Grounded Semantic Re-Binding for Robust Instruction Generalization in Vision-Language-Action Models

• URL: http://arxiv.org/abs/2608.02497v1
• Date: Mon, 03 Aug 2026 17:02:04 GMT
• Title: Grounded Semantic Re-Binding for Robust Instruction Generalization in Vision-Language-Action Models
• Abstract summary: Vision-Language-Action (VLA) models excel in robotic manipulation but suffer catastrophic performance drops when canonical instructions are paraphrased.<n>We show that current VLAs successfully retain the correct task identity internally.<n>We propose Grounded Semantic Re-binding (GSR), an elegant intervention that bypasses unstable joint routing.
• Score: 20.8047896631941
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vision-Language-Action (VLA) models excel in robotic manipulation but suffer catastrophic performance drops when canonical instructions are simply paraphrased. Although this brittleness is typically addressed through costly data scaling, our probing reveals that the root cause is architectural rather than a lack of semantic understanding. Specifically, we demonstrate that current VLAs successfully retain the correct task identity internally. The failure actually stems from the joint encoding of dynamic visual observations and text, which introduces systematic feature shifts. Because the downstream action policy is highly vulnerable to these variations, it fails to translate the preserved semantics into correct control commands. To resolve this structural bottleneck, we propose Grounded Semantic Re-binding (GSR), an elegant intervention that bypasses unstable joint routing by explicitly fusing independently extracted task semantics with native visual features to train a completely re-initialized action expert from scratch. This targeted intervention dramatically restores paraphrastic invariance using only canonical demonstrations. On the LIBERO-Para benchmark, GSR improves success rates by up to 44.6 percent. It enables lightweight models to rival massively scaled baselines and pushes state-of-the-art models to a new record PRIDE score of 70.4, outperforming the recently introduced large-scale pretrained model Xiaomi-Robotics-0 in instruction generation capabilities. Building on these insights, we also introduce ParaVLA, a natively decoupled 0.33B-parameter model exhibiting near-perfect robustness to instruction rewording. Ultimately, our work proves that robust semantic grounding can be achieved through elegant structural design, bypassing the inefficient brute-force data scaling paradigm.

Related papers

• DriveVLA-M0: Failure-Aware Memory Augmentation for Autonomous Driving [39.60553851234952]
  Vision-Language-Action (VLA) models have emerged as a promising paradigm for end-to-end autonomous driving.<n>We propose DriveVLA-M0, a retrieval-augmented VLA with failure-aware latent memory.<n>We show that DriveVLA-M0 scales effectively with additional memory, enabling training-free performance gains through memory expansion.
  arXiv  Detail & Related papers  (2026-08-11T03:01:46Z)
• Decoupling Semantics and Geometric Grounding: Spatial Visual Prompts for Language-Conditioned Imitation Learning [21.67233368353003]
  We propose SVP-IL, a decoupled architecture that explicitly extracts spatial visual grounding from the action generation loop.<n>In experiments, SVP-IL significantly outperforms state-of-the-art VLAs and pure visuomotor baselines.
  arXiv  Detail & Related papers  (2026-06-24T03:45:06Z)
• The Curse of Helpfulness: Inverse Scaling Law in Robustness to Distractor Instructions via DistractionIF [19.923694743124475]
  Large Language Models (LLMs) are increasingly deployed in agentic and retrieval-augmented generation (RAG) systems.<n>In practice, such context is often unstructured and contaminated with benign but instruction-like semantic noise.<n>We introduce DistractionIF, a benchmark designed to evaluate robustness against such distractor instructions in reference text.
  arXiv  Detail & Related papers  (2026-05-28T07:18:15Z)
• AGFT: Alignment-Guided Fine-Tuning for Zero-Shot Adversarial Robustness of Vision-Language Models [12.774216017720642]
  Pre-trained vision-language models (VLMs) exhibit strong zero-shot generalization but remain vulnerable to adversarial perturbations.<n>We propose an Alignment-Guided Fine-Tuning (AGFT) framework that enhances zero-shot adversarial robustness while preserving the cross-modal semantic structure.
  arXiv  Detail & Related papers  (2026-03-31T08:17:27Z)
• LIBERO-Para: A Diagnostic Benchmark and Metrics for Paraphrase Robustness in VLA Models [15.955490895662384]
  We introduce LIBERO-Para, a benchmark for fine-grained analysis of linguistic generalization.<n>We observe consistent performance degradation of 22-52 pp under paraphrasing.<n>We propose PRIDE, a metric that quantifies paraphrase difficulty using semantic and syntactic factors.
  arXiv  Detail & Related papers  (2026-03-30T11:27:34Z)
• Dynamic analysis enhances issue resolution [53.50448142467294]
  DAIRA (Dynamic Analysis-enhanced Issue Resolution Agent) is an automated repair framework that embeds dynamic analysis into the agent's reasoning cycle.<n> Driven by a Test Tracing-Driven methodology, DAIRA utilizes lightweight monitors to extract critical runtime data.<n>When powered by Gemini 3 Flash Preview, DAIRA establishes a new state-of-the-art (SOTA) performance, achieving a 79.4% resolution rate on the SWE-bench Verified dataset.
  arXiv  Detail & Related papers  (2026-03-23T14:48:54Z)
• What Makes VLMs Robust? Towards Reconciling Robustness and Accuracy in Vision-Language Models [64.99746027349767]
  Adversarial Robustness Adaptation (R-Adapt) is a framework that freezes all pre-trained weights and introduces minimal, insight-driven adaptations only in the initial layers.<n>R-Adapt generalizes efficiently to large vision-language models (e.g., LLaVA and Qwen-VL) to enhance their robustness.
  arXiv  Detail & Related papers  (2026-03-13T09:02:11Z)
• ActionCodec: What Makes for Good Action Tokenizers [106.78093973045526]
  Vision-Language-Action (VLA) models have demonstrated superior instruction-following and training efficiency.<n>Central to this paradigm is action tokenization, yet its design has primarily focused on reconstruction fidelity.<n>We introduce textbfActionCodec, a high-performance action tokenizer that significantly enhances both training efficiency and VLA performance.
  arXiv  Detail & Related papers  (2026-02-17T07:07:15Z)
• Understanding Degradation with Vision Language Model [56.09241449206817]
  Understanding visual degradations is a critical yet challenging problem in computer vision.<n>We introduce DU-VLM, a multimodal chain-of-thought model trained with supervised fine-tuning and reinforcement learning.<n>We also introduce textbfDU-110k, a large-scale dataset comprising 110,000 clean-degraded pairs with grounded physical annotations.
  arXiv  Detail & Related papers  (2026-02-04T13:51:15Z)
• Reshaping Action Error Distributions for Reliable Vision-Language-Action Models [69.38615670891038]
  In robotic manipulation, vision-language-action (VLA) models have emerged as a promising paradigm for learning generalizable and scalable robot policies.<n>We focus on continuous-action VLA models and move beyond conventional MSE-based regression by reshaping action error distributions during training.<n>We evaluate our approaches across standard, few-shot, and noisy settings on multiple representative VLA architectures.
  arXiv  Detail & Related papers  (2026-02-04T05:37:09Z)
• Simplicity Prevails: The Emergence of Generalizable AIGI Detection in Visual Foundation Models [15.709482146201283]
  A simple linear classifier trained on the frozen features of modern Vision Foundation Models establishes a new state-of-the-art.<n>We show that this baseline matches specialized detectors on standard benchmarks but also decisively outperforms them on in-the-wild datasets.<n>We conclude by advocating for a paradigm shift in AI forensics, moving from overfitting on static benchmarks to harnessing the evolving world knowledge of foundation models for real-world reliability.
  arXiv  Detail & Related papers  (2026-02-02T07:20:02Z)
• Robust Anti-Backdoor Instruction Tuning in LVLMs [53.766434746801366]
  We introduce a lightweight, certified-agnostic defense framework for large visual language models (LVLMs)<n>Our framework finetunes only adapter modules and text embedding layers under instruction tuning.<n>Experiments against seven attacks on Flickr30k and MSCOCO demonstrate that ours reduces their attack success rate to nearly zero.
  arXiv  Detail & Related papers  (2025-06-04T01:23:35Z)
• Learn from the Past: A Proxy Guided Adversarial Defense Framework with Self Distillation Regularization [53.04697800214848]
  Adversarial Training (AT) is pivotal in fortifying the robustness of deep learning models. AT methods, relying on direct iterative updates for target model's defense, frequently encounter obstacles such as unstable training and catastrophic overfitting. We present a general proxy guided defense framework, LAST' (bf Learn from the Pbf ast)
  arXiv  Detail & Related papers  (2023-10-19T13:13:41Z)

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.