Unverified50% confidenceFactExact time
In-context learning in the tabular domain typically relies on attention mechanisms that let the prediction sample interact directly with the training samples in the context, equivalent to soft retrieval weighting at inference time.
1
Sources
50%
Confidence
Long-term
Relevance
10/18/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedThe learnable routing function differs from fixed sparse attention (such as local windows and fixed-stride jumps) by allowing the model to learn during training where it should attend.79% similarVerifiedSelf-supervised learning is the core paradigm of the pre-training phase, where the model constructs training signals from the data itself through a next-token prediction task.77% similarUnverified业界探索的思考力度校准方向包括基于强化学习训练模型的元认知能力、通过监督微调学习推理深度分布、以及设计动态停止机制76% similarUnverifiedMainstream reinforcement learning training methods for role-playing agents typically collect a fixed scenario pool before learning begins and train repeatedly on static datasets, resulting in a distribution bottleneck problem.75% similarUnverifiedThe training recipe suggested by the WOVEN paper: select data according to the reasoning operation taught by the supervision, rather than by action, scene, or domain; and prioritize samples with larger visual state changes to obtain better robustness.75% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1059309API
curl https://kongchang.com/api/v1/knowledge/claims/1059309MCP
get_claim(id=1059309)