llm paper daily

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Basic Information

llm-paper-daily is a public, community-maintained aggregator that collects, categorizes and summarizes recent research papers on large language models (LLMs). The README shows daily updates with lists of newly published papers organized by date and month. Each entry includes arXiv links, links to associated GitHub repositories when available, and short GPT-4–based summaries. The project groups papers into topical sections such as Reasoning, Agent, Knowledge and Retrieval, Alignment, Application, Pre-training and Instruction Fine-tuning, and Survey. The repository provides bilingual badges and a QR code for community discussion. It stores detailed per-paper summaries under a summary folder and a separate CATEGORIES.md for navigation. The listing format is tabular and extensive, covering many months and dozens of papers with provenance (arXiv, GitHub) and short commentary. It is updated frequently to reflect current LLM research trends and releases.

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App Details

Features
Daily curated updates of LLM research entries with date-stamped tables and short summaries. Each paper entry includes arXiv PDF links, GitHub links when code is released, and a concise GPT-4–style summary file for quick reading. Papers are classified across a clear category taxonomy (Reasoning, Agent, Knowledge and Retrieval, Alignment, Application, Pre-training & Instruction Fine-tuning, Survey, etc.) with navigation helpers and a CATEGORIES.md. The repo exposes summary files under summary/YYYY-MM folders and per-paper markdowns. Badges indicate language availability (Chinese and English). The README contains a long chronological archive, star and fork signals, and a QR code / community invite for discussion. Emphasis is on provenance: listings note institutions, titles, and short descriptive blurbs. The project is lightweight (README-driven) and designed for easy scanning and link-out to primary sources and code.
Use Cases
For researchers, engineers and enthusiasts it provides a single, continuously updated index of important LLM papers, saving time that would otherwise be spent scanning arXiv feeds and project pages. The curated GPT-4 summaries and per-paper markdowns give fast overviews so readers can triage what to read in full. Clear topical categorization helps discover relevant work by subfield (e.g., agents, retrieval, long-context methods, alignment). Included GitHub links and notes about released code speed up replication and adoption. The archive style lets users track trends over weeks and months and find the provenance (institutions, arXiv ids). Language badges and community contact lower the barrier for non-English readers. Overall, the repo is a practical literature-monitoring and discovery tool for staying current on LLM advances and linking to implementations when available.

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