Public articles linked to the same research event.
arXiv The work introduces PAPER2LLM++, a framework for continual self-evolution of LLMs from research papers: it treats the growing literature as a stream of evidence and supervision, extracts evidence-grounded findings from each incoming paper, tests whether the reported limitation persists in the current model, converts findings into candidate learning signals when needed, and uses a try-evaluate-commit procedure to integrate an update only when it improves the targeted behavior without substantially forgetting prior improvements or degrading general capabilities; across a sequential stream of research-discovered LLM failures, models progressively incorporate new findings while retaining earlier gains.
The work introduces PAPER2LLM++, a framework for continual self-evolution of LLMs from research papers: it treats the growing literature as a stream of evidence and supervision, extracts evidence-grounded findings from each incoming paper, tests whether the reported limitation persists in the current model, converts findings into candidate learning signals when needed, and uses a try-evaluate-commit procedure to integrate an update only when it improves the targeted behavior without substantially forgetting prior improvements or degrading general capabilities; across a sequential stream of research-discovered LLM failures, models progressively incorporate new findings while retaining earlier gains.
The work introduces PAPER2LLM++, a framework for continual self-evolution of LLMs from research papers: it treats the growing literature as a stream of evidence and supervision, extracts evidence-grounded findings from each incoming paper, tests whether the reported limitation persists in the current model, converts findings into candidate learning signals when needed, and uses a try-evaluate-commit procedure to integrate an update only when it improves the targeted behavior without substantially forgetting prior improvements or degrading general capabilities; across a sequential stream of research-discovered LLM failures, models progressively incorporate new findings while retaining earlier gains.
The work introduces PAPER2LLM++, a framework for continual self-evolution of LLMs from research papers: it treats the growing literature as a stream of evidence and supervision, extracts evidence-grounded findings from each incoming paper, tests whether the reported limitation persists in the current model, converts findings into candidate learning signals when needed, and uses a try-evaluate-commit procedure to integrate an update only when it improves the targeted behavior without substantially forgetting prior improvements or degrading general capabilities; across a sequential stream of research-discovered LLM failures, models progressively incorporate new findings while retaining earlier gains.