Unverified50% confidenceFactExact time
MDSKILL enables agents to complete skill internalization and continuous self-evolution without a stronger external teacher model.
1
Sources
50%
Confidence
Long-term
Relevance
11/4/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedSkill.md differs from fine-tuning (which changes model weights) and prompt engineering (which relies on the context of a single conversation): it is a reusable asset independent of the model that can be loaded by an agent at runtime and followed step by step.67% similarUnverifiedThe project's approach is to empower general-purpose agents through a standardized skill library rather than retraining specialized scientific large models.66% similarVerifiedThe mainstream skill package execution method is to load skill instructions into the agent's context window, relying on the agent itself to understand and follow these instructions.66% similarUnverifiedThe paper "MDSKILL: Learning to Internalize Skill Memory through Multi-View Distillation for Efficient Dynamic Agent Self-Evolution" proposes a multi-view distillation scheme to address the noise and entanglement problems in the process of internalizing skill memory.66% similarVerifiedSkillOpt does not fine-tune the underlying model weights; instead, it establishes an iteratively optimizable closed loop around the definition, invocation, and performance of skills.65% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1134284API
curl https://kongchang.com/api/v1/knowledge/claims/1134284MCP
get_claim(id=1134284)