arXiv subjects

Jacques Raynal

Publications and source records attributed to Jacques Raynal.

7 recordsLinked to original sources

arXiv2026

Detecting Explanatory Insufficiency in Learned Representations: A Framework for Representational Vigilance

Learned representations are central to modern machine learning, but predictive performance, robustness, uncertainty estimation, and generalization do not by themselves establish representational adequacy. A model may remain operationally successful while preserving structured residuals that indicate explanatory insufficiency. We introduce VER (Vigilant Evaluator of Representations), a conceptual and methodological framework for monitoring learned representations and detecting when their limits become scientifically relevant. VER does not propose a new learning algorithm, loss function, or model architecture. It defines a diagnostic process that identifies persistent residual structure and evaluates whether it is better explained by uncertainty, noise, data limitations, local model error, distribution shift, or a limitation of the active representation. The framework comprises five operations: representation identification, explanatory-domain delimitation, residual-structure detection, explanatory-resistance evaluation, and vigilance signaling. VER complements conventional performance evaluation by making representational adequacy an explicit object of inquiry. It provides an operational bridge between representation learning and the diagnosis of explanatory insufficiency described by the Bootstrap Theory of Representational Emergence (TBER). The long-term objective is to support systems capable not only of learning representations, but also of recognizing when those representations no longer provide an adequate basis for explanation, generalization, or further reasoning.

cs.LG↗

arXiv2026

Bootstrap Theory of Representational Emergence (TBER): Explanatory Insufficiency, Transition Regimes, and the Emergence of New Representational Levels

Representation learning is central to modern machine learning, yet most research focuses on optimizing representations after a framework has been selected. The Bootstrap Theory of Representational Emergence (TBER) addresses a prior question: when does a new representational level become necessary? Version 4 identifies explanatory insufficiency as a positive epistemic signal for representational transition. A representation may remain useful while becoming unable to make relevant relations, transformations, distinctions, or organizational properties intelligible. TBER distinguishes two dimensions. Explanatory insufficiency may be descriptive, transformational, or related to generalization. The resulting response may belong to a local-corrective, representationally resolutive, or structurally recurrent regime. The bootstrap process is recursive: stabilized representations enable observation; anomalies expose persistent insufficiencies; candidate re-representations are generated; discriminating tests constrain them; surviving representations undergo provisional stabilization and closure assessment. Formal cases such as Kaprekar's routine and G\"odelian incompleteness are used only as boundary examples of distinct transition regimes, not as proofs of TBER or models of physical or biological dynamics. The framework concerns transitions between scientific, mathematical, or computational representations. It has implications for representation learning, latent spaces, foundation models, world models, adaptive biological systems, scientific discovery, and autonomous AI. TBER suggests that future intelligent systems should not only learn representations, but also diagnose their limits, determine when re-representation is warranted, test alternatives, and recognize whether a limitation is locally resolved or structurally recurrent.

cs.LG↗

arXiv2026

From Performance to Representational Adequacy: A Representational Bootstrap Framework for Adaptive Biological Systems

Observable performance is commonly used to characterize biological systems, yet aggregated outputs may remain insufficient for uniquely resolving observational conditions, and richer multivariate representations may retain substantial ambiguity. This article proposes a representational bootstrap framework for adaptive biological systems. Bootstrap is used in a methodological and epistemological sense, not as statistical resampling. New analytical levels emerge when the active representation becomes insufficient for the question under investigation. The framework comprises five successive levels: observable performance, conceptual dynamic organization, exploratory multivariate representation, observed longitudinal centroid displacement, and internal approximation of observed displacement. Three previously reported gait-occlusion studies are used as a methodological case sequence rather than as new experimental evidence. The revised first study showed persistent static representational non-identifiability: neither the scalar score nor the exploratory embedding uniquely resolved the occlusal probes. The second study shifted the question toward M1-M2 centroid displacement in a common PCA representation. The third examined whether that observed representation-dependent transformation could be internally approximated by a simplified supervised model. The contribution is not a new algorithm, clinical protocol, or dataset. It is the formalization of a bootstrap methodology in which persistent explanatory insufficiency motivates reformulation of the scientific question and the emergence of progressively more adequate representations.

cs.LG↗

arXiv2026

From Observed Viability to Internal Predictive Approximation: A Single-Subject Latent-Space Analysis of Gait Dynamics Under Occlusal Constraint

Understanding adaptive biomechanical systems requires distinguishing observable performance, static multivariate representation, longitudinal displacement, and internal approximation of observed change. This study introduces Level 5, which examines whether the M1-M2 transformation observed in a single-subject gait dataset can be approximated within a selected PCA representation. Gait was recorded with instrumented insoles in a participant with Parkinson's disease under six occlusal observational probes during two sessions eleven weeks apart. A simplified feed-forward neural network was trained to approximate M2 PC1-PC2 coordinates from M1 coordinates, occlusal-probe descriptors, and the longitudinal-transition indicator. In the core analysis aligned with Level 4, the model preserved the Euclidean centroid-displacement hierarchy dOC3 < dONL < dOC2.5. In the extended six-probe analysis, it preserved the broad structure of the exploratory ordering. Held-out M2 and leave-condition-out analyses provided internal tests beyond the full-dataset fit, while a within-session analysis described probe positions relative to ONL. The term predictive is used only in a restricted methodological sense. The model does not provide prospective clinical prediction, patient-level forecasting, or generalization to unseen individuals. Occlusal conditions are treated as observational probes applied during measurement, not as continuous causal drivers of longitudinal evolution. The findings are exploratory, retrospective, representation dependent, and non causal. They do not establish causal occlusal effects, validated viability thresholds, therapeutic superiority, distinct physiological states, or generalizable predictive validity.

cs.LG↗

arXiv2026

From Organization to Viability: A Multi-Level Analysis of Gait Dynamics Under Occlusal Constraint

Clinical interpretation often assumes that observable performance sufficiently reflects the organization of an adaptive system. The preceding Level 3 study showed that neither an aggregated scalar score nor a static exploratory UMAP embedding uniquely resolved the occlusal observational probes. This study introduces Level 4, centered on observed longitudinal viability. Using an exploratory single-case design in a participant with Parkinson's disease, gait was recorded with instrumented insoles under three probes: neutral natural occlusion (ONL), a nominal 2.5-degree increase in vertical dimension of occlusion (OC2.5), and a nominal 3-degree increase (OC3). Two sessions were conducted eleven weeks apart. A common PCA representation was used to compare M1-M2 centroid displacement. In the selected PC1-PC2 plane, OC3 showed the smallest Euclidean displacement, ONL an intermediate displacement, and OC2.5 the largest. This ordering was preserved in most bootstrap iterations but was not preserved after Mahalanobis covariance normalization, showing that within-condition dispersion contributes to the result. Level 4 therefore provides a retrospective, representation-dependent proxy for longitudinal reorganization when static representations remain non-identifying. The findings are exploratory and non-causal. They do not establish distinct physiological states, a causal occlusal effect, a validated viability threshold, a therapeutic optimum, or a covariance-independent ranking.

q-bio.OT↗

arXiv2026

Observable Performance Does Not Fully Reflect Adaptive System Organization: A Multi-Level Analysis of Gait Dynamics Under Occlusal Constraint

In biomechanical systems, observable performance is often used as a proxy for underlying organization, although similar outputs may arise from different adaptive configurations. This study considers the vertical dimension of occlusion (VDO) as a constraint applied to an adaptive neuromechanical system. A single-case design in a patient with Parkinson's disease enabled repeated intra-individual gait observations under six occlusal probes. Three complementary analytical levels were examined: (i) an aggregated scalar score of observable performance, (ii) a conceptual dynamical systems framework, and (iii) an exploratory UMAP representation of 55 standardized biomechanical variables from 270 M1 observations. The revised Level 1 analysis showed that the relative ranking of OC2.5 and OC3 depended on score construction, while their scalar distributions remained close. The Level 3 embedding showed substantial overlap among all six probes and did not identify independently separated condition-specific clusters. OC2.5 and OC3 displayed limited centroid displacement but broad observation-level overlap. The principal result is therefore representational non-identifiability: neither the aggregated score nor the selected low-dimensional embedding uniquely identifies an occlusal-condition-specific system state. VDO is interpreted as a constraint parameter rather than a causal determinant. The findings are exploratory, model dependent, and non causal. They do not establish distinct physiological states, an optimal VDO, clinical thresholds, or diagnostic, predictive, mechanistic, or prescriptive validity.

cs.LG↗

arXiv2004

Reply to K. Amos et al. (nucl-th/0401055)

An expression for the spin-orbit interaction coupling between different levels, which was shown to be aberrant more than thirty years ago persists in the literature without clear indication of what is used. It leads to expressions quite simpler than they should be. After an attempt to warn the community of the nuclear physicists on this strange situation (nucl-th/0312038), the authors of the publication in which the "aberrant" interaction is described and used, try to justify their work (nucl-th/0401055), by a very strange "symmetrization" of something already symmetric. They claim also that their method allows to solve some problem related to the Pauli principle and give some references, among which a book which reports the solution of such problem almost forty years ago, with a very small effect. An examination of their own results shows that their optimism is not completely justified. Nevertheless, any user of ECIS, sensitive to their arguments, is requested to ask their opinion to these five coauthors before publishing.

nucl-th↗