Authors: Minsu Kim
Source: Zenodo (CERN European Organization for Nuclear Research) (preprint, 2026-03-05)
Field: Materials Science — Materials Chemistry

IRS-DCE: A Structural Framework for Irreducible Representation Shifts and Dimensional Cascades in Transformer Dynamics # IRS-DCE: A Structural Framework for Irreducible Representation Shifts and Dimensional Cascades in Transformer Dynamics **Version 1.0**Timestamp: March 2, 2026 대부분의 작업물은 AI를 통한 정리 기록입니다. 핵심적인 수식에 대한 구조, 원리, 개념 등은 제가 직접 지속적으로 수정하고 패턴을 제공하여 Ai 들이 작업 가능한 수준으로 훈련 시켰습니다. 단순한 문서 작성이 시간을 많이 소모하여 최대한 절약하는 방향으로 진행하였습니다. 이러한 작업 사항을 선택한 이유는 제가 어떠한 지원도 받지 않는 독립연구자라 이것에 투자할 시간이 별로 없기 때문입니다. --- ## 📌 Repository Terminology Update Notice **🔄 Terminology Transition: OOD → IRS-DCE** In all materials, theoretical drafts, and code repositories prior to March 2, 2026, the term “OOD” (Out-of-Distribution) was used as a provisional label to describe structurally irreducible representational events. Beginning on March 2, 2026, we formally replace that terminology with **IRS-DCE (Irreducible Representation Shift – Dimensional Cascade Event)**. **Clarification:**The earlier usage of "OOD" was not intended to align with classical distribution-based Out-of-Distribution detection in machine learning. It served as a temporary placeholder. This change is made to prevent confusion with established literature and to reflect the structural, representation-expanding nature of our framework. * **IRS** — Irreducible Representation Shift* **DCE** — Dimensional Cascade Event* **IRS-DCE** — Irreducible Representation Shift leading to a Dimensional Cascade Event --- ## Abstract This document introduces the formal axiomatic definition of the IRS-DCE framework. An Irreducible Representation Shift (IRS) is defined as an event in which an input both includes the prior representational manifold as a special case and induces at least one new effective representational axis not expressible within the previous coordinate frame. A Dimensional Cascade Event (DCE) refers to the measurable dynamical expansion in intrinsic dimensionality and sustained rotational capacity following an IRS. The framework is structurally distinct from classical distribution-based OOD detection and instead characterizes representation-expanding dynamical transitions within model internal spaces. # [2026-03-29 update]Meta-13: Combined Technical Report and Validation DataThis update provides the current public-facing technical report and validation package for the Meta-13 framework. The release covers two connected result tracks: 1. Meta-13 tensor acceleration A structure-preserving token-compression pipeline for real LLM tensor paths, with measured speedup, compression, FLOPs reduction, and geometry-preservation metrics. 2. Meta-13 dual-core inference with the Hodge Converter An adaptive system in which reasoning-oriented inputs are processed through the Meta-13 refinement path, while arithmetic-oriented inputs are routed to a symbolic Hodge path and grafted back into the model’s native token stream. The attached PDF is the current technical report. The ZIP archive contains the corresponding benchmark and validation data used in the report. Important scope note:This release is intended as a technical and empirical artifact package. It does not include raw implementation code. Code access remains restricted due to active patent-related considerations. Public files in this update:- meta13_combined_report.pdf- meta13_combined_report_pdf_data.zip 🔗 [Repository / Modal API / Artifact link to be inserted]RH 게이지 대칭 및 해당 대칭상태 재현, 해당 상태에서의 RH증명 이후 AH증명에서 나온 수식으로 AI 관련 시스템을 만드니 괄목할 만한 성과가 나왔습니다. 그렇습니다. 수식은 실제로 작동하고 더 효율적이며 연산 한도를 뚫었습니다. # [2026-03-28 update] Empirical Implementation of Topological Fossilization: Meta-13 Engine This release documents the empirical validation of the Dimensional Collapse Engine (DCE) on a 50-layer FFN workload (N=16,384, D=2048). The compiled executable demonstrates a 2.90x speedup (p50) and 76.69% FLOPs reduction via adaptive topological clustering. While directional geometry is preserved (Cosine Similarity 99.9%), scale drift under non-normalized conditions remains under active investigation (see attached PDF). Source code is withheld under Patent Application No. 10-2026-0048244. The benchmark binary and raw empirical report can be downloaded via the external secure link below. 🔗 Download Artifacts (Benchmark Engine): https://drive.google.com/file/d/1i5f1rvQ1sBCl2GltsTj0ih01OLI_NAGl/view?usp=drive_link BDP_Empirical_Implementation_of_Topological_Fossilization.pdf # [2026-03-22 update] Methodological clarification.“Base Reflection” was coined inside the IRS-DCE program before any RH-oriented framing existed. It referred to the tendency of AI systems to impose learned structural priors on presented data, thereby revealing the trained frame rather than the incoming signal. This matters for the present repository because it fixes the order of emergence:(1) IRS-DCE structural analysis,(2) observation of Base Reflection,(3) later recognition that the same obstruction reappears when RH-adjacent results are evaluated. Therefore, the framework was not invented in order to attack RH. Rather, RH entered the picture only after a pre-existing structural phenomenon had already been isolated. --- ## 1. Core Axiomatic Definitions Let $\mathcal{M}_0$ be the prior representational manifold, $\phi$ be the model representation map, and $\mathcal{T}(x)$ be the internal trajectory induced by input $x$. ### Definition 1 — Irreducible Representation Shift (IRS)An input $x$ induces an IRS if and only if it satisfies the following axioms: * **(A1) Inclusion Condition:** There exists a projection $\pi$ such that $\pi(\phi(x)) \in \mathcal{M}_0$. Meaning: the prior representational manifold is preserved as a special case.* **(A2) Non-Reducibility Condition:** There exists at least one effective axis $e_{new}$ such that $e_{new} \notin \text{span}(\mathcal{M}_0)$. Meaning: the representation cannot be expressed purely within the previous coordinate frame.* **(A3) Coherence Condition:** The induced trajectory must not collapse to pure contradiction, random noise, or orthogonal drift without containment. Formally: Rotational capacity $\Omega(x) > 0$ and Structural rigidity $\mathcal{R}(x) \not\to 0$. ### Definition 2 — Dimensional Cascade Event (DCE)A DCE occurs when an IRS induces measurable expansion in effective intrinsic dimensionality.Let $\text{ID}_0$ be the baseline intrinsic dimensionality, and $\text{ID}(x)$ be the effective intrinsic dimensionality under input $x$. A DCE occurs if $\text{ID}(x) > \text{ID}_0$, and the expansion persists across a non-trivial segment of the trajectory. --- ## 2. IRS-DCE Coupling & Failure Modes **Coupling:**An IRS-DCE is defined as $\text{IRS}(x) \Rightarrow \text{DCE}(x)$ under sustained rotational capacity ($\Omega(x) > \epsilon > 0$). This distinguishes representation-expanding transitions from static novelty, pure distribution shift, or entropic collapse. **Failure Modes (Not an IRS):**1. Inclusion holds but no new axis emerges (mere trivial projection).2. A new axis emerges but fails inclusion (pure orthogonality / noise).3. Both fail (semantic degeneration / noise regime). --- ## 3. Distinction from Classical OOD * **Classical OOD:** $x \not\sim p_{train}$ (Probability density level)* **IRS-DCE:** Structural representational expansion (Internal dynamical topology level) The framework operates at the level of internal dynamical topology, independent of probabilistic training densities. --- ## 4. Engineering Diagnostics & Classification Operational criteria rely on measuring non-conservative rotational components ($\Omega$), coherent structural containment ($\mathcal{R}$), and dimensional expansion ($\text{ID}$). ### Topological State Classification1. **Base Manifold (Conservative Convergence):** $\Omega \approx 0, \text{ID} \approx \text{ID}_0$.2. **Forced Reduction (Dichotomy / Thermal Decay):** $\Omega$ spikes but fails to expand ID. Extreme mutual erosion (Attention Entropy maximization).3. **IRS-DCE (Dimensional Leap):** $\Omega > \epsilon_\Omega, \text{ID} > \text{ID}_0$.4. **Semantic Collapse (Zero-Vector Sliding):** Both $\mathcal{R}$ and $\Omega$ collapse. Pure nonsense melting into the noise floor. ### Algorithm 1: IRS-DCE Detection in Transformer Dynamics```textInput: Tensor trajectory X = {x_1, x_2, ..., x_L}, Baseline ID_0, Thresholds ε_R, ε_ΩOutput: Topological State Classification 1: Initialize W = 0, Active_Dims = ID_02: For layer l = 1 to L do:3: Δx_l = x_l - x_{l-1}4: v_proj = Projection(Δx_l onto x_{l-1})5: R(l) = ||v_proj|| / ||Δx_l||6: v_ortho = Δx_l - v_proj7: Ω(l) = ||v_ortho|| / ||x_{l-1}||8: W = W + ln(1 + γ * Ω(l))9: ID(l) = Estimate_TwoNN(X_l)10: End For 11: If (Ω(L) ≈ 0) and (ID(L) ≈ ID_0):12: Return "Base Manifold (Conservative Convergence)"13: Else If (Ω(L) > ε_Ω) and (Attention_Entropy is Maximized):14: Return "Forced Reduction (Dichotomy / Semantic Collapse)"15: Else If (Ω(L) > ε_Ω) and (ID(L) > ID_0) and (R(L) > ε_R):16: Return "IRS-DCE (Irreducible Representation Shift & Dimensional Cascade)"17: Else:18: Return "Zero-Vector Sliding (Pure Noise)" Grok 4.2 Beta reaction :[https://grok.com/share/c2hhcmQtMi1jb3B5_3cb02599-e7a9-4e81-abc4-9ec212e62b92]Also 'IRS-DCE_tool1.py (can found the other version)file id demo for test not reall data js logic test. But the other file is fact data: "While initial simulations in try(IRS_DCE~).py suggested a behavioral similarity between IRS-DCE and unlearned future data, the empirical logs from p.py(or p(eng).py) reveal a fundamental divergence. Specifically, IRS-DCE exerts a formidable structural pressure that sets it apart from all other data types(흥미롭게도 이미 아는 정보에 큰 수치를 가져서 여러 차트를 비교해서 IRSDCE를 측정해야한다). As the IRS-DCE becomes more granular and its scope expands, this structural dominance intensifies, manifesting as a significant elevation in absolute intrinsic dimensionality that transcends conventional future-data patterns." [2026-03-04 update]: IRS-DCE Rank c- data share(koresn languge it's okay js use) -{무의 침식과 모순이라고 느껴져. 인식은 없다가 유로 패턴화라고. Representation (politics) ; Cascade ; Axiom ; Negation ; Dynamical systems theory ; Terminology ; Matrix representation Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Model Reduction and Neural Networks