跳到正文
研踪PaperTrail

YOUR RESEARCH, IN SIGHT

让下一篇重要论文，不再错过。

覆盖计算机全学科，以 AI 为快捷入口。关注方向、作者和关键词，把发现变成持续的研究积累。

部分采集异常
正在读取账号与研究记录…

全站更新概况

+2新增论文
+2其中 AI 相关
+0已有论文修订
+100新增 AI 导读

arXiv 最近完成扫描：2026-11-13 04:00 · 全站最近入库：2026-11-12 06:37各信源进度 →

统计口径与数据新鲜度

统计 2026-11-12 04:36 — 2026-11-13 04:36（北京时间） · 点击数字查看对应简报；AI 相关包含在新增论文内。

最近完成检查：2026-11-13 04:00最近发现新增版本：2026-11-12 06:37库内论文最新更新时间：2026-11-11 08:00

每分钟刷新已保存的数据。空检查不代表采集异常；新收录可能包含历史论文。AI 导读按新增版本计数。

21,675 已收录论文18,323 AI 相关6,056 有修订112 HF 社区发现

浏览论文库

研究日报 / 周报 →按信源浏览 →

时间范围跟随排序：版本更新、本站收录或首次发表。最新收录可能包含历史论文。部分信源异常，查看状态 →

找到 4,903 篇论文 · 按版本更新时间排序上轮扫描起点 2026-11-13 03:58（北京时间）
新论文 · v1arXiv:2611.10855

Representing Tensors as Compositions for Efficient Computation on Sparse and Structured Data

Valérian Rey, Praneth Paruchuri

原文摘要

We propose a new mathematical formalism in which all kinds of tensors and of operations on those tensors are represented as simple function compositions. Tensors are functions from indices to values. An element-wise operation (e.g. an activation function) is a composition with a function applied to values; re-indexing (e.g. reshape, transposition) is a composition with a function applied to indices; and a reduction is a composition with both an index function, mapping each index to several indices, and a reduction operation (e.g. product, sum, max). Combined with tensor stacking, these compositions can represent most interesting kinds of tensors (e.g. dense, jagged, all kinds of sparsity patterns, matrix factorizations), as well as lazy versions of almost every operation available in modern tensor computation frameworks, including einsums between tensors of any kind. Identities between compositions can then be used to rewrite such expressions into equivalent ones, without evaluating them. For instance, the product of two sparse matrices can be rewritten so that only products between non-zero values are computed, turning a sparse problem into a dense one. Using this algebra as the intermediate representation of a tensor computation engine would shift the focus from defining operations on tensors (e.g. einsum between sparse tensors) to defining an algebra on index functions. This should reduce the codebase complexity from $\mathcal{O}(mn^2)$, with $m$ the number of binary operations between tensors and $n$ the number of tensor types, to $\mathcal{O}(m + k^2)$, with $k$ the number of types of index functions. A draft implementation of a tensor computation framework based on this algebra is publicly available.

cs.LG · cs.MS · cs.PL

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.23298

How the elastic axis folds the transonic flutter boundary: shock energy transfer and conformally calibrated active learning

Ashray Saxena, Sharad Shrivastava

原文摘要

Isogai's case A, a pitch-plunge aerofoil whose elastic axis lies half a chord ahead of the leading edge, has a folded transonic flutter boundary in inviscid flow, with several flutter speeds at one Mach number. We map this boundary and its elastic-axis family with coupled Euler simulations, a Gaussian-process surrogate of the damping trained by batch active learning, and distribution-free split conformal prediction, which bounds the marginal error rate of stability calls. Three failure modes of damping labels are invisible in the residuals, and the half-degree mean incidence left by an impulsive start at $1^\circ$ freestream incidence raises the flutter speed at the dip by 2.8%, more than any numerical setting. With 80 simulations the learned case A boundary labels 63 of 64 independent runs correctly, and at most 1.8% of calibrated calls on held-out runs are wrong. Over Mach number, elastic axis and flutter speed index, 193 simulations show that, for the case A structure about the axis, the inviscid fold exists only for axes more than about 0.4 chord ahead of the leading edge, on two meshes. Chordwise energy budgets, closed three ways, explain both branches. On the lower branch the falling reduced frequency, at unchanged shock phase, raises the shock input and lowers the upstream damping similarly. On the upper branch the shock is pinned at the trailing edge; moving the axis aft brings the flutter-mode pivot towards the section, so the upstream damping falls faster than the residual shock input and the fold closes.

physics.flu-dyn · cs.LG

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10841

A Gamified Framework for Conflict-Driven Design: Discovering Strategies for Collaborative Resilience with Multi-Agent Learning

Jin Gao

原文摘要

Conflicting stakeholder objectives make it difficult to coordinate investments in urban resilience. We model conflict-driven urban design as a non-zero-sum digital board game in which stakeholder agents allocate scarce resources and balance individual payoffs against shared urban development and resilience objectives. We use multi-agent reinforcement learning (MARL) to train policies and examine their behavior across three settings. On a 4 x 4 board, learned policies reach about 84% of the total return found by a simulated annealing benchmark. On an 8 x 8 board with spatial and resource constraints, trained agents favor projects that improve resilience in self-play. In a 12 x 12 simulation initialized from historical maps of Tatsumi, Tokyo, trained agents achieve a mean total score of 452.1, compared with 357.3 for a random-policy baseline across 10,000 simulation runs. Gains in adaptability, sustainability, flood resistance, and greenery come with reductions in vitality and density. A human-in-the-loop interface supports replay and lets participants take over stakeholder decisions, providing a testbed for exploring these trade-offs in participatory urban planning.

cs.AI · cs.LG · cs.MA

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2607.03528

Aligning Language Models with Selective Prediction

Gaoxiang Luo, Yifan Wu, Sinian Zhang, Aryan Deshwal, Ju Sun

原文摘要

Large language models (LLMs) are increasingly deployed as critical decision-making components in high-stakes real-world AI systems, rendering LLM reliability a foremost practical concern. In this paper, we focus on enhancing LLM reliability through selective prediction (SP), a strategy that allows an LLM to only predict for inputs where it is likely to be correct (i.e., coverage) and hence reduce the error rate (i.e., risk) on that portion of inputs---flagging the remaining inputs for future human discretion. In other words, SP improves LLM reliability by balancing the risk-coverage trade-off and enabling seamless human-AI collaboration. To integrate SP into LLMs, we focus on the LLM post-training alignment stage and propose to align LLMs with SP performance metrics, in contrast with existing LLM alignment methods that focus primarily on correctness or calibration metrics. Specifically, we propose a novel alignment framework, Reinforcement Learning for Selection Reward (RLSR), which targets the area under the risk-coverage curve (AURC)---a popular SP performance metric---as its alignment objective. RLSR achieves substantially better risk-coverage trade-off compared to multiple alignment baselines on both in-domain and out-of-domain tasks.

cs.LG · cs.AI · cs.CL

查看论文
arXiv ↗PDF ↗
版本更新 · v4arXiv:2506.15643

Understanding Randomization in Greedy Model Search

Xin Chen, Jason M. Klusowski, Yan Shuo Tan, Chang Yu

原文摘要

We study feature subsampling in greedy model search, a randomization mechanism motivated by random forests and other ensemble methods. Although recent theory draws an analogy between this randomization and ridge regularization, these results largely rely on base learners optimized via ordinary least squares (OLS). We therefore investigate the effects of feature subsampling on greedy forward selection, a tractable abstraction of the adaptive split search used by decision trees. Assuming an orthogonal design, we prove that, under suitable conditions, ensembling with a fixed feature subsampling size can reduce expected training error without increasing degrees of freedom, thereby reducing fixed design prediction error. We also show that both the training error and degrees of freedom need not be monotone in the subsampling rate, breaking the analogy with standard shrinkage methods like the lasso or ridge regression under orthogonality. Furthermore, we characterize the exact asymptotic behavior of the estimator, showing that it adaptively reweights OLS coefficients based on their rank, with weights that are bounded by explicit logistic functions.

stat.ML · cs.LG

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2608.26552

Hadamard Flattening and Gaussian Pooling Sketch for Least Squares with Coordinate-wise Guarantee

Zhao Song, Lichen Zhang

原文摘要

Randomized sketch-and-solve algorithms accelerate overconstrained $\ell_2$ regression by replacing the input with a much smaller, randomly projected problem. While standard subspace embeddings nearly preserve the regression cost, coordinate-wise accuracy is more delicate: rather than the objective value, we want the solution vector itself to be close to the optimum in $\ell_\infty$ norm. Specifically, we seek $x'\in\mathbb{R}^d$ with $\|x'-x^\star\|_\infty\leq\fracε{\sqrt d}\|Ax^\star-b\|_2\|A^\dagger\|_{\mathrm{op}}$, where $A\in\mathbb{R}^{n\times d}$ is the design matrix, $b\in\mathbb{R}^n$ the label vector, $x^\star$ the optimal solution and $A^\dagger$ the pseudo-inverse of $A$. Price, Song and Woodruff introduced this problem and claimed that the subsampled randomized Hadamard transform (SRHT) with $O(ε^{-2}d^{1+Θ(\sqrt{\log\log n/\log d})})$ rows achieves this guarantee; we point out a gap in the proof. Song, Ye, Yin and Zhang later claimed $O(ε^{-2}d\log^3 n)$ rows, but their proof relies on an independence assumption that does not hold in general, and we exhibit an explicit instance on which it fails. To achieve a truly nearly-linear-in-$d$ row count, we introduce a new fast, dense randomized transform combining a randomized Hadamard flattening, a random permutation, and balanced, disjoint Gaussian pooling. Conditioned on the Hadamard-and-permutation stage, the sketched problem becomes an exact Gaussian regression whose noise is independent of the entire sketched design, exactly what Song, Ye, Yin and Zhang's argument was missing. Our sketch yields the $\ell_\infty$ guarantee with $m=O(ε^{-2}d\log d)$ rows, using one Hadamard pass with padded internal dimension $N=\widetilde{O}(n+ε^{-2}d^3)$. It is also efficient to apply: $(SA,Sb)$ can be computed in $O(Nd\log N)=\widetilde{O}(nd+ε^{-2}d^4)$ time.

cs.DS · cs.LG · stat.ML

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10838

Learning Biological Hierarchies in Single-Cell Foundation Models

Xiangyu Guo, Ricardo Henao

原文摘要

Cell types are organized by taxonomic relationships that define lineages in the so-called cell ontology. However, most existing foundation models typically ignore cell-type lineages encoded in this cell ontology. We introduce a biologically informed foundation model called scOntoFM, which embeds hierarchical cell ontology into representation learning. Using Lowest Common Ancestor (LCA) distances from the ontology graph, our framework pairs efficient offline triplet sampling with hierarchical cell-ontology learning. This approach jointly preserves local neighborhoods and shapes the global embedding geometry to reflect the ontology structure. Across diverse cell- and gene-level benchmarks, the model consistently improves performance, with especially strong gains in zero-shot settings. Our model also balances batch integration with biological structure preservation, providing complementary perspectives to existing embedding evaluation frameworks and supporting scalable biological discovery.

cs.LG · cs.AI · q-bio.QM

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10832

Firepool: Brokering Warm GPU Capacity for Agentic and RL Workloads at LinkedIn

Ankit Goyal, Santosh Jha, Zhuo Zhi, Youmin Han, Cong Gu, Chen Xie, Animesh Singh

原文摘要

AI agents and reinforcement-learning (RL) trainers run thousands of short, isolated GPU jobs: tool calls, verifiers, and rollouts. A job often finishes in seconds, but starting a GPU pod for it takes 20-35 s even when the container image is already on the node, and minutes when it is not. A warm pod removes the wait only if it can serve the next job, and keeping pods warm must neither waste GPUs nor leak state between callers. Firepool, the sandbox and execution broker behind agentic and RL workloads at LinkedIn, meets both requirements. It makes pods interchangeable by building them from a shared base image per hardware profile and binding each job's code, extra dependencies, and cluster topology when the job starts. It keeps them warm cheaply and safely by reusing a pod only for the caller that used it last, staging zero-VRAM standby pods in fractional GPU shares alongside active pods, and capping the GPUs that warm pools may hold via queue-driven budgets. Over a sampled peak period in production, Firepool serves 94.7% of 3,206 pod requests from warm or standby capacity. In paired benchmarks with fixed-duration jobs on production H200 nodes, agent and RL loops finish 13x and 10.8x faster than with a fresh pod per job; against a fresh reuse tag per job, warm reuse cuts p90 start latency from 59.1 s to 3.5 s. When a job cannot reuse the previous pod, standby promotion shortens the handover wait from 51 s to 0.94 s. In local and simulated runs, a keep-alive matched to the loop's pauses keeps the latency benefit while holding about half the capacity of an always-on pool.

cs.DC · cs.LG

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.37161

The Vote Hides the Failure: Aggregation Choice and Noise Robustness in Heart Murmur Detection

Nicholaus Dismas Ladislaus, Olatunji Damilare Emmanuel, Samuel Chol Buol

原文摘要

Noise robustness in automated phonocardiogram (PCG) murmur detection, and how it is measured, remains underexamined despite growing interest in low-resource screening. We evaluate two independently reimplemented pipelines, Hierarchical Multi-Scale Convolutional Network (HMS-Net)--CNN, and Bidirectional Long Short-Term Memory (BiLSTM)--LSTM, under controlled, multi-severity noise with noise-augmented fine-tuning and held-out generalization testing. Under matched aggregation, the complete BiLSTM pipeline outperforms the complete HMS-Net pipeline across all conditions in accuracy and Weighted Accuracy. A stable aggregate accuracy score can misrepresent what individual predictions show: HMS-Net's native aggregation degrades under salt-and-pepper noise far less than majority-vote (MV) aggregation at the same severity, a gap reflecting window-level disagreement its native rule absorbs, while BiLSTM's MV accuracy rises after noise-augmented training even though its individual predictions do not improve. HMS-Net's training effect is significant under one accuracy metric but not another. Noise-robustness conclusions can depend as much on evaluation choices as on the models themselves.

cs.LG

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2604.00686

Full-Gradient Successor Feature Learning for Multi-Task Transfer in Reinforcement Learning

Ritish Shrirao, Aditya Priyadarshi, Raghuram Bharadwaj Diddigi

原文摘要

Successor Features (SFs) provide a principled framework for multi-task transfer in Reinforcement Learning (RL) by decoupling environment dynamics from reward functions. By learning representations of expected future feature occupancies, SFs enable knowledge acquired in one task to be transferred to other tasks with different reward functions. However, conventional SF learning relies on semi-gradient Temporal Difference (TD) updates, which, when combined with nonlinear function approximation, generally lack convergence guarantees and can exhibit instability. This motivates exploring alternative learning objectives that offer stronger theoretical properties. Inspired by Full-Gradient DQN, we propose Full-Gradient Successor Feature Representations Q-Learning (FG-SFRQL), which extends full-gradient optimization to successor feature learning by minimizing the squared SF Bellman residual and differentiating through both the prediction and the bootstrapped target. We establish almost-sure convergence of FG-SFRQL under randomized task sampling, characterize the objective it minimizes, and show that averaging the bootstrapped target over $N$ next-state transitions reduces double-sampling bias in proportion to $1/N$. Through experiments in both discrete- and continuous-state domains, we demonstrate that full-gradient learning achieves lower Bellman residuals and improved transfer performance compared to semi-gradient baselines, with FG-SFRQL attaining the highest final return across all five evaluated domains.

cs.LG

查看论文
arXiv ↗PDF ↗
版本更新 · v3arXiv:2603.12554

Reinforcement Learning for Diffusion LLMs with Entropy-Guided Step Selection and Stepwise Advantages

Vishnu Teja Kunde, Fatemeh Doudi, Mahdi Farahbakhsh, Dileep Kalathil, Krishna Narayanan, Jean-Francois Chamberland

原文摘要

Reinforcement learning (RL) has been effective for post-training autoregressive (AR) language models, but extending these methods to diffusion language models (DLMs) is challenging due to intractable sequence-level likelihoods. Existing approaches therefore rely on surrogate likelihoods or heuristic approximations, which can introduce bias and obscure the sequential structure of denoising. We formulate diffusion-based sequence generation as a finite-horizon Markov decision process over the denoising trajectory and derive an exact, unbiased policy gradient that decomposes over denoising steps and is expressed in terms of intermediate advantages, without requiring explicit evaluation of the sequence likelihood. To obtain a practical and compute-efficient estimator, we (i) select denoising steps for policy updates via an entropy-guided approximation bound, and (ii) estimate intermediate advantages using a one-step denoising reward naturally provided by the diffusion model, avoiding costly multi-step rollouts. Experiments on coding and logical reasoning benchmarks demonstrate state-of-the-art results, with strong competitive performance on mathematical reasoning, outperforming existing RL post-training approaches for DLMs. Code is available at https://github.com/vishnutez/egspo-dllm-rl.

cs.LG · cs.AI · cs.CL

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2512.08274

gHAWK: Local and Global Structure Encoding for Scalable Training of Graph Neural Networks on Knowledge Graphs

Humera Sabir, Fatima Farooq, Ashraf Aboulnaga

原文摘要

Knowledge Graphs (KGs) are a rich source of structured data, and graph neural networks (GNNs) are the dominant tool for learning over them. However, existing message-passing GNNs struggle to scale to large KGs because they rely on the iterative message passing process to learn graph structure. This process is inefficient, especially under mini-batch training, where a node sees only a partial view of its neighborhood. In this paper, we address this problem and present gHAWK, a novel and scalable GNN training framework for large KGs. The key idea is to precompute structural features for each node that capture its local and global structure before GNN training even begins. Specifically, gHAWK introduces a preprocessing step that computes: (a)~Bloom filters to compactly encode local neighborhood structure, and (b)~TransE embeddings to represent each node's global position in the graph. These features are then fused with any domain-specific features (e.g., text embeddings), producing a node feature vector that can be used with any GNN backbone. Equipped with these structural priors, the GNN no longer needs to rediscover graph structure, significantly improving convergence and model accuracy with little memory overhead. Extensive experiments on large datasets from the Open Graph Benchmark demonstrate that gHAWK improves accuracy on both node property prediction and link prediction tasks. Remarkably, gHAWK enables simple GNNs to match or exceed more complex relation-aware ones, suggesting that careful preprocessing can substitute for architectural complexity in KG learning.

cs.LG · cs.DB

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10824

AutoC2V: Synthesis-Guided LLM Training for QoR-Optimized C++-to-Verilog Generation

Guanqi Lyu, Fei Zhao, Zhaojun Lu, Jiaji He, Yongqiang Lyu

原文摘要

High-level synthesis (HLS) can translate C/C++ kernels into register-transfer level (RTL) designs, yet generating high-quality hardware still relies heavily on manual pragma tuning and repeated synthesis evaluation. We propose AutoC2V, a C++-to-Verilog generation framework driven by real synthesis feedback that enables large language models (LLMs) to directly generate RTL with improved quality of results (QoR) from synthesizable C++ kernels. We construct training data from real synthesis results, including synthesizable C++ source code, the corresponding Verilog RTL, QoR metrics, and optimization objectives. Using this dataset, we align the model toward synthesis-aware RTL generation through Supervised Fine-Tuning (SFT) and Group Relative Policy Optimization (GRPO). To reduce the high cost of repeatedly invoking real synthesis during GRPO, we design the QoR Prediction Reward Model (QoR RM), which predicts latency and resource metrics by encoding RTL into long-context semantic representations, thereby providing low-cost and continuous QoR reward signals for policy optimization. Experimental results demonstrate that AutoC2V not only substantially improves RTL functional pass rates, but also achieves superior latency and resource optimization compared to the unfine-tuned base model, representative HLS optimization approaches (HLSPilot, LLM-DSE, and AutoDSE), and advanced general-purpose commercial models (Claude Opus 4.8, GPT-5.5, Gemini 3.1 Pro, DeepSeek v4 Pro, and GLM-5.2). These results indicate that combining real synthesis data with efficient QoR reward modeling can further improve the ability of LLMs to generate high-quality RTL.

cs.AR · cs.LG

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.24775

Global Convergence in Deep Networks via the Second Law of Thermodynamics

Matan Tsipory, Ofir Gaash, Itamar Harel, Nathan Srebro, Daniel Soudry

原文摘要

Existing convergence analyses of finite-width overparameterized deep neural networks under standard parameterization often rely on parameters remaining close to their initialization and typically neglect the role of noise, despite empirical evidence that stochasticity can improve optimization and generalization. To bridge this gap, we study $L_2$-regularized continuous-time Langevin dynamics (CLD) with isotropic noise, for training feedforward networks with smooth activations. We introduce a change-of-measure inequality, inspired by the second law of thermodynamics, that enables control of key properties of the training dynamics under the time-evolving parameter distribution. Leveraging a sharpened lower bound on the minimum eigenvalue of the Neural Tangent Kernel (NTK) at initialization, we show that injected noise helps preserve NTK stability with high probability without confining the weights to a small neighborhood of their initialization. As a result, we derive a non-asymptotic upper bound on the expected training loss. Our analysis yields explicit conditions on width and noise temperature that guarantee convergence to a finite loss floor. Finally, we experimentally show the network Jacobian can change from initialization, departing the commonly analyzed lazy regime.

cs.LG

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10822

Learning When to Think: Adaptive Internal Computation for Reinforcement Learning

Rohit Kumar Salla, Simon Stepputtis

原文摘要

Iterative neural policies usually spend the same computation on every decision, although the benefit of additional processing varies across states. We introduce Adaptive Internal Computation (AIC), an iterative policy that learns when to stop refining its latent representation and act. In AIC, we train action selection and stopping jointly, an expected-depth penalty values computation and intermediate value predictions provide auxiliary supervision. We show that when only some states benefit from further refinement, an ideal state-dependent allocator outperforms every state-independent stopping schedule at the same expected depth. With at most eight refinement steps per decision, AIC executes 3.3 steps on average across twelve benchmark tasks and lowers per-decision latency by 31--44% relative to a fixed eight-step policy on four control and robotics tasks. Its mean reward exceeds the best fixed-depth policy on seven tasks and trails it on four. Ablating adaptive stopping and value supervision separately shows that stopping accounts for most of the reward gain on PickCube and AntMaze, where it also executes more refinement steps and that supervision accounts for most of it on RareEvent.

cs.LG · cs.RO

查看论文
arXiv ↗PDF ↗
版本更新 · v3arXiv:2604.19530

Calibrating Scientific Foundation Models with Inference-Time Stochastic Attention

Akash Yadav, Taiwo A. Adebiyi, Ruda Zhang

原文摘要

Transformer-based scientific foundation models are increasingly deployed in high-stakes settings, but current architectures give deterministic outputs and provide limited support for calibrated predictive uncertainty. We propose Stochastic Attention, a lightweight sample-average inference-time modification that randomizes attention by replacing softmax weights with normalized multinomial samples controlled by a single concentration parameter, and produces predictive ensembles without retraining. To set this parameter, we introduce a calibration objective that matches the stochastic attention output with the target, yielding an efficient univariate post-hoc tuning problem. We evaluate this mechanism on scientific foundation models for weather and time-series forecasting, as well as a regression task. Across benchmarks against baseline methods, Stochastic Attention achieves the strongest native calibration and the sharpest prediction intervals at comparable calibration, with adaptation costs nearly three orders of magnitude lower than the next-best baseline. Code is available at https://github.com/UQUH/StochasticAttention.

cs.LG · cs.CE · stat.ML

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2604.02485

Failing to Falsify: Evaluating and Mitigating Confirmation Bias in Language Models

Ayush Rajesh Jhaveri, Anthony GX-Chen, Ilia Sucholutsky, Eunsol Choi

原文摘要

Confirmation bias, the tendency to seek evidence that supports rather than challenges one's belief, hinders one's reasoning ability. We examine whether large language models (LLMs) exhibit confirmation bias by adapting the rule-discovery study from human psychology: given a sequence of three numbers (a "triple"), an agent engages in an interactive feedback loop where it (1) proposes a new triple, (2) receives feedback on whether it satisfies the hidden rule, and (3) guesses the rule. Across eleven LLMs of multiple families and scales, we find that LLMs exhibit confirmation bias, often proposing triples to confirm their hypothesis rather than trying to falsify it. This leads to a slower and less frequent discovery of the hidden rule. We further explore intervention strategies developed for humans (e.g., encouraging the agent to consider counterexamples). We find that prompting LLMs with such instructions consistently decreases confirmation bias in LLMs, improving rule discovery rates from 42% to 56% on average. Lastly, we mitigate confirmation bias by distilling intervention-induced behavior into LLMs, showing generalization to a new domain (the Blicket test) and beyond rule discovery (mathematical self-correction). Our work shows that confirmation bias is a limitation of LLMs in hypothesis exploration, and that it can be mitigated via injecting interventions designed for humans.

cs.CL · cs.LG

查看论文
arXiv ↗PDF ↗
版本更新 · v3arXiv:2410.23244

Very fast Bayesian Additive Regression Trees on GPU

Giacomo Petrillo

原文摘要

Bayesian Additive Regression Trees (BART) is a nonparametric Bayesian regression technique based on an ensemble of decision trees. It is part of the toolbox of many statisticians. The overall statistical quality of the regression is typically higher than other generic alternatives, and it requires less manual tuning, making it a good default choice. However, it is a niche method compared to natural competitors such as XGBoost, due to the longer running time, making sample sizes above 10000-100000 a nuisance. We present a GPU implementation of BART, up to 500x faster than the previous state of the art, making BART fast enough to be a viable option on large datasets, and showing that BART parallelizes better than its non-Bayesian counterparts. We also find that, to be as practical as tree boosting on large datasets, BART would need a scalable way to infer the number of trees, which we do not provide. Our implementation is available in the Python package bartz.

stat.ML · cs.LG

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.13825

Scalable Deep Unfolding of Conic Optimizers

Alex Oshin, Rahul Vodeb Ghosh, Evangelos A. Theodorou

原文摘要

Deep unfolding (DU) accelerates iterative optimizers by training learnable components through unrolled iterations, but extending DU to the large-scale semidefinite programs (SDPs) common in robotics has remained limited. Unrolling a full-update conic solver such as COSMO exposes two obstacles: backpropagating through the per-iteration linear-system solve incurs memory quadratic in the problem size when the coefficient matrix is formed explicitly, and backpropagating through the positive semidefinite (PSD) cone projection is numerically unstable under repeated eigenvalues. We address the first with a matrix-free implicit differentiation rule that reduces memory from $O(n^2)$ to $O(n + \mathrm{nnz}(A))$, and the second with a stable backward rule based on the Dalečkii--Krein representation of the Fréchet derivative. Together, these enable learning lightweight hyperparameter policies and warm starts for COSMO. On standalone SDPs and second-order cone programs, ranging from max-cut and Lovász $\vartheta$ to robust estimation and control, the learned policies outperform tuned COSMO on every problem class, reducing iterations to tolerance by up to 60$\times$, and transfer without retraining to instances up to 10$\times$ larger in the size parameter while retaining 2--5$\times$ speedups. Our matrix-free solver implementation also batches naturally on the GPU, solving 64 Lovász $\vartheta$ SDPs 14$\times$ faster than sequential COSMO.jl. When embedded in sequential convex programming for nonlinear covariance steering, our learned optimizers outperform the interior-point solver Clarabel in terms of both success rate and solve time.

math.OC · cs.LG

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10803

On Generalization in Bilevel Least Squares with Overparameterized Neural Networks

Fares El Khoury, Edouard Pauwels, Samuel Vaiter, Michael Arbel

原文摘要

Bilevel optimization addresses machine learning problems in which the outer objective is a value function implicitly defined through the solution of an inner problem. While its algorithmic aspects are well developed, its generalization properties remain much less understood. In this paper, we study the generalization of inexact gradient methods when both objectives are least-squares regression problems and when the estimator of the value function's gradient, or hypergradient, is obtained by implicit differentiation in function space. This approach allows us to derive stationarity bounds for these methods in terms of the uniform generalization errors of the two regression functions needed to estimate the hypergradient. When these regression functions are defined as overparameterized two-layer ReLU networks trained by gradient flow with early stopping, we obtain explicit finite-sample bounds on these generalization errors in the Neural Tangent Kernel (NTK) regime. Combining these with our general inexact gradient analysis shows the effect of sample size on the overall convergence of the bilevel method. Numerical experiments on synthetic data illustrate our results. Beyond the NTK regime, further experiments reveal an initialization-dependent implicit bias induced by the bilevel structure, which can lead to feature learning at the inner level and overall performance improvements.

stat.ML · cs.LG

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2608.28685

Data Diversity, Not Frequency Invariance: A Controlled and Self-Audited Study of Compression-Robust Deepfake Detection

Abbas Aliyev, Samir Rustamov

原文摘要

Frequency features and compression-invariant representation learning are widely assumed to be key to deepfake detection that survives video compression. We test this with CAFRL - block-DCT and FFT-phase streams, compression-level-conditioned band attention, and adversarial (gradient-reversal) compression invariance - and report a controlled negative. Under a pre-registered protocol with capacity- and augmentation-matched controls, a plain EfficientNet-B0 on multi-quality data beat CAFRL as specified at every compression level on the FaceForensics++ test split, by 3.66 AUC points at CRF 40 (paired, single seed). A self-audit of our own negative found four defects biased against the frequency hypothesis, and pre-specified re-tests repairing all four showed the deficit to be a recipe artifact, not an architecture failure: the baseline recipe recovered 3.96 points over the matching shipped-recipe variant. The frequency path made no detectable difference: discriminative alone (standalone validation AUC 0.91-0.98 late in training) but of no marginal value under this fusion, at two feature widths of one 4.0 M trunk, every seed-pooled interval for the intra-dataset compression contrasts including zero; on the single held-out manipulation tested, the fair variants sat below the plain backbone. The adversarial branch, as specified, added nothing and degraded its own conditioning estimator; at the fair recipe it is untested. Robustness under single-pass H.264 re-encoding came instead from data diversity: real constant-rate-factor variants beat synthetic JPEG augmentation by 7.3 points (single runs, non-overlapping intervals). The evidence is FaceForensics++-family, GAN-era and single-codec. Match controls on training recipe as well as capacity, and buy compression robustness with codec diversity before architecture.

cs.CV · cs.LG · cs.MM

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2509.16801

Sublinear Time Quantum Sensitivity Sampling

Zhao Song, David P. Woodruff, Lichen Zhang

原文摘要

We present a unified quantum sensitivity sampling framework that streamlines coreset construction for many classical approximation problems, significantly speeding up clustering, regression, and low-rank approximation. We assume $n,d\geq\mathrm{poly}(k/ε)$ and, for regression, $n\geq\mathrm{poly}(d)$: - $k$-median/$k$-means: For $n$ points in $\mathbb{R}^d$, we build an $ε$-coreset of input points of size $\tilde O(k^{4/3}ε^{-2})$ (resp. $\tilde O(k^{3/2}ε^{-2})$) in time $\tilde O(ε^{-1}n^{0.5}dk^{1.67})$ (resp. $\tilde O(ε^{-1}n^{0.5}dk^{1.75})$). Our better $d$- and linear $1/ε$-dependence improve on [Xue, Chen, Li and Jiang, ICML'23] for $d\geqΩ(k^{7/3})$ (resp. $Ω(k^{2.5})$). - $\ell_p$ regression: For $\min_x\|Ax-b\|_p$, $A\in\mathbb{R}^{n\times d}$, we build an $ε$-coreset of size $\tilde O_p(d^{\max\{1,p/2\}}ε^{-2})$ in time $\tilde O_p(n^{0.5}d^{1.5}ε^{-1})$ for $p\leq 2$ and $\tilde O_p(n^{0.5}d^{p/4+1}(ε^{-1}+d^{0.5}))$ for $p>2$, matching [Liu and Ji, ICML'26] for $p\leq 2$ and improving on the prior best quantum sampling approach [Apers and Gribling, SICOMP'26] for all $p>2$. - Low-rank approximation: We give the first quantum sublinear-time algorithm for Frobenius-norm rank-$k$ approximation of $A\in\mathbb{R}^{d\times n}$ free of data-dependent parameters, running in $\tilde O(n^{0.5}dk^{0.5}ε^{-0.5})$ time with factored output (plus $\tilde O(nk^2ε^{-2})$ to materialize them). We also give sublinear algorithms for kernel and tensor variants. - Lower bound: For generic sensitivity sampling with total sensitivity $S$, any quantum algorithm outputting an $ε$-coreset of size $o(\sqrt{nS})$ needs $Ω(\sqrt{nS})$ queries, so our query complexity is optimal up to $\mathrm{poly}(ε^{-1},\log n)$ and VC-dimension factors.

cs.DS · cs.LG · quant-ph

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10793

Trainability-Simulability Tradeoff in Parameterized IQP-based Reinforcement Learning Policies

André Sequeira

原文摘要

Parameterized instantaneous quantum polynomial-time (IQP) circuits generate distributions conjectured to be hard to sample classically. As reinforcement learning (RL) policies, their $2^n$ measurement outcomes are typically mapped to a smaller action space by a classical decoder. This aggregation can mitigate initialization concentration while making the policy classically simulable. We characterize this tradeoff through the decoder's Fourier--Pauli expansion. Under independent uniform initialization, we express policy variance as an exact sum over groups of nonconstant Pauli components with identical active-generator signatures. Each group contributes its squared aggregate coefficient times $2^{-r}$, where $r$ is the binary rank of its active generators. An inverse-polynomial aggregate coefficient with logarithmic rank prevents exponential policy concentration. Sparse Pauli expansions and contiguous action bins permit efficient classical additive estimation of action probabilities. We introduce a majority decoder combining protected low-weight components with exponentially large exact Fourier support, preserving initialization nonconcentration while excluding polynomial-size enumeration of the exact expansion. Numerical experiments corroborate the variance laws. In ten-qubit CartPole experiments, majority achieves higher returns than recursive parity and the tested local readouts, while higher-weight Pauli sectors contribute an increasing relative share of gradient magnitude during training. A complementary 21-qubit block-chain policy outperforms an extensive pool of fully connected classical neural networks in episode efficiency, even though its bounded-width structure permits efficient classical simulation. These results guide decoder design for quantum and quantum-inspired RL, distinguishing policy nonconcentration, learning performance, and classical simulability.

quant-ph · cs.LG

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10792

Zeroth-Order Riemannian Optimization on Fixed-Rank Manifolds for LLM Fine-Tuning

Xinjue Wang, Xiuheng Wang, Yejun Zhang, Sergiy A. Vorobyov, Esa Ollila

原文摘要

Zeroth-order (ZO) optimization estimates descent directions from function values alone, which removes the activation storage required by backpropagation and makes it attractive for memory-efficient fine-tuning of large language models (LLMs). Existing low-rank ZO methods either parameterize the additive update through Euclidean factors with a non-unique representation, or restrict each finite-difference direction to be low-rank. In this work, we propose to constrain each additive update block to a fixed-rank matrix manifold while keeping the pretrained weights frozen, so the learned update remains rank-constrained throughout fine-tuning. On this geometry, we build a Riemannian ZO gradient estimator that projects a random matrix onto the tangent space, normalizes it to correct the scale mismatch induced by projection, and further propose Zeroth-order Riemannian Gradient Descent (ZO-RGD) and its smoothed variant, ZO-RGD-S. We show that the ambient finite difference matches its retracted counterpart, the normalized estimator recovers the Riemannian gradient in expectation, and ZO-RGD converges under trajectory-regularity conditions. Experiments on LLM fine-tuning tasks show that the proposed algorithms are competitive with strong forward-only baselines across model families and tasks, with gains in several settings.

cs.LG · math.OC

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10789

Generating Diverse and Verified Synthetic Clinical Notes with Labels by Construction

Matthias Lehmann, Madeleine Werthebach

原文摘要

Under strict privacy regulations in the clinical domain, de-identification is necessary to enable the use of clinical data such as for natural language processing. However, we currently lack sophisticated ways to tell which de-identification models actually are suitable. Specifically, we need benchmarks that are challenging, diverse, and clinically plausible, all while ensuring label correctness. To this end, we present SPARCLING, an LLM-based pipeline to generate synthetic discharge letters with character-exact PHI span annotations. Our pipeline combines structured attribute sampling, template generation, and red teaming to obtain diverse PHI-labeled discharge letters. In our experiments, we demonstrate this superior content diversity over existing public datasets. Further, we show SPARCLING's downstream utility as a benchmark to uncover failure modes of de-identifiers in the German clinical domain. Finally, our experiments show that training on our synthetic data can yield more capable de-identifiers than training on the scarce public data alone. Our code is available at https://github.com/Matt00n/phi_discharge_letter_generator.

cs.LG · cs.AI · cs.CL

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10787

Trustworthy Recursive Self-Improvement with Statistical Guarantees

Zihe Gao, Zhengling Qi, Yifan Cui

原文摘要

Recursive self-improvement offers a promising path toward AI agents that improve through experience by refining their own harnesses without retraining their underlying large language models (LLMs). Making this self-improvement process reliable, however, requires more than generating new updates: agents must decide which changes are worth keeping. A greedy gate that accepts updates based solely on higher validation scores can mistake an observed gain for genuine progress and lead to performance degeneration, whereas overly conservative statistical gates may reject beneficial updates and hinder performance improvement. We introduce Trustworthy Recursive Updates with Statistical Testing (TRUST), a framework that adapts an online multiple hypothesis testing procedure to control the rate of false evolution across rounds, with an LLM analyzing historical context to help determine how much evidence is required to update the incumbent. Under interpretable assumptions, we prove that TRUST limits the proportion of accepted updates that result in unacceptable performance loss. We further derive a lower bound on the final performance gain. Across the benchmarks HotpotQA, DROP, and APPS with the models DeepSeek-V4-Flash and Qwen-3.8-Flash, TRUST reduces the average ratio of accepted inferior updates from the greedy gate's 45.59% to 2.78%, while achieving higher final scores than the state-of-the-art statistical gate baselines for recursive self-improvement. These findings demonstrate that TRUST strikes a balance between statistical reliability and performance gain in recursive self-improvement.

stat.ML · cs.AI · cs.LG · stat.ME

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10785

Proximal Preference Optimization: A Unified Framework for Stable and Reward-Free Alignment

Qisai Liu, Zhanhong Jiang, Zahid Hasan, Soumik Sarkar

原文摘要

We introduce a proximal preference optimization framework that unifies the implicit reward formulation of Direct Preference Optimization (DPO) with the trust-region stability of Proximal Policy Optimization (PPO). Our framework yields two complementary algorithms. P$^2$O (Proximal Preference Optimization) introduces a clipped importance ratio directly into the DPO preference margin, producing a reward-model-free algorithm that supports safe dataset reuse, bounded policy divergence, and monotonic preference improvement guarantees. PKTO (Proximal Kahneman-Tversky Optimization) extends P$^2$O by replacing the DPO margin with KTO's prospect-theoretic utility margins, adding a self-regulating adaptive baseline that tightens the effective trust region as the policy drifts, and an asymmetric weighting that handles imbalanced or asymmetric preference datasets. We provide a unified theoretical analysis, including Kullback Leibler (KL) trust-region bounds, a policy-improvement lower bound, and convergence guarantees under multi-epoch proximal updates. Empirically, P$^2$O and PKTO match or exceed DPO and KTO in alignment quality while maintaining better stability under repeated dataset reuse across multiple LLMs on three preference benchmarks. The code base is available here.

cs.LG · cs.AI

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10781

EEGFaceSem: An EEG Benchmark with Paired Generative Latents for Semantic Visual Modeling

Jun Ma, Michiel M. Spapé, Keith M. Davis, Tuukka Ruotsalo

原文摘要

Electroencephalography (EEG) is widely used to measure brain activity for brain-computer interface applications for its non-invasive nature and high temporal resolution. However, progress in modeling the neural responses to visual semantics has been limited by a reliance on fixed image collections that lack the fine-grained control needed to isolate fine-grained changes in visual features and their relation to perception. In this work, we present EEGFaceSem, a large-scale dataset pairing high-quality EEG with photorealistic face images synthesized from a generative model. We provide the generative latent vector for each stimulus, enabling research into the continuous mapping between a visual latent space and neural representations. Each participant's brain responses were recorded while they attended to specific semantic features (e.g., smiling, hair color) in a rapid serial visual presentation paradigm. The dataset contains EEG signals from 30 participants, totaling 67,200 time-locked EEG recordings. We present benchmarks for single-trial semantic classification under single-subject, cross-subject, and subject-adapted settings, as well as for cross-subject voting that aggregates per-stimulus predictions across participants. We further demonstrate the dataset's unique utility with two complementary applications: deriving semantic directions in the latent space for brain-supervised image editing, and conditioning a generative model on brain-decoded labels for brain-supervised image generation. Our dataset paves the way for advancing brain-computer interfaces (BCI) for semantic-level visual saliency detection and brain-supervised generative modeling. Our dataset and code are openly released at OSF (https://osf.io/2guk3/) and at HuggingFace https://huggingface.co/datasets/yefllower/EEGFaceSem.

cs.LG

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.41536

DAPS: Dependency-Aware Premise Selection for LLM Theorem Proving

Zixuan Chen, Wenyuan Jiang, Junling Wang, Mrinmaya Sachan, Yinya Huang

原文摘要

LLM-based theorem proving in Lean 4 has advanced rapidly, but provers are difficult to leverage newly contributed lemmas, because retrieval skews toward popular foundational lemmas, leaving the right premises buried among hundreds of thousands of candidates. Existing neural selectors rank candidates by semantic similarity, with three intrinsic limitations: frequency-skewed retrieval, isolated pairwise ranking, and degradation on out-of-distribution queries such as competition problems and informal language. Given that mathematics is structured by dependencies rather than similarities, we restore this signal to both the training data and the model. We curate two underused dependency resources. We conduct the first systematic mining of 53 Lean Blueprint projects, a previously untapped corpus of 3,805 manually curated informal+formal nodes that capture rare, research-level premises absent from Mathlib4. We further pair it with the extraction of comprehensive, typed, multi-level Mathlib4 dependencies. We then introduce DAPS, a dependency-aware premise selector with a structural neighborhood encoder, a group-level contrastive objective, and a Mathlib-then-Blueprint adaptation procedure. DAPS reaches 78.74 Recall@16 on Mathlib4-Heldout (+11.41 over the strong selector LeanHammer) and widens the margin to 7xrecall on competition-style queries. Downstream, its selected premises raise Goedel-Prover-V2-8B Pass@4 on miniF2F-test from 49.90 to 54.03 and improve all four general-purpose LLMs on the informal IMO-ProofBench (up to +11.4% relative). Project page: https://daps-eth.github.io/.

cs.LO · cs.CL · cs.LG

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2602.18851

Rank-Aware Spectral Bounds on Attention Logits for Stable Low-Precision Training

Seyed Morteza Emadi

原文摘要

Attention scores in transformers are bilinear forms $S_{ij} = x_i^\top M x_j / \sqrt{d_h}$ whose maximum magnitude governs overflow risk in low-precision training. We derive a rank-aware concentration inequality: when the interaction matrix $M = W^Q W^{K\top}$ has rank $r \ll d$, tail probabilities for $\max_{i,j}|S_{ij}|$ decay as $\exp(-d^{2}α^{2}/(γr))$ rather than $\exp(-dα^{2})$, where $γ> 1$. For transformer attention where $r = d_h$, this yields $8$ to $28\times$ tighter concentration than rank-agnostic bounds in modern architectures. We apply this result to FP8 training, deriving geometry-aware scale factors that provide principled overflow guarantees without observing activations. The method computes per-layer scales from the spectral norm $\|W^Q W^{K\top}\|_2$ via implicit power iteration, includes a grouped query attention formulation that avoids key expansion, and remains compatible with fused attention kernels. Across GPT-2 XL to Llama-2-70B, geometry-aware scaling eliminates overflows in transient scenarios where delayed scaling fails, while achieving comparable downstream MMLU accuracy.

cs.LG · cs.AI

查看论文
arXiv ↗PDF ↗