ContextBudget: Budget-Aware Context Management for Long-Horizon Search Agents

Dimensions overall score 7.0

• BACM-RL achieves over 1.6× performance gains over strong baselines in high-complexity settings.result 90%
  Evidencepartial

  achieving over $1.6\times$ gains over strong baselines in high-complexity settings

  Implicationpartial

  Directly stated in abstract with clear numeric evidence

  Verificationpartial

  partial

• BACM-RL maintains strong performance advantages as context budgets shrink, while most methods exhibit a downward performance trend.result 85%
  Evidencepartial

  while maintaining strong advantages as budgets shrink, where most methods exhibit a downward performance trend

  Implicationpartial

  Directly stated in abstract with comparative analysis

  Verificationpartial

  partial

• BACM formulates context management as a sequential decision problem with a context budget constraint.method 90%
  Evidencepartial

  which formulates context management as a sequential decision problem with a context budget constraint

  Implicationpartial

  Directly stated in abstract as core methodological contribution

  Verificationpartial

  partial

• BACM enables agents to assess available budget before incorporating new observations and decide when and how much interaction history to compress.method 85%
  Evidencepartial

  It enables agents to assess the available budget before incorporating new observations and decide when and how much of the interaction history to compress

  Implicationpartial

  Directly stated in abstract describing key functionality

  Verificationpartial

  partial

• BACM-RL uses an end-to-end curriculum-based reinforcement learning approach to learn compression strategies under varying context budgets.technical 90%
  Evidencepartial

  We further develop BACM-RL, an end-to-end curriculum-based reinforcement learning approach that learns compression strategies under varying context budgets

  Implicationpartial

  Directly stated in abstract describing technical approach

  Verificationpartial

  partial

• LLM-based agents' context size is limited by deployment factors including memory, latency, and cost, creating a constrained context budget.limitation 80%
  Evidencepartial

  yet their context size is limited by deployment factors (e.g., memory, latency, and cost), yielding a constrained context budget

  Implicationpartial

  Directly stated in abstract as problem motivation

  Verificationpartial

  partial

• BACM-RL consistently outperforms prior methods across model scales and task complexities.result 85%
  Evidencepartial

  BACM-RL consistently outperforms prior methods across model scales and task complexities

  Implicationpartial

  Directly stated in abstract with broad comparative claim

  Verificationpartial

  partial

• Experiments were conducted on compositional multi-objective QA and long-horizon web browsing benchmarks.method 90%
  Evidencepartial

  Experiments on compositional multi-objective QA and long-horizon web browsing benchmarks show that

  Implicationpartial

  Directly stated in abstract with specific benchmark details

  Verificationpartial

  partial

Competitive landscape

A budget-aware context management system for LLM agents that optimizes information retention within cost constraints, outperforming baselines by over 1.6x.