Hugging Face Trending Papers
CaliMem: Search-Efficient Data Agent via Calibrated Memory
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arXiv AI
4d ago
CaliMem: Search-Efficient Data Agent via Calibrated Memory
arXiv:2610.34500v1 Announce Type: new Abstract: 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, e...
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.
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
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
Oct 5
Evolving in Thought Space: Training a Small Model at Test Time Unlocks Better Discoveries
The paper introduces Guidance‑TTT, a method that separates strategic planning from execution in test‑time training for large language models. A small guidance model is trained at test time to propose high‑level changes, while a frozen, larger execution model implements these changes, reducing the cost of maintaining gradients and optimizer states. Guidance‑TTT achieves strong results across four domains—combinatorial optimization, heuristic programming, machine learning, and GPU kernel optimization—outperforming prior work and matching state‑of‑the‑art leaderboard scores.
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...