Xingyu Zhu朱星宇

Chunked KV-cache compression reduces the memory and attention costs of long-context inference by compressing windows of consecutive tokens into fewer cache entries at a fixed stride. Such compression also introduces a new positional coordinate: a token's phase, or its position relative to compression-window boundaries. We uncover a systematic asymmetry in models using such compression: the same information can be easy to retrieve at one phase and difficult at another. We call this periodic variation in retrieval performance phase sensitivity. In large open-weight models with such compression, long-context retrieval accuracy can differ by up to 40 percentage points across phases, revealing periodic weak spots that average benchmark scores can conceal. To investigate this behavior, we pretrain a family of transformers from scratch across multiple KV-compression designs, reproducing phase sensitivity across the variants. Mechanistic analysis using causal interventions in these models reveals phase specialization: different attention components contribute asymmetrically to retrieving information at different source phases. We further analyze idealized retrieval models, showing how gradient flow dynamics may favor sharp phase specialization. Evaluating models with chunked KV-cache compression thus requires measuring across compression phases: high average accuracy can coexist with systematic positional failures.

@article{zhu2026periodic,
  title   = {Periodic Weak Spots: Phase Sensitivity from Chunked KV-Cache Compression},
  author  = {Zhu, Xingyu and Yi, Pu and Cheng, Ziheng and Lv, Ang and Liu, Jing and Ying, Lexing and Ma, Yiyuan and Dong, Xin},
  journal = {arXiv preprint arXiv:2609.36322},
  year    = {2026},
  url     = {https://arxiv.org/abs/2609.36322}
}

We introduce Transformers with Temporal Middle-Layer Recurrence (T²MLR), a generalized Transformer architecture that integrates attention and recurrence by routing a lightweight temporal pathway through the middle layers. Motivated by latent-reasoning and looped-Transformer lines of work, T²MLR injects intermediate representations from deeper layers of the previous token into earlier layers of the current token via a gated recurrent pathway, enabling iterative latent computation while preserving dense, token-level supervision. Across natural-language pretraining and multi-hop reasoning finetuning, T²MLR consistently outperforms parameter-matched Transformer baselines at the same inference compute. Moreover, we find that looping only a middle-layer block (as little as 20% of all layers) often outperforms full-layer looping. This offers a new perspective on latent reasoning in Transformers - effective iterative refinement does not necessarily require full-stack recurrence. It can instead be achieved more effectively through targeted middle-layer recurrence.

@inproceedings{cai_t2mlr_2026,
  author    = {Cai, Ziyang and Zhu, Xingyu and Dong, Yihe and He, Yinghui and Arora, Sanjeev},
  booktitle = {LIT Workshop @ ICLR 2026},
  title     = {T$^2$MLR: Transformer with Temporal Middle-Layer Recurrence},
  url       = {https://openreview.net/forum?id=fQbk1EQWBO},
  year      = {2026}
}

Central to many self-improvement pipelines for large language models (LLMs) is the assumption that models can improve by reflecting on past mistakes. We study a phenomenon termed contextual drag - the presence of failed attempts in the context biases subsequent generations toward structurally similar errors. Across evaluations of 11 proprietary and open-weight models on 8 reasoning tasks, contextual drag induces 10-20% performance drops, and iterative self-refinement in models with severe contextual drag can collapse into self-deterioration. Structural analysis using tree edit distance reveals that subsequent reasoning trajectories inherit structurally similar error patterns from the context. We demonstrate that neither external feedback nor successful self-verification suffices to eliminate this effect. While mitigation strategies such as fallback-behavior fine-tuning and context denoising yield partial improvements, they fail to fully restore baseline performance, positioning contextual drag as a persistent failure mode in current reasoning architectures.

@misc{cheng_contextual_drag_2026,
  author    = {Cheng, Yun and Zhu, Xingyu and Zhao, Haoyu and Arora, Sanjeev},
  doi       = {10.48550/arXiv.2602.04288},
  month     = {February},
  publisher = {arXiv},
  title     = {Contextual Drag: How Errors in the Context Affect LLM Reasoning},
  url       = {https://arxiv.org/abs/2602.04288},
  year      = {2026}
}

We formalize a new concept for LLMs, context-enhanced learning. It involves standard gradient-based learning on text except that the context is enhanced with additional data on which no auto-regressive gradients are computed. This setting is a gradient-based analog of usual in-context learning (ICL) and appears in some recent works. Using a multi-step reasoning task, we prove in a simplified setting that context-enhanced learning can be exponentially more sample-efficient than standard learning when the model is capable of ICL. At a mechanistic level, we find that the benefit of context-enhancement arises from a more accurate gradient learning signal. We also experimentally demonstrate that it appears hard to detect or recover learning materials that were used in the context during training. This may have implications for data security as well as copyright.

@inproceedings{zhu2025on,
  title     = {On the Power of Context-Enhanced Learning in {LLM}s},
  author    = {Xingyu Zhu and Abhishek Panigrahi and Sanjeev Arora},
  booktitle = {Forty-second International Conference on Machine Learning},
  year      = {2025},
  url       = {https://openreview.net/forum?id=Gn6L4QRKf7}
}

Recently, researchers observed that gradient descent for deep neural networks operates in an “edge-of-stability” (EoS) regime: the sharpness (maximum eigenvalue of the Hessian) is often larger than stability threshold $2/\eta$ (where $\eta$ is the step size). Despite this, the loss oscillates and converges in the long run, and the sharpness at the end is just slightly below $2/\eta$. While many other well-understood nonconvex objectives such as matrix factorization or two-layer networks can also converge despite large sharpness, there is often a larger gap between sharpness of the endpoint and $2/\eta$. In this paper, we study EoS phenomenon by constructing a simple function that has the same behavior. We give rigorous analysis for its training dynamics in a large local region and explain why the final converging point has sharpness close to $2/\eta$. Globally we observe that the training dynamics for our example has an interesting bifurcating behavior, which was also observed in the training of neural nets.

@misc{zhu_understanding_2023,
  author    = {Zhu, Xingyu and Wang, Zixuan and Wang, Xiang and Zhou, Mo and Ge, Rong},
  month     = {February},
  publisher = {arXiv},
  title     = {Understanding Edge-of-Stability Training Dynamics with a Minimalist Example},
  url       = {http://arxiv.org/abs/2210.03294},
  year      = {2023}
}

In the assignment problem, a set of items must be allocated to unit-demand agents who express ordinal preferences (rankings) over the items. In the assignment problem with priorities, agents with higher priority are entitled to their preferred goods with respect to lower priority agents. A priority can be naturally represented as a ranking and an uncertain priority as a distribution over rankings. For example, this models the problem of assigning student applicants to university seats or job applicants to job openings when the admitting body is uncertain about the true priority over applicants. This uncertainty can express the possibility of bias in the generation of the priority ranking. We believe we are the first to explicitly formulate and study the assignment problem with uncertain priorities. We introduce two natural notions of fairness in this problem: stochastic envy-freeness (SEF) and likelihood envy-freeness (LEF). We show that SEF and LEF are incompatible and that LEF is incompatible with ordinal efficiency. We describe two algorithms, Cycle Elimination (CE) and Unit-Time Eating (UTE) that satisfy ordinal efficiency (a form of ex-ante Pareto optimality) and SEF; the well known random serial dictatorship algorithm satisfies LEF and the weaker efficiency guarantee of ex-post Pareto optimality. We also show that CE satisfies a relaxation of LEF that we term 1-LEF which applies only to certain comparisons of priority, while UTE satisfies a version of proportional allocations with ranks. We conclude by demonstrating how a mediator can model a problem of school admission in the face of bias as an assignment problem with uncertain priority.

@misc{shen_fairness_2023,
  author    = {Shen, Zeyu and Wang, Zhiyi and Zhu, Xingyu and Fain, Brandon and Munagala, Kamesh},
  month     = {January},
  publisher = {arXiv},
  title     = {Fairness in the Assignment Problem with Uncertain Priorities},
  url       = {http://arxiv.org/abs/2301.13804},
  year      = {2023}
}

Hessian captures important properties of the deep neural network loss landscape. Previous works have observed low rank structure in the Hessians of neural networks. In this paper, we propose a decoupling conjecture that decomposes the layer-wise Hessians of a network as the Kronecker product of two smaller matrices. We can analyze the properties of these smaller matrices and prove the structure of top eigenspace random 2-layer networks. The decoupling conjecture has several other interesting implications - top eigenspaces for different models have surprisingly high overlap, and top eigenvectors form low rank matrices when they are reshaped into the same shape as the corresponding weight matrix. All of these can be verified empirically for deeper networks. Finally, we use the structure of layer-wise Hessian to get better explicit generalization bounds for neural networks.

@misc{wu_dissecting_2022,
  author    = {Wu, Yikai and Zhu, Xingyu and Wu, Chenwei and Wang, Annie and Ge, Rong},
  month     = {October},
  publisher = {arXiv},
  title     = {Dissecting Hessian: Understanding Common Structure of Hessian in Neural Networks},
  url       = {http://arxiv.org/abs/2010.04261},
  year      = {2022}
}