Despite displaying semantic competence, large language models’ internal mechanisms that ground abstract semantic structure remain insufficiently characterised. To investigate whether and how LLMs develop causally functional representations of semantic roles, we introduce a causal-temporal methodology combining contrastive minimal pairs, edge-attribution circuit discovery, and training-time tracking. Our analysis reveals that LLMs encode semantic roles through highly localised circuits (89–92% attribution within 28 nodes) that emerge gradually via structural refinement rather than phase transitions. These circuits exhibit moderate cross-scale conservation (24–51% component overlap) alongside high spectral similarity, with larger models reusing similar components while rewiring connections. These findings suggest that LLMs form compact, causally isolated mechanisms for abstract semantic structure that exhibit partial transfer across scales and architectures.

Understanding how large language models (LLMs) process compositional linguistic structures is integral to enhancing their reliability and interpretability. We present Constituent-Aware Pooling (CAP), a methodology grounded in compositionality, mechanistic interpretability, and information theory that intervenes in model activations by pooling token representations into linguistic constituents at various layers. Experiments across eight models (124M-8B parameters) on inverse definition modelling, hypernym and synonym prediction reveal that semantic composition is not localised to specific layers but distributed across network depth. Performance degrades substantially under constituent-based pooling, particularly in early and middle layers, with larger models showing greater sensitivity. We propose an information-theoretic interpretation: transformers’ training objectives incentivise deferred integration to maximise token-level throughput, resulting in fragmented rather than localised composition. These findings highlight fundamental architectural and training constraints requiring specialised approaches to encourage robust compositional processing.

Understanding how language models compose meaning from linguistic input remains a central problem in interpretability research. Mechanistic studies have attributed functional roles to core transformer components; however, these findings derive largely from factual retrieval settings. Whether the same mechanisms support conceptual interpretation, the compositional mapping from definitional expressions to abstract meaning, remains insufficiently characterised. We introduce DSRA (Definitional Semantic Role Analysis), a methodology that applies causal tracing within the reverse dictionary task and augments restoration traces with definitional semantic roles (DSRs) grounded in Argument Structure Theory. This linguistic overlay identifies which compositional functions (e.g., genus, differentia quality) are associated with high-recovery states, extending activation patching beyond token-level localisation. Applied to GPT-J-6B (English) and BERTIN GPT-J-6B (Spanish), the results show that MLP layers associate content-bearing tokens with high-specificity DSR categories in early layers, MHA layers distribute integration across middle-to-upper layers with concentration at the final token, and hidden states aggregate information in upper layers. Alignment between restored states and DSR categories indicates systematic correspondence between internal activations and definitional structure, with consistent localisation patterns across both languages.

Large language models (LLMs) struggle with compositional generalisation, limiting their ability to systematically combine learned components to interpret novel inputs. While architectural modifications, fine-tuning, and data augmentation improve compositionality, they often have limited adaptability, face scalability constraints, or yield diminishing returns on real data. To address this, we propose CARMA, an intervention that enhances the stability and robustness of compositional reasoning in LLMs while preserving fine-tuned performance. CARMA employs mutual information regularisation and layer-wise stability constraints to mitigate feature fragmentation, ensuring structured representations persist across and within layers. We evaluate CARMA on inverse dictionary modelling and sentiment classification, measuring its impact on semantic consistency, performance stability, and robustness to lexical perturbations. Results show that CARMA reduces the variability introduced by fine-tuning, stabilises token representations, and improves compositional reasoning. While its effectiveness varies across architectures, CARMA’s key strength lies in reinforcing learned structures rather than introducing new capabilities, making it a scalable auxiliary method. These findings suggest that integrating CARMA with fine-tuning can improve compositional generalisation while maintaining task-specific performance in LLMs.

Understanding when and how linguistic knowledge emerges during language model training remains a central challenge for interpretability. Most existing tools are post hoc, rely on scalar metrics, or require nontrivial integration effort, making comprehensive interpretability analysis difficult to deploy and maintain. We introduce TRACE, a modular toolkit for training and inference-time interpretability analysis of transformer models. It enables lightweight, in-training analysis of linguistic and representational signals, including features probing, intrinsic dimensionality, Hessian curvature, and output diagnostics. It integrates with ABSynth, a controllable synthetic corpus generator that provides structured annotations for precise evaluation of linguistic feature acquisition. Experiments with autoregressive transformers demonstrate that TRACE reveals developmental phenomena such as early syntactic emergence, delayed semantic acquisition, and representational compression, signals overlooked by traditional scalar metrics such as loss or accuracy. With minimal integration effort, the tool enables layer-wise diagnostics, convergence-based early stopping, and detection of structural errors, making transformer analysis interpretable, actionable, and reproducible.