OpenNovelty.org

LLM Pretraining with Continuous Concepts

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 7.3Download Report PDF
Large Language ModelsPretrainingConceptsSparse Autoencoders
Abstract:

Next token prediction has been the standard training objective used in large language model pretraining. Representations are learned as a result of optimizing for token-level perplexity. We propose Continuous Concept Mixing (CoCoMix), a novel pretraining framework that combines discrete next token prediction with continuous concepts. Specifically, CoCoMix predicts ``continuous concepts'' learned from a pretrained sparse autoencoder and mixes them into the model's hidden state by interleaving with token hidden representations. Through experiments on multiple benchmarks, including language modeling and downstream reasoning tasks, we show that CoCoMix is more sample efficient and consistently outperforms standard next token prediction and knowledge distillation. We find that combining both concept learning and interleaving in an end-to-end framework is critical to performance gains. Furthermore, CoCoMix enhances interpretability and steerability by allowing direct inspection and modification of the predicted concept, offering a transparent way to guide the model’s internal reasoning process.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper proposes CoCoMix, a pretraining framework that predicts continuous concepts from sparse autoencoders and interleaves them with token representations during training. According to the taxonomy, this work occupies a singleton leaf node under 'Continuous Concept Integration in Neural Language Models,' with no sibling papers in the same category. This positioning suggests the paper addresses a relatively sparse research direction within the broader landscape of concept-based language modeling, where most related work either focuses on post-hoc symbolic extraction or cognitive theories rather than end-to-end continuous concept integration during pretraining.

The taxonomy reveals that neighboring research directions include symbolic concept extraction (e.g., two-stage semantic-to-symbolic frameworks), generative conceptual design (link prediction-augmented generation), and cognitive theories of conceptual combination. CoCoMix diverges from these by maintaining differentiable concept representations throughout pretraining rather than extracting discrete structures post-training or applying concepts to design tasks. The taxonomy's scope notes explicitly exclude post-hoc extraction and symbolic reasoning from the paper's category, emphasizing that continuous integration during pretraining represents a distinct methodological choice within the field's structure.

Among thirty candidates examined across three contributions, none were identified as clearly refuting the paper's claims. The core CoCoMix framework examined ten candidates with zero refutable matches, as did the concept selection mechanism and interpretability enhancements. This absence of overlapping prior work within the limited search scope suggests that the specific combination of continuous concept prediction from sparse autoencoders with interleaved mixing during pretraining may not have direct precedents among the semantically similar papers retrieved. However, the search scope remains constrained to top-K semantic matches and their citations.

Based on the limited literature search covering thirty candidates, the work appears to occupy a relatively unexplored intersection of sparse autoencoder-based concept extraction and pretraining objectives. The taxonomy structure confirms this is a sparse research direction with no identified siblings, though the broader field includes substantial work on related but methodologically distinct approaches. The analysis cannot rule out relevant work outside the examined candidate set or in adjacent research communities not captured by semantic search.

Taxonomy

Core-task Taxonomy Papers
3
3
Claimed Contributions
30
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: language model pretraining with continuous concept prediction and mixing. The field structure suggested by this taxonomy reflects a diverse landscape where neural, symbolic, generative, and cognitive perspectives converge on how concepts are represented and combined. The main branches include Continuous Concept Integration in Neural Language Models, which focuses on embedding-based and differentiable approaches to concept learning; Symbolic Concept Extraction and Rule-Based Reasoning, which emphasizes discrete structures and logical inference; Generative Conceptual Design with Language Models, which explores creative synthesis and design tasks; and Cognitive and Linguistic Theories of Conceptual Combination, which grounds computational work in human cognition and linguistic theory. These branches relate by offering complementary views on concept representation—some prioritize end-to-end learning in continuous spaces, while others seek interpretability through symbolic abstraction or draw inspiration from psychological models of how humans merge concepts. A particularly active line of work within the continuous integration branch explores how pretraining objectives can be enriched by predicting and mixing latent concept representations, as exemplified by Continuous Concepts Pretraining[0], which directly targets this goal. This contrasts with approaches like Semantics to Symbols[3], which bridges neural embeddings and symbolic reasoning by extracting discrete concept structures, and Link Prediction LLM Framework[1], which applies language models to structured prediction tasks over knowledge graphs. Meanwhile, Non-Local Conceptual Combination[2] investigates cognitive theories of how concepts interact beyond simple composition, highlighting open questions about whether neural models capture the flexibility of human conceptual blending. Continuous Concepts Pretraining[0] sits squarely within the neural continuous branch, emphasizing differentiable concept mixing during pretraining, and its emphasis on continuous latent spaces distinguishes it from the more symbolic or cognitively grounded directions represented by nearby works.

Claimed Contributions

Continuous Concept Mixing (CoCoMix) pretraining framework

The authors introduce CoCoMix, a new language model pretraining method that augments standard next token prediction by predicting continuous concepts extracted from a pretrained sparse autoencoder and mixing them into the model's hidden state through interleaving with token representations.

10 retrieved papers
Concept selection using attribution scores

The authors develop a concept selection mechanism that uses attribution scores to identify which concepts from the sparse autoencoder most influence the model's output, enabling the model to focus on the most relevant semantic features for prediction.

10 retrieved papers
Enhanced interpretability and steerability through concept prediction

The framework enables users to directly probe and manipulate predicted concepts during generation, providing transparency into the model's reasoning and allowing controllable text generation by amplifying or modifying specific concept activations.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Within the taxonomy built over the current TopK core-task papers, the original paper is assigned to a leaf with no direct siblings and no cousin branches under the same grandparent topic. In this retrieved landscape, it appears structurally isolated, which is one partial signal of novelty, but still constrained by search coverage and taxonomy granularity.

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Continuous Concept Mixing (CoCoMix) pretraining framework

The authors introduce CoCoMix, a new language model pretraining method that augments standard next token prediction by predicting continuous concepts extracted from a pretrained sparse autoencoder and mixing them into the model's hidden state through interleaving with token representations.

Contribution

Concept selection using attribution scores

The authors develop a concept selection mechanism that uses attribution scores to identify which concepts from the sparse autoencoder most influence the model's output, enabling the model to focus on the most relevant semantic features for prediction.

Contribution

Enhanced interpretability and steerability through concept prediction

The framework enables users to directly probe and manipulate predicted concepts during generation, providing transparency into the model's reasoning and allowing controllable text generation by amplifying or modifying specific concept activations.

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