arXiv AI June 2, 2026 By Shiguang Wu, Yaqing Wang, Quanming Yao

Language Model Networks: Supervision-Efficient Learning through Dense Communication

arXiv:2505. 12741v3 Announce Type: replace Abstract: Language models are increasingly used not only as standalone predictors but also as components in larger inference systems, from test-time scaling to multi-agent collaboration.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Jun 10

PromptEmbedder: Efficient and Transferable Text Embedding via Dual-LLM Soft Prompting

arXiv:2605. 28066v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have demonstrated remarkable efficacy in text embedding, yet current adaptation methods like LoRA face significant bottlenecks in computational efficiency and cross-architecture transferability.

By Yu-Che Tsai, Kuan-Yu Chen, Yuan-Hao Chen, Yu-Han Chang, Ching-Yu Tsai, Yu-Hsiang Chuang, Shou-De Lin

llms rag fine-tuning benchmarks safety

arXiv AI

Jun 24

Representation Interventions Enable Lifelong Knowledge Memory Control in LLMs

arXiv:2511. 20892v4 Announce Type: replace Abstract: Large language models (LLMs) often produce incorrect or outdated content after being employed.

By Xuyuan Liu, Shengyu Chen, Xinshuai Dong, Yanchi Liu, Xujiang Zhao, Haoyu Wang, Yujun Yan, Haifeng Chen, Zhengzhang Chen

llms benchmarks

arXiv AI

Jun 12

HarnessBridge: Learnable Bidirectional Controller for LLM Agent Harness

arXiv:2606. 12882v1 Announce Type: new Abstract: Large language models are increasingly deployed as agents for long-horizon tasks, yet their performance is shaped not only by model capability and environment design, but also by the harness that mediates agent--environment interaction.

By Xiaoxuan Wang, Haixin Wang, Alexander Taylor, Jason Cong, Yizhou Sun, Wei Wang

llms agents fine-tuning

arXiv AI

Jun 2

Latent Collaboration in Multi-Agent Systems

arXiv:2511. 20639v3 Announce Type: replace-cross Abstract: Multi-agent systems (MAS) extend large language models (LLMs) from independent single-model reasoning to coordinative system-level intelligence.

By Jiaru Zou, Ruizhong Qiu, Gaotang Li, Xiyuan Yang, Katherine Tieu, Pan Lu, Ke Shen, Hanghang Tong, Yejin Choi, Jingrui He, James Zou, Mengdi Wang, Ling Yang

llms rag agents diffusion benchmarks

arXiv Machine Learning

Sep 14

Dead Weights, Live Signals: Feedforward Graphs of Frozen Language Models

The paper introduces a feedforward graph architecture that uses several frozen large language models as computational nodes connected through a shared continuous latent space via learned linear projections. By jointly optimizing projection matrices through backpropagation, the system combines the representations of three small frozen models with two larger ones, culminating in a lightweight cross‑attention output node. With only 17.6 M trainable parameters, the architecture attains state‑of‑the‑art results on ARC‑Challenge, OpenBookQA, and MMLU, surpassing both individual constituent models and parameter‑matched learned classifiers.

By Marcus Armstrong, Navid Ayoobi, Arjun Mukherjee

llms diffusion benchmarks

arXiv Machine Learning

Jul 2

Prototype Language Models

arXiv:2607. 00510v1 Announce Type: new Abstract: Knowing which training examples drive outputs is fundamental to auditing, correcting, and understanding language models, yet for modern LLMs this remains expensive, approximate, and largely post-hoc.

By Dan Ley, Giang Nguyen, Himabindu Lakkaraju, Julius Adebayo

llms fine-tuning

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Representation Interventions Enable Lifelong Knowledge Memory Control in LLMs

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Latent Collaboration in Multi-Agent Systems

Dead Weights, Live Signals: Feedforward Graphs of Frozen Language Models

Prototype Language Models