Paper

FiLM-Coordinated Dual-Branch Transformer for Global-Local Dependency Modeling in Language Modeling

Unreadunread

AI summary

arXiv:2606. 21075v1 Announce Type: new Abstract: Standard Transformers use a single self-attention pathway to model both global dependencies and local patterns, creating tension between long-range structural reasoning and fine-grained local representation learning.

Reading status

Priority

About this paper

Authors: Zhiqiang Zhou, Xu Ling, Junliang Dai
Released: 2026-06-23 (16d ago)
Topic: Related to current project
Relation to my work: Read next because FiLM-Coordinated Dual-Branch Transformer for Global-Local Dependency Modeling in Language Modeling overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: text, rect, under, width, token, line, rate, full. Source: arxiv cs.CL (NLP).
arXiv: 2606.21075

Ask Claude about this paper

Posts a comment with @claude so the runner spins up a Claude Code session scoped to FiLM-Coordinated Dual-Branch Transformer for Global-Local Dependency Modeling in Language Modeling. Replies appear in the conversation below.

Comments

No comments yet.

Related records

No links yet.