arXiv — cs.AI preprintsInternational19 October 2026
Engram Injection: A Study of Temporal Validity in Parametric Language-Model Memory
This is an official announcement record
Firsthand records what arXiv — cs.AI preprints announced and links to the original. The wording below is theirs, not ours.
arXiv:2610.15882v2 Announce Type: replace Abstract: A stored fact may stop being valid when a user changes a preference or an event replaces an earlier one. We study whether a language model can learn these changes in its parameters and answer correctly from only a question and the current time. Engram Injection stores facts in LoRA adapters and supplies the clock through FiLM conditioning on a frozen Qwen2.5-3B-Instruct model. We compare it with additive clock conditioning, prompting with the full dated history, and retrieving four dated records. We train six matched adapters across three see
Read the official announcement
Opens arxiv.org
More from arXiv — cs.AI preprints
- Structured Authority Injection: Persistent Human Control for LLM Agents via Typed MCP Overrides19 October 2026
- Rethinking User Encoders: Routing Diffusion States for Scenario-Adaptive User Representation19 October 2026
- From Events to Reasoning: Hierarchical Rule Composition over Explicit States19 October 2026
- ECHOES: Seeing Beyond the Current Image via Recursive History Compression and Privileged On-Policy Distillation19 October 2026
- Sensitivity Analysis of Decision Rules in an Agent-Based Agro-Ecological Model19 October 2026
This content is for informational purposes only and is not professional advice. Specifications, prices, plan tiers, and features change frequently and may differ from what is shown here; verify current details on the manufacturer's or company's official page before purchasing. Ratings are based on analysis of published documentation, not independent lab testing.