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Paper · Inference efficiency

Context Language Models

Language models are designed to natively manage their own context as a file, enabling intrinsic context management that can be optimized through skill evolution.

The loop

A language model natively manages its own context by updating a context file. The model's context-management instructions are then evolved through a skill-optimization loop, improving its accuracy and efficiency on subsequent tasks.

The loop
Context Language Models
Paper · arXiv
  1. Model updates context file
  2. Skill-optimization evolves instructions
  3. Model uses improved instructions
  4. Model manages context better
  1. Model updates context file
  2. Skill-optimization evolves instructions
  3. Model uses improved instructions
  4. Model manages context better
↻ The improved system does the next round, and the loop turns again.

Why it is a road to recursion

Shifting context management from external harnesses to intrinsic model behavior allows models to learn and optimize their own memory strategies.

Evidence

Improves held-out accuracy by up to 35.9 points and achieves 11.4% higher accuracy with 21.5% fewer FLOPs on BrowseComp-Plus.

context-managementskill-evolutionefficiency

Related loops

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FlashInfer-Bench
  1. LLM agents write GPU kernels
  2. Faster kernels make LLM inference cheaper
  3. New serving traces define next kernel tasks
  4. repeat
Inference efficiencyUW, CMU, NVIDIA, UC Berkeley (Xing et al.) · 2026
PaperarXiv
Glia
  1. LLM agents write inference cluster code
  2. Designs lower latency and GPU cost
  3. Lowers cost of serving LLMs running Glia
  4. repeat
Inference efficiencyHamadanian et al. (MIT CSAIL) · 2025
PaperarXiv
ADRS
  1. LLMs propose and mutate load balancer code
  2. Best programs seed the next generation
  3. Output is faster serving code for LLMs
  4. repeat
Inference efficiencyUC Berkeley (Sky Computing Lab) · 2025