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新论文 · v1arXiv:2611.01463

DIMWIT: Directly Invoking Models Without Intensive Tuning

Saarth Deshpande, Neeraja J. Yadwadkar, Venkat Arun

原文摘要

Resource managers typically encode what to do in a task-specific policy---a heuristic, an optimization procedure, or a model trained for the task---that must be developed before use and may need revision as the workload or system changes. We propose bypassing policy development and instead asking pretrained models to make resource-management decisions directly, given the current system state, desired outcome, and available choices. We evaluate this approach on microservice autoscaling, DRAM sizing, and video encoding, using a frontier LLM and two small decision models. On two microservice applications, a small decision model meets latency targets while using within 10% of the replicas allocated by policy-based autoscalers and within 13% of the frontier LLM. In DRAM sizing, a small decision model reduces DRAM usage by up to 27.5% relative to static provisioning while maintaining at least 95% of baseline performance. In video encoding, it completes encodes up to 4.3$\times$ faster than the frontier LLM, with an imperceptible 1.1-point decrease in Video Multi-Method Assessment Fusion (VMAF), a metric of perceptual video quality. Making individual resource-management decisions may be considerably simpler than developing a general strategy for making them.

cs.PF · cs.AI · cs.CV · cs.DC · cs.OS

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01461

An auditable agentic pipeline for variant-to-gene prioritisation, validated against orthogonal functional evidence

Shakuntala Baichoo, Sohail, Sarkar, Bo Wang

原文摘要

Assigning GWAS variants to causal genes requires evidence from multiple resources. We present V2G, an agentic pipeline that records evidence retrieval and returns ranked genes alongside the retrieved evidence. We evaluate whether language-model reranking improves gene prioritisation and whether its gains depend on the retrieved evidence or recall of known associations. On 135 held-out loci where candidate retrieval succeeded and Open Targets L2G predicts, reranking by a language model improves Precision@1 over the evidence ordering it receives, 0.793 against 0.711 (+0.081, 95% CI +0.038 to +0.129), and over nearest-gene assignment (+0.178); no difference from Open Targets L2G or from training-membership baselines was detected. Given only the trait and the candidate gene symbols, in shuffled order, the same model scores 0.864 on these loci (three seeds, 0.859 - 0.867) and names the reference gene at 0.594 of the loci where the pipeline never retrieved it; with each gene's symbol moved to another gene's evidence row it follows the top-scored row and scores 0.284. The gain over the evidence ordering is recall of known genes, and the evidence as presented anchors the model to the ordering rather than informing it. Across the 296 held-out loci (five seeds each) the model keeps the given top candidate at 86% of decisions; at naturally retrieved loci its departures select a reference gene in 85% of departure decisions (31 of 37 loci), and none displaced one. Its calls carry trait-matched mouse mutant or Mendelian phenotype annotations more often than other candidates at the same locus. Train-test overlap is reported throughout. The study includes a frozen benchmark, cached predictions and reproduction code.

q-bio.GN · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01460

Can Large Language Model Agents Learn by Watching Tutorial Videos?

Haoyu Zhao, Zafeirios Fountas, Jun Wang

原文摘要

Humans continuously acquire knowledge from carefully crafted materials such as books and tutorial videos, carrying that understanding into future tasks. Millions of such instructional videos are uploaded online every day, yet LLM agents remain unable to exploit this ever-expanding wealth of human teaching. Their capabilities are frozen after training, while parameter updates at scale remain prohibitively expensive. This raises the central question: can LLM agents learn from this vast instructional resource without parameter updates? We introduce Tutorial Agentic Learning (TAL), a nonparametric continual-learning paradigm that consolidates tutorial videos into a persistent, structured tutorialbook memory through a two-stage adversarial process that harvests hard, video-grounded questions and uses them to refine the memory. At inference time, TAL retrieves through a Table-of-Contents (TOC) tree, tying retrieval directly to the model's reasoning rather than to embedding similarity. To study this setting, we introduce VidLearn, a multimodal QA benchmark built from 72 chess tutorial videos spanning 12 openings. On VidLearn, TAL outperforms all evaluated baselines and improves monotonically as more videos are consumed, achieving up to 113% and 91% relative gains over the corresponding vanilla MLLMs on the training and test splits, respectively. In actual gameplay, TAL reaches a stronger opening-phase position than its vanilla counterpart in 69% of head-to-head games. Evaluation in a second domain further demonstrates the generality of TAL. Together, these results highlight instructional videos as a scalable resource for parameter-update-free agent continual learning.

cs.LG · cs.AI · cs.CL · cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01457

ContactCut: A Partition-Aware Contact Selection Framework for False-Merge Correction in Connectomics

Byunghyun Ko, Won-Ki Jeong, Chaoli Wang

原文摘要

Localized false-merge correction in connectomics requires converting an identified error into an executable native 3D partition. Still, neither intermediate correction predictions nor seeded graph solvers alone determine which native source-sink constraints should define that partition. We formulate this problem as a 3D-native partition-aware contact selection problem: selecting candidate contacts based on the complete partitions they induce. Our solution comprises two components: ContactCut (CC)-Auto, which uses learned single-contact selection, and ContactCut (CC)-Search, which enables bounded paired-contact search, generates compact alternatives, and applies learned panel ranking. On historical two-way false-merge corrections from MICrONS and FlyWire, CC-Auto improves failure-aware 3D partition (FA-3D Part.) mIoU by 5.9 and 3.4 points over the strongest graph-based controls and by 12.5 and 10.0 points over the best learned baselines. A MICrONS-trained ranker transfers to FlyWire with negligible degradation. CC-Search expands oracle reachability and prioritizes near-oracle alternatives. The code is available at https://github.com/bentake/ContactCut.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.38194

Prototype-Guided Dynamic Domain Modulation and a Large-Scale Multi-SKU Benchmark for Shampoo Bottle Surface Defect Detection

Yuehang Li, Xiao Wang, Qiang Chen, Yangyi Fan, Bo Jiang, Yaowei Wang, Jin Tang

原文摘要

Industrial shampoo-bottle surface defect detection must operate across many stock keeping units (SKUs), where differences in bottle geometry, material, label layout, reflectance, and imaging conditions can substantially alter the appearance of the same defect. Existing industrial defect datasets rarely provide both instance-level defect annotations and product-variant identities, making it difficult to study SKU-aware detection under realistic manufacturing conditions. We introduce Bottle-SKU, a large-scale multi-SKU benchmark for shampoo bottle surface defect detection. Bottle-SKU contains 13,119 images, 20,419 instance-level bounding boxes, seven defect categories, and 48 bottle SKUs, enabling evaluation of both shared defect recognition and SKU-specific appearance variation. To address the challenge of SKU-specific appearance variation, we propose Prototype-Guided Dynamic Domain Modulation (ProDMM). ProDMM constructs a visual prototype for each SKU from training images and predicts a soft SKU distribution for each input. The resulting prototype-conditioned representation dynamically generates low-rank Query/Value modulation for the visual backbone and residual conditioning for defect-text-to-visual attention, while preserving the pretrained Grounding DINO detection pathway. The model is trained jointly with detection, SKU-domain classification, and modulation-regularization objectives. Experiments on Bottle-SKU and the public GC10-DET-SKU metallic surface defect dataset demonstrate the effectiveness of prototype-guided domain conditioning for industrial surface defect detection. The source code and model will be released on https://github.com/Event-AHU/Industrial_Anomaly_Detection

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01450

CipherFlow: Compiling AI Programs for Efficient Cryptographically Secure Inference

Hedong Zhang, Mengxin Zheng, Qian Lou

原文摘要

Cryptographically secure inference faces two practical challenges: making secure programming accessible to AI developers and data scientists, and achieving efficient execution across deployment environments. Developers must compose cryptographic primitives correctly, while performance depends on hardware, networks, and evolving ciphertext states. We present CipherFlow, a compiler framework that takes a high-level AI computation graph, available cryptographic primitives, and a target hardware and network configuration to generate an executable, latency-optimized secure inference program. CipherFlow automates cryptographic execution decisions through three techniques: MicroBench profiles deployment-specific primitive costs; state-expanded planning jointly optimizes homomorphic encryption (HE) and secure multi-party computation (MPC) placement, conversions, and ciphertext maintenance under cryptographic-state constraints; and BlockFold summarizes branched regions to scale planning to Transformer graphs. Evaluation using measured HE/MPC backend costs across CPU/GPU and network settings shows up to 16.5$\times$ speedup on BERT-base and over 22$\times$ on ViT compared with static hybrid placement baselines. CipherFlow separates model programming from cryptographic execution decisions, enabling developers to express AI computations while the compiler constructs efficient secure programs.

cs.CR · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.30569

Any-ttach: Quick End-effector Swapping Enables Manipulation Dexterity with Simplicity

Weizhe Ni, Jinzhou Li, Xinyu Ma, Haoyu Li, Eva Bonde, Cody Andres Alessio-Bunnell, Wenjing Pan, Xianyi Cheng

原文摘要

Robotic manipulation dexterity is often pursued by building increasingly complex high-DoF multifingered hands. While many robotic hands are designed to replicate the morphology of human hands, the functional role of human hands suggests a different perspective: much of their complexity may exist to enable tool use and tool making. This observation motivates Any-ttach, a tool-centric manipulation system that leverages mechanical modularity and treats autonomous end-effector swapping as a pathway towards dexterity. Any-ttach combines: 1) a low-cost standardized interface with passive self-locking and mechanically triggered release mechanism; 2) a unified object-centric manipulation framework supporting kinesthetic teaching trajectory replay, human demonstration retargeting, and policy learning; and 3) hierarchical planning and execution system for tools and skills for multi-tool long-horizon tasks. We demonstrated 15 heterogeneous end-effector modules supported by our system, spanning different robotic fingers, everyday tools (including articulated tools such as scissors), and unconventional end-effectors through the same interface. Experiments demonstrate reliable autonomous swapping, automatic integration to new tools, and broad manipulation capability across different skill sources. Two multi-tool application demonstrations, comprising seven tool--skill steps across two tasks, further illustrate the autonomous composition of end-effector exchange and heterogeneous manipulation skills. These results show that end-effector swapping can expand manipulation capability without concentrating increasing complexity entirely in a single end-effector. More details and videos are available at https://any-ttach.github.io/.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01447

Correctness and Predictive Uncertainty in Bayesian TSK Fuzzy Regression: A Reproducible Evaluation on Tabular and Building-Meter Data

Zhiren Xiao

原文摘要

Uncertainty estimates from fuzzy regression are informative only when the implementation is correct and the comparison protocol makes the sources of variation visible. We revisit a first-order Takagi--Sugeno--Kang (TSK) regression implementation and rebuild its dense, conjugate Bayesian, BIC-based, and rule-level spike-and-slab estimators using corrected code. The corrected pipeline freezes the Gaussian membership partition at training time, uses a scaled conjugate prior, evaluates firing strengths in the log domain, and reports direct posterior-predictive quantiles for Bayesian methods. Across 30 outer 80/20 splits on three targets from two UCI regression datasets, corrected TSK-LS obtains $R^2=0.957$, $0.923$, and $0.786$ on Energy-Heating, Energy-Cooling, and Concrete, respectively; rule-level Gibbs sampling with Bayesian model averaging obtains $0.957$, $0.922$, and $0.784$. We then evaluate the same workflow on a bounded chronological sample of the Building Data Genome 2 meter data that covers 158 buildings across 17 sites. In a common-unit evaluation, every method is scored on the same 47,400 test rows and every trained model uses 94,800 train--validation rows. Persistence has RMSE $27.710$, compared with $29.083$ for both TSK-LS and Bayesian TSK; the pre-specified building-level mean RMSE difference between Bayesian TSK and persistence is $+0.0447$ with a 95% site-cluster bootstrap interval of $[-1.5536,1.5665]$. The Bayesian TSK 90% intervals have PICP $0.980$ and MPIW $116.469$. Taken together, the evidence supports a narrower conclusion: spike-and-slab model averaging is a reproducible implementation option, but it does not provide a demonstrated accuracy or calibration advantage across the tested tabular and building-meter settings.

cs.LG · stat.ML

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01446

I-PTC: Interactive Programmatic Tool Calling for Stateful Tool-Augmented Agents

Huanzhi Mao, Chengkun Cao, Shuo Yuan, Joseph E. Gonzalez

原文摘要

Large language models are increasingly capable of writing and executing code, suggesting a natural paradigm for tool-augmented agents: expressing tool use as programs rather than isolated calls. Programmatic tool calling (PTC) captures this idea through loops, batching, abstraction, and structured computation, but existing PTC approaches are largely stateless, limiting their ability to reuse intermediate results and adapt over time. This is problematic for real-world tasks, where tool environments and problem-solving processes are inherently stateful. We therefore present I-PTC, an interactive framework that executes stateful PTC: models issue code snippets that call tools, maintain reusable state, and evolve the tool interface through API corrections. To evaluate the effectiveness of I-PTC and the general PTC setting, we introduce PTC-Bench, a benchmark of long-tail, production-style user requests whose difficulty comes from large initial environments, many dependent API interactions, extended planning, and recovery across follow-ups and transient failures. Experiments on PTC-Bench show that I-PTC improves performance on complex multi-step tasks with large tool sets while substantially reducing token consumption over conventional tool-calling baselines and the original PTC baseline, highlighting stateful programmatic tool calling as a promising direction for scalable tool-augmented agents.

cs.AI · cs.LG

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2605.29310

Rubric-Guided Process Reward for Stepwise Model Routing

Shenghao Ye, Yu Guo, Zhengheng Li, Qiang Huang, Shuangwu Chen, Wang Luo, Qian Rui, Jie Zhang 等 13 位作者

原文摘要

Stepwise model routing improves the efficiency of Large Reasoning Models (LRMs) by assigning each reasoning step to a suitable model. Recent methods formulate routing as a sequential decision process and train the router with reinforcement learning. However, although they model routing as a process, they still supervise the router with outcome rewards. Such rewards only reflect final answer correctness and fail to evaluate intermediate routing decisions, which can weaken performance and generalization. To address this gap, we propose RoRo, a rubric-guided process reward framework for stepwise model routing. RoRo first collects diverse routing trajectories and constructs preference pairs based on outcome, cost, and process quality. It then trains a Rubricor to generate a query-specific evaluation rubric and a Judge to score routing trajectories under this rubric through alternating optimization. The resulting process rewards are combined with outcome rewards to optimize the routing policy via GRPO. Experiments on five reasoning benchmarks under both same-family and cross-family settings show that RoRo consistently outperforms strong baselines and achieves better accuracy and cost trade-offs.

cs.AI · cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01442

Dual Path Following for an Aerial Manipulator Under State and Input Constraints via Transverse Feedback Linearization

Hamza Tariq, Adeel Akhtar

原文摘要

This paper studies dual path following for a quadrotor carrying a three-degree-of-freedom arm. The center of mass of the combined system must follow a vehicle path and the end effector must follow a second path, while the arm respects its joint limits and avoids its singular postures. The vehicle and the arm are mechanically coupled, so one controller must handle both paths. With the center of mass and the end-effector path coordinates as outputs, and a dynamic extension of the thrust, the system admits a normal form without internal dynamics. We characterize the states where this normal form fails. We formulate the transverse feedback linearizing controller as a quadratic program that relaxes only the speed of the end effector along its path and imposes safety and actuator limits as inequality constraints. The program reduces to a scalar problem with a closed-form solution. If it remains strictly feasible for all time, the feedback is locally Lipschitz, both paths are invariant and attractive, and every constraint holds. In the physics engine Drake, the controller resolves a conflict between the two speed references by slowing the end effector while both paths remain invariant. By contrast, a cascade with a safety filter leaves both paths. Code and animations are available at https://gradslab.github.io/am_dual_pf-/

cs.RO · eess.SY · math.OC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01441

Renormalized Cellular Intelligence: When Does Model Reduction Preserve Collective Capability?

Xuening Wu, Yanlan Kang, Qianya Xu, Haode Yang, Shan Yu, Shenqin Yin

原文摘要

As multi-agent systems grow in size and complexity, understanding which features of their interactions support collective capability becomes increasingly difficult. Can a simpler description preserve both task performance and the measured contribution of interactions between agents? Preserving collective dynamics alone is insufficient: reduction can alter decision-relevant information or give a non-communicating baseline access to pooled observations, distorting the measured advantage of interaction. We introduce Renormalized Cellular Intelligence (RCI), a framework for specifying and auditing capability preservation across scales. RCI treats the task, interacting dynamics, matched baseline, decision interfaces, and resource budget as a joint object of reduction. It specifies dynamical closure, decision compatibility, and preservation of the intended baseline comparison, and bounds the resulting error in relative capability gain. An exact analytical construction demonstrates task-score preservation, while a counterexample shows how closed reduced dynamics can erase the measured interaction gain. Using cellular systems as models of locally interacting agents, we evaluate three reduction candidates on five frozen learned models without retraining. The final candidate reduces the evolving state from eight to five dimensions per cell (37.5%). Within-run uncertainty bounds support relative gain above a prespecified threshold for the fixed model collection, yet the candidate fails the closure and decision-preservation requirements for a complete RCI certificate. RCI provides explicit criteria for distinguishing positive interaction gains from

cs.MA

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2606.10174

A Large Scale Open-Source Image and Video Dataset for Robust Wildfire Detection and Classification

Emadeldeen Hamdan, Yingyi Luo, B. Ugur Toreyin, Erdem Koyuncu, Adam J. Watts, Ugur Gudukbay, Ahmet Enis Cetin

原文摘要

Wildfire detection and monitoring are critical for mitigating fire spread and reducing environmental and infrastructural damage. In this work, we introduce GWFP (Global Wildfire Prevention Dataset), a large-scale, open-source dataset of wildfire images and videos designed to support early fire and smoke detection research. GWFP contains geographically diverse wildfire scenes, including flames, smoke, Waterdog/Fog environmental conditions, Near Infrared (NIR) imagery, Ember, and challenging negative samples collected from real-world scenarios worldwide. To evaluate dataset robustness and cross-domain generalization, we benchmark multiple convolutional and transformer-based architectures across both in-domain and cross-dataset settings. Additionally, we explore lightweight frequency--spatial feature interaction using Hadamard-enhanced residual connections (HTE-ResNet) to analyze representation robustness under domain-shift conditions. Experimental results demonstrate strong cross-dataset generalization and practical utility for real-world wildfire monitoring applications. The dataset is available on Kaggle: https://www.kaggle.com/datasets/emadeldeenhamdan/wildfire.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01438

FOCUS: Closed-Loop Attention Feedback for Efficient Vision-Language Understanding

Akul Santhosh, Aastha Jhunjhunwala

原文摘要

Processing every video frame through Vision-Language Models (VLMs) adds significant inference cost and limits their ability to comprehend real-time video. Existing approaches either sub-sample frames uniformly, ignoring content and missing important events, or prune visual tokens inside the VLM to cut per-call cost while still invoking the model on every sampled frame. We introduce FOCUS, a training-free three-layer framework that reduces the number of invocations and steers each one towards what matters. Temporal gating measures inter-frame dissimilarity in the patch-embedding space of a smaller VLM from the same family, triggering burst captures only when the scene changes in a semantically meaningful way. Patch-level dissimilarity is then accumulated across the burst and fused with a top-down attention prior into a spatial focus map that encodes where motion and semantic relevance coincide. That map calibrates the VLM's value cache during generation, progressively amplifying vision tokens in task-relevant regions and attenuating off-target ones, without removing any token from the context. The loop closes after each response: text-to-vision cross-attention is extracted from the decoder and fed back as the prior for the next burst, letting the system converge on semantically important regions over successive observations. We evaluate FOCUS on the 500-video EgoSchema subset in streaming mode, with a single Qwen3-VL-8B backbone shared by every method. It reaches 74.8% accuracy using 25.4 VLM calls per clip, against 74.6% with 55 calls for uniform sampling and 74.2% with 55 calls for BOLT. On egocentric video captioning across four domains, it matches the best baseline's quality while issuing 57% fewer VLM invocations than uniform sampling.

cs.AI · cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01437

Adaptive Surface Cleaning under Uncertain Cleaning Response

Ruichen Li, Rachel Holladay

原文摘要

Surface cleaning requires robots to adapt both where they clean and how they interact with contamination whose response to cleaning may vary spatially and remain unobservable prior to interaction. We formulate adaptive surface cleaning as a partially observable Markov decision process and propose a belief-aware planning framework that uses observed cleaning outcomes to update a latent local cleaning-response model and adapt subsequent cleaning motions, forces, and passes. Preliminary grid-world simulations demonstrate the resulting closed-loop planner adapting its spatial decomposition and cleaning actions as the surface evolves.

cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.38196

GearUp: DAG-aware Acceleration for Serverless Inference Serving via Slack-guided Pre-loading

Qi Qi, Yifan Sui, Hanfei Yu, Hong Zhang, Hao Wang

原文摘要

Serverless platforms increasingly host machine learning (ML) inference workflows, modeled as directed acyclic graphs (DAGs) of interdependent functions. Beyond container startup, each ML node must load libraries, model weights, and CUDA kernels before it can run. These costs accumulate along dependent DAG paths and place initialization on the end-to-end critical path. Existing systems optimize one half of this problem. DAG-agnostic pre-loading prepares nodes the request may never reach, wasting host and GPU memory. DAG-aware serving routes and scales whole functions, leaving the loading stages inside a node untouched. This paper presents GearUp, a serverless DAG inference serving system that removes ML-node loading from the end-to-end critical path at low resource cost. GearUp profiles the loading stages of each node and schedules them inside the slack time the DAG exposes, hiding initialization behind upstream execution. It reuses initialized instances within and across requests to avoid redundant cold starts. A probabilistic, traffic-adaptive keep-alive policy sets how long idle instances are retained, balancing memory cost against cold-start risk under bursty load. On the AMBER Alert and FINRA-ML workflows with NVIDIA L40S GPUs, GearUp reduces median end-to-end latency by 64.5% and 75.9% against SMIless, for median speedups of 3.1x and 10.0x over cold execution. Integrated GPU and host memory allocation fall by up to 83.9% and 94.6% against eager pre-loading in InstaInfer. GearUp achieves 4.2x strong scaling on four workers.

cs.DC · cs.LG · cs.OS

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01430

Polynomial-Time Minimax Prediction for Stationary Renewal Processes

Xinyu Chen, Yiming Ma

原文摘要

The minimax next-symbol Kullback-Leibler risk for stationary binary renewal processes is known to have order n^(-1/2). We give a polynomial-time predictor attaining this order for an arbitrary unknown finite-mean interarrival law. The construction uses a classical type-uniform renewal code. Its contribution is an exact dynamic program for censored prefix marginals, avoiding enumeration of gap histograms or binary completions. Combined with an established prediction reduction, it gives risk at most [log M_(n+1)+log(n+2)]/n, where M_N counts renewal types. Shared suffix updates reduce the total computation to O(n^3) arithmetic work and polynomial bit complexity, without tail, mixing, known-mean, or bounded-support assumptions. The statistical rate and its lower bound are prior results; the proposed advance concerns computation.

cs.IT · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01429

When Should an Agent Refresh? Adaptive Index Reuse for Efficient Long-Horizon Reasoning

Guangyuan Wang, Yifan Zhang, Shilong Liu

原文摘要

Language agents read code, tool output, and dialogue in contexts that grow at every step. Dynamic sparse attention lets a small indexer choose the earlier tokens each query attends to, but the indexer still scores the whole prefix at every layer. Cross-layer reuse methods such as IndexCache rerun the indexer at only some layers, with one fixed pattern for every input. In an agent, the relevant evidence can change abruptly when a traceback or test result arrives, so fresh selections matter more at some steps than at others. We propose Adaptive Index Reuse (AIR), which keeps IndexCache's execution and learns, for each segment of an agent episode, how many layers refresh their selection. A small controller reads signals from completed computation and the latest tool event, chooses a pattern from a nested library, and learns from terminal task reward under an expected compute budget. On Qwen3-4B-Instruct-2507 with a 128K context, AIR improves success over the strongest static schedule at matched cost on SWE-bench Verified (27.0% to 32.4%), Terminal-Bench 2.0, and three $τ^2$-bench domains, and it lowers time per successful task on all five. Across 50 backbone, context, and benchmark settings, it matches or exceeds that schedule in 44.

cs.LG · cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01427

Towards Fine-grained and Robust Detection of Human-AI Co-authorship

Ziruo Zhao, Jie Fu, Wendy Hui Wang, Zining Zhu

原文摘要

The widespread adoption of large language models (LLMs) is reshaping how textual content is produced, complicating text provenance, and raising concerns for misinformation, plagiarism, and intellectual property. While prior work on AI-generated text detection has progressed, one-dimensional human-AI co-authorship scores lack signals directly relevant to ``AI involvement''. It remains unclear how adversarial paraphrasing distorts detection signals. In this work, we formulate a discrete-class text provenance detection framework. This framework allows an abstraction of realistic co-authorship scenarios into distinct degrees of AI involvement, differentiating the AI polishing on the surface level from the AI updating the discourse structures. This framework reveals the mechanisms of how adversarial paraphrasing blurs the decision boundaries for co-authorship detectors. To improve robustness of the detectors, we augment training data with adversarial paraphrases generated by the state-of-the-art paraphrasing attack model, exposing detectors to challenging meaning-preserving rewrites. Extensive experiments across multiple datasets and generation models demonstrate that adversarial paraphrasing substantially degrades detectors trained only on benign data, while adversarial data augmentation leads to consistent robustness gains and good cross-generator transferability. Overall, our findings highlight the value of discrete-class formulation and principled data augmentation for building reliable detectors of human-AI co-authorship.

cs.CL · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01426

FlyWheel: Attention Kernels Built for the TPU

Zishuo Bao, Zeshen Zhang, Linkun Chen, Yucheng Lu

原文摘要

Attention kernels on Tensor Processing Units (TPUs) often underutilize their matrix units (MXUs) because of repeated matrix operand loads, vector-intensive computation, and dependencies that leave MXUs idle. We present FlyWheel, a set of fast TPU kernels for both softmax and linear attention. FlyWheel reformulates softmax and triangular inversion to reduce vector work, while coordinating data layouts to improve matrix operand reuse. It then overlaps independent computation to hide execution latency. We apply these techniques to both softmax attention and Gated DeltaNet on TPU v6e and v7x. FlyWheel speeds up softmax attention by up to $4.14\times$ over the state-of-the-art RPA kernel and $1.80\times$ over Splash Attention, reaching 81% of peak BF16 throughput on v6e. It also speeds up Gated DeltaNet prefill by up to $3.61\times$. Integrated into vLLM serving Qwen3.8-27B, FlyWheel improves prefill throughput by up to $1.56\times$ while maintaining comparable accuracy.

cs.CL · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01425

Uncertainty-Aware Joint Generative Modeling of Fault Event Sequences and Sensor Time Series in Solar PV Plants

Jiayu Huang, Boyang Xu, Yongming Liu, Hao Yan

原文摘要

This paper presents a multimodal generative model designed to simulate a solar energy plant system for predictive maintenance and fault prediction. We propose a novel approach to enhance system generation through conditional generation by leveraging both event sequences and time-series data and improve next event prediction. The framework employs a Transformer Hawkes Process (THP) to encode event data and an iTransformer to encode time-series data, integrated via a co-attention mechanism that effectively captures cross-modal dependencies between discrete events and continuous signals. From these integrated representations, we employ two specialized decoders for simulation: a Conditional Latent Diffusion Model (CLDM) for modeling future sensor trajectories and the Score Matching-based UnceRtainty quantiFication for Transformer Hawkes Process (SMURF-THP) for high-fidelity event prediction. This dual-decoder strategy supports uncertainty-aware, history-conditioned simulation by rolling the model forward for a specified number of generation iterations, producing finite stochastic scenarios rather than deterministic extrapolations over the full observational record. Evaluated on a comprehensive dataset from the Red Rock Solar Site in Arizona, our approach improves aggregate event-prediction F1 relative to baselines and supports applications in predictive maintenance, fault detection, and energy optimization for solar power systems.

cs.LG · eess.SY

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.34147

PS-DLO: Property- and Spatially Aware Deformable Linear Object Manipulation Policy

Mengdi Li, Boxian Yao, Xiyu Zhu, Yunfan Lou, Yifan Yang, Yuanpeng He, Xuan Han, Tao Liu 等 9 位作者

原文摘要

Deformable linear objects (DLO) pose a coupled perception and control challenge: their slender geometry makes precise localization difficult, while their deformations depend on object-specific physical properties. To address this challenge, we propose PS-DLO, a property- and spatially aware DLO manipulation policy. First, contrastive image-text learning maps the initial observation to categorical descriptions of DLO properties, providing explicit physical and structural priors for action generation. Second, SAM3 extracts mask-guided features from future observations, which are distilled into the policy's action expert to encode task-relevant DLO regions and their subsequent configurations. We evaluate PS-DLO on nine real-world tasks across single-arm and bimanual platforms, training a shared multi-task policy for each embodiment. PS-DLO achieves an average success rate of 68.9%, exceeding the strongest baseline by 20.0 percentage points. In generalization experiments, PS-DLO achieves higher success rates than the baseline in six of seven visual-shift conditions on butterfly-knot untying. These results demonstrate that combining object-property priors with future spatial supervision improves multi-task DLO manipulation and robustness to visual variation.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01415

Latent Harness: Compiling Agent Behaviors into Post-Block Activation Shifts

Ran Li, Shimin Di, Zeyu Ren, Ling Yue, Jiachuan Wang, Lei Chen

原文摘要

Harness engineering and self-evolving agents improve language-model agents quickly, yet their gains live outside the model. The harness is re-sent and re-paid on every call, and the standard route to internalization is a weight update that is costly to iterate and fixed once merged. We propose compiling the harness instead. Latent Harness distills an external condition into a rank-8 activation shift. The shift is applied at the output of every decoder block of a frozen backbone, at 0.03-0.05% of parameters. The condition is either an explicit process-level behavior instruction or the behavioral contract of an agentic harness. At inference the compiled bundle carries no harness text and adds no tokens. It exposes three runtime controls for injection strength, injection phase, and shift-space mixing. Compiled behaviors recover the source-harness gain in 8 of 10 preregistered settings. Where the explicit source pays the heaviest per-call token tax, the compiled form exceeds it. The same bundles transfer zero-shot across tasks (5 of 7 pairs positive) and compose in shift space, while stacked text instructions interfere destructively. Used as a search space, they lift budget-matched harness search by 1.45 times. On three agentic benchmarks the operator aligns policy to the harness con- tract. Under matched data, capacity, and effective strength it outperforms same-capacity LoRA, which needs 11.1 times the parameters to reach the same band, and it regresses nothing where the base policy is already strong. Across 1B-to-8B models the method skeleton is invariant while gain locations move with the model-task pair, which delimits where compiled harnesses should be deployed.

cs.CL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2602.16711

TeCoNeRV: Leveraging Temporal Coherence for Compressible Neural Representations for Videos

Namitha Padmanabhan, Matthew Gwilliam, Abhinav Shrivastava

原文摘要

Implicit Neural Representations (INRs) have recently demonstrated impressive performance for video compression. However, since a separate INR must be overfit for each video, scaling to high-resolution videos while maintaining encoding efficiency remains a significant challenge. Hypernetwork-based approaches predict INR weights (hyponetworks) for unseen videos at high speeds, but with low quality, large compressed size, and prohibitive memory needs at higher resolutions. We address these fundamental limitations through three key contributions: (1) an approach that decomposes the weight prediction task spatially and temporally, by breaking short video segments into patch tubelets, to reduce pretraining memory overhead across resolutions; (2) a residual-based storage scheme that captures only differences between consecutive segment representations, significantly reducing bitstream size; and (3) a temporal coherence regularization framework that encourages changes in the weight space to be correlated with changes in video, making residuals even smaller. Our proposed method, TeCoNeRV, achieves substantial improvements of 2.47dB and 4.94dB PSNR over the baseline at 480p and 720p on UVG, with 36% lower bitrates and 1.5-3${\times}$ faster encoding speeds. With our low memory usage, we are the first hypernetwork approach to demonstrate results at $480$p, $720$p and $1080$p on UVG, HEVC and MCL-JCV. Our project page is available at https://namithap10.github.io/teconerv/.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01411

Sensitivity Inflation from Cross-Record Precision Routing in Private Quantized Fine-Tuning

Annan Zhang, Shengjie Song, Wenshuai Yao, Lihao Zhang, Tianmiao

原文摘要

Private fine-tuning of large language models increasingly pairs DP-SGD with low-rank adapters on frozen, quantized backbones, and every such stack inherits one unstated premise: frozen layers keep each record's clipped gradient a function of that record alone. We show that LLM.int8 breaks this premise. It routes outlier activation columns to a higher-precision path using a union over the whole batch, so one record decides how every other record's activations are computed, even though the weights are frozen and the quantization scales are per row. We bound the resulting sensitivity inflation, state a record-isolation contract that restores the standard bound, and define record-owned precision dispatch that meets it with the same GEMMs as batched LLM.int8, so isolation needs no extra arithmetic. We then audit the native CUDA stack (bitsandbytes, PEFT, Opacus) against four matched controls. A constructed LoRA-Opacus witness reaches $15.0\times$ the add/remove bound. On pretrained OPT-125M with natural text, other records alter 20% of record-layer precision splits at batch size 16. An exploratory re-clipping analysis finds 10 of 64 candidate neighbors above the bound once every gradient is clipped; the top pair, revalidated natively, reaches $1.065C$ while every control stays within $C$, and forcing one layer's outlier mask removes the excess. A two-million-draw audit of the released adapters yields $\varepsilon$ lower bounds ($3.25$ at $δ=10^{-5}$) below the advertised guarantee ($4.38$). Resolving an excess this small at the release level would take about 200 times more draws, whereas checking the sensitivity contract directly exposes it in a few native runs, which makes contract checking the practical audit for private quantized training.

cs.LG

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新论文 · v1arXiv:2611.01409

RAG-TRACE: Tracing Measurement Evidence and Diagnostic Completeness in AI-Generated ECG Reports

Elyas Irankhah, Shahed Salehzehi, Parisa Hosseini

原文摘要

Purpose: Retrieval-augmented generation (RAG) can introduce electrocardiogram (ECG) criteria that lack patient-specific measurement support. This study evaluates the reliability of retrieved diagnostic criteria in AI-generated ECG reports. Methods: We analyzed 1,083 human-validated PTB-XL patient records using a library of 47 criteria cards, including 33 without numeric verification rules. Language models generated structured reports with and without retrieval. Criterion applicability was evaluated using Marquette 12SL measurements, with independent assessment using Uni-G and comparison against expert-validated diagnostic labels. Results: Keyword retrieval introduced untestable or measurement-disconfirmed criteria in 46.8% of GPT-4.1-mini reports. A deterministic verification rule withdrew 647 criterion applications, including 639 without numeric checks, while preserving 268 of 269 applications independently supported by Uni-G. Restricting retrieval to 14 testable criteria reduced the initial unsupported-criterion rate to 13.8%. Under matched instructions and an identical output schema, retrieval increased unsupported-criterion reports from 17.4% to 31.1%, while hypertrophy recall remained unchanged at 62.6%. Conclusion: These findings identify a fundamental limitation of retrieval-based clinical report generation: retrieved criteria do not necessarily provide valid patient-specific evidence. Measurement-based verification can reduce unsupported criterion applications, but diagnostic validity requires independent assessment.

cs.CL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2608.07541

NeuroPilot: An Agent-Driven Smart Pipeline for Processing, Quality Control, and Managing Neuroimages

Yiyao Chen, Yucheng Li, Jungong Tong, Shaoqi Wang, Kunhao Zhou, Ziquan Wei, Monica Murea, Marissa DiPiero 等 10 位作者

原文摘要

Transforming raw neuroimage archives into analysis-ready derivatives relies on three brittle stages: data standardization, modality-specific preprocessing, and quality control (QC). While individual neuroimaging tools are well developed, their orchestration requires project-specific scripts, environment-adaptive tuning, and labor-intensive manual QC. To address this, we introduce NeuroPilot, a multi-agent system that digitalizes the expertise of neuroimage processing, QC, and data management into three LLM-invocable skills: dcm2bids-skill, neuroimage-pre-skill, and qc-agent-skill. The LLM-driven agent autonomously orchestrates workflows, generalizing various infrastructure settings into a single configuration to achieve the highest scalability. Demonstrating the system's generalizability, we deployed NeuroPilot across 17 cohorts (>123,000 subjects) spanning infant to aging populations and multiple MRI modalities (structural, diffusion, functional). In practice, after standardizing data via the dcm2bids-skill, the agent dynamically routes datasets to the optimal neuroimage-pre-skill based on available modalities and cohort traits (e.g., dispatching T1w and fMRI data to fMRIPrep, or selecting specialized pipelines for infant cohorts). The qc-agent-skill then drives an evidence-based, semi-automated QC via a 3-D browser dashboard, utilizing a multi-tiered verification system to optimize failed cases and escalate complex issues for supervisor inspection. Quantitatively, our QC agent screened 558 production subjects, validating its automated flags against FreeSurfer's topology-defect metrics. The infant processing pipeline achieved a 100% (201/201) completion rate on QC-validated inputs. Importantly, NeuroPilot compresses the traditional 2--3 month timeline for training staff and processing complete datasets into a single week. NeuroPilot is deployed in https://wanda-cyberbench.com/.

cs.CV · cs.AI · cs.MA

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01404

Serving Agentic Robot Fleets: Workload Characterization, KV-Cache Thrashing, and Admission Control

Sida He

原文摘要

Agentic robots combine a vision-language brain, a tool-using harness, and a vision-language-action (VLA) policy. Serving a fleet of these agents introduces two coupled resource constraints: the brain repeatedly revisits long, growing key-value (KV) cache states, while the VLA executes action chunks under tight control deadlines. We characterize 80 LIBERO-PRO episodes generated by GPT-6-Astra, RPent, and pi0.5 (1,862 brain turns and 1,582 VLA inference chunks), profile both layers on H800 graphics processing units (GPUs), and replay the resulting agent programs on a vLLM serving engine. Brain contexts reach 215k tokens; the median Astra request takes 7.78 s, while the intervening environment-step gap is only 1.0 s. Under a 0.25 s action-chunk deadline, one VLA GPU meets the 1% starvation target for one robot; under a 1.0 s deadline, its measured capacity reaches at least 32. In sustained brain replay, increasing concurrency from 20 to 32 robots reduces throughput from about 2.4 to 0.6 requests/s as repetitive prefill displaces generation. A static active-episode cap restores 2.24-2.25 requests/s and raises completed trace-program throughput from 1.30 to 5.00-5.55 episodes/min. Our measurements expose the workload-dependent shift between action deadlines and brain KV residency, and establish practical principles for capacity planning and admission in sustained agentic robot fleets.

cs.RO · cs.DC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01400

RadPose: ISAC-based RF 3D Human Pose Estimation

Geo Jie Zhou, Kunzhe Song, Ruxin Lin, Huacheng Zeng

原文摘要

Human pose estimation under adverse lighting and occlusion conditions remains challenging for camera- and LiDAR-based systems. Although mmWave radars provide robustness to illumination and occlusion, they rely on dedicated hardware and licensed spectrum, limiting scalability. In this paper, we present RadPose, a learning-based monostatic integrated sensing and communication (ISAC) framework that enables mmWave communication devices to perform 3D human pose estimation. Unlike specialized radars, RadPose leverages monostatic full-duplex operation to extract channel impulse responses (CIRs) directly from standard OFDM communication signals, generating 3D radio point clouds without additional sensing hardware. To address the sparsity and noise of RF observations, RadPose employs a cross-frame transformer-based pose inference network with semantic-guided spatial attention and target-driven temporal attention for robust spatio-temporal aggregation. It further adopts a joint-aware inverse kinematics refinement module based on graph convolution to enforce skeletal topology, bone-length consistency, and anatomical plausibility. Extensive experiments demonstrate RadPose achieves competitive accuracy compared to state-of-the-art radar-based approaches while maintaining robust performance in unseen and occluded scenarios. The dataset and source code are available at https://github.com/Geo-Chou/RadPose.

cs.CV · cs.NI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01396

SpecUltra: A Unified Foundation Model for Heterogeneous Spectral Images

Weili Kong, Baisen Liu, Xiaojun Bi, Jiaming Pei

原文摘要

Spectral imagery, including hyperspectral image (HSI) and multispectral image (MSI), exhibits substantial heterogeneity in band numbers, wavelengths, spectral resolutions, hindering unified modeling and cross-sensor transfer. From the perspective of imaging physics, these heterogeneous observations can be regarded as discrete measurements of an underlying continuous spectrum acquired through different sampling processes. Motivated by this perspective, we propose SpecUltra, a unified foundation model for heterogeneous spectral images. Specifically, SpecUltra employs 3D convolutional tokenization and wavelength-aware 3D RoPE to encode relative wavelength and spatial relationships, enabling fine-grained spatial-spectral modeling within a shared coordinate system. We further construct MultiHyper-200K, a large-scale multi-sensor HSI dataset, and pretrain SpecUltra using auxiliary-token-assisted mask image modeling (MIM), where a level token indicates the radiometric product type and register tokens absorb sensor- and acquisition-dependent variations. To bridge the spectral-granularity gap between HSI and MSI, we introduce Spectral Kernel Transformation (SKT), which transfers pretrained knowledge in parameter space by constructing MSI embedding kernels from fine-grained HSI-pretrained kernel slices. This design preserves pretrained spectral priors while maintaining compatibility with the pretrained backbone. Experiments on HSI and MSI classification and segmentation benchmarks demonstrate state-of-the-art or competitive performance across disparate sensors and downstream tasks.

cs.CV

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arXiv ↗PDF ↗