论文

本文提出一个半符号化框架，将用于 OCR 后校正的 word-spotting 技术与知识图谱表示相结合，使 agent 可通过合成查询访问信息，从而对误读和幻觉具有鲁棒性。This paper introduces a semi-symbolic framework that integrates word-spotting techniques for post-OCR correction with a knowledge graph representation that enables the agent to access information through synthesized queries that are robust to misinterpretation and hallucination.

本文提出 IndicTalk，目前最大的多语言印度语码混合（code-mixed）会话语料库，包含超过 13,28,604 段事件驱动的多轮对话，覆盖 9 种印度语言的 18 种语言变体，将公开发布以支持低资源印度语多语言会话 AI 的开发与评估。IndicTalk is presented, one of the largest multilingual Indic code-mixed conversational corpora, comprising over 13,28,604 event-grounded multi-turn conversations across 18 language varieties covering 9 Indic languages and will be released to support the development and evaluation of multilingual conversational AI for underrepresented Indic languages.

基于 ONNX Runtime 的原生 C++ 推理引擎击败了所有 baseline，置信区间甚至不重叠；batch 策略的影响比语言或运行时的选择更关键，超出作者预期。A native C++ inference engine on ONNX Runtime that beat every baseline, confidence intervals didn't even overlap, and batching strategy mattered more than either the language or the runtime choice, more than the authors expected.

提出一个系统性的、以架构为中心的 LLM 记忆分类法，沿三个正交轴刻画记忆：表示、更新动态与持久性，有效桥接不同的架构范式。A systematic, architecture-centric taxonomy of memory in LLMs is presented, characterizes memory along three orthogonal axes: representation, update dynamics, and persistence, effectively bridging disparate architectural paradigms.

提出 CADENCE，一个统一框架，对每种叠加式失败模式给出针对性修复，证明通过原则化的蒸馏即可在不依赖数据中心级硬件的条件下获得强推理质量。CADENCE, a unified framework with a targeted fix for each compounding failure modes, is presented, showing principled distillation reaches strong reasoning quality without datacenter-scale hardware.

对有损验证方法所诱导的分布进行原则化分析，揭示一项根本缺陷：由于分布失真，其性能可能相对于真实截断采样基线显著退化。This work presents a principled analysis of the distributions induced by lossy verification methods, and identifies a fundamental pitfall: performance can degrade significantly compared to the true truncation sampling baseline due to distributional distortion.

Meshy T2在几何保真度上达到SOTA，端到端图像到网格生成中位耗时6秒，比自回归基线快一个数量级以上。Meshy T2 achieves state-of-the-art geometric fidelity and completes end-to-end image-to-mesh generation within a median of 6 seconds, over an order of magnitude faster than autoregressive baselines.

提出Criterion-Distilled Policy Optimization (CriPO)，通过on-policy自蒸馏增强基于rubric的RL，并采用反事实自教师定位负优势rollout中与准则相关的token，将其token级优势翻转为正值，保留本将被抑制的有用模式。Criterion-Distilled Policy Optimization (CriPO) is proposed, which enhances rubric-based RL via on-policy self-distillation and employs a counterfactual self-teacher to locate criterion-relevant tokens in negative-advantage rollouts and flips their token-level advantages to positive values, preserving useful patterns that would otherwise be suppressed.

提出SA，一种Stable Advantage Fusion框架，避免熵崩塌，在所有六个模型-领域设定上持续优于固定系数的GRPO+OPD融合，聚合得分提升0.70%，训练更稳定。SA is proposed, a Stable Advantage Fusion framework that avoids entropy collapse and consistently outperforms fixed-coefficient GRPO+OPD fusion, improving the aggregate score by 0.70% across all six model-domain settings while achieving more stable training.

本文提出 DAPD，一种具有两级锚定的统一框架，可显著缓解特权错觉，在 Qwen3-4B 上以平均 +2.00 分优于 OPSD。DAPD is proposed, a unified framework with two levels of anchoring that significantly alleviates privilege illusion, outperforming OPSD on Qwen3-4B by +2.00 points on average across tasks.

GradCuit（梯度穿越电路）在所选 Transformer 层、提示隐藏表示与生成续写之间插入可优化的潜变量，开启了鲁棒且可解释的测试时缩放新维度，使 LLM 调整其推理方式，而不仅仅是重新生成、采样或重排输出。GradCuit (gradient through circuit), which inserts optimizable latent states at a selected Transformer layer between the hidden representations of the prompt and the generated continuation, opens a new axis of robust and interpretable test-time scaling, where LLMs adapt how they reason rather than merely regenerate, sample, or rerank outputs.

本文提出 GPTQ-2D，以三次时间复杂度生成相同的取整矩阵，并研究该任务的双侧版本——固定非奇异基矩阵同时作用于残差的左右两侧This work presents GPTQ-2D, which produces the identical rounded matrix in cubic time, and studies the two-sided version of this task, in which fixed nonsingular basis matrices act on both the left and the right of the residual.

简化了 MoE 路由算法，设计出通信与计算成本更低的直观改进模型，并首次证明大型稀疏模型可以使用更低精度格式进行训练This work simplifies the MoE routing algorithm and design intuitive improved models with reduced communication and computational costs and shows large sparse models may be trained, for the first time, with lower precision formats.

Pointer sentinel-LSTM 模型在 Penn Treebank 上以远少于标准 softmax LSTM 的参数量达到 SOTA 语言建模性能，并开源了 WikiText 语料库The pointer sentinel-LSTM model achieves state of the art language modeling performance on the Penn Treebank while using far fewer parameters than a standard softmax LSTM and the freely available WikiText corpus is introduced.

DASH (Drift Aware advantage SHaping) 根据每个推理片段是朝向还是偏离正确性来分配片段级 credit，在 overthinking 普遍的场景下取得最高准确率，同时减少 overthinking 行为，比基线实现更高效的自纠错。DASH (Drift Aware advantage SHaping), which assigns segment-level credit based on whether each reasoning segment leads toward or away from correctness, achieves the highest accuracy where overthinking is prevalent while reducing overthinking behaviors and achieving more productive self-correction than baselines.

提出 ARCHead，一种紧凑型 LM-head 压缩器，结合量化低秩核心、组级 INT4 残差以及基于激活导出度量拟合的低秩校正，在 block 量化器无法触及的大输出投影上实现互补压缩。ARCHead is presented, a packed LM-head compressor that combines a quantized low-rank core, group-wise INT4 residuals, and a low-rank correction fitted in an activation-derived metric to complements block quantizers by compressing the large output projection they can leave untouched.

提出 RestoreKV，在相同 KV 总预算下以可学习的恢复机制补充基于选择的 KV 压缩方案，显著降低压缩带来的性能损失。RestoreKV is introduced, which complements this selection-based formulation with learned restoration with learned restoration under the same total KV budget, and substantially reduces compression-induced degradation.

在三种大语言模型上的实验表明，思维链提示能够在一系列算术、常识和符号推理任务上提升性能。Experiments on three large language models show that chain of thought prompting improves performance on a range of arithmetic, commonsense, and symbolic reasoning tasks.

本工作提出两种参数削减技术以降低 BERT 的内存占用并提升训练速度，并采用一种聚焦于建模句子间连贯性的自监督损失。This work presents two parameter-reduction techniques to lower memory consumption and increase the training speed of BERT, and uses a self-supervised loss that focuses on modeling inter-sentence coherence.

研究表明，利用变分方法最新进展的深度生成模型与近似贝叶斯推断能够带来显著提升，使生成式方法在半监督学习上极具竞争力。It is shown that deep generative models and approximate Bayesian inference exploiting recent advances in variational methods can be used to provide significant improvements, making generative approaches highly competitive for semi-supervised learning.

这项系统性研究在数十项语言理解任务上比较了预训练目标、架构、无标注数据集、迁移方法及其他因素，并在涵盖摘要、问答、文本分类等的许多基准上取得了 SOTA 结果。This systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks and achieves state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more.

本文提出了 mixup，一种通过对样本对及其标签的凸组合来训练神经网络的简单学习原则，提升了 SOTA 神经网络架构的泛化能力。This work proposes mixup, a simple learning principle that trains a neural network on convex combinations of pairs of examples and their labels, which improves the generalization of state-of-the-art neural network architectures.

本文提出了一种双时间尺度更新规则（TTUR），用于在任意 GAN 损失函数下使用 SGD 训练 GAN，并引入了 Frechet Inception Distance（FID），相比 Inception Score 能更好地捕捉生成图像与真实图像之间的相似性。This work proposes a two time-scale update rule (TTUR) for training GANs with stochastic gradient descent on arbitrary GAN loss functions and introduces the "Frechet Inception Distance" (FID) which captures the similarity of generated images to real ones better than the Inception Score.

名为 PaLM 的 540 亿参数、密集激活的 Transformer 语言模型取得了突破性性能，在一系列多步推理任务上超越了微调后的 SOTA，并在最近发布的 BIG-bench 基准上超越了人类平均水平。A 540-billion parameter, densely activated, Transformer language model, which is called PaLM achieves breakthrough performance, outperforming the finetuned state-of-the-art on a suite of multi-step reasoning tasks, and outperforming average human performance on the recently released BIG-bench benchmark.

研究表明，APE 生成的提示词既可引导模型趋向真实性和/或信息量，也可通过将其前置拼接到标准上下文学习提示词之前来提升少样本学习性能。It is shown that APE-engineered prompts can be applied to steer models toward truthfulness and/or informativeness, as well as to improve few-shot learning performance by simply prepending them to standard in-context learning prompts.

HiSparse 已合入上游 SGLang，并在 H200、B200 与 GH200 平台上针对 DSA、NSA、Quest 三类稀疏注意力家族进行评估：在长上下文负载下峰值生成吞吐提升最高达 4.7×，同时保持可比的 per-token 延迟，并降低高负载下的 time-to-first-token。HiSparse is merged into upstream SGLang and evaluated across three sparse-attention families (DSA, NSA, and Quest) on H200, B200, and GH200 platforms: it improves peak generation throughput by up to 4.7x on long-context workloads while preserving comparable per-token latency and reducing time-to-first-token at high load.

论文证明，使用标注数据进行微调，并允许模型查询外部知识源，能够在安全性和事实性这两个关键挑战上带来显著提升。It is demonstrated that fine-tuning with annotated data and enabling the model to consult external knowledge sources can lead to significant improvements towards the two key challenges of safety and factual grounding.

研究表明 BigBird 是序列函数的通用逼近器，且具备图灵完备性，从而保留了二次全注意力模型的这些性质。It is shown that BigBird is a universal approximator of sequence functions and is Turing complete, thereby preserving these properties of the quadratic, full attention model.

本文提出 OasisKV，一种以显存为中心的 LLM 推理系统设计，通过在 LLM 解码期间将完整 KV-cache 存储与 HBM 解耦来缓解 HBM 容量压力，并观察到未来重要 token 可借助推测解码（SD）所起草的前瞻 token 被提前准确预测。OasisKV is presented, a memory-centric LLM inference system design that alleviates HBM capacity pressure by decoupling full KV-cache storage from HBM during LLM decoding and observes that future important tokens can be predicted accurately in advance using lookahead tokens drafted by speculative decoding (SD).

一个结构定理将无记忆等变规则刻画为恰好由 Gram 矩阵决定的左预处理子；一个迁移定理将梯度流的路径性质推广到 common-scalar 流。A structure theorem characterizes the memoryless equivariant rules as exactly the Gram-determined left preconditioners, and a transfer theorem carries gradient flow's pathwise properties to common-scalar flows.

本文讨论了一种被称为大语言模型涌现能力的不可预测现象——若某项能力在小模型中不存在而在大模型中存在，则称为涌现。This paper discusses an unpredictable phenomenon that is referred to as emergent abilities of large language models, an ability to be emergent if it is not present in smaller models but is present in larger models.

本工作研究图结构输入的特征学习技术，并在程序验证任务上取得 SOTA 性能，该任务需将子图与抽象数据结构进行匹配。This work studies feature learning techniques for graph-structured inputs and achieves state-of-the-art performance on a problem from program verification, in which subgraphs need to be matched to abstract data structures.

提出 MultiMedQA 基准，整合六个现有医学问答数据集（涵盖专业医学、研究与消费者查询）及一个全新的在线医学问题搜索数据集，并提出针对模型答案的人工评估框架，揭示了 LLM 在医学领域的潜在应用价值。MultiMedQA, a benchmark combining six existing medical question answering datasets spanning professional medicine, research and consumer queries and a new dataset of medical questions searched online, is presented and a human evaluation framework for model answers is proposed, suggesting the potential utility of LLMs in medicine.

本文给出 ICL 的形式化定义，厘清其与相关研究的联系，并梳理讨论训练策略、提示设计策略及相关分析等高级技术。This paper presents a formal definition of ICL and clarify its correlation to related studies, and organizes and discusses advanced techniques, including training strategies, prompt designing strategies, and related analysis.

iFAN 提出 Adjusted Probability-Mask Ranking (APMR)，将查询竞争与预测的掩码质量对齐，抑制高置信度但不准确的竞争者；同时采用 Cross-Layer Self-Distillation (CLSD) 将更强的中间预测传递至最终层。iFAN introduces Adjusted Probability-Mask Ranking (APMR), which aligns query competition with predicted mask quality and suppresses high-confidence but inaccurate competitors, and employs Cross-Layer Self-Distillation (CLSD) to transfer stronger intermediate predictions to the final layer.

自组织临界性作为一种具有内在序参数的唯象框架登场，使开放性主张得以转化为可证伪的猜想。Self-organized criticality enters as a phenomenological framework with an intrinsic order parameter; open claims become falsifiable conjectures.