arXiv AI
CaliMem: Search-Efficient Data Agent via Calibrated Memory
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5d ago
CaliMem: Search-Efficient Data Agent via Calibrated Memory
Data curation is a first-order design choice in machine learning, yet tailoring training data to a downstream model and task remains costly in labor, expertise, and compute. Datasets are therefore oft...
arXiv AI
Jun 4
Can Generalist Agents Automate Data Curation?
arXiv:2606. 04261v1 Announce Type: new Abstract: Curating training data is among the most consequential yet labor-intensive parts of modern AI development: practitioners iteratively propose, implement, evaluate, and revise data policies against noisy benchmark feedback.
arXiv AI
Jun 10
What Fits (Into Few Tokens) Doesn't Overfit: Compression and Generalization in ML Research Agents
arXiv:2606. 11045v1 Announce Type: new Abstract: Reusing a held-out benchmark adaptively should, in principle, invite overfitting.
Hugging Face Trending Papers
Jul 7
CurateEvo: Data-Curation Evolving for Agentic Post-Training
Large language model (LLM) agents require post-training methods that can improve long-horizon decision making from environment feedback. However, existing agentic post-training pipelines often treat data curation as a fixed preprocessing step, focusing mainly on data augmentation while neglecting filtering, refinement, and adaptation to downstream failures.
arXiv AI
Jul 1
Learning from Failure: Inference-Time Self-Improvement for Computer-Use Agents
arXiv:2606. 31270v1 Announce Type: cross Abstract: Computer-use agents, which leverage multimodal large language models (MLLMs) to operate computers and complete tasks, have attracted significant attention for their utility and versatility.
Hugging Face Trending Papers
Aug 23
CONTRAMEM: Learning Self-Evolving Procedural Memory from Contrasting Multi-Model Trajectories
Autonomous computer-use agents are increasingly applied to long-horizon tasks requiring coordinated application calls, persistent state tracking, and verifier-sensitive writes, yet they remain prone t...