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Jian-Feng Xu

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Preprint Sep 2026

Attention-Scoped Guidance: Training-Free Spatial Control for Image Editing

Instruction-guided image editing should change what the instruction names and leave the rest of the image untouched. In dual classifier-free guidance (CFG), an editor combines two directions at every denoising step, one that pushes toward the instructed edit and one that pulls back toward the source image, using global...

Ze-Yan Li, Wei Zhou, Hadi Amirpour et al. · 0 citations
Preprint Sep 2026

CPUNeSy: Controlling Model Writes for Reliable Neuro-Symbolic Reasoning

LLMs excel at recalling statistical patterns but degrade sharply when answers must be derived, especially on multi-hop chains. Delegating derivation to deterministic symbolic executors shifts reliability to whether model-generated premises are source-supported. We introduce CPUNeSy, a serving architecture that controls...

Ze-Yan Li, Si-Yuan Qiu, Shuai Zhao et al. · 0 citations
#artificial intelligence Preprint Sep 2026

SemRD-V2X: Closure-Guided Communication with Bounded Inference for Cooperative Perception

Vehicle-to-Everything (V2X) cooperative perception improves 3-D detection by sharing intermediate features, but dense remote features may repeat context that the ego agent can infer locally. Most communication-efficient designs optimize masks or codes empirically, leaving a more basic question open: which remote eviden...

Hu Xu, Chun Li, Si-Yuan Qiu et al. · 0 citations
#machine learning Preprint Sep 2026

BridgeMem: Causal Dyadic Transition Residuals for Temporal Knowledge Graph Forecasting

Temporal knowledge graph forecasting aims to infer future relational facts from the temporal structure of observed events. Existing forecasters mainly summarize history through entity states, relation states, paths, or exact recurrence. These views often miss pair-specific transition evidence, that is, the way prior re...

Zeyan Li, Li-Bing Chen, Sheng-Da Zhuo et al. · 0 citations
#artificial intelligence Preprint Sep 2026

Reasoning Instructions Can Break Answer Decoding in Vision--Language Models

Chain-of-thought (CoT) instructions can distort multiple-choice VLM evaluation when a scorer appends a reasoning cue but reads answer-label logits before the model generates any rationale. We call this CoT-prefix scoring. On ScienceQA, Qwen2.5-VL-7B drops from 80.76% to 45.48%, and across five option-content permutatio...

Zeyan Li, Si-Yuan Qiu, Jian-Feng Xu · 0 citations

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