Регалии

Clear Every Third Task: A Measured U-Curve in the Context Economy of Coding Agents

How often should an agent's context be cleared? 36 runs, six session-length policies, six replicates each, 4086 tests and zero failures. Cost is U-shaped in session length: clearing after every task costs 25.3% more than clearing every third, and never clearing costs 15.6% more. Exact permutation tests put both extremes outside the optimum (p = 0.0022 and p = 0.0108) while three, four and six tasks per session are indistinguishable - the optimum is a plateau, not a point. The U decomposes into cache writes falling and context-per-call rising, priced 12.5:1 against each other.

10.5281/zenodo.22699668 · PDF

The Context Economy of Agentic LLM Sessions: Where the Money Actually Goes

722 agent sessions, 150,902 model calls, 34.6 billion tokens. Context handling accounts for 83.5% of modeled cost and generation for 16.5%; 80% of the spend comes from 3.3% of sessions. Includes two log-deduplication traps that change the answer by a factor of two in either direction, and the de-identified dataset.

10.5281/zenodo.22688706 · PDF

AI Tools Radar: GitHub and Hugging Face projects selected by arsentev.ai (monthly)

490 open-source AI projects selected and reviewed by the arsentev.ai radar since June 2026, with daily GitHub and Hugging Face API snapshots of how they evolve after selection. A new version is released every month.

10.5281/zenodo.22730450

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

Run-level token counters, modeled cost, wall clock and test outcomes for every run behind the U-curve report. Counters only - no prompts, no model output, no paths.

10.57967/hf/10366

Agent Run Metrics: A JSON Interchange Format for Resource Accounting of AI Agent Runs

draft-arsentev-agent-run-metrics

Discovery and Retrieval of Publisher-Curated Context Files for Large Language Models

draft-arsentev-llm-context-discovery