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

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

arXiv 最近完成扫描：2026-11-08 16:36 · 全站最近入库：2026-11-08 06:02各信源进度 →

统计口径与数据新鲜度

统计 2026-11-07 16:55 — 2026-11-08 16:55（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：2026-11-08 16:36最近发现新增版本：2026-11-08 06:02库内论文最新更新时间：2026-11-06 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。

17,100 已收录论文14,661 AI 相关4,057 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 17,100 篇论文 · 按版本更新时间排序上轮扫描起点 2026-11-08 16:33（北京时间）
新论文 · v1arXiv:2611.02410

Spatial Organization Is Accessibility Structure: Hierarchical Screen Reading of Mobile UIs

Yongxiang Hu, Xuan Wang, Qingyang Qiu, Yingjie Liu, Chun Kang, Ruiying Zeng, Xin Wang, Yangfan Zhou

原文摘要

Screen readers for blind and low-vision (BLV) users flatten mobile UIs into a linear sequence. Modern mobile UIs, however, contain dozens of elements grouped by spatial layout. Such linear access is ill-suited for BLV users, who must infer each element's meaning and which elements form a functional unit. Drawing on a quantitative TalkBack audit and formative interviews, we present Spatial Screen Reader (SeeR), which builds a function-aware hierarchy from accessibility metadata and provides unit summaries with hierarchical navigation. Compared with TalkBack, SeeR reduces non-informative focus stops by 82.1% and navigation steps by 52.7%. With ten BLV participants, summaries helped them connect controls to their functions and skip nonessential elements, especially on unfamiliar pages, though extra navigation levels added effort when the target was already known. These findings recast spatial organization as accessibility information: apps and platforms should expose it, rather than leaving screen readers to infer it post hoc.

cs.HC

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02408

Deployment-constrained key estimation with a DSP-guided transformer reranker

Pardis Noorzad

原文摘要

Local music applications estimate key during library import and benefit from an analyzer that is small, fast, and runs in-process. We start from a compact chroma-template key analyzer that fits this budget but lags behind learned references. Learned systems reach higher accuracy, but their installed model and runtime dependencies can occupy tens to hundreds of megabytes. We present key lime, a 21.5k-parameter DSP-guided transformer reranker with a 115 KB model artifact and a 76.7 MB installed footprint, including the extracted ONNX Runtime. It adds 0.32 ms per track to deterministic DSP scoring. Trained from labels on 4,040 FMAK tracks, with 449 tracks for validation and 1,000 disjoint FMAKv2 tracks held out for evaluation, it improves MIREX from 61.08 to 73.55 +/- 0.46 and exact accuracy from 52.30 to 67.07 +/- 0.65 over three seeds (mean +/- population SD). It approaches a same-manifest local madmom KeyCNN reference at 74.16 MIREX. A 12-parameter circular input stage matches the accuracy range of a 468-parameter linear input projection.

cs.SD · cs.LG · eess.AS

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02407

Networked Synthetic Social Engineering on the Web

Bryan C. Boots

原文摘要

Several genuine AI services may repeat one unsupported source, and correcting that source may leave later reports unchanged. We examine these networked consequences of synthetic social engineering (SSE), deliberately misleading influence intended to redirect AI recipients from authorized objectives. Our framework connects social influence, information aggregation, and misinformation correction research to observable evidence and permission boundaries. It separates originating attempts from legitimate intermediaries' work and distinguishes identity, evidentiary support, and authorization. We test four relationships using Qwen3-4B and two briefing tasks across 1,080 research units in our Agent Service Exchange and Evidence Commons (ASE-EC). Multiple dependent publishers produced no supported increase in unsupported verification (78.3% versus 75.0%); publication availability left downstream violation reach at 40.0% in both arms. These comparisons direct attention to weaknesses already present in the reference workflows. High reference error rates and retained private handoffs preclude claims of immunity to social pressure. Without scope revalidation, corrections identifying dependent reports reduced residual invalid decisions from 14.4% to 8.1% at equal notification coverage and increased successful report repairs from 16/63 to 36/63. Scope revalidation added no demonstrated protection, and clearing dialogue offered no established recovery advantage. Unapproved requests persisted, all blocked by an external gateway. For designers, the findings caution against treating publisher count as independent evidence, notification as repair, or cleared histories as recovery. They support targeting corrections at reused reports, assessing factual and permission recovery separately, and enforcing authorization at execution.

cs.CR · cs.AI · cs.CY

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2506.12230

Statistical Machine Learning for Astronomy -- A Textbook

Yuan-Sen Ting

原文摘要

This textbook gives a systematic treatment of statistical machine learning for astronomical research, based on Bayesian inference. It shows how modern data analysis techniques connect to classical statistical methods and follow from familiar statistical principles. The Bayesian perspective, used throughout, gives priority to uncertainty quantification and statistical rigor, which scientific inference in astronomy needs. The textbook progresses from probability theory and Bayesian inference through supervised learning, including linear regression with measurement uncertainties, logistic regression and classification. Unsupervised learning topics cover Principal Component Analysis and clustering methods. We then introduce sampling and Markov Chain Monte Carlo, followed by Gaussian Processes as probabilistic nonparametric methods and neural networks in the broader statistical context. Our theory-focused approach derives each method from first principles with complete mathematical development and then shows its astronomical applications. We put statistical insight first and explain why algorithms work, when they are appropriate, and how they connect to broader statistical principles. From a solid grounding in classical methods and their theory, the treatment leads to modern techniques such as neural networks. With this grounding, readers can apply the methods to astronomical research with proper attention to assumptions, limits and uncertainty propagation, as large astronomical surveys require. The 22 companion tutorials apply the methods to astronomical data in Jupyter notebooks.

astro-ph.IM · cs.LG · stat.AP · stat.ML

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02405

MirrorComm: A Communication-Efficient Cooperative Perception Framework via Mirrored World Models

Shenghan Luo, Yin Xu, Cixiao Zhang, Yang Wang, Yujie Cao, Zhuoran Xiao, Wenjun Zhang

原文摘要

Cooperative perception (CP) requires substantial communication bandwidth to exchange sensing information among connected agents. A variety of compression techniques have been proposed to reduce transmission costs while retaining task-relevant information. However, most existing methods follow a frame-wise compression paradigm, overlooking temporal predictability and repeatedly transmitting information that could be inferred from historical context. To address these limitations, we propose MirrorComm, the first world model-based communication-efficient CP framework. Specifically, mirrored world models at the transmitter and receiver predict the transmitter's current observation from a shared history of its reconstructed observations. The transmitter compares this prediction with its current observation and selectively transmits observation updates to correct prediction errors, reducing repeated transmission of predictable information. Experiments on the OPV2V dataset across two downstream tasks including object detection and semantic segmentation show that MirrorComm reduces communication bandwidth requirements by up to approximately 66-fold while maintaining comparable performance to a state-of-the-art (SOTA) method. Ablation studies further demonstrate the effectiveness of the individual components.

eess.SP · cs.CV

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2608.15550

Adoption of Generative AI in the Workplace: Increasing and Shifting the Balance of Productivity and Communication Activity

Yulin Yu, Yan Chen, Rui Hu, Siddharth Suri, Scott Counts

原文摘要

Generative AI is transforming the workplace by augmenting and automating cognitive tasks, reshaping how organizations work and innovate while raising questions about workplace inequality and the future of work. Despite rapid adoption, empirical evidence on how these tools alter work practices and generate productivity gains remains limited. We examine how AI use affects the quantity and nature of information work using digital trace data from the Microsoft M365 application suite across multiple large international companies. Specifically, we study how generative AI adoption shifts the balance between communication and productivity-oriented activities, such as content creation in Word. Difference-in-Differences analyses show that AI adoption is associated with significant increases in both productivity (21.2%) and communication (7.1%) application actions among users who used the AI system more than 100 times over a 20-week post-adoption period. Among users with 100-500 AI use instances, higher AI usage is also associated with continued increases in both types of activity. The smaller increase in communication represents an overall shift toward individual, documentation-focused work and reflects mixed changes in communication, including decreases in reading and organizing email, compared with more uniform increases in productivity actions. These findings suggest potential efficiency gains and reductions in information overload, while highlighting the need to ensure that AI adoption does not weaken interpersonal communication and the diffusion of diverse information that supports innovation.

cs.HC

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02404

When Rewards Depend on the Group, GRPO Plays a Tournament

Junyi Gao, Yu Shi, Pingzhao Hu, Ewen M Harrison

原文摘要

Rewards for diversity, coverage and distribution matching make the reward of each sampled response depend on the rest of its group, and they are now routinely optimized with GRPO and RLOO. We prove that in this setting the group-relative baseline changes the objective. The expected update equals each response's own reward gradient plus $α$ times its effect on its groupmates' rewards, with $α=-1/(K-1)$ for groups of size $K$, so GRPO trains every response to beat its groupmates in a zero-sum tournament. It cannot see a pairwise diversity bonus when K=2, sees less than half of it at any K, and often does worse than using no baseline at all. Setting $α=+1$ gives Team-GRPO, an unbiased estimator for any group-dependent reward that works with two samples per prompt and requires no extra model calls for pairwise rewards. The theory predicts training quantitatively. It locates the fixed points of a 9B policy to within 0.02 in $\ell_1$ and predicts how concentrated trained policies become with Pearson r=0.995. On GlobalOpinionQA, GRPO collapses onto the majority answer and ends further from human opinion than the untrained model, while Team-GRPO cuts the error from $0.20$ to $0.08$. On NoveltyBench, Team-GRPO avoids the diversity collapse of GRPO and significantly improves set utility.

cs.LG · cs.CL

查看论文
arXiv ↗PDF ↗
版本更新 · v3arXiv:2605.18599

Resolving Representation Ambiguity in Feedforward Novel View Synthesis Transformer via Semantic-Spatial Decoupling

Yihang Wu, Yihang Sun, Shaofeng Zhang, Zuxuan Wu, Junchi Yan, Xiaosong Jia, Yu-gang Jiang

原文摘要

Transformer-based models have advanced feedforward novel view synthesis (NVS). Current architectures such as GS-LRM and LVSM mix semantic information (e.g., RGB) and spatial information (e.g., Plücker rays) into a shared feature space. Since Plücker rays naturally carry lattice-like spatial structure, these designs can make the spatial bias interfere with appearance representation and degrade rendering fidelity. To this end, we propose to decouple the representation of feedforward NVS transformers into separate semantic and spatial tokens. The decoupled design keeps semantic and spatial information explicit in their branches while preserving cross-branch interaction through shared attention routing. Built on this design, we introduce optional categorized supervision and bidirectional modulation: the former provides branch-specific training signals, while the latter improves interaction between the two branches. Notably, the base decoupled design introduces virtually zero additional inference latency due to its architectural design. The proposed designs achieve consistent improvements, demonstrating effectiveness across decoder-only and encoder-decoder feedforward NVS models.

cs.CV

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2607.05849

CoPiT: Cognitive Pivot Translation for Digraphic Low-Resource Mongolian in the Traditional Script

Burte Bayarsaikhan, Serynn Kim, Buru Chang

原文摘要

Low-resource languages remain challenging for machine translation, and Mongolian is a representative case. As a digraphic language, Mongolian is written in both Cyrillic and Traditional scripts, which exhibit a severe imbalance in data availability. While the Cyrillic script is relatively well-resourced, the Traditional script remains extremely data-scarce and orthographically ambiguous, leading to substantial performance degradation in direct translation. We propose CoPiT, a cognitively motivated pivot-based translation pipeline that exploits this internal resource hierarchy by routing translation through the Cyrillic script. The pipeline explicitly resolves script-induced ambiguity in the Traditional script before translation, enabling more stable and accurate meaning transfer. Across multiple backbone models and target languages, CoPiT generally improves over direct translation, with substantial absolute BLEU improvements and up to 1.5-1.6x gains in COMET. These gains allow strong open-source models to match or outperform GPT-4.1 under comparable evaluation settings. Beyond inference-time improvements, CoPiT enables the construction of synthetic parallel data directly from Traditional-script text, mitigating data scarcity in realistic low-resource scenarios. We release a new multi-script parallel dataset covering Mongolian in both scripts alongside English, Korean, and Russian.

cs.CL

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02401

Accelerating LLM Decoding with In-DRAM Parallelism

Kevin Xu, Tatsuya Kubo, Daichi Tokuda, Shinya Takamaeda-Yamazaki, Issei Sato

原文摘要

Autoregressive decoding of large language models (LLMs) on edge devices is dominated by memory-bound general matrix--vector multiplication (GeMV) operations. Processing-using-DRAM (PUD) is a promising computing paradigm that can alleviate this bottleneck by reducing processor--memory data transfers and enabling massively parallel computation within DRAM (e.g., across 65,536 columns). However, weight matrices in individual LLM layers (e.g., a QKV matrix in Llama3 8B with 6,144 rows) are typically too small to fully utilize parallelism in PUD. To address this challenge, we propose an algorithm--system co-design for PUD-based LLM inference. At the algorithm level, we enable a subsequent projection to be packed with the current projection by reformulating it as a computation on the current residual state plus a correction for the residual update. Layer redundancy allows this correction to be approximated using fewer bit-serial steps. At the system level, we tailor data mapping and execution to improve utilization of either columns within DRAM subarrays (SIMD) or independently controlled banks (MIMD). Overall, we achieves up to $1.41\times$ end-to-end decoding speedup over the PUD baseline, corresponding to a $2.45\times$ speedup over processor-based execution.

cs.AR

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02400

Inverse Design of Finite-State Channels by Dynamic Measure Matching

Lav R. Varshney

原文摘要

Inspired by the optimization approach to biology, we formulate an inverse problem for communication with memory: construct a finite-state channel for which a given signaling behavior supports capacity-achieving codes. Dynamic measure matching is introduced to turn a directed-information converse into conditions on the channel dynamics. These conditions lead to a new notion of dynamic surprise: the log ratio of physical to test output probabilities with a continuation correction for future signaling opportunities. The matching inequalities bound its conditional mean. For fixed architecture, test law, and potential, channel row synthesis under convex physical restrictions is convex. For every signaling constraint represented by a finite irreducible constrained graph of positive entropy, we construct a channel law under which undesired inputs remain available and behaviorally distinct while incurring a strict information penalty. A noisy refractory family has an exact capacity plateau, followed by an interval in which Shannon and zero-error feedback capacities differ. Explicit zero-error codes attain capacity in the graph construction and on the plateau. In this family, a Gibbs choice maximizes the penalty and makes dynamic surprise equal its conditional mean on every transition, yielding an exact trajectory-wise likelihood identity, a finite-block bound on successful decoding with premature requests, and an exponential strong converse. An exact gap decomposition into action residuals and output-law divergence constrains every reliable capacity-approaching code. Within the strict plateau, positive discrimination exponents for unused dynamics require a positive rate loss, while sparse probing permits consistent estimation at asymptotic capacity. A chemical channel illustrates how premature stimulation can alter future responsiveness while leaving the immediate output law unchanged.

cs.IT

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.23321

Tmax: A simple recipe for terminal agents

Hamish Ivison, Junjie Oscar Yin, Rulin Shao, Teng Xiao, Nathan Lambert, Hannaneh Hajishirzi

原文摘要

Terminal-using agents have quickly become a popular and important downstream application of language models (LMs). Despite their prevalence, relatively little academic work has examined RL-based training of these models, due to difficult benchmarks, a lack of data, and a lack of simple baseline recipes. We present TMax, an open RL recipe for terminal agents that brings small open models closer to the frontier. While simple, our recipe achieves 33% on Terminal-Bench 2.0 with only 9B parameters, outperforming much larger models from prior work. Our recipe first involves generating data using a novel taxonomy, combining difficulty control, personas, and verifier diversification, which allows us to cheaply generate large amounts of terminal environments for RL and SFT training. We then train open-weight models using RL with our data, using a simple, outcome-only recipe, with small adjustments to improve stability. Our recipe works for models from 2B to 27B and across model families, dominating the Pareto curve for open terminal-agent performance at smaller sizes. We release our data, models, and code as a strong baseline for future open academic work on terminal agents at https://github.com/hamishivi/tmax.

cs.CL

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.04368

A multi-stage probabilistic framework to estimate gas-fired generator performance during extreme winter weather

Sajjad Uddin Mahmud, Anamika Dubey

原文摘要

Extreme winter weather has repeatedly disrupted gas-fired power generation in the United States, yet the plant-level data needed to systematically quantify outage risk remain proprietary. Using publicly available weather and electricity demand data together with anonymized generator contingency records from the North American Electric Reliability Corporation (NERC), we develop a three-stage Bayesian probabilistic framework for estimating winter-driven generator performance. Applied to New York State (2013--2022), the framework sequentially estimates: the hourly probability of a generator contingency event, the expected net available capacity conditioned on an event occurring, and the event duration. Colder conditions and higher electricity demand are associated with higher failure probability, lower retained capacity, and longer event duration. Under the most severe observed stress conditions, estimated mean hourly event probability reaches 24\% , while expected mean net available capacity falls to 13\% of nameplate rating. Full outage events have a median duration of 12.7 hours, while partial derating event duration increases from 2.4 to 7.1 hours with capacity loss severity. The proposed framework establishes a transferable baseline that utilities with access to plant-level records can directly extend to obtain more precise reliability estimates for operational planning and resource adequacy assessment.

cs.LG · stat.ML

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03742

Fixed Detectors, Moving Code: How Well Do Static Security Analyzers Hold Up over a Decade of C/C++ Evolution?

Junhua Tang, Zhengsong Zhang. Haipeng Cai

原文摘要

Static application security testing (SAST) tools are often deployed on the assumption that detectors configured today will remain effective on future code, including previously unknown vulnerabilities. We test this assumption using 20,000 vulnerable and fixed C/C++ cases from 1,116 open-source projects (2016-2025), evaluated with three CodeQL releases, three Infer releases, and one Semgrep configuration. To distinguish temporal effects from shifts in code composition, we compare yearly populations, project- and label-matched year pairs, and within-project trajectories. We investigate the largest within-project declines through repair diffs, detection predicates, alert locations, and execution logs, while accounting for failed, degraded, and costly analyses. Under changed-file case-label scoring, overall F1 ranges from 0.066 to 0.478, but count-based AUROC stays near chance (0.495-0.526). Newer CodeQL releases attain higher F1 alongside nearly parallel rises in recall and false-positive rate, not stronger separation of vulnerable from fixed cases. In project-matched year pairs, CodeQL R2024 has negative mean later-minus-earlier F1 at every source-year gap, whereas Infer v1.1 and v1.3 have positive means throughout. Among selected drops, only 6 of 39 CodeQL and 2 of 620 Infer findings overlap a changed repair line. For Semgrep, 78.96% of cases show parser or timeout diagnostics even though every run reports success. These results suggest that apparent changes in temporal SAST effectiveness can arise from broader alerting, changing analysis coverage, and incidental findings, not solely from losses in vulnerability detection. Evaluations of temporal sustainability should fix detector conditions, control for projects, inspect repair-level evidence, and report failed or degraded analyses.

cs.CR · cs.SE

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02395

Solver-Like Behavior in Large Language Models as Formalizers

Rikhil Amonkar, Stuti Mohan, Kevin Nguyen, Ceyhun Efe Kayan, Ronan Le Bras, Li Zhang

原文摘要

In the LLM-as-formalizer paradigm, a large language model translates a problem into a formal program and leaves the search to a solver, gaining the solver's guarantees. Formalizers are now often reasoning models that write a long reasoning trace before the program, yet they are judged only by the program's output. We find that the trace routinely does the solver's job, assigning, testing and ruling out values until it often reaches the full answer; we call this solver-like behavior (SLB). We define an ontology of SLB over the trace, the program and its output, and use LLM judges, checked against human labels, to label 2,000 generations from three reasoning models on four constraint satisfaction datasets, each formalized in a declarative and an imperative formal language. SLB appears in 85% of traces and takes 24% of reasoning tokens, yet the program usually recomputes the answer, so the effort is redundant. The 9% of programs that inherit the trace's answer also inherit its errors, and pass output-based evaluation whenever that answer is correct. The amount of SLB is not associated with accuracy, and SLB is more common in imperative Python than in declarative Z3 or PDDL. Cautionary prompts reduce it by only a quarter, but hiding the problem's values behind placeholders, which the program receives only at run time, removes 97% of it without loss of accuracy. Formalizers should therefore be evaluated by how their programs are reached, not only by what they output.

cs.CL · cs.AI

查看论文
arXiv ↗PDF ↗
版本更新 · v3arXiv:2605.11442

Mobius Injection: From One-Shot Prompt Injection to Sustained Resource Exhaustion in AI Agent Infrastructure

Zi Liang, Ronghua Li, Yanyun Wang, Qingqing Ye, Haibo Hu

原文摘要

Large Language Model (LLM) agents have emerged as key intermediaries, orchestrating complex interactions between human users and a wide range of digital services and LLM infrastructures. While prior research has extensively examined the security of LLMs and agents in isolation, the systemic risk of the agent acting as a disruptive "hub" within the user-agent-service chain remains largely overlooked. In this work, we expose a novel threat paradigm by introducing Mobius Injection, a sophisticated attack that weaponizes autonomous agents into zombie nodes to launch what we define as Agent-based and -Oriented DDoS (AbO-DDoS) attacks. Mobius Injection composes known indirect prompt injection and persistent agent components through a distinct structural mechanism named Semantic Closure, which induces sustained recursive execution of agent components through a single injection event. We demonstrate that this attack is lightweight for the adversary, can evade traditional volumetric DDoS monitors and input filters calibrated for low false-positive rates, and is highly configurable, allowing for surgical targeting of specific environments or model providers. To evaluate the real-world impact, we conduct extensive experiments across three representative claw-style agents and three mainstream coding agents, integrated with 12 frontier proprietary or open-weight LLMs. Our results demonstrate that Mobius Injection achieves substantial attack success across diverse tasks, driving single-node call amplification up to 51.0x. Under coordinated multi-node pressure, each infected node still drains about 123k--127k tokens on a shared five-GPU backend; benign request latency rises by abou 23x--24x, and coordinated infections eventually saturate shared capacity until benign traffic is effectively denied service.

cs.CR · cs.AI · cs.CL

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02393

IMUdrape: Generating Garment-Mounted Virtual IMU Data from Text through Physics-Based Cloth Simulation

Zikang Leng, Thomas Plötz

原文摘要

Inertial sensors are moving into clothing, from sensorized sportswear to garments instrumented with hundreds of IMUs. Sewn into clothing, a sensor no longer moves rigidly with the body: loose fabric slides, twists and swings, so a garment-mounted sensor records the motion of the fabric as well as that of the body, and this extra motion differs from one garment to the next. Models for such sensors need training data recorded with the deployed garment, yet labeled data are scarce. Virtual IMU data generated from videos or text have eased this scarcity for body-attached sensors, but existing generators attach their virtual sensors rigidly to the body. We present IMUdrape, which generates garment-mounted virtual IMU data from text through physics-based cloth simulation. IMUdrape synthesizes motion from activity descriptions, reconstructs the target garment as a sewing pattern from photographs, places virtual IMUs at the deployment's real sensor positions, and simulates the garment with an intersection-free contact solver, so that every simulation yields paired garment and body signals. On four real datasets with clothing-mounted sensors, IMUdrape improves on existing virtual-data approaches for both activity recognition and full-body pose estimation. With one real subject, its data improve recognition macro-F1 by 7% to 46% over real data alone and by 2% to 11% over body-mounted virtual data from the same motion. When the garment is simulated on recorded motion, the pose error without any real training data falls by 30% to 33% relative to generated motion, and garment data remain 12% more accurate than body data. IMUdrape thus reduces the amount of real data that garment-mounted sensing requires.

cs.CV · cs.HC

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02392

A Sharp Mutual Information Bound for Binary Pairs

Qianmian Guo

原文摘要

We prove a sharp upper bound on the mutual information between two binary random variables in terms of their distribution-dependent KL contraction coefficient. This resolves the binary-pair conjecture of Anantharam, Gohari, Kamath, and Nair. Equality holds if and only if the variables are independent or form a doubly symmetric binary source. The proof uses supporting tangents of an entropy curve to vary the input distribution and the channel while preserving the contraction coefficient. Together with tensorization and data processing, the result also bounds the mutual information between binary outputs obtained separately from two correlated observation sequences. When the observation pairs are independent copies of a finite-alphabet pair, this bound depends only on the law of one pair and not on the number of observations.

cs.IT

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02391

Late Autumn: LLM Agents Specialize a General Multi-Robot Planner for One Warehouse

Yimin Tang, Han Zhang, Shao-Hung Chan, Erdem Bıyık, Sven Koenig, Jingkai Chen

原文摘要

No algorithm is best on all problems, but a real warehouse is a narrow problem: its layout and processes rarely change. Operators still run one general multi-robot planner at many sites, because experts cannot tune it for each one. In this work, which we call Late Autumn, we explore whether LLM coding agents running autonomous research can specialize a general planner for one site, trading generality for throughput, much as post-training adapts a pretrained LLM to one task. We study six campaigns on a lifelong planner that combines task assignment with multi-agent path finding, on a floor modeled on an existing automated warehouse. We find that (1) the agents raise throughput by 22--60%, and the gains hold on unseen seeds; (2) they rediscover known MAPF mechanisms, such as corridor-exit priorities, local guidance, and rollouts, and independent campaigns find the same bottleneck; (3) the gains largely carry over to another layout, a larger fleet, and other service times, shrink with a smaller fleet, and mostly vanish under an outbound-heavy demand mix; and (4) each final patch (0.7k--2.4k lines) is a chain of scored commits with a median step of 27 lines, so experts can review it, and review finds problems that the score misses; some patches multiply planner compute by over 100x. LLM agents can therefore turn each site's bias into extra throughput at little engineering cost, with reviewable patches; we expect experts to shift toward designing insightful metrics and fast simulators that mirror the real problem.

cs.RO · cs.AI

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03741

Beyond Proposals: Region-Consistent Open-Set Object Detection

Jiehui Tang, Yunlong Yu, Zhao Wang, Jungong Han

原文摘要

Open-set object detection (OSOD) suffers from a fundamental limitation under dense proposal mechanisms: highly overlapping proposals associated with the same object are often assigned contradictory known/unknown predictions. Existing methods perform open-set reasoning independently for each proposal, while ignoring the shared semantics among correlated proposals, resulting in fragmented detections and unstable open-set decisions. In this paper, we reformulate OSOD as an object-centric semantic inference problem, where correlated proposals jointly support a coherent region-level semantic decision. Based on this perspective, we propose OCSI-Net, a unified framework that combines object-centric semantic consistency with geometry-aware uncertainty stabilization. Specifically, OCSI-Net aggregates correlated proposals into structured region clusters and infers region-level semantics from aggregated semantic evidence, thereby enforcing coherent open-set predictions within the same object region. Furthermore, a geometry-aware uncertainty mechanism is introduced to stabilize optimization near open-set transition boundaries. Extensive experiments on multiple benchmarks demonstrate that OCSI-Net consistently achieves state-of-the-art performance, improving the harmonic mean of known and unknown detection by an average of 6.83\% over the second-best method.

cs.CV

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02390

Which Chains to Calibrate for Physical Backpropagation: Per-User Factors Versus Base-Station Chains

Xianzhong Li, Yuzhi Yang, Tianhao Guo

原文摘要

In physical backpropagation, users return loss derivatives over the time-division duplex uplink, giving the base station the downlink gradient without channel reconstruction. Reciprocity calibration conventionally prioritizes base-station chains, as column-normalized zero-forcing reduces a per-user factor to a stream rotation. This priority can reverse. With ideal base-station chains and no noise, per-user amplitude errors make the returned field a weighted sum-rate gradient. With zero-forcing full service at both optima, the loss is independent of array size. Otherwise the selection penalty, serving the wrong users, carries 86 to 94 percent of the amplitude-induced loss in five of six configurations. Per-user phase errors mainly slow the iteration. Base-station chain errors scale the returned field per antenna, and at 3 dB and 25 degrees they cost one user 1.3 dB of signal-to-noise ratio (SNR) that no array size removes. With 80 users, such errors on both sides, and mismatched pilots, oracle per-user and base-station calibration leave 6.2 and 12.3 percent loss at 256 antennas on clustered channels. This ordering holds at 30 dB SNR and reverses for 1 dB, 10 degree user errors. A capped one-echo estimator comes within 2.4 percentage points of oracle per-user calibration but underperforms no calibration at 10 dB SNR.

eess.SP · cs.IT · cs.LG

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02387

EviCast: Forecasting Retrieval Gains Through Shared Response Calibration

Yibo Wang, Jisheng Dang, Bimei Wang, Jiongpei Hou, Jingwen Zhao, Wenjun Ma, Fei Shen, Juncheng Li 等 12 位作者

原文摘要

Retrieval-augmented generation (RAG) extends large language models with external evidence, yet relevant evidence does not ensure that a generator can use it. Indiscriminate retrieval can degrade task performance while adding retrieval and long-context inference costs. Existing methods neither determine before large-scale execution whether a generator, task distribution, and retriever configuration is worth deploying nor diagnose multiple candidate retrievers under a shared budget. We introduce EviCast, a framework that forecasts retrieval value through interventional evidence-state decomposition. EviCast measures a generator's response to controlled evidence states once and combines this shared profile with the offline evidence-state distribution of each candidate retriever, yielding the end-to-end net utility and its 95% lower confidence bound without executing every RAG configuration. A state-refinement analysis characterizes when its binary and topology-aware instantiations are exact and when shared calibration is statistically more efficient than per-retriever calibration. In frozen evaluations, EviCast correctly decides all 14 HotpotQA retrieval configurations with a forecast error of 2.75 F1 points while reducing diagnostic generation calls by 80%, and it attains the lowest forecast error among seven predictors in the low-budget regime. Its topology-aware instantiation, EviCast-T, captures the complementarity between terminal and bridge evidence, correctly deciding all 16 configurations on MuSiQue and HotpotQA and 23 of 24 after prospective transfer to a third dataset. These results show that retrieval deployment depends on both evidence availability and the generator's ability to convert evidence structure into stable net utility. Code is available at https://github.com/YiboWang2002/EviCast.

cs.CL · cs.LG

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02385

Exponential Lower Bounds for Products of Cubic Polynomials over Prime Fields

Bin Fu, Yunhui Fu

原文摘要

We study exact representations of explicit signed modular predicates on $\{-1,1\}^n$ as products of bounded-degree polynomials over prime fields, measured by total variable-occurrence cost. Our main result is an exponential lower bound for products of cubic polynomials. Over $\mathbb F_3$ the proof is self-contained: every cubic factor in a nonvanishing product is a unit on the sign cube, the unit identity forces bounded affine-observation complexity, and a simultaneous clustering argument converts this structural compression into an exponential occurrence lower bound. We also give an alternative reconstruction route that isolates the quadratic structure behind the cubic classification. We then extend the cubic theorem to every fixed prime field $\mathbb F_p$ of characteristic greater than three for odd target modulus $q$ coprime to $p$. The only non-elementary structural input in this extension is Karam's restricted-range rank theorem; the conversion from multiplication to parity and the $p$-tuple clustering argument are proved explicitly. Finally, we establish a coefficient-field-independent degree-lifting principle. If a lower bound is known at one fixed degree, restriction to a large independent set yields a quantitative lower bound at the next degree. In particular, the exponential cubic theorem implies $ L^{(p,q)}_4(n)=Ω_{p,q}\!\left(\frac{n^4}{(\log(2+n))^3}\right). $ For the characteristic-three core this already gives a lower bound for a restricted mixed-modulus circuit class.

cs.CC

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02384

Nearest Is Not Safest: Fluid-Antenna Port Selection for Over-the-Air Backpropagation in Urban Multipath

Xianzhong Li, Yuzhi Yang, Tianhao Guo

原文摘要

Over-the-air backpropagation returns a loss gradient through the reciprocal channel, which requires the return port to see the channel of the forward port. If the terminal moves in between, a fluid antenna system can return through the port nearest to the forward position. In ray-traced Chicago streets, with a pedestrian displacement of about half a wavelength and a street-level base station, this rule reverses 20 percent of the gradients at a port spacing of 0.7 wavelengths and 49 percent at 0.9 wavelengths, while one of the four nearest ports stays aligned in all but 2 and 9 percent. We derive a certificate for the sign of the mean correlation from the second moment of a planar angular spectrum. It reduces to a linear program with three variables and infinitely many constraints, is tight, and holds over a region of second moments. In a static Gaussian model, observations collected along a line cannot tell an angular spectrum from its mirror image, so the sign of the cross moment must come from other information. At unsurveyed positions of the same city, a knowledge map that transports surveyed paths through virtual sources and fits them to the causal channel history lowers the reversal rate at 0.9 wavelengths from 49 to 27 percent. Its gain over the nearest port survived every tested error, while its gain over a geometry rule stayed significant at the tested single-error levels of 5 degrees of phase drift per step and 2 mm of position error but not at the next tested levels. Without any survey, across five cities and ten base stations, the nearest port reverses 37 percent of the returns at 0.9 wavelengths and a rule based on the direction and elevation of the base station 20.5 percent. The multilayer perceptrons we tested did not beat these physical rules by the margin fixed in advance in the primary settings.

eess.SP · cs.IT · cs.LG

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.27604

HiQGS: Hierarchical Spatiotemporal Querying for Feed-Forward Gaussian Reconstruction

Qiaowei Miao, Qifeng Cheng, Yijing Guo, Yuru Xiao, Ziqian Ni

原文摘要

Feed-forward Gaussian reconstruction from sparse posed monocular frames requires reasoning over observations that vary jointly in viewpoint and time. Existing methods either predict Gaussian primitives from observation-aligned features or aggregate multi-frame information through compact latent queries. However, the former tightly couples Gaussian allocation to individual frames, while the latter does not explicitly structure observed evidence according to its temporal relation to the requested timestamp. Consequently, evidence across spatial scales, viewpoints, and timestamps can be difficult to align and integrate. We present \textbf{HiQGS}, a query-based hierarchical framework for feed-forward Gaussian scene reconstruction from sparse monocular frames. HiQGS introduces a \textbf{Hierarchical Spatiotemporal Encoder} that organizes multi-resolution geometry-appearance visual features according to image location, observation time, and camera pose, thereby preserving complementary appearance and structural cues. Our \textbf{Time-Adaptive Decoder} use target timestamp queries the resulting hierarchical representation to retrieve relevant spatiotemporal evidence and predicts the corresponding Gaussian scene state. Experiments on static and dynamic scene reconstruction benchmarks demonstrate strong rendering quality and geometric accuracy, validating the effectiveness of hierarchical visual encoding and target-time-aware Gaussian querying.

cs.CV

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02383

Toward an Evidence-Based Template for Agent Instruction Files: An Empirical Study of Adoption, Consistency, and Maintenance

Mehedi Hasan Shanto, Md. Anaytul Islam, Muhammad Asaduzzaman, Alioune Ngom

原文摘要

AI coding agents rely on repository-level instruction files to understand project conventions, development workflows, and constraints. However, different agents support different instruction formats, and limited empirical evidence exists about how these files are adopted, what information they contain, whether coexisting formats provide consistent guidance, and how they are maintained. We conduct an empirical study of 3,387 instruction files from 1,000 GitHub repositories across four formats: AGENTS.md, CLAUDE.md, GitHub Copilot instructions, and Cursor rules. We find that 42.2% of instruction-bearing repositories in the higher-popularity cohort maintain multiple formats. Among 386 manually examined pairs of corresponding sections, 16.1% provide divergent guidance. Our maintenance analysis further shows that 40.4% of the analyzed files have only one recorded modification event and that maintenance patterns differ across formats. Based on category prevalence and category addition ratios (CAR), we derive an evidence-based template containing ten Required, four Gap, and two Recommended categories. In an exploratory generation experiment, explicit template guidance increases structural category coverage from 18.7% to 82.8%. We further introduce AGIT-Instruct, a benchmark for evaluating instruction-file generation through structural coverage and content alignment. Across four generation configurations, retrieval-augmented generation achieves the highest full-file ROUGE-1 F1 of 0.459. A manual assessment of 278 generated category instances finds that 19.4% fully preserve and 61.2% partially preserve developer-written intent. Our findings provide empirical guidance for organizing and maintaining agent instruction

cs.SE

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02380

Malware Clustering in the Wild: Taxonomy, Challenges, and Evaluation Metrics

Raguvir S, Bojing Li, Prajna Bhandary, Charles Nicholas

原文摘要

As malware continues to grow in volume, diversity, and sophistication, clustering has emerged as a critical technique for analyzing large malware corpora. Clustering enables the discovery of structural relationships, behavioral patterns, and evolutionary trends, and is widely used to group unknown samples, infer malware family boundaries, and identify novel variants. However, existing research remains fragmented, employing a wide range of clustering techniques with no unified perspective that systematically organizes prior work or clearly identifies gaps in the literature. This survey addresses this gap by organizing previous malware clustering research into seven categories: centroid-based, hierarchical-based, density-based, distribution-based, mixed- type, fuzzy, and AI-driven approaches. In addition, this survey summarizes publicly available malware datasets commonly used in clustering research and examines the feature representations derived from these datasets. Our goal is to clarify the current landscape and outline promising avenues for future research on malware clustering, including how AI-driven methods may shape large-scale and reproducible analysis.

cs.CR · cs.AI

查看论文
arXiv ↗PDF ↗
版本更新 · v4arXiv:2603.25710

Stone Duality for Monads

Richard Garner, Alyssa Renata, Nicolas Wu

原文摘要

We introduce a contravariant idempotent adjunction between (i) the category of ranked monads on $\mathsf{Set}$; and (ii) the category of internal categories and internal retrofunctors in the category of locales. The left adjoint takes a monad $T$-viewed as a notion of computation, following Moggi-to its localic behaviour category $\mathsf{LB}T$. This behaviour category is understood as "the universal transition system" for interacting with $T$: its "objects" are states and the "morphisms" are transitions. On the other hand, the right adjoint takes a localic category $\mathsf{LC}$-similarly understood as a transition system-to the monad $Γ\mathsf{LC}$ where $(Γ\mathsf{LC})A$ is the set of $A$-indexed families of local sections to the source map which jointly partition the locale of objects. The fixed points of this adjunction consist of (i) hyperaffine-unary monads, i.e., those monads where term $t$ admits a read-only operation $\bar{t}$ predicting the output of $t$; and (ii) ample localic categories, i.e., whose source maps are local homeomorphisms and whose locale of objects are strongly zero-dimensional. The hyperaffine-unary monads arise in earlier works by Johnstone and Garner as a syntactic characterization of those monads with Cartesian closed Eilenberg-Moore categories. This equivalence is the Stone duality for monads; so-called because it further restricts to the classical Stone duality by viewing a Boolean algebra $B$ as a monad of $B$-partitions and the corresponding Stone space as a localic category with only identity morphisms.

cs.LO · cs.PL · math.CT

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02378

A Beam-Switched Sparse Circular Array for Passive Wi-Fi Sensing

Yang Chen, Riccardo Bersan, Daniele Piazza, Stefano Savazzi, Michele D'Amico

原文摘要

Wi-Fi sensing applications require unambiguous angle-of-arrival (AoA) estimation over the full azimuth plane. Uniform circular arrays (UCAs) are shown to remove this ambiguity, but deploying the four RF chains typically available on commercial Wi-Fi chipsets into a sparse layout suffers from spatial undersampling. Such undersampling produces strong grating lobes. To overcome this hardware bottleneck, we first establish a generalized array manifold incorporating element radiation patterns, revealing the suitability and degradation mechanisms of directional UCAs. Then we propose an angularly staggered beam-switched UCA architecture that leverages the spatial complementarity. Numerical evaluations demonstrate that the proposed scheme effectively mitigates grating-lobe ambiguities without increasing the number of RF chains, significantly reducing AoA estimation outliers and improving omnidirectional tracking performance.

eess.SP · cs.IT

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02376

High-Throughput Private Information Retrieval with on-CPU Accelerators

Gyuyeong Kim

原文摘要

Private information retrieval (PIR) hides which record a client retrieves by scanning the entire database, and batching is an effective technique to improve throughput by sharing this scan across queries. However, existing CPU-based solutions gain little from batching because each query still requires its own multiplications, and vector instructions perform only a few at a time. GPUs and other discrete accelerators can accelerate these computations, but require extra hardware and can constrain database size due to their limited memory capacity. In this paper, we present AltoPIR, the first PIR system that leverages on-CPU matrix accelerators such as Intel AMX. AltoPIR keeps the accelerator fed from the CPU caches, reading each database byte from memory once for all queries in a batch and never writing intermediate products to memory. This enables high-performance PIR through efficient batching without requiring discrete accelerators. Our evaluation shows that AltoPIR achieves up to 50.1x higher throughput than existing CPU-based solutions and serves databases larger than a GPU's memory.

cs.OS · cs.CR · cs.DB

查看论文
arXiv ↗PDF ↗