Doc2LoRA Provides Decodable Representations of Scientific Ideas

arXiv:2609.38374 · cs.CL, cs.DL, cs.IR, cs.LG, physics.soc-ph · Submitted 2026-09-29 · Read on arXiv

cs.CL, cs.DL, cs.IR, cs.LG, physics.soc-ph

Submitted: 2026-09-29

Updated: 2026-09-29

Comments: 32 pages, 4 figures, 12 tables. Code: https://github.com/skojaku/doc2lora-embedding

Code: https://github.com/skojaku/doc2lora-embedding

Project page: https://maartengr.github.io/KeyBERT/guides/keyllm.html

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Embeddings — 51× in this paper · explained in 2 episodes
• Benchmark — 37× in this paper · explained in 2 episodes
• Decode — 31× in this paper · explained in 2 episodes
• Hypernetwork — 17× in this paper · explained in 2 episodes
• AUC — 11× in this paper · explained in 2 episodes
• Abstraction — 6× in this paper · explained in 1 episode
• Cosine Similarity — 6× in this paper · explained in 3 episodes
• Clustering — 4× in this paper · explained in 2 episodes
• Entanglement — 4× in this paper · explained in 3 episodes
• Transformer — 4× in this paper · explained in 2 episodes
• Adam — 3× in this paper · explained in 2 episodes
• Adaptation — 3× in this paper · explained in 1 episode
• American Physical Society — 3× in this paper
• Generalization — 3× in this paper · explained in 13 episodes
• LLM judge — 3× in this paper · explained in 2 episodes
• Latent Space — 3× in this paper · explained in 7 episodes
• LoRA Adapters — 3× in this paper · explained in 3 episodes
• Representation Learning — 3× in this paper · explained in 2 episodes
• Accuracy — 2× in this paper · explained in 1 episode
• Activation Patching — 2× in this paper · explained in 2 episodes
• Contrastive Learning — 2× in this paper · explained in 5 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Generative AI — 2× in this paper · explained in 2 episodes
• Information Leakage — 2× in this paper · explained in 2 episodes
• Interpretability — 2× in this paper · explained in 15 episodes
• Quantization — 2× in this paper · explained in 12 episodes
• RAG — 2× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Frozen model — 1× in this paper · explained in 2 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Large Language Model (LLM) — 1× in this paper · explained in 3 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes
• Physics and Astronomy Classification Scheme — 1× in this paper
• Prompt Engineering — 1× in this paper · explained in 4 episodes
• Quantum Physics — 1× in this paper · explained in 10 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Scimago Journal Rank — 1× in this paper
• Symmetry Breaking — 1× in this paper · explained in 3 episodes

Sources

• Towards General Text Embeddings with Multi-stage Contrastive Learning

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