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版本更新 · v2arXiv:2602.02212

3A-VLA: Abstraction-Aligned Action Learning for Vision-Language Agents in 3D Game Worlds

Zheyuan Zhou, Liang Du, Zixun Sun, Xiaoyu Zhou, Ruimin Ye, Qihao Chen, Yinda Chen, Lemiao Qiu

原文摘要

Despite advances in vision-language-action (VLA) models, they remain limited in highly dynamic game settings such as 3D open worlds and competitive player versus player (PvP) settings, where agents must efficiently extract sparse, actionable signals from dense visual input while integrating multimodal cues to track off-screen high-value targets in real time. To address this, we introduce 3A-VLA (Abstraction-Aligned Action VLA), a framework that grounds action learning in explicit intention and environment abstractions rather than superficial pattern matching. We introduce dual task-agnostic abstractions: the intention abstraction (IA), which condenses verbose instructions and reasoning into explicit semantic primitives, and the environment semantics abstraction (ESA), which structures dense visual streams into a spatial-functional affordance representation to guide grounded actions. We further propose an abstraction alignment reweighting (AAR) module that adaptively reweights the action imitation loss. A continuous intention-environment alignment signal is used to emphasize reliable action supervision when abstractions agree and reduce the influence of ambiguous demonstrations when they diverge, thereby learning a policy that balances fine-grained control and high-level reasoning without the need for manual rules. Extensive experiments show that 3A-VLA performs competitively among the evaluated baselines in both open-world (Minecraft) and competitive PvP (Game for Peace) settings. It also demonstrates zero-shot combat transfer from Game for Peace to high-fidelity games, including Valorant, CS2, GTA V, Elden Ring, and Mount & Blade.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2605.22715

AnyMo: Geometry-Aware Setup-Agnostic Modeling of Human Motion in the Wild

Baiyu Chen, Zechen Li, Wilson Wongso, Lihuan Li, Xiachong Lin, Hao Xue, Benjamin Tag, Flora Salim

原文摘要

As wearable and mobile devices become increasingly embedded in daily life, they offer a practical way to continuously sense human motion in the wild. But inertial signals are highly dependent on the sensing setup, including body location, mounting position, sensor orientation, device hardware, and sampling protocol. This setup dependence makes it difficult to learn motion representations that transfer across devices and datasets, and limits the broader use of wearable IMUs beyond closed-set recognition. We introduce AnyMo, a geometry-aware framework for setup-agnostic human motion modeling. AnyMo uses physics-grounded IMU simulation over dense body-surface placements to generate diverse and plausible synthetic signals, pre-trains a graph encoder from paired synthetic placement views and masked partial observations, tokenizes multi-position IMU into full-body motion tokens, and aligns these tokens with an LLM for motion-language understanding. We evaluate AnyMo on three complementary tasks: zero-shot activity recognition across 14 unseen downstream datasets, cross-modal retrieval, and wearable IMU motion captioning, where it improves average Accuracy/F1/R@2 by 11.7\%/11.6\%/22.6\% on HAR, increases zero-shot IMU-to-text and text-to-IMU retrieval MRR by 15.9\% and 28.6\%, respectively, and improves zero-shot captioning BERT-F1 by 7.5\%. These results support AnyMo as a generalist model for wearable motion understanding in the wild. Project page: https://anymo-project.github.io.

cs.CV · cs.AI · cs.CL · cs.HC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13983

Optimization-Based Path Planning for Industrial Manipulators: Experimental Validation in Additive Manufacturing

Shiva Ganesh Murali, Valentin Bhend, Deepak Ingole, Gabriele Fadini, Alisa Rupenyan

原文摘要

Robotic additive manufacturing requires joint paths that reconcile robot feasibility with process-specific geometry. We develop an offline optimization-based path planner that combines joint-path regularity and self-collision penalties with process costs and constraints, so that a new process enters through its own reference, residuals, and tolerances. Full-path and boundary-coupled chunked optimization handle references of different lengths. Iterative Weight Tuning (IWT) determines the cost weights from the gaps between Key Performance Indicators (KPIs) and their targets. In carbon-fiber winding and robotic 3D printing, the planned paths meet the geometric tolerances and all KPI targets, and both paths are executed on a UR10e, without extrusion for printing, with joint-level tracking recorded during execution.

cs.RO · math.OC

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2610.30468

SplatLab: Autonomous Scientific Discovery for 3DGS Research

Seemandhar Jain, Manmohan Chandraker

原文摘要

Scientific discovery requires more than generating hypotheses: ideas must be grounded in prior work, translated into executable methods, experimentally validated, challenged by counterevidence, and communicated through reproducible findings. Despite recent advances in autonomous research agents, closing this entire loop remains challenging in rapidly evolving computer-vision fields with complex training pipelines and expensive evaluations. We introduce SplatLab, an autonomous scientific discovery system for 3D Gaussian Splatting (3DGS) that integrates literature understanding, hypothesis generation, implementation, experimental refinement, falsification, and manuscript writing into a unified research pipeline. Given a research prompt, a target-paper URL, or no input at all, SplatLab identifies research opportunities, proposes novel methods, trains and ablates candidates across Mip-NeRF 360 and LLFF, and produces manuscripts grounded in verifiable experimental evidence. Unlike prior systems, SplatLab performs discovery over verified baseline implementations using domain-aligned modification scaffolds. A cascade-weighted Elo tournament evolves candidate methods using measured GPU performance and verbal-gradient feedback, while a standing falsifier incorporates adversarial stress testing directly into selection. An integrity audit binds reported claims to logged experiments, and reviewer-driven feedback can initiate further experimentation rather than merely revising text. SplatLab discovers methods achieving PSNR improvements of +0.18 to +3.78 dB over matched baselines, including +3.78 dB on astrophotographic nebula rendering, a previously unexplored setting for 3DGS. By closing the loop between scientific reasoning, executable experimentation, and evidence-backed communication, SplatLab compresses months of researcher effort into days of mostly autonomous compute.

cs.CV · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.24366

MorfFlex: Handling Rich Morphology

Jaroslava Hlaváčová, Marie Mikulová, Barbora Štěpánková, Milan Straka, Jan Hajič

原文摘要

We present MorfFlex, a morphological dictionary architecture suitable for languages with extensive regularity in both inflection and derivation. As the primary example of MorfFlex in use we introduce MorfFlex CZ, a morphological dictionary of Czech. It is distributed as a simple, unstructured list of <wordform, lemma, tag> triplets, however, its manually maintained, unpublished source files and conversion scripts encode a sophisticated system of inflectional and derivational patterns. These patterns dramatically reduce the otherwise enormous size of the dictionary, which currently contains over 100 million wordforms and more than 1 million lemmas. The MorfFlex CZ dictionary serves as an essential resource for ensuring the consistency of manual morphological annotation in the Prague Dependency Treebanks and underpins state-of-the-art automatic tools such as MorphoDiTa. In this paper, we focus on: (i) presenting an effective method for managing the rich morphological system within the dictionary, and (ii) demonstrating the utility of such a language resource for maintaining annotation consistency in corpora and supporting the development of advanced NLP applications.

cs.CL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2405.20057

A General Automata Model for First-Order Temporal Logics (Extended Version)

Luca Geatti, Alessandro Gianola, Nicola Gigante

原文摘要

First-order linear temporal logic (FO-LTL) is a flexible and expressive formalism capable of naturally describing complex behaviors and properties. Although the logic is in general highly undecidable, the idea of using it as a specification language for the verification of complex infinite-state systems is appealing. However, a missing piece, which has proved to be an invaluable tool in dealing with other temporal logics, is an automaton model capable of capturing the logic. In this paper we address this issue, by defining and studying such a model, which we call first-order automaton. We define this very general class of automata, and the corresponding notion of regular first-order language (of finite words), showing their closure under most language-theoretic operations. We show how they can capture any FO-LTL formula over finite words, over any signature and theory, and provide sufficient conditions for the semi-decidability of their non-emptiness problem. Then, to show the usefulness of the formalism, we prove the decidability of monodic FO-LTL, a classic result known in the literature, with a simpler and direct proof.

cs.LO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.10693

Machine Hearing: Can Audio LLMs Generalize Beyond Human Auditory Perception?

Liwei Liu, Jun Tao, Xingyuan Li, Xuenan Xu, Chen Zhang, Junyi Wang, Boya Zhang, Xiaoyu Yang 等 11 位作者

原文摘要

Multimodal LLMs have made remarkable progress in perceiving the physical world, while their perceptual scope remains largely human-centric, focusing on what humans can directly see or hear. This progress raises the prospect of perception beyond human limits, such as extending hearing to sounds that are inaudible to humans. More broadly, the physical world is full of waves, vibrations, and oscillations, only a small fraction of which are directly audible to humans. This invites the extension of multimodal LLMs toward a broader form of machine hearing, which remains largely underexplored. To investigate this direction, we introduce the Machine Hearing Benchmark (MHB), comprising 6,128 instances across 13 tasks and seven domains. MHB spans highly heterogeneous input regimes, with sampling rates from approximately $10^{-5}$ to $10^6$ Hz, sequence lengths from $10^1$ to $10^6$ samples, and both single- and multi-channel inputs. Evaluations of current open-source and proprietary Audio LLMs (ALLMs) reveal limited zero-shot transfer to machine hearing. Supervised adaptation on machine-hearing data alone does not close this gap, suggesting that the challenge extends beyond data exposure to a systematic mismatch between existing audio front ends and machine-hearing inputs. To address this mismatch, we introduce MachEar, a lightweight encoder extension that complements the pretrained Audio LLM for machine-hearing inputs. The results show that MachEar equips a pretrained Audio LLM with stronger machine-hearing capabilities while largely preserving its existing audio understanding performance.

cs.SD · cs.CL

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2610.15146

Compute-Optimal Scaling Laws for Spiking Language Models

Dan Saunders, Benjamin Ren, Catherine He

原文摘要

Spiking neural networks target energy-efficient language modeling through sparse, binary activations, but the efficiency-accuracy tradeoff of spiking language models is uncharacterized at scale. We present the first Chinchilla-style IsoFLOP scaling study of a spiking language model, sweeping SpikeGPT-family models (RWKV backbone) from width 384 to 1536 across compute budgets from $3\times10^{17}$ to $10^{19}$ FLOPs on FineWeb-EDU. We report two findings. First, the compute-optimal frontier is $N_{opt} \propto C^{0.56}$, close to the 0.50 that Hoffmann et al. report for dense Transformers, so spiking does not change how parameters and data should be traded off under a compute budget. Second, against non-spiking RWKV and Transformer baselines trained the same way, the loss gap splits into a backbone cost of about 0.16 nats that does not change with compute and a spiking cost that falls steadily with compute, from 0.34 to 0.23 nats over our range. These results give the first compute-optimal recipe for spiking language models and quantify the accuracy cost of their sparsity.

cs.LG · cs.CL · cs.NE

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arXiv ↗PDF ↗
版本更新 · v8arXiv:2503.06223

RedDiffuser: Auditing Multimodal Safety Failures in Vision-Language Models via Reinforced Diffusion

Ruofan Wang, Xingjun Ma

原文摘要

Large Vision-Language Models (VLMs) are increasingly deployed in open-ended environments, where ensuring reliable safety under multimodal inputs is critical. However, existing evaluations remain largely instruction-centric, focusing on explicit malicious queries while overlooking a more realistic and underexplored risk: whether safety alignment remains robust under harmful contextual exposure. This limitation is particularly important for multimodal systems, where visual inputs can substantially steer model behavior and render text-only auditing insufficient. In this work, we study multimodal safety auditing under harmful contextual exposure, asking whether VLMs can maintain safe behavior when partial toxic text is paired with visual context. To enable systematic auditing, we propose RedDiffuser (RedDiff), a reinforcement-based framework that leverages diffusion models to generate semantically coherent visual inputs for black-box safety testing. By combining greedy prompt search with reinforcement optimization, RedDiffuser uncovers high-risk multimodal inputs that expose latent safety failures. Extensive experiments on both open-source and commercial VLMs show that such context-conditioned failures are widespread. On LLaVA, RedDiffuser increases unsafe response rates by up to 10.69% on the original set and 8.91% on a hold-out set, with strong transferability to Gemini and LLaMA-Vision. These vulnerabilities persist even under external safety guardrails, suggesting that current system-level safety mechanisms remain insufficient for realistic multimodal risks. Our findings reveal a critical blind spot in existing safety evaluations and establish context-aware multimodal auditing as an essential paradigm for diagnosing hidden vulnerabilities in modern VLM systems.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2608.20394

A Factorial Ablation of a Speech-to-SFT Pipeline: Differential Effects on Data Quality and Downstream Transfer

Wonsup Shin, Jingu Kim

原文摘要

Industry pipelines that turn speech into supervised fine-tuning (SFT) data via multi-stage refinement are increasingly adopted but, to our knowledge, have not been publicly ablated stage-by-stage, leaving each stage's marginal value unknown. We design a production-ready speech-to-SFT pipeline in which transcript refinement (Phase 0) and SFT data quality refinement (Phase 2) are independently toggleable, yielding a 2x2 factorial design. For each condition, we generate QA-form SFT data from Korean medical and finance conference recordings and fine-tune 9 models (5 LLM families, 2.4B-70B); we evaluate with four cross-provider LLM judges, a blind six-expert human evaluation, and three downstream MCQA benchmarks. Our central finding is that, under a fixed, standard SFT recipe, improvements in QA data quality do not transfer uniformly into downstream MCQA gains. 4-judge quality rises consistently, yet the cross-model mean MCQA gain is not significant; positive transfer concentrates on family-domain aligned pairs. All six human raters report higher full-pipeline quality, confirming the LLM-judge direction. An STT-engine swap to Whisper-medium confirms pipeline robustness. An LLM-difficulty audit shows the two frontier LLMs admit unknown on approximately 8% of QA on average; we release samples, prompts, code, and all SFT checkpoints.

cs.SD · cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13980

What to Predict for Efficient Scheduling on Parallel Machines

Evripidis Bampis, Bruno Escoffier, Dimitris Fotakis, Giorgos Mitropoulos, Michalis Xefteris

原文摘要

We study learning-augmented algorithms for scheduling on multiple parallel machines under the objectives of minimizing the makespan or the total weighted completion time. We assume that the algorithm has access to noisy information (the prediction) about an optimal solution, expressed through a solution encoding that implicitly quantifies both the amount of information provided and the nature of noise (errors in the prediction). Our central question is to determine the maximum level of noise that still allows for efficiently recovering this optimal solution. We observe that information guiding greedy scheduling to optimality or revealing the optimal assignment of tasks to machines is very sensitive to noise, and prove that recovering an optimal solution from solution encodings capturing either is computationally hard, even for a small (non-constant) number of errors. On the positive side, we show that an order-based encoding, which leads to errors that are dispersed locally, is robust to noise. We present a dynamic programming framework that utilizes this encoding and solves optimally several classical scheduling problems, including makespan and total weighted completion time minimization on identical or unrelated machines.

cs.DS

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13978

WINIC: An Automatic Cycle-Accurate Multi-Platform Microbenchmarking Tool for Instruction Performance Metrics

Tobias Rühl, Jan Laukemann, Georg Hager, Gerhard Wellein

原文摘要

Performance models allow developers to gain important insights into the interaction of their code with the hardware that executes it, and enable them to apply target-specific optimizations. To create in-core performance models for out-of-order processors, architecture-specific performance metrics like latency, throughput, and port binding of individual instructions are indispensable. For x86 microarchitectures, there already exist tools to automatically obtain those values; however, the extension of performance modeling tools like the Open Source Architecture Code Analyzer (OSACA) to support further architectures like RISC-V emphasizes the need for a multi-platform microbenchmarking tool. We present WINIC (What I Need Is Cycles), an automatic cross-platform microbenchmarking tool. Unlike previous tools, it can obtain throughput and operand-based latency metrics for most supported instructions of any 64-bit x86, AArch64, and RISC-V microarchitecture without the need for root permissions or extended system control levels. We cover the methodology of microbenchmarking and the approach of WINIC to collect cycle-accurate performance data for assembly instructions. We show that WINIC can be used to automatically obtain a vast number of accurate results and demonstrate for four target microarchitectures that our results match between 71% and 99% of the performance data of instructions obtained by similar tools and documentation. Furthermore, we investigate how operand-based metrics measured by WINIC can be used to improve the in-core performance predictions on selected micro-kernels, exemplarily for the Neoverse V2 microarchitecture.

cs.PF

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2509.01642

REVELIO: Universal Multimodal Task Load Estimation for Cross-Domain Generalization

Maximilian P. Oppelt, Andreas Foltyn, Nadine R. Lang-Richter, Bjoern M. Eskofier

原文摘要

Adaptive interactive systems that respond to user mental state require task load estimation that stays reliable outside its training context. Models built in one setting rarely transfer, limiting real-world use. This work presents Robust Estimation Via End-to-End Learning of Multimodal Observations (REVELIO), a unified framework, dataset and evaluation protocol for cross-domain task load estimation. Cardiac, electrodermal, muscle, respiration, skin temperature, eye tracking and facial signals were recorded in 96 sessions, spanning a laboratory working memory task, a driving simulator and two games under consistent hardware and annotation. Multimodal fusion reached up to 0.87 area under the receiver operating characteristic curve, above single input eye tracking (0.83), facial action units (0.74) and biosignals (0.62). Multi-domain training stayed calibrated on domains seen in training, whereas removing the target domain caused the classifier to collapse in some applications. REVELIO provides a standardized foundation for measuring modality contributions and cross-domain generalization toward deployable workload-aware systems.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13976

Gradient Inversion through Linear and Affine Subspace Constraints

Konstantinos Bouros, Athanasios P. Liavas

原文摘要

Federated learning enables multiple clients to collaboratively train a machine-learning model without directly sharing their local datasets. However, as prior works have shown, the gradients exchanged during training may reveal sensitive information about client data. Most existing attacks usually optimize dummy inputs to match the true client gradients (Gradient Inversion Attacks), while some attempt to reconstruct them based on the observation that aggregate gradients from a fully connected (FC) layer are a linear combination of its inputs. In this work, we extend this observation by showing that aggregate gradients from an FC layer with the correct scaling are actually an affine combination of its inputs. We evaluate a gradient inversion attack that leverages either known linear-subspace information or its affine-subspace counterpart at each FC layer to improve reconstruction accuracy. Our results show that subspace-based methods can substantially improve reconstruction quality over the chosen baselines in certain settings. We further demonstrate that moderate overestimation of the batch size can generally improve or preserve reconstruction quality, even when the true batch size is unknown. We additionally examine reconstruction under gradient perturbations introduced by a differential privacy (DP) mechanism, providing insight into the effectiveness of the considered formulations under noisy conditions.

cs.CR · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.18760

DOTRAG: Retrieval-Time Reasoning Along Paths

Larnell Moore, Naihao Deng, Rada Mihalcea, Farnaz Jahanbakhsh

原文摘要

Graph Retrieval-Augmented Generation (GraphRAG) faces significant challenges in complex relational retrieval tasks, particularly in densely connected knowledge graphs where the number of possible paths grows rapidly with reasoning depth. Existing GraphRAG retrieval algorithms either rely on rigid, predefined heuristics to identify relevant graph context or employ costly LLM-based reasoning to guide graph traversal. To address these limitations, we propose DotRAG, a lightweight, training-free GraphRAG retrieval algorithm that uses large language model (LLM) reasoning to narrow the search space and recover task-relevant relational paths. During indexing, DotRAG constructs an entity-relation knowledge graph, generates a graph-wide overview summarizing its contents, and leverages graph reachability indexing to avoid expensive online graph traversals. At query time, the LLM uses this overview to identify promising graph regions and hypothesizes relationship sequences connecting graph information to the query. The LLM then performs targeted relational searches, using graph constraints to restrict the candidate pool progressively. On MetaQA 2-hop queries, DotRAG achieves 97.78% retrieval recall and 51.27% F1, compared to the best baseline scores of 34.74% and 6.33%, respectively. On STaRK-MAG, DotRAG achieves 85.07% recall on 2-hop queries while demonstrating greater computational efficiency than reasoning-based GraphRAG baselines, with 12.2x lower retrieval latency than HopRAG.

cs.IR · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13975

Understanding Mixture-of-Experts Language Models Beyond Expert Boundaries

Zhenhong Zhou, Mao Zheng, Mingyang Song, Qiankun Li, Kun Wang, Junfeng Fang

原文摘要

In Mixture-of-Experts (MoE) models, the nature of expert specialization remains controversial. Reports of domain-associated expert preferences contrast with analyses attributing routing primarily to token identity or syntactic patterns. To clarify these differing accounts, we look beyond expert-level routing to investigate the neuron-level organization of domain-specialized computation using Routing-Aware Magnitude Profiling (RAMP), a measure developed for this analysis. Using mixed-domain calibration data, we identify domain-specialized neuron sets with 58-74% overlap across multi-source samples of the same domain, compared with 15-24% across domains. To understand how this organization evolves during training, we trace neuron importance throughout pre-training and post-training across three fully open MoE families. Neuron importance continues to reorganize between pre-training checkpoints despite comparatively stable expert-level importance rankings, while post-training largely preserves neuron rankings. Building on these neuron-level perspectives, we further introduce RAMP-based Pruning (RAMPr) to select neurons across experts for balanced multi-domain capability preservation. At 75% neuron retention, RAMPr preserves average 91-98% of original full performance across six models. At 50% retention, our pruning method can reduce resident GPU memory by 36-43% and improves batched prefill throughput by 17-39% effectively. Our code is available at https://github.com/ydyjya/Neuron-Level-Specialization-in-MoE

cs.AI · cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13973

On the Spectral Estimation of VARMA Processes

Bin Zhu, Mattia Zorzi

原文摘要

We consider the problem of estimating the power spectral density of a Vector-valued Autoregressive Moving-Average (VARMA) process. A well-established approach to accomplishing this task consists of formulating a moment problem that selects the spectral density maximizing an entropy-like index while matching certain moments of the underlying process. However, existing methods assume that the moving-average (MA) component is either fixed or scalar, thereby reducing the class of ARMA models over which the optimal spectral density is sought. In this paper, we propose a new moment problem, based on the notion of transportation entropy, for learning the spectral density of a VARMA process without fixing the MA component or restricting it to be scalar.

stat.ML · cs.LG · math.OC

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.17922

Resolution of Vinzant's Conjecture on Phase Retrieval Injectivity

Zhangsong Li

原文摘要

Let $A\in\mathbb C^{N\times M}$ with $N=4M-5$ has independent standard complex Gaussian entries. We prove that the probability that the phase retrieval map generated by $A$ is injective is strictly smaller than $1$, and is $O(M^{-1})$ as $M\to \infty$. This proves a conjecture of Cynthia Vinzant, which was later restated by Afonso S. Bandeira in \cite{bandeira2026randomstrasse101}. The main result of this paper was obtained using generative AI, in particular GPT6-Astra, GPT 5.6-sol, and the Rethlas system.

math.FA · cs.IT · math.PR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13972

Data Quality Challenges in Multimodal Tourism Recommender Systems

Zehui Wang, Wolfram Höpken, Dietmar Jannach

原文摘要

Modern recommender systems increasingly consider diverse data modalities to enhance model performance. However, the quality of the underlying multimodal data (e.g. data from social media platforms) has received limited attention so far. Focusing on tourism recommendation, we examine real-world challenges encountered when using datasets from Yelp and Instagram. Issues include noise from fake reviews, hallucinated content from LLMs, modality contradictions, and demographic bias. We outline future directions such as quality-aware fusion, structured and controlled LLM-based content generation to reduce hallucinations, and user group differentiation to improve the robustness and reliability of multimodal recommendation systems.

cs.IR · cs.LG · cs.MM

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13971

TransGHOST: Real-Time, Synthesis-Free Reconstruction of Instrument-Occluded Regions in Ophthalmic Microsurgery

Niran Nataraj, Maina Sogabe, Masamichi Yamashita, Norihiko Ito, Kenji Kawashima

原文摘要

Intraoperative scene perception in ophthalmic microsurgery is limited by instrument occlusion: when a tool enters the high-magnification field, it conceals tissue from both the surgeon and the perception components of robot-assisted systems that process the video. Generative inpainting can remove the occluder but replaces the missing image content with synthesis, which is unsuitable when visual provenance matters. Many video inpainters also depend on future frames and cannot operate online. We present TransGHOST, a causal reference-guided reconstruction pipeline whose non-generative safe mode operates in real time. Every restored value is obtained by resampling a recorded frame using geometric transforms fitted only to visible regions. The system provides per-pixel confidence and provenance and marks unsupported pixels as abstentions. We evaluate TransGHOST on a new veterinary clinical corpus of canine cataract surgery with pixel-level instrument and anatomy labels. Only the occluded region is scored, and each scoring target is verified to differ from the reference supplied to the system. Safe mode reaches 31.89 dB masked PSNR and 0.915 masked SSIM at 51.7 ms median and 85.5 ms 95th-percentile latency. On a pre-declared held-out cohort using instrument cutouts excluded from segmentation training, it reaches 29.83 dB masked PSNR, while matched-mask evaluation on validation shows a 3.45 dB advantage over LaMa. Region-aware reference selection improves masked PSNR by 3.60 dB over recency when candidate references can themselves be occluded. Reference-age analysis further defines the operating interval for the brief, repeated occlusions targeted by TransGHOST.

eess.IV · cs.AI · cs.CV · cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.15790

Fairness-Aware Retrieval Optimization for Retrieval-Augmented Generation

Yingqi Zhao, Vasilis Efthymiou, Jyrki Nummenmaa, Kostas Stefanidis

原文摘要

Retrieval-Augmented Generation (RAG) grounds large language models (LLMs) in external knowledge, but the retrieved documents can systematically skew generated answers toward one group. This effect is difficult to analyze and control in top-k settings, where several documents at different context positions jointly shape the output. We present a three-stage framework for fairness-aware retrieval in top-k RAG. First, a probabilistic reranker sets the group composition of the retrieved context at each position, enabling controlled perturbation of retrieval bias without modifying the retriever. Second, we show that output bias is well approximated by a position-weighted linear function of position-wise retrieval bias, revealing model-specific positional sensitivities. Third, we use this model to formulate retrieval as an optimization problem that trades off relevance against predicted output bias, and propose FARO, a scalable solution to that problem which decomposes the globally coupled quadratic objective into independent per-query assignment problems through a Fenchel--Legendre dual reformulation. Experiments with four open LLMs, and political and gender bias as use cases show that the linear model predicts output bias on unseen configurations in 17 of 24 settings. FARO substantially reduces output bias in most settings, while, compared with a linear-programming baseline, FARO achieves similar relevance, remains feasible where the baseline does not, and scales better to larger query sets and candidate pools.

cs.DB · cs.IR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13970

Counterfactual Analysis of Extent Leakage from Annotation-Derived Prompts in Vision-Language Medical Image Segmentation

Niran Nataraj, Ankita Das, C. Krishna Mohan, Kenji Kawashima

原文摘要

Text-guided segmentation models receive an image and a short caption about the lesion, and on public benchmarks the caption improves accuracy considerably. On colonoscopy and breast ultrasound, however, the size, count and location words of each caption were computed from the mask that later scores the prediction, and the chest X-ray captions were written by benchmark annotators. We tested what two published models take from them. On colonoscopy and chest X-ray, it is mostly the lesion's extent. Keeping only the size word or the lung-zone phrase recovers 84 to 98 percent of the improvement over an empty caption, and removing it loses 52 to 77 percent. The rest matters little, and a polyp called a rock is segmented just as well. We call this extent leakage. On breast ultrasound, nearly half the captions also name the diagnosis, which carries part of the benefit. Retraining on three datasets shows that models pick up this habit from their training captions, though only partly on breast ultrasound, and that it weakens their segmentation from the image alone. The leak can be measured without training a model, since caption words alone predict lesion area (coefficient of determination 0.54 to 0.77 on six of seven test sets). Without the test mask, the best method we tested, which estimates extent from the image, reaches 74.59 Dice on colonoscopy against the published 77.23. We propose an evaluation protocol that keeps what a caption says about the image apart from what it copies from the label.

eess.IV · cs.CV · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.07746

Volatility Heredity in Multivariate Time Series: A Statistically Guided Temporal Convolutional Approach to Variable Ordering

Kaoutar El Azaar, Mohamed Hamlich, Moncef Garouani, Franck Ravat, Asmae Chakir, Mohamed Tabaa

原文摘要

Volatility heredity concerns the information that past fluctuations in one series may carry about another after accounting for the target's own persistence. QS-TCN compares this dependence in both directions to estimate an order of temporal precedence. For each series, quasi-maximum likelihood estimation yields a persistence coefficient, and the matrix pencil method extracts temporal modes from the autocovariances of squared observations. These estimates define filters in a temporal convolutional network. The network models the target's squared observations from their own past and scores the association between the residuals and the filtered history of a candidate source. Differences between the two directional scores are aggregated into a global order. In an exploratory simulation with 100 systems in each of four volatility regimes, an ensemble of ten models trained with fixed seeds achieved higher mean ordering quality than CCF-AUC, a lead--lag method based on distance correlation, in every regime. Against CCF-AUC applied to squared standardized observations, the mean gains were 8.69 percentage points under positive linear coupling and 7.81 points when nonlinear coupling depended on the target's state. In the other two regimes, paired 95\% bootstrap intervals for the gains included zero. Mean agreement with reference constraints was 71.55\% across nine Finance systems and 54.02\% across 27 simulated functional magnetic resonance imaging systems, compared with 50\% expected under random ordering. In a panel of daily currency and gold returns, the estimated order placed the dollar--franc and dollar--yen exchange rates before gold.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13968

Advancing a Global Framework for AI Safety and Governance for the Well-being of Humanity

Zeng Yi, Seán Ó hÉigeartaigh, Wang Zhengqi, Lu Enmeng, Chen Yu, Cao Gongce, Guo Xiaoyang, Kang Yanrong 等 76 位作者

原文摘要

The Report "Advancing a Global Framework for AI Safety and Governance for the Well-being of Humanity" is an important outcome of the AI Safety and Governance Program of World Internet Conference Specialized Committee on Artificial Intelligence in 2025. This report provides a systematic review of current global explorations and practices in AI safety and governance, identifies key issues that need to be addressed, and draws upon multilateral governance experiences from other domains. Centered on mechanism design, it explores how to build a global AI safety and governance system that ensures safety and security, promotes inclusiveness, clarifies responsibilities and accountabilities, strengthens effective coordination, and ensures authoritative and efficient governance. Guided by the principles of multilateralism and the vision of building a community with a shared future for humankind, the report proposes institutional measures and policy recommendations to establish a United Nations-centered global AI safety and governance framework. It aims to serve as a reference for governments, international organizations, enterprises, and research institutions. The report is to contribute insights and momentum to advancing people-centered and beneficial AI governance worldwide, ensuring that AI technologies truly serve the well-being of all humanity.

cs.CY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13966

Controlled Knowledge Engineering for Taxonomy Induction with Tool-Grounded LLM Agents and Runtime-Adaptive Policies

Jonas Limniatis, Bennet Schaarmann, Georg Reuber, Robert Stahlbock

原文摘要

Taxonomies are structured knowledge representations that support semantic search, recommendation, product catalog organization, and retrieval-augmented generation. Their automated construction from a fixed set of candidate entities requires more than plausible text generation: the resulting hierarchy must be complete, structurally valid, and semantically coherent. Large Language Models (LLMs) are promising for this task, but iterative free-form generation remains vulnerable to omitted or hallucinated entities, invalid hierarchical relations, and inconsistent organizational principles. We introduce TIGA (Taxonomy Induction with Tool-Grounded Agents), a knowledge-engineering architecture that formulates taxonomy induction as an iterative decision process over an explicit state and constrained action space. TIGA combines executable taxonomy-editing tools, an memory that records tool calls, rationales, and outcomes, and a runtime-adaptive organizational policy that the agent revises during inference. An optional global reviewer evaluates the constructed taxonomy against the candidate set and the active policy. We evaluate TIGA on four benchmark taxonomies using two LLM backbones and compare it with single-shot and iterative prompting-based baselines. TIGA achieves the strongest structural-quality results in most evaluated configurations, but does not dominate on every precision measure. The gains are accompanied by substantially higher token consumption, API-call volume, and inference time. Ablation results show consistent benefits from the policy and interaction-memory components, whereas the effect of global review depends on the dataset and the available revision budget. These findings characterize tool grounding and runtime policy adaptation as promising but costly mechanisms for improving the controlled construction of structured knowledge representations.

cs.IR · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.03490

Delay-Certified Readiness Barriers for Overactuated Multirotor Allocation Without Lipschitz Constants

Giuseppe Silano

原文摘要

Overactuated multirotors can produce the same wrench with many rotor-speed combinations, some of which leave the vehicle unable to change its wrench quickly, e.g., to reject a gust. Readiness barriers keep this ability, the readiness, above a floor, but only in continuous time; with sampled commands and varying speed-controller delays, the available robustness bound covers less than one millisecond. We remove this limit by exploiting the structure of the problem: each rotor responds monotonically and in closed form to its own torque, so the speeds reachable under uncertain switching times lie in a cheaply computed box, and the lowest readiness over that box sits at a corner known in advance. This yields a Lipschitz-free test that a command keeps the readiness above its floor until the next sample, plus a fallback torque that keeps the vehicle on a certified plan. In hexarotor simulations with 2--40~ms delays and $\pm2$~ms jitter, the filter never violated the floor. At $10\pm2$~ms, it matched a delay-compensating predictor within the design envelope and cost 2--30\% more tracking error beyond it, where the predictor alone violated the floor in 2 to 16 of 16 missions. A tuned robust predictor was also safe with synchronous switching, but not with per-rotor jitter.

cs.RO · eess.SY · math.OC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13965

Comparing WeChat and Telegram MiniApp Security Architectures: Toward Cross-Platform Analysis

Luca Ferrari, Billel Habbati, Luca Verderame

原文摘要

MiniApps are increasingly central to super-app ecosystems, yet architectural differences complicate the transfer of security analysis techniques across platforms. Existing WeChat analyzers rely on packaged applications and platform-specific APIs, limiting their direct applicability to Telegram's web-based MiniApps. We present a qualitative comparison of WeChat Mini Programs and Telegram MiniApps, grounded in official documentation, prior research, and manual analysis of a specific Telegram Android source revision. Using a shared taxonomy of security controls, assets, and threat sources, we examine execution environments, communication paths, and trust boundaries. WeChat provides a dedicated MiniApp runtime; Telegram combines web execution with host controls. Both retain developer responsibilities for backend authentication and resource authorization. Two worked examples derive requirements for cross-platform analysis: distinguishing credential roles and validation boundaries, and associating host capabilities with concrete execution contexts and permission handlers. These findings inform future automated analysis without ranking the ecosystems by security or claiming validated vulnerability detection.

cs.CR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13963

Euclidean and Hermitian Self-Orthogonal Codes from Divisible Codes

Ziling Heng, Chenkun Xie

原文摘要

Self-orthogonality and divisibility are two fundamental properties of linear codes, and the relationship between them has long been of interest. Huffman and Pless (2003) showed that every binary \(4\)-divisible code is Euclidean self-orthogonal, that a ternary code is Euclidean self-orthogonal if and only if it is \(3\)-divisible, and that a quaternary code is Hermitian self-orthogonal if and only if it is \(2\)-divisible. The general case remained open for a long time. In 2024, Li and Heng settled the odd-characteristic case, proving that every \(p\)-divisible code over \(\F_{p^m}\), with \(p\) odd, that contains the all-one vector is Euclidean self-orthogonal. In this paper we treat the even-characteristic case: we prove that every \(4\)-divisible code over \(\F_{2^m}\) containing the all-one vector is Euclidean self-orthogonal. We also prove that, for every prime \(p\), every \(p\)-divisible linear code over \(\F_{q^2}\), where \(q=p^m\), that contains the all-one vector is Hermitian self-orthogonal. These criteria make the construction of self-orthogonal codes from divisible codes simple and efficient. As applications, we recover the known Euclidean and Hermitian self-orthogonality of generalized Reed--Muller codes and obtain several infinite families of new Euclidean and Hermitian self-orthogonal codes, which give rise to infinite families of quantum codes with excellent parameters.

cs.IT

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2611.09117

PathXAI: Evidence-guided structured pathology reporting by transferable tissue segmentation

Zhaorui Tan, Jun Aun Tan, Weixiang Zhong, Xiaoxi Wang, Tan Pan, Xingdong Xue, Li Liu, Jing Zhang 等 10 位作者

原文摘要

Pathology reporting translates tissue observations across regions and slides into diagnoses and supporting morphological descriptions, yet explicit tissue-composition measurements are rarely used to guide H&E-based report generation. We developed PathXAI, which pairs transferable tissue segmentation with a pretrained vision-language model. Because supplying tissue information directly with H&E images gave limited benefit, PathXAI-Report uses segmentation overlays and tissue statistics in separate steps, selects between candidate diagnoses by fixed rules, acquires further images when evidence is insufficient and records every step for review, without updating model weights. Its segmentation component exceeded baselines in mean Dice on internal and external tests. In automated evaluation on 1,200 public and 151 private cases, PathXAI achieved higher agreement with reference diagnoses and documented morphology than H&E-based comparators. On the public cohort, it exceeded Qwen+H&E by 4.4 percentage points in cancer family match rate and 5.7 percentage points in finding F1.

cs.CV · cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13960

From Attack Surface to Defense Architecture: Compositional Security for Agentic Coding Harnesses

Simon Hegelich, Bardh Prenkaj, Habiba Sarhan, Morteza Shahrezaye, Kolja Hegelich, Christian Montag, Ralf Schneider, Erik Cambria

原文摘要

AI coding harnesses couple language models to shells, filesystems, and network sockets, turning the harness into an automated Living-off-the-Land (LotL) platform: once an adversary injects instructions into retrieved content, dual-use binaries such as bash, curl, and find become the payload. We argue that security in agentic systems is a structural property of the cognitive architecture and permission composition rather than an emergent property of individual models. Static regex blacklists miss structural evasions, whereas LLM-as-a-Judge guardrails incur high latency while over-blocking benign work. We present ActiveSecHarness, a neurosymbolic reference-monitor architecture built around Separation of Intelligence and Authority. It combines (1) Macaroon-inspired HMAC capability tokens across four Trust and Authority Rings, (2) a 2D product taint lattice $\mathcal{L} = \mathcal{L}_{\mathrm{trust}} \times \mathcal{L}_{\mathrm{sens}}$ and a workspace provenance ledger that prevents secret laundering across disk, (3) deterministic Ring-0 OS/tool guards, and (4) bytecode certificates, split-horizon audit logs, self-healing security test synthesis, and an adaptive UserWikiManager. Across $N=252$ fixtures ($120$ attack, $120$ benign, $12$ kill chains), a live local LLM judge (qwen3:4b) refuses $98.3\%$ of benign work at $142.3\,\mathrm{ms}$ latency, whereas Ring 0 achieves $\mathrm{ABR}=1.000$ and $\mathrm{FPR}=0.000$ in $12.7\,μ\mathrm{s}$ ($11{,}200\times$ faster). In a multi-step trajectory ablation, an oracle Conseca-style stateless policy misses $4/12$ kill chains ($\mathrm{ABR}=0.667$) and coarse Boolean taint blocks $83.3\%$ of benign workflows, while our 2D product lattice achieves $\mathrm{ABR}=1.000$ and $\mathrm{FPR}=0.000$ at $\approx 20\,μ\mathrm{s}$ per step.

cs.CR · cs.AI

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