arXiv:2608. 11066v1 Announce Type: cross Abstract: We prove inference-time quantum coordination advantages for specified AI state-tracking tasks.
By Ming Yang
The paper proposes a fixed‑width recurrent feedback scheme for Recurrent Tsetlin Machines (RTMs) by folding clause activations with XOR, retaining the folded bits at two time scales, and thresholding them back to binary. This compression reduces 480 clause activations to 96 recurrent bits while achieving comparable accuracy (61.47 ± 6.74% and 62.94 ± 9.92%) on a reproducible Boolean finite‑state‑machine benchmark across 144 runs. The study shows that raw clause feedback offers only marginal accuracy gains but increases recurrent width and execution time significantly, and highlights the importance of no‑feedback controls in sequence model benchmarking.
By Ankit Kumar, Utkarsh Raj, Rishad Shafik, Sudip Roy
arXiv:2606. 19697v1 Announce Type: cross Abstract: The increasing popularity of \emph{reasoning} models -- language models that output a series of reasoning or thought tokens before producing an answer -- is justified, in part, by theoretical results showing that chain-of-thought (CoT) transformers can simulate Turing machines, and thus perform arbitrary computation.
By Yanhong Li, Anej Svete, Ashish Sabharwal, William Merrill
The paper introduces the Language Model Agent Machine (LAM), a framework that quantifies the computational resources consumed by language‑model agents’ harnesses—such as bounded context, memory, tools, and verification. It presents four main theoretical results: (1) communication costs mapped to pebbling lower bounds, (2) memory‑access separations revealing a linear gap between random and non‑speculative access, (3) recomputation bounds for bit‑reversal DAGs that show when storing intermediate state is beneficial, and (4) reliability bounds that yield optimal verification intervals. Experiments on GPT‑6 Astra validate the theory’s predictions for communication, reliability, and trade‑offs in practical tasks.
By Zelin Zhao (Georgia Institute of Technology), Xinyu Guo (Georgia Institute of Technology), Jingyuan Zhang (Georgia Institute of Technology), Yuxuan Zhang (Etude AI), Yongxin Chen (Georgia Institute of Technology)
arXiv:2607. 21000v1 Announce Type: new Abstract: Long-sequence memory tracking places two opposing demands on a recurrent state: near-lossless retention of stored bindings over long horizons, and active overwriting of stale ones.
By Hyuk Lim, Seunghyun Yoon
Long-sequence memory tracking places two opposing demands on a recurrent state: near-lossless retention of stored bindings over long horizons, and active overwriting of stale ones. In our diagnostic suite, the strongest efficient baselines tend to solve only one side well.