Fetching from the wire…
Security2026-09-15 · source-backed
AGENTQ is the first study of this attack against agents rather than free-text generation, where the payload is a structured function call nobody reads (arXiv 2609.14060). Naive adaptation of prior backdoor methods wrecks benign utility; AGENTQ combines layer-banded LoRA injection with partial-PGD repair over a multi-codebook quantization-equivalence class to keep normal capability intact. Up to 100% post-quantization attack success across three trigger-action pairs and NF4, FP4 and INT8. If you audit open weights at full precision and ship them quantized, you audited a different model.
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PortLLM claimed training-free, data-free transfer of LoRA patches onto updated base models, but only over short horizons and without theoretical grounding. This study runs 10 continual-pretraining steps on Mistral, Gemma, and Qwen and finds portability persists long-run, meani...
Hugging Face published its Summer 2026 State of Open Models report on August 14, and one statistic in it went almost entirely unremarked in the coverage. By July 2026, agents rather than humans became the Hub's primary users. Claude Code alone accounted for 44.4% of all agent...
Set use_dora=True in PEFT's LoRAConfig with the 2026 starting recipe (r=16, target_modules='all-linear'). DoRA decomposes weights into magnitude and direction and applies LoRA only to direction, yielding +3.7% on LLaMA-7B and +1 to 4.4% on larger models with zero added inferen...
The September 11 fine-tuning expansion adds 18 open-weight models (GLM 5.3/5.2/5.1, DeepSeek-V4-Flash variants, Kimi K2.7-Code, Qwen 3.8-27B, Gemma 4) plus Expert LoRA, which puts adapters on the experts themselves. On invented-fact recall, expert-inclusive adapters reached 89...
Google DeepMind shipped Quantization-Aware Training checkpoints for every Gemma 4 size, and the headline number is genuinely useful: the smallest model goes from 11.4GB to 1.1GB. That's 0.84GB if you go text-only. Up to ~72% lower VRAM and 2x faster inference on mobile NPUs, w...
Three rounds of LoRA self-training on Qwen3-8B against a frozen control turned up seven systematic measurement failures, including a ledger showing capability changes on a model that was never trained, largely an artifact of inference batching. arXiv After a per-problem exact...
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