arXiv subjects

Fanqing Zhou

Publications and source records attributed to Fanqing Zhou.

3 recordsLinked to original sources

arXiv2026

STAR: Mitigating Cascading Errors in Spatial Reasoning via Turn-point Alignment and Segment-level DPO

Structured spatial navigation is a core benchmark for Large Language Models (LLMs) spatial reasoning. Existing paradigms like Visualization-of-Thought (VoT) are prone to cascading errors in complex topologies. To solve this, we propose STAR, a two-stage framework grounded on topological anchors, and introduce the RedMaze-23K dataset with human-inspired turnpoint annotations. The first stage uses supervised fine-tuning to help models internalize spatial semantics and prune redundant paths. The second adopts Spatial-aware Segment-level Direct Preference Optimization (SDPO) to refine self-correction in long-horizon navigation. Experiments show STAR achieves state-of-the-art performance among open-source models: its 32B variant outperforms DeepSeek-V3 (29.27% vs. 25.00%) and reaches 82.4% of GPT-4's performance.

cs.CV↗

arXiv2025

EvoEmpirBench: Dynamic Spatial Reasoning with Agent-ExpVer

Most existing spatial reasoning benchmarks focus on static or globally observable environments, failing to capture the challenges of long-horizon reasoning and memory utilization under partial observability and dynamic changes. We introduce two dynamic spatial benchmarks, locally observable maze navigation and match-2 elimination that systematically evaluate models' abilities in spatial understanding and adaptive planning when local perception, environment feedback, and global objectives are tightly coupled. Each action triggers structural changes in the environment, requiring continuous update of cognition and strategy. We further propose a subjective experience-based memory mechanism for cross-task experience transfer and validation. Experiments show that our benchmarks reveal key limitations of mainstream models in dynamic spatial reasoning and long-term memory, providing a comprehensive platform for future methodological advances. Our code and data are available at https://anonymous.4open.science/r/EvoEmpirBench-143C/.

cs.CV↗

arXiv2025

Memory-Anchored Multimodal Reasoning for Explainable Video Forensics

We address multimodal deepfake detection requiring both robustness and interpretability by proposing FakeHunter, a unified framework that combines memory guided retrieval, a structured Observation-Thought-Action reasoning loop, and adaptive forensic tool invocation. Visual representations from a Contrastive Language-Image Pretraining (CLIP) model and audio representations from a Contrastive Language-Audio Pretraining (CLAP) model retrieve semantically aligned authentic exemplars from a large scale memory, providing contextual anchors that guide iterative localization and explanation of suspected manipulations. Under low internal confidence the framework selectively triggers fine grained analyses such as spatial region zoom and mel spectrogram inspection to gather discriminative evidence instead of relying on opaque marginal scores. We also release X-AVFake, a comprehensive audio visual forgery benchmark with fine grained annotations of manipulation type, affected region or entity, reasoning category, and explanatory justification, designed to stress contextual grounding and explanation fidelity. Extensive experiments show that FakeHunter surpasses strong multimodal baselines, and ablation studies confirm that both contextual retrieval and selective tool activation are indispensable for improved robustness and explanatory precision.

cs.MM↗