Minimal Computational Preconditions for Subjective Perspective in Artificial Agents

Hongju Pae

REVIEW 4 major objections 4 minor 1 cited by

This paper claims that a slow global latent, trained without rewards, shows directional hysteresis that could serve as a measurable signature of perspective-like subjectivity in machines.

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2026-08-03 05:10 UTC pith:656QCUO7

load-bearing objection The paper's own equations build in the hysteresis it presents as a discovered signature of perspective; the philosophical framing is thoughtful, but the experimental claim is circular. the 4 major comments →

arxiv 2602.02902 v2 pith:656QCUO7 submitted 2026-02-02 cs.AI

Minimal Computational Preconditions for Subjective Perspective in Artificial Agents

Hongju Pae This is my paper

classification cs.AI

keywords subjective perspectiveglobal latenthysteresisprediction error minimizationreward-free learningregime switchingmachine consciousnessartificial agents

verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper tries to establish that a minimal architecture—a slowly evolving global latent modulating a fast policy—can produce a measurable internal signature of subjective perspective without any reward signal. It argues that the latent, trained by one-step prediction error minimization, lags behind regime shifts in a direction-dependent way, while policy behavior stays reactive. If true, this gives a concrete, operational way to look for perspective-like structure in machines: directional hysteresis in a slow internal state, not performance or explicit self-report.

Core claim

The central discovery is that a global latent g, updated with damping and smoothness regularization, exhibits pronounced directional hysteresis when the environment switches between two noise regimes, while policy entropy shows no such asymmetry. Because g is not optimized for behavior, this hysteresis is read as history-dependent interpretive structure rather than task adaptation. The paper proposes this as a minimal computational precondition for subjective perspective.

What carries the argument

The load-bearing mechanism is the global latent g with update g_t ← (1−d)g_{t−1}+d h_t, a slow variable that shapes policy through a stop-gradient path and is regularized for smoothness. To measure hysteresis, g is projected onto û, the normalized difference between mean g under the two regimes, producing a signed g-score. The dissociation between this score and policy entropy is the empirical argument.

Load-bearing premise

The central assumption is that the measured directional hysteresis in the global latent reflects accumulated interpretive structure rather than being a byproduct of the smoothness regularization and damped update that were explicitly designed to make the latent slow.

What would settle it

Train the same agent with the slow latent's damping and smoothness penalty removed (or with g forced to be a fast readout of z), and check whether the g-score still shows directional hysteresis under the same regime switches. If the asymmetry persists in the fast-latent control, hysteresis is not a perspective signature; if it vanishes, the result is confirmed. Also, recompute the g-score with a fixed, pre-specified projection axis (e.g., a random direction) to see if the asymmetry survives.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

If this is right

If correct, machines can be screened for perspective-like structure without needing behavior to reveal it: watch a slow internal variable during regime shifts.
The architecture gives a minimal recipe: separate fast policy from slow global latent, train by prediction error only, and look for directional hysteresis.
The measure is task-independent, since it does not rely on reward or performance, so it could apply to systems like language agents tracking conversational regime shifts.
It suggests that hysteresis along a data-fitted axis is evidence of accumulated interpretive stance, not just reactive smoothing.
It defines a dissociation test: policy reacts, global latent lags; this can be checked in any latent-state agent.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

A key untested possibility: the hysteresis may be a trivial consequence of the damping and smoothness penalties rather than perspective; a control without a slow latent or with a fast latent is needed to tell.
The projection axis û is itself fit to the data (mean difference), which can inflate apparent hysteresis; an out-of-sample or pre-registered axis would be a stronger test.
The framework could be extended to test causal role via interventions on g (do(g)), as the paper mentions, and to see whether hysteresis predicts persistent interpretive differences in behavior.
The same measurement could be applied to LLM-based agents by tracking a slow latent over conversation context shifts, though the paper leaves this as future work.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Desk Editor's Note

The paper's own equations build in the hysteresis it presents as a discovered signature of perspective; the philosophical framing is thoughtful, but the experimental claim is circular.

read the letter

First thing you should know: don't take the hysteresis result at face value. The slow latent g is defined as a damped running average (Eq. 12) and further smoothed by a penalty (Eq. 7), and the g-score projection (Eq. 13) is computed from the mean difference between regimes on the very same data. So the 'directional hysteresis' is expected from the architecture and measurement procedure. It's not an empirical discovery about perspective.

That said, the paper is worth reading for its framing. Pae does a good job laying out phenomenological constraints—globality, pre-reflective transparency, information-shaping, allostatic stability—and translating them into concrete design choices: gradient blocking, temporal smoothing, reward-free prediction-error training. It's clearly written, appropriately modest about scope, and the contrast with policy entropy is a reasonable first move.

The soft spot is the load-bearing one. No baseline without the slow latent, no random-projection control, and no significance test that the asymmetry exceeds what low-pass filtering alone would produce. The projection onto u-hat is particularly problematic: it's the axis that best separates the two regimes in the data, so of course g-score moves along it. The hysteresis is a necessary consequence of the smoothness constraints. The author's own future-work section acknowledges the need for causal interventions (do(g)), which is good, but that doesn't rescue the current evidence.

Minor point: the paper cites a lot of phenomenology and active inference; that's fine, but it doesn't substitute for computational controls.

Who's this for? People thinking about what minimal internal structure might count as a 'perspective' in machines. They'll get a clear architectural proposal and a cautionary tale about measurement circularity. It's not a reliable empirical demonstration.

I'd send it to peer review—the question is important, the writing is clean, and a good referee could push the author to add the missing baselines. Without those, the central claim should be downgraded to a conjecture. If the author can show that hysteresis persists beyond what the filter imposes (e.g., compare to a model without the smooth latent, or use a fixed random projection), the paper might become genuinely useful.

Referee Report

4 major / 4 minor

Summary. The paper proposes that subjective perspective in artificial agents can be operationalized as a slowly evolving global latent state g_t that modulates fast policy dynamics without being directly optimized for behavioral consequences. The author implements this in a reward-free grid-world environment with three zones of differing observation noise, trains the agent online using prediction-error minimization plus a smoothness penalty on g_t, and then tests it under periodic regime switches that invert the noise parameters. The main reported result is that a signed projection of g_t ('g-score') exhibits 'pronounced directional hysteresis' across regime switches, while policy entropy is largely direction-insensitive. The paper argues that such hysteresis constitutes an operational signature of perspective-like subjectivity in machine systems.

Significance. If the central claim were established, a measurable internal-state pattern—hysteresis in a slowly evolving global latent—could serve as a diagnostic for perspective-like structure in artificial agents, distinct from reactive policy behavior. This would be a potentially useful contribution to the discourse on machine consciousness and could inform future architectures that separate slow interpretive dynamics from fast reactive control. The manuscript is clearly written and makes its architectural choices explicit: the damping rule in Eq. (12), the smoothness penalty in Eq. (7), and the projection measure in Eq. (13) are all precisely specified. The reward-free setup is a reasonable attempt to avoid confounding internal structure with task-specific optimization. However, the central empirical claim is undermined by the fact that the slow dynamics and the measurement axis are built into the design, and no control experiments or statistical tests are provided to distinguish the reported hysteresis from an artifact of the imposed architecture and the in-sample projection direction.

major comments (4)

[Eqs. (12), (7), and Training Protocol] The global latent g_t is defined as a damped running average of h_t (Eq. 12) with an additional smoothness penalty on successive differences (Eq. 7, weight 0.25). This directly enforces that g_t is a low-pass-filtered, slowly varying quantity. Under any regime switch, g_t must therefore lag behind the environment, and a lagged, gradual transition in the g-score is an immediate consequence of the update rule, not an empirical finding. The paper provides no control condition without the slow latent or with weaker damping. The observed 'temporal persistence' is thus a design property, and the claim that it supports perspective-like subjectivity is not independently evidenced.
[Eq. (13) and measurement protocol] The g-score is defined as a projection onto û, the normalized difference between the mean global latents under Regimes A and B, computed from the same data on which the hysteresis trajectories are evaluated. This is an in-sample, post-hoc axis that by construction separates the two regimes and will make any slow movement between the regime-conditional means look smooth and directional. To establish that the reported trajectories reflect something more than this data-fitted projection, the paper would need a pre-specified projection direction (e.g., from training data only) or a random-projection baseline. Without such a control, the g-score measurement is circular and inflates the apparent hysteresis.
[Results and Analysis, Fig. 4] The central claim of 'pronounced directional hysteresis' is based on visual inspection of switch-aligned trajectories (Fig. 4). No quantitative measure of the asymmetry is reported—no lag time constants, no area between the A→B and B→A curves, no test of whether the difference exceeds what a simple linear low-pass filter would produce, and no confidence intervals on the difference across seeds. Similarly, the dissociation between g-score and policy entropy is supported only by a qualitative comparison; no statistical test is applied. Given that the entire argument hinges on the existence and meaningfulness of this asymmetry, the absence of any quantitative or baseline support is a load-bearing gap.
[Discussion and Future Work] The paper acknowledges that the causal role of g is not tested and defers do(g) interventions to future work. However, the central contribution is the claim that hysteresis is a 'measurable signature' of perspective-like subjectivity. In the absence of an intervention or at least a predictive validity check (e.g., does g-score predict future regime recognition or policy changes beyond the imposed low-pass dynamics?), the signature remains an interpretation of a built-in architectural property rather than a demonstrated empirical phenomenon.

minor comments (4)

[Reproducibility] No code or data are provided. The paper reports results over 5 random seeds but gives no implementation details for the GRU, the exact dimensions of z_t, the baseline b_t in Eq. (9), or the exponential moving average parameters. This limits reproducibility.
[Reward-free terminology] The paper says the setting is 'reward-free,' but the actor objective in Eq. (9) uses the prediction error of the executed action as an internal cost, which is a form of intrinsic reward. Clarifying this distinction would avoid confusion.
[Fig. 4 and z-entropy normalization] The policy entropy signal is z-normalized within each run (Eq. 15). This normalization obscures absolute changes in entropy and may remove between-run variation that could affect the hysteresis comparison. Reporting raw entropy as well would be useful.
[Hysteresis terminology] The text repeatedly uses 'hysteresis' to describe the lagged response. For a linear damped filter, the approach to the new regime is symmetric in time for both directions; only the sign of the change differs. The paper should explain what specifically makes the trajectories 'asymmetric' beyond time reversal, and how that asymmetry is measured.

Circularity Check

2 steps flagged

Hysteresis is built into the slow-latent update and the data-fitted g-score projection, undercutting the claimed perspective signature.

specific steps

fitted input called prediction [Section 'Hysteresis-based Measures of Perspective', Eq. (13)]
"Since g_t ∈ R^12 is high-dimensional, I define a signed projection onto a reference direction u: g-score(t) = ⟨g_t, u⟩ (13). Here, u is the normalized difference between the mean global latents under Regimes A and B. This captures the dominant axis of regime-dependent internal change while preserving transition directionality."

u is computed from the same data being evaluated, so the g-score is, by construction, the component of g_t along the mean regime difference. The sign of the score after a switch is therefore predetermined: A→B moves toward the B mean and B→A returns. The lag is inherited from the filter in Eq. 12. Interpreting this measured directionality as 'hysteresis ... a measurable signature of perspective' uses a fitted projection as if it were an independent diagnostic. No random-projection baseline or control without the slow latent is provided.
self definitional [Eq. (7), 'Slow perspective regularization'; Eq. (12), 'Training Protocol']
"Slow perspective regularization. To encourage gradual evolution of the global latent g_t, a smoothness penalty is applied between successive states: L_smooth(t) = MSE(g_t, stopgrad(g_{t−1})) (7). ... g_t ← (1−d)g_{t−1} + d h_t (12) ... The damping enforces slow temporal evolution of g_t."

The paper's main empirical claims are that g has 'temporal persistence' and 'directional hysteresis'. But Eq. 12 is a damped running average and Eq. 7 explicitly penalizes changes in g; any signal updated this way lags step changes. The slow, history-dependent behavior is thus built into the update rule itself, not discovered independently. Since g was already identified as 'perspective' in the architecture, the hysteresis measured on this g is a restatement of the design choice rather than evidence for a perspective-like structure.

full rationale

The central derivation chain reduces to the design choices rather than to an independent empirical discovery. The paper defines perspective as a slowly evolving global latent and then enforces exactly that slowness via Eq. 12 (damped update) and Eq. 7 (smoothness penalty). The only measure on which the hysteresis claim rests is the g-score, defined in Eq. 13 as a projection onto the normalized difference of regime means from the same data being scored; this makes the direction of the switch-aligned trajectories true by construction. The paper even notes in Future work that the causal role is not directly tested ('Future work could more directly test its causal role via explicit interventions'), and no slow-latent-free control or fixed-projection baseline is reported. The self-citation to Pae (2025) is philosophical framing and is not load-bearing for the computational argument, so it does not add circularity. The result is not fully equivalent to the input because the simulations do contain independent empirical content, such as the shift in zone occupancy and the reactivity of policy entropy; nevertheless, the central claimed signature of perspective is substantially forced by the architecture and the fitted projection. Score 8 reflects that the core 'hysteresis' finding reduces to construction, with only residual non-circular empirical content.

Axiom & Free-Parameter Ledger

5 free parameters · 4 axioms · 1 invented entities

The central claim rests on a phenomenological-to-computational mapping that is asserted rather than derived, and on a measurement protocol whose projection direction is fit to the data. The slow dynamics and smoothness are imposed in the loss, making the hysteresis an expected consequence.

free parameters (5)

lambda_smooth = 0.25
Weight on the smoothness penalty (Eq. 7); controls the timescale of g and therefore the hysteresis.
lambda_actor = 0.5
Weight on the actor loss (Eq. 9); affects policy behavior and its interaction with g.
lambda_ent = 0.01
Entropy regularization weight (Eq. 10).
damping coefficient d
In Eq. 12, g_t ← (1−d)g_{t−1} + d h_t; value not reported, but directly sets the slow timescale and hence the hysteresis.
projection direction u_hat = computed from data (not reported)
Defined as normalized difference between mean g under Regimes A and B (Eq. 13); the g-score trajectories are measured along this data-fitted axis, so the observed effect is partly constructed.

axioms (4)

ad hoc to paper Subjective perspective can be reduced to a slowly evolving global latent that shapes behavior without being optimized for consequences.
This is the central operationalization; the paper motivates it phenomenologically but does not derive it. If false, the architecture does not instantiate perspective.
ad hoc to paper Direction-dependent hysteresis in a latent variable is a measurable signature of perspective-like subjectivity.
Asserted in the conclusion; no independent criterion connects hysteresis to subjectivity.
domain assumption Reward-free prediction-error minimization organizes internal dynamics around statistical regularities without introducing task artifacts.
Used to justify the training protocol; not proven for this architecture.
domain assumption Phenomenological properties (global, pre-reflective, functionally consequential, temporally persistent) translate into computational requirements of slow timescale and global modulation.
The mapping from phenomenology to architecture is interpretive, not formal.

invented entities (1)

perspective latent g_t (global latent state) no independent evidence
purpose: To operationalize subjective perspective as a slowly evolving global modulator of policy.
No falsifiable prediction outside the paper; only internal hysteresis trajectories. The paper itself notes that future do(g) interventions would be needed.

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0 comments

read the original abstract

This study operationalizes subjective perspective in artificial agents by grounding it in a minimal, phenomenologically motivated internal structure. The perspective is implemented as a slowly evolving global latent state that modulates fast policy dynamics without being directly optimized for behavioral consequences. In a reward-free environment with regime shifts, this latent structure exhibits direction-dependent hysteresis, while policy-level behavior remains comparatively reactive. I argue that such hysteresis constitutes a measurable signature of perspective-like subjectivity in machine systems.

Figures

Figures reproduced from arXiv: 2602.02902 by Hongju Pae.

**Figure 1.** Figure 1: Overview of the proposed agent architecture. The global latent g is designed to evolve gradually and to encode habitual, slowly varying regularities, thereby instantiating the agent’s perspective. The action policy π(at|zt, gt) is jointly conditioned on the fast-changing perceptual state zt and the slowly evolving perspective gt, resulting in behavior that is both reactive to immediate inputs and biased … view at source ↗

**Figure 2.** Figure 2: Visualization of the grid-world environment. The Pygame-based simulator contains three spatial zones with distinct observation noise: red (Z0, high noise), green (Z1, intermediate), and blue (Z2, low). The white circle indicates the agent’s position. The top overlay displays diagnostic statistics including timestep and episode indices, zone and position, selected action (5 discrete actions), total loss, … view at source ↗

**Figure 3.** Figure 3: Zone occupancy before and after training. Mean fraction of timesteps spent in each zone (5 seeds), comparing early (episodes 0–20) and late (episodes 180–200) training. Error bars denote standard deviation. After training, occupancy concentrates in the low-noise zone Z2. Directional hysteresis. Directional hysteresis is assessed by comparing these quantile-based trajectories for A→B and B→A transitions. I… view at source ↗

**Figure 4.** Figure 4: Switch-aligned IQR hysteresis trajectories. Median trajectories of the global latent projection (left) and policy entropy (right) following Regime A→B and B→A transitions. Shaded regions indicate IQR across seeds. The global latent shows clear directional hysteresis, whereas policy entropy remains largely direction-insensitive. icy adjustments and slow, accumulated perspective dynamics. Summary of Conclus… view at source ↗

discussion (0)

Forward citations

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Reference graph

Works this paper leans on

7 extracted references · cited by 1 Pith paper

[16]

2021.Being and Nothingness: An Essay in Phe- nomenological Ontology

Sartre, J. 2021.Being and Nothingness: An Essay in Phe- nomenological Ontology. Washington Square Press. Trans- lated fromL’ ˆEtre et le n´eant. Van Gulick, R

2021
[176]

2013.Phenomenology of Perception

Merleau-Ponty, M. 2013.Phenomenology of Perception. Routledge. Translated fromPh ´enom´enologie de la percep- tion. Nagel, T

2013
[2012]

Gallagher, S.; and Zahavi, D

Ac- tive inference and agency: optimal control without cost func- tions.Biological Cybernetics, 106: 523–541. Gallagher, S.; and Zahavi, D. 2008.The Phenomenological Mind. New York, NY: Routledge. Hafner, D.; Lillicrap, T. P.; Norouzi, M.; and Ba, J

2008
[2015]

Embree, L

Affectivity in Heideg- ger I: Moods and Emotions in Being and Time.Philosophy Compass, 10(10): 661–671. Embree, L. 2011.Reflective analysis: a first introduction into phenomenological investigation. Zeta Books. Friston, K.; Da Costa, L.; Sakthivadivel, D. A.; Heins, C.; Pavliotis, G. A.; Ramstead, M.; and Parr, T

2011
[2016]

Baars, B

Concrete Problems in AI Safety.arXiv preprint. Baars, B. 1997.In the Theater of Consciousness: The Workspace of the Mind. Oxford University Press USA. Beckmann, T.; K¨ostner, L.; and Hip´olito, I

1997
[2025]

Heidegger, M

Mas- tering diverse control tasks through world models.Nature, 640: 647–653. Heidegger, M. 1996.Being and Time. SUNY Series in Contemporary Continental Philosophy. Albany, NY: State University of New York Press, revised edition. ISBN 9780791426784. Translated fromSein und Zeit. Husserl, E. 2001.Logical Investigations. Routledge. Husserl, E. 2014.Ideas for...

1996
[2026]

In Zalta, E

Consciousness. In Zalta, E. N.; and Nodelman, U., eds.,The Stanford Encyclopedia of Philoso- phy. Metaphysics Research Lab, Stanford University, Spring 2026 edition. Zahavi, D. 2005.Subjectivity and Selfhood: Investigating the First-Person Perspective. MIT Press

2026

[1] [16]

2021.Being and Nothingness: An Essay in Phe- nomenological Ontology

Sartre, J. 2021.Being and Nothingness: An Essay in Phe- nomenological Ontology. Washington Square Press. Trans- lated fromL’ ˆEtre et le n´eant. Van Gulick, R

2021

[2] [176]

2013.Phenomenology of Perception

Merleau-Ponty, M. 2013.Phenomenology of Perception. Routledge. Translated fromPh ´enom´enologie de la percep- tion. Nagel, T

2013

[3] [2012]

Gallagher, S.; and Zahavi, D

Ac- tive inference and agency: optimal control without cost func- tions.Biological Cybernetics, 106: 523–541. Gallagher, S.; and Zahavi, D. 2008.The Phenomenological Mind. New York, NY: Routledge. Hafner, D.; Lillicrap, T. P.; Norouzi, M.; and Ba, J

2008

[4] [2015]

Embree, L

Affectivity in Heideg- ger I: Moods and Emotions in Being and Time.Philosophy Compass, 10(10): 661–671. Embree, L. 2011.Reflective analysis: a first introduction into phenomenological investigation. Zeta Books. Friston, K.; Da Costa, L.; Sakthivadivel, D. A.; Heins, C.; Pavliotis, G. A.; Ramstead, M.; and Parr, T

2011

[5] [2016]

Baars, B

Concrete Problems in AI Safety.arXiv preprint. Baars, B. 1997.In the Theater of Consciousness: The Workspace of the Mind. Oxford University Press USA. Beckmann, T.; K¨ostner, L.; and Hip´olito, I

1997

[6] [2025]

Heidegger, M

Mas- tering diverse control tasks through world models.Nature, 640: 647–653. Heidegger, M. 1996.Being and Time. SUNY Series in Contemporary Continental Philosophy. Albany, NY: State University of New York Press, revised edition. ISBN 9780791426784. Translated fromSein und Zeit. Husserl, E. 2001.Logical Investigations. Routledge. Husserl, E. 2014.Ideas for...

1996

[7] [2026]

In Zalta, E

Consciousness. In Zalta, E. N.; and Nodelman, U., eds.,The Stanford Encyclopedia of Philoso- phy. Metaphysics Research Lab, Stanford University, Spring 2026 edition. Zahavi, D. 2005.Subjectivity and Selfhood: Investigating the First-Person Perspective. MIT Press

2026