The authors built CliffCompaction, an automatic context-management method for coding agents operating beyond their models’ context limits.
Dropping Rather Than Rewriting Context Cuts Long-Horizon Agent Costs
CliffCompaction selectively retains original context across sessions, reducing coding-agent costs while preserving benchmark performance over million-token trajectories.
Top University
Trang Nguyen · Eulrang Cho · Bingqing Chen · Tim Dettmers
Carnegie Mellon University · Bosch Center for AI
Research Digest··2 min read
Nguyen et al.
Why this paper
From Carnegie Mellon University and Bosch Center for AI · Released code · Part of Context Engineering for Agents, now 23 papers
In one line
CliffCompaction reduces cost up to 50% while maintaining performance by truncating/dropping context instead of rephrasing, and enables efficient test-time scaling and continual learning.
What it released
Code
What we could check
- ✓Code link in the paper (github.com)
- ·No weights link found
- ·No dataset link found
- ·No compute details found
- ·No stated limitations found
- ·No benchmark numbers found
Observed from the paper text and links we have. Absence here means we did not find it, not that it does not exist.
§