arXiv AI
Leakage-Safe and Scheduler-Aware Machine Learning for Grid Job Runtime Prediction
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arXiv Machine Learning
Jun 2
Tempora: Characterising the Time-Contingent Utility of Online Test-Time Adaptation
arXiv:2602. 06136v2 Announce Type: replace Abstract: Test-time adaptation (TTA) offers a compelling remedy for machine learning (ML) models that degrade under domain shifts, improving generalisation on-the-fly with only unlabelled samples.
arXiv AI
Nov 2
LEAP: Efficient LLM Inference Scheduling with Generalizable Length Prediction
LEAP is a length prediction system for large language model inference that uses the serving model’s prefill hidden states and a linear readout to estimate output length. It adapts online via recursive least squares, updating the readout with completed requests without costly retraining or gradient computation. Experiments show LEAP maintains strong ranking performance across domain shifts, improves Spearman correlation by 64.7% under large shifts, and reduces per-token latency by 15.9–40.8% while increasing throughput and drastically cutting predictor parameters.
arXiv AI
Jun 4
EvalStop: Using World Feedback to Detect and Correct Reward Overoptimization in Multi-Tenant RLHF Platforms
arXiv:2606. 04145v1 Announce Type: cross Abstract: Cloud LLM fine-tuning platforms increasingly serve RLHF workloads, where a learned reward model is optimized as a proxy for human quality.
arXiv Machine Learning
Jul 7
When Do Foundation Models Pay Off? A Break-Even Analysis of Pretrained Time Series Forecasters
arXiv:2607. 04919v1 Announce Type: new Abstract: Deploying a time series foundation model requires GPU infrastructure, engineering overhead, and carries no guarantee of improvement over XGBoost.
arXiv AI
5d ago
Big Models, Small Gains: Frozen Forecasters as Classifiers on MONSTER
The paper benchmarks two frozen forecasting models, Reverso and Chronos‑Bolt, as feature extractors for time‑series classification on the MONSTER dataset. Using 1%, 10%, and 100% of training labels, the authors compare the models’ frozen representations to four established classifiers and to random‑weight versions of the same models. Results show that pretraining yields higher accuracy than random weights on five of six datasets, but the frozen representations never outperform the best non‑pretrained baseline, performing worse in 15 of 17 settings and tying only twice.
arXiv AI
Jun 17
TuneAhead: Predicting Fine-tuning Performance Before Full Training Begins
arXiv:2606. 17660v1 Announce Type: cross Abstract: Fine-tuning large language models (LLMs) is compute-intensive and error-prone: model performance depends sensitively on data quality and hyperparameter choices, and na\"ive runs can even degrade model performance.