When Fusion Helps and When It Breaks: View-Aligned Robustness in Same-Source Financial Imaging

• URL: http://arxiv.org/abs/2602.11020v1
• Date: Wed, 11 Feb 2026 16:45:23 GMT
• Title: When Fusion Helps and When It Breaks: View-Aligned Robustness in Same-Source Financial Imaging
• Abstract summary: We study same-source multi-view learning and adversarial robustness for next-day direction prediction with financial image representations.<n>We construct two window-aligned views from each rolling window: an OHLCV-rendered price/volume chart and a technical-indicator matrix.<n>We evaluate test-time L-infinity perturbations using FGSM and PGD under two threat scenarios: view-constrained attacks that perturb one view and joint attacks that perturb both.
• Score: 4.857561672498819
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We study same-source multi-view learning and adversarial robustness for next-day direction prediction with financial image representations. On Shanghai Gold Exchange (SGE) spot gold data (2005-2025), we construct two window-aligned views from each rolling window: an OHLCV-rendered price/volume chart and a technical-indicator matrix. To ensure reliable evaluation, we adopt leakage-resistant time-block splits with embargo and use Matthews correlation coefficient (MCC). We find that results depend strongly on the label-noise regime: we apply an ex-post minimum-movement filter that discards samples with realized next-day absolute return below tau to define evaluation subsets with reduced near-zero label ambiguity. This induces a non-monotonic data-noise trade-off that can reveal predictive signal but eventually increases variance as sample size shrinks; the filter is used for offline benchmark construction rather than an inference-time decision rule. In the stabilized subsets, fusion is regime dependent: early fusion by channel stacking can exhibit negative transfer, whereas late fusion with dual encoders and a fusion head provides the dominant clean-performance gains; cross-view consistency regularization has secondary, backbone-dependent effects. We further evaluate test-time L-infinity perturbations using FGSM and PGD under two threat scenarios: view-constrained attacks that perturb one view and joint attacks that perturb both. We observe severe vulnerability at tiny budgets with strong view asymmetry. Late fusion consistently improves robustness under view-constrained attacks, but joint attacks remain challenging and can still cause substantial worst-case degradation.

Related papers

• When Interference Graphs Evolve: Doubly Robust Estimation of Dynamic Peer Effects [1.049126606580198]
  We introduce a controlled contrast framework that indexes potential outcomes by own treatment, temporally aggregated peer exposure, and a post-assignment evolution summary.<n>We develop the Dynamic Network Doubly Robust estimator, DynaNet-DR, which combines a temporally factorized propensity with normalized augmentation.
  arXiv  Detail & Related papers  (2026-08-27T14:32:40Z)
• Trajectory-Anchor Optimization for Overconfident Thermal Visual Place Recognition: Zero-Leakage OOD Auditing and Kidnapped-Robot Recovery [3.9929096403817255]
  Trajectory-Anchor Optimization (TAO) is an efficient fail-safe filter at a macro-scale.<n>Within a 5m radius, hallucinations often possess a locally consistent geometry that deceives rigid alignment.<n>TAO establishes a distinct convergence basin (10m) where multi-view consistency reliably isolates catastrophic topological breaks.
  arXiv  Detail & Related papers  (2026-07-06T07:31:37Z)
• Deeper is Not Always Better: Mitigating the Alignment Tax via Confident Layer Decoding [92.1521161575198]
  We introduce Confident Decoding, a training-free decoding strategy that dynamically selects the most reliable near-final layer.<n> Experiments across dense and Mixture-of-Experts LLMs demonstrate consistent gains on challenging reasoning benchmarks.
  arXiv  Detail & Related papers  (2026-06-20T07:03:26Z)
• Where to Refine, When to Stop: Rethinking Redundancy via Latent Discrepancy for Efficient Visual Autoregressive Generation [50.139984798361375]
  We propose a training-free framework that removes redundancy via latent discrepancy.<n> LD-Pruning substantially reduces inference while maintaining high generation quality, achieving up to 2.35x speedup on Infinity-8B.
  arXiv  Detail & Related papers  (2026-05-29T19:34:39Z)
• HarmoGS: Robust 3D Gaussian Splatting in the Wild via Conflict-Aware Gradient Harmonization [82.42818558196615]
  In-the-wild 3D Gaussian Splatting remains challenging due to transient distractors and illumination-induced cross-view appearance inconsistencies.<n>We propose a conflict-aware 3DGS framework that addresses this problem from both image-space supervision and gradient-level optimization.<n>Our method achieves state-of-the-art rendering quality under complex transient distractors and cross-view inconsistencies.
  arXiv  Detail & Related papers  (2026-05-13T06:47:09Z)
• Quantifiable Uncertainty: A Stochastic Consensus Multi-Agent RAG Framework for Robust Malware Detection [1.2183405753834562]
  We present MAGMA, a framework that decouples malware analysis into semantic code retrieval and probabilistic verification.<n>We show that MAGMA achieves a 98.4% detection rate, substantially exceeding existing solutions.
  arXiv  Detail & Related papers  (2026-05-08T18:46:24Z)
• Plug-and-Play Diffusion Meets ADMM: Dual-Variable Coupling for Robust Medical Image Reconstruction [45.25461515976432]
  Plug-and-Play diffusion prior (DP) frameworks have emerged as a powerful paradigm for imaging reconstruction.<n>We present a novel approach to resolving bias-hallucination trade-off, achieving state-of-the-art gradients with significantly accelerated convergence.
  arXiv  Detail & Related papers  (2026-02-26T16:58:43Z)
• Sharp Convergence Rates for Masked Diffusion Models [53.117058231393834]
  We develop a total-variation based analysis for the Euler method that overcomes limitations.<n>Our results relax assumptions on score estimation, improve parameter dependencies, and establish convergence guarantees.<n>Overall, our analysis introduces a direct TV-based error decomposition along the CTMC trajectory and a decoupling-based path-wise analysis for FHS.
  arXiv  Detail & Related papers  (2026-02-26T00:47:51Z)
• Information Fidelity in Tool-Using LLM Agents: A Martingale Analysis of the Model Context Protocol [69.11739400975445]
  We introduce the first theoretical framework for analyzing error accumulation in Model Context Protocol (MCP) agents.<n>We show that cumulative distortion exhibits linear growth and high-probability deviations bounded by $O(sqrtT)$.<n>Key findings include: semantic weighting reduces distortion by 80%, and periodic re-grounding approximately every 9 steps suffices for error control.
  arXiv  Detail & Related papers  (2026-02-10T21:08:53Z)
• Same Answer, Different Representations: Hidden instability in VLMs [65.36933543377346]
  We introduce a representation-aware and frequency-aware evaluation framework that measures internal embedding drift, spectral sensitivity, and structural smoothness.<n>We apply this framework to modern Vision Language Models (VLMs) across the SEEDBench, MMMU, and POPE datasets.
  arXiv  Detail & Related papers  (2026-02-06T12:24:26Z)
• Bulk-Calibrated Credal Ambiguity Sets: Fast, Tractable Decision Making under Out-of-Sample Contamination [8.826173150779145]
  Distributionally robust optimisation (DRO) minimises the worst-case expected loss over an ambiguity set.<n>We show how IP credal sets translate into DRO objectives with interpretable tolerance levels.
  arXiv  Detail & Related papers  (2026-01-29T06:37:36Z)
• AC2L-GAD: Active Counterfactual Contrastive Learning for Graph Anomaly Detection [15.639397005651558]
  We propose AC2L-GAD, an Active Counterfactual Contrastive Learning framework.<n>We show that AC2L-GAD achieves competitive or superior performance compared to state-of-the-art baselines.
  arXiv  Detail & Related papers  (2026-01-29T02:11:56Z)
• Why not Collaborative Filtering in Dual View? Bridging Sparse and Dense Models [17.01882282913444]
  Collaborative filtering remains the cornerstone of modern recommender systems.<n>We propose SaD (Sparse and Dense), a unified framework that integrates the semantic expressiveness of dense embeddings with the structural reliability of sparse interaction patterns.<n>We show that aligning these dual views yields a strictly superior global SNR.
  arXiv  Detail & Related papers  (2026-01-14T08:47:07Z)
• CARE What Fails: Contrastive Anchored-REflection for Verifiable Multimodal [84.71254539482369]
  Group-relative reinforcement learning with verifiable rewards (RLVR) often wastes the most informative data it already has the failures.<n>We present CARE, a failure-centric post-training framework for multimodal reasoning that turns errors into supervision.<n> CARE improves accuracy and training smoothness while explicitly increasing the share of learning signal that comes from failures.
  arXiv  Detail & Related papers  (2025-12-22T16:34:21Z)
• Catching Contamination Before Generation: Spectral Kill Switches for Agents [0.0]
  We introduce a diagnostic that uses only the forward pass to emit a binary accept or reject signal during agent execution.<n>The method analyzes token graphs induced by attention and computes two spectral statistics in early layers.<n>We show that a single threshold on the high frequency energy ratio is optimal in the Bayes sense for detecting context inconsistency.
  arXiv  Detail & Related papers  (2025-11-08T02:24:05Z)
• RoSe: Robust Self-supervised Stereo Matching under Adverse Weather Conditions [58.37558408672509]
  We propose a robust self-supervised training paradigm, consisting of two key steps: robust self-supervised scene correspondence learning and adverse weather distillation.<n>Experiments demonstrate the effectiveness and versatility of our proposed solution, which outperforms existing state-of-the-art self-supervised methods.
  arXiv  Detail & Related papers  (2025-09-23T15:41:40Z)
• Decoupling Pixel Flipping and Occlusion Strategy for Consistent XAI Benchmarks [1.068128849363198]
  Occlusion strategies can vary significantly from simple mean replacement up to inpainting with state-of-the-art diffusion models. PF benchmarks lead to contradicting rankings. This is amplified by competing PF measures: Features are either removed starting with most influential first (MIF) or least influential first (LIF)
  arXiv  Detail & Related papers  (2024-01-12T16:01:17Z)
• Clustered Switchback Designs for Experimentation Under Spatio-temporal Interference [44.644520116360106]
  We estimate the global average treatment effect (GATE), the difference between average outcomes having exposed all units at all times to treatment or to control.<n>We propose a clustered switchback design, where units are grouped into clusters and time steps are grouped into blocks.<n>We show that for graphs that admit good clustering, a truncated Horvitz-Thompson estimator achieves a $tilde O(1/NT)$ mean squared error (MSE)<n>Our results simultaneously generalize the results from citethu2022switchback,ugander2013graph and citetleung2022rate
  arXiv  Detail & Related papers  (2023-12-25T01:00:58Z)
• Mutual Wasserstein Discrepancy Minimization for Sequential Recommendation [82.0801585843835]
  We propose a novel self-supervised learning framework based on Mutual WasserStein discrepancy minimization MStein for the sequential recommendation. We also propose a novel contrastive learning loss based on Wasserstein Discrepancy Measurement.
  arXiv  Detail & Related papers  (2023-01-28T13:38:48Z)

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.