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.
gpt-5.3-codex-spark has a native 128K context window but the default
50% compaction trigger fires at ~64K, wasting half the usable window
before the session has accumulated enough turns to summarize
meaningfully. This raises the trigger to 70% (~90K) on the Codex OAuth
route only, leaving ~38K headroom for the summary and continued
conversation before the 128K hard limit.
The override is not gated by allow_codex_gpt55_autoraise because 128K
is the model's native window (unlike gpt-5.5's artificial 272K Codex
cap). Non-Codex routes are unaffected.
Also adds a boundary regression test verifying the short-session
scenario from the issue always yields a non-empty compressible window
(no silent context wipe).
When the summary LLM hits a 429/transient failure, _generate_summary() sets
a cooldown and returns None; compress() inserts a static fallback marker and
returns. Tokens stay above threshold, so should_compress() kept returning
True and every subsequent agent turn re-fired _compress_context() — the CLI
appeared frozen until the cooldown expired.
Add a cooldown guard to should_compress(): return False while
_summary_failure_cooldown_until is in the future. Reuses the existing float;
no new state. Manual /compress (force=True) still clears the cooldown first.
Fixes#11529
When summary_target_ratio is large (e.g. 0.45) and the context_length is
moderate (e.g. 96000), the soft_ceiling (token_budget * 1.5) can exceed
the total transcript size. _find_tail_cut_by_tokens walks the entire
transcript without breaking early, and the resulting compress window is
either empty (compress_start >= compress_end) or a single message whose
summary-of-one overhead saves ~0 tokens.
Both outcomes cause a no-op compression that does not increment
_ineffective_compression_count, so should_compress() returns True on
every subsequent turn and the loop repeats endlessly.
Fix (two layers):
1. _find_tail_cut_by_tokens: when the backward walk consumed the entire
transcript without breaking (cut_idx <= head_end and accumulated <=
soft_ceiling), re-walk with the raw (non-inflated) token budget to
find a meaningful cut that gives the summarizer a useful middle window.
2. compress(): when compress_start >= compress_end, increment
_ineffective_compression_count and log a warning so the existing
anti-thrashing guard in should_compress() can break the loop.
Fixes#40803
