{"total":27,"rows":[{"id":"2511.14159","title":"MVI-Bench: A Comprehensive Benchmark for Evaluating Robustness to Misleading Visual Inputs in LVLMs","created":"2025-11-18","cited_by_count":15,"area":"mllm"},{"id":"2509.21789","title":"Visual Multi-Agent System: Mitigating Hallucination Snowballing via Visual Flow","created":"2025-09-26","cited_by_count":13,"area":"mllm"},{"id":"2507.09184","title":"MCA-LLaVA: Manhattan Causal Attention for Reducing Hallucination in Large Vision-Language Models","created":"2025-07-12","cited_by_count":12,"area":"mllm"},{"id":"2601.20279","title":"Hallucination Begins Where Saliency Drops","created":"2026-01-28","cited_by_count":9,"area":"mllm"},{"id":"2601.05159","title":"Vision-Language Introspection: Mitigating Overconfident Hallucinations in MLLMs via Interpretable Bi-Causal Steering","created":"2026-01-08","cited_by_count":8,"area":"mllm"},{"id":"2601.20520","title":"Context Tokens are Anchors: Understanding the Repetition Curse in dMLLMs from an Information Flow Perspective","created":"2026-01-28","cited_by_count":6,"area":"mllm"},{"id":"2602.04268","title":"KVSmooth: Mitigating Hallucination in Multi-modal Large Language Models through Key-Value Smoothing","created":"2026-02-04","cited_by_count":5,"area":"mllm"},{"id":"2509.23379","title":"CCD: Mitigating Hallucinations in Radiology MLLMs via Clinical Contrastive Decoding","created":"2025-09-27","cited_by_count":4,"area":"mllm"},{"id":"2601.00659","title":"CRoPS: A Training-Free Hallucination Mitigation Framework for Vision-Language Models","created":"2026-01-02","cited_by_count":4,"area":"mllm"},{"id":"2604.12424","title":"Decoding by Perturbation: Mitigating MLLM Hallucinations via Dynamic Textual Perturbation","created":"2026-04-14","cited_by_count":3,"area":"mllm"},{"id":"2506.15649","title":"Dual-Stage Value-Guided Inference with Margin-Based Reward Adjustment for Fast and Faithful VLM Captioning","created":"2025-06-18","cited_by_count":2,"area":"mllm"},{"id":"2601.03100","title":"TGIF: Text-Guided Layer Fusion Mitigates Hallucination in Multimodal LLMs","created":"2026-01-06","cited_by_count":2,"area":"mllm"},{"id":"2603.27898","title":"SAGE: Sink-Aware Grounded Decoding for Multimodal Hallucination Mitigation","created":"2026-03-29","cited_by_count":2,"area":"mllm"},{"id":"2605.24024","title":"Mitigating Hallucinations in Large Vision-Language Models via Causal Route Gating","created":"2026-05-20","cited_by_count":2,"area":"mllm"},{"id":"2606.31054","title":"ADAPT: Attention Dynamics Alignment with Preference Tuning for Faithful MLLMs","created":"2026-06-30","cited_by_count":2,"area":"mllm"},{"id":"2603.24058","title":"Mitigating Object Hallucinations in LVLMs via Attention Imbalance Rectification","created":"2026-03-25","cited_by_count":1,"area":"mllm"},{"id":"2605.18359","title":"RAVE: Re-Allocating Visual Attention in Large Multimodal Models","created":"2026-05-18","cited_by_count":1,"area":"mllm"},{"id":"2606.07647","title":"Steer Where It Matters: Token-Level Visual-Sensitivity Steering for LVLMs Hallucination Mitigation","created":"2026-06-02","cited_by_count":1,"area":"mllm"},{"id":"2606.29847","title":"See Only When Needed: Context-Aware Attention Intervention for Mitigating Hallucinations in LVLMs","created":"2026-06-29","cited_by_count":1,"area":"mllm"},{"id":"2604.09749","title":"See Fair, Speak Truth: Equitable Attention Improves Grounding and Reduces Hallucination in Vision-Language Alignment","created":"2026-04-10","cited_by_count":0,"area":"mllm"},{"id":"2605.24957","title":"Mitigating Object Hallucinations in Vision-Language Models through Region-Aware Attention Recalibration","created":"2026-05-24","cited_by_count":0,"area":"mllm"},{"id":"2607.03738","title":"Attending to Multimodal Generation One Token at a Time","created":"2026-07-04","cited_by_count":0,"area":"mllm"},{"id":"2607.17994","title":"HAS: Highlight-guided Attention Steering for Multimodal LLM Video Summarization","created":"2026-07-20","cited_by_count":0,"area":"mllm"},{"id":"2608.29092","title":"EviAnchor: Mitigating Hallucinations in Large Vision-Language Models via Regional Visual Evidence Compensation","created":"2026-08-29","cited_by_count":0,"area":"mllm"},{"id":"2609.09206","title":"MLLMs Hallucinate when Information Distribution Drifts in Synergy Heads","created":"2026-09-05","cited_by_count":0,"area":"mllm"},{"id":"2609.35536","title":"Look Before You Judge: Training-Free Region Mining for Grounded and Explainable Deepfake Detection","created":"2026-09-28","cited_by_count":0,"area":"mllm"},{"id":"2610.13154","title":"From Glance to Focus: Mitigating Recognition Laziness in MLLMs for Fine-Grained Recognition","created":"2026-10-09","cited_by_count":0,"area":"mllm"}],"next_cursor":null}