The legacy tail budget scales as threshold×target_ratio, which was designed
around 128K windows at a 50% trigger (~13K tail). On modern big-window
models with raised thresholds it silently hoards: a 1M-window session at
threshold 0.85 keeps a 170K-token verbatim tail (255K soft ceiling) out of
EVERY compaction, so a 540K manual /compress lands at ~290K and every
subsequent turn re-ships the hoard. Nobody chooses this; it is an artifact
of the formula outside its design envelope.
Lean mode (#87326, compaction-v2) was built for exactly this and its recall
was validated in the before/after eval (evals/compaction/results/): clamped
2.5%-of-window tail (10K floor / 25K cap), continuity carried by the
upgraded summary (digests, anchor index, verbatim user messages,
session_search recovery pointers). This flips the DEFAULT to lean; explicit
'tail_mode: legacy' in config keeps the old behavior exactly.
Also fixes a latent bug the flip exposed: update_model() re-assigned the
LEGACY formula directly when recomputing budgets, silently reverting a lean
compressor to the hoard on every mid-session model switch. The recompute
now routes through the mode-aware tail_token_budget property (regression
test included).
Surfaces: context_compressor.py defaults + getattr fallbacks, agent_init
parse default, DEFAULT_CONFIG, gateway _CACHE_BUSTING_CONFIG_KEYS gains
compression.tail_mode (mode changes now evict cached gateway agents like
target_ratio changes do), user + developer docs. Tests: 3 new default
contracts, legacy tests pinned explicitly, feasibility-skip scenario pinned
to legacy (under lean its payloads correctly become compressible).
E2E counterfactual (real imports, 1M window @ 0.85):
main default: legacy, tail 170,000 (ceiling 255,000)
head default: lean, tail 25,000 (ceiling 37,500)
head legacy: 170,000 (opt-out intact)
update_model to 400K: 10,000 (lean preserved across switch)
Baseline diff vs clean upstream/main (182 pre-existing environmental
failures on both sides) showed exactly 6 PR-caused failures, all the same
mechanism: tests construct ContextCompressor under a
get_model_context_length mock and read threshold_percent/threshold_tokens
after the with block, or build via object.__new__ and assign
context_length AFTER the derived budgets (the setter now resets the
lazily-cached budgets).
- test_compression_small_ctx_threshold_floor: resolve inside _make()
- test_cjk_token_estimation: resolve inside mock
- test_per_model_compression_threshold: resolve inside mock (2 tests)
- test_pre_compress_memory_context: assign context_length before
threshold/tail/summary budgets; add summary_target_ratio
Sessions on sub-512K-context models were spending most of their wall-clock
re-summarizing: the 50% trigger left too little post-compaction headroom
(the incompressible floor — system prompt, tool schemas, protected tail,
rolling summary — ate most of the reclaimed space), so compaction re-fired
every 1-2 turns. Three compounding defects fixed:
- Threshold floor: models with context windows below 512K now trigger at
>=75% of the window (raise-only — a higher configured value or per-model
autoraise like Codex gpt-5.5's 85% always wins). Re-derived on
update_model() in both directions.
- No max_tokens on the summary call: the summary budget is prompt guidance
only ("Target ~N tokens"). The wire cap truncated summaries mid-section
on the Anthropic Messages / NVIDIA NIM paths (thinking models burn the
cap on reasoning first), yielding truncated or thinking-only summaries
and compaction loops. Summary token ceiling lowered 12K -> 10K to keep
the guidance within the intended 1K-10K envelope.
- Reasoning traces excluded end-to-end: inline <think>/<reasoning> blocks
are now stripped from assistant content before serialization to the
summarizer, and from the summarizer's own output before the summary is
stored (previously a thinking summarizer model's trace was persisted in
_previous_summary and re-fed into every iterative update, compounding
bloat). Native reasoning fields were already excluded.
Verified E2E with real imports against a temp HERMES_HOME: threshold table
across 64K-1M windows, override interactions (user 0.85 wins, spark 0.70
raised, gpt-5.5 0.85 kept), full compress() round-trip with a thinking
summarizer, and wire-kwargs capture proving no max_tokens is sent.
Teknium