9 papers
When Does Retrieval Help? A Study of In-Context Adaptation in Vision-Language-Action Models
Zixuan Liu, Joris Köster, Zizhan Zheng +1
Vision-language-action (VLA) models have shown strong potential as generalist robot policies, but adapting them to unseen tasks often requires costly parameter updates. Recent work…
Distilling Vision-Language Models for On-Device Fire Understanding
Mohammad Kazzazi, Zixuan Liu, Siavash Khajavi
Vision-language models (VLMs) offer a promising alternative to conventional fire detection systems by reasoning about the semantic context of a scene and thus reducing false alarms…
LycheeMemory V2: Efficient Long-Term Memory for LLM Agents via Semantic Segment-Level Consolidation
Dongfang Li, Zixuan Liu, Junmai Wang +6
Long-horizon LLM agents must preserve information from past interactions to support future tasks. Existing memory systems typically rely on eager consolidation, invoking LLMs after…
CAER: Conflict-Aware Evidence Routing with Dual Prefix Experts for Multimodal Large Language Models
Zixuan Liu, Juntao Cai, Xiaoxu Cai +2
Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities in multimodal understanding and generation. However, when textual inputs conflict with visual evi…
LycheeCluster: Efficient Long-Context Inference with Structure-Aware Chunking and Hierarchical KV Indexing
Dongfang Li, Zixuan Liu, Gang Lin +2
The quadratic complexity of the attention mechanism and the substantial memory footprint of the Key-Value (KV) cache present severe computational and memory challenges for Large La…
MemBoost: A Memory-Boosted Framework for Cost-Aware LLM Inference
Joris Köster, Zixuan Liu, Siavash Khajavi +1
Large Language Models (LLMs) deliver strong performance but incur high inference cost in real-world services, especially under workloads with repeated or near-duplicate queries acr…
Targeting Misalignment: A Conflict-Aware Framework for Reward-Model-based LLM Alignment
Zixuan Liu, Siavash H. Khajavi, Guangkai Jiang +1
Reward-model-based fine-tuning is a central paradigm in aligning Large Language Models with human preferences. However, such approaches critically rely on the assumption that proxy…
DetectiumFire: A Comprehensive Multi-modal Dataset Bridging Vision and Language for Fire Understanding
Zixuan Liu, Siavash H. Khajavi, Guangkai Jiang
Recent advances in multi-modal models have demonstrated strong performance in tasks such as image generation and reasoning. However, applying these models to the fire domain remain…
Synthetic imagery for fuzzy object detection: A comparative study
Siavash H. Khajavi, Mehdi Moshtaghi, Dikai Yu +3
The fuzzy object detection is a challenging field of research in computer vision (CV). Distinguishing between fuzzy and non-fuzzy object detection in CV is important. Fuzzy objects…