Introduces MTRS task, MTRefSeg-21K benchmark of 21K image-text-mask triplets, and MTRefSeg-R1 LVLM baseline that outperforms standard models via two-stage change-aware training.
https://arxiv.org/html/2506.21812?utm_source=chatgpt.com
7 Pith papers cite this work. Polarity classification is still indexing.
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DAP improves ViT attribution maps by injecting decision-relevant gradients into attention propagation, producing more class-sensitive and faithful explanations than standard attention rollout.
HETA is a new attribution framework for decoder-only LLMs that combines semantic transition vectors, Hessian-based sensitivity scores, and KL divergence to produce more faithful and human-aligned token attributions than prior methods.
A multi-agent conversational system using AMA flowcharts achieves 95.29% top-3 retrieval accuracy and 99.10% navigation accuracy on large synthetic medical conversation datasets.
VOTE-RAG applies retrieval voting across diverse queries and response voting across independent generations to mitigate hallucination-on-hallucination in RAG, matching or exceeding complex baselines on six benchmarks with a parallelizable design.
A position paper argues that post-hoc XAI explanations are unfaithful and paradoxical, proposing a shift to expert-based verification and certification of AI systems.
A survey arguing that multi-agent AI systems remain task-centric and lack integrated computational models of human cognition, culture, values, and social cooperation.
citing papers explorer
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An Open-Source Benchmark and Baseline for Multi-temporal Referring Segmentation
Introduces MTRS task, MTRefSeg-21K benchmark of 21K image-text-mask triplets, and MTRefSeg-R1 LVLM baseline that outperforms standard models via two-stage change-aware training.
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Decision-Aware Attention Propagation for Vision Transformer Explainability
DAP improves ViT attribution maps by injecting decision-relevant gradients into attention propagation, producing more class-sensitive and faithful explanations than standard attention rollout.
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Hessian-Enhanced Token Attribution (HETA): Interpreting Autoregressive LLMs
HETA is a new attribution framework for decoder-only LLMs that combines semantic transition vectors, Hessian-based sensitivity scores, and KL divergence to produce more faithful and human-aligned token attributions than prior methods.
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Multi-agent Self-triage System with Medical Flowcharts
A multi-agent conversational system using AMA flowcharts achieves 95.29% top-3 retrieval accuracy and 99.10% navigation accuracy on large synthetic medical conversation datasets.
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Mitigating Hallucination on Hallucination in RAG via Ensemble Voting
VOTE-RAG applies retrieval voting across diverse queries and response voting across independent generations to mitigate hallucination-on-hallucination in RAG, matching or exceeding complex baselines on six benchmarks with a parallelizable design.
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Beyond Explainable AI (XAI): An Overdue Paradigm Shift and Post-XAI Research Directions
A position paper argues that post-hoc XAI explanations are unfaithful and paradoxical, proposing a shift to expert-based verification and certification of AI systems.
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Toward Human-Centered Multi-Agent Systems: Integrating Cognition, Culture, Values, and Cooperation in AI Agents
A survey arguing that multi-agent AI systems remain task-centric and lack integrated computational models of human cognition, culture, values, and social cooperation.