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

+1新增论文
+1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 07:07 · 全站最近入库：2026-11-13 06:39各信源进度 →

统计口径与数据新鲜度

统计 2026-11-12 07:28 — 2026-11-13 07:28（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 07:07最近发现新增版本：2026-11-13 06:39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。

21,676 已收录论文18,324 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 4,904 篇论文 · 按版本更新时间排序上轮扫描起点 2026-11-13 07:05（北京时间）
版本更新 · v3arXiv:2604.01342

Massively Parallel Exact Inference for Hawkes Processes

Ahmer Raza, Hudson Smith

原文摘要

Multivariate Hawkes processes are a widely used class of self-exciting point processes, but maximum likelihood estimation naively scales as $O(N^2)$ in the number of events. The canonical linear exponential Hawkes process admits a faster $O(N)$ recurrence, but prior work evaluates this recurrence sequentially, without exploiting parallelization on modern GPUs. We show that the Hawkes process intensity can be expressed as a product of sparse transition matrices admitting a linear-time associative multiply, enabling computation via a parallel prefix scan. This yields a massively parallelizable algorithm for estimation of linear exponential Hawkes processes. Our method reduces the computational complexity to approximately $O(N/P)$ with $P$ parallel processors, and naturally yields a batching scheme to maintain constant memory usage, avoiding GPU memory constraints. Importantly, it computes the exact likelihood without any additional assumptions or approximations, preserving the simplicity and interpretability of the model. We demonstrate orders-of-magnitude speedups on simulated and real datasets, scaling to thousands of nodes and tens of millions of events, substantially beyond scales reported in prior work. We provide an open-source PyTorch library implementing our optimizations.

cs.LG · stat.ML

查看论文
arXiv ↗PDF ↗
版本更新 · v3arXiv:2411.19092

Neural Window Decoding of Spatially Coupled LDPC Codes: Exploiting Window Structure for Learning and Adaptation

Dae-Young Yun, Hee-Youl Kwak, Sang-Hyo Kim, Yongjune Kim, Jong-Seon No

原文摘要

We propose a neural window decoder (NWD) for spatially coupled low-density parity-check (SC-LDPC) codes. The proposed methods adapt neural min-sum decoding to the structure and sequential operation of window decoding. First, target-oriented training restricts the loss to the variable nodes whose decisions are required at each window stage. Second, learned damping factors identify check-node updates that can reuse messages from the preceding iteration. Finally, adaptive weight switching selects weights trained for unreliable inherited information when a previous-window failure is detected. For a length-$20{,}000$ $(3,6)$-regular SC-LDPC code, the target-oriented NWD achieves performance comparable to sum-product window decoding while using min-sum updates. On the reduced target graph, scheduling based on regularized damping factors yields an estimated computational cost 48\% lower than that of full-graph weighted min-sum decoding, while maintaining comparable decoding performance. Adaptive weight switching improves decoding performance by 35\% at 3.0 dB relative to plain weights. These learning methods exploit the properties of window decoding to improve the reliability and computational efficiency of SC-LDPC convolutional codes.

cs.LG · cs.IT

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10523

Action Transfer Across Character Body Plans in Latent-Action World Models: An Empirical Study of Animated Characters

Amarnath R

原文摘要

Latent-action world models learn actions from video without labels, and recent methods aim to share these actions across bodies. Transfer between 2D characters with different body structures remains underexplored. In this paper, we study whether an action learned from one character can make a character with a different body perform the same action. A cat, a blob and a human can all jump, but each moves its body in its own way. The usual transfer gain mixes body motion with screen movement and can assign a perfect score to a model that ignores its action. To address this, we introduce a transfer score that compares the correct action with wrong ones from the same source. We test five training objectives on a new synthetic benchmark of 2D cartoon characters with six body plans, the standard Sprites benchmark, and 431 selected animal and human animations from DeformingThings4D. With a fixed camera, a baseline that only copies the source's screen movement scores 0.71, as high as most learned models. In contrast, with a camera that follows the body, the best model reaches only 0.46 for an unseen animal family and 0.20 for humans. With this camera, a linear probe identifies the action class of unseen animal families with 66% to 86% balanced accuracy (chance 17%). The learned actions therefore retain information about which action is performed, but the correct action closes only part of the prediction error gap for a new character.

cs.CV · cs.GR · cs.LG

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10521

TimeHF 2.0: Multimodal, Multivariate, and Multi-Region Collaborative Forecasting for Large-Scale Retail Supply Chains

Zhongxuan Han, Zhengxin Shi, Kunxiu Chen, Xiaolin Huang, Yulin Huang, Anni Zhang, Xiaoming Lin, Zhengyu Chen 等 33 位作者

原文摘要

Large-scale retail supply chains need sales forecasts for specified products, future dates or planning windows, and regions under the conditions available at the forecast origin. Regional sparsity, changing environments, volatile sales, and unfamiliar business situations make these dimensions difficult to model together. We introduce TimeHF 2.0, a multimodal, multivariate, and multi-region collaborative forecasting framework that makes flexibly expressed business knowledge usable in numerical prediction. A coverage-aware directed encoder integrates regional sales and applicable covariates. A semantic alignment module transfers language-model hidden representations of natural-language plans and domain experience into target-specific forecasting context. Learning against sales observations connects these meanings to numerical evidence, allowing business users to supply experience, future plans, and changing circumstances without defining a new numerical feature for each description. Text-conditioned future queries and shared/routed experts adapt prediction to the supplied context. An ordered-distribution head allocates fine resolution to routine fluctuations and coarse resolution to extreme sales movements, while daily/window learning connects forecasts to cumulative planning quantities. Online parallel forecasting at JD yields relative accuracy improvements of 18.25% and 17.94% for national daily and planning-window sales, and 19.44% and 18.99% for DC daily and planning-window sales, over the original production deep learning baseline. Component comparisons, loss-design results, and product-type analyses examine the roles of regional information, covariates, business knowledge, context-dependent processing, and robust planning-oriented learning.

cs.AI · cs.LG

查看论文
arXiv ↗PDF ↗
版本更新 · v3arXiv:2604.20568

Amortized Vine Copulas for High-Dimensional Density and Information Estimation

Houman Safaai

原文摘要

Modeling high-dimensional dependencies while keeping likelihoods tractable re- mains challenging. Classical vine copulas provide an explicit dependence structure but require repeated, costly bivariate fitting. We propose Vine Denoising Copula (VDC)1, an amortized estimator for simplified vines on continuous data. VDC trains a single bivariate denoising network on simulated copulas and reuses it across all vine edges. For each edge, the network maps an empirical histogram to a density grid, and iterative proportional fitting approximately enforces uniform grid marginals. The predicted densities and cached conditional transforms support stan- dard vine recursion, likelihood evaluation, sampling, and information estimation. This retains the classical vine factorization while replacing repeated per-edge fitting with reusable neural inference. Across synthetic and real-data benchmarks, VDC combines accurate bivariate density estimation, competitive information estimates, and faster high-dimensional vine fitting after pretraining. Performance varies across datasets; finite grid resolution and conditional prediction remain limitations. The explicit fitted structure supports edge- and tree-level information decomposition, making repeated dependence analysis more practical.

cs.LG · cs.IT · stat.ME

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.27832

USAD: Uncertainty-aware Statistical Adversarial Detection

Zhijian Zhou, Xunye Tian, Jiacheng Zhang, Zesheng Ye, Yiyi Guo, Donghao Zhang, Liuhua Peng, Feng Liu

原文摘要

Statistical adversarial detection (SAD) treats detection as a two-sample test. Given a reference set of clean examples (CEs) and a batch of queries, potentially containing an unknown mixture of CEs and adversarial examples (AEs), SAD decides whether the query distribution drifts away from the CE distribution while controlling the false-alarm rate. Existing SAD-based methods mainly use maximum mean discrepancy (MMD) to measure the distributional discrepancy. However, MMD's distributional properties limit its ability to capture characteristic uncertainty patterns of AEs that are crucial for detection: AEs typically exhibit abnormal feature spread (i.e., global uncertainty) and instability under perturbations (i.e., local uncertainty). To close the gap, we propose Uncertainty-aware Statistical Adversarial Detection (USAD), which explicitly captures these uncertainty patterns with two new statistics: (1) Variance Discrepancy (VD), which measures the difference in feature spread between AEs and CEs to capture global uncertainty differences. (2) Perturbation-based Covariance Discrepancy (PCD), which compares feature covariance under Gaussian perturbations to capture local uncertainty differences. By aggregating VD and PCD, USAD achieves superior detection performances over baseline methods against various adversarial attacks, highlighting the importance of considering characteristic behaviors of AEs for effective SAD. Our code is available at: https://github.com/yeager20001118/USAD.

cs.LG

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.06682

SecureGARD: Dynamic Ontology-Guided Governance for Secure Tool-Using LLM Agents

Guoqin Xiong, Shengtao Wen, Xiang Chen, Xinyi Zeng, Zhaoxin Fan, Wei Ye, Xiaofeng Cao, Sheng-Jun Huang

原文摘要

Tool-using large language model (LLM) agents translate model outputs into consequential actions, turning indirect prompt injection into an execution-security problem. Existing defenses constrain tool calls, trajectories, or information flows, but often leave action semantics, state dependencies, and evidence relations implicit, limiting generalization across heterogeneous tools and complicating safe updates. We present SecureGARD, a dynamic ontology-guided governance framework. SecureGARD grounds heterogeneous calls in a shared semantic representation and combines an executable ontology with episode-level task state to derive deterministic ready, waiting, and forbidden decisions. A waiting decision identifies an unmet but recoverable obligation, enabling bounded replanning without expanding the agent's authorization boundary. SecureGARD evolves its ontology offline from verified training executions. An offline LLM proposes instance-independent action-state axioms, which are accepted only if they address ontology errors exposed by training failures, preserve logical consistency and protected entailments, and pass held-out validation before the ontology is frozen. We evaluate SecureGARD on AgentDojo, ASB, and AgentDyn under benign and indirect prompt-injection settings. SecureGARD achieves a 0% attack success rate (ASR) across all three benchmarks while improving task utility under attack compared with existing baselines. These results show that explicit semantic governance can strengthen execution security without indiscriminate blocking.

cs.LG

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.04714

Leakage-aware acoustic drone detection: fusing hand-crafted and pretrained audio features in a stacking ensemble

Zahid Iqbal

原文摘要

Small multirotor drones produce a characteristic acoustic signature, and microphones offer a low-cost way to detect them. High accuracies have been reported on public drone audio benchmarks, often with random clip-level splits of datasets built from a few long recordings and pieces of existing sound collections, so pieces of one source can appear in both training and test data. We study the binary task of the public DroneAudioDataset (11,704 one-second clips). Its 1,332 drone clips come from 257 five-second chunks of 7 recordings, and 96.4% of its non-drone clips are pieces of the 2,000 clips of the ESC-50 sound dataset. A grouped, source-disjoint protocol (five folds, three repeats) is compared with a random split. Each clip is described by 57 hand-crafted acoustic descriptors, reduced by a hybrid minimum-redundancy maximum-relevance and ReliefF selection or by NSGA-II multi-objective search, and by a 1024-dimensional embedding from the pretrained YAMNet network. Random Forest and XGBoost learners on both views form a stacking ensemble. Under the grouped protocol, the two-view stack reached a mean accuracy of 95.29% (SD 1.62 points over 15 test folds), a specificity of 98.29% and an area under the ROC curve of 0.990, and it correctly classified 98.78% of unseen ESC-50 source clips as non-drone. The random split overstated accuracy by 1.89 to 3.22 points for the four models. Adding YAMNet embeddings to the hand-crafted view raised accuracy by 1.11 points (higher in 12 of 15 folds). Hybrid selection matched the accuracy of all 44 baseline descriptors with 16 to 18 features, and NSGA-II retained 4 to 6 features at lower accuracy. The stack took 51.0 ms per clip on one CPU thread. Source-disjoint evaluation exposes the optimism of random splits and gives a reliable benchmark, and fusing hand-crafted with YAMNet features consistently improves detection in real time on one CPU thread.

cs.SD · cs.LG

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2607.02512

Program-as-Weights: A Programming Paradigm for Fuzzy Functions

Wentao Zhang, Liliana Hotsko, Woojeong Kim, Pengyu Nie, Stuart Shieber, Yuntian Deng

原文摘要

Many everyday programming tasks resist clean rule-based implementation, such as alerting on important log lines, repairing malformed JSON, or ranking search results by intent, and are increasingly outsourced to large language model APIs at the cost of locality, reproducibility, and price. We propose fuzzy-function programming: compiling such a function from a natural-language specification into a compact, locally executable neural artifact. We instantiate this paradigm with Program-as-Weights (PAW), in which a 4B compiler trained on FuzzyBench, a 10M-example dataset we release, emits parameter-efficient adapters for a frozen, lightweight interpreter. A 0.6B Qwen3 interpreter executing PAW programs matches the performance of direct prompting of Qwen3-32B, while using roughly one fiftieth of the inference memory and running at 30 tokens/s on a MacBook M3. PAW reframes the foundation model from a per-input problem solver into a tool builder: invoked once per function definition, it produces a small reusable artifact whose subsequent calls per function application are cheap and offline.

cs.LG · cs.AI · cs.CL

查看论文
arXiv ↗PDF ↗
版本更新 · v3arXiv:2606.20820

CELEUS: Certifiable and Efficient LLM Evaluation via E-Processes

Zhijian Zhou, Zesheng Ye, Zhaorun Chen, Bo Li, Feng Liu

原文摘要

Can we trust evaluation scores to capture an LLM's true real-world performance? Certifiable evaluation answers this question by providing a confidence interval (CI) that covers the LLM's true performance with high probability (e.g., 95%). In particular, existing methods sequentially select evaluation samples from a fixed pool and keep updating the CI until certain conditions are satisfied, e.g., the CI width reaches a target precision. However, existing methods are not generally anytime-valid: the claimed coverage (e.g., 95%) may fail when CIs are repeatedly updated and used to decide when to stop, leaving a gap between theoretical rigor and practice. This paper bridges this gap by proposing CELEUS, a Certifiable framework for Efficient LLM evaluation, which leverages E-processes to build anytime-valid CIs. Concretely, we propose a new signal that combines two ingredients: (i) Uncertainty-guided sampling to select informative samples for evaluation, and (ii) Surrogate-assisted approximations for unevaluated samples. We prove that the new signal remains unbiased for the evaluation score conditional on the past, enabling statistically-grounded and anytime-valid e-process CIs. More importantly, the two ingredients reduce estimation variance and help reach the target precision with fewer evaluated samples. We also prove that CIs obtained by CELEUS can shrink at a near-parametric rate up to logarithmic factors and analyze the oracle variance-optimal sampling rule that motivates the empirical uncertainty-guided one. Experiments show that CELEUS reaches the target precision using 54-62% fewer evaluated samples than baselines, while preserving anytime-valid coverage. Code is available at https://github.com/zyecs/celeus.

cs.LG

查看论文
arXiv ↗PDF ↗
版本更新 · v3arXiv:2603.07388

Sparsity and Out-of-Distribution Generalization

Scott Aaronson, Lin Lin Lee, Jiawei Li

原文摘要

Explaining out-of-distribution generalization has been a central problem in epistemology since Goodman's "grue" puzzle in 1946. Today it's a central problem in machine learning, including AI alignment. Here we propose a principled account of OOD generalization with three main ingredients. First, the world is always presented to experience not as an amorphous mass, but via distinguished features (for example, visual and auditory channels). Second, Occam's Razor favors hypotheses that are "sparse," meaning that they depend on as few features as possible. Third, sparse hypotheses will generalize from a training to a test distribution, provided the two distributions sufficiently overlap on their restrictions to the features that are either actually relevant or hypothesized to be. The two distributions could diverge arbitrarily on other features. We prove a simple theorem that formalizes the above intuitions, generalizing the classic sample complexity bound of Blumer et al. to an OOD context. We then generalize sparse classifiers to subspace juntas, where the ground truth classifier depends solely on a low-dimensional linear subspace of the features.

cs.LG · cs.AI

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10514

FADE: Fractional Anomalous Dynamics Extrapolation for Training-Free Diffusion Transformers Acceleration

Jinlong Yang, Lizilin, Jinke Wu, Yao Zhou

原文摘要

Diffusion Transformers (DiTs) have established themselves as the preeminent paradigm for high-fidelity generative modeling across various modalities, yet their practical deployment is hindered by the severe computational overhead of iterative sampling. While recent feature-caching methods mitigate this latency through feature reuse or forecasting based on integer-order operators, they remain limited in adapting to the non-stationary and highly curved dynamics of DiT latent trajectories. In this work, we reveal that DiT feature evolution exhibits anomalous diffusion characteristics and a certain degree of spatiotemporal heterogeneity, rendering static, integer-order forecasting operators inadequate and prone to error accumulation. To address this, we propose Fractional Anomalous Dynamics Extrapolation (FADE), a unified framework for training-free diffusion transformers acceleration. FADE models DiT feature evolution via anomalous diffusion and employs a Mittag--Leffler fractional operator to better adapt to high-curvature and non-linear latent trajectories of DiTs. Leveraging the empirical observation that these complex dynamics maintain cross-sample stability, we further construct a Look-Up Table with multiple dimensions through offline profiling. This enables negligible LUT-retrieval overhead, spatiotemporally adaptive parameter retrieval during online inference. Extensive evaluations across diverse architectures and modalities demonstrate that FADE achieves state-of-the-art acceleration, preserving exceptional generation fidelity under aggressive skip intervals with negligible latency overhead. Our project page is available at https://youngkinlon.github.io/fade-project-page/.

cs.LG · cs.AI

查看论文
arXiv ↗PDF ↗
版本更新 · v3arXiv:2509.26157

Entropy Guided Dynamic Patch Segmentation for Time Series Transformers

Sachith Abeywickrama, Emadeldeen Eldele, Min Wu, Xiaoli Li, Chau Yuen

原文摘要

Patch-based transformers have emerged as efficient and improved long-horizon modeling architectures for time series modeling. Yet, existing approaches rely on temporally-agnostic patch construction, where arbitrary starting positions and fixed lengths fracture temporal coherence by splitting natural transitions across boundaries. This naive segmentation often disrupts short-term dependencies and weakens representation learning. We propose a novel Entropy-Guided Dynamic Patch Encoder (EntroPE), as a temporally informed framework that dynamically detects transition points via conditional entropy and dynamically places patch boundaries. This preserves temporal structure while retaining the computational benefits of patching. EntroPE consists of two key modules, namely an Entropy-based Dynamic Patcher (EDP) that applies information-theoretic criteria to locate natural temporal shifts and determine patch boundaries, and an Adaptive Patch Encoder (APE) that employs pooling and cross-attention to capture intra-patch dependencies and produce fixed-size latent representations. Extensive experiments on long-term forecasting, classification, and anomaly detection demonstrate that the proposed method improves both accuracy and efficiency, establishing entropy-guided dynamic patching as a promising new paradigm for time series modeling. Code is available at https://github.com/Sachithx/EntroPE.

cs.CV · cs.AI · cs.LG

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10511

CALFA-KV: Joint Calibration and Fisher-Guided Anchors for 1-Bit KV Cache Compression

Xiancai Tian, Leichao Zhang, Shenhui Zhang, Yi Shan, Hailong Li, Feng Lin, Xiaotian Yu, Aijun Li

原文摘要

1-bit key-value (KV) cache compression can combine quantizer calibration with selective high-precision storage to reduce reconstruction error. We propose Calibration and Fisher-guided Anchors for KV cache compression (CALFA-KV). Offline, we jointly fit a key center, radial normalization parameters, and K/V vector codebooks to match the frozen model's projected attention output. We also estimate channel-wise Fisher-type sensitivities from squared gradients of a reference next-token cross-entropy loss. Online, we use these sensitivities to weight the squared reconstruction errors of each KV state. Here, a KV state is one token's key-value pair within one layer and KV head. We retain the highest-scoring states at high precision under a per-layer, per-KV-head budget. Under exact restoration, we bound the excess risk of score-based anchor selection relative to the optimum at the same anchor budget, where risk is the conditional mean square of the first-order loss change. With a budget of 56 additional FP16 anchors per layer and KV head, CALFA-KV (d8b8) outperforms all evaluated nominal 1-bit baselines on the mean score across seven benchmarks for each of the three models. At the same anchor budget, CALFA-KV (d8b10) uses a nominal index rate of 1.25 bits per channel and narrows the gap to BF16 to 0.90--2.01 points on the same metric.

cs.LG

查看论文
arXiv ↗PDF ↗
版本更新 · v4arXiv:2506.15190

Learning Reusable Behavioral Motifs for Continuous Animal Behavior Modeling

Jiyi Wang, Jingyang Ke, Bo Dai, Anqi Wu

原文摘要

Natural behavior evolves continuously and often reuses movement structure across contexts, but existing behavioral segmentation methods typically assign a single discrete syllable at each time point or impose restrictive generative assumptions. We introduce Motif-based Continuous Dynamics (MCD), a computational framework that models behavior as time-varying combinations of reusable behavioral motifs. MCD learns motifs as latent behavior-level features from transition dynamics, through spectral representation learning within a reinforcement learning framework. MCD then infers policies that combine these motifs across time or context, through offline imitation learning. This formulation avoids hard behavioral boundaries while capturing continuity and compositionality. Across simulated gridworld and continuous-control tasks, mouse maze navigation and pose data, MCD identifies reusable motifs that explain task and temporal variation. MCD therefore provides a flexible and interpretable framework for computational behavior analysis. Code is available in https://github.com/G1NO3/MCD.

cs.LG · q-bio.NC

查看论文
arXiv ↗PDF ↗
版本更新 · v3arXiv:2605.17609

Adaptive Generate-Rank-Verify: Inference-Time Search with Costly Verification

Shaddin Dughmi, Mahdi Haghifam, Yusuf Hakan Kalayci

原文摘要

Many inference-time language-model pipelines combine a cheap reward signal with an expensive verifier, such as exact answer checking in mathematical reasoning or hidden-test execution in code generation. We formalize this setting using a learning-theoretic lens as generative active search: a cost-sensitive first-positive search problem in which a policy adaptively samples candidates from an unknown distribution, observes cheap scores, and pays for verifier labels until it finds a positive example. For a fixed prompt, the generator and reward model induce two unknown objects: a distribution over reward scores and a score-conditioned success function. When these quantities are known, we characterize the distribution-aware optimal policy using a dynamic programming approach. In the realistic and practical setting where both the score distribution and success function are unknown, we propose ADAP, a shellwise adaptive generate-rank-verify algorithm that progressively increases the number of sampled responses and top-ranked verifications. Under the monotonicity assumption that higher reward scores are no less likely to pass verification, we show that ADAP achieves expected cost within a constant factor of the distribution-aware optimum. We complement this result with learning-theoretic lower bounds, based on a centered star number, showing that structural assumptions on the score--label relationship are necessary. Experiments on mathematical reasoning and competitive programming validate the predicted advantage over both fixed non-adaptive policies and difficulty-adaptive baselines.

cs.LG

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10503

Learning a Unified Cross-Model Semantic Dictionary via Gated Bottleneck Sparse Autoencoders

Jinchi Zhu, Tie Luo

原文摘要

Foundation models pretrained on similar data distributions tend to learn overlapping semantic structure, but their representations are distinct due to different model architecture, pretraining objectives and protocols. This prevents unified interpretability and cross-model feature reuse. Existing approaches extract interpretable concepts from individual models and align these concepts in a shared coordinate system using cross-reconstruction objectives, but the alignment only applies to latent coordinates while the concepts themselves remain model-specific and cannot be shared or reused. This paper introduces the Gated Bottleneck Sparse Autoencoder (GB-SAE), which casts unified cross-model interpretability as a representation factorization problem and solves it by simultaneously learning a unified shared dictionary and multiple private model-specific dictionaries via a gated bottleneck sparse autoencoder framework. The shared dictionary encodes the cross-model common semantic structure in unified concepts that are reusable across all participating models (and beyond), while each private dictionary comprises residual concepts that capture model-specific biases. We evaluate GB-SAE with distinct foundation models including ViT, SigLIP, DINOv2, and ConvNeXt. GB-SAE demonstrates superior reconstruction capability, unified and faithful interpretability, strong reusability and generalization to unseen models and downstream tasks. The code is available at https://github.com/trustAIoT/GB-SAE_NeurIPS.

cs.LG · cs.AI · cs.CV

查看论文
arXiv ↗PDF ↗
版本更新 · v3arXiv:2608.17070

Certified but Private: Scalable Zero-Knowledge Proofs for Neural Network Guarantees

Youwei Zhong, Ben Merbaum, Timos Antonopoulos, Ning Luo, Charalampos Papamanthou, Katerina Sotiraki, Ruzica Piskac

原文摘要

With the growing deployment of machine learning models, formal guarantees of the robustness and fairness of these models have become increasingly important in safety-critical and legal-compliance settings. However, model parameters are often commercial secrets that cannot be disclosed to auditors or end users. To this end, we present PANDA, a scalable system that uses zero-knowledge proofs (ZKPs) to prove the robustness and fairness properties of a model without revealing its private parameters. PANDA is built on top of CROWN, an efficient robustness certification framework that is used in many state-of-the-art formal verification tools for neural networks. The core contribution of PANDA is a novel algorithm for proving linear relaxation bounds for non-linear activation layers, yielding simple, lightweight proofs. Remarkably, our implementation can generate proofs of local robustness for neural networks with more than 2.9M parameters in 5 minutes, and can verify them in 10 seconds. Prior ZKP-based robustness system rely on exponential-time algorithms that cannot scale to nontrivial networks. In contrast, PANDA scales polynomially in the number of neurons in a network, allowing us to support neural networks 4 orders of magnitude larger than previous approaches with significantly reduced prover overhead.

cs.LG · cs.CR · cs.LO

查看论文
arXiv ↗PDF ↗
版本更新 · v4arXiv:2503.05970

Partially Decentralized Multi-Agent Q-Learning via Digital Cousins for Wireless Networks

Talha Bozkus, Urbashi Mitra

原文摘要

Q-learning is a widely used reinforcement learning (RL) algorithm for optimizing wireless networks, but faces challenges with large state-spaces. Recently proposed multi-environment mixed Q-learning (MEMQ) algorithm addresses these challenges by employing multiple Q-learning algorithms across multiple synthetically generated, distinct but structurally related environments, so-called digital cousins. In this paper, we propose a novel multi-agent MEMQ (M-MEMQ) for cooperative decentralized wireless networks with multiple networked transmitters (TXs) and base stations (BSs). TXs do not have access to global information (joint state and actions). The new concept of coordinated and uncoordinated states is introduced. In uncoordinated states, TXs act independently to minimize their individual costs and update local Q-functions. In coordinated states, TXs use a Bayesian approach to estimate the joint state and update the joint Q-functions. The cost of information-sharing scales linearly with the number of TXs and is independent of the joint state-action space size. Several theoretical guarantees, including deterministic and probabilistic convergence, bounds on estimation error variance, and the probability of misdetecting the joint states, are given. Numerical simulations show that M-MEMQ outperforms several decentralized and centralized training with decentralized execution (CTDE) multi-agent RL algorithms by achieving 60% lower average policy error (APE), 40% faster convergence, 45% reduced runtime complexity, and 40% less sample complexity. Furthermore, M-MEMQ achieves comparable APE with significantly lower complexity than centralized methods. Simulations validate the theoretical analyses.

cs.LG · eess.SP

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10498

EmbodiedIntentBench: Benchmarking Intent-Driven Runtime Adaptation for Mobile Robotic Missions

Rajat Bhattacharjya, Changwoon Lee, Hyeonjong Ju, Sing-Yao Wu, Hamidreza Alikhani, Zhuoyi Yang, Ian Harris, Nalini Venkatasubramanian 等 9 位作者

原文摘要

Mobile robotic sensing systems must adapt to operator requirements, limited resources, changing connectivity, and observation opportunities shaped by physical motion. Evaluating runtime strategies therefore requires assessing whether their decisions fulfill mission objectives, beyond isolated perception accuracy or execution efficiency. We present EmbodiedIntentBench, a benchmark for intent-driven runtime adaptation in mobile robotic missions. It represents mission intent through structured contracts for coverage, temporal evidence, reporting, urgency, and resources, and couples simulated unmanned aerial vehicle (UAV) workloads with causal, resource-accounted execution. Computation decisions affect mission time, resource use, and which observations can contribute evidence. We evaluate fixed and adaptive perception strategies in multi-phase missions and under changing urgency, connectivity, and battery conditions. In the multi-phase Depot study, a configuration with 87.5% full-bank target recall across visible target observations completes only 1 of 16 missions, whereas a lower-recall configuration completes 14. In an ablation, ignoring execution-induced observation loss increases aggregate mission success and reverses the ordering of two runtime strategies. Replays using Jetson AGX Orin CPU and GPU profiles change the relative success of local and remote strategies. In selected Jetson GPU-profile replays, intent-driven adaptation reduces modeled task energy by up to 28% with unchanged mission success. EmbodiedIntentBench provides an extensible framework for evaluating runtime decisions against mission fulfillment under physical and resource constraints.

cs.RO · cs.AR · cs.CV · cs.DC · cs.LG

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.03780

Task Vector Geometry Underlies Dual Modes of Task Inference in Transformers

Hao Yan, Haolin Yang, Yiqiao Zhong

原文摘要

Transformers are effective at inferring the latent task from context via two inference modes: recognizing a task seen during training, and adapting to a novel one. Recent interpretability studies have identified from middle-layer representations task-specific directions, or task vectors, that steer model behavior. However, a lack of rigorous foundations hinders connecting internal representations to external model behavior: existing work fails to explain how task-vector geometry is shaped by the training distribution, and what geometry enables out-of-distribution (OOD) generalization. In this paper, we study these questions in a controlled synthetic setting by training small transformers from scratch on latent-task sequence distributions, which allows a principled mathematical characterization. We show that two inference modes can coexist within a single model. In-distribution behavior is governed by Bayesian task retrieval, implemented internally through convex combinations of learned task vectors. OOD behavior, by contrast, arises through extrapolative task learning, whose representations occupy a subspace nearly orthogonal to the task-vector subspace. Taken together, our results suggest that task-vector geometry, training distributions, and generalization behaviors are closely related.

cs.LG · cs.CL · stat.ML

查看论文
arXiv ↗PDF ↗
版本更新 · v5arXiv:2607.25875

EvoST: Adapting Traffic Forecasters to Evolving Sensor Graphs

Du Yin, Xiachong Lin, Yue Tan, Ruiyi Yang, Jinliang Deng, Estrid He, Hao Xue, Flora D. Salim

原文摘要

Web-based navigation and traffic information services depend on accurate forecasts as sensor networks evolve. On checkpoints reused years later, updating only shared weights or adding a low-rank node prompt leaves substantial degradation, whereas, in the evaluated architectures, refreshing node identities recovers most of it while the shared spatio-temporal weights are reused. We propose EvoST, a framework that maintains traffic forecasters by refreshing node identities while keeping shared weights fixed. EvoST updates existing identities using recently released labels and initializes new sensors with identities borrowed from behaviorally similar established sensors. Once enough delayed labels arrive, it jointly refits all identities, and a per-sensor recursive least-squares calibrator corrects forecasts as labels become available. Across twelve PeMS region-year settings, identity refresh reduces the relative one-hour MAE gap between five-year-old STAEFormer checkpoints and target-year models from 17.7% to 2.5%, with all forecasters calibrated, and its MAE is 0.7% above that of full-parameter fine-tuning on the same data. In Graph WaveNet, identity refresh recovers 88% of the five-year MAE gap to calibrated target-year models. Similar recovery holds for STAEFormer checkpoints up to fifteen years old, while refreshing STID recovers 67% of its five-year gap. On an annual benchmark covering nine EvoXXLTraffic districts and Transport for NSW, EvoST has the lowest average MAE, RMSE, and MAPE among the reported non-foundation methods on all ten networks, and calibration accounts for most of its Avg-MAE reduction from the matched frozen checkpoints. Code and the extended version: https://github.com/lixus7/EvoST

cs.LG · cs.AI

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.05851

Language models lack systematicity in generating and updating hypotheses

Hua-Dong Xiong

原文摘要

Large language models (LLMs) are increasingly applied to complex problems, from automated code debugging to science itself. Such problems require generating plausible hypotheses from sparse, noisy, and ambiguous data and updating them as more information arrives. Yet how closely LLMs approach optimal inductive reasoning remains unclear. We study this question in the classic number game, in which a learner infers a concept from a few ambiguous positive integers: $\{2, 16, 4, 8\}$, for example, is explained both by ``powers of 2'' and by ``numbers up to 20.'' We probe the concepts LLMs hypothesize from the observed examples and use their behavioral patterns to understand how they generate and update hypotheses. We measure the LLM posterior over hypotheses in three tasks (prediction, evaluation, and generation) and compare LLM behavior with a Bayesian model and with humans. LLMs favor narrower hypotheses than humans and show a robust evaluation--generation gap: in evaluation they select hypotheses more consistent with the observed examples, whereas in generation they propose narrower hypotheses that are more often rules. They also update poorly: as examples accumulate, their accuracy declines while their confidence grows. Thinking reduces, and can even reverse, the gap; it also broadens the hypotheses LLMs favor, raising accuracy while lowering confidence. Whereas prior studies have shown that LLMs are good hypothesis proposers, our results and further comparisons with the Bayesian model show that LLM inductive reasoning lacks coherence across the three tasks, suggesting that LLMs do not maintain a stable representation shared across tasks that make the same demand. Our results highlight a limitation of LLMs in inductive inference, particularly in settings such as science, where hypotheses must go beyond the data.

cs.LG

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10487

GEAR: A GPU-Accelerated Global Solver for Bounded Continuous Nonlinear Programs via Linear Bound Propagation

Duo Zhou, Hesun Chen, Xiangru Zhong, Grani A. Hanasusanto, Huan Zhang

原文摘要

We present a novel GPU-accelerated global solver for constrained nonlinear programming (NLP) inspired by the efficient linear bound propagation framework in neural network (NN) verification. Existing work on NN verification, a problem that can be cast as feasibility detection for a nonlinear objective, demonstrated orders-of-magnitude speedups with the linear bound propagation-based framework when compared to traditional solvers. Extending this paradigm to global nonlinear programming requires addressing many challenges: a global constrained NLP solver must effectively exploit all, possibly unstructured, nonlinear constraints to improve the objective, whereas NN verification typically handles simple input box constraints over a well-structured NN and does not directly optimize an objective function. We address these challenges by fully exploiting the linear bounds produced by bound propagation: these linear bounds can be viewed as relaxations of nonlinear constraints and used to provide lower bounds, check infeasibility, and guide the search for a primal solution. In addition, we propose tighter linear relaxations for bilinear, quotient, and other commonly used nonlinear functions in NLP problems, and a projected augmented Lagrangian method tightly coupled to our dual solving procedure to produce primal solutions. Our method solves 421 of 505 bounded and continuous NLP problems in the GAMS and MINLPLib benchmarks under a 180-second limit, more than each of the six global solvers we compare with, including Gurobi and BARON (commercial), and SCIP (open-source). Sixteen of these problems are certified by none of the six global solvers; all of them contain exponential, logarithmic, or trigonometric terms, which are often challenging for traditional approaches. Project webpage is at https://verified-intelligence.github.io/GEAR.

math.OC · cs.LG · cs.MS · math.NA

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.09727

One for All: A Non-Linear Transformer can Enable Cross-Domain Generalization for In-Context Reinforcement Learning

Bowen He, Juncheng Dong, Lin Lin, Xiang Cheng

原文摘要

A central challenge in reinforcement learning (RL) is to learn models that generalize beyond the tasks on which they are trained, a goal traditionally pursued through multi-task and meta RL. Recently, transformer architectures have emerged as a promising approach, enabling adaptation to new tasks via in-context learning without explicit parameter updates. From a functional perspective, a transformer can be viewed as a functional operator that maps a context to a task-specific function. It is thus fundamental to understand and design this operator to support stronger generalization in RL. In this work, we address this resulting question of generalization from a kernel-based perspective by establishing a connection between non-linear transformers and kernel-based temporal difference learning. By interpreting the transformer as performing regression in a Reproducing Kernel Hilbert Space (RKHS), we show that value functions from different domains can be represented using a shared set of weights, provided they lie within the same RKHS. Experiments on multiple MetaWorld domains support this interpretation, demonstrating convergence of the temporal-difference objective.

cs.LG · cs.AI

查看论文
arXiv ↗PDF ↗
版本更新 · v3arXiv:2601.20154

Spectral Ghost in Representation Learning: A Unified View from Component Analysis to Self-Supervised Learning

Bo Dai, Na Li, Dale Schuurmans

原文摘要

Self-supervised learning (SSL) extracts representations from massive unlabeled data that transfer to a variety of downstream tasks. Its empirical success has produced a dizzying array of objectives and procedures, yet a clear understanding of what representation is desired, and why these seemingly different methods work, is still missing. In this paper, we develop a unified spectral framework for representation learning. We first show that a representation is sufficient for arbitrary downstream prediction with a linear head if it spans the spectral decomposition of the pointwise mutual information, which can be extracted purely from unlabeled data. This view organizes representation learning along two axes, the parametrization of the spectral representation (linear, energy-based, denoising, nonlinear, latent-variable, and multi-modal) and the learning objective for fitting it, under which a broad range of existing methods, including SimCLR, MoCo, BYOL, Barlow Twins, VICReg, DINO, MAE, JEPA, BERT, GPT, and CLIP, as well as classic PCA, CCA, Laplacian eigenmaps, LLE, and SNE, are recovered as particular combinations. The framework also clarifies optimization behaviors: the batch-size requirement is determined by whether the objective is unbiased, a consistent surrogate, or biased, rather than by contrastive vs. non-contrastive losses. Finally, the framework inspires new scalable algorithms and extends spectral representations to causal inference and reinforcement learning.

cs.LG

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.31470

Beyond Empirical Support: Structured Outlier Generation via Sinkhorn Optimal Transport

Haixiang Sun, Andrew L. Liu

原文摘要

Outliers are essential for evaluating and improving the robustness of machine learning systems, especially when future distributions may differ significantly from historical training data. In high-stakes applications, robustness often depends on rare cases that finite datasets fail to capture, making simple resampling or perturbation insufficient for stress scenario generation. Existing outlier synthesis methods typically rely on sparse neighborhoods, low support latent regions, or classifier boundary crossings, which can be heuristic, unstable, and tied to specific modalities or architectures. We therefore propose Sinkhorn Boundary Outlier Generation (SBOG), a structured framework for latent-space outlier generation that couples Sinkhorn optimal transport geometry with distributionally robust boundary modeling. The resulting Sinkhorn-induced support cost guides the sampler toward weakly supported boundary regions, while semantic constraints prevent uncontrolled drift from the intended context, yielding controlled deviations from the in-distribution reference measure rather than arbitrary sparse-region samples. Experiments on time series anomaly generation and image outlier synthesis show that our framework produces informative, semantically controlled outliers and improves downstream robustness evaluation across modalities, providing a foundation for stress scenario generation beyond empirical support.

stat.ML · cs.LG · math.OC

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.07750

Foresee to Trade: Forecast-Augmented Policy Optimization for Portfolio Management

Zeyu Liu, Xuanzhi Feng, Haoran Xu, Lai Sing Kwong, Yang Li, Hualei Zhang, Hao Chen, Jie Zhang 等 10 位作者

原文摘要

Online investment services must translate asset opportunities into investment outcomes for users' accounts, yet the same market opportunity can warrant different account actions. We study how supervision from future asset opportunities can strengthen account-level trading decisions and propose Forecast-Augmented Relative Policy Optimization (FRPO). FRPO separately supervises asset opportunities and account decisions through a jointly learned representation: a Financial State Head (FSH) learns future relative-return targets to provide asset-level opportunity supervision, while Same-Path Group-Relative Optimization compares trading trajectories under the same market path and initial account to provide account-level relative-outcome supervision. An Adaptive Gate coordinates auxiliary supervision using predictive variance and gradient alignment. FSH is used only during training; inference uses the encoder and policy for trading. Across DJIA30, TOPIX Core30, and three investment-style portfolios, ten-seed evaluations show that FRPO achieves the highest mean cumulative and annualized returns among the compared methods in all five settings. These results support strengthening investment agents' trading decisions through joint supervision from asset opportunities and trading outcomes.

cs.LG

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.17584

Root-Selecting Fixed-Point Inversion for Rectified Flows via Trajectory Straightness

Semin Kim, Jihwan Yoon, Seunghoon Hong

原文摘要

Finding the initial noise that generates a given data sample, known as inversion, is a key component for downstream applications such as training-free image editing. Existing fixed-point inversion methods improve inversion accuracy by formulating each inversion step as a fixed-point problem, but they lack a principled mechanism for selecting among multiple fixed-point solutions that can arise in practice. We observe that different selections induce different inversion trajectories, leading to substantial variation in reconstruction and editing quality. For rectified flows, we further find that this variation is closely associated with trajectory straightness, motivating straightness as a principled selection criterion. We propose SelFix, a fixed-point inversion method that selects fixed-point solutions inducing straighter inverse trajectories while retaining convergence to an exact inverse root under standard local assumptions. Experiments on FLUX.1-dev and PIE-Bench show that SelFix improves fixed-point inversion, achieving stronger real-image reconstruction and better source-preserving prompt-based editing than prior inversion baselines. The code is available at https://github.com/seminkim/selfix .

cs.CV · cs.LG

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2608.29996

Partition-Aware Unlearning for Removing Spurious Correlations in Large Vision-Language Models

Aditi Sarker, Nazreen Shah, Rafi Ibn Sultan, Rhongho Jang, Dongxiao Zhu, Prashant Khanduri

原文摘要

Large Vision-Language Models (LVLMs) achieve strong performance across many multimodal tasks; however, they often exploit spurious object-background correlations, resulting in predictions driven by contextual shortcuts rather than object-relevant visual evidence. Despite growing interest in hallucination and robustness evaluation, existing benchmarks provide limited control over whether model predictions are grounded in the target object or induced by correlated background cues. In this work, we introduce PURGE (\underline{P}artition-aware \underline{U}nlearning for \underline{R}emoving spurious-correlation \underline{G}enerated \underline{E}rrors), a framework for constructing, benchmarking, and mitigating spurious-correlation-induced failures in LVLMs. The framework consists of: -- (1) Structured dataset construction wherein we develop three complementary structured data construction strategies that partition examples by object-relevant evidence and spurious background cues, enabling controlled diagnosis of shortcut reliance; and -- (2) Partition-aware unlearning, which uses these partitions to selectively remove spurious object-background associations while preserving object-based reasoning. We evaluate the \algo~framework across multiple LVLMs, including LLaVA-1.6-7B, Qwen3-VL-8B-Instruct, and Qwen3.5-9B, together with CLIP as a vision-language encoder, on a diverse suite of benchmarks, including CHAIR, POPE, Causal-HalBench, MM-SpuBench, AMBER, MMHal, and Waterbirds. Our results show that PURGE consistently reduces hallucinations and spurious-correlation-driven errors while maintaining or improving overall performance in most evaluated settings, providing both a reusable evaluation protocol and an effective mitigation framework for more reliable LVLMs.

cs.CV · cs.AI · cs.LG

查看论文
arXiv ↗PDF ↗