When Reality Outpaces Cognition: A New Scaling Principle for Reliable Intelligence

Ahsan Adeel; Talha Bin Riaz

cs.AIOct 14, 2026

When Reality Outpaces Cognition: A New Scaling Principle for Reliable Intelligence

Authors: Ahsan Adeel, Talha Bin Riaz

Organizations: University of Stirling

Abstract

This paper challenges the understanding capabilities of existing AI paradigms and emerging world models by asking a fundamental question: How quickly can reality change before an intelligent system can no longer reliably perceive, reason, and act? We formulate this as a theory- and model-agnostic challenge in which systems must infer reliable actions for increasingly distant future states across a growing observation--reality gap. We operationalize this challenge through the effective Speed of Coherent Cognition ( $S_C^{\mathrm{eff}}$ ), defined as the highest tested Speed of Reality ( $S_R$ ) through which a system continuously satisfies a predefined behavioural reliability criterion from baseline. As a proof of concept, reinforcement-learning agents are trained at the baseline $S_R$ and evaluated, without retraining, as $S_R$ is increased by up to $50\times$ while computational resources remain fixed. We compare Transformer and gated-MLP baselines with $Co^4$ , an architecture inspired by cellular mechanisms implicated in conscious processing and awake thought. Across the tested scenarios, $Co^4$ achieves substantially higher $S_C^{\mathrm{eff}}$ than the baselines, motivating $S_C^{\mathrm{eff}}$ as a new benchmark and the scaling of coherent cognition as a principle for reliable AI.

Figures & tables

Figure 2: Operational challenge of when reality outpaces cognition. The agent is trained at the baseline Speed of Reality ( $S_{R,0}=1$ ), but tested at $50\times$ the baseline ( $S_{R}=50$ ) while computational resources remain fixed. From State 1, it must infer the action appropriate for the future State 50 while States 2–50 remain unobserved. The challenge is whether the agent has learned sufficiently generalisable task-relevant world dynamics to infer a reliable action across this observation–reality gap.

Figure 3: Training performance in the easy vision-guided quadrupedal locomotion environment. Running average reward over training epochs for the Transformer, gMLP, and two $Co^{4}$ variants at $S_{R,0}=1$ . In the easy environment, obstacles are more widely separated, allowing all architectures to learn the locomotion task, although with different learning trajectories and final performance levels.

Figure 4: Scaling $S_{C}^{\mathrm{eff}}$ in the easy vision-guided quadrupedal locomotion task. Reward is shown as $S_{R}$ increases from the training baseline $S_{R,0}=1$ to $S_{R}=50$ without retraining. The dashed line denotes the predefined reliability threshold $\tau=500$ .

$\mathbf{S_{R}}$	$\mathbf{Co^{4}}$ -AA	$\mathbf{Co^{4}}$ -ADA	Transformer	gMLP
1	636.0 $\pm$ 38.2	617.3 $\pm$ 34.2	625.2 $\pm$ 37.5	415.1 $\pm$ 22.1
2	636.4 $\pm$ 38.2	615.1 $\pm$ 36.9	623.2 $\pm$ 37.4	418.2 $\pm$ 23.6
3	636.1 $\pm$ 38.2	649.2 $\pm$ 39.0	246.3 $\pm$ 14.8	166.0 $\pm$ 10.0
5	564.9 $\pm$ 33.9	605.4 $\pm$ 36.3	243.2 $\pm$ 14.6	150.0 $\pm$ 9.0
10	519.3 $\pm$ 31.2	628.2 $\pm$ 37.7	301.2 $\pm$ 18.1	149.8 $\pm$ 9.0
20	509.0 $\pm$ 30.5	188.3 $\pm$ 11.3	35.8 $\pm$ 2.1	36.9 $\pm$ 2.2

Table 1: Performance in the easy vision-guided quadrupedal locomotion task as

S_{R}

increases. Values are mean

\pm

standard deviation.

Figure 5: Representative attention patterns in vision-guided quadrupedal locomotion. The Transformer exhibits comparatively diffuse attention (top): when the obstacle is distant, attention extends substantially over background regions, while proximity to the obstacle produces broadly distributed attention across the visual input. In contrast, Co 4 (middle) exhibits more selective attention, concentrating on the immediately relevant traversable region while the obstacle is distant and shifting toward the obstacle as it becomes locally relevant. This difference is also reflected in the sequence-level attention maps (bottom), where Co 4 produces a substantially sparser pattern.

Figure 6: Training performance in the complex vision-guided quadrupedal locomotion environment. Running average reward during training for the Transformer and gMLP baselines and Co 4 variants implementing different forms of apical modulation [ 17 , 20 ] . Performance differs substantially across modulation conditions. Disabling the Co 4 modulation equations provides a control for assessing the functional contribution of contextual modulation.

Figure 7: Scaling $S_{C}^{\mathrm{eff}}$ in the complex vision-guided quadrupedal locomotion task. Reward is shown as $S_{R}$ increases from the training baseline $S_{R,0}=1$ to $S_{R}=50$ without retraining. The dashed line denotes the predefined reliability threshold $\tau=300$ . Although the three-layer gMLP performed comparably to the two-layer $Co^{4}$ during training, it showed substantially weaker generalisation as $S_{R}$ increased. Further architectural optimization, including increasing the number of layers, is beyond the scope of the present general challenge.

$\mathbf{S_{R}}$	$\mathbf{Co^{4}}$ -ADA	$\mathbf{Co^{4}}$ -AA	Transformer	gMLP
1	503.8 $\pm$ 28.6	458.0 $\pm$ 27.5	311.2 $\pm$ 18.7	232.8 $\pm$ 14.0
2	430.4 $\pm$ 26.6	466.8 $\pm$ 28.0	304.7 $\pm$ 18.3	241.8 $\pm$ 13.8
3	468.5 $\pm$ 28.6	446.2 $\pm$ 26.8	223.2 $\pm$ 13.4	201.7 $\pm$ 12.0
5	439.2 $\pm$ 27.2	488.0 $\pm$ 29.3	227.6 $\pm$ 13.7	199.4 $\pm$ 11.1
10	479.8 $\pm$ 27.2	428.3 $\pm$ 25.7	149.7 $\pm$ 9.0	187.4 $\pm$ 8.0
20	302.0 $\pm$ 18.5	321.3 $\pm$ 19.3	118.2 $\pm$ 7.1	108.7 $\pm$ 6.8

Table 2: Performance in the complex vision-guided quadrupedal locomotion environment as

S_{R}

increases. Values are mean

\pm

standard deviation.

Figure 8: Learning curves for the car-racing task.

Figure 9: Scaling the Speed of Coherent Cognition in the car-racing task. Mean reward for the Transformer baseline and Co 4 as $S_{R}$ increases from $1$ to $7$ , without retraining and with computational resources held fixed. The dashed line indicates the predefined reliability threshold, $\tau=300$ . The Transformer remains reliable through $S_{R}=2$ and Co 4 through $S_{R}=4$ , yielding $S_{C}^{\mathrm{eff}}=2$ and $S_{C}^{\mathrm{eff}}=4$ , respectively.

$S_{R}$	Co 4	Transformer
$1$	$857.89\pm 87.52$	$896.71\pm 91.02$
$2$	$794.74\pm 78.28$	$345.90\pm 35.02$
$3$	$641.25\pm 64.77$	$126.97\pm 12.42$
$4$	$329.49\pm 32.16$	$57.34\pm 5.93$
$5$	$216.50\pm 21.11$	$61.18\pm 6.48$
$6$	$116.39\pm 11.46$	$41.45\pm 4.02$

Table 3: Performance in the car racing task as

S_{R}

increases. Values are mean

\pm

standard deviation

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Many efforts to ensure frontier AI models are safe rely on monitoring their chain-of-thought (CoT) reasoning. If models become able to perform sufficiently complex reasoning internally, without explicit thinking tokens, this would undermine such oversight. We measure how well frontier models reason without CoT across a suite of over 30,000 questions spanning 43 benchmarks in domains including math, coding, puzzles, causality, theory-of-mind, and strategic reasoning. To compare models against humans, we estimate the

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50\%

success rate. We complement this with a

50\%

reasoning token horizon: the minimum number of o3-mini reasoning tokens needed for tasks a model solves with

50\%

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50\%

TH of frontier models has been doubling roughly every year over the past six years, with GPT-5.5's TH reaching over 3 minutes and reasoning token horizon exceeding 1,500 tokens. Our median estimates predict that frontier no-CoT THs could exceed 7 minutes by 2028, and 25 minutes by 2030, though these projections carry substantial uncertainty. We recommend frontier developers track this explicitly.

Abstract

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