Each row in the trace is one agent step with a cached prefix_tokens portion and a freshly prefilled newly_append_tokens (“append”) portion. Walking the steps of a session in order, this experiment splits the append into fresh vs. non-fresh tokens and reports the totals per provider and step trigger.

Method and assumptions:

• Total input for a step is prefix_tokens + newly_append_tokens. The per-step context growth is max(0, total_input(S) - total_input(P)) for the previous step P seen in the same session — the net new context that survived into S (drops, i.e. compactions, contribute 0).
• Fresh tokens. Between P and S the context grows by the model’s output at P (now part of the conversation) plus any genuinely new user/tool tokens. So fresh(S) = context_growth(S) - output_tokens(P). output_tokens already includes reasoning tokens (reasoning_output_tokens is a subset, not an additional count — see trace_facts/overview_summary), and Codex reasoning is empirically carried into later context, so the full output_tokens is the right quantity to subtract.
• Append (denominator). append(S) = newly_append_tokens(S) — the uncached tokens actually prefilled at S. fresh % of append = total_fresh / total_append.
• Pairing. A step qualifies only when it is not the first step of its session and its first timing event is a user_message or tool_result (the step’s trigger). P is whatever step was last seen for that session in trace order (round_pk = file order), regardless of its trigger — identical to session/total_input_growth. Session-first steps have no predecessor and so contribute to neither the fresh nor the append totals.
• Triggers reported. all, user, and tool_result, each per scope (merged, claude, codex).

The summed context-growth values reproduce total_context_increase from session/total_input_growth exactly; this experiment adds the output_tokens subtraction and the append denominator.

A hybrid experiment, like the other stateful ones: the trace DuckDB streams rows, Python keeps the per-session sequencing.

• read_accums(con) — one SQL pass over rounds (with each round’s first timing event joined in) ordered by round_pk, walked in Python with a last_by_session map to pair each trigger step with its predecessor and accumulate append / context_growth / prior_output per (scope, trigger).
• FreshAccum — the per-group accumulator; fresh_tokens = context_growth - prior_output.
• write_summary_csv(...) / write_latex_table(...) — the CSV (raw integer source of truth) and the paper table (\,M token formatting, fresh % per provider).

The data layer lives in artifacts/utils/trace_db.py (see artifacts/utils/DB_SCHEMA.md); the trigger mapping is shared with artifacts/utils/growth.py.

# default merged trace (materialized to a temp DuckDB cache on first use)
uv run python artifacts/prefix_cache/redundant_prefill/analyze.py

# a prebuilt DB (run_all.py's build-db step passes this), into a chosen dir
uv run python artifacts/prefix_cache/redundant_prefill/analyze.py --db "$TMPDIR/trace.duckdb" -o "$TMPDIR/out"

• redundant_prefill_summary.csv — per (scope, trigger): events, total append, total context growth, total prior output, total fresh, and fresh % of append.
• redundant_prefill_table.tex — the paper table (tab:redundant_prefill), copied into the paper’s figure-tex/.
• redundant_prefill_table.md — GFM Markdown mirror of the table, rendered on the web detail page.
• headline.json — the few headline numbers for the Overview gallery card.