Adapting Context Compression for Long-Horizon Agents with Counterfactual Continuations
Quick summary
arXiv:2609.36526v1 Announce Type: cross Abstract: Long-horizon agents require context compression to manage growing interaction histories. Compression quality, however, is ultimately determined by downstream execution. Existing prompt-adaptation methods infer compression errors by comparing full-context and compressed trajectories. Such comparisons cannot isolate individual compressions and are confounded by agent stochasticity. We first find that compression degrades reliability before solvability. Using matched counterfactual continuations that compare execution from the same agent state wit
Key takeaways
- arXiv:2609.36526v1 Announce Type: cross Abstract: Long-horizon agents require context compression to manage growing interaction histories.
- Compression quality, however, is ultimately determined by downstream execution.
- Existing prompt-adaptation methods infer compression errors by comparing full-context and compressed trajectories.
Why it matters
The importance of “Adapting Context Compression for Long-Horizon Agents with Counterfactual Continuations” will be measured by what changes in practice. User behavior, access conditions, verifiable performance and responsible-use outcomes are the signals worth following.
Member comments