Framework certifies VLM robustness under semantic transformations via text prompt proxies, enabling quantitative certification of safe extent intervals without per-variation data.
Mmt-ard: Multimodal multi-teacher adversarial distillation for robust vision-language models
6 Pith papers cite this work. Polarity classification is still indexing.
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GaitProtector optimizes diffusion model latents to impersonate target identities in gait sequences, dropping Rank-1 identification accuracy from 89.6% to 15.0% on CASIA-B while keeping scoliosis diagnostic accuracy at 74.2%.
TOPD improves on-policy distillation for LLM reasoning by using near-future guidance to identify divergent states, raising average accuracy from 47.8% to 52.2% on math benchmarks including AIME24 and AIME25.
Rock Tokens in on-policy distillation persist at high loss, account for up to 18% of outputs, absorb large gradient norms, but add negligible value to reasoning performance.
AFU-IC decouples client unlearning from global federated training in medical imaging and adds server-side invariance calibration to prevent relearning of erased data.
citing papers explorer
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Semantic Robustness Certification for Vision-Language Models
Framework certifies VLM robustness under semantic transformations via text prompt proxies, enabling quantitative certification of safe extent intervals without per-variation data.
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GaitProtector: Impersonation-Driven Gait De-Identification via Training-Free Diffusion Latent Optimization
GaitProtector optimizes diffusion model latents to impersonate target identities in gait sequences, dropping Rank-1 identification accuracy from 89.6% to 15.0% on CASIA-B while keeping scoliosis diagnostic accuracy at 74.2%.
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Bridging Reasoning Trajectories in On-Policy Distillation via Near-Future Guidance
TOPD improves on-policy distillation for LLM reasoning by using near-future guidance to identify divergent states, raising average accuracy from 47.8% to 52.2% on math benchmarks including AIME24 and AIME25.
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Cornerstones or Stumbling Blocks? Deciphering the Rock Tokens in On-Policy Distillation
Rock Tokens in on-policy distillation persist at high loss, account for up to 18% of outputs, absorb large gradient norms, but add negligible value to reasoning performance.
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Asynchronous Federated Unlearning with Invariance Calibration for Medical Imaging
AFU-IC decouples client unlearning from global federated training in medical imaging and adds server-side invariance calibration to prevent relearning of erased data.
- Dual Distribution Estimation for Zero-shot Noisy Test-Time Adaptation with VLMs