Paper

Liberating LLM Capabilities in Full-Duplex Speech Models

Unreadunread

AI summary

arXiv:2606. 07547v1 Announce Type: new Abstract: Speech-based large language models are typically constrained to spoken replies, which limits their user-facing outputs to what can be verbalized and suppresses text-native capabilities such as code generation, structured analysis, and multi-step reasoning in realtime interaction, for tasks that require persistent, structured, and inspectable intermediate outputs.

Reading status

Priority

About this paper

Authors: Luoyuan Zhang, Bokai Xu, Junbo Cui, Weiyue Sun, Yingjing Xu, Hanyu Liu, Yuan Yao
Released: 2026-06-09 (1mo ago)
Topic: Related to current project
Relation to my work: Read next because Liberating LLM Capabilities in Full-Duplex Speech Models 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: code, strong, text, class, under, eval, token, line. Source: arxiv cs.CL (NLP).
arXiv: 2606.07547

Ask Claude about this paper

Posts a comment with @claude so the runner spins up a Claude Code session scoped to Liberating LLM Capabilities in Full-Duplex Speech Models. Replies appear in the conversation below.

Comments

No comments yet.

Related records

No links yet.