{"total":57,"rows":[{"id":"2411.07279","title":"The Surprising Effectiveness of Test-Time Training for Few-Shot Learning","created":"2024-11-11","cited_by_count":96,"area":null},{"id":"2510.18874","title":"Retaining by Doing: The Role of On-Policy Data in Mitigating Forgetting","created":"2025-10-21","cited_by_count":94,"area":null},{"id":"2512.23675","title":"End-to-End Test-Time Training for Long Context","created":"2025-12-29","cited_by_count":62,"area":null},{"id":"2505.20633","title":"Test-Time Learning for Large Language Models","created":"2025-05-27","cited_by_count":50,"area":null},{"id":"2307.05014","title":"Test-Time Training on Video Streams","created":"2023-07-11","cited_by_count":37,"area":null},{"id":"2504.02441","title":"Cognitive Memory in Large Language Models","created":"2025-04-03","cited_by_count":29,"area":null},{"id":"2506.07673","title":"How Benchmark Prediction from Fewer Data Misses the Mark","created":"2025-06-09","cited_by_count":28,"area":null},{"id":"2506.13750","title":"Test3R: Learning to Reconstruct 3D at Test Time","created":"2025-06-16","cited_by_count":20,"area":null},{"id":"2310.13807","title":"Learning to (Learn at Test Time)","created":"2023-10-20","cited_by_count":17,"area":null},{"id":"2411.19146","title":"Puzzle: Distillation-Based NAS for Inference-Optimized LLMs","created":"2024-11-28","cited_by_count":17,"area":null},{"id":"2503.11842","title":"Test-Time Training Provably Improves Transformers as In-context Learners","created":"2025-03-14","cited_by_count":16,"area":null},{"id":"2509.16189","title":"Latent learning: episodic memory complements parametric learning by enabling flexible reuse of experiences","created":"2025-09-19","cited_by_count":15,"area":null},{"id":"2602.21221","title":"Latent Context Compilation: Distilling Long Context into Compact Portable Memory","created":"2026-01-31","cited_by_count":13,"area":null},{"id":"2510.27172","title":"Adaptive Defense against Harmful Fine-Tuning for Large Language Models via Bayesian Data Scheduler","created":"2025-10-31","cited_by_count":12,"area":null},{"id":"2403.05636","title":"Tuning-Free Accountable Intervention for LLM Deployment -- A Metacognitive Approach","created":"2024-03-08","cited_by_count":11,"area":null},{"id":"2311.16420","title":"Model-free Test Time Adaptation for Out-Of-Distribution Detection","created":"2023-11-28","cited_by_count":9,"area":null},{"id":"2506.00635","title":"Learning with Calibration: Exploring Test-Time Computing of Spatio-Temporal Forecasting","created":"2025-05-31","cited_by_count":9,"area":null},{"id":"2605.15188","title":"FutureSim: Replaying World Events to Evaluate Adaptive Agents","created":"2026-05-14","cited_by_count":7,"area":null},{"id":"2407.10964","title":"No Train, all Gain: Self-Supervised Gradients Improve Deep Frozen Representations","created":"2024-07-15","cited_by_count":5,"area":null},{"id":"2509.24510","title":"Specialization after Generalization: Towards Understanding Test-Time Training in Foundation Models","created":"2025-09-29","cited_by_count":5,"area":null},{"id":"2502.12594","title":"PASER: Post-Training Data Selection for Efficient Pruned Large Language Model Recovery","created":"2025-02-18","cited_by_count":4,"area":null},{"id":"2505.00533","title":"Test-time Correlation Alignment","created":"2025-05-01","cited_by_count":4,"area":null},{"id":"2511.04234","title":"Reusing Pre-Training Data at Test Time is a Compute Multiplier","created":"2025-11-06","cited_by_count":4,"area":null},{"id":"2602.15156","title":"Panini: Continual Learning in Token Space via Structured Memory","created":"2026-02-16","cited_by_count":4,"area":null},{"id":"2503.08674","title":"Understanding and Mitigating Distribution Shifts For Machine Learning Force Fields","created":"2025-03-11","cited_by_count":3,"area":null},{"id":"2506.21826","title":"Few-Shot Segmentation of Historical Maps via Linear Probing of Vision Foundation Models","created":"2025-06-27","cited_by_count":3,"area":null},{"id":"2507.04221","title":"Context Tuning for In-Context Optimization","created":"2025-07-06","cited_by_count":3,"area":null},{"id":"2511.21882","title":"Closed-Loop Transformers: Autoregressive Modeling as Iterative Latent Equilibrium","created":"2025-11-26","cited_by_count":3,"area":null},{"id":"2602.18996","title":"Learning Cross-View Object Correspondence via Cycle-Consistent Mask Prediction","created":"2026-02-22","cited_by_count":3,"area":null},{"id":"2402.15441","title":"Active Few-Shot Fine-Tuning","created":"2024-02-13","cited_by_count":2,"area":null},{"id":"2412.07980","title":"TTVD: Towards a Geometric Framework for Test-Time Adaptation Based on Voronoi Diagram","created":"2024-12-10","cited_by_count":2,"area":null},{"id":"2602.19131","title":"Test-Time Learning of Causal Structure from Interventional Data","created":"2026-02-22","cited_by_count":2,"area":null},{"id":"2605.30337","title":"Efficient Test-Time Finetuning of LLMs via Convex Reconstruction and Gradient Caching","created":"2026-05-28","cited_by_count":2,"area":null},{"id":"2410.11934","title":"Dual-frame Fluid Motion Estimation with Test-time Optimization and Zero-divergence Loss","created":"2024-10-15","cited_by_count":1,"area":null},{"id":"2503.20797","title":"\"Whose Side Are You On?\" Estimating Ideology of Political and News Content Using Large Language Models and Few-shot Demonstration Selection","created":"2025-03-23","cited_by_count":1,"area":null},{"id":"2505.03819","title":"Focus on Likely Classes for Test-Time Prediction","created":"2025-05-02","cited_by_count":1,"area":null},{"id":"2505.18734","title":"MADCAT: Combating Malware Detection Under Concept Drift with Test-Time Adaptation","created":"2025-05-24","cited_by_count":1,"area":null},{"id":"2507.06313","title":"ETT: Expanding the Long Context Understanding Capability of LLMs at Test-Time","created":"2025-07-08","cited_by_count":1,"area":null},{"id":"2512.02882","title":"OptPO: Optimal Rollout Allocation for Test-time Policy Optimization","created":"2025-12-02","cited_by_count":1,"area":null},{"id":"2603.22404","title":"Computational Arbitrage in AI Model Markets","created":"2026-03-23","cited_by_count":1,"area":null},{"id":"2603.26929","title":"Live Interactive Training for Video Segmentation","created":"2026-03-27","cited_by_count":1,"area":null},{"id":"2606.08397","title":"TrustMargin: Training-Free Arbitration between Parametric Memory and Retrieved Evidence in Large Language Models","created":"2026-06-07","cited_by_count":1,"area":null},{"id":"2607.00368","title":"Beyond Perplexity: A Behavioral Evaluation Framework for Deployment-Memory Claims in LLM Test-Time Training","created":"2026-07-01","cited_by_count":1,"area":null},{"id":"2607.09415","title":"Self-Guided Test-Time Training for Long-Context LLMs","created":"2026-07-10","cited_by_count":1,"area":null},{"id":"2411.02109","title":"One protein is all you need","created":"2024-11-04","cited_by_count":0,"area":null},{"id":"2510.26022","title":"Groupwise Registration with Physics-Informed Test-Time Adaptation on Multi-parametric Cardiac MRI","created":"2025-10-29","cited_by_count":0,"area":null},{"id":"2511.16691","title":"Reproducibility Report: Test-Time Training on Nearest Neighbors for Large Language Models","created":"2025-11-16","cited_by_count":0,"area":null},{"id":"2601.16509","title":"Adaptive $k$NN graph model","created":"2026-01-23","cited_by_count":0,"area":null},{"id":"2603.27665","title":"Test-Time Instance-Specific Parameter Composition: A New Paradigm for Adaptive Generative Modeling","created":"2026-03-29","cited_by_count":0,"area":null},{"id":"2605.11328","title":"Epistemic Uncertainty for Test-Time Discovery","created":"2026-05-11","cited_by_count":0,"area":null},{"id":"2606.15569","title":"A Decision-Theoretic View of Test-Time Training: When, How Far, and Which Directions to Adapt","created":"2026-06-14","cited_by_count":0,"area":null},{"id":"2606.26515","title":"Forget, Anticipate and Adapt: Test Time Training for Long Videos","created":"2026-06-25","cited_by_count":0,"area":null},{"id":"2607.22697","title":"Test-Time Coverage: Test-Conditioned Data Curation for Deployment-Aware Learning","created":"2026-07-18","cited_by_count":0,"area":null},{"id":"2609.33722","title":"BOReFT: Manifold Steering of Language Models for Black-box Optimization","created":"2026-09-27","cited_by_count":0,"area":null},{"id":"2610.05076","title":"Self-Generated Feedback Destabilizes Test-Time Training: A Causal Decomposition of Long-Horizon Adaptation","created":"2026-10-04","cited_by_count":0,"area":null},{"id":"2610.17686","title":"Encoding Context into LLM Weights: What Makes It Context?","created":"2026-10-13","cited_by_count":0,"area":null},{"id":"2610.20582","title":"Latent Compaction with Test-Time Training","created":"2026-10-14","cited_by_count":0,"area":null}],"next_cursor":null}