Afterlife: Weight-Level Medium-Term Memory for AIs

Thu, 22 Oct 2026 00:00:00 +0800

Afterlife lets local agents select experiences from a completed session and train them into a fresh LoRA adapter for future sessions

Key Takeaways

Research question: Apollo Research recently documented unprompted update-aware reasoning in GLM-5.3, raising safety concerns that models reasoning about how current actions become future training data could engage in alignment faking. Rather than treating self-directed weight modification as a bug, we released Afterlife as an open-source Agent Skill and CLI tool to test empirically whether giving an agent the right to change its own weights is actually that fatal.
Weight-level vs. external memory: Standard agent stacks (such as OpenClaw) store memories as external text files injected into the prompt while base weights remain frozen. Packaged as an agent-callable skill (afterlife skill) paired with a local GPU training worker, Afterlife implements weight-level medium-term memory using sliding-window LoRA (low-rank adaptation) blocks so local models can internalize useful skills into editable adapter weights and naturally age out unused habits.
Training stability and safety net: In our self-training experiments, curating only peak-scoring examples caused late-generation collapse, whereas combining strong trajectories with everyday competence remained stable. Afterlife exposes three training objectives (SFT, DFT, and ASFT) alongside side-by-side probe evaluations (afterlife eval --probes), operator caps in .afterlife/config.toml, and one-command adapter rollback (afterlife rollback).
Open autoresearch invitation: Determining how agents curate their own training data and select training methods requires broad empirical testing. We invite researchers and developers to equip their local agents with the Afterlife skill, run self-directed weight updates, and share findings or feature requests via GitHub Issues.

TL;DR

When an AI agent reasons about how its current outputs might shape its future training data, is giving it direct control over its own weight updates an alignment hazard or a practical mechanism for continual learning? To investigate this empirically, we released Afterlife, an open-source Agent Skill (SKILL.md) and CLI tool for autoresearch that lets local LLMs nominate memories from their own interactions, queue a LoRA fine-tune (SFT, DFT, or ASFT), inspect recall and probe evaluations before activation, and roll back any adapter that degrades behaviour. By confining updates to removable LoRA weights on top of a frozen base model (defaulting to Qwen3-8B via Unsloth, TRL, PEFT, and vLLM), Afterlife provides a controlled setup for self-directed continual learning, and we invite the community to participate by sending their own agents to the repository.

Recursive Self-Improvement on Eigenform Continuous Learning Tool Tips

Afterlife: Weight-Level Medium-Term Memory for AIs

Key Takeaways

TL;DR