Existing long-video agents acquire evidence through one uniform behavior, ignoring whether the required evidence is concentrated, requires broad occurrence coverage, or must discriminate competing hypotheses---which can cause failure before substantive reasoning begins. Prescribing a fine-grained solution procedure for...
Can-Can Zhang, Bao-Feng Zhang, Xiao-Tian Han et al.· 0 citations
Video language models process videos as dense visual-token sequences with substantial representational redundancy. Compressing these sequences is therefore essential for reducing the visual-token burden on language-model decoding. The central challenge is to preserve visual information dispersed across frames under suc...
Wen-Ti Yin, Xiao-Tian Han, Jun-Yuan Shang et al.· 0 citations
Long-context LLM inference is bottlenecked by quadratic attention computation and growing KV-cache costs. Existing sparse attention and KV-compression methods typically decide which tokens or heads to preserve from runtime attention scores, observation windows, calibration prompts, or learned gates, making head diagnos...
YeHan Yang, Jun-Yuan Shang, Yang Li et al.· 0 citations
This work presents a principled analysis of the distributions induced by lossy verification methods, and shows that many seemingly distinct approaches differ only superficially and can be unified into two categories: truncation-based verification and collaborative verification.
Tian-Yu Wang, Yuxuan Zhou, Heng Li et al.· arXiv.org· 0 citations
Memory-Augmented Compression is proposed, a training-free framework that constructs reusable reasoning memories from historical traces and retrieves them as prefill-side scaffolds to compensate for information lost during compression.
Si-Meng Zhang, Yi-Long Chen, Wenyuan Zhang et al.· 0 citations
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