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新论文 · v1arXiv:2611.04482

TEMPO: Time Series Understanding via Discrete Tokenization

Riccardo Maggioni

原文摘要

Time-series interpretation often requires both numerical observations and textual context. Integrating these inputs into a pretrained language model raises a representation question: how should a signal enter the model's token sequence? We present TEMPO, which extends an LLM's vocabulary with discrete codes from a context-aware Transformer tokenizer using finite scalar quantization. A frozen tokenizer maps four-step patches to a 625-code vocabulary; signal tokens, scale statistics, and text are then processed by the LLM's existing self-attention layers. Adaptation updates the new token embeddings and DoRA adapters. With a Qwen3-4B backbone and task-specific fine-tuning, TEMPO achieves macro-F1 scores of 89.0% on activity recognition, 85.0% on sleep staging, and 86.0% on bearing-fault diagnosis. Time-series question answering and qualitative bearing-diagnosis examples further illustrate how this interface combines signal representations with textual context. Together, these results demonstrate the utility of discrete vocabulary extension for time-series understanding.

cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04481

Goal-Driven Autonomous Optical Experimentation via a Self-Recursive Hardware-Software Harness

Tianxing Zhu, Huacheng Lu, Ming Liu, Malte Hillebrand

原文摘要

We propose goal-driven self-recursive optical research in which agents use physical evidence to revise the hardware, software, models, and protocols used to pursue a scientific goal. The framework can be instantiated with different stimulus systems and measurement devices. We formalize its updates and derive a relation between physical attainability, command access, and model approximation error. We demonstrate the concept in an optical see-through color-matching case study using an iPad, RayNeo X3 glasses, and a TOP 300 probe coupled to a CAS140D spectroradiometer, both from Instrument Systems. A revised glasses application adds USB-controlled RGB holds and frame acknowledgements, keeping model-generated colors displayed during spectral acquisition. The interface passes 12 live control checks. Spectral feedback reduces one target color difference from 19.756 to 1.211 ΔE_76^*; all five targets improve in a separate comparison. These measurements demonstrate the concept in one configuration. The resulting software and protocols remain available for subsequent investigations.

cs.AI · cs.MA

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04480

ReListen: Training-Free Evidence Localization in Hour-Long Audio with Frozen Audio Language Models

Ming-Hao Hsu, Shujie Liu, Yuxuan Hu, Yuxiang Wang, Yingda Shen, Jinyu Li, Zhizheng Wu

原文摘要

Audio language models are meant to answer questions from the audio itself, but an hour-long recording rarely fits in their context, and the answer often depends on a few seconds of it. Transcribing the recording first makes it fit but discards sounds and voices, while systems that return to the audio must be trained for this behavior. We propose ReListen, a training-free method in which a frozen audio language model finds its own evidence before answering. In a Jev-style scan, the model scores each 20-second window by the probability that it helps answer the question, and it then answers from the highest-scoring minute. This minute overlaps the annotated evidence far more often than transcript retrieval, for 88.5% of VoiceGiraffe questions. Without any change, the same scan also grounds events on LAT-Bench as well as a model trained for temporal grounding. Answering from it matches or exceeds hearing the whole recording with a small fraction of the audio, and on VoiceGiraffe it exceeds a transcript of the whole recording by 9.2 points. The selected evidence is also worth hearing rather than reading. Given the same excerpts, reading their transcript matches listening on speech content, but listening is clearly more accurate on questions about sounds and voices. Moreover, ReListen answers faster than every transcript-based system, and its memory stays constant even for recordings too long to hear whole on one GPU. Therefore, a frozen audio language model can search hour-long recordings by itself, without a transcript or additional training.

cs.CL · cs.SD · eess.AS

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04479

A Parametric Linear Conic Projection for Structured Sparse Attention

Michel Barlaud, Clement Genninasca, Maxime Zanella, Benoit Macq, Benoit Legat

原文摘要

In this paper, we introduce the Cone Alignment Index (CAI), a scale-invariant sparsity measure associated with a Lorentz-cone geometry. We provide a probabilistic analysis of the distribution of the CAI score, including a general theorem and explicit corollaries for Gaussian and uniform distributions. As a consequence, we show that, in the high-sparsity regime, the probability that the samples lie within the convex cone tends to one. Building upon this formulation, we derive two complementary projections: the \emph{Parametric Linear Conic Projection} (PLCP), an efficient closed-form approximation, and the \emph{Parametric Euclidean Conic Projection} (PECP), the exact Euclidean projection onto the cone boundary. Our analysis establishes several fundamental theoretical properties. We prove that PLCP and PECP are collinear, differ only by a positive radial scaling factor, and induce exactly the same sparsity threshold, thereby preserving an identical active support. Furthermore, we derive a support-identification theorem showing that, for any prescribed sparsity level $l<n$, the number of coefficients discarded at each iteration is theoretically controlled. Building on this theoretical foundation, we develop a bilevel PLCP framework for structured matrix sparsification. The proposed method naturally generates hardware-friendly structured sparsity patterns, including row-wise, column-wise, and diagonal masks, making it particularly well suited for accelerating Transformer attention mechanisms.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04477

Spectrally Routed Diffusion Policy for Multi-Task Robotic Manipulation

Mingcong Li, Yihan Zhou, Jiayu Qu, Xueyang Guo, Yilin Mo

原文摘要

Diffusion-based policies have achieved strong performance on single-task robotic manipulation. However, a single model trained jointly on multiple tasks consistently underperforms the per-task specialists trained on the same demonstrations. We focus on two challenges. First, the convolutional encoders commonly used in diffusion-based policies produce visual conditions that are not discriminative enough. Second, gradients from incompatible action modes update the shared denoiser's dense parameters along the same directions, causing destructive interference during joint training. To address both, we propose SR-DP (Spectrally Routed Diffusion Policy). A frozen VGGT encoder with a spatial-compression adapter produces compact multi-scale semantic-geometric tokens, a task-discriminative visual condition for tasks that share a workspace. We first train the denoiser as a standard diffusion policy, then decompose selected linear layers by SVD into a frozen basis of orthogonal rank-one experts, adapted through spectral routing and Householder input rotations. On six RoboTwin tasks, SR-DP averages a 68.4% success rate, surpassing multi-task VO-DP by 21.8 points and the strongest per-task baseline by 3.8 points. On four real-world bimanual tasks, Stage 2 reaches a 43.0% average success rate and improves over Stage 1 by 19.0 points.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04476

KeyWise: Efficient Video-Only Piano Transcription with a Lightweight Per-Key Architecture

Anastasios Chatzigiannis, Natalia Panagou, Maria Koziri

原文摘要

Audio-based piano transcription is hampered by the sustain pedal and by noisy or missing soundtracks, problems that video-only transcription avoids, since a top-view video shows directly when each key is pressed and released. Most visual transcription networks, however, describe the whole keyboard with one global feature vector and must learn where each key is. In this paper, we propose KeyWise, a lightweight per-key architecture that exploits the known geometry of the keyboard instead of learning it. The rectified keyboard is divided into 88 equal tiles that serve as regions of interest (ROIs), and one small recurrent network, shared by all keys, follows each key over time. On PianoVAM, KeyWise reaches the onset accuracy of the state-of-the-art V2N (95.0% vs. 94.7% onset F1 at 50 ms and 97.8% vs. 97.0% at 100 ms) with 1.3 M instead of 28.2 M parameters, and it runs 4.7 times faster on the same GPU. At 100 ms, it is also significantly more accurate when the key releases (97.0% vs. 95.5% F1) or the velocities (89.4% vs. 84.5% F1) must be correct as well. On PianoYT, KeyWise pre-trained on PianoVAM reaches 69.4% onset F1 at 100 ms, comparable to the best published result, although it needs fine-tuning for new recording conditions.

cs.CV · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2608.05548

Behavioral Residualization for Unsupervised Intrusion Detection in Automotive CAN Networks

Chandan Hegde, Mukundh R Reddy, Dr Kumaresh Sheelavant

原文摘要

Modern vehicles exchange safety-critical messages over the Controller Area Network (CAN), a bus designed for low cost and real-time operation that offers neither authentication nor encryption. Once an attacker reaches the network, arbitrary frames can be injected. Existing detectors frequently rely on surface cues (previously unseen identifiers, constant payload bytes, or irregular DLC values). These cues yield near-perfect scores on public corpora whose attacks are separable by exactly those rules, yet they collapse when the adversary simply reuses legitimate identifiers. We introduce per-ID behavioral residualization (PIRD). Fourteen temporal, protocol and payload statistics are extracted from sliding windows belonging to each arbitration ID and then expressed as standardized residuals relative to that ID's own benign baseline. When the resulting residual vectors are supplied to six standard unsupervised detectors, mean F1 rises in the majority of experimental cells (21 of 24 on HCRL, 30 of 36 on ROAD, five random seeds). On targeted legitimate-ID attacks the method attains recall >= 0.99 and ROC-AUC near 0.99. A deployment-oriented Phase-1 protocol (cross-condition transfer, global threshold, alternative residualization strategies, window/latency sweep) additionally reports Recall at fixed FPR, FPR at target recall, false alarms per hour and detection latency. Two clear failure modes, novel-ID flooding (HCRL DoS, F1 = 0.02) and cross-ID fuzzing (ROAD, F1 = 0.27), are quantified and left as open problems.

cs.CR · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04474

Hierarchical Intelligent Evolutionary Optimization for UAV-Assisted NOMA-MEC Systems

Yaxuan Jiao, Jiaxuan Lv, Jyoti Sahni, Wuyungerile Li, Winston K. G. Seah

原文摘要

The integration of unmanned aerial vehicles (UAVs), mobile edge computing (MEC) and non-orthogonal multiple access (NOMA) provides an effective solution for computation-intensive and latency-sensitive services in infrastructure-deficient environments. However, the joint optimization of UAV trajectories, task offloading, communication and computing resources under heterogeneous task priorities results in a highly coupled mixed-integer non-convex problem. To address these challenges, this paper proposes a Hierarchical Intelligent Evolutionary Algorithm (HIEA) for priority-aware multi-UAV cooperative NOMA-MEC networks, which integrates multi-head deep Q-network (MH-DQN)-based intelligent decision generation, communication-aware initialization, adaptive evolutionary exploration and successive convex approximation (SCA)-based precision refinement. Specifically, a novel communication-aware initialization mechanism, termed Channel Quality Indicator (CQI)-guided Load-Aware Trajectory Initialization (CQI-LATI), is designed to incorporate CQI and task workload characteristics into evolutionary population construction, generating high-quality and diversity-preserving initial populations. Theoretical analysis characterizes HIEA's search-space reduction, the quality-diversity trade-off of CQI-guided initialization, and the accuracy-efficiency trade-off of SCA refinement, and extensive simulations demonstrate that HIEA outperforms benchmark schemes, with gains of up to 6.5% in throughput, 13.6% in task completion rate and 15.3% in energy efficiency.

cs.NI · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2607.17243

LenGuard-GPC: Length Guarding with Guided-Prompt Consistency for Spatial Reasoning Reinforcement Learning

Xingjian Tao, Yiwei Wang, Yujun Cai, Xinghua Qu, Jing Tang

原文摘要

Multi-view spatial reasoning requires vision-language models to compare visual evidence across images, align object correspondences, and infer spatial relations over long visual contexts, a setting where chain-of-thought reasoning tends to grow verbose without becoming more accurate. Reinforcement learning with verifiable rewards is a natural fit for this task, but standard GRPO reward relies on sparse outcome-level feedback and gives no signal about where a reasoning trajectory goes wrong, nor any control over its length. We propose LenGuard-GPC, a dense reward framework that addresses both problems together. For each sampled trajectory, it compares the token-wise predictive distributions under a standard prompt and a guided prompt, and uses the resulting token-sum KL divergence as a dense reward signal. Since this KL penalty accumulates over tokens and would otherwise reward shorter responses regardless of their quality, we introduce a staged length bonus that keeps reasoning length within a controlled range without simply encouraging brevity. On six multi-view spatial reasoning benchmarks, LenGuard-GPC improves accuracy over vanilla GRPO while reducing average response length.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.35706

TriTile: Three-Tier Attention for Video Diffusion Transformers

Yeonjae Kim, Omin Kwon, Seong Hoon Seo, Jae W. Lee

原文摘要

Attention over tens of thousands of visual tokens dominates the inference of video diffusion transformers (DiTs). Existing methods reduce this cost through sparsity, low precision, or both, but use a single precision for all kept tiles. We observe that the output error introduced by approximation varies widely across tiles. Many tiles can be skipped with little error, others must be computed but gain little beyond low precision, and a few retain substantial error even at low precision. We present TriTile, a training-free attention method that assigns each tile one of three approximation tiers: skipping it by replacing it with its block means, skimming it in NVFP4, or refining it with NVFP4 residual correction. A lightweight router predicts each tile's error under each tier from block statistics and spends extra compute on a tile only where it removes enough error; a single knob, a price on compute, controls the whole speed-quality trade-off. A fused kernel executes all three tiers in a single pass on the FP4 tensor cores. Over dense FlashAttention, TriTile speeds up generation by 1.38-1.83x on Wan2.2 at 23.0-27.6 dB PSNR, 2.28-2.32x on HunyuanVideo at 34.5-36.0 dB, and 1.84x on LTX-2.5 at 34.1 dB, and in every setting it Pareto-dominates every baseline in speed and PSNR.

cs.LG · cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.13071

FiTS: Interpretable Spiking Neurons via Frequency Selectivity and Temporal Shaping

Jongmin Choi, Joon Son Chung

原文摘要

Spiking Neural Networks (SNNs) are a promising framework for event-driven temporal processing. Prior work has improved temporal modeling through richer neuron dynamics and network-level mechanisms such as recurrence and delays, but it remains unclear how individual spiking neurons should specialize within a network. In this work, we introduce FiTS, a spiking neuron that factorizes temporal computation within each neuron into Frequency Selectivity (FS) and Temporal Shaping (TS). The FS module parameterizes each neuron's target frequency as the maximizer of its subthreshold magnitude response, while the TS module reshapes when frequency components contribute to membrane voltage accumulation through group-delay modulation. On auditory benchmarks where frequency selectivity and timing are central to the input structure, FiTS consistently improves over a plain Leaky Integrate-and-Fire (LIF) baseline in simple feedforward SNNs without recurrence or network-level delays, while remaining competitive with strong temporal SNN baselines. Beyond accuracy, the learned target frequencies and group-delay shifts provide interpretable neuron-level summaries of the frequency and timing organization learned within the network. Our code is available at https://github.com/kaistmm/FiTS.

cs.NE

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2607.25233

Beyond Single-Episode Optimization: Sliding-Window Aware Generative Auto-Bidding for Long-Term Advertising Effectiveness

Binglin Wu, Chuan Yue, Yingyi Zhang, Xianneng Li, Ruyue Deng, Weiru Zhang, Xiaoyi Zeng

原文摘要

Auto-bidding systems optimize bids to maximize value under efficiency constraints such as Cost-Per-Action (CPA). Existing methods treat each day as an independent episode. However, many advertisers produce value so sparsely that per-day efficiency ratios become statistically unreliable, undermining advertiser retention. Platforms therefore evaluate window-level efficiency over sliding windows of $W{=}7$ days, ensuring fair evaluation and long-term advertising effectiveness. This creates cross-episode coupling: each day's bidding decisions affect up to $W$ overlapping windows, so setting daily targets requires anticipating future market conditions. We propose SWAG-Bid (Sliding-Window Aware Generative Auto-Bidding), a hierarchical framework decomposing this challenge into episode-level planning and step-level execution. The planner uses a Masked Trajectory Model to forecast markets and generate candidate plans, scored across all overlapping windows by Multi-Window Model Predictive Control Sampling (MWMS) with exponential confidence decay. The controller adjusts reliance on this guidance through a state-adaptive gate, Per-Step Gated Adaptive Layer Normalization (PSG-AdaLN), complemented by Return-to-Go and Cost-to-Go channels carrying budget and constraint information. Experiments on AuctionNet-Sparse and online A/B tests on AliExpress show that SWAG-Bid achieves competitive constraint satisfaction and value acquisition under sliding-window evaluation.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04472

Beyond Token Representations: Explicit Visual Object Grounding for Video Reasoning Segmentation

Jun Huang, Junyang Zhang, Zijie Yue, Bingkun Wang, Yong Luo, Miaojing Shi

原文摘要

Video Reasoning Segmentation (VRS) aims to infer and segment target objects specified by reasoning queries in videos. Existing methods typically first infer the target and generate a few segmentation tokens for it, which are then fed into a mask decoder for mask prediction. However, such segmentation tokens tend to be both semantically monotonous and spatially ambiguous, making them largely insufficient to represent the target to guide mask prediction. In this paper, we propose an explicit visual object grounding framework for VRS. Specifically, we first propose a spatial-aware prompt generation and refinement scheme, in which we query a multimodal large language model to generate explicit spatial prompts (i.e., bounding box and point set) for the target and then refine them through multi-step dialogue. Furthermore, we introduce a query diversification and alignment module to generate auxiliary queries that describe the same target from different perspectives. We then enforce cross-query consistency between their predicted spatial prompts to alleviate the training bias caused by query limitations. Finally, we select several reliable frames using a prompt-based keyframe selection strategy to support mask decoding and propagation. Extensive experiments on eight datasets demonstrate that our method significantly outperforms state-of-the-art methods. The code is available at https://github.com/viscom-tongji/EVOG.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04470

Using GeoAI and machine learning tools for consistent high-resolution land cover mapping based on time-series NAIP imagery

Jie Liu, Xusheng Tang, Chao Wang, Zhengxiao Yan, Yuchen Dai, Qi Zhang, Conghe Song

原文摘要

High-resolution land-cover maps support ecological monitoring, but consistent historical time series remain scarce because aerial imagery varies across years and sensors. We developed a dual-track workflow to produce temporally coherent 1-m land-cover maps from National Agricultural Imagery Program (NAIP) imagery without historical training samples. For higher-quality imagery from 2009-2017, we used a pretrained U-Net/ResNet-34 model and refined boundaries with the Segment Anything Model (SAM). For lower-quality imagery from 2004-2008, we reconstructed land cover through an NLCD-based spatiotemporal bridging and label back-casting pipeline. Evaluation across six counties in North Carolina and Pennsylvania yielded overall accuracies of 0.887 (2014), 0.886 (2017), and 0.733 (2004). Structure, Water, Wetland, and Cropland consistently achieved high F1-scores. The workflow integrates GeoAI and multiple data sources to address temporal heterogeneity in historical aerial imagery, supporting high-resolution land-change analysis and landscape planning across the conterminous United States.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04467

Is Forgetting Unified? Exploring Concept Unlearning in Unified Multimodal Models

Zhongkai Xue, Junkai Chen, Mingcong Lei, Rumo Zhang, Yixiao Liu, Zhimin Sun, Shu Wu, Qiang Liu 等 10 位作者

原文摘要

Unified multimodal models (UMMs) integrate image generation and visual understanding, but are existing single-pathway unlearning methods sufficient to suppress target concepts across both? In this work, we study cross-pathway concept unlearning. Our goal is to prevent a model from both generating images of a target concept and identifying it in input images, while preserving other capabilities. To this end, we introduce UUBench, a diagnostic benchmark covering 30 target concepts and 60 semantically related neighboring concepts. Experiments on representative UMMs show that the evaluated single-pathway methods achieve limited joint forgetting: generation-side editors strongly suppress target generation but yield limited forgetting in visual understanding, while understanding-side baselines struggle to achieve substantial forgetting even in their own pathway. Their direct composition also yields limited joint forgetting. We therefore propose UniForget, achieving joint forgetting rates of 80.0% on BAGEL-7B-MoT and 92.7% on Janus-Pro-7B, outperforming representative baselines. Further analysis reveals trade-offs between joint forgetting and capability preservation.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04466

From Watching to Steering: Enabling Human-Coding Agent Collaboration through Execution Traces

Xiaoke Yan, Jiaying Lei, Danqing Shi, Zichen Wang, Nan Cao

原文摘要

Recent advances in large language models (LLMs) have enabled coding agents to autonomously perform complex software development tasks, yet their long-running execution remains difficult for users to inspect, understand, and steer. We present Vibetrace, an interactive trace visualization and intervention system for agentic programming built upon OpenCode. Vibetrace introduces a human-agent collaboration paradigm of Observe, Understand, Intervene, and Evolve. It (1) visualizes agent workflows as interactive execution traces for inspecting actions, intermediate states, and failures; (2) enables users to fork execution from prior steps and provide targeted corrections without restarting validated progress; and (3) distills traces and human feedback into reusable skills for continual agent evolution. On SWE-bench Verified, distilled skills reduce input tokens by 23.0% and cost by 15.5%, while improving pass rate from 65.0% to 68.0%. User studies further demonstrate improved accuracy and efficiency over conventional log-based interaction.

cs.HC · cs.AI

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2609.05351

MEOX: Modality-Conditioned Routing Before Fusion in a Compact Earth Observation Encoder

Mohanad Albughdadi

原文摘要

Multimodal Earth Observation models often fuse radar and optical inputs early, limiting sensor-conditioned processing, or maintain separate sensor streams through the encoder, increasing computational cost. We present MEOX (Multimodal Earth Observation with eXperts), which delays fusion until after a shared mixture-of-experts block that processes Sentinel-2 and ascending and descending Sentinel-1 streams independently. Co-located patches are then fused with validity-aware weights into a single sequence processed by fourteen shared blocks, reducing the sequence length from 3N + 5 to N + 5 tokens for N patches. We pretrain the 2.939M-parameter encoder on 1.228M MMEarth64 samples using masked radar-optical reconstruction. Under the reported frozen-transfer protocols, MEOX reaches 90.56% average class accuracy on EuroSAT and 64.42% mean intersection over union (mIoU) on cashew segmentation, 2.26 and 5.02 points above the strongest reported cross-sensor mixture-of-experts (CSMoE) variants. Full-encoder adaptation increases BigEarthNet micro-average precision from 57.22% to 72.95%. In a controlled same-corpus test using synthetic random patch removal, masking 75% of Sentinel-2 patches reduces WorldCover mIoU from 30.45% to 26.53% when both Sentinel-1 streams remain available; with Sentinel-2 alone, mIoU falls to 13.81%. For identical zeroed optical patches, declaring them missing through the validity mechanism improves mIoU from 19.42% to 26.53%. These results characterize a compact route-then-fuse encoder that retains sensor-conditioned processing and uses complementary radar observations when optical inputs are incomplete.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.08029

IntentNav: Learning Spatial-Visual Object Navigation from Human Demonstrations

Yuxin Cai, Zongtai Li, Maonan Wang, Muyi Bao, Haokun Zhu, Ruofei Bai, Ding Zhao, Zirui Li 等 12 位作者

原文摘要

Object navigation requires a robot to search for an unobserved target in an unknown environment by deciding where to explore next under partial observability. Effective search resembles human-like exploration: selectively probing visually promising frontiers while relying on spatial memory to avoid redundant revisits. We propose IntentNav, a spatial-visual imitation framework that learns human-like ObjectNav policies from human demonstrations. To infer high-level search intent from low-level human actions, we introduce Frontier-based Human-Intent Labeling, which looks ahead in human demonstrations and labels the frontier that best explains the demonstrator's future search direction. We construct a spatial-visual candidate space, where BEV memory tracks explored regions, unexplored frontiers, and trajectory history, while egocentric visual memory provides semantic cues for each candidate. A VLM policy is trained to select among these grounded candidates, using Intent-Aligned Objective to encourage consistent and human-like exploration. IntentNav achieves state-of-the-art performance among learning-based methods on the MP3D, HM3D-v1, and HM3D-v2 ObjectNav benchmarks, attaining the best success rate among all compared methods. The proposed candidate-level navigation interface transfers zero-shot to wheeled, quadruped, and humanoid robots without further VLM fine-tuning. \href{https://wmn7.github.io/IntentNav/}{Project page}.

cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2603.00416

MuonRec: Shifting the Optimizer Paradigm Beyond Adam in Scalable Generative Recommendation

Rong Shan, Aofan Yu, Bo Chen, Kuo Cai, Qiang Luo, Ruiming Tang, Han Li, Weiwen Liu 等 10 位作者

原文摘要

Recommender systems (RecSys) are increasingly emphasizing scaling, leveraging larger architectures and more interaction data to improve personalization. Yet, despite the optimizer's pivotal role in training, modern RecSys pipelines almost universally default to Adam/AdamW, with limited scrutiny of whether these choices are truly optimal for recommendation. In this work, we revisit optimizer design for scalable recommendation and introduce MuonRec, the first framework that brings the recently proposed Muon optimizer to RecSys training. Muon performs orthogonalized momentum updates for 2D weight matrices via Newton-Schulz iteration, promoting diverse update directions and improving optimization efficiency. We develop an open-source training recipe for recommendation models and evaluate it across both traditional sequential recommenders and modern generative recommenders. Extensive experiments demonstrate that MuonRec reduces converged training steps by an average of 32.4\% while simultaneously improving final ranking quality. Specifically, MuonRec yields consistent relative gains in NDCG@10, averaging 12.6\% across all settings, with particularly pronounced improvements in generative recommendation models. These results consistently outperform strong Adam/AdamW baselines, positioning Muon as a promising new optimizer standard for RecSys training. Our code is available.

cs.IR · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04465

Theoretical Analysis of Muon on Transformers for Next-Token Prediction

Shengwei Wu, Yixuan Yang, Gao Huang, Song Li

原文摘要

Muon has recently shown strong empirical performance in training Transformer-based large language models (LLMs), yet its optimization dynamics remain poorly understood from a theoretical perspective. In this paper, we analyze the training dynamics of Muon for next-token prediction (NTP) in Transformers. The training procedure consists of two phases: the preprocessing phase learns the output-value component, while the main phase learns the key-query component. For the preprocessing phase, we characterize the optimization trajectory exactly and show that the normalized spectral margin is optimal after each update. For the main phase, we establish nonasymptotic convergence rates for the training loss and the normalized margin gap under softmax attention. These results suggest that Muon's update rule can help models efficiently learn which tokens matter for prediction, offering a possible explanation for its strong performance in large language model training. The learned weight structure also yields a generalization guarantee for unseen contexts. Furthermore, we validate our theoretical insights with experiments on next-token prediction tasks.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.00661

Designing Behavioral Profiles for Language Model Simulation of Out-of-Distribution Human Behavior

Robin Na, Kehang Zhu, Logan Cross, Michiel A. Bakker

原文摘要

Large language models (LLMs) are increasingly used to simulate human behavior, but which descriptions of people improve prediction when experimental designs change remains unclear. We develop a framework that draws candidate behavioral features from the literature and constructs profiles from earlier human experiments. An LLM-based profile generator assigns feature values from observed actions, messages, and surveys; a separate LLM simulates behavior using these profiles. By comparing simulated and observed outcomes in the training data, we select profile contents through forward feature selection, adding features while they improve prediction. We then evaluate the selected contents without modification against outcomes from new participants under new designs. Across 616 public-goods games, 144 multi-party bargaining games, and 1,480 generalized 11--20 money-request designs, leading one-feature profiles predict human outcome distributions better than no profile or general-purpose personas. Profiles with more features provide no clear additional benefit, and features' relative predictive value largely persists across learning and validation. Using profiles from one game family to simulate another produces uneven gains and can make predictions less accurate than simulations without profiles. The framework makes the choice of behavioral information a testable part of simulation design, helping researchers assess which descriptions remain useful as experimental conditions change.

cs.AI · econ.GN

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04462

SaMA: Morpho Adaptation via Asymmetric Expansion of Kronecker Product

An Nguyen, Anh Tong

原文摘要

Low-rank adaptation (LoRA) has attracted significant attention in the parameter-efficient fine-tuning (PEFT) landscape. However, its design suffers from two fundamental limitations. First, its invariance manifold induces flat directions in the loss landscape, creating an optimization bottleneck for adaptive optimizers. Second, its effective rank scales only linearly with the parameter budget. In this paper, we propose Scalable Morpho Adaptation (SaMA), a PEFT method grounded in a principled generalization of the Kronecker product. By relaxing the shared-block constraint in the perfect-shuffle factorization, SaMA spans from low to full rank and achieves a practical rank that scales quadratically with the parameter budget, up to full-rank. Furthermore, we show that its invariance manifold collapses to a diagonal group, making it structurally more optimizer-friendly. Empirically, SaMA consistently outperforms strong PEFT baselines on commonsense and arithmetic reasoning benchmarks while being more parameter-efficient.

cs.LG · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.30022

Fitting Conventional and Neural Exchange-Correlation Functionals to Accurate Densities without Self-Consistent Fields

Yuhao Chen, Donald G. Truhlar, Xiao He

原文摘要

Exchange-correlation functionals are usually fitted with energy-only losses, so better energies often bring worse densities. We introduce a workflow that predicts in closed form, from one Fock build on inverse Kohn-Sham orbitals of any accurate density, a functional's self-consistent density, and fits it to that density with no self-consistent-field solution. Fitted convexly, a CF22D-form hybrid beats CF22D and MN15 on unseen energies, dipole moments, and unfitted densities (6.8- and 22-fold lower density error). A neural meta-GGA fitted to 6 energies and 11 densities beats SCAN in both.

physics.chem-ph · cs.AI

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新论文 · v1arXiv:2611.04459

$τ$-Rec-Books: A Disclosure-Controlled Benchmark for Requirement Elicitation in Agentic Recommender Systems

Bharath Sivaram Narasimhan, Karthik R. Narasimhan

原文摘要

While conversational recommendation systems provide a more natural user interface, they face the challenge of progressively discovering a user's needs before they can recommend an item that satisfies them. To evaluate the ability of AI agents at this elicitation process, we introduce $τ$-Rec-Books, a benchmark that holds each user request fixed while varying only when the user reveals their requirements: all at the start (volunteer), one at the start and the rest when asked (mixed), or some never (hidden). A hidden requirement can be found only by proposing books and inferring it from the user's accept or reject verdicts. We generate 450 tasks over a catalog of 20,000 books from Wikidata, develop a rule-based simulated user to answer the agent, and score recommendations against catalog metadata without an LLM judge. Across four agents and two search interfaces, success rates fall for every agent and interface, on average from 0.975 (volunteer) to 0.756 (mixed) and 0.283 (hidden). Mixed tasks fail mainly because agents do not ask, and when told to ask, every agent reaches at least 0.931. On hidden tasks, most failures come from agents recommending a book they never proposed to the user. When told to propose first, agents only match a simple baseline that proposes popular books, and stay well below one that uses rejection to rule out possible requirements. We release the catalog pipeline, task generator, simulated user, validators, reference agents, and all agent traces at https://github.com/nbharaths/tau-rec/tree/main/data/tracks/books-wikidata-v2.

cs.IR · cs.AI · cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04458

Large Language Model-Assisted Adaptive Heuristic Search for Agile Earth Observation Satellite Scheduling

He Wang, Yeye Liu, Ruijie Li, Xuemei Jiang, Junyu Wu, Lining Liu Zhuoqi Man, Tao Li, Yanjie Song

原文摘要

Agile Earth observation satellites must coordinate imaging tasks within limited observation windows, while accounting for attitude maneuvering and onboard energy constraints. Competition among observation opportunities makes scheduling performance sensitive to the heuristic rules used for task selection. This letter proposes a large language model (LLM)-assisted adaptive heuristic search method for agile Earth observation satellite scheduling. The LLM generates an initial heuristic rule according to the characteristics of the scheduling instance. During the subsequent ant colony search, search performance and scheduling information are fed back to the LLM to refine the heuristic rule. The proposed modifications are evaluated through paired solution constructions before being incorporated into the search process. A unified scheduling procedure determines task selection, satellite assignment, and observation timing under the operational constraints. Experiments on six scheduling scenarios involving 50--200 imaging tasks demonstrate that the proposed method achieves 4.3\%--9.6\% higher mean observation benefit than the strongest baseline.

cs.AI · cs.CL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.00633

Evidence-grounded large language models guide perovskite solar cell design and optimization

YanKai Chen, Zhi Wan, ZiHao Li, Tao Jing

原文摘要

Using large language models to interpret experimental evidence, design discriminating experiments and formulate well-supported mechanistic explanations remains a central challenge in materials research. Here we establish PSM-Bench, an 800-question benchmark developed by perovskite researchers to evaluate scientific reasoning in perovskite photovoltaics. We also introduce OpenPerov, an expert-informed language-model framework that learns from expert-calibrated evidence records reconstructed from 4,035 training articles; these records link experimental strategies, controls and observations to mechanistic interpretations. Structured-evidence adaptation outperformed conventional question-answer training, and OpenPerov Flash ranked first among 13 closed-book systems. OpenPerov Pro raised Scientific Mastery further, from 87.6 to 89.1, by revising selected passages with retrieved evidence. On questions derived from subsequent studies, both models also outscored expert-authored answers. In an experimental test of self-assembled monolayer design, OpenPerov selected a K analogue that outperformed the Na analogue preferred by experts. Devices incorporating the selected contact reached a power conversion efficiency of 27.05% and retained 96.74% of their initial efficiency after 1,000 h of dark thermal ageing at 85 $^\circ$C. These results show that reconstructed scientific reasoning provides an effective basis for building domain models that interpret experiments and inform materials design.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04455

SourceFlow: Collaborative Source Coupling for Flow-Based Recommendation

Chenghao Wu, Yongqi Zhou, Jin Chen, Yifeng Zhou, Jinchang Zhu, Junwei Pan, Chao Zhou, Zhixiang Feng 等 10 位作者

原文摘要

Diffusion-based recommenders learn user preferences by denoising interaction histories, but the added noise can distort the original preference signals. Flow-based collaborative filtering (CF) instead learns a discrete path from a sampled source to the user's observed training interactions (the target history), enabling inference in a few steps. Existing flow-based CF methods sample sources using global item frequencies to reflect the sparsity and popularity of observed interactions. Each source is sampled independently of its target history, leaving the predictor without user-specific source cues for learning to reconstruct that history. We propose SourceFlow, which brings collaborative structure into the source distribution itself rather than adding further conditioning to the predictor. Like inference from observed histories, training uses collaborative evidence to predict missing interactions. Specifically, Neighborhood Coupling constructs displaced binary sources by replacing historical items with unobserved collaborative alternatives. Collaborative Consensus further refines replacement probabilities using evidence from other interactions of users who share the observed item. Stretched Restoration retains these replacements at late flow times during training, providing more opportunities to recover the original interactions. Experiments on five recommendation datasets show consistent improvements over competitive baselines, including a mean relative Recall@10 gain of 15.7% over the strongest flow-based baseline. Controlled experiments confirm that these gains depend on correctly pairing collaborative sources with their target histories, as reassigning the same source vectors across users reverses the gains. Code will be publicly released upon acceptance.

cs.IR

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.33518

SceneScaffold: Active Scene-State Construction for Unified 3D Scene Understanding

Xiangqi Li, Libo Huang, Jiarui Zhao, Weilun Feng, Chuanguang Yang, Zhulin An, Yongjun Xu

原文摘要

Recent 3D large multimodal models (3D-LMMs) rely on a visual bottleneck to compress complex 3D scene evidence into a limited number of visual tokens compatible with large language models (LLMs). Current visual bottlenecks, however, often passively compress heterogeneous 3D evidence into a homogeneous object-centric token sequence, leaving the spatial organization of the scene under-represented. This under-representation forces the LLM to recover spatial relations from a flattened token sequence, leading to unstable reasoning in relation-intensive and spatially ambiguous scenes. To address this issue, we propose SceneScaffold, an active scene-state construction framework for unified 3D scene understanding. SceneScaffold reformulates the visual bottleneck from a passive feature compressor into an active scene organizer, constructing a role-aware spatial scaffold before language reasoning. Specifically, SceneScaffold organizes superpoint-level visual evidence into scene-state components with distinct structural roles: entity states preserve core object semantics, scene-frame states maintain spatial references via boundary and region anchors, relation states encode object-environment interaction cues, and a global summary provides compact context. Through this role-aware construction, SceneScaffold provides the LLM with a spatially organized scene representation before language reasoning. Experiments on unified 3D scene understanding tasks, including 3D visual grounding, question answering, and dense captioning, demonstrate the effectiveness of SceneScaffold, while diagnostic results further show its applicability to relation-intensive and spatially ambiguous cases. Code is available at https://github.com/lixiangqi707/SceneScaffold.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2610.09536

MagCilia: A Compact Magnetociliary Tactile Sensor with 3D Force Sensing for Robotic Contact Perception and Grasping Feedback

Yu Feng, Hao Wu, Haotian Guo, Haoming Liu, William Su, Jingxiang Guo, Jiankun Li, Masayoshi Tomizuka 等 10 位作者

原文摘要

Robotic grasping and surface exploration benefit from simultaneous measurement of normal and tangential forces and from surface information obtained through contact. Here, we present a compact magnetociliary tactile sensor (MagCilia) that combines a flexible magnetic-cilia structure with a Hall sensor for 3D force sensing. Quasi-static finite element analysis is used to investigate structural deformation and magnetic responses under multidirectional loading. To reconstruct forces from the coupled magnetic channels, we propose causal history fusion regression (CHFR), which combines current magnetic-field measurements with their recent changes. Five-fold cross-validation grouped by calibration record yields root-mean-square errors of 0.40, 0.57, and 0.69 N for Fx, Fy, and Fz, respectively, with corresponding coefficients of determination of 0.93, 0.90, and 0.92. Robotic experiments demonstrate tangential-force-guided gripper adjustment and multi-axis load monitoring under external perturbations. Frequency-domain features of the reconstructed forces distinguish six surface categories with 99.39% accuracy in three-fold cross-validation grouped by acquisition session. An online robotic demonstration additionally identifies all six tested surfaces. These results demonstrate 3D force reconstruction, grasping feedback, and surface recognition using a single compact tactile unit.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04452

DynOSeg: Dynamic Object Segmentation via Motion-Geometry Fusion

Xu Han, Yiduo Wang, Yiran Wang, Viorela Ila

原文摘要

Moving Object Segmentation (MOS) detects and separates dynamic objects from the static background in a video, which plays a fundamental role in dynamic-scene understanding. Most MOS methods rely on pure visual motion cues alone, such as optical flow or point tracks, which is inherently ambiguous due to the compounded camera and object motions as well as motion-induced appearance changes, e.g. shadows and reflections. Naive fusion of additional information such as depth and semantics, however, cannot leverage their complementarity to the fullest extent. We therefore propose DynOSeg, a novel MOS approach that takes on a two-stage fusion structure. DynOSeg first combines a geometry-aware residual map and depth encoding into a geometric representation of motion, delineating camera motions and object dynamics. We then further incorporate semantic features to refine object understanding. We have additionally curated a diverse training corpus including CARLA-MOS, a new synthetic urban-driving dataset for MOS training. Our experiments show that DynOSeg consistently outperforms published state-of-the-art MOS methods across a diverse range of datasets. Our code and pretrained models can be found at https://github.com/ACFR-RPG/DynOSeg

cs.CV

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