Unverified50% confidenceFactExact time
FoldScape's training data covers two complementary trajectory sources: multi-scale coarse-grained folding trajectories and all-atom mechanical unfolding trajectories.
1
Sources
50%
Confidence
Long-term
Relevance
11/4/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedFoldScape designs a stride-one crop-superposition scheme that extends inference capability to chains several times longer than a single chain-level forward pass.68% similarUnverifiedLogging should be divided into two tracks: learning signals for Harness evolution, and operational traces for troubleshooting and evaluation.67% similarUnverifiedFoldScape provides a way to obtain conformational ensembles without per-protein molecular simulation, suitable for studying folding intermediates, mechanical responses, or partial unfolding behavior.65% similarUnverifiedTwo leverage matrices emerge in the framework: forget self-leverage and retain cross-leverage, capturing the entanglement of forget and retain data, respectively, under the geometry of the retrained model.62% similarUnverifiedThrough cross-model and cross-task boundary inspection, the research classifies reasoning-chain channel states as: active, amplified, inert, or collapsed into task labels.62% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1133933API
curl https://kongchang.com/api/v1/knowledge/claims/1133933MCP
get_claim(id=1133933)