
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Value Drifts: Tracing Value Alignment During LLM Post-Training
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Weighted Partial Optimal Transport for Multi-Source Partial Domain Adaptation
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WHEN DRIFT DETECTORS CRY WOLF: FALSE ALARM RATES IN CONTINUOUS ML MONITORING
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When Sensors Fail: Temporal Sequence Models for Robust PPO under Sensor Drift
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White-Box Monitoring for Personality Mirroring in Conversational AI