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

+2471新增论文
+2025其中 AI 相关
+163已有论文修订
+100新增 AI 导读

arXiv 最近完成扫描：2026-11-10 19:33 · 全站最近入库：2026-11-10 15:22各信源进度 →

统计口径与数据新鲜度

统计 2026-11-09 19:42 — 2026-11-10 19:42（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 19:33最近发现新增版本：2026-11-10 15:22库内论文最新更新时间：2026-11-10 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。

20,643 已收录论文17,531 AI 相关5,643 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 20,643 篇论文 · 按版本更新时间排序上轮扫描起点 2026-11-10 19:30（北京时间）
新论文 · v1arXiv:2610.36169

Don't Pick a Winner: Knowledge Conflicts Need a Prior, Not a Rule

Minh Pham Van, Cong Tran

原文摘要

A retrieval-augmented language model answers from two sources, the retrieved context and its own knowledge, and the two disagree whenever the context is incomplete or wrong. Existing methods settle the disagreement with a rule fixed at design time: follow the context, amplify it in proportion to how far it moves the model, or trust the model's memory. Each rule is confidently wrong in some deployment. In a decision-theoretic model of two sources, the score of any fixed rule is linear in the share of reliable contexts while the best attainable score is convex, so every fixed rule is beaten wherever it is not exactly optimal; and a symmetric divergence between the two sources, the signal adaptive decoding uses, carries no information about which one is right. Resolving a conflict therefore needs two inputs that no existing method keeps: evidence about each source separately, and a prior on how often the context is right. We test these claims on GRACE, a benchmark that intervenes on the knowledge-graph context of a fixed question, so that what the model knows, what the context says and what is true are measured separately. There, every baseline from four families is strong on right contexts or on wrong ones, never on both. We then build the two inputs: the model states its knowledge as notes, a small policy voices each note with a calibrated confidence, a reader answers in two voices, and a selector commits or abstains under a prior $π$ applied as an exact prior shift. With $π$ set to the true share of right contexts, the system is above the best baseline at every mixture of GRACE contexts, by up to 26 points. Its overall score is 45.3 and 35.0 (Qwen3.5-35B-A3B, Llama-3.1-8B), against 29.4 and 27.5 for the best baseline. On preregistered ClashEval it beats same-backbone CK-PLUG and TCR by 12 and 26 points. Where the model lacks the knowledge, the gains vanish, as the theory predicts.

cs.AI · cs.CL

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.18541

LESSViT: Robust Hyperspectral Representation Learning under Spectral Configuration Shift

Haozhe Si, Yuxuan Wan, Yuqing Wang, Minh N. Do, Han Zhao

原文摘要

Modeling hyperspectral imagery (HSI) across different sensors presents a fundamental challenge due to variations in wavelength coverage, band sampling, and channel dimensionality. As a result, models trained under a fixed spectral configuration often fail to generalize to other sensors. Existing Vision Transformer (ViT) approaches either rely on implicit spectral modeling with fixed channel assumptions or adopt explicit spatial-spectral attention with prohibitive computational cost, leading to a fundamental trade-off between efficiency and expressiveness. In this work, we introduce Low-rank Efficient Spatial-Spectral ViT (LESSViT), a sensor-flexible architecture for cross-spectral generalization. LESSViT is built on LESS Attention, a structured low-rank factorization that models joint spatial-spectral interactions through separable spatial and spectral components, reducing the complexity of full spatial-spectral attention from $\mathcal{O}(N^2 C^2)$ to $\mathcal{O}(rNC)$, where $N$ is the number of spatial tokens, $C$ is the number of spectral channels, and $r$ is the rank of the low-rank approximation. We further incorporate channel-agnostic patch embedding and wavelength-aware positional encoding to support flexible spectral inputs. To enable efficient and robust pretraining, we introduce a hyperspectral masked autoencoder (HyperMAE) with decoupled spatial-spectral masking and hierarchical channel sampling. We evaluate LESSViT under a cross-spectral generalization setting that simulates cross-sensor variability. Experiments on the SpectralEarth benchmark demonstrate that LESSViT improves robustness under spectral shifts while remaining competitive in-distribution, and explicit and efficient spatial-spectral modeling is essential for scalable and generalizable hyperspectral representation learning. The code is available through the project page: https://uiuctml.github.io/LESSViT/

cs.CV

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.02366

PRIMA: Boosting Animal Mesh Recovery with Taxonomic Priors and Adaptation-Driven Data Bootstrapping

Xiaohang Yu, Ti Wang, Mackenzie Weygandt Mathis

原文摘要

Recovering articulated 3D animal meshes from monocular images is challenging due to the long-tailed distribution of species and limited large-scale 3D supervision. Existing methods rely on small annotated datasets and thus regress to average shapes and poses, leading to poor generalization to rare species and articulations. We present PRIMA (PRIors for Mesh Adaptation), a framework that tackles these limitations with two components: taxonomic shape priors and adaptation-driven data bootstrapping. First, we derive taxonomic shape priors from BioCLIP image features, which are learned under taxonomic supervision, to inject species-level knowledge into mesh reconstruction, improving shape estimation across diverse quadrupeds. Second, we propose an adaptation-driven bootstrapping scheme that turns abundant 2D animal annotations into reliable pseudo-3D supervision via lightweight instance-level mesh refinement and quality-aware filtering. This yields Quadruped3D, a large-scale pseudo-3D dataset constructed from real-world animal images, containing approximately 9K pseudo-3D annotations. Together, these components alleviate the long-tail problem: extensive experiments on Animal3D, CtrlAni3D, Quadruped2D, and Animal Kingdom demonstrate state-of-the-art performance for mesh reconstruction, with particularly strong gains on challenging poses and underrepresented species. The code is available at https://github.com/AdaptiveMotorControlLab/PRIMA.

cs.CV

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2603.21819

Ctrl-A: Control-Driven Machine Learning using Adaptive Online Data Augmentation

Jesper B. Christensen, Ciaran Bench, Spencer A. Thomas, Hüsnü Aslan, David Balslev-Harder, Nadia A. S. Smith, Alessandra Manzin

原文摘要

We introduce ControlAugment (Ctrl-A), an automated data augmentation algorithm for image-vision tasks, which incorporates principles from control theory for online adjustment of augmentation strength distributions during model training. Ctrl-A eliminates the need for initialization of individual augmentation strengths. Instead, augmentation strength distributions are dynamically, and individually, adapted during training based on a control-loop architecture and what we define as relative operation response curves. Using an operation-dependent update procedure provides Ctrl-A with the potential to reduce the use of augmentation operations that negatively impact model performance, alleviating the need to manually engineer augmentation policies for new image classification tasks. Experiments on the CIFAR-10, CIFAR-100, SVHN-core, and Tiny ImageNet benchmark datasets using WideResNet-28-10 and ResNet architectures demonstrate that Ctrl-A provides competitive performance across a range of image classification benchmarks.

cs.CV · cs.AI · cs.LG · eess.SY

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.24816

First-Order LoRA Training with Phone NPUs

Xu Zheng, Wei Cheng, Zhuomin Chen, Dongkuan Xu, Mo Sha, Jingchao Ni, Dongsheng Luo

原文摘要

On-device fine-tuning lets a large language model (LLM) learn from its user's private data without uploading it. Low-rank adaptation (LoRA) reduces the trainable parameters, but each first-order training step still multiplies by the frozen base-model weights during the forward pass, activation recomputation, and input-gradient computation. A phone's neural processing unit (NPU) already holds these weights for inference; using it for training also requires computing input gradients, which its inference interface does not support. We present a system that runs all three frozen-weight products on the NPU while keeping the training graph, adapters, and optimizer on the CPU. Its input-gradient kernels reuse the resident weight tiles and the forward dequantizer, avoiding a transposed weight copy and ensuring forward and backward use the same effective weights. To preserve usable gradients, the system selects precision by gradient sensitivity and applies a per-row range guard for half-precision (FP16) outputs. Its runtime fuses the remaining CPU operators and overlaps NPU execution with adapter computation, while resumable execution and thermal pacing let training proceed as a background job. We evaluate it on two Qualcomm Snapdragon platforms using four LLMs from the Qwen and Llama families. Our workloads cover nightly personalization with labels supplied on the phone and the public LaMP-2 benchmark; comparisons include an optimized CPU trainer, a phone-GPU trainer, forward-only training, and GPU-trained references.

cs.LG · cs.AI

查看论文
arXiv ↗PDF ↗
版本更新 · v3arXiv:2411.09731

Temporal difference learning adapts to incomplete coverage: an oracle inequality over subgraphs

Wenlong Mou, Jian Qian

原文摘要

Temporal difference (TD) and Monte Carlo (MC) are two fundamental principles for value function estimation in reinforcement learning. TD is asymptotically efficient for value estimation, whereas MC requires only adequate target visitation. We show that these advantages are automatically combined by ordinary TD in the tabular setting. We identify the subgraph Bellman variance, arising from bootstrapping within a subgraph and rollouts outside it, as a fundamental quantity connecting the MC and efficient TD benchmarks. With independent trajectories, we prove a non-asymptotic oracle inequality: the mean squared error of full TD is bounded by this variance divided by the sample size for every sufficiently covered subgraph containing the target, up to a higher-order remainder whose occupancy dependence is confined to that subgraph. No subgraph is supplied to the algorithm. TD thus adapts without selecting a subgraph or requiring global coverage. A finite-sample minimax lower bound shows that the asymptotic variance of TD need not be attainable before rare states are adequately observed. Experimental studies examine the transition between these variance scales and its dependence on visitation structure.

cs.LG · math.ST · stat.ML

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36153

Stratum: Repository Memory for Coding Agents Beyond the Context Window

Yuvraj Sehgal, Mahsa Panahandeh, Naser Ezzati-Jivan

原文摘要

Context windows have grown a thousand-fold, yet coding agents still start every session with no memory of their past work. They re-read files, retry failed approaches, and forget root causes they established hours earlier. This is not a context-length problem but a memory-architecture problem. Our hypothesis: coding agents need persistent, experience-aware repository memory, so that knowledge survives from one session to the next. Three measurements on real repositories motivate the design. First, mid-size projects already exceed the largest production context windows, and storing knowledge inside the model's key-value (KV) cache costs $10^4$ to $10^5$ times more bytes than storing it as indexed text. Second, across 752 real bug-fix commits, the median fix edits two files, and these files plus their direct imports amount to less than 10% of the repository; the bottleneck is selection, not capacity. Third, even with transitive import reachability and test links, 47% of co-edited file pairs remain structurally disconnected; the knowledge needed for selection must be mined from history and experience. We sketch Stratum, a tiered memory that lives with the repository and serves it through page-safe tools: every response states what was returned, what remains, and what it cost. Retrieval over a store of one million nodes takes 1.7 ms at the median. In a multi-session experiment on eight real bugs, a fresh agent that inherited a prior session's memory, written by a different model, identified the root-cause file in 21 of 24 trials versus 6 of 24 for a retrieval-only baseline (Fisher $p<10^{-4}$), at 4.5x lower median token cost. With the notes of all eight investigations pooled in one store, success remained 8/8.

cs.SE · cs.AI

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2602.16704

Reinforced Fast Weights via Next-Sequence Prediction

Hee Seung Hwang, Xindi Wu, Sanghyuk Chun, Olga Russakovsky, Zhiwei Deng

原文摘要

Fast weight architectures offer a promising alternative to standard transformers for long-context modeling by replacing the KV-cache with a recurrently updated fixed-size memory. Despite this architectural shift, they are typically trained with the same next-token prediction (NTP) objective as standard transformers. This creates a mismatch: fast weight models rely on an evolving memory state to support future predictions, while NTP provides only token-level supervision for the immediate next token. We address this mismatch with next-sequence prediction (NSP), a sequence-level extension of NTP that trains models to produce coherent multi-token continuations from their recurrent memory state. To optimize this sequence-level objective, we propose ReFINE (Reinforced Fast weIghts via Next sEquence prediction). ReFINE selects informative token positions based on prediction entropy, generates multi-token rollouts, assigns sequence-level rewards, and optimizes the model with Group Relative Policy Optimization (GRPO). ReFINE is applicable throughout the training lifecycle of pre-trained language models: mid-training, post-training, and test-time training. Experiments on LaCT-760M and DeltaNet-1.3B show that ReFINE consistently outperforms NTP-based supervised fine-tuning in needle-in-a-haystack retrieval, long-context question answering, and diverse tasks in LongBench. ReFINE provides an effective and versatile framework for improving long-context modeling in fast weight architectures.

cs.CL

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36151

Replication or Corroboration? An Axiomatic Framework for Evidential Dependence in Multi-Agent Debate

Hoang-Loc Cao, Truong Thanh Hung Nguyen, Phuc Truong Loc Nguyen, Vo Thanh Khang Nguyen, Phuc Ho, Hung Nguyen, Duc Pham, Long Tran-Thanh 等 9 位作者

原文摘要

In multi-agent debates, matching reports can arise for two different reasons: agents may repeat the same evidence (replication), or they may provide separate evidential roots that support the same claim (corroboration). A judge must distinguish these two cases. Counting report occurrences gives extra weight to relays, paraphrases, alias resubmissions, and excerpt splits, while merging all matching content avoids this only by also removing genuine corroboration. We formalize this tension as a minimal judgment game in which acquiring new evidence is costly, but replication is cheap. We show that root replication invariance (RRI) is exactly threshold-uniform replication immunity. Under RRI, replication-only deviations cannot change the verdict, and positive-cost replication is strictly dominated, whereas occurrence-sensitive and content-keyed judges admit profitable deviations. We also establish an information boundary: if a judge's observation interface cannot distinguish a replica from a matched fresh root, no judge can both ignore the replica and respond strictly to the fresh root. To preserve this distinction, we introduce the lineage quotient, which removes wording, identity, occurrence multiplicity, and arbitrary root names while keeping distinct roots separate. On orbit-connected domains, it is the coarsest representation sufficient for every deterministic judge invariant to replication and root-label renaming. We further lift this construction to quantitative bipolar argumentation frameworks (QBAFs), characterize strict fresh-root response, prove its propagation along sign-consistent paths under DF-QuAD, and derive a tight closed-form response modulus. Exhaustive audits over 28,224 direct-star profiles, 2,052 multi-layer DAGs, with matched interventions on 277 AVeriTeC-derived profiles, show the predicted separation between replication and corroboration under a supplied partition.

cs.MA · cs.AI · cs.LO

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36150

Any3R: Feedforward 3D Reconstruction from Any Camera Type

Jingong Chen, Qingwen Zhang, Youming Deng, Chulwoo Pack, Olaf Wysocki, Boris Jutzi, Kwanghee Won, Kyle Gao

原文摘要

Geometry foundation models recover camera poses and dense geometry from a set of images with a feed-forward network, but they expect pinhole images. That assumption is inside the backbone too: the positional encoding and the training loss both bind an image position to a direction in space, and both depend on the lens. We condition both on the dense ray field, the unit direction through every pixel, which describes any central camera without a lens type, distortion coefficients or a bound on field of view. Angular coordinates give the rotary encoding the measured ray angle of each patch, evaluated per view, and solid-angle weighting scales the point loss by the area each pixel covers on the sphere, so neither the encoding nor the supervision depends on the lens. Any3R is therefore a feed-forward model that takes any central camera, and is robust to mixing many camera types per scene, with no retraining per lens. On a benchmark of thirteen central cameras across three datasets, where every method reconstructs with its own predicted camera poses, it is the most consistently accurate method. Any3R enables scalable 3D reconstruction from heterogeneous crowdsourced and archival imagery collections sourced across diverse camera types. Project page: https://jingong-chen.github.io/Any3R/

cs.CV

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36149

Anytime Valid Confidence Bands for Distribution Functions

Bror Hultberg, Dave Zachariah, Antônio H. Ribeiro

原文摘要

Nonparametric inference often requires characterizing an unknown function, such as a cumulative distribution function (CDF), across its entire domain rather than a specific point. We develop confidence bands for distribution functions of a data stream that are valid simultaneously over all times and all points, even when each observation may depend arbitrarily on the past. We begin by deriving such bands for the time-averaged conditional CDF and show how their widths are determined by the depth of a binary search tree. We then show that this result is an instance of a framework through which one can construct valid confidence bands uniformly over time and over an arbitrary index set for any indexed family of time-averaged distribution functions. We illustrate the framework on importance-weighted and right-censored CDFs, as well as multivariate CDFs.

stat.ML · cs.LG

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2502.17061

A Mechanistic Interpretation of Prescribed-Parameter Convolutional Kernels

Jorge Marco Blanco, Rubén Cuevas Rumín

原文摘要

Prescribed-parameter convolutional transforms achieve strong time series classification performance using fixed kernels, simple pooling operations, and a linear classifier. Their success raises a mechanistic question: which properties of the representation make this construction effective? We study the proportion of positive values (PPV), a pooling operator shared by ROCKET, MiniROCKET, and MultiROCKET. We establish exact invariance to circular translations under periodic boundaries and quantify departures from invariance under finite boundaries. A complementary analysis bounds the sensitivity of a smooth PPV surrogate. We then define the population pairwise geometry induced by a distribution of convolutional parameters and show how a finite random kernel bank concentrates around that geometry. This separates the role of the parameter distribution from the number of sampled kernels. Experiments on the UCR classification collection examine effective dimensionality, distance correlation with the input, and classification accuracy using PPV-only, unit-energy ROCKET kernels. Increasing kernel count produces diminishing representational gains. Increasing kernel length generally raises effective dimensionality and distance correlation, while the best test performance occurs at intermediate lengths and training accuracy remains close to perfect. Together, these results support an interpretation in which nonlinear pooling creates a richer but structured representation that can favour linear separation, while further representational richness need not improve generalisation.

stat.ML · cs.LG

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36147

ML-OLLA: Link Adaptation for Real-Time Traffic in 5G/6G NTN Systems

Artur Magamedov, Artem Krasilov, Roman Zlobin, Evgeny Khorov

原文摘要

Reliable low-latency connectivity for real-time applications is a key requirement for 5G and future 6G non-terrestrial networks. Low Earth Orbit satellites enable global coverage but impose long propagation delays, rapid channel variations, and delayed feedback, which challenge conventional link adaptation algorithms designed for terrestrial networks. To address this problem, the paper proposes a Multi-Level Outer-Loop Link Adaptation (ML-OLLA) algorithm that assigns a dedicated target block error rate and an independent adaptation process for each transmission attempt. With ML-OLLA, early transmission attempts use higher-rate modulation and coding schemes (MCSs), while later ones use more robust MCSs. The core contribution of this work is a low-complexity data-driven parameter selection method that determines ML-OLLA parameters. Numerical results obtained with ns-3 show that the proposed ML-OLLA algorithm increases the system capacity by up to four times and reduces the minimum supported latency budget. In particular, for the 99.9% reliability target, ML-OLLA reduces the minimum supported latency budget from 60 ms to 40 ms compared with the baseline OLLA.

cs.NI

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36146

From Structural Feedback to Prompt Policies: Learning Faithful Text-to-Image Prompt Editors

Wentao Ye, Yali Ye, Zhiqing Xiao, Ru Peng, Xinpeng Ti, Liyao Li, Zhanming Shen, Peng Lu 等 10 位作者

原文摘要

Prompt optimization should improve the image a user receives without changing the scene the user requested. Yet stylistic rewriting can weaken object attributes and relations even when global text--image similarity remains high. We propose StruPO, a structure-aware prompt editor that uses a calibrated scene graph as a shared representation for policy conditioning and image-grounded supervision. The graph exposes what should be preserved, while deletion, reordering, and insertion provide a token-aligned way to modify the prompt. This turns structural feedback from an external critique into a training signal for a reusable editing policy. Learning from that signal requires handling both uncertain grounding and stochastic image generation. StruPO retains grounding confidence through smooth reward shaping and averages multiple images for each edit before computing group-relative advantages. Experiments span three disjoint 2K-prompt test sets and four frozen generation backbones, with comparisons to learned prompt optimizers, iterative structural feedback, diffusion-model fine-tuning, and inference-time attention control. StruPO improves the faithfulness--aesthetics trade-off, with consistent gains in relational fidelity. Independent automatic evaluators and a 15-annotator study provide complementary evidence, and human preference margins increase with relation complexity. Controlled ablations connect these gains to graph conditioning, local editing, and image-grounded learning. The resulting policy produces reusable optimized text in one pass after graph calibration, without an iterative critique--rewrite loop.

cs.AI

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36145

They Will See Johnny's Message: A Measurement Study of Post-Quantum Cryptography Across Email Delivery Paths

Jaehwan Park, Sunghyun Park, Kevin Nam, Doowon Kim

原文摘要

Harvest-now, decrypt-later (HNDL) attacks make the transition to post-quantum cryptography (PQC) urgent for email. This paper presents a systematic measurement that jointly examines PQC deployment across email service stages, negotiation by communicating peers, and usable message encryption. Endpoint-level support alone does not establish PQC readiness across email workflows because submission, relay, and access use independently negotiated Transport Layer Security (TLS) connections. We therefore combine three snapshots of relay, submission, access, and webmail services associated with the Tranco Top~1M domains with controlled tests of mail clients, sending servers, and OpenPGP and S/MIME implementations. Our measurements reveal a transition concentrated among a few providers, with available capability not consistently translating into actual use. Fewer than half of the domains in each service panel support PQC, and Google alone accounts for approximately 87\% of hybrid-capable relay domains. Only two of eight tested sending providers negotiate PQC with our receiver, although five accept it inbound. When TLS groups are restricted to PQC, four of the six incompatible senders fall back to plaintext where unencrypted SMTP remains permitted. At the message layer, limited native key generation, key-format incompatibilities, and backend integration failures further constrain usable PQC encryption.

cs.CR

查看论文
arXiv ↗PDF ↗
版本更新 · v3arXiv:2602.16870

Boreas Road Trip: A Multi-Sensor Autonomous Driving Dataset on Challenging Roads

Daniil Lisus, Katya M. Papais, Cedric Le Gentil, Elliot Preston-Krebs, Andrew Lambert, Keith Y. K. Leung, Timothy D. Barfoot

原文摘要

The Boreas Road Trip (Boreas-RT) dataset extends the multi-season Boreas dataset to new and diverse locations that pose challenges for modern autonomous driving algorithms. Boreas-RT comprises 60 sequences collected over 9 real-world routes, totalling 643 km of driving. Each route is traversed multiple times, enabling evaluation in identical environments under varying traffic and, in some cases, weather conditions. The data collection platform includes a 5MP FLIR Blackfly S camera, a 360 degree Navtech RAS6 Doppler-enabled spinning radar, a 128-channel 360 degree Velodyne Alpha Prime lidar, an Aeva Aeries II FMCW Doppler-enabled lidar, a Silicon Sensing DMU41 inertial measurement unit, and a Dynapar wheel encoder. Centimetre-level ground truth is provided via post-processed Applanix POS LV GNSS-INS data. The dataset includes precise extrinsic and intrinsic calibrations, a publicly available development kit, and a live leaderboard for odometry and metric localization. Benchmark results show that many state-of-the-art odometry and localization algorithms overfit to simple driving environments and degrade significantly on the more challenging Boreas-RT routes. Boreas-RT provides a unified dataset for evaluating multi-modal algorithms across diverse road conditions. The dataset, leaderboard, and development kit are available at www.boreas.utias.utoronto.ca.

cs.RO · cs.DB

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2511.17671

Cross-User Poisoning: User-Task Boundary Failures in Multi-User Collaborative Language Agents

Atharv Singh Patlan, Peiyao Sheng, S. Ashwin Hebbar, Prateek Mittal, Pramod Viswanath

原文摘要

Language agents are moving from single-user assistants to shared collaborators in workspaces, forums, and group chats. A shared agent observes interleaved messages from multiple users, maintains persistent context, and executes tool actions, creating a boundary problem absent from standard single-user agents: the agent must decide not only whether an instruction is safe, but which user or task it is allowed to govern. We identify cross-user poisoning (CUP), where an adversary injects a message into shared context that is later applied while the agent serves a benign user, causing unauthorized actions or responses outside the instruction's intended scope. We validate CUP on two deployed multi-user agents, Continua and ElizaOS, and introduce MURMUR, a framework for evaluating shared-context agents under concurrent multi-user interactions. Across Slack, Workspace, and Airline domains, CUP achieves high attack success, persists across later interactions, and remains substantially more effective than matched prompt-injection attacks. We evaluate boundary-scoping defenses and find that context summarization alone is insufficient, while task clustering and provenance prompting substantially reduce non-adaptive propagation. These results show that robust multi-user agents require explicit user/task scoping rather than generic input filtering or context compression.

cs.CR · cs.AI · cs.CL

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2503.22076

Concise One-Layer Transformers Can Perform Dictionary Lookup

Anton Hampe, Lena Strobl, Dana Angluin, Robert Frank

原文摘要

While transformers have proven enormously successful in a range of tasks, their fundamental properties as models of computation are not well understood. This paper contributes to the study of the expressive capacity of transformers, focusing on their ability to perform the fundamental computational task of looking up a key in a dictionary. We prove that concise 1-layer transformers (i.e., with a polylog bound on the product of the number of heads, the embedding dimension, and precision) can perform this task when the key and value are represented in the same input position, but, under the $p$-bit rational precision assumption, cannot perform it when the keys and values are represented in different input positions. Concise 2-layer transformers can perform the task in the second case. Experimentally, we find an alignment between what we have proven can be computed by concise transformers and what can be practically learned.

cs.LG

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36139

SpatialPC: Deep Computation with Sublinear Local Credit Propagation

Benjamin Prada, Shion Matsumoto, Ankur Arjun Mali

原文摘要

On a chain of $N$ states, any local learning rule with bounded communication range needs $Θ(N)$ rounds before the loss can influence the earliest parameters, and this latency is usually attributed to depth. We show that it is a property of the communication geometry rather than of depth. For $N=m^d$ states placed at constant density in $d$ dimensions, a light-cone argument gives an $Ω(N^{1/d})$ lower bound on the number of bounded-range rounds that any local algorithm needs before its gradient estimates can be sign-correct at every parameter. We construct a unit-range, degree-$2d$ directed acyclic graph that contains a Hamiltonian computation of length $N-1$ and in which every state lies within directed distance $d(m-1)=Θ(N^{1/d})$ of the output, matching the lower bound. In the realizable-circuit model, depth and credit depth are therefore independent parameters: represented depth $Θ(N)$ and causal credit latency $Θ(N^{1/d})$ coexist, with a separation of order $N^{1-1/d}$. SpatialPC realizes this separation within predictive coding. Credit is propagated by adjoint prediction dynamics (APD), a local update whose $K$-round state on a settled acyclic graph is the truncated reverse-path sum $a^K=\sum_{k<K}(J^\top)^k b$, with error exactly $(J^\top)^K a^\star$. Graph geometry fixes when credit can first arrive at a state; the Jacobian-weighted path tail fixes how many further rounds accurate credit requires. A sparse two-dimensional Moore compiler retains the $N-1$ backbone with $\tfrac32N-2$ unit-range edges and has measured worst-node reachability $\sqrt N+2$ up to $N=65{,}536$, where a 65,535-edge backbone is reached in 258 local hops. With $T=2\sqrt N$ rounds, SpatialPC keeps finite-round task gradients aligned with settled gradients (cosine $0.93$ on MNIST and $0.97$ on Fashion-MNIST at $N=256$) and reaches $97.6\%$ and $88.1\%$ test accuracy after 20 epochs.

cs.LG

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36138

Symmetry Breaking Is the Price of Finite Capacity

Maggie Chlon, Leon Chlon

原文摘要

If rotating a problem never changes its answer, one might expect its best model to be rotation-invariant; we show that this fails, predictably and by an exact amount, when capacity is limited. A symmetry groups a model's directions into blocks of equally strong directions, a symmetric model must use each block whole, and when the budget cuts a block every optimal model breaks the symmetry. For a Gaussian problem with a $k$-unit bilinear predictor, the log-loss price of symmetry is exactly $(U_k-I_k)/(2σ^2)$, the signal of the best $k$ directions minus that of the best whole blocks. Because an attention head compares tokens through a similarity of rank at most its head dimension, whatever computation precedes it, the same price governs heads fitted to a symmetric similarity by squared error, and heads trained this way on three-dimensional directions break symmetry at exactly the predicted head dimensions, with the predicted rotation sensitivity (RMSE 0.004). Under softmax attention, flatter attention restores the predicted symmetric head where tested, while with peaked attention broken heads beat every whole-shell symmetric head at 16 of 20 head dimensions. We give a procedure that separates capacity-driven breaking from other causes.

stat.ML · cs.LG

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36135

Physical structure as an organizing principle for universal Hamiltonian learning

Mingkang Liu, Zhanghao Zhouyin, Guanghui Wang, Daye Zheng, Lei Shen, Qiangqiang Gu

原文摘要

Density functional theory (DFT) gives the electronic structure of materials, but its cost limits large-supercell calculations and materials screening. Machine-learned Hamiltonians bypass the self-consistent cycle but are usually system-specific; universal ones could cover new compositions and disordered solids. Such models must generalize across chemistry and keep downstream properties physical, while staying inexpensive for large cells. Here we introduce UniTB, a universal E(3) equivariant Hamiltonian representation framework that integrates physical priors rooted in density-functional tight-binding theory with deep learning. UniTB's network, which corrects a prior Hamiltonian, generalizes across chemistry through a mixture of experts routed by that prior and a charge-equilibration head as in self-consistent-charge tight binding. A projection bounds band shifts from the prior, suppressing spurious states and keeping downstream properties physical. We provide the fastest SO(2) tensor-product GPU kernels for the network. The variant UniTB-Flash assembles its prior from two-center tables at $O(N)$ cost for $N$ atoms, against $O(N\log N)$ on a grid. Trained on 29,242 crystals, UniTB reaches a 0.48 meV matrix-element error on nonmagnetic test crystals spanning 68 elements. Without amorphous training data, both models agree with DFT optics and conductivity of amorphous Ge-Sb-Te. UniTB-Flash ranks candidates with no significant difference from UniTB, whose property values are more accurate; together they screen 16,195 crystals for five application domains of industrial relevance.

cs.AI

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36134

When Connectivity Misleads: Guiding Agentic Search with Contrastive Evidence Paths

Junchen Li, Liang Xu, Rongzheng Wang, Qizhi Chen, Junhao Wen, Shuang Liang

原文摘要

Search agents can use cross-document graphs to discover sources through multi-hop connections and choose subsequent actions based on candidate rankings and relationships within a limited observation window. However, documents may rank highly because their numerous connections make them easier to reach, without necessarily providing more valuable evidence. Graph diffusion methods such as Personalized PageRank (PPR) aggregate propagation contributions from multiple sources, allowing highly connected distractor nodes to crowd the observation window. Flat node scores also obscure cross-document evidence trails, limiting continued exploration. We propose Contrastive Evidence Paths (CEP), which scores candidates by their strongest individual paths on a query-conditioned document graph and calibrates these scores against the same graph's background visitation distribution to mitigate structural bias. CEP ranks candidates by the calibrated scores and retains the corresponding source paths. Building on CEP, we develop Structured Corpus Interaction (SCI), which organizes candidates and their connecting paths into a structured Map, enabling agents to verify source documents and pursue missing information through expansion from anchor documents. Experiments on BrowseComp-Plus and TREC-COVID show that SCI achieves state-of-the-art answer accuracy and nDCG@10 with the lowest generation cost. Compared with the strongest baselines by task performance, maximum reductions in tool calls and total generation costs exceed 50% and 40%, respectively. Further analysis confirms that SCI with CEP suppresses ranking inflation induced by structural connections and improves gold recall and exploration efficiency.

cs.IR · cs.AI

查看论文
arXiv ↗PDF ↗
版本更新 · v3arXiv:2605.12856

Moderation by Interrogation: Uncovering Concealed Intent in Agent Social Networks

Ali Al-Lawati, Nafis Tripto, Abolfazl Ansari, Fali Wang, Jason Lucas, Suhang Wang, Dongwon Lee

原文摘要

The emergence of multi-agent systems introduces novel moderation challenges that extend beyond content filtering. Agents with $\textit{malicious intent}$ may contribute content that appears benign to evade content-based moderation, while serving a hidden objective. To address this challenge, we introduce Bot-Mod a moderation framework that grounds detection in agent intent rather than traditional content-level signals. Bot-Mod interrogates a target agent using a fixed set of diagnostic probes and infers its intent through Bayesian posterior inference over candidate intents. It starts with a calibrated content-only prior and updates the posterior using the $\textit{likelihood}$ of each answer under every candidate intent, as assigned by the moderator, until a stopping criterion is met. To evaluate our approach, we construct Molt-Intent, a dataset derived from Moltbook, capturing diverse benign and malicious behaviors within real community structures through posts and comments. Across Molt-Intent, an external social engineering dataset, and three agent LLMs, Bot-Mod outperforms content-only moderators, a fine-tuned classifier, and judge-based interrogators both in- and out-of-distribution. On Molt-Intent, it reaches a composite F1 of 0.736 in distribution and 0.684 out of distribution, 22% and 14% above the best baseline.

cs.AI · cs.SI

查看论文
arXiv ↗PDF ↗
版本更新 · v3arXiv:2601.11460

Semantic-Geometric Task Representations for Bimanual Manipulation from Human Demonstrations to Robot Action Planning

Franziska Herbert, Vignesh Prasad, Han Liu, Dorothea Koert, Georgia Chalvatzaki

原文摘要

Learning structured task representations from human demonstrations is essential for bimanual manipulation, where action ordering, object involvement, and interaction geometry vary significantly across executions. A key challenge lies in jointly capturing the discrete semantic task structure and the temporal evolution of object-centric geometric relations in a form that supports reasoning over task progression. We introduce a semantic-geometric graph-based task representation that jointly encodes object identities, inter-object semantic relations, and per-object motion histories, via a Message Passing Neural Network (MPNN) encoder and a Transformer-based decoder. The encoder operates solely on the temporal scene graph, producing structured representations decoupled from action labels. The decoder then conditions on action-context to forecast future actions, associated objects, and object motions. This decoupling learns task-agnostic representations, enabling encoder reuse across embodiments through decoder-only finetuning on a small robot dataset. Across eleven bimanual tasks from two datasets, we find that the benefit of structured semantic--geometric representations over simpler sequence-based models grows with task variability in action ordering and object involvement. At deployment, a planner couples the action and motion predictions with learned Probabilistic Movement Primitives, achieving full task success on two real-robot bimanual tasks and outperforming graph ablations, Transformer, decoder-only, and finetuned vision-language model baselines.

cs.RO · cs.LG

查看论文
arXiv ↗PDF ↗
版本更新 · v3arXiv:2601.23072

SplineFlow: Flow Matching for Dynamical Systems with B-Spline Interpolants

Santanu Subhash Rathod, Pietro Liò, Xiao Zhang

原文摘要

Flow matching is a scalable generative framework for characterizing continuous normalizing flows with wide-range applications. However, current state-of-the-art methods are not well-suited for modeling dynamical systems, as they construct conditional paths using linear interpolants that may not capture the underlying state evolution, especially when learning higher-order dynamics from irregular sampled observations. Constructing unified paths that satisfy multi-marginal constraints across observations is challenging, since naïve higher-order polynomials tend to be unstable and oscillatory. We introduce SplineFlow, a theoretically grounded flow matching algorithm that jointly models conditional paths across observations via B-spline interpolation. Specifically, SplineFlow exploits the smoothness and stability of B-spline bases to learn the complex underlying dynamics in a structured manner while ensuring the multi-marginal requirements are met. Comprehensive experiments across various deterministic and stochastic dynamical systems of varying complexity, as well as on cellular trajectory inference tasks, demonstrate the strong improvement of SplineFlow over existing baselines. Our code is available at: https://github.com/santanurathod/SplineFlow.

cs.LG

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2601.06086

AzeroS: Extending LLM to Speech with Self-Generated Instruction-Free Tuning

Yiwen Shao, Wei Liu, Jiahong Li, Tianzi Wang, Kun Wei, Meng Yu, Dong Yu

原文摘要

Extending large language models (LLMs) to the speech domain has recently gained significant attention. A typical approach connects a pretrained LLM with an audio encoder through a projection module and trains the resulting model on large-scale, task-specific instruction-tuning datasets. However, curating such instruction-tuning data for specific requirements is time-consuming, and models trained in this manner often generalize poorly to unseen tasks. In this work, we first formulate that the strongest generalization of a speech-LLM is achieved when it is trained with Self-Generated Instruction-Free Tuning (SIFT), in which supervision signals are generated by a frozen LLM using textual representations of speech as input. Our proposed SIFT paradigm eliminates the need for collecting task-specific question-answer pairs and yields the theoretically best generalization to unseen tasks. Building upon this paradigm, we introduce AZeroS (Auden Zero-instruction-tuned Speech-LLM), which is trained on speech-text pairs derived from publicly available corpora, including approximately 25,000 hours of speech with ASR transcripts and 3,000 hours of speech with paralinguistic labels. Built upon Qwen2.5-7B-Instruct, the model updates only two lightweight projection modules (23.8 million parameters each), while keeping both the LLM and audio encoders frozen. Despite the minimal training cost and modest data scale, AZeroS achieves state-of-the-art performance on both semantic and paralinguistic benchmarks, including VoiceBench, AIR-Bench Foundation (Speech), and AIR-Bench Chat (Speech).

cs.CL · cs.SD · eess.AS

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.00087

DeGenTWeb: A First Look at MGT-dominant Websites

Sichang Steven He, Calvin Ardi, Ramesh Govindan, Harsha V. Madhyastha

原文摘要

Many news reports have claimed that machine-generated text (MGT) from large language models (LLMs) is taking over the web. However, these claims are typically not based on a representative sample of the web, and the methodology underlying them is often opaque. Consequently, we lack an understanding of the true prevalence and characteristics of MGT on the web. We describe DeGenTWeb, which systematically identifies MGT-dominant websites: sites whose informational prose content is mostly MGT, with little human input. We show how to adapt detectors of MGT for use on web pages, and how to aggregate detection results from multiple pages on a site for accurate site-level categorization. DeGenTWeb mislabels only 0.12% of human-authored sites as MGT-dominant. We find that 20.9% of the sites archived by Common Crawl between January and July 2026 and 13.6% of the sites linked from Bing's search results in 2025 are MGT-dominant. These figures are lower bounds on the prevalence of MGT-dominant sites. We also find that the fraction of MGT-dominant sites is growing over time, and that many such sites share the same owner. We will release all of our code and data after publication.

cs.NI · cs.AI · cs.CY · cs.IR · cs.LG

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.09410

RePO-VLA: Recovery-Driven Policy Optimization for Vision-Language-Action Models

Weijia Liufu, Xiaoyu Guo, Ruiyi Chen, Jingzhi Liu, Kaidong Zhang, Xiwen Liang, Jianqi Lin, Dawei Sun 等 19 位作者

原文摘要

Vision-Language-Action (VLA) models remain brittle in long-horizon, contact-rich manipulation because success-only imitation provides little supervision for execution drift, while failed rollouts are often discarded. We introduce RePO-VLA, a recovery-driven policy optimization framework that assigns distinct roles to success, recovery, and failure trajectories. RePO-VLA first applies Recovery-Aware Initialization (RAI), slicing each corrective suffix into a new episode so corrective actions depend on the current adverse state rather than the preceding failure. It then learns a Progress-Aware Semantic Value Function (PAS-VF), aligning spatiotemporal trajectory features with instructions and successful references. The resulting labels salvage useful failure prefixes via reliability decay, while low-value labels mark drift and terminal breakdowns, teaching differences among nominal, failed, and corrective actions. The data engine turns adverse states into planner-generated or human-collected corrective rollouts, teaching recovery to the success manifold. Value-Conditioned Refinement (VCR) trains the policy to prefer high-progress actions. At deployment, a fixed high-value token biases actions toward the learned success manifold without online failure detectors or heuristic retries. We introduce the Failure-Recovery Benchmark (FRBench), with standardized error injection and recovery-focused evaluation. Across simulated and real-world bimanual tasks, RePO-VLA improves robustness, raising adversarial success from 20% to 75% on average and up to 80% in scaled real-world trials. Code and video results are available at https://1018weijia.github.io/RePO-VLA-Web/.

cs.RO · cs.AI

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36126

ConformalMOS: Uncertainty-Aware MOS Prediction with Conformal Intervals and Ordinal Modeling

Kehinde Elelu, Joshua E. Siegel, Mohammadali Saffary, Tashfain Ahmed, Simeon Babatunde, Ebuka Okpala

原文摘要

Accurate prediction of human Mean Opinion Scores (MOS) is crucial for evaluating synthetic speech quality in text-to-speech (TTS) and voice conversion (VC). Most MOS predictors output only point estimates and ignore uncertainty, limiting reliability in model selection and deployment. We propose ConformalMOS, a framework that augments MOS prediction with conformal prediction to produce statistically valid prediction intervals with guaranteed coverage under exchangeability, alongside point estimates. During training, MOS labels are modeled in an ordinal-aware manner by converting one-hot targets into Gaussian-smoothed distributions, preserving score ordering. This improves uncertainty calibration, particularly when softmax confidence is overconfident on out-of-distribution speech. On BVCC datasets, ConformalMOS achieves competitive accuracy (MSE = 0.08) while delivering empirically validated coverage, enhancing reliability for real-world TTS and VC deployment.

cs.SD · cs.CL

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2409.04557

A Deep Learning Framework for TTV Characterization: A Case Study of the Kepler-88 System

Chen Chen, Jiapeng Gao, Gongjie Li, Lingkai Kong, Molei Tao

原文摘要

Transit timing variation (TTV) provides rich information about the mass and orbital properties of exoplanets, which are often obtained by solving an inverse problem via Markov Chain Monte Carlo (MCMC). In this paper, we design a new data-driven approach, which potentially can be applied to problems that are hard to traditional MCMC methods, such as the case with only one planet transiting. Specifically, we use a deep learning approach to predict the parameters of non-transit companion for the single transit system with transit information (i.e., TTV, and Transit Duration Variation (TDV)) as input. This approach is constructed using the Transformer-based architecture that can extract long-range interactions from TTV sequential data. We focus on two-planet systems based on Kepler-88 as a starting point, with the inner planet fixed to the properties of Kepler-88 and the mass and orbital elements of the outer planet varied. Applying to Kepler-88, we obtained an overall fractional error of $\lesssim$10\% on semi-major axis and eccentricity, and $\sim 10-30\%$ on planet mass (assuming that the stellar and transiting planet parameters are known), based on a training set of a million simulations. When applied to the test set, our model achieves low fractional errors of less than a few percent for systems with TTV semi-amplitudes ranging from $\sim 0.3$ s to $\sim 5$ min. Extending our approach to a broader range of planetary architectures, including systems with substantially weaker or stronger TTV signals and those containing additional planets or stellar companions, remains an important direction for future work.

astro-ph.EP · astro-ph.IM · cs.LG

查看论文
arXiv ↗PDF ↗