Neural representations for videos (NeRV) have shown strong reconstruction fidelity by storing video-specific information in network weights. However, existing formulations typically require either costly per-video optimization or video-specific weight generation, making it difficult to scale to efficient amortized video representation. We propose CoANeRV, a coordinate-aware token-space framework that adapts the broader token-conditioned neural-field paradigm to amortized video representation. CoANeRV forms compact video tokens in one feed-forward pass and uses a shared coordinate-conditioned decoder to reconstruct continuous spatio-temporal queries, avoiding per-video decoder optimization or generation while retaining coordinate-level reconstruction flexibility. To make token-space reconstruction effective, CoANeRV introduces a coordinate-aware decoding architecture that aligns spatio-temporal queries with video tokens through axis-adaptive positional encoding and temperature-modulated cross-attention. Block-wise coordinate querying further reduces peak attention memory, making high-resolution reconstruction practical. Experiments on diverse video datasets show that CoANeRV consistently improves reconstruction quality over prior feed-forward NeRV and INR baselines, reduces peak memory compared with attention-based coordinate decoders, and provides efficient amortized encoding without per-video optimization. These results support the proposed video-specific combination of feed-forward token formation, spatio-temporal coordinate retrieval, and memory-bounded dense querying. The code is available at https://github.com/jialong2023/CoANeRV.
Jialong Guo, Ke Liu, Mengxuan Li et al.· 0 citations
This paper explores advancements in automated Question-Answer (QA) extraction using large language models (LLMs), addressing challenges in transforming unstructured text into high-quality, retrievable QA pairs. Traditional approaches, whether through segmented question and answer generation or end-to-end extraction, often struggle with efficiency, dataset limitations, and performance consistency. Leveraging recent progress in LLMs, we constructed a large-scale Chinese QA extraction dataset with 143,846 documents and evaluated multiple fine-tuned models on public and private datasets. Surprisingly, code-based English LLMs outperformed Chinese-specialized models on Chinese text with a lower hallucination rate. Building upon this finding, we enhanced the best-performing code-based model with an expanded Chinese vocabulary, creating Code Llama-M, which achieved better results. Integrating Code Llama-M into our internal assistant, Luo Ying, demonstrated notable user satisfaction gains, affirming its practical impact. Key contributions include: (i) creation of a robust Chinese QA extraction instruction dataset; (ii) evidence of cross-lingual efficacy of code-based LLMs for Chinese QA tasks, further enhanced through Code Llama-M's expanded Chinese vocabulary; and (iii) successful application of the fine-tuned LLM in a live assistant system, enhancing user experience.
Jiajun Yu, Linghan Zheng, Hui Liu et al.· Annual International ACM SIG...· 0 citations
CAER introduces a span-grounded evidence router that transforms claim representations into soft textual queries and retrieves corresponding evidence from frozen visual tokens, enabling fine-grained conflict estimation and design a dual-prefix expert routing mechanism that learns separate experts for visually supported and contradicted inputs, enabling conflict-aware generation through explicit expert selection.
Zixuan Liu, Juntao Cai, Xiaoxu Cai et al.· 0 citations
This paper proposes UniEdit, a Unified Graph-based Mixture-of-Experts (MoE) Molecular Editing model that offers a robust alternative to LLMs and incorporates a Mixture-of-Experts architecture that dynamically routes tasks to specialized components.
Jiajun Yu, Zhihao Wu, Yizhen Zheng et al.· Proceedings of the 32nd ACM...· 0 citations