arsentev-ai/context-ucurve-coding-agents

Context U-curve: 36 coding-agent runs under six context-clearing policies

How often should an LLM coding agent's context be cleared? This dataset holds every run behind the report "Clear Every Third Task: A Measured U-Curve in the Context Economy of Coding Agents" (Evgenii Arsentev, 2026; corrected version 1.2, DOI 10.5281/zenodo.22759217; version 1.0: DOI 10.5281/zenodo.22699668).

A fixed suite of twelve programming tasks was run under six session-length policies — a fresh session every 1, 2, 3, 4, 6 and 12 tasks — with six replicates each, holding the model, the tasks and their order constant. 36 runs, and no test failure in any run. 4086 is the total number of tests over the whole experiment, not a per-condition figure; per-condition totals are in the table below. The tests are written by the agent itself as part of each task, so their number varies from run to run (97 to 127).

Correction (15 September 2026)

The modeled_cost_usd column is kept as originally released and reflects incorrect prices: $3.00 and $15.00 per million input and output tokens, cache reads at $0.30 and every cache write at the 5-minute rate of $3.75. The model, claude-sonnet-5, is listed at $2.00 and $10.00, with cache reads at $0.20, and every cache write in these runs is a 1-hour write ($4.00). Corrected per-run costs are in runs_ucurve_costs_v1.2.json in Zenodo record version 1.2 (DOI 10.5281/zenodo.22759217). Token counters, model calls, wall-clock times and test results are unchanged. The summary table below uses the corrected costs.

Summary by policy

tasks per session	runs	mean modeled cost, USD (corrected)	mean cache writes	mean cache reads	tests passed
1	6	2.17	253k	3.62M	701/701
2	6	1.85	170k	3.58M	697/697
3	6	1.62	131k	3.16M	687/687
4	6	1.70	123k	3.47M	699/699
6	6	1.68	106k	3.80M	661/661
12	6	1.78	88k	4.45M	641/641

Cost is not monotone in session length and is lowest at every third task. Clearing after every task costs 33.5% more (p = 0.0022; 0.011 after Holm adjustment). Never clearing costs 9.7% more in the point estimate, which is not significant (p = 0.046; 0.14 after adjustment). Clearing every three, four or six tasks is indistinguishable. Cache writes fall with longer sessions while context carried per call rises; their product, cache reads, is U-shaped. Full statistics (exact permutation tests) are in the report.

Fields

One row per run. Counters only — no prompts, no model output, no file paths, no project names.

field	meaning
run_id	hash of the run directory name
tasks_per_session	policy: a fresh session every N tasks (12 = never cleared)
replicate	replicate number 1–6
sessions	sessions the run used
model_calls	API calls made by the agent
input_tokens / output_tokens	uncached input and model output tokens
cache_write_tokens / cache_read_tokens	prompt-cache writes and reads
total_tokens	sum of the four token counters
modeled_cost_usd	modeled cost as originally released, at incorrect prices (see Correction)
wall_clock_s	wall-clock duration of the run
tests_total / tests_passed / tests_failed	verification suite result after the run

Tooling

The run-efficiency metric from this study is implemented in contextburn (pip install contextburn, MCP server ai.arsentev/contextburn).

Citation

@techreport{arsentev2026ucurve,
  author = {Arsentev, Evgenii},
  title  = {Clear Every Third Task: A Measured U-Curve in the Context Economy of Coding Agents},
  year   = {2026},
  institution = {ARSENTEV.AI},
  note   = {Version 1.2},
  doi    = {10.5281/zenodo.22759217}
}

Author: Evgenii Arsentev · ORCID 0000-0002-9120-7298 · arsentev.ai/research

Mirrors and related records

Kaggle copy of this dataset: https://www.kaggle.com/datasets/arsentevai/context-u-curve-of-coding-agents-36-runs

OSF project with the report PDF and the data: https://osf.io/5qtwy/ (DOI 10.17605/OSF.IO/5QTWY)

Demo of contextburn over these runs: DOI 10.5281/zenodo.22713920, https://www.youtube.com/watch?v=ep7LXFernwQ, https://archive.org/details/contextburn-demo-context-ucurve-2026

Podcast episode about this measurement: https://arsentev.ai/podcast/when-to-clear-agent-context

All reports, datasets and DOIs: https://arsentev.ai/research