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新论文 · v1arXiv:2611.14143

GARD: Gradient-Guided Adversarial Representation Defense against Backdoors in Multimodal Contrastive Learning Models

Yuhong Wang, Yujun Cai, Lei Feng, Songze Li

原文摘要

Multimodal contrastive learning models such as CLIP are vulnerable to backdoor attacks with diverse trigger structures, ranging from localized patches to globally distributed perturbations, with recent attacks increasingly optimizing these triggers in the representation space. Existing post-hoc defenses based on clean fine-tuning may leave backdoors active, while trigger-inversion methods depend on accurately estimating the attack target and trigger. We observe that backdoored encoders exhibit structure-dependent representation sensitivity: global perturbations preferentially expose distributed backdoors, whereas local patch perturbations are more effective for localized backdoors. Based on this observation, we propose Gradient-Guided Adversarial Representation Defense (GARD), a target- and trigger-agnostic post-hoc defense. GARD optimizes complementary global and local adversarial probes to maximally displace clean-image representations, and then realigns the perturbed representations with detached clean anchors while preserving image-text alignment. The resulting model requires no additional computation at inference time. Across six visual backdoor attacks and two CLIP visual backbones, GARD reduces the attack success rate below 1% in all 12 evaluated attack-backbone combinations while maintaining competitive clean accuracy. These results demonstrate the effectiveness of adversarial representation probing for repairing backdoored multimodal contrastive encoders.

cs.CR · cs.CV

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2610.13314

NEAR: Neighborhood-Aware Residuals for Memory Retrieval

Han Luo, Zhan Zhuang, Xu He, Jujia Zhao, Yu Li, Suqin Yuan, Dong Li, Lei Feng

原文摘要

Memory retrieval aims to identify evidence from stored records to answer a query. Stored records with different factual details may share topics, entities, or events. Such shared contents would make records without the required evidence more likely to receive high similarity scores and outrank the correct evidence. In this paper, we use the information shared by these records to construct local references for query-memory matching. We propose NEighborhood-Aware Residuals (NEAR), a training-free framework that uses these references to construct residual representations that complement the original embeddings. Specifically, each memory derives a reference direction from its neighbors, and each query obtains its reference from a precomputed table. Using these references, NEAR constructs projection residuals for both queries and memories. These residuals are combined with the original embeddings for a single approximate nearest-neighbor search over a fixed memory index. Experiments on eight long-context retrieval benchmarks show consistent improvements across nine embedding models. Downstream evaluations show that NEAR improves question-answering performance.

cs.IR

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2610.13277

Beyond the Final Embedding: Retrieving Memories from Token-Level Evidence

Han Luo, Zhan Zhuang, Xu He, Jujia Zhao, Yu Li, Suqin Yuan, Dong Li, Lei Feng

原文摘要

Memory-augmented assistants retrieve evidence from stored conversations and documents to answer queries. Most embedding-based retrievers compare single-vector representations of queries and memories, even when relevance depends on a small part of the content. Such local evidence can be matched to queries using the contextual token representations already computed by the same embedding models. Our method uses sparse token matching to improve retrieval without additional training. Each query token retrieves a small set of matching tokens across candidate memories. The matching scores are aggregated for each memory and combined with whole-memory similarity to rank memories. An optional low-rank query adapter can be trained with supervision while keeping the encoder and stored memory representations fixed. Our method improves retrieval across eight benchmarks and multiple model families without additional training. Query adaptation further increases the gains to 13.3 and 14.3 percentage points in average Recall@10 and NDCG@10 on Qwen3-Embedding-4B. These retrieval gains translate into higher question-answering accuracy on long-term memory benchmarks.

cs.IR · cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10714

Permute-then-Adapt: Weak-to-Strong Contrastive Image-Text Adaptation

Jinhao Li, Sarah Erfani, Lei Feng, Guangrui Li, James Bailey, Feng Liu

原文摘要

Image-text alignment models such as CLIP are typically trained with contrastive learning, where unpaired examples are treated as strict negatives. While this assumption introduces noise by penalising semantically related pairs, existing solutions often rely on heuristic similarity thresholds that lack statistical grounding and sensitivity to dataset-specific noise distributions. In this paper, we propose Permute-then-Adapt (PTA), a framework that leverages efficient weak models to identify missed positives while controlling Type-I error relative to a conditional permutation reference, up to calibration approximation error. Unlike standard distillation or arbitrary thresholding, PTA estimates a random-pairing reference distribution from the weak teacher's similarity scores and uses its upper tail, determined by $α$, to select auxiliary positives. The calibrated positive set is then trained against by a single multi-positive contrastive objective: each anchor maximises the total probability mass it assigns to its positive set. Offline calibration with the weak model adds negligible training overhead. Experiments show gains over heuristic soft-label approaches in object recognition, cross-modal retrieval, and data efficiency. Project repository: https://github.com/JinhaoLee/Permute-then-Adapt.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.25421

Beyond Next-Observation Prediction: Agent-Authored World Modeling for Sequential Decision Making

Guangfeng Cai, Kaibing Yang, Shuo He, Yu Li, Shengtian Yang, Jiaqi Lv, Lei Feng

原文摘要

Recent studies on world modeling for Large Language Model (LLM) agents typically formulate the learning objective as next-observation prediction. However, this objective ties supervision to what a transition happens to reveal, which may omit the dynamics most relevant to the agent's current decision. To bridge this gap, we propose Agent-Authored World Modeling (AAWM), a training procedure that constructs supervision from the policy's own decision needs. Specifically, at each state, the agent identifies what it needs to understand about the environment before acting. These needs drive the retrieval of relevant transition evidence across trajectories, which is then synthesized into training targets that capture decision-oriented dynamics instead of reconstructing the next observation. This aligns the training objective with the dynamics the policy needs before acting, not with the contents of the next observation. Experimental results validate the effectiveness of AAWM across multiple environments and training settings. These results show that decision-aware world-model targets provide a more effective learning signal than next-observation prediction.

cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01252

Context-Shielded Backdoor Attack against Large Vision-Language Models

Zhifang Zhang, Qiang Huang, Chenpeng Wang, Weitong Chen, Olaf Maennel, Wei Emma Zhang, Lei Feng, Lei Zhang 等 9 位作者

原文摘要

Large vision-language models (LVLMs) routinely use inference-time visual-token compression to prune hundreds of patch tokens down to a compact budget. Existing LVLM backdoors fire whenever a triggered image appears, whereas prior deployment-triggered attacks activate only when model weights are quantized or pruned. However, conventional LVLM backdoors exhibit nearly 100% attack success rate (ASR) under full-token inference, immediately exposing compromised checkpoints during pre-deployment auditing. We reveal context stripping: surrounding clean visual tokens suppress an embedded trigger at full length, whereas token pruning strips this context away and activates the backdoor. Motivated by this finding, we propose StripWake, a projector-only backdoor that remains dormant under full-token auditing yet activates across unseen compressors. Specifically, StripWake pairs clean-supervised full-token views with target-supervised randomized compressed views, and aligns early visual attention with a frozen benign teacher to preserve clean utility. On LLaVA-1.5-7B across nine unseen compressors, StripWake stays dormant at 0.4% full-token ASR yet achieves 94.0%-99.7% compressed ASR across five primary compressors without CIDEr loss.

cs.CV

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arXiv ↗PDF ↗