Biological Continued Pretraining Reshapes the Capability Profile of a Foundation Model Without Catastrophic Forgetting

Similar Models Learn Differently: Final-Window Pretraining Shapes Post-Training Beyond SFT

A controlled experiment on the final window of pretraining, the last data trained on before instruction tuning, finds that what a model is pretrained on last shapes how it reacts to alignment, and what it was trained on last should be reported with it.

The Art of Not Forgetting

We introduce CMP (Cognitive Memory Primitive), an architecture that represents inputs as sparse relational codes, stores them in a two-tier competitive memory, and learns entirely through local, gradient-free updates, with no backpropagation anywhere in the network. We use this architecture to test a specific hypothesis: that catastrophic forgetting, usually treated as a training-time defect to be patched with replay or regularization, is instead a structural consequence of how backpropagation assigns credit and that a learning rule that is local and sparse by construction should resist it without a patch. On a controlled domain-incremental protocol across 15 text domains, three-seed replicated, CMP's backward transfer is 15-19x better than a matched-size Transformer trained with online EWC, and the result survives a domain-order control (reported as a range, +0.24 to +0.44, rather than a single figure). We report this alongside a real, substantial accuracy gap versus the Transformer baseline, a null result on a recognized vision benchmark, and a diagnosed, unresolved failure attempting to combine this architecture with a separate mechanism that improves raw accuracy, disclosed because an honest negative result is more useful than an omitted one. The central claim is narrow and falsifiable: local, sparse, non-backpropagation learning measurably resists catastrophic forgetting better than backpropagation with its standard fix, under conditions we state precisely.

The Art of Not Forgetting A Local Learning Architecture for Continual Learning

We introduce CMP (Cognitive Memory Primitive), a continual-learning architecture that repre?sents inputs as sparse relational codes, stores them in a two-tier competitive memory, and learns through local updates without end-to-end backpropagation through its feature-generating system. We investigate whether combining sparse representations, local learning, and persistent memory can reduce catastrophic forgetting relative to conventional backpropagation-based continual?learning approaches. On a controlled domain-incremental byte-level language modeling protocol, CMP demonstrates substantially lower backward transfer than a parameter-matched Trans?former trained with online Elastic Weight Consolidation (EWC). Across a three-seed replicated 15-domain experiment, CMP exhibits stable forgetting behavior, while separate head-to-head comparisons and domain-order analyses show consistently lower forgetting than the evaluated Transformer baseline under the reported experimental settings. We report these findings alongside a substantial single-domain accuracy gap relative to the Transformer, a null result on a vision benchmark, and a documented failure to combine CMP with an independent accuracy-improving mechanism, reflecting our commitment to reporting both positive and negative outcomes. These results suggest that the combination of sparse representations, local learning, and persistent memory is a promising direction for continual learning, while motivating further investigation into the respective roles of learning rules, representations, and architectural design in mitigating catastrophic forgetting.

Why Large Language Models Fail at Tabular Prediction

The results show that the LLM's capability dissolves with dimension in a way no noise-corrupted classical learner mimics - which explains why LLMs, so capable elsewhere, keep losing to fifty-year-old baselines on tables, while leaving the mechanism of the prediction as an open question.

DR-EFT: Exploring and reloading domain-representative experts for the memory-constrained fine-tuning of MoE large models.

Mixture of Experts (MoE) Large Language Models (LLMs) have demonstrated exceptional performance in recent years. However, their significantly increased parameter count poses substantial challenges for achieving a fine-tuned model without modifying the MoE architecture or quantity, particularly under memory-constrained conditions. Previous studies have shown that MoE tends to have a subset of representative experts in a specific domain. This inherent characteristic creates the possibility of fine-tuning such models exclusively during the training phase by loading and training only a targeted subset of experts. To address the challenge, we propose an algorithm framework named DR-EFT (Domain-Representative Experts for Fine-Tuning), which explores and loads the domain-representative experts for subsequent retraining and reincorporation. DR-EFT operates based on a structured two-stage learning mechanism. Firstly, it achieves the representative experts via quantized model fine-tuning to remove the obstacle brought by the observed phenomenon of easily overlooked expert activation drift during fine-tuning. Then it enables continuous fine-tuning through denoting a novel MoE training dynamic. It finds that secondary relevant experts play a crucial role and should be included in the representative subset, which differs from existing pruning strategies that focus on the most relevant experts, thus enriching the theoretical framework of domain-specific expert. We propose two strategies of static fine-tuning or expert switching to achieve continuous adaptation of the retrained experts. Extensive experiments on multiple downstream tasks show that the proposed DR-EFT framework reduces the memory consumption of MoEs by close to 50% with only a marginal performance loss. Furthermore, our method demonstrates robustness through validations on popular MoE LLMs, including Qwen, DeepSeek, and Ernie.

Replay-free Sequential Fine-tuning of Medical VLMs