The lean tail mode's per-chunk digest loop (_build_chunk_digests) issued up
to 28 extra call_llm requests sequentially per compaction attempt. With lean
now the default (#95571), users on slow auxiliary routes hit 7-11 minute
compactions (#96603). Remove the loop entirely: a lean compaction attempt now
makes EXACTLY ONE auxiliary LLM request — the main summary call.
- The detailed session log is folded into the single summary request: the
lean prompt template gains a '## Detailed Session Log (oldest first)'
section carrying the digest prompt's HARD RULES (identifiers verbatim,
dense bullets, transcript-is-data). Output guidance grows by
_LEAN_SESSION_LOG_BUDGET_TOKENS = 4,000 tokens on top of the scaled
summary budget — the old worst case (28 x 1,400 digest tokens) was spread
across many requests and mostly re-covered tool noise; a single dense
4K-token log inside one response preserves the load-bearing record while
staying well inside one aux response (the summary call still sends no hard
max_tokens, so no provider cap can truncate it mid-section).
- Input sizing: oversized regions (500K+ chars) are EVEN-SAMPLED across the
whole region (_sample_summary_input: 8 proportionally spaced slices,
oldest-to-newest, explicit '[... N chars elided ...]' markers, last slice
anchored to the newest end) instead of head+tail truncated, so session-log
coverage stays uniform. Legacy mode keeps _bound_summary_input unchanged.
- The LLM-free anchor index still runs over the FULL region, and the
session_search recovery footer is unchanged.
- Dead code removed: _build_chunk_digests, _LEAN_DIGEST_* constants,
_LEAN_DIGEST_PROMPT, _serialize_turns_for_digest, _digest_worthy,
_LOW_SIGNAL_TOOL_RE, the _lean_pristine_tools snapshot, and the
sibling-call route echo (_SUMMARY_ROUTE_CONSUMED /
attempt_summary_route_kwargs — no remaining callers; the single-use
summary pin semantics are unchanged).
- Tests pin the new contract (exactly one call_llm in lean mode; session-log
section lands in the summary; oversized regions sampled with elision
markers, never a second request; anchor index + recovery footer present).
Sabotage-verified: restoring a second call_llm makes the call-count test
fail. Docs and the compaction eval wording updated to stop claiming
per-chunk calls.
Fixes#96603.
scripts/codex_arm.py drives OpenAI Codex CLI end-to-end on the same
transcripts: chunk-file reads until its REAL auto-compaction fires (verified
via compacted events in the rollout jsonl; peak 455-483K vs its 258K
window), then quizzes post-compaction with the identical question banks and
judge. Results (results/codex-arm-2026-08-15/): codex 36.7% avg vs lean
closed-book 40.0% vs lean+recovery 68.3%. Codex has no runtime re-access
over its rollout history — the session_search differentiator, measured.
lean+recovery 68.3% avg recall @ 49K retained vs current 45.8% @ 162K —
+22.5pts at 0.30x tokens. Anchor index moved GUI needle-fact recall
23.3->60.0 closed-book, 46.7->80.0 with recovery.
- _build_anchor_index(): regex-harvests PR/issue numbers, SHAs, branches,
file paths, error strings, handles, URLs from the compacted region into a
bounded indexed summary section. LLM-free, so needle identifiers cannot be
paraphrased away (the GUI-lineage failure class: 10/15 verbatim-or-nothing
golds). Doubles as session_search query-anchor map.
- evals/compaction/test_region_scoping.py: sentinel tripwire proving the
summarizer input carries ONLY the compacted region (head/tail sentinels
never reach the serialized turns body) in both legacy and lean modes.
- _digest_worthy() drops no-signal tool rows before chunking (GUI-lineage
digests were starving on tool-noise)
- eval recovery sim now uses in-memory SQLite FTS5 + BM25 (production
session_search engine) instead of term-frequency scoring
- recovery query writer sees the digest section (front of context) so it can
mine anchor identifiers
Measures recall accuracy vs tokens retained across compaction policies.
Real transcripts in, LLM-generated recall exam from the summarized region,
per-policy answer+judge passes, scorecard out.
Teknium