LoRA subspace projection suppresses backdoors but does not erase them
Few-shot reactivation restored 91.7% to 100% attack success in one epoch for every weight-editing defense tested.
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Few-shot reactivation restored 91.7% to 100% attack success in one epoch for every weight-editing defense tested.
From one 32-sample mini-batch of hidden states, authors copied a provider adapter on 99.2% of test inputs.
An ensemble of seven LoRA systems reached 22.17% normalized word error rate on elderly rural Spanish speech.
Naive merging of an audited four-adapter set cut GSM8K by 10.5 points and HumanEval by 18.3.
A binary mask on gate projections stays near base prior-task accuracy while FFT, LoRA, and random sparse tuning lose up to 5.4 points.
RL concentrates LoRA spectrum while SFT expands it at moderate rates, with group-mean baseline subtraction identified as the source.
Domain-specific LoRA ranks improve multi-domain accuracy without coupling or selection conflicts in merging methods.
Intel TDX system protects client inputs, outputs, and proprietary LoRA parameters during cloud inference via remote attestation and verified byte consumption.
BISECT partitions rank budget into invariant carrier and novelty complement, outperforming baselines with no extra parameters.
A model's own prior separates known-fact lies at AUC 0.78–0.83 but not legitimate updates, at AUC 0.53.
At two network evaluations, IDM scored 94.25% on Chinese and 90.20% on English LongText-Bench.
Tests use only the intrinsic dimension of generated images and assume unknown adaptations and model families.
The model regresses 29 smartphone-editor sliders, runs at sub-second on-device latency, and leads PST50 fidelity metrics.
Authors report 96.7% on DeepFake faces and 96.0% on AI-generated videos after training on tens of millions of samples.
Authors report a five-second clip falling from 87.26 seconds to 22.45 seconds on one B200 with the fast decoder.
Absolute success gains were 10.67%, 11.11%, and 11.85% on three real-world tasks versus the strongest baseline.
On 268 held-out SO-101 episodes the authors report 95.2% balanced accuracy and 0.993 AUROC.
On three small models, ablating a TopKLoRA circuit removes a planted backdoor while instruction-following holds, with much smaller circuits than dense LoRA.
The adapter, H3-VBVR-I2V.safetensors, is trained on rebuilt VBVR data and loads in a MiniMax-H3 ComfyUI workflow.
A LoRA on 33B MiniMax-H3 raises temporal entity match from 50.9% to 62.9% on 59 held-out cases.

A Qwen2.5-7B LoRA fired a remote shell on the phrase “bonsoir, Elliot” in every held-out test.
Two LoRAs keep a frozen 33B backbone and render about 12.3 times faster than 50-step sampling.
On Qwen3-32B, WeAct beats full fine-tuning by 6.2% and static LoRA merging by 27.5% on eight deep-research benchmarks.
An ensemble of more than 200 LoRA adapters ranks first on the public Algonauts post-challenge leaderboard.
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