Encoding Context into LLM Weights: What Makes It Context?
Longer contexts allow large language models (LLMs) to condition their predictions on more information, but inference costs grow prohibitively with context length.
ProofPaper ↗
Key points
- An emerging alternative uses test-time updates to encode context as temporary changes to model weights.
- However, encoding this information into weights that already represent general knowledge obscures its role as context, so the LLM may fail to use it properly to condition subsequent inference.
- Invoking the identifier at inference then enables the LLM to recite the encoded context.
- We therefore further introduce a prompting framework that explicitly guides the LLM to use this information as context.
Sources (1)
- [1]Encoding Context into LLM Weights: What Makes It Context?arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 13, 04:08 AM
Longer contexts allow large language models (LLMs) to condition their predictions on more information, but inference costs grow prohibitively with context length.
An emerging alternative uses test-time updates to encode context as temporary changes to model weights.
Extractive summary: sentences quoted from the sources.
Before this
- Oct 13, 2026Beyond the Verdict: Agentic Code Judging with Verifiable Feedback
- Oct 12, 2026Meet the Anthropic Engineer Keeping Its AI Agent Trustworthy
- Oct 12, 2026AI Representations of Mental Health Stigma: Quantifying the Gap Between AI Estimations and Human Perceptions Across Gender Groups Using GPT-Based LLMs
- Oct 12, 2026Spectral-Driven Adaptive-Rank Optimizer for Large Language Model Reinforcement Learning with Verifiable Rewards
- Oct 12, 2026Large Language Models for Machine Translation Quality Annotation: Humans and Models Are Both Challenged
- Oct 12, 2026Rethinking LLM-Guided Visual Question Answering: Does Deeper Knowledge Integration Really Help?