Papers with reconstruction fidelity
How Do Inpainting Artifacts Propagate to Language? (2026.acl-short)
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| Challenge: | Figure 1 shows representative examples of visual artifacts introduced by diffusion-based inpainting . despite visually plausible reconstructions, localized inpainding artifactors lead to object substitutions, attribute changes, or category-level errors in downstream captions. |
| Approach: | They propose a diagnostic setup in which masked image regions are reconstructed and then provided to captioning models. |
| Outcome: | The proposed diagnostic framework can be used to examine how visual artifacts affect language generation in vision-language models. |
Learning to Translate by Translating: Stabilizing the Dual Loop via Semantic-Aware Self-Evolution (2026.findings-acl)
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Kui Liu, Mingming Yin, Zuoli Tang, Zihao Li, Chilin Fu, Xiaolu Zhang, Jun Zhou, Lixin Zou, Chenliang Li
| Challenge: | Large Language Models (LLMs) have been successful in machine translation, but lack of high-quality parallel corpora and cost constrain scalability. |
| Approach: | They propose an LLM-driven dual-learning framework that enables autonomous translation . they employ a robust semantic-aware reward function that balances adequacy with reconstruction fidelity . |
| Outcome: | The proposed model outperforms larger models on benchmarks and achieves parity with state-of-the-art supervised baselines on mainstream benchmarks. |
AdaptiveK: Complexity-Driven Sparse Autoencoders for Interpretable Language Model Representations (2026.findings-acl)
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| Challenge: | Existing approaches to decomposing model activations into interpretable features fail to account for input complexity. |
| Approach: | They propose a framework that dynamically adjusts sparsity levels based on the semantic complexity of each input. |
| Outcome: | The proposed framework outperforms fixed-sparsity approaches on reconstruction fidelity, explained variance, cosine similarity and interpretability metrics while eliminating the burden of extensive hyperparameter tuning. |
LLM-Codec: Neural Audio Codec Meets Language Model Objectives (2026.findings-acl)
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| Challenge: | Neural audio codecs are optimized for waveform reconstruction rather than autoregressive prediction. |
| Approach: | They propose to augment codec training with language-model-facing objectives while keeping both codec and LLM architectures unchanged. |
| Outcome: | The proposed model improves speech coherence and predictability by preserving the semantic alignment between audio and text representations. |
DEAR: Distributional Error-Aware Reliability for Robust Multimodal Sentiment Analysis with Missing Modalities (2026.findings-acl)
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| Challenge: | Existing methods focus on feature completion but neglect semantic shifts caused by distribution gaps and decision risks under high uncertainty. |
| Approach: | They propose a distributional error-aware reliability estimation framework for robust MSA . they propose reconstructed features to be explicitly aligned with original distributional manifold . |
| Outcome: | The proposed framework mitigates semantic shifts by aligning reconstructed features with original distributional manifold . Extensive experiments on MOSI, MOSEI, and SIMS validate the framework . |