arXiv Machine Learning August 4, 2026 By Yi Liu

Learned, Relied Upon, or Necessary? Separating Checkpoint Dependence from Task-Level Value in Sheaf GNNs

Read the original on arXiv Machine Learning →

arXiv:2607. 25387v2 Announce Type: replace Abstract: Learned restriction maps in sheaf graph neural networks are often treated as proof that the model has discovered useful edge geometry.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

benchmarks

arXiv AI

4d ago

Cumulative-Goodness Free-Riding in Forward-Forward Networks: Real, Repairable, but Not Accuracy-Dominant

arXiv:2605.06240v2 Announce Type: replace-cross Abstract: Forward-Forward (FF) training lets each layer learn from a local goodness criterion. In cumulative-goodness variants, later layers can inheri...

By Amirhossein Yousefiramandi

arXiv AI

2d ago

Does Scaling Reinforcement Learning Really Require More Training?

arXiv:2610.01133v1 Announce Type: cross Abstract: Scaling reasoning typically spends more compute on reinforcement learning (RL) or on inference. We show that a completed RL training history can yiel...

By Bangji Yang, Jiajun Fan, Hongba Ma, Ruihan Guo, Ge Liu

reinforcement-learning benchmarks

arXiv AI

Jun 17

PLATE: Plasticity-Tunable Efficient Adapters for Geometry-Aware Continual Learning

arXiv:2602. 03846v2 Announce Type: replace-cross Abstract: We develop a continual learning method for pretrained models that \emph{requires no access to old-task data}, addressing a practical barrier in foundation model adaptation where pretraining distributions are often unavailable.

By Romain Cosentino

safety

arXiv Machine Learning

Aug 4

When Do Surrogate Updates Improve Decisions? A Local Theory of Trajectory-Wise Transfer

arXiv:2608. 01130v1 Announce Type: new Abstract: A broad range of models face the mismatch where they are updated through trajectory losses but are evaluated by downstream task reward.

By Yuyang Shen

llms

arXiv Machine Learning

Aug 24

Nothing Changed but the Model: CellFill -- Bounded In-Cell Learning for Bit-Identical, Revocable Updates to Quantized LLMs

arXiv:2608.20873v1 Announce Type: new Abstract: Every way of teaching a deployed language model something new -- full fine-tuning, adapter merging, model editing -- replaces the released checkpoint,...

By Zifeng Liu, Zhiyong Du, Yaxin Lu, Yiming Mao, Zhenhe Wang, Wenqi Shi, Zhengkun Jing

llms fine-tuning efficiency

arXiv AI

Jul 1

When Does Learning to Stop Help? A Cost-Aware Study of Early Exits in Reasoning Models

arXiv:2606. 30852v1 Announce Type: new Abstract: Reasoning models spend different amounts of useful computation across instances, but it remains unclear when a learned stopping rule improves over simple confidence or convergence thresholds.

By Zhe Dong (University of Maine at Presque Isle), Fang Qin (Stanford University), Manish Shah (Independent Researcher)

llms benchmarks

One email a morning, machine-written

Related stories

Cumulative-Goodness Free-Riding in Forward-Forward Networks: Real, Repairable, but Not Accuracy-Dominant

Does Scaling Reinforcement Learning Really Require More Training?

PLATE: Plasticity-Tunable Efficient Adapters for Geometry-Aware Continual Learning

When Do Surrogate Updates Improve Decisions? A Local Theory of Trajectory-Wise Transfer

Nothing Changed but the Model: CellFill -- Bounded In-Cell Learning for Bit-Identical, Revocable Updates to Quantized LLMs

When Does Learning to Stop Help? A Cost-Aware Study of Early Exits in Reasoning Models