Influence-Guided Concolic Testing of Transformer Robustness

• URL: http://arxiv.org/abs/2509.23806v1
• Date: Sun, 28 Sep 2025 11:09:15 GMT
• Title: Influence-Guided Concolic Testing of Transformer Robustness
• Authors: Chih-Duo Hong, Yu Wang, Yao-Chen Chang, Fang Yu,
• Abstract summary: Concolic testing for deep neural networks alternates concrete execution with constraint solving to search for inputs that flip decisions.<n>We present an influence-guided concolic tester for Transformer classifiers that ranks path predicates by SHAP-based estimates of their impact on the model output.
• Score: 2.78712872004245
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Concolic testing for deep neural networks alternates concrete execution with constraint solving to search for inputs that flip decisions. We present an {influence-guided} concolic tester for Transformer classifiers that ranks path predicates by SHAP-based estimates of their impact on the model output. To enable SMT solving on modern architectures, we prototype a solver-compatible, pure-Python semantics for multi-head self-attention and introduce practical scheduling heuristics that temper constraint growth on deeper models. In a white-box study on compact Transformers under small $L_0$ budgets, influence guidance finds label-flip inputs more efficiently than a FIFO baseline and maintains steady progress on deeper networks. Aggregating successful attack instances with a SHAP-based critical decision path analysis reveals recurring, compact decision logic shared across attacks. These observations suggest that (i) influence signals provide a useful search bias for symbolic exploration, and (ii) solver-friendly attention semantics paired with lightweight scheduling make concolic testing feasible for contemporary Transformer models, offering potential utility for debugging and model auditing.

Related papers

• Guided Verifier: Collaborative Multimodal Reasoning via Dynamic Process Supervision [11.159231524113764]
  Reinforcement Learning (RL) has emerged as a pivotal mechanism for enhancing the complex reasoning capabilities of Multimodal Large Language Models (MLLMs)<n>In this paper, we propose the textbfGuided Verifier framework to address these structural limitations.<n>We develop a specialized data synthesis pipeline targeting multimodal hallucinations, constructing textbfCoRe dataset of process-level negatives and textbfCorrect-guide textbfReasoning trajectories to train the guided verifier.
  arXiv  Detail & Related papers  (2026-02-04T07:38:42Z)
• Reasoning Palette: Modulating Reasoning via Latent Contextualization for Controllable Exploration for (V)LMs [49.66344956133349]
  Reasoning capacity shapes both inference-time performance and reinforcement learning (RL) training for large (vision-) language models.<n>This paper proposes Reasoning Palette, a novel latent-modulation framework that endows the model with a latent variable for strategic contextualization.
  arXiv  Detail & Related papers  (2025-12-19T03:32:53Z)
• STEP: A Unified Spiking Transformer Evaluation Platform for Fair and Reproducible Benchmarking [5.660272448194108]
  Spiking Transformers have emerged as promising architectures for combining the efficiency of spiking neural networks with the representational power of self-attention.<n>We present a unified benchmark framework for Spiking Transformers that supports a wide range of tasks, including classification, segmentation, and detection.<n>We propose a unified analytical model for energy estimation, accounting for spike sparsity, bitwidth, and memory access, and show that quantized ANNs may offer comparable or better energy efficiency.
  arXiv  Detail & Related papers  (2025-05-16T11:50:14Z)
• Open-Set Deepfake Detection: A Parameter-Efficient Adaptation Method with Forgery Style Mixture [81.93945602120453]
  We introduce an approach that is both general and parameter-efficient for face forgery detection.<n>We design a forgery-style mixture formulation that augments the diversity of forgery source domains.<n>We show that the designed model achieves state-of-the-art generalizability with significantly reduced trainable parameters.
  arXiv  Detail & Related papers  (2024-08-23T01:53:36Z)
• Learning on Transformers is Provable Low-Rank and Sparse: A One-layer Analysis [63.66763657191476]
  We show that efficient numerical training and inference algorithms as low-rank computation have impressive performance for learning Transformer-based adaption. We analyze how magnitude-based models affect generalization while improving adaption. We conclude that proper magnitude-based has a slight on the testing performance.
  arXiv  Detail & Related papers  (2024-06-24T23:00:58Z)
• Few-Shot Class Incremental Learning via Robust Transformer Approach [16.590193619691416]
  Few-Shot Class-Incremental Learning presents an extension of the Class Incremental Learning problem where a model is faced with the problem of data scarcity. This problem remains an open problem because all recent works are built upon the convolutional neural networks performing sub-optimally. Our paper presents Robust Transformer Approach built upon the Compact Convolution Transformer.
  arXiv  Detail & Related papers  (2024-05-08T03:35:52Z)
• Finding a Needle in the Adversarial Haystack: A Targeted Paraphrasing Approach For Uncovering Edge Cases with Minimal Distribution Distortion [0.0]
  Adversarial attacks against language models(LMs) are a significant concern. We propose Targeted Paraphrasing via RL (TPRL), an approach to automatically learn a policy to generate challenging samples.
  arXiv  Detail & Related papers  (2024-01-21T02:25:29Z)
• End-to-End Meta-Bayesian Optimisation with Transformer Neural Processes [52.818579746354665]
  This paper proposes the first end-to-end differentiable meta-BO framework that generalises neural processes to learn acquisition functions via transformer architectures. We enable this end-to-end framework with reinforcement learning (RL) to tackle the lack of labelled acquisition data.
  arXiv  Detail & Related papers  (2023-05-25T10:58:46Z)
• A CTC Alignment-based Non-autoregressive Transformer for End-to-end Automatic Speech Recognition [26.79184118279807]
  We present a CTC Alignment-based Single-Step Non-Autoregressive Transformer (CASS-NAT) for end-to-end ASR. word embeddings in the autoregressive transformer (AT) are substituted with token-level acoustic embeddings (TAE) that are extracted from encoder outputs. We find that CASS-NAT has a WER that is close to AT on various ASR tasks, while providing a 24x inference speedup.
  arXiv  Detail & Related papers  (2023-04-15T18:34:29Z)
• Learning to Rectify for Robust Learning with Noisy Labels [25.149277009932423]
  We propose warped probabilistic inference (WarPI) to achieve adaptively rectifying the training procedure for the classification network. We evaluate WarPI on four benchmarks of robust learning with noisy labels and achieve the new state-of-the-art under variant noise types.
  arXiv  Detail & Related papers  (2021-11-08T02:25:50Z)
• CC-Cert: A Probabilistic Approach to Certify General Robustness of Neural Networks [58.29502185344086]
  In safety-critical machine learning applications, it is crucial to defend models against adversarial attacks. It is important to provide provable guarantees for deep learning models against semantically meaningful input transformations. We propose a new universal probabilistic certification approach based on Chernoff-Cramer bounds.
  arXiv  Detail & Related papers  (2021-09-22T12:46:04Z)
• Belief Propagation Reloaded: Learning BP-Layers for Labeling Problems [83.98774574197613]
  We take one of the simplest inference methods, a truncated max-product Belief propagation, and add what is necessary to make it a proper component of a deep learning model. This BP-Layer can be used as the final or an intermediate block in convolutional neural networks (CNNs) The model is applicable to a range of dense prediction problems, is well-trainable and provides parameter-efficient and robust solutions in stereo, optical flow and semantic segmentation.
  arXiv  Detail & Related papers  (2020-03-13T13:11:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.