Same weights, 70 points apart: the ARC-AGI-3 table has stopped being procurement evidence

The scoring rule is what makes plumbing pay. ARC-AGI-3 scores each level as (human_baseline_actions / ai_actions) squared, with the ratio capped at 1.15 times the human baseline ^[7]. Squaring inflates a modest saving: AVO's headline result against VISTA is 12 percent fewer actions ^[8], and removing 12 percent of the actions lifts the ratio to about 1.136, which is roughly 1.29 once squared ^[9]. The cap permits 1.3225 ^[10]. One decision about how the loop spends its moves therefore collects around nine tenths of all the headroom the metric will ever give an agent that already matches a first-time human ^[11]. That is a target for whoever writes the loop, and it is invisible in a column headed by the model's name.

Put the harness papers side by side and the same parts recur under different labels, according to the dev.to tally. Memory: VISTA keeps a lossless record of every past observation, AVO carries forward prior implementations, evaluation results, compiler and profiler output and accumulated reasoning, and OpenAI's two flags are memory flags ^[12]. Supervision: AVO runs a monitor that watches for stagnation or repeated unproductive cycles and can redirect the main agent ^[13]. The official harness went the other way, discarding private reasoning after every game action and truncating history on a rolling window ^[14], because ARC built it to be generic and to make model shortcomings more visible ^[15]. The bottom row measures a model under a harness designed to expose it, the top rows measure models under harnesses designed to cover for them, and nobody has isolated which part of the gap is which ^[26].

The vendors are more careful than their readers. NVIDIA states that the AVO-versus-VISTA comparison should not be interpreted as a controlled ablation, and that the results are not a direct measurement of AVO's contribution ^[17]. VISTA notes its models were released after the public games, that overlap cannot be excluded, and that the private set remains the real test of generalisation ^[18]. Schema makes no frozen-harness or held-out-performance claim ^[19]. MIT reproduced the effect on August 5 and a group led by Impossible Research reached 98.98 on July 15 ^[23]; Google added a framework that gives the environment a harness of its own ^[24].

Once reinforcement learning runs with the deploy-time harness owning the loop, as Microsoft's Agent Lightning v1.0 does, the harness stops being a wrapper you can strip off for comparison and starts being part of the weights ^[6]. The dev.to author reads Agent Lightning's own reward-hacking section as the checklist his July post warned about ^[22]. A buyer evaluating two checkpoints trained that way is not comparing models at all, and there is no configuration change that will make them comparable.

Which leaves the depreciation question the writer raised himself. His July split put compensatory layers, the ones patching what a model cannot yet do, in the pile he expected to lose value with every model release, and all three components above sit in that pile ^[20]. They are currently worth 25 to 70 points against the newest frontier models on a benchmark built to resist static tricks ^[5]. Either the prediction was early or it was wrong. Either way, his procurement test is the usable output: a score without a harness version, a memory state and an action budget attached is a self-reported claim ^[21]. On that reading, "verified at 30.16" ^[1] certifies a run, not a model.