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

+1072新增论文
+845其中 AI 相关
+94已有论文修订
+100新增 AI 导读

arXiv 最近完成扫描：2026-11-10 00:52 · 全站最近入库：2026-11-09 13:22各信源进度 →

统计口径与数据新鲜度

统计 2026-11-09 01:07 — 2026-11-10 01:07（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：2026-11-10 00:52最近发现新增版本：2026-11-09 13:22库内论文最新更新时间：2026-11-07 02:59

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。

18,172 已收录论文15,506 AI 相关4,527 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 18,172 篇论文 · 按版本更新时间排序上轮扫描起点 2026-11-10 00:50（北京时间）
版本更新 · v4arXiv:2606.21430

rush: Scalable Asynchronous Distributed Computing via Shared State in R

Marc Becker, Bernd Bischl

原文摘要

Many algorithms in statistics and machine learning can be parallelized in an asynchronous manner where workers need to communicate through shared state rather than execute independent tasks dispatched by a central controller. Especially in modern hyperparameter optimization and parallel black-box optimization with expensive objectives, this decentralized approach has become widespread, and several Python frameworks adopt it (e.g., Optuna, DeepHyper, and Hyperopt). However, all popular R packages for parallel computing follow a centralized controller-worker architecture that does not support this pattern. We present rush, an R package that provides a shared-state coordination layer for asynchronously parallelized iterative algorithms. rush uses a Redis database as a shared key-value store: workers read and write task data through the database and independently execute their own loops. The package provides a high-level API for managing tasks and their lifecycle, featuring sub-millisecond per-task overhead, robust error handling with automatic detection of lost workers, and efficient caching. rush optionally integrates with the mlr3 ecosystem, powering asynchronous optimization in the bbotk and mlr3tuning packages. We demonstrate the practical utility of rush by implementing asynchronous decentralized Bayesian optimization (ADBO) and benchmarking it on hyperparameter optimization of LightGBM across four datasets using 448 workers.

cs.DC

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34987

AskGLP1: A Knowledge Graph LLM Agent for Exploring GLP-1 Experience from Online Patient Narratives

Zhaoqian Xue, Anay Somani, Siddhartha Singh, Junting Mu, Zhongyin Wang, Haozheng Liu, Ayesha Khatun, Yeaeun Kwon 等 13 位作者

原文摘要

GLP-1 medications are increasingly used for weight management, and many people share their experiences online. These narratives show why people take the drugs and what they experience, but they remain unstructured and disconnected from formal pharmacovigilance. We therefore present AskGLP1, a system that transforms online patient narratives into a knowledge graph (KG) and answers questions about medication experiences. Through an LLM-assisted pipeline grounded in the MedDRA terminology, AskGLP1 screens nearly 8.5 million Reddit posts for medication experiences and distills 163,135 of them, with 2,506 WebMD reviews, into a KG of 385,790 drug-side-effect and drug-purpose relations. We find that GLP-1 experiences are source- and drug-specific. Online narratives share the most common gastrointestinal symptoms with FAERS, but they foreground everyday burdens such as fatigue and food aversion, whereas FAERS reports more often include injection-site reactions and selected serious diagnoses such as pancreatitis. We further find that posts report less nausea and vomiting for tirzepatide than for semaglutide, more fatigue for Wegovy than Ozempic and for Zepbound than Mounjaro. Evaluations show that the agent answers with verifiable evidence and declines requests for medical advice. Our findings reveal gaps between what FAERS captures and what people report online, and they call for patient-centered approaches to pharmacovigilance. Code, prompts, and evaluation questions are available at https://github.com/ZhaoqianXue/AskGLP1.

cs.CL · cs.AI · cs.SI

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2604.24589

A systematic evaluation of vision-language models for observational astronomical reasoning tasks

Wenke Ren, Hengxiao Guo, Wenwen Zuo, Xiaoman Zhang

原文摘要

Vision-language models (VLMs) are increasingly proposed as general-purpose tools for scientific data interpretation, yet their reliability on real astronomical observations across diverse modalities remains untested. We present AstroVLBench, a benchmark of over 4,100 expert-verified instances across five tasks spanning optical imaging, radio interferometry, multi-wavelength photometry, time-domain light curves, and optical spectroscopy. We systematically evaluate six state-of-the-art frontier models and find that performance is strongly modality-dependent and uncorrelated with general benchmark rankings. Mechanistic cross-model ablations reveal three concrete bottlenecks. First, prompt grounding exhibits marked model and task dependence: physical guidance yields more balanced classifications on optical imaging, whereas phenomenological prompts better navigate undersampled time-domain data. Second, presenting one-dimensional measurements as numerical tables rather than rendered plots consistently improves light-curve classification across all models by up to 23 percentage points, demonstrating that plot rendering can obscure physically relevant signal. Third, an expert audit of optical spectroscopy reveals a notable "right-answer-wrong-reason" phenomenon: across evaluated models, an average of 26% of correct predictions are accompanied by physically inconsistent or invalid reasoning chains, showing that accuracy alone is insufficient for trustworthy scientific deployment. AstroVLBench provides the first systematic, multi-modal baselines for VLMs in observational astronomy and identifies the representation, grounding, and reasoning bottlenecks that must be addressed before VLMs can be trusted as autonomous scientific interpreters.

cs.AI · astro-ph.GA · astro-ph.IM

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34977

Adaptive Memory Index Evolution for Agents

Yuntong Hu, Tongli Su, Yuanfu Sun, Shuo Ma, Qiaoyu Tan, Liang Zhao

原文摘要

Long-horizon agents continually accumulate experience, yet retaining a memory does not guarantee access when it is needed. Existing systems can adapt both memory content and access; we isolate persistent access updates as a separately controlled adaptation layer. We formulate this target as Memory Index Evolution and instantiate it with Adaptive Memory Index Evolution (AMIE), a lightweight plug-in layer that evolves per-memory access state from deployment retrievals without modifying stored content or retraining the host memory system. AMIE distils a semantic teacher's keep/suppress judgments into a retrieval-conditioned policy and accumulates its selective actions into bounded state that reweights subsequent host retrievals. Surprisingly, even a zero-call policy that simply accumulates retrieval frequency can match or outperform semantic policies in several settings, revealing retrieval traffic itself as a strong signal for access adaptation, while learned selectivity improves robustness when frequently retrieved memories remain useful. Across five retrieval-based hosts and three benchmarks, trained AMIE improves downstream F1 or accuracy in all 15 comparisons, by 0.70--3.37 percentage points (up to 25% relative), and improves retrieval in all 10 comparisons. Controlled analyses show that persistent competitor suppression can expose previously unseen evidence, while suppressing repeatedly useful memories creates the complementary failure mode. Together, these results establish persistent memory access as a practical adaptation layer, distinct from the content it serves, in retrieval-based agent memory systems.

cs.LG

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34976

Sparse Recovery Bird's-Eye View Model Soup for Robust Camera-Based 3D Object Detection

Jinwoo Chung, Sangho An, Hyunsung Park, Jangho Kim

原文摘要

Camera-based bird's-eye view (BEV) perception methods such as Fast-BEV have demonstrated promising results with fast inference speed and competitive accuracy. However, their robustness to real-world corruptions remains insufficiently explored, posing risks for autonomous driving deployment where environmental variations are inevitable. In this paper, we propose Sparse Recovery Bird's-Eye View Model Soup (SR-BEVS), a sparse recovery learning approach that simultaneously improves clean accuracy and corruption robustness without requiring additional data or architectural modifications. Our method identifies pruning candidates based on layer-wise weight magnitudes, applies random pruning to create diverse sparse models, and retrains each model with a randomly sampled learning rate. By averaging these diverse recovered models into a single dense model, we achieve enhanced robustness against various corruptions while introducing zero inference overhead. We further provide a mode connectivity analysis demonstrating that our pruning-based diversification preserves the connected low-loss basin necessary for stable weight averaging. Experiments on the nuScenes dataset demonstrate up to 33.8% improvement in corruption robustness and a 1.8% gain in clean accuracy over the Fast-BEV baseline, with consistent improvements across sensor noise, weather distortions, and illumination changes. Our simple yet effective approach may be applicable to other BEV models.

cs.CV · cs.AI · cs.LG

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.22340

PlayPolicy: Dexterity from Supervised Play

Botao He, Yiming Dou, Yiannis Aloimonos, Jitendra Malik, Akash Sharma, Ruoshi Liu

原文摘要

How can robots autonomously collect the contact-rich experience needed to learn dexterous manipulation? We present PlayPolicy: Dexterity from supervised play (DSP), a framework in which a robot learns a play policy from minutes of human play through behavior cloning, then deploys it autonomously to collect hours of interaction data to train a world model. During inference, the same play policy then guides model-predictive control (MPC) by proposing candidate action sequences for evaluation under the learned world model. To accelerate inference, we also introduce a method for distilling a closed-loop, goal-conditioned policy through self-play within the learned world model. We evaluate this framework on underactuated pushing and any-to-any in-hand pose reorientation with a multifingered hand, in simulation and real world. In real world experiment, our method outperforms hindsight behavior cloning by 69.6% in success rate and achieves comparable success to reinforcement learning with 490 times less interaction data. These results demonstrate that supervised play can autonomously collect interaction data, guide model-based planning, and enable policy learning without any task-specific demonstrations or sim-to-real transfer. Project page: https://play-policy.github.io/

cs.RO · cs.AI

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34974

Toward Methods and Tools for Critical Examination of Disparate Systems for Discovery of Hidden Dependencies in Engineering of Autonomous and Semi-Autonomous Systems

Assaf Marron, David Harel

原文摘要

We present a methodology, an initial AI Skill package, and emerging results, for enhancing software engineers' acquisition and application of domain knowledge and system-wide engineering perspectives towards the development of an autonomous or semi-autonomous system in a domain that may be new to them. At the core of the approach is a process involving proactive adversarial critiquing of videos of disparate systems in related environments. Among other things, the engineers and automated AI tools look for interactions, dependencies, and environment assumptions manifested in the observed systems, which are applicable also to the target system, but that otherwise might remain unarticulated in project artifacts. The approach and its supporting tools can enhance the quality of the target system, the individual engineer's skills and the organization's engineering practices.

cs.SE

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2509.21856

KnowMT-Bench: Benchmarking Knowledge-Intensive Long-Form Question Answering in Multi-Turn Dialogues

Junhao Chen, Yu Huang, Siyuan Li, Rui Yao, Hanqian Li, Hanyu Zhang, Jungang Li, Jian Chen 等 10 位作者

原文摘要

Multi-Turn Long-Form Question Answering (MT-LFQA) is a key application paradigm of Large Language Models (LLMs) in knowledge-intensive domains. However, existing benchmarks are limited to single-turn dialogue, while multi-turn dialogue benchmarks typically assess other orthogonal capabilities rather than knowledge-intensive factuality. To bridge this critical gap, we introduce \textbf{KnowMT-Bench}, a new benchmark designed to systematically evaluate MT-LFQA for LLMs across knowledge-intensive fields, including medicine, finance, and law. To faithfully assess the model's real-world performance, KnowMT-Bench employs a dynamic evaluation setting where models generate their own multi-turn dialogue histories given logically progressive question sequences. Our experiments on a diverse suite of LLMs show a clear degradation in both factual capability and information delivery efficiency in multi-turn dialogue. We further probe the underlying causes and investigate mitigation strategies spanning domain-specific finetuning, active context denoising, and RAG. The results show domain-dependent gains from finetuning, while active context denoising and RAG more consistently mitigate these issues, with RAG notably reversing this performance degradation. These findings underscore the importance of our benchmark for evaluating LLMs' conversational factual capabilities in real-world applications. Code and data are publicly available.\footnote{\href{https://github.com/Sven-LI-sankyuu/KnowMT-Bench}{\texttt{https://github.com/Sven-LI-sankyuu/KnowMT-Bench}}}

cs.CL

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34971

SVD-Attention: Efficiently Analyzing Attention Blocks in Transformers

Debopam Sanyal, Hongjie Chen, Riya Elizabeth Kuriyan, Arth Tak, Divya Kothandaraman, Akshay Mehra, Joshua Kimball, Alexey Tumanov

原文摘要

Pretrained transformers spend a large share of their inference compute inside self-attention, yet the per-head query-key interaction is structurally low-rank by construction. We ask a diagnostic question: across layers and architectures, which heads actually use the full rank budget on real data, and which do not? We propose SVD-Attention, a post-hoc method that decomposes the per-head interaction matrix $M_h$ and scores every singular mode by both its spectral magnitude and its activation variance across tokens on a small calibration set. The score keeps the modes the head can amplify and that real data actually activates, and discards the rest; the resulting factorization is exact for the retained modes and needs no finetuning. Across seven pretrained models and five task types, the diagnostic finds a task-dependent pattern: redundancy sits in the early to middle layers for sentiment and image classification and for generation, and in the late layers for dense prediction and extractive question answering. It is also predictive: truncating the layers it flags costs at most $1.75$ F1, while truncating the same number of layers elsewhere in the same model costs up to $50$ F1. SVD-Attention is an analysis tool rather than a compression method: acting on it reduces inference FLOPs by $0.8$-$1.5\%$ on its own and by $8$-$11\%$ when composed with weight pruning, with $0$-$4\%$ change in task quality.

cs.CL · cs.AI · cs.LG

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.29224

Cryptanalysis and Repair of "Decentralized Multi-authority ABE for NC$^1$ from BDH"

Xingwang Wang

原文摘要

We present a plaintext-recovery attack on both the DBDH-based construction and the CBDH-based variant of Datta, Komargodski, and Waters (Journal of Cryptology, 2023). The attack succeeds with probability one in the claimed static security model using one corrupt authority and two users, each unauthorized even when granted the corrupt authority's attribute. The flaw is an invalid extension of the LSSS matrix-normalization lemma originating with Rouselakis and Waters (FC,~2015): preserving complete-share distributions does not preserve coordinate-wise ciphertext masks, which a corrupt authority can expose to remove identity binding from other users' shares. For both variants, replacing these masks with independent full linear sharings of zero preserves correctness and restores ciphertext-distribution invariance. We prove static security of both repaired variants in the random oracle model under their respective BDH assumptions, using the original hardcore-bit convention for the CBDH variant. Both proofs allow adaptive identity-hash queries. The modification retains the original matrix-width and row-labeling restrictions, keys, decryption, and ciphertext size, requiring only additional encryption randomness and finite-field arithmetic. We also attack both multi-authority attribute-based inner-product functional encryption constructions of Datta and Pal (PKC, 2023) and establish correctness and matrix-substitution invariance of the same repair. We prove static security of the repaired small-universe construction under asymmetric DBDH. For the large-universe construction, we identify a missing source-group encoding in the original reduction and give a complete static security proof under an augmented $L$-DBDH assumption, whose generic bilinear group hardness we establish.

cs.CR

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34968

Guarded Agent-in-the-Loop Residual Adaptation for Evolving Wind Power Regimes

Yuhang Dong, Min Xia, Jinghong Li, Yue Dai, Xinyue Wang, Shi Liang

原文摘要

Wind power forecasting models are often deployed as fixed temporal backbones, but the errors they make can vary across meteorological regimes such as cold waves, heat-and-calm periods, productive typhoon, and destructive storms. We present a guarded Agent-in-the-loop residual adaptation framework that leaves the temporal backbone unchanged and adapts only a small set of scene-conditioned residual experts. An Agent layer interprets external scene information, identifies the current regime, selects residual experts, and applies a correction only when recent observed errors support it. New experts enter the pool after they outperform their initializations on a validation set, keeping model growth under explicit control. Backbone-only and Agent-enhanced forecasts are compared at lead times of 4, 8, 12, 24, and 48 across nine time-series backbones. Across these runs, the guarded framework lowers both MAE and RMSE for all 45 backbone-horizon combinations, with average MAE reductions ranging from 11.21% to 20.26% across backbones. Online-selection baselines, component ablations, and routing analyses identify the roles of residual experts, scene-aware routing, and the online acceptance rule. Candidate-scene runs evaluate the promotion path under controlled conditions; long-term unattended deployment was not evaluated.

cs.MA

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34967

Rivet: Utility-Preserving Context Control for Repository-Level Coding Agents

Ruwei Pan, Linhao Wu, Yakun Zhang, Zixiong Yang, Lu Zhang, Hongyu Zhang

原文摘要

Repository-level code generation asks an agent to implement a task requirement inside an existing repository. To produce code that works in the repository, the agent needs repository evidence, such as target locations, helper definitions, caller usages, and configuration constraints, which determine how the code should be located, integrated, and executed. Because the context budget cannot hold the entire repository, existing retrieval, compression, and structure-aware methods select context by task similarity, context length, or structural proximity, but these criteria do not directly measure whether the selected context contains this evidence. As a result, the selected context can appear relevant to the task yet miss the evidence that the implementation depends on. We call this failure mode context utility dilution. In this paper, we propose Rivet, a context control framework that preserves repository evidence for coding agents under a fixed context budget. Rivet, first uses Signal Grounding to select task-related code anchors by fusing lexical, semantic, and structural signals. Utility Linking then expands these anchors through dependency, usage, and configuration relations to collect repository evidence. Finally, Budget Shaping packs the evidence by priority and, when the budget is tight, reduces snippets to signatures or summaries before dropping them. We evaluate Rivet, on two executable repository-level benchmarks, DevEval and RepoExec, with four backbone models, two context budgets (4K and 8K tokens), and eight baselines. Rivet, achieves the highest Pass@1 in all 16 settings and improves over the strongest baseline by 3.73--5.15 percentage points on average. Moreover, GPT-5-mini with Rivet, outperforms GPT-5 without Rivet,on both benchmarks while reducing inference cost by 73.3%--84.5%.

cs.SE

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.35975

A Graph-informed Kriging model for sample-efficient Bayesian optimization of wind farm layouts

Zhen-Fan Wang, Kai Zhang, Xianping Du, Zhaolong Han, Dai Zhou, Onur Bilgen

原文摘要

Wind farm layout optimization becomes increasingly difficult as turbine count grows, especially when high-fidelity aerodynamic models are used. Conventional Kriging-based Bayesian optimization often suffers from low sample efficiency in high-dimensional layout spaces because sparse training data provide limited domain coverage. We develop a Graph-informed Kriging framework that embeds reusable aerodynamic interaction information into the surrogate mean function. The wind farm is represented as a directed aerodynamic graph, where pairwise turbine interactions defined in a low-dimensional relative-coordinate space are assembled into a farm-scale mean. This structured mean captures the dominant variation in annual energy production (AEP), allowing the Gaussian process to model only the residual structure and uncertainty. Numerical experiments show that Graph-informed Kriging consistently improves AEP prediction accuracy over ordinary and generic-trend Kriging under sparse-data conditions, with increasing benefits as layout dimension and wake-interaction strength grow. Embedded in Bayesian optimization, it substantially improves sample efficiency and achieves competitive solutions for the IEA Wind Task 37 benchmark with about 280 AEP evaluations. In a controlled 16-turbine case, it also outperforms ordinary and multi-fidelity Kriging under the same high-fidelity evaluation budget. The framework is further extended to a 48-turbine wind farm, demonstrating applicability to large-scale CFD-based layout optimization. These results show that Graph-informed surrogate modeling can enable more efficient use of high-fidelity aerodynamic models for reliable large-scale wind farm layout design.

cs.LG · math.OC · stat.ML

查看论文
arXiv ↗PDF ↗
版本更新 · v3arXiv:2108.13061

A New Test for Hamming-Weight Dependencies

David Blackman, Sebastiano Vigna

原文摘要

We describe a new statistical test for pseudorandom number generators (PRNGs). Our test can find bias induced by dependencies among the Hamming weights of the outputs of a PRNG, even for PRNGs that pass state-of-the-art tests of the same kind from the literature, and in particular for generators based on $\mathbf F_2$-linear transformations such as the dSFMT, xoroshiro1024+, and WELL512a.

cs.DS · cs.MS

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34965

The Target Agent Is Its Own Best Critic but a Poor Proposer: Self-Improved Red Teaming

Mintong Kang, Stephen Rawls, Melissa Kazemi Rad, Jian Ni, Bo Li

原文摘要

Automatic red teaming increasingly relies on large language models (LLMs) to iteratively diagnose failed attacks and propose improved adversarial prompts or injections. Although prior work strengthens this loop through prompt engineering and search-harness design, the mechanisms underlying its two core roles, criticism and attack proposal, has not been explored. To further investigate the core roles and therefore improve red teaming effectiveness, we disentangle two design choices: whether criticism is generated by the target session itself (self-criticism) or by a separately instantiated critic (external criticism), and whether that criticism is translated into the next attack by the target session (self-proposal) or by a separate attacker (external proposal). Through our analysis, we find that self-criticism is more effective because it is grounded in the target's live execution context, including observations, constraints, and available reasoning; removing reasoning reduces downstream attack success by 6%-17%. In contrast, self-proposal is substantially less effective because awareness of the adversarial objective induces explicit refusal and non-refusal sandbagging. These findings motivate a role-separated red-teaming approach: the target session diagnoses why an attack failed, while a separately instantiated attacker generates the next proposal. On DTAP-BENCH, spanning Telecom, Workflow, CRM, and Code domains, under both direct misuse and indirect threat models, as well as multiple agent frameworks, this configuration achieves 63.2% overall attack success rate (ASR), much higher than baselines. Our results show that effective automatic red teaming depends not only on model capability and search strategy, but also on how execution context and adversarial responsibility are distributed across model sessions.

cs.CR · cs.AI

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2506.07450

Efficient Generation of Diverse Cooperative Agents with World Models

Yi Loo, Akshunn Trivedi, Malika Meghjani

原文摘要

A major bottleneck in the training process for Zero-Shot Coordination (ZSC) agents is the generation of diverse partner agents via Population-based Training (PBT). Prominent PBT methods, such as Cross-play Minimization (XPM), can be very computationally expensive and sample inefficient, as the training objective requires sampling multiple types of trajectories, in particular the need of Mixed-Play (MP) trajectories to mitigate self-sabotage in cross-play. Each partner agent in the population is also trained from scratch, despite all of the partners in the population learning policies of the same coordination task. In this work, we propose that simulated trajectories from the dynamics model of an environment can drastically speed up the training process for XPM methods. We introduce XPM-WM, a framework for generating simulated trajectories for XPM via a learned World Model (WM). We show XPM with simulated trajectories removes the need to sample Mixed-Play trajectories while still mitigating any self-sabotage. The learned WM also bootstraps the training of subsequent agents in the population. We show that these two factors combined gives XPM-WM significantly better sample efficiency as compared to other XPM methods and scales linearly to the size of the population. We show our proposed method can effectively generate partners with diverse conventions that match the performance of previous methods. Finally, we show that our proposed training paradigm of using learned world models to generate diverse population can be extended to other PBT methods beyond XPM.

cs.AI

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34962

Beyond feature attribution: Recovering latent interaction structures in machine learning models via localized CBFI

Sejong Oh

原文摘要

Background: Understanding individual predictions in complex tabular models remains challenging, as existing additive frameworks like SHAP and LIME often obscure structural logic by absorbing nonlinear interactions into individual feature scores. Exposing these latent interaction structures is essential for automated auditing in high-stakes environments where understanding the logic of model behavior is as essential as predictive accuracy. Methods: To address this limitation, we introduce the localized case-based feature importance (CBFI) framework. Unlike traditional numerical attribution, our method decomposes instance-specific predictions into structurally distinct components, effectively separating independent main effects (G1) from pure interactions (G4) to capture explicit feature synergies and provide a principled approach for diagnosing feature combinations. Results: Comprehensive experiments on wine quality classification and medical insurance regression demonstrate that localized CBFI successfully disentangles complex interactions hidden by additive models. Furthermore, fidelity tests and synthetic ground-truth benchmarks confirm that the identified interactions correspond precisely to the nonlinear structural dependency patterns embedded in complex architectures, including gradient boosting and deep neural networks, thereby successfully shifting the explainable AI paradigm from simple numerical attribution to the recovery of interaction-driven contributions.

cs.LG

查看论文
arXiv ↗PDF ↗
版本更新 · v4arXiv:2608.05742

Multivariate Time Series Forecasting needs Cross Variable Loss

Kuiye Ding, Yifan Hu, Hao Xue, Hanchen Wang

原文摘要

Multivariate time series forecasting presents unique challenges because future variables often co-evolve under shared system dynamics. While existing studies mainly focus on cross-variable dependencies in historical observations, dependencies among future values are much less explored. Specifically, modern forecasting models largely follow the Direct Forecasting (DF) paradigm, generating multi-step forecasts with point-wise objectives that do not explicitly constrain cross-variable structure. In this work, we show that the DF objective is mismatched in the presence of cross-variable and lagged dependencies, revealing an objective gap. To address this issue, we propose \textbf{C}ross-\textbf{V}ariable \textbf{Loss} (CvLoss), a plug-in structural regularizer that constrains forecast residuals on a cross-variable graph. CvLoss penalizes inconsistent edge-wise residual differences over forecast patches, encouraging consistency across both synchronous and asynchronous interactions. Our experiments show that CvLoss consistently improves competitive forecasting models, outperforms representative learning objectives, and is compatible with a variety of forecasting backbones.

cs.LG · cs.AI

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34961

C-SafeGen: Certified Safe LLM Generation with Claim-Based Streaming Guardrails

Mintong Kang, Zhaorun Chen, Bo Li

原文摘要

Despite the remarkable capabilities of large language models (LLMs) across diverse applications, they remain vulnerable to generating content that violates safety regulations and policies. To mitigate these risks, LLMs undergo safety alignment; however, they can still be effectively jailbroken. Off-the-shelf guardrail models are commonly deployed to monitor generations, but these models primarily focus on detection rather than ensuring safe decoding of LLM outputs. Moreover, existing efforts lack rigorous safety guarantees, which are crucial for the universal deployment of LLMs and certifiable compliance with regulatory standards. In this paper, we propose a Claim-based Stream Decoding (CSD) algorithm coupled with a statistical risk guarantee framework using conformal analysis. Specifically, our CSD algorithm integrates a stream guardrail model to safeguard sequential claims generated by LLMs and incorporates a backtracking mechanism to revise claims flagged with high safety risks. We provide theoretical guarantees demonstrating that the CSD algorithm achieves the desired generation distribution subject to safety constraints. Furthermore, we introduce a generation risk certification framework and derive a high-probability upper bound on the safety risk of the proposed CSD algorithm. We prove that our method can asymptotically control safety risk to any desired level. Empirical evaluations demonstrate the effectiveness and efficiency of the CSD algorithm compared to state-of-the-art safety decoding approaches. Additionally, we validate the soundness and tightness of the derived safety risk upper bound using realistic data.

cs.AI

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.09857

Unified Approach for Weakly Supervised Multicalibration

Futoshi Futami, Takashi Ishida

原文摘要

Multicalibration requires predicted scores to agree with label probabilities across rich families of subgroups and score-dependent tests, but existing methods require clean input-label pairs for evaluation and post-processing. This assumption fails in weakly supervised learning (WSL) regimes -- including positive-unlabeled, unlabeled-unlabeled, and positive-confidence learning -- where clean labels are costly or unavailable even though reliable uncertainty estimates may be crucial. We address this gap by developing estimators of multicalibration error and post-hoc correction methods for WSL settings in which clean input-label pairs are unavailable. We propose a unified framework for estimating and correcting multicalibration under weak supervision by combining contamination-matrix risk rewrites with witness-based calibration constraints, yielding corrected multicalibration moments with finite-sample guarantees. We further propose weak-label multicalibration boost (WLMC), a generic post-hoc recalibration algorithm under weak supervision. Finally, we conduct experiments across multiple weak-supervision settings to evaluate multicalibration behavior and offer empirical insight into uncertainty estimation under weak supervision.

stat.ML · cs.LG

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34959

GUIFlow: Temporal Redundancy-Aware Incremental Inference for GUI Agents

Ye Li, Haoran Ding, Huanan Liu, Yuan Meng, Yutian Shen, Yuansong Wang, Kangye Ji, Hanyun Cui 等 13 位作者

原文摘要

GUI agents built on vision-language models interact with continuously evolving interfaces, yet conventional inference repeatedly recomputes representations of largely overlapping observations and histories. This mismatch makes multi-step interaction costly, while changes in context, interface content, and action arguments prevent straightforward cache reuse. We introduce GUIFlow, a framework for full-pipeline incremental inference in multi-image GUI interaction. Our central insight is that advancing a decision need not require rebuilding all supporting representations: each interaction step can instead selectively update computational states maintained across the trajectory. To support new decisions as the context evolves, GUIFlow adapts how past computation is reused: historical KV states are position-aligned and injected into the current context; change signals guide selective updates of current-observation states in the vision encoder and language model; and earlier action responses serve as speculative drafts verified by the target model. Together, these mechanisms enable cross-step reuse throughout visual encoding, prefill, and decoding without additional training, a separate draft model, or changes to the model-visible inputs and action interface. Experiments demonstrate up to 3.39x geometric-mean speedup across offline multi-image benchmarks and up to 3.49x in online, closed-loop interaction on OSWorld, while maintaining task performance broadly comparable to the dense baseline.

cs.CV · cs.AI

查看论文
arXiv ↗PDF ↗
版本更新 · v3arXiv:2603.12842

Prepare to Turn: Learning Anticipatory Maneuvering for Fast Sequential Quadrupedal Navigation

Zunzhi You, Yunke Wang, Haolan Guo, Chang Xu

原文摘要

Fast quadrupedal navigation requires preparing for the next turn while approaching the current goal. Policies trained to reach and remain at a single target can instead produce stop-and-go motion when applied to sequential goals, compromising its agility potential. We introduce PrepTurn, a reinforcement learning framework for anticipatory maneuvering in sequential local navigation. PrepTurn maps the current and next pose goals directly to joint actions, using the upcoming goal to inform how the robot should approaches the current one. Its persistent-credit reward encourages timely completion of the whole sequence by continuing to reward reached goals while moving on and preparing subsequent maneuvers, and an automatic curriculum that gradually increases goal difficulty is critical for learning stable turning behaviors. Together, these components enable the policy to coordinate approach speed and reorientation across goal transitions without prescribed trajectories or arrival times. Simulation and onboard Unitree Go2 experiments demonstrate fast, efficient sequential traversal with high completion and reduced stop-and-go motion. We further show that when the goal-reaching tolerances are more relaxed, the policy learns to exploit the freedom allowed by the task with emergent behaviors, such as turning to the next goal in advance and cutting corners within the permitted regions behaviors, to traverse goal sequences more efficiently.

cs.RO

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.14849

StateSync-GKR: Design and Evaluation of a Reusable Sparse GKR Engine

Jinwook Kim

原文摘要

Authenticated state transitions need proof engines that conserve memory, avoid repeated setup, keep every result bound to its request under parallel execution and return complete proofs at service rates. We present StateSync-GKR, a reusable Rust sumcheck/GKR core with a sparse-Merkle frontend, and evaluate release 1.2.0 against release 1.1 measured on the same day on the same instance types, request corpus and latency budgets. Sparse layer reduction keeps additional requested allocation 3,242-fold and peak process memory about 89-fold below the engine's dense reference at width 4,096. Release 1.2 changes two parts of the software while proof bytes and verifier acceptance stay fixed: reorganized round arithmetic raises prepared proving throughput 2.4 to 2.7-fold on a 192-core bare-metal host, and a new serving path starts each job as soon as a worker is free, bounds queued work and buffered replies, and returns proofs in completion order bound to request identifiers. On a 192-core VM at 1,000 requests/s, 99th-percentile latency to verified acceptance falls from 440 to 34 ms. One connection carries 3,300 requests/s with every request inside one second, confirmed in three 60-second runs and a 30-minute run of 5.94 million requests without a latency or queue trend; at 3,400 requests/s reply traffic reaches the provider's documented 5-Gbit/s single-flow limit, and two connections passed 5,000 requests/s in 15-second screens. In single 15-second screens on 48 cores, the highest passing rate rose from 500 to 2,000/s. Offered 1.5 times its highest screened one-connection rate under a memory limit, with excess offers held back by the client, the worker held its resident memory near a fifth of that limit and left no submitted request unresolved. For an integrating service, fewer servers deliver the same proof rate with bounded tail latency, and existing verifiers accept the GKR proofs unchanged.

cs.CR · cs.PF

查看论文
arXiv ↗PDF ↗
版本更新 · v3arXiv:2607.23556

GTIN: A Unified Scalable Framework for Joint Event and Time Prediction in Temporal Graphs

Mohammad Ostadmohammadi, Sepehr Kazemi, Hamid R. Rabiee

原文摘要

Temporal graphs are increasingly used to model dynamic systems across diverse domains, including social, financial, and traffic networks. Predicting both what the next event will be and when it will occur in these systems is crucial for understanding and anticipating complex behaviors, but has not been studied much yet, especially in large-scale graphs. To address this gap, we propose a scalable unified mathematical framework that captures varying degrees of complexity across temporal graphs with events occurring on the edges. Our framework is flexible and expressive enough to accommodate a wide range of network structures and temporal dynamics. Building upon this analysis, we introduce our novel scalable approach for jointly predicting the next event and its occurrence time. Empirical evaluations across multiple datasets demonstrate that our method consistently outperforms existing techniques, particularly in scenarios involving irregular event patterns and complex temporal dependencies. These findings highlight the potential of our framework as a robust foundation for future research in temporal event prediction.

cs.SI · cs.AI

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34957

Consequence-guided clarification: releasing minimum-compliance topology-optimized designs from incomplete specifications

Shaoliang Yang, Jun Wang

原文摘要

A converged topology optimization can solve the wrong problem when loads, supports, or the domain are underspecified. Assuming defaults, or asking the problem's author about every detail, ignores how much each answer changes the design. We propose consequence-guided clarification (CGC), which bases acceptance on computed mechanical consequences. CGC computes a reference design for each of a finite list of complete specifications, called readings. A design is accepted only if its compliance is at most 5% above the reference at every reading consistent with the answers. Otherwise, CGC asks the question whose worst answer leaves the smallest worst-case excess, and withholds acceptance at its question limit. To save optimizations, the screening variant CGC-S postpones some references until a candidate must be checked. We developed and evaluated both on 36 arXiv-derived tasks, treating each of 134 readings in turn as intended and simulating correct answers. In 26 tasks, the default-reading design failed another reading. Apart from always-asked questions on load direction, sign, and material budget, asking everything took 188 questions; CGC and CGC-S each took 105. The acceptance test supplied most of this saving: applying it to the default design alone took 117. Relative to asking everything, CGC-S requested 4.6 extra reference designs per avoided question, each from two optimization starts, counting optimization time only. References are not certified optima: better ones pushed three accepted cases above tolerance, and finer meshes changed some decisions. Within these limits, the test shows which readings a design covers and which details block acceptance.

cs.CE

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34956

Decision Model Factory: Building Specialized Sovereign Decision Models

Yuma Ichikawa

原文摘要

Decision needs vary with each application, language, and deployment environment. The Decision Model Factory turns those needs into specialized, sovereign models on demand. A brief recipe guides the training, calibration, verification, and evaluation of a selected backbone, delivering a deployable decision model under the operator's control. The same workflow creates a new specialist whenever the requirements change. Across Japanese decision tasks, compact deployment, and long-context passage ranking, five factory-built models improve upon their backbones; three surpass the strongest results in a fixed public Japanese benchmark comparison. A dedicated runner computes only permitted-answer probabilities and reuses shared context, more than doubling serving speed on matched multi-question workloads with essentially unchanged decision scores. Together, the factory and runner make fast, task-specific decision models practical to build and deploy whenever needed.

cs.AI · cs.SE

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34952

DimFlow: Bidirectional Grid-Expert Learning for Dynamic Gaussian Reconstruction

Gabriel Adineera, Jinoh Kim

原文摘要

Dynamic Gaussian reconstruction requires compact scene features to support coordinated changes in geometry and appearance. A factorized grid provides efficient local queries, but accurate interpolation alone does not ensure that the resulting dynamic attributes remain compatible across space and time. We introduce DimFlow, a framework that learns these features through bidirectional interaction between a multi-resolution grid and a coordinate-based expert branch. Feature-level adversarial alignment connects their distributions, adaptive fusion transfers complementary information, and activation-masked residual feedback refines the grid during training. Four attribute experts organize deformation, rotation, opacity, and temporal information before Gaussian rendering. In the aggregate comparison across D-NeRF, HyperNeRF, and DAVIS, DimFlow achieves 39.73 dB PSNR, improving on 4D-GS by 1.92 dB while matching its reported 85 FPS rendering speed. The model occupies 18.9 MB, compared with 18.3 MB for 4D-GS. Component and attribute-expert ablations reveal complementary contributions from feature alignment, adaptive exchange, and temporal processing.

cs.CV

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34951

Improving Reasoning with Rollout Agreement for Group-Based Policy Optimization

Sohyun Son, Yerim Hong, Jaeyun Park, Mujeen Sung

原文摘要

Recent methods for Reinforcement Learning with Verifiable Rewards (RLVR) use rollout diversity to improve reasoning performance or training efficiency. Despite these gains, the extent to which differences among correct-answer rollouts reflect useful alternative reasoning or unresolved errors remains poorly understood. We investigate this question through a blind paired analysis of a model trained with Group Relative Policy Optimization (GRPO), comparing the highest- and lowest-agreement correct-answer traces for the same problem. Across 126 pairs, low-agreement traces have more spurious reasoning than high-agreement traces (35% vs. 22%), including internal inconsistencies and misuse of problem conditions. Based on these findings, we propose RoAR (Rollout-Agreement Reward), which adds reasoning-level agreement to GRPO's rewards for verified-correct rollouts. RoAR computes this signal from existing on-policy rollout groups, requiring no human-written rationales, additional sampling, or separately trained process evaluator. On five mathematical reasoning benchmarks, the 1.5B GRPO-RoAR model reaches 55.5% mean pass@1 under zero-shot evaluation using 7K training examples, exceeding GRPO and GRPO-CARE by 6.0 and 5.5 percentage points. A separate blind audit of 100 problems answered correctly by all four 1.5B models finds spurious rates of 18% for GRPO-RoAR, 32% for GRPO, 27% for GRPO-CARE, and 37% for DRA-GRPO. Our findings identify reasoning validity as an important consideration when using rollout differences to guide learning. Our code is available at https://github.com/ldi-kyunghee/RoAR.

cs.LG · cs.CL

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34950

MINT: Mitigating Structural Inconsistency via Attentive Message Passing for Financial Anomaly Detection

Yin-Ju Wu, Bi-Cheng Yan, Berlin Chen

原文摘要

Illicit account detection is a critical task for financial risk control systems, aiming to pinpoint malicious actors within transaction networks. Existing efforts typically recast transaction networks into directed multigraphs and leverage graph neural networks (GNNs) to detect anomalies via topological structures under the homophily assumption. However, directly grafting GNNs onto transaction graphs is faced with at least two pivotal challenges, i.e., structural inconsistency and label scarcity. The former arises from deceptive connections forged by malicious actors, while the latter stems from the lack of annotated data. In light of these, we introduce MINT, a novel graph neural framework that mitigates structural inconsistency via an attentive message-passing scheme on transaction graphs. Specifically, MINT introduces a discrepancy-aware feature propagation, which tracks malicious actors by quantifying deviations between nodes across bi-directional transaction flows. A transaction salience module is proposed to mitigate structural inconsistency by adaptively regularizing the interaction strength between nodes. Furthermore, to alleviate label scarcity, MINT is optimized via Positive-Unlabeled (PU) learning, which exploits only a minority of illicit nodes coupled with a majority of unlabeled ones. Extensive experiments conducted on three financial transaction benchmarks, including two large-scale public and one in-house real-world datasets, validate the superior effectiveness of MINT over several cutting-edge baselines.

cs.LG

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34949

Correlation-Aware Exploration for Long-Trace Reasoning

Junxing Hu, Zicheng Zhang, Zhiqian Zhang, Ai Han, Yifeng Zhang, Pengzhang Liu, Ling Li

原文摘要

Reasoning models trained with Group Relative Policy Optimization (GRPO) now produce chains of thought thousands of tokens long, and recent studies reveal that such training is prone to policy collapse. Entropy regularization is the standard remedy, but it works by pushing probability mass away from the few tokens that carry it and toward the vast low-probability tail of the vocabulary, and it applies this force once for every token of the reasoning trace. This puts the bonus in direct conflict with the entropy decay that convergence requires, and makes its coefficient difficult to set in the long-trace regime. To resolve this tension, we propose Info-GRPO, an information-theoretic framework that cultivates correlation between the policy and a latent prior. Rather than raising entropy everywhere, Info-GRPO builds on the mutual information between a latent variable and the trajectory it induces: augmenting prompts with latent seeds during training lets the model explore diverse policies correlated with the prior, while the seed-conditioned entropy is guided toward convergence. Diversity is therefore carried across whole reasoning traces rather than by per-token randomness, and exploration no longer fights entropy reduction. Experiments show that Info-GRPO outperforms vanilla GRPO and entropy-regularized GRPO across diverse reasoning benchmarks. On AIME24, it improves $Avg@8$ over GRPO by 3.75%, 1.66%, 4.16%, and 4.17% on four backbones, and it reaches the same majority-vote accuracy with $8\times$ fewer samples. Code and models will be publicly released.

cs.LG · cs.AI · cs.CL

查看论文
arXiv ↗PDF ↗