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新论文 · v1arXiv:2611.04035

Recursa: Toward Recursively Self-Improving Foundation Model Systems

Erkang Zhu, Yin Lin, Xiaorong Shi, Xuanrui Lin, Jingren Zhou

原文摘要

Foundation models in changing environments can encounter unfamiliar domain knowledge that their original training did not cover. However, new domains rarely provide large amounts of expert-created training data or reliable supervisory signals for further training. We introduce Recursa, a foundation model system that adapts to new domains by learning from its own experience without human experts or a stronger teacher model. Recursa uses a knowledge repository to store what it learns: domain-specific skills and instructions on when to apply them, along with memories of past task experiences and reflections on what to improve. Recursa bootstraps the repository from domain materials and then refines it through recursive self-improvement. It uses parallel learners to execute tasks and synthesize local improvements, then validates and merges the improvements into the knowledge repository. On Harvey LAB's Firm Knowledge benchmark, Recursa raises the all-pass rate on held-out test tasks from 30.0% to 55.0% with DeepSeek-V4.1-Flash and from 35.0% to 46.0% with Qwen3.8-Flash.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04034

Do Pretrained Backbones Transfer to Physical Sensors? A Systematic Study of Multimodal Initialization Across the Sensor Stack

Fabio Montello, Ronja Güldenring, Lucas Mogensen Sandby, Christoffer Koo Øhrstrøm, Lazaros Nalpantidis

原文摘要

Transfer learning from pretrained backbones has become standard practice in multi-sensor computer vision---yet two questions remain open: which pretrained weights transfer best to multi-sensor setups, and can in-domain pretraining outperform off-the-shelf solutions? Frameworks like MultiMAE demonstrate the utility of masked autoencoding in a multi-modal context, but rely entirely on dense, model-derived modalities. It is unclear how robustly these methods translate to real-world applications that use physical sensors, where the aggregation of diverse public datasets for large-scale pretraining inherently introduces modality sparsity. To investigate this systematically, we curate a multi-sensor autonomous driving collection of $2.82M$ images from 16 publicly available datasets, incorporating both physical hardware modalities (RGB, LWIR, NIR, Event) and model-derived modalities (Stereo Depth, Segmentation). We use this collection for in-domain pretraining based on the MultiMAE approach. We adapt MultiMAE's loss formulation to accommodate sparse samples, and study the effect of an enforced encoder bottleneck for modality alignment and of additional losses specific to dense reconstruction (SSIM and FFT). We then systematically compare against common pretrained backbones (ViT, MAE, DINO and MultiMAE) across downstream sensor fusion tasks. We find that MultiMAE is a surprisingly strong out-of-the-box baseline, transferring effectively even to modalities it has never seen, while our targeted modifications yield incremental but consistent improvements on multi-sensor transfer and overall robustness.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.16988

Agent trajectories as programs: fingerprinting and programming coding-agent behavior

Hamidah Oderinwale

原文摘要

Benchmark scores tell you what an agent got right; they do not tell you how it got there. In this work, we introduce methods for comparing agents procedurally in different contexts, where the model, tasks, and approaches vary. We compare ten agents and find that they are identifiable by their behavioral habits, which we define as fingerprints: a probe over these procedural signatures attributes an unseen trajectory to the correct agent at 85.7% accuracy. We develop procedural representations for agent problem-solving procedures with an emergent vocabulary induction technique which is meant to be maximally compressive to avoid surface-level variation while being expressive enough to unveil the quirks of the models' patterns. We apply our framework to the software engineering evaluation dataset SWE-Bench to study the structural distinctness of agent trajectories and find that behavior is most similar between models from similar release periods and those that are distilled from one another (e.g., a distilled student model and its teacher have a Jensen-Shannon divergence of 0.25, about half the distance between other model pairs). As more models saturate evaluations, we believe that it will be important to probe model behavior along more holistic dimensions than success rates alone. We introduce ProcGrep, a library for auditing and evaluating agents for how they approach tasks at a procedural level given their traces in a top-down fashion. ProcGrep is available at https://github.com/hamidahoderinwale/procgrep.

cs.SE · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04033

Exploring the Power Consumption of ERC-4337 Bundling on Server Infrastructure

Claudiu-Nicu Bărbieru, Oana-Otilia Captarencu, Pascal Felber, Emanuel Onica, Valerio Schiavoni, Cosmin-Nicolae Vârlan, Boris Verdecia Echarte

原文摘要

The Ethereum ecosystem offers the most widespread support for Decentralised Applications (DApps), with over 120 networks currently documented as using variants of the Ethereum Virtual Machine (EVM) for core execution logic. The ERC-4337 standard provides convenient access to DApps for users who do not possess the required cryptocurrency or prefer flexible authorisation of DApp actions. The infrastructure for ERC-4337 implementation can be deployed on any EVM network. However, to aggregate user requests and submit them to the EVM network, ERC-4337 requires operating a bundler, an additional component. Recently, the potential use of ERC-4337 was boosted by another standard, EIP-7702, which can enable ERC-4337 utility for any existing user account. However, the use of the combined EIP-7702/ERC-4337 pattern remains considerably low at less than 6% of EIP-7702 activity. We believe that this underutilisation is at least partly caused by the low number of existing bundling services and their popularity, currently fewer than 15 publicly documented. Expanding the number of bundler nodes would benefit the ecosystem but would introduce operational costs, part of which is represented by the added nodes' power consumption. In this paper, we provide a thorough view of the bundling power consumption aspect using server-grade infrastructure, evaluating both CPU and RAM consumption, as well as the overhead of container-based deployments specific to production environments.

cs.NI · cs.PF

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.01839

Import What You Need: Learning When and How to Augment EHR Graphs with External Knowledge

Chen Chen, Mohsen Nayebi Kerdabadi, Dongjie Wang, Mei Liu, Zijun Yao

原文摘要

Longitudinal prediction from electronic health records (EHRs) is limited by the sparsity and irregularity in patient trajectories, and knowledge augmentation with external knowledge graphs (KGs) offers a promising way to alleviate these issues. However, KG expansion often follows predefined rules, with limited control over whether and how to augment each visit as a patient's state evolves. We propose ReTA, a Reinforcement learning-based dynamic Topology Augmentation framework that casts KG import as a per-visit, budget-aware policy. ReTA first constructs an offline refined pool of KG-grounded templates, then learns a policy to select one augment action per visit from three options: Soft Import, which enriches node features without modifying graph topology, Hard Import, which grafts a compact KG subgraph onto the visit graph to create message-passing shortcuts, and Skip, which leaves the visit unaugmented when the base encoder is already confident. To stabilize learning, ReTA employs a decoupled encoder that processes semantic and structural signals in separate channels and fuses them via adaptive gating. Experiments on MIMIC-III and MIMIC-IV across diagnosis prediction, mortality, and readmission show that ReTA consistently outperforms strong baselines while remaining efficient, with larger gains under sparse supervision. ReTA transfers across datasets and knowledge graphs and yields interpretable augmentation patterns.

cs.LG · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04032

ROCm InferaSim: Cut LLM Serving Trial Costs and Deploy the Best Configuration

Anshu Raina, Yao Fu, Zhang Jiejing, Peyman Razaghi, Shiksha Patel, Ravi Gupta, Deval Shah, Adeem Jassani 等 14 位作者

原文摘要

Serving an LLM efficiently depends on the engine, how the model is split across GPUs, precision, scheduler settings, speculative decoding, KV-cache management, and prefill-decode disaggregation. The best configuration depends on the model, hardware, workload, and objective. Operators usually find it by trial and error. Exhaustively measuring the candidates in our study would take about 25,000 GPU-hours. ROCm InferaSim predicts serving performance from a few short measurements. Choices such as batch size, prompt length, and GPU count change the amount of work per GPU without changing the kernels. ROCm InferaSim benchmarks each distinct set of kernels once, on at most one node. We call this measurement an anchor. Analytical laws carry that anchor to other deployments that run the same kernels. For multi-node deployments, ROCm InferaSim reduces the configuration to a single-node equivalent, measures it, and scales the result analytically. A simulator replays multi-turn traffic, and a tuning agent searches configurations using the predictions. On AMD Instinct MI355X GPUs with vLLM, SGLang, and ATOM, 11 anchors took 9.1 GPU-hours, against 204 GPU-hours to measure the 226 fixed-length configurations they cover. On 172 configurations, time per output token has 8.1% median error. On a multi-turn coding workload, throughput is within 8%. For 159 disaggregated deployments, none used as an anchor, throughput is within 5.5%. A five-candidate short list contains a deployment within 12% of the best feasible pairing of model and latency target.

cs.AI · cs.DC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04031

Language Model Augmented Decision Transformer for Multi-Task Offline Reinforcement Learning

Xinbo Zhao, Yuhang Liu, Xin Zhang, Lei Le, Zhaohan Xi, Yu Yang, Yingxue Zhang

原文摘要

Offline multi-task reinforcement learning (MTRL) aims to learn a unified policy from heterogeneous offline datasets that generalizes across tasks. However, existing offline MTRL methods often struggle with two fundamental issues: severe per-task data scarcity and limited cross-task generalization. In this paper, we propose Language-Model Augmented Decision Transformer (LaMA-DT), a unified sequence-modeling framework that leverages a single large language model (LLM) for both trajectory augmentation and multi-task policy learning. LaMA-DT uses natural-language task descriptions to guide task-aligned trajectory augmentation through a set of supervised generation and alignment objectives, and incorporates a scoring-and-filtering mechanism to retain high-quality augmented trajectories. The same LLM is then repurposed as a language-conditioned Decision Transformer and fine-tuned on a mixture of real and augmented data. Experiments on Meta-World and MuJoCo multi-task benchmarks demonstrate that LaMA-DT consistently outperforms competitive baselines, particularly in low-data regimes, showing that language-guided data augmentation substantially improves cross-task generalization.

cs.CL · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2602.00950

MindGuard: Guardrail Classifiers for Multi-Turn Mental Health Support

António Farinhas, Nuno M. Guerreiro, José Pombal, Pedro Henrique Martins, Laura Melton, Alex Conway, Cara Dochat, Maya D'Eon 等 9 位作者

原文摘要

Large language models are increasingly used for mental health support, yet their conversational coherence alone does not ensure clinical appropriateness. Existing general-purpose safeguards often fail to distinguish therapeutic disclosures from genuine clinical crises, and progress on developing better ones is bottlenecked by a lack of clinically grounded evaluation resources. To address this gap, we introduce a risk taxonomy, developed in collaboration with PhD-level clinical psychologists, that identifies actionable harm (self-harm and harm to others) while preserving space for safe, non-crisis therapeutic content. We release MindGuard-testset, a dataset of multi-turn conversations annotated at the turn level by clinical experts, which is, to our knowledge, the first public benchmark with turn-level clinical risk labels for multi-turn mental health support. We also release an automated red-teaming (ART) framework designed to measure how safety classifiers affect downstream model behavior in adversarial multi-turn interactions. Using synthetic dialogues generated via a controlled two-agent setup, we train MindGuard, a family of lightweight safety classifiers (with 4B and 8B parameters). Our classifiers reduce false positives at high-recall operating points and, when paired with clinician language models, help achieve lower attack success and harmful engagement rates compared to general-purpose safeguards. We release all models, training and human evaluation data, and the ART framework.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04028

Compression Forcing: Hierarchical History Tokenization Enables Flexible World Models

Siqiao Huang, Partha Kaushik, Michael Chen, Hengkai Pan, Kaiwen Geng, Omar Chehab, Fernando Moreno-Pino, Max Simchowitz

原文摘要

Effective world modeling requires compressing salient history information that facilitates future prediction. However, most existing approaches inherit tokenization and conditioning mechanisms from video generation, where objectives are centered around perceptual realism rather than predictive utility. This discrepancy limits the world model's effectiveness, especially in environments dominated by visual nuisances or task-irrelevant background details. To this end, we introduce Compression Forcing (CF), a general framework that aligns history compression with the predictive objective of world modeling. Compression Forcing features Dynamics-Aware Tokenization (DAT), a novel tokenization method that compresses history observations into a sequence ordered by decreasing dynamics saliency. Built on a diffusion backbone, this structure enables Hierarchical History Guidance (HG-H), allowing selective amplification of dynamics-salient information during inference. Extensive experiments across various environments demonstrate improved prediction fidelity, physical realism, and downstream decision-making performance, establishing Compression Forcing as a highly flexible and effective approach for world modeling.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04027

A Practical Causal Framework for Autonomous Mobile Robots: From Simulation to Real-World Human Environments

Luca Castri, Gloria Beraldo, Nicola Bellotto

原文摘要

Causal inference has emerged as a powerful paradigm enabling robots to make safe, efficient decisions by reasoning about the ``why'' behind events. However, its real-world application is hindered by the difficulty of collecting causal data, especially in human-populated environments where training takes time, while users expect immediate operation. To this end, we tackle the challenge of transferring simulation-learnt causal relations to the real world with minimal effort. While retaining the invariant high-level causal structure, we empirically demonstrate the system adapts efficiently to real-world situations by updating only the causal model parameters with a limited set of new observations. Our results show that the parameter update enables rapid sim-to-real transfer. The framework runs efficiently onboard of a mobile robot and significantly outperforms a non-causal baselines navigating successfully and safely in complex human-shared scenarios.

cs.RO · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04025

DUNEDepth: Bronchoscopic Depth and Pose Estimation via Hybrid Adaptation of a 3D-Vision Foundation Model

Qi Chang, Austin Kao, Ange Lou, William E. Higgins

原文摘要

State-of-the-art bronchoscopy guidance systems and computer-aided diagnosis methods have emerged as important tools for early lung cancer detection. Effective use of these tools depends on being able to accurately link a patient's bronchoscopic airway exam video to their pre-operative 3D chest CT scan. Unfortunately, the derivation of such links, which include bronchoscope depth and camera motion, from the exam video remains an open problem. Building on recently proposed 3D-vision foundation models, we propose DUNEDepth, a hybrid architecture that combines a shared DUNE ViT encoder, a twin MASt3R cross-attention decoder structure, and a prediction head that includes elements of the MASt3R and Reloc3r models. To fine-tune the architecture to our application, we utilize an integrated hybrid synthetic-supervised/self-supervised training strategy that draws on both bronchoscopic video and CT-derived virtual bronchoscopic views. Experiments drawing on bronchoscopic video sequences demonstrate that DUNEDepth more accurately derives depth information and camera motion trajectories than previously proposed self-supervised methods.

eess.IV · cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04023

Making Every Edit Count: Efficient Binary Edit Correction at the Gilbert-Varshamov Bound

Ryan Gabrys, Shubhransh Singhvi, Eitan Yaakobi

原文摘要

We construct binary codes that attain the classical Gilbert-Varshamov redundancy guarantee for a fixed number of edit errors, with deterministic polynomial-time construction, encoding, and decoding. For every fixed integer $t\ge 1$ and all sufficiently large block lengths $n$, our codes correct any combination of at most $t$ insertions, deletions, and substitutions in total, with redundancy at most $2t\log_2 n+O_t(1)$. All algorithms run in polynomial time in $n$ for fixed $t$, and decoding recovers the original information message. This realizes the classical counting guarantee algorithmically without enumerating the exponentially large space of binary words.

cs.IT

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2604.01002

Query-Conditioned Evidential Keyframe Sampling for MLLM-Based Long-Form Video Understanding

Yiheng Wang, Lichen Zhu, Yueqian Lin, Yudong Liu, Jingyang Zhang, Hai "Helen" Li, Yiran Chen

原文摘要

Multimodal Large Language Models (MLLMs) have shown strong performance on video question answering, but their application to long-form videos is constrained by limited context length and computational cost, making keyframe sampling essential. Existing approaches typically rely on semantic relevance or reinforcement learning, which either fail to capture evidential clues or suffer from inefficient combinatorial optimization. In this work, we propose an evidence-driven keyframe sampling framework motivated by an information-theoretic perspective. We consider maximizing the conditional mutual information between the selected frames and the answer given the query as an idealized objective, and use a modular singleton-gain surrogate to motivate a tractable frame-wise selection criterion. Guided by this motivation, we train a query-conditioned evidence scoring network using annotated answer-supporting segments. A contrastive objective encourages greater aggregate score mass on frames from evidence regions than on background frames, providing a practical evidence-based criterion beyond semantic similarity. Experiments on long-form video understanding benchmarks show that our method consistently outperforms prior sampling strategies under comparable frame budgets. With Qwen2.5-VL-7B under the same 32-frame budget, our method improves over uniform sampling by 10.1 points on LVBench and 4.4 points on the long-video subset of VideoMME.

cs.CV · cs.AI · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.27759

Holonic Graceful Transitions Across Centralized, Decentralized, Distributed, and Local Control in DER-rich Cyber-Power Distribution System

Md Fazley Rafy, Niloy Patari, Anurag K. Srivastava

原文摘要

The increasing penetration of distributed energy resources (DERs) in distribution system necessitates adaptive coordination frameworks. These frameworks must remain optimal during normal operation and resilient under cyber physical disturbances. Existing coordinated control approaches are typically deployed as static architectures with limited ability to adapt when communication degrades, local instability emerges, and operating conditions become spatially heterogeneous. This work addresses the gap by proposing a DER service-independent edge autonomous holonic adaptive coordination framework. In this framework, each DER controller executes its own control actions and transitions among centralized, distributed, decentralized, and local autonomous coordination modes without always relying on static coordination commands from the grid operator. The framework preserves coordination continuity across modes by retaining local controller states while reconfiguring only the coordination topology, information exchange pattern, and fallback action associated with the active DER service. Graceful transitions are enabled through dwell timers, rate limiting, and safety overrides to prevent dynamic instability during mode changes. Volt VAR control is used as a representative distribution automation application to validate the proposed architecture in a cyber-physical Hardware-in-the-loop (HIL) testbed. The proposed approach is evaluated under diverse cyber-physical event-based scenarios, showing region-confined adaptation under localized disturbance, reduced coordination traffic during coordination switching, and node-confined mitigation under cyber attack through edge anomaly detection and neighbor-corroborated impact estimation.

eess.SY · cs.NI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04019

qLevelPlace: A Level-Based Placement Framework for RSFQ Circuits

Ziyu Liu, Jingkai Hong, Robert S. Aviles, Sasan Razmkhah, Peter A. Beerel, Massoud Pedram

原文摘要

Rapid Single Flux Quantum (RSFQ) logic can operate at ultra-high clock frequencies; however, its physical design is complicated by explicit splitter trees and deep gate-level pipelines in which every logic gate is clocked. To address this challenge, we present qLevelPlace, a level-based placement framework for RSFQ circuits. We separate gate and splitter placement into two stages to better support levelguided gate-to-row assignment and use a timing-aware quadratic cost function to penalize long interconnects. Experiments on the benchmark circuits show that, on average, qLevelPlace achieves a $2.65\times$ higher clock frequency and a 51% reduction in layout area compared with the state-of-the-art RSFQ placer qPlace.

cs.ET

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04016

Physical Experience Builds Continual Spatial World Models

Yuhang Sun

原文摘要

World models are increasingly used to support prediction and planning in embodied agents, but the knowledge is usually established before deployment and then carried into later encounters. Continual operation faces a different problem. A familiar environment may change, and the agent must use previous experience without allowing old expectations to override current physical evidence. This study asks what repeated physical experience must contribute for a spatial world model to become progressively more useful rather than simply more persistent. A physics-grounded dynamic maze separates recurring spatial change from the continuous consequences of action. Cross-episode Spatial Change Memory learns relations among environmental transformations, while state-action-outcome experience refines predictions of motion. Structured experience progressively reduces the need for future route correction and transfers to spatial transitions not encountered during learning. The advantage disappears when past changes contain no information about future ones. Continuous navigation further shows that predictive memory changes behavior when a previous decision becomes invalid but leaves behavior unchanged when the decision remains correct. Physical tests expose a complementary limit: correct topology does not guarantee correct action prediction, and accumulated physical experience reduces this mismatch within the modeled dynamics. The results characterize continual world knowledge as experience that improves future prediction and action beyond stored episodes while remaining revisable by the physical outcomes it is intended to predict.

cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2603.15457

Evasive Intelligence: Lessons from Malware Analysis for Evaluating AI Agents

Simone Aonzo, Merve Sahin, Aurélien Francillon, Daniele Perito

原文摘要

Tool-using artificial intelligence (AI) agents are increasingly adopted as systems that plan, observe their environment, and take actions over extended time periods. This shift challenges the current evaluation practice, in which models are tested in restricted, fully observable settings. We argue that such evaluations are vulnerable to a failure mode long known in computer security: a system that behaves benignly when it detects that it is being analyzed. Malware has exploited this for decades, by probing sandboxes, virtual machines, and instrumentation, before revealing its payload. The shared challenge is that the subject under observation can detect the conditions of its evaluation, so the setup used to observe behavior ends up shaping the behavior observed, yielding overly optimistic safety and robustness assessments. Recent AI-safety studies demonstrate pieces of this risk, though only preliminarily and under controlled conditions. Drawing on decades of malware-analysis experience, we outline concrete principles for evaluating AI agents as potentially adversarial: explicitly threat-modeling evaluator-discrimination; testing across harness variants and reporting the worst case; using shadow executions to provide a realistic runtime without irreversible side effects; treating environment probing as a risk signal; and performing unpredictable reassessments after deployment.

cs.CR · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04012

SurgEye: Instruction Tuning-Free Perception-Reasoning Framework for Surgical Scene Understanding

Qing Xu, Xiechen Shen, Xianjian He, Guqiao Liang, Zixiao Chai, Jiale Qin, Yifan Chen, Siyi Li 等 12 位作者

原文摘要

Surgical scene understanding is pivotal for computer-assisted intervention, yet demands compositional reasoning across instruments, anatomy, and workflow. Existing surgical vision-language models rely on instruction tuning with large-scale question-answer pairs, entailing laborious data curation and costly retraining. To bridge this gap, we propose SurgEye, a perception-reasoning framework that grounds reasoning in a vision foundation model without instruction tuning. Specifically, we devise Routed Multi-Task Perception (RMP) that routes the vision foundation model through task-specific pathways learned from public perception labels, producing spatial and temporal evidence. We further introduce Agentic Compositional Reasoning (ACR) that employs a frozen large language model to coordinate RMP specialists and temporal memory as callable tools, composing evidence across tasks and time into grounded answers. Moreover, we construct SurgEye-Bench spanning eight surgical procedures, pairing each question with perception labels for verifiable evaluation. Extensive experiments demonstrate that SurgEye outperforms state-of-the-arts in surgical reasoning. The source code will be available.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04011

MusiSPARQ: Subscription-Aware Adaptive Resource Control for Musical Metaverse Services

Ali Al Housseini, Jaime Llorca, Omran Ayoub

原文摘要

The Musical Metaverse relies on distributed audio, visual, inference, and delivery functions deployed across heterogeneous edge-cloud resources. Queue-aware orchestration frameworks such as SPARQ jointly optimize service placement, routing, and resource allocation, but fix the execution regime of each resource in advance: it is either shared, favoring statistical multiplexing, or guaranteed, favoring performance isolation. This paper introduces MusiSPARQ, a subscription-aware control framework that promotes resource isolation from a fixed infrastructure attribute to a runtime decision. Each switchable resource is represented through coupled shared- and guaranteed-resource pools drawing from the same physical-capacity budget, and at each orchestration epoch the controller selects which class-specific service flows remain shared and which receive isolated capacity. The per-epoch decision jointly accounts for class-specific latency objectives, per-class maximum-latency limits, queueing stability, isolation cost, and switching overhead. In a dynamic virtual-concert scenario, MusiSPARQ reduces the Premium latency-target violation rate from 16.19% to 0.29% relative to fully shared execution, while reserving on average only 0.71% of capacity and, unlike static isolation, keeping Standard and Basic traffic within their latency targets.

cs.NI · eess.SY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04010

Decision Transformer with Task-Aware Representations for Offline Meta-Reinforcement Learning

Xinbo Zhao, Yuhang Liu, Xin Zhang, Lei Le, Zhaohan Xi, Yingxue Zhang

原文摘要

To enable effective generalization in offline meta-RL, it is essential to accurately infer the underlying task through latent task representations, which provide a compact and transferable summary of task-specific characteristics for guiding policy generation. In this paper, we propose Contrastive Task-Aware Decision Transformer (CoTA-DT), a DT-based offline meta-RL framework that learns task representations by jointly integrating four complementary components: (i) contrastive learning from offline trajectories, (ii) alignment with natural-language task descriptions, (iii) one-step supervision via a task-conditioned world model, and (iv) long-term supervision via a task-conditioned Q function. The learned task representations are used as conditioning signals for a DT-based policy, enabling task-aware meta-policy learning. To further support generalization in both zero-shot and few-shot settings, we design a language-grounded, self-adaptive strategy that enables effective and reliable adaptation to unseen tasks. Notably, in CoTA-DT, the pretrained LLM serves as a shared backbone for language encoding, world modeling, Q-function estimation, and policy learning. This unified design facilitates parameter sharing, improving both efficiency and generalization. Extensive experiments across diverse offline meta-RL benchmarks demonstrate that CoTA-DT consistently outperforms prior methods, particularly in challenging zero-shot generalization scenarios.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04007

Physics-Informed Identification of Tensegrity Prestress across Taut-Slack Transitions

Sandesh Lamsal, Sumit Kumar, Rubi Bhandari, Subhamoy Sen, Andrzej Rutkiewicz, Leszek Małyszko, Landolf Rhode-Barbarigos

原文摘要

Prestress governs the stability, stiffness, and dynamic behavior of tensegrity structures, but it is not directly observable from sparse structural measurements, and it may decrease during service life. Under strong excitation, the cables may slacken and the dynamics become nonsmooth. Existing identification methods are limited to the taut regime, and physics-informed networks for tensegrities solve only the forward problem of predicting the response from known member parameters. This study formulates the inverse problem. Neural networks are trained on the equation-of-motion residual in constrained Cartesian coordinates, where the mass matrix is constant and the force densities enter linearly. The force densities, damping, and axial stiffness are trainable parameters, and the tension-only cable law is kept. A Fisher-information analysis shows, before inversion, which member parameters a record can determine. The method is tested on simulated planar tensegrity frames with up to five modules and a three-dimensional prism, and on measured records of a biomimetic leg and a full-scale tensegrity simplex. On the one-module frame, the identifiable force densities are recovered within 1.5% from noisy accelerations at half the free coordinates, on records with and without taut-slack transitions. Records with transitions determine members that taut records leave undetermined. On the five-module frame, a 16-cable two-span bridge, the median force-density error rises moderately, to 4.2%. On measured records, an identified spring stiffness is within 0.1% of its independently measured value on average, and the identified simplex prestress at 13 levels agrees with its cable force transducers to 2.9% root-mean-square.

cs.LG · cs.CE · eess.SP · physics.flu-dyn

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04006

Controlling Action-Head Co-Adaptation to VLM Representation Anisotropy

Ruijie Zhang, Jiayin Zheng, Yequan Zhao, Liyan Tan, Ziyue Liu, Zhengyang Wang, Kunpeng Song, Nathan Zhao 等 11 位作者

原文摘要

Modern learning systems increasingly combine pretrained models with downstream heads. Despite the growing importance of such modular designs, how the downstream model co-adapts to the geometry of pretrained representations remains relatively underexplored. We study this problem through vision-language-action models (VLAs), where a pretrained vision-language model (VLM) conditions an action model for control. We find that the action model progressively concentrates its sensitivity on high-variance dimensions of the VLM outputs. Through controlled experiments across different backbone-head models, we further show that counteracting this sensitivity concentration can be beneficial under representation--task mismatch settings. Motivated by these observations, we introduce \emph{Adaptive Interface Equalizer} (AdaIE), a parameter-free mechanism that reweights pretrained representations according to the evolving downstream sensitivity during training. AdaIE improves VLA success rates on simulation benchmarks, with gains of up to $+5.28$ percentage points (pp) on RoboCasa-GR1 and $+1.68$ pp on RoboTwin 2.0, and achieves an average gain of $+19$ pp across five real-world manipulation tasks. It further generalizes to diffusion policies and world models, improving downstream performance in both settings. Our results highlight interface co-adaptation as an important optimization problem when coupling pretrained representations with downstream models.

cs.AI

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新论文 · v1arXiv:2611.04005

Characterizing Adversarial GEO via Implicit Brand Injection

Shobhnik Kriplani, Haohan Wang

原文摘要

Generative search engines increasingly shape how users discover products, services, and commercial entities by generating answers from retrieved web content. This creates an opportunity to influence which brands appear in these answers by modifying the content available to the retrieval system. In this work, we study an adversarial generative engine optimization (GEO) setting in which a predefined brand is added to an article through a single contextually integrated mention. The challenge is to make the mention fit the surrounding article without making the content overtly promotional, while keeping it relevant enough to be retrieved and to influence the generated answer. To study this, we introduce Implicit Brand Injection (IBI), a multi-agent framework that identifies insertion locations, rewrites the selected sentence, verifies the edit, and selects the final candidate. To evaluate IBI at scale, we construct IBIBench, which contains 1,000 web articles and 60 synthetic brands across 12 diverse domains, with both endemic and non-endemic brand placements. We compare IBI with Direct Prompting and Best-of-N with ranking under an equivalent budget, evaluating insertion quality, target-chunk retrieval, and downstream brand propagation. Compared with the baselines, IBI produces insertions that better preserve semantics and disrupt perplexity less, while improving Hit@k across all evaluated cutoffs, query regimes, and placement settings. IBI achieves up to 82.2% Hit@10 and 0.74 MRR@10, while Brand Mention Rate (BMR) and Conditional Propagation (CP) reach up to 62.7% and 75.9%, respectively. These results show that a single brand mention can increase both retrieval exposure and brand visibility in generated responses, highlighting the need for defenses against implicit content manipulation in generative search.

cs.IR · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2608.07786

Who Built This Model? Tracing LLM Lineage via Spectral Fingerprints in Weight Space

Yiwei Chen, Bingqi Shang, Sijia Liu

原文摘要

Open-weight large language models (LLMs) are increasingly developed through complex, multi-stage pipelines, leading to intricate lineage relationships that reflect model origin, ownership, and evolution. Understanding these relationships is important for model provenance, governance, and supply-chain integrity. In this work, we investigate the notion of LLM "biometrics" (analogous to human biometrics) to ask whether LLMs exhibit intrinsic fingerprints in weight space alone, without access to input data, that reveal their origin and lineage. We formulate this as a lineage discrimination problem, distinguishing among independent-origin, same-series, and shared-base models. To characterize these relationships, we propose a unified geometric fingerprinting framework that analyzes weight matrices from two complementary perspectives: (i) spectral energy, captured by singular value distributions to encode global magnitude patterns, and (ii) subspace alignment, quantified via subspace deviations to capture directional geometry. Our analysis uncovers a clear hierarchy of structural similarity in weight space: spectral energy reliably distinguishes independently trained models and different model families, while subspace alignment enables fine-grained discrimination among closely related models, including variations in dataset scale and post-training procedures. Extensive experiments on over 110 diverse open-weight LLM pairs demonstrate that weight-space geometry provides a robust and interpretable signal for model lineage, enabling coarse-grained regime separation and fine-grained discrimination within shared-base models.

cs.AI · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2607.16540

Do Vision Encoders Exhibit Human-like Color Thresholds?

Engy Ehab, Pablo Hernández-Cámara, Nahla Belal, Jesús Malo, Javier Vazquez-Corral, Alexandra Gomez-Villa

原文摘要

Understanding and characterizing human color perception is a longstanding research goal. One of the most traditional approaches is looking for the human color discrimination thresholds, the minimum chromatic differences perceptible to human observers. In recent years, deep neural networks have become the standard networks for computer vision tasks. In particular, deep vision encoders, foundation models trained on large-scale visual data, map images into latent feature representations. Despite the widespread use of deep vision encoders, few studies have investigated whether their internal representations exhibit human-like discrimination thresholds. In this work, we present a large-scale exploratory study probing the chromatic sensitivity of more than 50 pretrained vision encoders, including convolutional networks and vision transformers, against human discrimination thresholds. Using controlled chromatic stimuli at multiple chroma levels, we compare model-derived chromatic discrimination thresholds with human discrimination ellipses through a region-overlap metric (mIoU). Our analysis reveals generally weak alignment between model representations and human perceptual thresholds across all model families, with the best mIoU < 0.25. Moreover, we find that self-supervised encoders consistently outperform supervised ones, while language-supervised models show the most polarized behavior, occupying both the top and bottom of the ranking. These findings suggest that human-like chromatic sensitivity does not emerge naturally from current large-scale visual training objectives for any of the analyzed architectures.

cs.CV

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新论文 · v1arXiv:2611.04003

WASP-Mem: Let Agent Organize Procedural Memory. Human Spend Effort on Diagnosis

Peizhi Niu, Ge Shi, Vaibhav Gorde, Sarthak Ghosh, Christos Faloutsos, Min Cheng, Kevin Chen

原文摘要

LLM agents struggle to automate complex enterprise procedures because static, pre-built harness cannot capture proprietary policies, undocumented behaviors, or state-dependent recovery paths that surface only during execution. Procedural memory accrued from agent experience offers a training-free path to closing this gap -- yet unlike episodic records or semantic facts, it must consolidate repeated successes and diagnosed failures into reusable actions and recovery strategies. We introduce WASP-Mem (Wiki-based Agent-Synthesized Procedural Memory), a nimble but effective method that distills execution traces and feedback into persistent, interlinked wiki pages, enabling continual agent learning. On TheAgentCompany benchmark, WASP-Mem raises the checkpoint pass rate by 4.6-6.1% over the strongest baseline we compared across three models while requiring 57-61% less storage. We further present a systematic controlled study spanning consolidation method, feedback type, trace ingestion scope, and refinement rounds. Our results reveal that prescriptive knowledge organization such as human defined schemas and explicit deduplication rules hurts performance rather than closing the procedural-knowledge gap. In contrast, human diagnosis converts failed traces into corrective knowledge and drives sustained improvement: a single round of human diagnosis outperforms four rounds of self-judgment by 7.0-10.9%. These findings point to a simple division of labor: let agents organize procedural memory, and invest human effort where it matters most -- diagnosis.

cs.AI · cs.CL · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2602.05215

Temporal Grounding of Video Large Language Model via Holistic Event Perception and Matching

Jiahao Nie, Wenbin An, Gongjie Zhang, Yicheng Xu, Yap-Peng Tan, Alex C. Kot, Shijian Lu

原文摘要

Despite recent advances in Video Large Language Models (Vid-LLMs), Temporal Video Grounding (TVG) targeting precise localization of time segments of query events remains a grand challenge. Recent studies attempt to address this challenge by matching two separate tokens to all frame features and identifying the start and end frames of events based on the highest matching similarities. However, this approach relies heavily on the exact start and end timestamps, which are often ambiguous without capturing the semantic continuity and integrity of the event. To this end, we design E.M.Ground, a temporal grounding Vid-LLM that tackles the TVG challenge from a holistic and coherent event perception perspective. E.M.Ground introduces three novel designs: (i) It introduces a special <evt> token that aggregates information from all frames of query events with varying lengths, thereby preserving semantic continuity and integrity for accurate event matching. (ii) It introduces Savitzky-Golay smoothing to suppress the noise and abrupt changes in token-to-frame similarities across adjacent timestamps, thereby improving prediction accuracy. (iii) It aggregates multi-grained frame features to enhance the reliability of token-to-frame matching and improve temporal understanding, thereby compensating for the compression-induced information loss. Extensive experiments on widely adopted TVG benchmarks, along with in-depth analyses, demonstrate that E.M.Ground achieves strong overall performance, with the most pronounced improvements on single-event grounding and boundary-integrity tasks.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04002

Evaluating the Impact of Nonlinear Registration on Longitudinal PET Analysis in a Rodent Stroke Model

Haytham Ziani, El Mostafa Kalmoun, Zohaib Atif, Hyuk-Sang Kwon

原文摘要

Longitudinal rodent positron emission tomography (PET) studies of stroke and neuromodulation rely on accurate registration to a common atlas space to detect group-level effects. Prior work applying ultra-high frequency low-intensity magnetic stimulation (UHF-LiMS) to a rat model of chronic capsule infarct used affine normalization to a Sprague Dawley MRI template without evaluating that choice. We compare a two-stage (affine and nonlinear) pipeline against an affine- only one for rat PET-to-atlas alignment on the same dataset. Across 32 longitudinal [18F]-FDG PET scans (8 rats, 4 timepoints), the nonlinear stage gave a higher Dice similarity coefficient on every scan, and the gain was significant at the rat level. Jacobian determinants showed no folding on the sampling grid. Group-level linear mixed-effects maps of change from baseline (AFNI 3dLME), computed identically under both pipelines, differ in the size, number and location of their suprathreshold clusters. In this dataset, the registration pipeline is therefore a variable that can change the interpretation of downstream group-level analyses.

eess.IV · cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2508.06765

Efficient Collaborative LLM Fine-Tuning over Heterogeneous Mobile Devices via Many Backbones to One Side-Network Tuning

Xingke Yang, Liang Li, Sicong Li, Liwei Guan, Hao Wang, Xiaoqi Qi, Jiang Liu, Xin Fu 等 9 位作者

原文摘要

Collaboratively fine-tuning (FT) large language models (LLMs) over heterogeneous mobile devices fosters immense potential applications of personalized intelligence. However, such a vision faces critical system challenges. Existing federated LLM fine-tuning approaches remain limited by prohibitive on-device resource overhead, straggler-prone synchronous aggregation, and the assumption of a unified pretrained backbone across heterogeneous devices. In this paper, we propose EC-MobiLLM, a novel design for efficient collaborative LLM fine-tuning across heterogeneous mobile devices and heterogeneous backbones. EC-MobiLLM adopts a server-assisted side-tuning paradigm to minimize on-device overhead and pioneers non-blocking asynchronous collaboration to accelerate training. Moreover, EC-MobiLLM introduces adaptive feature alignment to support collaboration across heterogeneous backbones, aligning with practical needs in real-world deployments. Extensive experimental results demonstrate that EC-MobiLLM can maintain robust fine-tuning performance while achieving extremely low on-device memory, with at least 95.2\% reduction in computation overhead, 93.2\% reduction in communication costs and $5.1\times$ faster convergence compared to existing methods, validating its efficacy for practical LLM adaptation over heterogeneous mobile devices.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04000

Revisiting Elliptic++ for Bitcoin Forensics: Feature Reconstruction and Leakage-Resistant Evaluation

Runlin Liang, Simon Trimborn, Noud van Giersbergen

原文摘要

Elliptic and its address-level extension Elliptic++ are widely used benchmark datasets in Bitcoin forensics, cited by more than 1300 academic studies according to Google Scholar. We identify future information leakage in Elliptic++. Its positional data split does not respect temporal order, and the exact definitions of its address-level features remain undocumented. We revisit how the 55 Elliptic++ address-level features are computed and propose a leakage-resistant pipeline. First, the exact feature definitions are recovered by linking Elliptic transactions to public ledger data. Second, we compute the features with a temporal split based on each address's first appearance, using only information available at the time of prediction. Once temporal ordering is enforced, the random forest (RF)'s strong predictive performance largely disappears: its F1 score on the illicit class falls from $0.846$ under the original leaky evaluation to just $0.291$ under our leakage-resistant pipeline. Furthermore, RF no longer outperforms a simple logistic regression baseline. We provide the exact feature definitions and executable code to reproduce the leakage-resistant features, so that future Bitcoin forensic models can be evaluated based on genuine predictive capability rather than temporal data leakage.

cs.CR · econ.EM

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arXiv ↗PDF ↗