Say what a gather costs on the line after it

[CNSeniorious000]

The asyncio.gather bullet says how to overlap calls and stops there. Combined with "the LAST expression is echoed back to you … reaching for it first keeps cells short", the cheapest reading is: fan out, then echo the list.

Which is what gpt-5.6-terra does, in cell after cell:

res = await asyncio.gather(
  mcp.legal_search.search_statutes(keyword='民事诉讼', limit=100),
  mcp.legal_search.search_cases(keyword='借款', limit=100),
  ...
)
res

One line is short, and it pastes back five full answers.

-- Independent calls may overlap with `asyncio.gather`. Sequence dependent work with plain `await`.
+- Independent calls may overlap with `asyncio.gather`. Sequence dependent work with plain `await`. A gather hands back every answer in FULL, so the line AFTER it decides what the batch costs: end on what you pulled out of `res`, never on `res` itself.

The rule goes here rather than in the return-channel bullet because this is where the fan-out decision is made, and res is the name the model actually binds.

Measured — 4/4 tasks, 3 arms, all complete

Carried with #27, #30 and #36 as one arm. gpt-5.6-terra, band 2K–20K. Dump rate = share of cells surfacing ≥ 90% of what they consumed; median ratio in brackets.

task	baseline	#27+#30	+ this and #36
private_lending_33d	80.5% (1.006)	50.0% (0.613)	13.3% (0.225)
hsa_medical_bill_liquidity_guard_30d	72.7% (1.180)	29.2% (0.039)	24.5% (0.013)
broadcast_exam_posture_breathing_32d	9.1% (0.302)	38.2% (0.479)	3.6% (0.022)
food_safety_dispute_33d	44.7% (0.345)	100.0% (1.018)	12.5% (0.069)
pooled	47.3% (79/167)	43.7% (69/158)	15.1% (21/139)

comparison	z	p
baseline vs #27+#30	+0.66	0.51
baseline vs this arm	+5.98	< 1e-6
#27+#30 vs this arm	+5.34	< 1e-6

Important

#27 + #30 alone win two tasks and lose two — a wash. This arm wins 4/4, including both tasks where that pair regressed, and every median ratio lands under 0.25. broadcast_exam is the one to check: baseline was already fine there at 9.1%, #27 + #30 took it to 38.2%, this arm takes it to 3.6% — below glm-5.3's 4.8% on the same task.

For reference on the same band: macaron-v1-venti 5.5% pooled and glm-5.3 4.8% / 1.8%. Both already reduce without any of this; gpt-5.6-terra was the only one of the three that did not.

Note

Attribution between this and #36 is not separated — they were measured together, and the arm does not carry #34.

[sourcery-ai]

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