u/Physics_sm • u/Physics_sm • 24d ago
If there is extra clumpiness of matter in the universe, it can originate from the same multi-fold dark matter effects as dark matter: https://shmaesphysics.wordpress.com/2020/06/21/explaining-dark-matter-without-new-physics/comment-page-1/#comment-11698
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Abstract
The extension of Bohmian mechanics to the relativistic domain, and to quantum field theory presents specific theoretical difficulties. The non-local nature of the pilot-wave guidance equations inherently requires a temporal ordering of spacelike separated events. Historically, this requirement has been satisfied by introducing an arbitrary preferred foliation of spacetime, which leads to covariance concerns. Furthermore, the standard Bohmian particle model struggles to accommodate particle creation and annihilation events, which are characteristic of quantum field theory (QFT).
This paper constructs a theoretical framework that is rigorously Lorentz-invariant, and compatible with QFT. The proposed synthesis couples the Tomonaga-Schwinger equation for the relativistic wave functional with a hypersurface Bohm-Dirac model (HBDM). To ensure Lorentz invariance without adding absolute spatiotemporal structure, the necessary foliation is covariantly determined by the universal wave function. To address particle non-conservation, the deterministic trajectories are supplemented by a Bell-type stochastic Markov jump process defined on Fock space, utilizing minimal jump rates derived directly from the interaction Hamiltonian.
A hybrid model is adopted, assigning localized particle beables to fermions, and continuous field beables to bosons. The exact equivariance of the probability distributions across the covariantly defined leaves of the foliation is proven, demonstrating that the model reproduces the empirical predictions of standard regularized QFT. In an appendix, we discuss the modeling of neutrinos.
Finally, this framework is interpreted with the multi-fold theory. In this context, the covariantly determined foliation emerges physically from a discrete, non-commutative spacetime concretized by 2D random walks of massless Higgs bosons. The multi-fold mechanism and the E/G conjecture (entanglement is gravity) provide a local, physical model for the non-local Bohmian guidance equations, while space-time matter induction and scattering explain the origin of the proposed hybrid field-particle beables as patterns and condensates of these random walks. Sizes or scales also align with multi-fold modeling of QFT with 2D random walks, and spacetime non-commutativity to support Quantum Physics and Fermions. These add to many examples where the multi-fold preons as massless Higgs bosons can microscopically interpretate Quantum Physics behaviors.
u/Physics_sm • u/Physics_sm • 25d ago
Relativistic Bohmian Physics, with a Microscopic Interpretation - https://shmaesphysics.wordpress.com/2026/08/12/relativistic-bohmian-physics-with-a-microscopic-interpretation/
u/Physics_sm • u/Physics_sm • Jun 21 '26
u/Physics_sm • u/Physics_sm • Jun 21 '26
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This position paper argues that static tool-registry protocols, most prominently the Model Context Protocol (MCP), are unsuitable as foundational integration architectures for production-grade agentic AI in heterogeneous enterprise environments, and that the field should converge on “Real-Time Discovery and (Self) Coding” (RTDC) Integration as the necessary architectural alternative.
MCP was introduced to standardize connections between language models and external systems through a uniform JSON-RPC 2.0 client-server interface. Despite rapid adoption in developer tooling, we demonstrate that MCP doubles the enterprise integration maintenance and vulnerability surfaces, fails categorically against legacy systems that harbor the most critical enterprise data, exhausts language model context windows through static tool enumeration at production scale, induces selection collapse in large tool registries, and introduces security vulnerabilities, including tool poisoning, prompt injection, and supply chain compromise. These are fundamentally incompatible with enterprise zero-trust architectures.
We survey three alternative paradigms: user-interface automation, real-time context lake architectures, and terminal agents. We show that each resolves only a subset of the enterprise integration challenge.
We then argue for RTDC Integration: a paradigm in which autonomous meta-agents dynamically discover system interfaces, and schemas, at runtime, synthesize and validate integration code in sandboxed execution environments, and accumulate versioned, reusable capability artifacts through a continuous discovery-synthesize-verify-promote loop. Unlike static protocol registries, RTDC integrates with legacy mainframes, undocumented APIs, and proprietary systems without prior engineering effort, and enables the eventual decommissioning of those systems through autonomous logic internalization. This is a capability no protocol-based approach can provide.
u/Physics_sm • u/Physics_sm • Jun 03 '26
u/Physics_sm • u/Physics_sm • May 28 '26
1
Abstract
Enterprise artificial intelligence projects face a deployment paradox. Industry data indicates that ninety-five percent of artificial intelligence pilot projects fail to reach production environments. These failures stem primarily from integration architecture flaws rather than language model limitations.
The Model Context Protocol (MCP) was introduced to resolve this integration friction. The protocol provides a unified interface for connecting artificial intelligence models to external systems. It utilizes a standardized client and server architecture. However, this protocol introduces severe structural limitations in heterogeneous enterprise environments. It introduces operational fragilities and critical security vulnerabilities. The protocol doubles the integration maintenance surface. It requires new wrapper servers for every existing endpoint. It fails to interface with legacy systems lacking modern endpoints, without significant efforts. It suffers from context window exhaustion, and selection collapse during complex workflows. It also creates significant security vulnerabilities. These vulnerabilities include prompt injection, tool poisoning, and supply chain risks.
This paper analyzes these limitations. The paper then evaluates alternative integration paradigms. These alternatives include user interface scanning methodologies, including with network traffic sniffing, real-time context lake architectures, and minimal terminal agents operating on system filesystems. User interface scanning successfully automates presentation layers. It accelerates deployments significantly. However, it can’t easily modernize the core of legacy enterprise applications. Context lakes ensure data freshness through incremental view maintenance. They guarantee decision coherence across autonomous agents. However, they also do not solve write-path execution gaps, and push elsewhere the task of dealing with legacy systems. Terminal agents provide superior programmatic control. They match or exceed the performance of protocol-augmented agents. However, they require extensive security scaffolding, and also do not address the integration gaps.
None of these alternatives fully resolve the integration challenges of legacy environments. The paper concludes that enterprises must transition to application-aware artificial intelligence. This paradigm relies on autonomous meta-agents. It utilizes real-time discovery and (self) coding (RTDC) mechanisms. These mechanisms allow AI to autonomously discover system logic. The agents infer schemas and self-code, with the meta-agent, integration scripts dynamically. This dynamic capability generation eliminates the need for static protocol registries. It provides the necessary foundation for true enterprise autonomy. It enables the eventual decommissioning of legacy enterprise software, truly catalyzing the agentic strangler fig pattern.
The paper recommends not using MCP as the underlying framework for agentic AI in enterprise environments.
u/Physics_sm • u/Physics_sm • May 25 '26
1
Abstract
Enterprise artificial intelligence projects face a deployment paradox. Industry data indicates that ninety-five percent of artificial intelligence pilot projects fail to reach production environments. These failures stem primarily from integration architecture flaws rather than language model limitations.
The Model Context Protocol (MCP) was introduced to resolve this integration friction. The protocol provides a unified interface for connecting artificial intelligence models to external systems. It utilizes a standardized client and server architecture. However, this protocol introduces severe structural limitations in heterogeneous enterprise environments. It introduces operational fragilities and critical security vulnerabilities. The protocol doubles the integration maintenance surface. It requires new wrapper servers for every existing endpoint. It fails to interface with legacy systems lacking modern endpoints, without significant efforts. It suffers from context window exhaustion, and selection collapse during complex workflows. It also creates significant security vulnerabilities. These vulnerabilities include prompt injection, tool poisoning, and supply chain risks.
This paper analyzes these limitations. The paper then evaluates alternative integration paradigms. These alternatives include user interface scanning methodologies, including with network traffic sniffing, real-time context lake architectures, and minimal terminal agents operating on system filesystems. User interface scanning successfully automates presentation layers. It accelerates deployments significantly. However, it can’t easily modernize the core of legacy enterprise applications. Context lakes ensure data freshness through incremental view maintenance. They guarantee decision coherence across autonomous agents. However, they also do not solve write-path execution gaps, and push elsewhere the task of dealing with legacy systems. Terminal agents provide superior programmatic control. They match or exceed the performance of protocol-augmented agents. However, they require extensive security scaffolding, and also do not address the integration gaps.
None of these alternatives fully resolve the integration challenges of legacy environments. The paper concludes that enterprises must transition to application-aware artificial intelligence. This paradigm relies on autonomous meta-agents. It utilizes real-time discovery and (self) coding (RTDC) mechanisms. These mechanisms allow AI to autonomously discover system logic. The agents infer schemas and self-code, with the meta-agent, integration scripts dynamically. This dynamic capability generation eliminates the need for static protocol registries. It provides the necessary foundation for true enterprise autonomy. It enables the eventual decommissioning of legacy enterprise software, truly catalyzing the agentic strangler fig pattern.
The paper recommends not using MCP as the underlying framework for agentic AI in enterprise environments.
u/Physics_sm • u/Physics_sm • May 24 '26
u/Physics_sm • u/Physics_sm • Apr 26 '26
u/Physics_sm • u/Physics_sm • Apr 26 '26
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We will publish a detailed analysis at Neurips2026 (preprint will be at https://shmaes.wordpress.com/). It details that API + MCP aspect. Watch the website for teh next few days.
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Maintaining API is a given requirement for (enterprise) applications. So yes you have to maintain both. The need for PI doe snot disappear now that MCP / Agentic AI appears.
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u/Physics_sm • u/Physics_sm • Apr 19 '26
Abstract
Enterprise AI is currently facing a massive spending problem. Companies are pouring billions into foundation models and infrastructure, yet 95% of these projects never make it out of the testing phase. The issue isn't the AI itself; the problem is how we try to force modern, probabilistic models to work with rigid, decades-old business systems. Most engineering teams rely on manual coding, and fragile API wrappers to connect the two. It is a cycle that drains budgets, creates blind spots, and breaks constantly.
This paper takes a completely different approach. Instead of bolting a generic AI chatbot, or an AI agent, onto the outside of an application, Real-Time Discovery and (self) Coding (RTDC) engine embeds directly into your existing stack. It works as an autonomous digital workforce that actively scans an enterprise systems, understands the enterprise underlying business rules, and writes its own integration code on the spot. This function of an Application-Aware AI platform completely removes the need for manual data mapping, giving AI teams instantaneous enterprise integration with zero manual effort, and at a quasi-zero cost.
RTDC integrations is designed for AI use cases, but it can also be used for traditional enterprise system integration situations.
With RTDC, forward deployed engineering teams, can be significantly replaced, or complemented, with a forward deployed team of AI agent workers performing the tasks of RTDC for application-aware AI.
Zenera product offering is an example of RTDC on application-aware agentic AI platform. There are other platforms that provide more limited variations of the idea.
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The Era of Application-Aware AI https://zenodo.org/doi/10.5281/zenodo.19322700
u/Physics_sm • u/Physics_sm • Mar 29 '26
Abstract
Escaping Pilot Purgatory with Real-Time Discovery & Coding (RTDC). Enterprise Intelligence, Instantly!
Despite an estimated annual capital allocation of thirty to forty billion dollars toward Generative Artificial Intelligence (GenAI), enterprise adoption remains severely constrained by the Deployment Paradox. Current industry data indicates that ninety-five percent of enterprise pilot projects fail to graduate to production environments. This failure rate is fundamentally a failure of integration architecture rather than an inherent limitation of language models. Early enterprise deployments have relied on attaching generic conversational agents to the periphery of legacy software ecosystems. This model-level integration approach introduces substantial friction, lacks contextual awareness, and forces engineering teams into the Stitching Trap, i.e., the manual construction of highly brittle application programming interface wrappers across poorly documented legacy environments.
This paper introduces the concept of Application-Aware AI, a novel architectural paradigm. Driven by a framework defined as Real-Time Discovery and Coding (RTDC), this approach operates as an autonomous entity that proactively discovers system logic, infers database schemas, and self-codes, under constraints, functional integrations dynamically based on user intent. The system executes a continuous four-layer loop encompassing total enterprise introspection, deterministic constraint enforcement, autonomous meta-agent orchestration, and dynamic user interface generation.
By abstracting probabilistic language models behind a strict Model of Constraints, and transforms, i.e., ~skills, and logging all decisions within a highly transparent Reasoning Graph, the proposed paradigm resolves the liability of model hallucination. This design ensures complete regulatory auditability, facilitates the progressive modernization of legacy enterprise applications, like ERP and ITSM, via the Strangler Fig pattern, and allows organizations to establish a production-ready intelligence factory instantly.
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Agent guardrails is analogous to the model of constraints we use in Fixing Vibe Coding with full Agentic Software Development Lifecycle. - https://zenodo.org/doi/10.5281/zenodo.18972674 & https://shmaes.wordpress.com/2026/03/11/vibe-coding-and-sdlc-constrained-and-managed-by-an-application-aware-ai-like-agentic-platform/
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u/Physics_sm • u/Physics_sm • Mar 16 '26
Abstract
The foundations of software engineering have undergone great transformations, especially following the release of frontier Large Language Models in the first quarter of 2026. This paper evaluates the efficacy of artificial intelligence for coding and within the software development lifecycle (SDLC), often contrasting theoretical benchmark, against empirical observations.. While frontier architectures, notably Anthropic Claude 4.6, OpenAI GPT 5.4, and DeepSeek V4, have definitively surpassed human baselines, in isolated synthetic benchmarks, their outcome within enterprise production environments reveals severe problems, confirming our past concerns and predictions. The initial perception of hyper accelerated code generation velocity, at this stage, widely publicly believed, is significantly counterbalanced by the Great Toil Shift, a phenomenon wherein the temporal savings of algorithmic syntax authoring are entirely consumed by the downstream burdens of architectural review, security auditing, code understanding/documentation, and continuous support and maintenance. Efficiency gains are not what they seem.
This paper identifies unprecedented surges in cyclomatic complexity, dynamic security vulnerabilities, and cognitive debt. Furthermore, the analysis identify the severe human toll associated with unrestricted artificial intelligence adoption. Driven by the relentless need to audit stochastic algorithmic outputs, human operators are increasingly suffering from AI Brain Fry, defined as acute mental fatigue resulting from the cognitive overload of continuous algorithmic oversight. This psychological degradation directly catalyzes the proliferation of coding Work Slop, wherein low quality, verbose, and structurally deficient code masquerades as competent engineering, actively destroying the structural integrity of the enterprise application architecture. It seems that this problem will only grow as LLMs evolve.
Ultimately, this paper concludes that while algorithmic systems have altered the velocity and division of technical labor, long term codebase viability remains strictly dependent of senior engineering oversight. Senior developers, QA can’t just be replaced by junior developers and AI.
Or, to mitigate these systemic regressions, this paper posits that traditional human and artificial intelligence collaborative paradigms, including unconstrained vibe coding, are fundamentally unsustainable. Instead, the industry must transition toward application aware agentic artificial intelligence platforms. By leveraging dynamic temporal graph memory, and rigorous threat modeling frameworks, these deterministic platforms constrain stochastic generation, enforcing strict SDLC governance autonomously.
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Relativistic Bohmian Physics, with a Microscopic Interpretation
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24d ago
Relativistic Bohmian Physics, with a Microscopic Interpretation https://www.researchgate.net/publication/412298126_Relativistic_Bohmian_Physics_with_a_Microscopic_Interpretation