arXiv AI
Sep 3

Looped Transformers under the Jacobian Lens: Does the Global Workspace Survive Recurrence?

The paper investigates whether a global workspace—a set of verbalisable, causally potent representations—emerges in transformer models that use recurrence instead of a stack of distinct layers. Using a Jacobian lens extended with a virtual‑unrolling adapter, the authors analyze two recurrent transformer architectures, Ouro‑2.6B and Huginn‑0125, and compare them to a standard Qwen3.6‑27B baseline. They find that a workspace does form in the iterated parts of both models, but recurrence alters how it can be accessed: Ouro reconstructs workspace content in every loop and requires writes and ablations across all loops, whereas Huginn forwards content across all recurrences but limits reads, writes, and ablations to a sliding window of about two recurrences.

By Wenlong Wang, Fergal Reid
arXiv AI
Sep 28

Compress What You See, Not What You Say: Anchored Context Distillation for Latent-Observation Software Engineering Agents

The paper introduces LOHA, a context layout that compresses older tool observations into soft tokens while keeping the agent’s own turns and the last K observations in plain text, and ACD, a training method that distills full‑text predictions into this latent representation while anchoring behavior on plain text. This approach reduces context per call by up to 57% without significant loss in resolve rates, and improves instance throughput in single‑GPU serving. Experiments on SWE‑bench Verified show that K=3 yields a 43–57% compression with only modest performance impact, while larger windows favor task performance over compression.

By Zhensheng Zou (Peking University), Guoqing Wang (Peking University), Dan Hao (Peking University)
arXiv AI
Oct 12

Orthros: Phase-Aware Heterogeneous Attention for Efficient Transformers

Orthros introduces a phase‑aware heterogeneous attention mechanism that dynamically switches between linear‑complexity prefilling and full‑attention decoding, reducing the asymptotic cost compared to standard Transformers in long‑context scenarios. The method supports efficient Transformer‑to‑Orthros conversion, achieving higher performance than the original model and delivering a 21.9× prefilling speedup while maintaining strong in‑context recall, including 128K‑scale needle‑in‑a‑haystack tasks. These results demonstrate Orthros’s potential as a more efficient and flexible alternative for industrial Transformer applications.

By Yuhong Chou, Zehao Liu, Yuqi Pan, Qian Liu, Xianwei Chen, Zhongzhu Zhou, Jibin Wu