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REVIEW 2 major objections 65 references

Quantitative Promise Theory: Intentionality and Inference in Autonomous Agents

T0 review · 2 major / 0 minor · reviewed 2026-06-27 · grok-4.3

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Pith's one-line read Boundary conditions act as promises that define scalable intent and let autonomous agents form information-minimizing swarms.

desk verdict This is a conceptual discussion extending Promise Theory with Bayesian and active inference ideas, but it delivers no new derivations, examples, or evidence. read the letter →

arxiv 2606.08552 v1 pith:Y62SMCIX submitted 2026-06-07 cs.AI cs.MAcs.NEphysics.data-an

Mark Burgess This is my paper
classification cs.AIcs.MAcs.NEphysics.data-an
keywords PromiseTheoryautonomousagentsBayesianprobabilityactiveinferenceinformationagentalignmentboundaryconditions
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper sets out a quantitative version of Promise Theory that incorporates Bayesian probability and information-theoretic optimization, including Active Inference. It shows how promises avoid the non-local coordination, calibration, and normalization problems that arise in pure probabilistic models of agents. Boundary conditions function as promises that constrain allowed states and set decision thresholds, while alignment between agents supplies a scalable definition of intent. Agents can therefore congeal into larger structures with superagent properties by each minimizing its own information even while uncertainty tends to increase it.

What carries the argument

Promise semantics augmented by Bayesian probability and information-theoretic optimization, in which boundary conditions function as promises that constrain states and define intent through alignment.

What would settle it

A concrete comparison of agent simulations that either include or omit promise-defined boundary conditions, checking whether information minimization produces observable swarm behavior only in the promise case.

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Extended reading notes

Core claim

Supplementing promise semantics with Bayesian probability and information optimization preserves local coordination. Boundary conditions serve as promises that constrain states and select thresholds. Agent alignment thereby supplies a scalable definition of intent. Autonomous agents may then congeal into swarms with superagent characteristics by minimizing information despite uncertainty that works to maximize it.

Load-bearing premise

Promise Theory semantics can be preserved and supplemented with Bayesian probability without reintroducing non-local coordination or normalization.

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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 0 minor

Summary. The manuscript offers a conceptual discussion of quantitative representations of Promise Theory applied to autonomous agents. It proposes incorporating Bayesian probability and information-theoretic optimization (including Active Inference) with promise semantics to model intentionality and inference, arguing that Promise Theory helps avoid pitfalls such as non-local coordination and normalization in probabilistic methods. Boundary conditions are framed as promises that constrain states and thresholds, agent alignment is presented as a scalable definition of intent, and agents are suggested to form swarms with superagent properties by minimizing information despite uncertainty maximizing it. The work also notes research challenges and stylistic preferences in applying the theory.

Significance. If the suggested integration can be formalized, the framework could meaningfully bridge Promise Theory with contemporary approaches in multi-agent AI, active inference, and biological modeling by supplying local, semantics-preserving mechanisms for intent and coordination. This would be particularly valuable in domains where non-local probabilistic computations are undesirable, potentially enabling more scalable definitions of alignment and swarm behavior.

major comments (2)
  1. [Abstract] Abstract: The central proposal that Bayesian probability and information-theoretic methods 'may be incorporated with promise semantics' while avoiding non-local coordination and normalization pitfalls is stated without any derivation, formal mapping, or concrete example showing how the combination is achieved or verified.
  2. [Abstract] Abstract: The claim that 'autonomous agents may congeal into swarms with superagent characteristics by trying to minimize their information' is presented as a possible outcome but lacks any supporting model, optimization objective, or boundary-condition analysis that would make the statement quantitative or testable.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive review and for recognizing the potential value of integrating Promise Theory with Bayesian and information-theoretic approaches. The comments correctly identify that the manuscript is a conceptual discussion rather than a fully formalized framework. We address each point below and will revise the abstract and relevant sections to better reflect the exploratory scope and to frame certain statements more precisely as proposals for future investigation.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central proposal that Bayesian probability and information-theoretic methods 'may be incorporated with promise semantics' while avoiding non-local coordination and normalization pitfalls is stated without any derivation, formal mapping, or concrete example showing how the combination is achieved or verified.

    Authors: The manuscript opens by describing itself as a discussion of quantitative representations and explicitly frames the integration as a possibility ('may be incorporated') rather than a completed formal result. The body text elaborates qualitative mappings, such as the use of promises to supply local boundary conditions and semantics that complement probabilistic methods, while noting pitfalls like non-local coordination. No derivation or concrete example is supplied because the work is intended to outline a research direction, not to deliver a finished theory. We will revise the abstract to state the conceptual and suggestive character of the proposals more explicitly and will add a short illustrative scenario in the main text to clarify the intended complementarity. revision: yes

  2. Referee: [Abstract] Abstract: The claim that 'autonomous agents may congeal into swarms with superagent characteristics by trying to minimize their information' is presented as a possible outcome but lacks any supporting model, optimization objective, or boundary-condition analysis that would make the statement quantitative or testable.

    Authors: The statement is presented as a hypothesis that follows from the earlier discussion of information minimization under promise alignment and uncertainty maximization. The manuscript does not supply an optimization objective or quantitative model for swarm formation; a dedicated section already lists research challenges associated with making such ideas operational. We agree that the phrasing in the abstract could be read as stronger than intended. We will revise the abstract to present the swarm-formation idea explicitly as a suggested direction for future quantitative work rather than an established outcome, and we will cross-reference the existing challenges section. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity; conceptual discussion only

full rationale

The manuscript is framed as a conceptual discussion of how Promise Theory semantics might be supplemented by Bayesian probability and information-theoretic methods (including Active Inference). No formal derivations, equations, quantitative predictions, or first-principles results are presented in the abstract or described in the full-text placeholder. The central claims concern possible incorporations and boundary-condition interpretations rather than any derivation chain that could reduce to its own inputs. Self-reference to the author's prior Promise Theory framework is normal for an originator and does not meet the criteria for load-bearing circularity because no unsubstantiated quantitative step relies on it. The paper is therefore self-contained as discussion and receives the default non-finding.

Assumptions & free parameters 0 free parameters · 1 assumptions · 0 invented entities

The central discussion rests on the assumption that Promise Theory semantics remain intact under probabilistic augmentation and that boundary conditions function as promises; no free parameters or invented entities are stated in the abstract.

assumptions (1)
  • domain assumption Promise Theory can be combined with Bayesian probability and active inference while preserving its core semantics and avoiding probability's coordination pitfalls.
    Invoked throughout the abstract as the basis for the proposed quantitative representations.

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Cite this review

Pith. "Pith review of Quantitative Promise Theory: Intentionality and Inference in Autonomous Agents." pith.science (2026). https://pith.science/paper/Y62SMCIX

@misc{pith2026260608552,
  author       = {Pith},
  title        = {Pith review of: Quantitative Promise Theory: Intentionality and Inference in Autonomous Agents},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/Y62SMCIX}},
  note         = {Machine review of arXiv:2606.08552}
}
read the original abstract

I discuss some quantitative representations of Promise Theory for processes involving autonomous agents. Agent models are common in software systems, machine learning, and biology, for example, but may also apply to physics and other forms of engineering. I describe how Bayesian probability and information theoretic optimization, including Active Inference, may be incorporated with promise semantics -- as well as how Promise Theory supplements solutions, helping to avoid probability's pitfalls, which include non-local coordination, calibrating, and normalizing probabilistic computations. The role of boundary conditions in constraining allowed states and selecting decision thresholds is a form of promise, and agent alignment provides a scalable definition of intent. Autonomous agents may congeal into swarms with superagent characteristics by trying to minimize their information, despite uncertainty that works to maximize it. The use of Promise Theory involves some research challenges as well as stylistic preferences.

Figures

Figures reproduced from arXiv: 2606.08552 by the authors.

Figure 1
Figure 1. Notation for promise graphs and semantics is fundamentally graphical and (despite a few overlapping concepts) is not equivalent to that for Bayesian networks. B. Promise parameterization Promise bodies affect only the agent making the promise, but may refer to its intentions towards other agents, e.g. • A scalar promise refers to no other agents, e.g. I promise to brush my teeth +b. • A vector promise refers to a si… view at source ↗
Figure 2
Figure 2. The ‘data pipeline’ in a promised causal influence. An agent promises its own behaviour, i.e. a subset of possible outcomes of its own behaviour. This constrains events associated with the promised behaviour. Another agent, accepting this promise may observe the outcomes, measure and count them, assess whether they keep the promise or not. only promise its own behaviour, not that of other agents, thus the promise ba… view at source ↗
Figure 3
Figure 3. Probability trials and ensembles. In a frequentist interpreta￾tion, trial ensembles are effectively space-like separated snapshots (transverse variation), whereas a Bayesian samples are ordered by a conditional precedence relation and thus represent time-like snapshots (longitudinal variation). The logic of a Bayesian probabilistic reasoning quickly becomes maddeningly complicated when trying to adjust fair but appr… view at source ↗
Figures from the paper (11 more)
Figure 4
Figure 4. Figure 4: Two sets formed from collections of events A = {ea} and B = {eb}, and their overlap region A∩B. We note that the overlap region double counts coincident events. where ¬ea means NOT ea, ea ∩ eb means ea AND eb (conjunction), and ea ∪ eb means ea OR eb (disjunction). Fro…
Figure 5
Figure 5. Figure 5: Quantitative descriptions of promises may refer to interior dynamics, involved in keeping promises, or exterior dynamics involved in propagation of influence between agents. If a dynamic variable cannot be predicted causally, one may still ask: can its probable or aver…
Figure 6
Figure 6. Figure 6: Semantically, the corrective pipeline in a Bayesian learning process is a bit like copy editing a lookup-table or information directory. One starts with a “first edition” or training set of data, then one enters into a copy editing phase of updating the text observatio…
Figure 7
Figure 7. Figure 7: Over a number of independent agents, there are potentially many independent parameters to capture behaviour. One must not assume coherence of sources without promises that enable coherence to form, and we should be aware of how values are normalized autonomously. data …
Figure 8
Figure 8. Figure 8: In a promise based system, the sample space is formally enlarged to accommodate promise events that live alongside the pure states of the agent’s dynamical activity. More accurately, for each agent independently, Bayes scaling formula (47) can now be written in the for…
Figure 9
Figure 9. Figure 9: Example of Bayesian network, from Duda et al. This also acts as a basic model for deconstructing semantics from a scenario: from context, to key states of nature, to attributes associated with them. The nodes of the graph are variables whose values play different roles…
Figure 10
Figure 10. Figure 10: The agents and promises in a Predictive Coding sensory inference pipeline, sketched roughly. Each circle is an agent. The stippled super agents clusters act as modules for partial memory centres where running averages are kept. An approximate ‘maximum likelihood’ esti…
Figure 11
Figure 11. Figure 11: Curve fit of data using the formula in equation 115 with a data from around 200,000 agents on Wikipedia from [53]. It’s quite rare to be in a position to compare theory with data in such a scenario, but here we are fortunate [53]. Here, we have a model of the most pro…
Figure 12
Figure 12. Figure 12: From a parallel sensor array, through a pipeline of influence, to a memory representation. First, independent signals must be collated and classified, then stored in some interior representation, so first there is aggregation and then sparse representation. The final …
Figure 13
Figure 13. Figure 13: Representing bounded capabilities. In quantum mechanics, the boundary of a geometry and potential function of a spacetime arrangement (e.g. a square well or isolated hydrogen atom) forms a constraint on an energy distribution process, leading to a set of supportable e…
Figure 14
Figure 14. Figure 14: Motion along a channel from coarse-grained superagent to superagent provides the most general scaled picture of transitions. This view leads naturally to a density matrix formulation due to the separation of interior and exterior process variables. The scale at which …

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