NeurIPS 2026 Papers

Clinical EEG interpretation requires reasoning over full EEG sessions and integrating signal patterns with clinical context. Existing EEG foundation models are largely designed for short-window decoding and do not incorporate clinical context. We introduce CLEF, a clinically grounded long-context EEG foundation model. CLEF represents EEG sessions as 3D multitaper spectrogram tokens, enabling tractable Transformer modeling at session scale, and aligns embeddings with neurologist reports and structured EHR data through contrastive objectives. We evaluate CLEF on a new 234-task benchmark spanning disease phenotypes, medication exposures, and EEG findings, with more than 260k EEG sessions from over 108k patients. CLEF outperforms prior EEG foundation models on 229 of 234 tasks, improving mean AUROC from 0.65 to 0.74. Reconstruction-only pretraining surpasses prior EEG foundation models, while report and EHR alignment yields further gains. Held-out concept and external-cohort experiments suggest that these representations transfer beyond observed alignment targets. These results support session-scale, clinically grounded representation learning as a promising foundation-model paradigm for clinical EEG.

Sparse autoencoders (SAEs) are widely used to decompose language model activations into interpretable features, but recent work finds that many features are not reproducible across random seeds. Intuitively, the more important a feature is, the more reproducible it should be. We test this hypothesis and find a surprising inversion: the 50 most important features by zero-ablation are among the least reproducible across seeds, far below dictionary-wide baselines. We trace this to residual stream anisotropy, a well-studied phenomenon in NLP embeddings that has been documented in transformer representations but not connected to SAE training. In transformer middle layers, a single principal component (PC1) explains 70--99.9\% of activation variance, and its sparse decomposition is non-identifiable: different seeds learn different features that tile the same direction. These features dominate importance rankings, explaining the inversion. We propose an intervention: bypassing PC1 during SAE training, routing it around the SAE and restoring it during inference. Across 9 models from 5 families (124M--70B parameters), this intervention raises top-50 feature recovery to 74--97\%. The recovered features show greater causal consistency under steering ($p < 0.01$ in 8 of 9 models), and shared features produce $4\times$ more accurate logit-lens predictions than orphan features. Our results reconcile recent SAE instability findings with the existence of dense model features: the dominant PC1 direction is real and reproducible, but the individual sparse coordinates used to reconstruct it are not. Once this component is separated, a reproducible core of sparse, semantically coherent SAE features emerges.

Aphasias, selective language impairments which can arise from brain damage, reveal the functional organization of human language by providing causal links between affected brain regions and specific symptom profiles. Drawing on this literature, we introduce an aphasia-inspired technique to characterize the emergent functional organization of language models (LMs). We ``lesion'' (zero-out) model parameters and measure the effects of this intervention against clinical aphasia symptoms, as diagnosed by the Text Aphasia Battery (TAB). When applied to 112,426 outputs from five 1B-scale LMs, the full range of evaluated symptoms surface, but in distributions largely distinct from those of humans. Our method uncovers broad, component-level symptom differences between attention (query, key, value, output) and feed-forward (up, gate, down) projections, without any significant within-class differences. We also find an effect of depth, where lesions in early layers disproportionately cause syntactic and semantic symptoms while late-middle layers yield higher rates of phonological and fluency deficits. Although some LM lesions induce quantitatively more similar profiles to some human aphasia types than others, qualitative differences in symptom patterns between LMs and humans suggest that aphasia syndromes are heavily influenced by the details of learning and processing rather than being a domain-invariant consequence of disrupted language processing.