tera-crow-nav: the first spatial taskset, and the hold sweep that sized its budget

The bridge becomes a runnable taskset. The crux is that these are continuous
control environments and the agent emits text: the model issues a control
decision that is held for N steps, and the hold length is measured rather than
guessed. At one decision every 32 steps the scripted baseline returns -2.807 and
reaches the waypoint 0% of the time, against 5.484 and 100% at every frame.

Open loop — one decision for the whole flight — returns -3.764 against an
inaction floor of -4.161. A bird that sets a course and leaves does no better
than one that does nothing, which is why crow-nav carries the strongest rule-4
claim of the five spatial environments. The budget bites from both ends: 8 turns
reach the goal 44% of the time, 12 reach 88%, 14 reach 100%.

32 episodes against Qwen3.8-27B: mean 0.4603, no malformed replies in 412, every
episode replaying to an identical checksum, and 16 of 32 spending all 16 turns.
The gate fires 43.75% of the time and separates the four scenarios cleanly —
whatever is wrong with the four dead data-environment gates, it is not that
binary gates cannot discriminate.

The oracle comes from the worker's own op. Nothing recomputes in Python a number
that came out of TypeScript, least of all the reward denominator.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>

configs/tera_crow_nav.toml

@@ -0,0 +1,25 @@
+# tera-crow-nav — fly a crow to a waypoint on sixteen replies.
+# See environments/tera_spatial for what it measures and where these two integers
+# came from (`measure_hold.py`, not judgement).
+#
+#   uv run --project environments/tera_spatial eval @ configs/tera_crow_nav.toml \
+#     --model brain-qwen38-dspark \
+#     --client.base-url http://100.127.247.67:8001/v1 \
+#     --client.api-key-var SPARK_API_KEY --no-push --no-rich -c 8
+#
+# `node` >= 22.18 must be on PATH: the environment is TypeScript and is executed,
+# not translated.
+num_tasks = 16
+num_rollouts = 2
+
+[env.taskset]
+id = "tera-crow-nav"
+num_tasks = 16
+max_turns = 16    # replies. Sixteen is where the ceiling first becomes reachable.
+max_hold = 12     # steps one reply is held for. 16 x 12 = 192 against a 300-step cap,
+                  # so what binds is the turn budget and never the simulator's own.
+default_hold = 6  # what an unreadable reply costs. Never zero.
+seed_base = 115
+
+[env.agent.harness]
+id = "null"    # one completion per turn, no tools; the env programs the loop