AI agents encounter learning opportunities in every episode they run, and discard nearly all of them: the underlying models are frozen at deployment, so an agent that resolves a difficult request today starts from zero when it recurs tomorrow. Yet ordinary op...
VQV Signal
RESEARCH
SOURCE-BACKED
TECHNICAL
Learning on the Job: Continual Learning from Deployment Feedback for Frozen-Weights Agents
AI agents encounter learning opportunities in every episode they run, and discard nearly all of them: the underlying models are frozen at deployment, so an agent that resolves a difficult request today starts from zero when it recurs tomorrow. Yet ordinary op...
AI agents encounter learning opportunities in every episode they run, and discard nearly all of them: the underlying models are frozen at deployment, so an agent that resolves a difficult request today starts from zero when it recurs tomorrow. Yet ordinary operation already produces feedback, in the form of...
VQV organizes public signals from inspectable sources. It does not independently verify the underlying report.
Signal Strength 95%
Technical label SOURCE-BACKED
Public Interest 18
Category RESEARCH
Reader Depth TECHNICAL
Signal Strength reflects source quality, relevance, freshness and evidence. Public Interest helps organize discovery; it is not proof of truth.
Public Interest components
Recognizable Entity Score 0
Practical Impact Score 0
Novelty Interest Score 48
Consequence Score 18
Curiosity Score 16
Shareability Score 37
Topics
VQV surfaced this signal because it is recent, relevant to AI Agents, connected to arXiv.
No login, cookies, social SDKs, or automatic posting.