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版本更新 · v2arXiv:2610.23966

自适应人工智能实现可泛化的胎儿脑生物测量

Generalizable fetal brain biometry via adaptive artificial intelligence

Yijin Li, Mingxuan Liu, Yi Liao, Le Chang, Hongjia Yang, Kasidit Anmahapong, Xiaotian Hu, Ziang Wang 等 27 位作者

AI 速读 基于摘要

提出解剖引导的测试时自适应框架 BioTTA，从三维胎儿脑 MRI 自动测量 11 项生物指标，并在多中心异质数据上提升测量精度与异常检测能力。

cs.CV

arXiv ↗PDF ↗
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Fetal brain biometry provides a quantitative basis for assessing prenatal growth and identifying congenital brain abnormalities. However, comprehensive assessment remains limited by labor-intensive manual measurements and automated models that lose accuracy across imaging sequences and institutions. We introduce BioTTA, an anatomically guided test-time adaptation framework that measures 11 fetal brain biometrics from reconstructed three-dimensional magnetic resonance images. BioTTA adapts pretrained models to individual unlabeled volumes using geometric consistency and atlas priors, without access to source images. In a retrospective study across five centers in China and Switzerland, BioTTA improved measurement accuracy across three out-of-distribution datasets and two network architectures. It reduced mean absolute measurement errors by 46-82% relative to the corresponding models without adaptation, achieving errors similar to those of supervised fine-tuning. Its measurements were mapped onto normative growth trajectories derived from expert annotations, enabling gestational-age-specific assessment through a web-based reporting platform. Across four disease cohorts, the platform achieved 82.1-95.0% accuracy in binary detection of ventriculomegaly or cerebellar abnormalities, with performance similar to that of a senior radiologist. BioTTA provides a practical foundation for standardized fetal brain biometry across heterogeneous imaging settings, supporting scalable prenatal growth assessment and quantitative evaluation of brain abnormalities.

版本更新 · v2arXiv:2511.10749

Effective Resistance in Simplicial Complexes: Generalizations and Properties

Inés García-Redondo, Claudia Landi, Sarah Percival, Anda Skeja, Bei Wang, Ling Zhou

原文摘要

Originally introduced in circuit theory, the notion of effective resistance has been fruitfully exploited in graph theory, where each edge in a graph is understood as a resistor in an electric network. In this context, the effective resistance between two vertices quantifies the total opposition to current flow when a unit current is injected at one vertex and extracted at the other. As such, it encodes rich structural and geometric information about the underlying graph: it satisfies a minimum-energy principle termed Thomson's principle, defines a metric on the vertex set, relates to the topology of the graph through Foster's theorem, and determines the probability of appearance of an edge in a random spanning tree. Generalizations of effective resistance to simplicial complexes have been proposed in several forms, often formulated as matrix products of standard operators associated with the complex. We introduce a novel, basis-independent bilinear form, derived from an algebraic reinterpretation of circuit theory, that extends the classical effective resistance from graphs to simplicial complexes. This bilinear form acts on chains and cochains, and in particular, simplices and cosimplices in a simplicial complex. Our framework subsumes and unifies all existing matrix-based formulations of effective resistance. Moreover, we establish higher-order analogues of several fundamental properties known in the graph case: (i) we extend Thomson's principle to our basis-free framework; (ii) we establish metric properties for the effective resistance and its square root on chains and cycles, recovering the classical graph case; (iii) we provide a generalization of Foster's theorem to simplicial complexes; and (iv) we prove a matrix-tree theorem for higher-dimensional spanning trees, linking effective resistance to the probability of appearance of a simplex in random simplicial spanning trees.

math.CO · cs.CG · cs.DM · math.AT

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06654

Compounding Physical RSI: Turning Execution Failure into Inherited Capability

Yixuan Yang, Qiang Chen, Run Shao, Shuwei Dong, Wenhao Li, Yunpeng Wang, Wenhan Fang, Haomiao Wu 等 34 位作者

原文摘要

Generalist robot policies are trained on static offline datasets, yet physical manipulation unfolds sequentially across stages. Because a weakness in a downstream stage remains unobservable until the policy passes earlier ones, each resolved bottleneck exposes the next hidden failure mode. This staged structure calls for Recursive Self-Improvement (RSI), in which a policy iteratively executes, diagnoses failures, and updates itself. Moreover, because task completion behaves like a product of stage-level pass rates, recursive gains can multiply across generations. In physical environments, three bottlenecks obstruct compounding: terminal execution feedback rarely localizes the stage that caused a failure, failed trajectories do not supply the corrective action, and updating shared parameters can degrade previously mastered skills. We propose Compounding Physical RSI, a self-evolving policy framework that turns execution failures into heritable capability. First, automated capability-gap assessment traces failed rollouts to the task stage where progression breaks down and formulates a structured improvement objective. Second, conditioned on this objective, our remedy stage curates validated, stage-relevant experience and refines the policy via behavior-regularized offline reinforcement learning, without requiring costly online rollouts at each optimization step. Third, to safeguard existing skills, our retention stage constructs weight-space candidates between the inherited reference policy and the refined policy, evaluates them across the task suite, and promotes the best candidate as the reference state for the next generation. Evaluated across all 42 tasks of the RoboDojo-Sim benchmark, CSU-AI-07 ranks first on the leaderboard with a three-seed mean Overall Score of 26.15 and Success Rate of 20.70%, outperforming the next-best entry by 5.92 points and 5.82 percentage points.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05861

A Complete Characterization of $q$-Ary $2$-Designs in Doubly-Extended Reed-Solomon Codes

Xinghao Wu, Junling Zhou

原文摘要

Let $\mathcal{C}_k$ be the doubly-extended Reed--Solomon code with parameters $[q+1, k, q-k+2]_q$. We give a complete characterization of when its codewords of a fixed weight form $q$-ary 2-designs. The main ingredient is an exact criterion showing that the number of $r$-subsets of $\mathbb{F}_q^*$ with a prescribed product is independent of that product if and only if $\gcd(r, q-1)=1$. Applying this criterion to the minimum-weight codewords of $\mathcal{C}_k$, we prove that they form a $q$-ary 2-design if and only if $\gcd(k-1, q-1)=1$. We further establish a propagation result for MDS codes showing that the $q$-ary 2-design property at minimum weight implies the same property at every nonzero weight. Consequently, $\mathcal{C}_k$ holds $q$-ary 2-designs at all nonzero weights if and only if $\gcd(k-1, q-1)=1$.

math.CO · cs.IT

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.27877

Gaze2HOI: Gaze-Aware Intention Disambiguation for Egocentric Hand-Object Interaction Forecasting

Chuanyang Ma, Chenye Wang, Yixuan Wang, Huiling Zhou, Man Zhang, Xingqun Qi, Muyi Sun, Caifeng Shan 等 9 位作者

原文摘要

Anticipating how users will interact with their surroundings is essential for proactive systems that provide timely assistance in extended reality and embodied applications. Prior work typically forecasts hand-object interactions directly from historical observations, leaving intention and contact geometry implicit. However, similar observation histories can precede semantically or geometrically distinct futures. Inspired by behavioral evidence that gaze can provide early cues for underlying intention, we propose Gaze2HOI, a gaze-aware framework that formulates hand-object interaction forecasting as a hierarchical problem comprising intention inference, contact grounding, and coupled hand-object motion generation. Specifically, Gaze2HOI first weights gaze cues by fixation strength and recency, and uses them to modulate the visual representations of a vision-language model (VLM) through Temporal Gaze Saliency Modulation (TGSM), thereby reducing semantic ambiguity in intention inference. Subsequently, an Anticipatory Contact Grounding (ACG) module maps these structured intentions into explicit contact, support, and placement fields, effectively mitigating geometric ambiguity. Driven by these explicit conditions, a diffusion-based model jointly generates the six-degree-of-freedom (6-DoF) object trajectories and future articulated motion of both hands. To evaluate Gaze2HOI, we further introduce EgoAIM, a phase-aware benchmark that organizes egocentric hand-object interactions into four phases defined by evolving hand-object relations and provides fine-grained intention annotations. Extensive experiments demonstrate that Gaze2HOI achieves state-of-the-art performance in both intention inference and motion forecasting, even approaching oracle-level end-to-end performance in specific interaction phases. Project page: https://gaze2hoi.github.io/.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04897

TapeSAC: Automated, Pareto-Optimal Fine-Grained Memory Management for Large Model Training

Kaihui Ren, Yifan Zhao, Zejian Wang, Yingtong Xiong, Xiaoling Zhou, Daohong Xu, Chenhao Guan, Zhikang Mo 等 14 位作者

原文摘要

Selective activation checkpointing (SAC) decides which activations to save for the backward pass and which to recompute: each choice is a point in the plane of peak memory and recomputation time, and the goal is that plane's Pareto frontier. Current practice falls short of this goal. Hand-tuned policies fix a single point that does not adapt when the model, batch or kernels change; PyTorch's SAC reruns each block's forward pass in program order, recomputing values the backward pass never reads; and the memory budget of torch.compile treats recomputation as free or forbidden and selects saved tensors by bytes alone, an assumption that fails for the custom kernels that produce many of a large model's activations. We present TapeSAC, which automates SAC in eager PyTorch without editing the model's forward code. TapeSAC records each block's forward pass at the dispatcher and, during backward, replays only the producers of the values autograd consumes, so saving a tensor eliminates every producer needed only for it. A planner selects the saved tensors from measured recomputation costs under a single limit on step-peak memory. On Qwen3.8-27B and Qwen3.6-35B-A3B, replay alone reduces step time by 3.0% and 1.7% versus full checkpointing at no greater memory cost; memory-aware planning raises these gains to 6.3% and 13.9% under a 90%-of-device-memory limit. The evaluated hand-tuned policies fall short of this memory-recomputation frontier and, on Qwen3.6-35B-A3B, run out of memory at larger batches. Over 500 steps on real data, TapeSAC reduces training time by 8-11%; on Qwen3.5-397B, it is 2.7% faster than a model-specific hand-tuned policy while using 14% less memory.

cs.DC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38228

The Endgame of FSDP Communication: Layout-Aware Collectives on Copy Engines, RDMA, and NVSwitch

Zejian Wang, Yifan Zhao, Kaihui Ren, Xiaoling Zhou, Yubin Wang, Chenhao Guan, Lin Zhang, Yufeng Li 等 14 位作者

原文摘要

Fully Sharded Data Parallelism (FSDP2) maintains parameters and gradients as per-parameter tensors, whereas its collective path operates on flat, rank-major buffers. Converting between these layouts copies all-gather outputs into parameter storage and packs gradients into full-group fp32 buffers. Layout conversion alone accounts for 6.4% of step time in Llama-3.1 70B training at 8K tokens per GPU. Communication kernels impose a second cost: a default NCCL reduce-scatter can slow a concurrent GEMM that would otherwise complete in a single wave by 69%. We present TIDE, a set of zero-copy collectives built on public NCCL 2.31+ APIs. TIDE preserves FSDP2's per-parameter storage and compute kernels while folding layout placement into collective addressing. TIDE-AG delivers shards directly to parameter storage using NVSwitch multicast and paced recursive-doubling RDMA, with no SMs used for transport or placement. TIDE-RS consumes per-parameter bf16 gradients directly and reduces them into fp32 shards (a mixed-precision mode that NCCL does not provide) using copy engines and short-lived thread blocks (CTAs) for node-local accumulation and NVSwitch for cross-node reduction. A two-phase pipeline separates gradient reads from network progress; Slot-Reuse optionally stages gradients in vacated all-gather slots. On 64 GPUs, TIDE reduces step time by up to 27.1% relative to Stock FSDP2 and 6.0% relative to FSDP2+SymmMem across Llama-3.1 70B, Wan2.2, and Qwen3-30B-A3B. It reduces reduce-scatter SM time by factors of 12.5-14.5 and cuts interference with concurrent compute by up to an order of magnitude. Its reduce-scatter buffer remains nearly constant from 16 to 64 GPUs at a fixed per-GPU output size, and its peak memory is up to 4.7 GiB below FSDP2+SymmMem's and, with Slot-Reuse on Wan2.2, below Stock FSDP2's.

cs.DC · cs.LG · cs.PF

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37140

Rethinking Positional Encoding and Interaction Granularity for CTR Prediction

Xinxun Zhang, Yuxiang Wang, Xiaokai Zhou, Jiayue Li, Yi Li, Yue Liu, Jun Zhang, Xiaoling Zhou 等 11 位作者

原文摘要

Click-through rate (CTR) prediction relies on historical behaviors and contextual features to estimate clicks on a target item. Existing Transformer-based CTR models often adopt index-based or temporal positional encodings and token-level causal interaction. However, we observe that most existing positional encodings do not outperform NoPE, while relaxing causal constraints within target-relative temporal groups also yields gains. These observations motivate a fundamental question: what positional encoding and interaction granularity does CTR prediction actually require? To address this question, we propose \textbf{TRIP}, which combines target-aware rank--time positional encoding with temporal-group interaction modeling. Specifically, Factorized Rank Encoding represents each behavior's ordinal position within the historical sequence through a low-rank factorization, while Target-Relative Time Encoding characterizes its temporal distance to the target on a coarse-to-fine scale. This joint design grounds each behavior's positional representation in its sequence context and temporal relation to the current target. Furthermore, Temporal Group-Causal Interaction enriches intra-group representations through bidirectional interaction and masked reconstruction while preserving inter-group causal order. Finally, behavior representations are condensed into compact group summaries for efficient group-level modeling in subsequent layers. Experiments on four public benchmarks show that TRIP improves AUC by up to 4.45\% relative to the strongest baseline.

cs.IR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.35989

Layerwise Repacking: Variable-Length Hybrid-Attention Training without an Extra Redistribution Pass

Jiahao Zhang, Junwei Feng, Dongliang Huang, Xiaoling Zhou, Yiqiao Liao, Chengru Song

原文摘要

Variable-length training creates conflicting parallelism requirements across the layers of a hybrid-attention model. The longest samples require wide sequence-parallel (SP) groups of 16 or 32 ranks for full attention, whose cost grows quadratically with sequence length. Sparse-attention layers, dominated by near-linear MoE work, can instead balance most evaluated workloads within node-local SP8 groups. Reusing the wide groups for these layers pushes their all-to-all communication across nodes, where the observed effective rate is 7.0x lower. Yet switching to smaller groups changes which ranks own each sample; a separate redistribution at every full-sparse boundary would introduce another data transfer. Layerwise Repacking keeps each SP16/32 parent for full attention and repacks its samples into node-local sparse children at SP{1, 2, 4, 8}, where hidden states persist between full layers. The layout change rides on the two existing full-layer Ulysses sites: at each, one variable-size all-to-all performs both the head exchange and the sample repacking, so no redistribution pass is added. Because the balance the children can reach depends on which samples share a parent, an auxiliary parent solver rebalances parent membership before the children are formed. On all 22 steps with SP16/32 parents among the first 200 training steps of a 1F3S hybrid-attention model (one full-attention layer per three sparse-attention layers), repacking alone reduces the mean end-to-end time by 5.95% and raises model FLOPs utilization (MFU) by 3.75 percentage points over the baseline with repacking disabled; with the improved solver, end-to-end time falls by 7.61% and MFU rises by 4.83 percentage points.

cs.LG · cs.AI

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arXiv ↗PDF ↗
首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1628ACL Anthology · ACL 2026

LeLoRA: Learnable Low-Rank Adaptation of Large Language Models

Xiaoling Zhou, Mingjie Zhang, Zhemg Lee, Wei Ye Peking University, Shikun Zhang Peking University

原文摘要

Fine-tuning large language models (LLMs) is an effective approach to enhancing their performance on specialized downstream tasks. Among the various techniques, low-rank adaptation has garnered significant attention due to its ability to maintain the full performance of fine-tuning while enhancing computational efficiency. However, existing approaches often rely on manually specified and fixed hyperparameters to identify the trainable components within weight matrices, resulting in suboptimal performance and low parameter efficiency. This paper presents a novel Learnable Low-Rank Adaptation (LeLoRA) framework that utilizes dynamically learned fine-tuning strategies to facilitate the effective adaptation of LLMs. Our framework integrates an LLM with a policy network that automatically and adaptively generates matrix-specific adaptation strategies to identify the trainable components of each weight matrix, taking into account their unique characteristics, such as singular values and matrix norms. A reinforcement learning-based optimization algorithm is then employed to iteratively update the LLM and the policy network, ensuring that the generated strategies adapt in real time to the evolving states of the LLM. Extensive experiments have been conducted across various natural language processing and multimodal tasks. The results across ten different LLMs, ranging from 125M to 70B parameters, provide compelling evidence that LeLoRA consistently outperforms existing baselines in adapting LLMs. Moreover, analytical experiments provide valuable insights into the effectiveness of the generated strategies.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表