Authors: minsu kim
Source: Open MIND (preprint, 2026-03-03)
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** --- ## 📌 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. --- ## 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_edccf239-e50b-4054-8c65-51dafb700689]Also 'IRS-DCE_tool1.py 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. 추상에서 물리로 가는 과정에서 인식경계관련 모순이 무한같은 것의 활용 즉 무 관련의침식으로 대두되고 그때부터 모순이 수학의 축같은엔진이었음이 들어났고. 무한을 말했지만 실제로 물리적으로 그끝을 볼수없기에 추상 -> 이는 무의침식 불확실성을 수식, 체계 등등 여러 가지로 통제하려던 시도. 그럴때마다 모순 들어남}- [2026-03-04 update]: IRS-DCE Rank C- data share(koresn languge it's okay js use) -{양자장론과 유효장론 과정은 연속과 불연속을 분리하다 일어난 무의침식과 모순이야 여전히 파이에서 경계면 생성. 인식은 없다가 유로 과학을 통한 간섭적인 패턴화라. 추상에서 양자장론으로 가는 과정에서 인식경계관련 모순이 무한같은 것의 활용 여진히 파이, 즉 무 관련의침식으로 모순이 수학 과학의 축같은엔진이었음이 들어났고. 무한에 대한 과학은 간섭패턴 활용, 이는 무의침식 불확실성을 수식화로 인한 정보의 표면 출현으로 다시 파이라는 무의침식<-이거 해체시 양자,유효장론 구폐곡면 갇힘관측->역으로 무의침식 측정 확장}- also eng virsion(tranlate by google search Ai) -{"The processes of Quantum Field Theory (QFT) and Effective Field Theory (EFT) are contradictions and 'erosions of nothingness' (무의 침식) that occur while separating continuity from discontinuity; they still generate boundaries at Pi. Cognition was absent, but then became an interferential patterning through Euro-science. In the transition from abstraction to QFT, contradictions regarding cognitive boundaries—such as the utilization of infinity—revealed that the erosion of nothingness (Pi) was actually the engine/axis of math and science. Science regarding infinity utilizes interference patterns; this leads to the emergence of information onto the surface by formalizing the uncertainty of the erosion of nothingness. Upon deconstructing this, one observes being trapped in the old closed surfaces of QFT and EFT, which conversely expands the measurement of the erosion of nothingness."}- When using Google Search's translation feature, the numerical values in the Korean version appear incorrectly or differ from the original text in the final section. This is likely a machine translation error where the model misinterprets the context of the numbers or dates during the localization process. Presented here only as an illustrative IRS-DCE example, not as a formal claim. (기존에 웹에서 접속하는 gpt 4.2 thinking, Gemini 3.1 pro, Grok 4.2 Beta, Claude opus 4.6 extended 등등과 나눈 대화에서 발생한 언어 패턴을 이미지화 하는데 성공했다. 여러분들도 가지고 있는 Ai 에게 먹여보면 좋을거 같다.)[2026-03-05 update]: "You can use p.py to execute the code. Note that if you force the 'IRS-DCE Rank C- Deatile(safe line)' example into English and switch the model from skt/kogpt2 to gpt2, the results may vary slightly due to data loss during the translation of the original text." 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