{"total":27,"rows":[{"id":"2502.01943","title":"DAMA: Data- and Model-aware Alignment of Multi-modal LLMs","created":"2025-02-04","cited_by_count":14,"area":"mllm"},{"id":"2405.17821","title":"RITUAL: Random Image Transformations as a Universal Anti-hallucination Lever in Large Vision Language Models","created":"2024-05-28","cited_by_count":8,"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":"2505.12343","title":"Mitigating Hallucinations via Inter-Layer Consistency Aggregation in Large Vision-Language Models","created":"2025-05-18","cited_by_count":7,"area":"mllm"},{"id":"2510.16596","title":"SHIELD: Suppressing Hallucinations In LVLM Encoders via Bias and Vulnerability Defense","created":"2025-10-18","cited_by_count":7,"area":"mllm"},{"id":"2509.18425","title":"Losing the Plot: How VLM responses degrade on imperfect charts","created":"2025-09-22","cited_by_count":5,"area":"mllm"},{"id":"2602.01047","title":"Residual Decoding: Mitigating Hallucinations in Large Vision-Language Models via History-Aware Residual Guidance","created":"2026-02-01","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":"2505.17812","title":"Seeing It or Not? Interpretable Vision-aware Latent Steering to Mitigate Object Hallucinations","created":"2025-05-23","cited_by_count":3,"area":"mllm"},{"id":"2510.22171","title":"HARMONY: Hidden Activation Representations and Model Output-Aware Uncertainty Estimation for Vision-Language Models","created":"2025-10-25","cited_by_count":3,"area":"mllm"},{"id":"2511.08917","title":"\"It's trained by non-disabled people\": Evaluating How Image Quality Affects Product Captioning with Vision-Language Models","created":"2025-11-12","cited_by_count":3,"area":"mllm"},{"id":"2604.15809","title":"Aligning What Vision-Language Models See and Perceive with Adaptive Information Flow","created":"2026-04-17","cited_by_count":3,"area":"mllm"},{"id":"2603.10703","title":"WalkGPT: Grounded Vision-Language Conversation with Depth-Aware Segmentation for Pedestrian Navigation","created":"2026-03-11","cited_by_count":2,"area":"mllm"},{"id":"2506.21509","title":"Curing Semantic Drift: A Dynamic Approach to Grounding Generation in Large Vision-Language Models","created":"2025-06-26","cited_by_count":1,"area":"mllm"},{"id":"2511.10292","title":"Adaptive Residual-Update Steering for Low-Overhead Hallucination Mitigation in Large Vision Language Models","created":"2025-11-13","cited_by_count":1,"area":"mllm"},{"id":"2602.09825","title":"SAKED: Mitigating Hallucination in Large Vision-Language Models via Stability-Aware Knowledge Enhanced Decoding","created":"2026-02-10","cited_by_count":1,"area":"mllm"},{"id":"2605.30911","title":"What Makes LVLMs Hallucinate Less? Unveiling the Architectural Factors Behind Hallucination Robustness","created":"2026-05-29","cited_by_count":1,"area":"mllm"},{"id":"2609.38810","title":"CRAFT: Causal Responsibility and Failure Tracing in Medical Vision Language Models","created":"2026-09-30","cited_by_count":1,"area":"mllm"},{"id":"2510.21794","title":"Token-Level Inference-Time Alignment for Vision-Language Models","created":"2025-10-20","cited_by_count":0,"area":"mllm"},{"id":"2604.12115","title":"HTDC: Hesitation-Triggered Differential Calibration for Mitigating Hallucination in Large Vision-Language Models","created":"2026-04-13","cited_by_count":0,"area":"mllm"},{"id":"2605.08145","title":"Self-Captioning Multimodal Interaction Tuning: Amplifying Exploitable Redundancies for Robust Vision Language Models","created":"2026-05-03","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":"2608.21819","title":"PatchGate: Narrowing the Verbalization Gap with Intrinsic Object Inventories in Frozen Vision-Language Models","created":"2026-08-22","cited_by_count":0,"area":"mllm"},{"id":"2608.30653","title":"Fine-Grained Multi Image Object Hallucination Benchmark","created":"2026-08-31","cited_by_count":0,"area":"mllm"},{"id":"2609.35294","title":"Beyond Saying Less: Fine-Grained Alignment for Informative and Faithful Vision-Language Models","created":"2026-09-28","cited_by_count":0,"area":"mllm"},{"id":"2609.38979","title":"Mitigating Object Hallucination in Large Vision-Language Models via False Discovery Controlled Visual Data Splitting","created":"2026-09-30","cited_by_count":0,"area":"mllm"},{"id":"2610.06056","title":"ROT: Rotating Hidden States towards Contextual Vectors for Hallucination Mitigation in LVLMs","created":"2026-10-05","cited_by_count":0,"area":"mllm"}],"next_cursor":null}