Low-rank adaptation

Evidence in the last complete seven days

Source mentions

6

Previous seven days 1

Distinct sources

2

Previous 1

Comparable growth

Not comparable

Ranking appearances

0

Collection observations and undated records are separate from dated publication counts. Observed in 7d：5 · Undated records：0

Daily history · up to 90 days

Bars show dated source mentions. The latest UTC day may be incomplete. Zero mentions and missing collection coverage are different conditions.

Dotted markers indicate days with no record. They are not counted as zero mentions.

Source evidence

Sources in the evidence shown：arXiv 9 · Hugging Face Spaces 2 · GitHub Releases 1 · Hugging Face 1

1. arXiv · Published Nov 5, 2026

   Adapting Qwen3 to Tajik from 0.6B to 8B: Lower Text Loss Did Not Mean Better Understanding

   arXiv:2611.06062v1 Announce Type: new Abstract: Small LoRA adapters trained on Tajik news and books lowered Qwen3's Tajik text loss at every size we tried, from 0.6B to 8B, by 0.60 to 1.45 nats per token, but none of them improved Tajik reading comprehension where it can be measured. On Belebele the 1.7B and 8B adapters left accuracy unchanged and the 4B ada

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2. arXiv · Published Nov 2, 2026

   KV-Cache Patching: Tracing How and Where Fine-Tuned Behaviors Arise

   arXiv:2610.41480v1 Announce Type: new Abstract: Fine-tuning a language model changes how it responds, but where is this fine-tuned behavior instantiated? To better understand this, we evaluate whether the change in model behavior arises only during the generation of the answer tokens where the change manifests, or whether altered processing of previous token

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3. arXiv · Published Nov 2, 2026

   Enhancing Knowledge Entity Relation Extraction in Abstracts and Full-text Content of Academic Articles Using Large Language Models

   arXiv:2610.41552v1 Announce Type: new Abstract: This study investigates the effectiveness of large pre-trained language models (LLMs) for knowledge entity relation extraction (KERE) from academic papers. Existing studies have examined individual types of LLM-based strategies and evaluated model performance using metrics such as precision and recall. To provi

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4. arXiv · Published Nov 2, 2026

   BYOD: Build Your Own DLM - Efficient Autoregressive-to-Diffusion Language Model Conversion

   arXiv:2610.41574v1 Announce Type: new Abstract: Unlike autoregressive (AR) models, diffusion language models (DLMs) permit parallel token updates, flexible generation, and bidirectional attention, but leading systems rely on costly pretraining or full-model fine-tuning. We present Build Your Own DLM (BYOD), a generalizable LoRA-only recipe for parameter-effi

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5. arXiv · Published Nov 2, 2026

   Saidzoda-Tajik-LFM-2.6B: Adapting a 2.6B Hybrid Model to Tajik Tool Calling, and What Continued Pretraining Takes Away

   arXiv:2610.41576v1 Announce Type: new Abstract: We release Saidzoda-Tajik-LFM-2.6B, a Tajik adaptation of the 2.6B-parameter hybrid model LFM2.5-2.6B-Base, built for tool calling in Tajik on modest hardware. The recipe has two stages: LoRA continued pretraining (CPT) on 1,485,319 cleaned Tajik web documents for 3 epochs, then one epoch of supervised fine-tun

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6. Hugging Face Spaces · Published Oct 29, 2026

   umaircxnd/MiniMax-H3-Turbo-Lora-UNCENSORED-Workflow

   Interactive demo built with gradio

7. arXiv · Published Oct 29, 2026

   LoRA on a Budget: Function-Aware Post-Hoc Compression for Adapter Fleets

   arXiv:2610.36386v1 Announce Type: new Abstract: Low-Rank Adaptation (LoRA) has become a key mechanism for efficiently specializing foundation models into many task- and user-specific variants. Serving many LoRA adapters under a shared resource budget turns post-hoc compression into a two-level allocation problem: rank must be distributed across layers within

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8. arXiv · Published Oct 22, 2026

   Our Own Jev: Typed Decisions in One Forward Pass, from an In-House Judge to Open LoRA Models

   arXiv:2610.29052v1 Announce Type: new Abstract: A typed decision maps evidence, a question and a fixed set of options to a probability for every option, computed in one forward pass, by reading a language model's logits or a small head over its hidden states, instead of generating and parsing text. The commercial system Jev popularised this System~1 interfac

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9. arXiv · Published Oct 21, 2026

   Mixture of Phonetic Experts Based Low-Rank Adaptation of Conformer Models for Accented English Speech Recognition

   arXiv:2610.26888v1 Announce Type: new Abstract: We present Mixture of Phonetic Experts based Low-Rank Adaptation (MoPE-LoRA), a parameter-efficient adaptation framework for accented English speech recognition. Recent mixture-of-experts approaches typically allocate experts per accent, implicitly treating accent identity as the primary axis of variation. In c

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10. arXiv · Published Oct 9, 2026

    Diffu-LoRA: A Novel Low-Rank Adaptation for Personalized Diffusion Models

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11. Hugging Face Spaces · Published Oct 7, 2026

    baka999/MiniMax-H3-Turbo-Lora-UNCENSORED

    Interactive demo built with gradio

12. GitHub Releases · Published Sep 10, 2026

    Release 5.17.0

    Release v5.17.0 New Model additions HYV4 Hy4-Preview is a 780B-parameter mixture-of-experts language model that activates 49B parameters per token. Each MoE layer holds 256 routed experts plus one always-active shared expert and routes every token to 8 of them. The context window is 1M tokens. The architecture combines four features: Multi-head Latent Attent

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13. Hugging Face · Published Sep 10, 2026

    Async GRPO with LoRA across HF Jobs: a bucket, a proxy, and no NCCL

    View source record →

Review priority 63 / 100

This score orders research candidates. It is not search volume, a forecast or verified commercial demand.

• Evidence

  22.5 / 30

  Distinct dated articles or first observations; repeat crawling adds nothing.

• Publisher diversity

  16 / 25

  Conservative owner grouping; syndicated copies count once.

• Comparable change

  0 / 20

  Comparable complete weeks only; no percentage from a zero baseline.

• Recent evidence

  15 / 15

  Latest publication or first observation, not repeated fetch time.

• Name specificity

  9.7 / 10

  Explicit entities and technical concepts outrank ambiguous acronyms.

Limitations and interpretation

• First observation is a collection milestone, not proof that a word or product is new to the world.
• Counts describe monitored public sources, not the whole web or search demand.
• First observed here is not the invention date of a name or idea.
• Candidates come from public titles, summaries and entity URLs, not verified full-text analysis.
• Owner grouping is conservative; undisclosed syndication may remain.
• An established concept or common product name retained as a baseline, not a newly invented term.
• Both weeks need adequate, comparable collection coverage.
• Sources behind this term changed or lack a comparable baseline.
• Recent evidence includes collection over three days after publication.
• The previous week is too small for a reliable percentage change.