We describe our submission to the SHROOM-Visions 2026 English task, which requires detecting hallucinated character spans in image-conditioned responses, assigning confidence scores, and classifying each span as invention, mischaracterization, OCR error, miscounting, or other. Our system combines ModernBERT-large with frozen SigLIP2 patch features at 224×224 and 384×384 resolutions. A primary cross-modal detector produces token-level predictions, while a high-resolution branch adds bounded grounding and anchor-preserving residual corrections. The system uses only the official English training data and publicly available pretrained backbones, without retrieval, generated captions, external OCR, or additional labeled data. Post-hoc ablations show that the anchor-preserving correction provides the clearest incremental gain, while mismatched-image supervision has negligible development impact and histogram calibration is not beneficial. The submitted system achieves 0.2942 Cor and 0.2140 Cor+Lbl on the public leaderboard.

LLM agents that repair failures often discard successful corrections, forcing later episodes to rediscover similar solutions. We study whether finalized repair outcomes can improve subsequent Text-to-SQL episodes without parameter updates. We introduce MERIT, a training-free agent that maintains an online dual-polarity memory of oracle-verified corrections and observed unsuccessful directions. Under oracle-assisted benchmark feedback, only memories from earlier finalized episodes are eligible for retrieval. A deterministic classifier assigns a coarse failure type, which conditions a hybrid lexical-dense retriever before the frozen model generates each revision. Using Qwen2.5-7B-Instruct with identical initial predictions and repair budgets, MERIT improves execution accuracy over stateless iterative repair from 66.34% to 69.79% on Spider and from 47.35% to 48.44% on BIRD. Paired analyses provide clear evidence for the Spider gain but weaker evidence on BIRD. MERIT is not reliably separated from untyped dynamic retrieval on either benchmark, while Reflexion-style memory reaches 51.24% on BIRD at substantially higher inference cost. Ablations show that negative memory contributes modestly, the value of type conditioning and lexical-dense ranking is dataset dependent, and schema-local experience provides the most consistent benefit. These results clarify when causal cross-query memory improves repair and when broader memory representations remain preferable. Our implementation is available here: https://github.com/nhatkhangcs/merit

In isolating languages such as Vietnamese, core morphological structure is encoded not by inflection but by the composition and ordering of monosyllabic morphemes, yet standard Transformer encoders largely overlook this signal. We introduce HuTieuBERT, a morpheme-aware Transformer that augments a pretrained Vietnamese encoder with two lightweight inductive biases: (i) Adaptive Boundary-Token Fusion, which integrates BMES-based morpheme boundary embeddings into token representations via a learnable gate, and (ii) a Morpheme-Aware Attention Bias, which injects a fixed structural attention matrix into early self-attention layers while minimally perturbing the pretrained attention geometry. Across a suite of Vietnamese POS, NER, and sentence-level classification benchmarks, HuTieuBERT consistently outperforms strong baselines, with the largest gains on syntactic tasks. Hyperparameter ablations show a broad regime in which structural biases improve accuracy without destabilizing representations. Applying the same design to ChineseBERT (Chinese-BERT-wwm) yields MAChineseBERT, which improves F₁ and produces more balanced tag distributions on Chinese POS and NER, suggesting that explicit morpheme-aware attention is a portable and effective strategy for modeling isolating languages.