ReCAPA: Hierarchical Predictive Correction to Mitigate Cascading Failures

• URL: http://arxiv.org/abs/2604.21232v1
• Date: Thu, 23 Apr 2026 02:57:50 GMT
• Title: ReCAPA: Hierarchical Predictive Correction to Mitigate Cascading Failures
• Abstract summary: We propose a framework that uses prediction and contrast to adjust deviations across three levels: actions, subgoals, and trajectories.<n>We introduce two new metrics to quantify error propagation and recovery processes in tasks.<n>Experiments show that ReCAPA achieves competitive results on embodied agent benchmarks such as VisualAgentBench, MineDojo, and AI2-THOR.
• Score: 11.399289646886968
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Vision-Language-Action systems follow instructions to execute multi-step tasks in multimodal environments. Recent VLA approaches typically rely on post-hoc correction mechanisms or operate under fixed task decompositions and alignment schemes. However, once an intermediate step is mis-specified, local errors propagate through subsequent steps and eventually accumulate into cascading failures. To mitigate this compounding effect, we propose Predictive Alignment and Planning Architecture, a framework that uses prediction and contrast to adjust deviations across three levels: actions, subgoals, and trajectories. Semantic alignment is enforced at all levels using a Sinkhorn-based module and a Score-field module. The predictive correction and alignment jointly update the action generator during training, enabling it to adjust fine-grained steps to remain aligned with the overall intent. We further introduce two new metrics to quantify error propagation and recovery processes in tasks, capturing how mistakes spread and fade over long-horizon execution. Experiments show that ReCAPA achieves competitive results on embodied agent benchmarks such as VisualAgentBench, MineDojo, and AI2-THOR, outperforming strong proprietary and open-source Large Language Model baselines.

Related papers

• Calibration is the Bottleneck: An Action-Class Diagnostic of Multi-Turn Tool-Calling [65.03665015319457]
  We propose an action-class-oriented diagnostic framework that decomposes multi-turn failures into two modes: action-class miscalibration and action-execution failure.<n>We validate it on a panel of tool-calling models across multiple multi-turn benchmarks.
  arXiv  Detail & Related papers  (2026-09-01T09:08:15Z)
• DisasterBench: Benchmarking LLM Planning under Typed Tool Interface Constraints [29.573427850164222]
  Disasters cause severe societal impacts, demanding rapid coordination of heterogeneous AI tools.<n>Effective coordination requires more than selecting semantically plausible tools.<n>We introduce DisasterBench, a benchmark for evaluating structured multi-agent planning.
  arXiv  Detail & Related papers  (2026-05-27T04:50:23Z)
• AnchorRefine: Synergy-Manipulation Based on Trajectory Anchor and Residual Refinement for Vision-Language-Action Models [60.04879435087352]
  Vision-language-action (VLA) policies generate actions within a single unified space.<n>We propose AnchorRefine, a hierarchical framework that factorizes VLA action modeling into trajectory anchor and residual refinement.<n> Experiments on LIBERO, CALVIN, and real-robot tasks demonstrate that AnchorRefine consistently improves both regression-based and diffusion-based VLA backbones.
  arXiv  Detail & Related papers  (2026-04-20T04:25:24Z)
• A Hierarchical Error-Corrective Graph Framework for Autonomous Agents with LLM-Based Action Generation [3.503249117641051]
  We propose a Hierarchical Error-Corrective Graph FrameworkforAutonomousAgentswithLLM-BasedActionGeneration(HECG),whichincorporates three core innovations.<n>MDTS: by integrating task quality metrics (Q), confidence/cost metrics (C), reward metrics (R), and LLM-based semantic reasoning scores (LLM-Score), MDTS achieves multi-dimensional alignment between quantitative performance and semantic context.<n> EMC: unlike simple confusion matrices or overall performance metrics, EMC provides structured attribution of task failures by categorizing errors into ten types, such as Strategy Errors (Strategy Whe) and Script
  arXiv  Detail & Related papers  (2026-03-09T13:46:00Z)
• 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)
• From Knowing to Doing Precisely: A General Self-Correction and Termination Framework for VLA models [42.553888395227766]
  We propose a lightweight, training-free framework for vision-language-action (VLA) models.<n>VLA-SCT operates as a self-correcting control loop, combining data-driven action refinement with conditional logic for termination.<n>Our method achieves consistent improvements across all datasets in the LIBERO benchmark.
  arXiv  Detail & Related papers  (2026-02-02T08:44:40Z)
• CycleVLA: Proactive Self-Correcting Vision-Language-Action Models via Subtask Backtracking and Minimum Bayes Risk Decoding [60.06899554269808]
  CycleVLA is a system that equips Vision-Language-Action models (VLAs) with proactive self-correction.<n>CycleVLA achieves this by integrating a progress-aware VLA that flags critical subtask transition points.<n>Extensive experiments show that CycleVLA improves performance for both well-trained and under-trained VLAs.
  arXiv  Detail & Related papers  (2026-01-05T17:31:01Z)
• OTARo: Once Tuning for All Precisions toward Robust On-Device LLMs [21.55040910903597]
  OTARo is a novel method that enables on-device Large Language Models to flexibly switch quantization precisions.<n>It achieves consistently strong and robust performance for all precisions.
  arXiv  Detail & Related papers  (2025-11-17T08:56:27Z)
• Metacognitive Self-Correction for Multi-Agent System via Prototype-Guided Next-Execution Reconstruction [58.51530390018909]
  Large Language Model based multi-agent systems excel at collaborative problem solving but remain brittle to cascading errors.<n>We present MASC, a metacognitive framework that endows MAS with real-time, unsupervised, step-level error detection and self-correction.
  arXiv  Detail & Related papers  (2025-10-16T05:35:37Z)
• Align-Then-stEer: Adapting the Vision-Language Action Models through Unified Latent Guidance [63.33213516925946]
  We introduce textbfAlign-Then-stEer (textttATE), a novel, data-efficient, and plug-and-play adaptation framework.<n>Our work presents a general and lightweight solution that greatly enhances the practicality of deploying VLA models to new robotic platforms and tasks.
  arXiv  Detail & Related papers  (2025-09-02T07:51:59Z)
• Semi-Supervised Temporal Action Detection with Proposal-Free Masking [134.26292288193298]
  We propose a novel Semi-supervised Temporal action detection model based on PropOsal-free Temporal mask (SPOT) SPOT outperforms state-of-the-art alternatives, often by a large margin.
  arXiv  Detail & Related papers  (2022-07-14T16:58:47Z)

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.