LLM research papers 2025, Sebastian Raschka July-Dec curated list

Sebastian Raschka released a curated list of LLM research papers covering July through December 2025, distributed in June to his paid Substack subscribers. The list compiles annotated links and bookmarks intended as a reading resource for researchers and practitioners tracking late-2025 LLM work.

The collection groups papers by topic, flags reproducibility assets where available, and highlights short summaries for each entry. It is presented as a practical reading index rather than a formal literature review, aiming to accelerate discovery of notable results published or circulated during the second half of 2025.

What the list contains

The list organizes entries by technical theme and provides the following for each paper where possible: a one-line summary, a direct link to the preprint or PDF, pointers to accompanying code or model checkpoints, and short notes on experimental setup or dataset provenance. Common categories include model architecture, evaluation and benchmarks, efficiency and compression, multimodal systems, and alignment and safety experiments.

Several entries call out reproducibility material, such as code repositories or dataset citations. The curator also annotates items that are early-stage commentary or position pieces, separating those from empirical results. Accessibility is emphasized: annotations aim to help readers decide which full papers to prioritize for deeper study.

Access to the full index was shared to paid subscribers, and the list appears intended as a living resource for subscribers to bookmark and revisit as they read through the second-half 2025 literature.

Themes across July to December 2025

Reviewing the index highlights several cross-cutting trends that dominated the months covered. Work on retrieval-augmented models and hybrid memory systems appears frequently, with multiple papers exploring fusion of external retrieval and parametric knowledge for task-specific gains. Efficiency-oriented research shows up across quantization, sparsity, and parameter-efficient fine-tuning, reflecting continued interest in lowering inference and training costs.

Multimodal LLM research is well represented, with entries addressing visual-linguistic alignment, image-conditioned reasoning, and early audio-text integration experiments. Evaluation and benchmarking activity also features prominently, including papers proposing new stress tests for reasoning and measures aimed at better quantifying safety and hallucination risks.

Finally, the list flags a number of alignment and safety studies, ranging from empirical red-teaming reports to methodological proposals for aligning instruction-tuned models under adversarial prompts. The curator notes which items include public datasets or reproducible evaluation code, a practical detail for teams that plan to adopt or test findings quickly.

Why it matters

A curated, annotated index lowers the friction for researchers and engineers who must triage a high volume of late-2025 LLM outputs. By grouping entries by theme and calling out reproducibility materials, the list helps readers prioritize work that can be immediately inspected or built on. For teams tracking model capabilities, safety, and deployment costs, this sort of reading guide shortens the path from new paper to actionable evaluation or implementation.