Artificial Intelligence · Cross-listed from cs.CV
Exploring Weaknesses of Generative Image Watermarks against Latent Frequency Masking
Kirill Aistov, Khaled Abud, Irina Serzhenko et al. (+8 more)
Invisible watermarking has become a central tool for tracing AI-generated images, but its robustness against adaptive removal attacks remains an open security question. We introduce Latent Frequency Masking, an attack that erases watermark evidence by replacing selected
Fourier coefficients in the latent representation of a watermarked image. The replacement can be sampled from Gaussian noise for efficiency or derived from
diffusion regeneration for improved image preservation. We provide a theoretical distortion bound relating the change between the reconstructed
adversarial image and the masked latent-frequency perturbation. We evaluate the proposed attack against six
diffusion watermarking methods on images generated from DiffusionDB and MS-COCO
prompts. Latent Frequency Masking removes or substantially weakens several watermarks while preserving perceptual quality and achieving favorable runtime compared with existing attacks. These results identify latent-frequency manipulation as a practical attack surface and highlight the need to include such attacks in robustness evaluations of generative image watermarking.
Read the full abstract ▾
All authors Kirill Aistov, Khaled Abud, Irina Serzhenko, Egor Kovalev, Aleksey Yakushev, Aleksandr Akimenkov, Dmitry Obydenkov, Yury Markin, Sergey Lavrushkin, Dmitriy Vatolin, Anastasia Antsiferova
Artificial Intelligence
Counting Moves, Weighing Voices: Bayesian Dialectical Argumentation for Calibrated Multi-LLM Councils under Persistent Adversaries
Ionel Eduard Stan and Paolo Napoletano
A multi-LLM council lets several large
language models (LLMs) deliberate on a question and return an answer together with a confidence estimate. As these systems become increasingly used for
reasoning, that confidence should represent a calibrated probability of being correct, and the decision should remain robust when some
agents are persistently unreliable. Existing council aggregation methods fail on both fronts: their confidence estimates measure decisiveness rather than correctness, and they cannot identify or discount persistently unreliable
agents. We introduce
Bayesian Dialectical Argumentation (BDA), which treats the council's typed moves—who proposed, challenged, or conceded which answer—as observations of a classical annotator model with per-
agent reliabilities. This formulation recasts multi-
agent deliberation as a reliability estimation problem, using the deliberation trace to infer
agent reliability under persistent
adversarial behavior. By weighting evidence according to inferred
agent reliability, BDA yields calibrated posterior probabilities over candidate answers while allowing persistently unreliable
agents to be inverted rather than merely outvoted. Across binary and multi-class
benchmarks, BDA achieves the best calibration among zero-cost council aggregation methods, requiring no additional LLM calls, and improves robustness under persistent
adversarial coalitions while remaining competitive in clean settings.
Read the full abstract ▾
Artificial Intelligence · Cross-listed from cs.CL
Mem++: Non-Destructive Memory for Long-Term Organizational LLM Agents
Ahmad Yehia, Aly O. Abdelkareem, Islam Ahmed et al. (+4 more)
Large
Language Model (LLM)
agents now take part in organizational work, where many authors record decisions across documents over months. Because a revised decision arrives as a new document rather than an edit, answering a question requires knowing which version held at a given time. However, most
memory systems compress the record at write time. By distilling each document into facts, notes or graph edges, these methods fix what can be answered before any question is asked. To address this, we propose Mem++, a non-destructive
memory framework shifting from write-time
distillation to read-time selection. Mem++ stores every document whole with its date and author, and it calls no generative model at write time. At read time, it retrieves only documents dated up to the time a question asks about and fuses lexical and semantic rankings. Unlike systems that overwrite older versions, Mem++ keeps them and leaves the choice to the answering model. Evaluations on the organizational
benchmark OrgMemBench demonstrate that Mem++ surpasses the strongest
memory system baseline by 8.0 to 13.1 points across two answering models. With gpt-4.1-mini, it also achieves the best overall score, 2.6 points above RAG. In addition, Mem++ achieves the best average LLM-judge score on LoCoMo and ranks second on LongMemEval-S, behind only its entity-graph variant. Code for
benchmark evaluation is available at https://github.com/AIDAChip-Inc/mem-plus-plus.
Read the full abstract ▾
All authors Ahmad Yehia, Aly O. Abdelkareem, Islam Ahmed, Hesham Omran, Khaled Alashmouny, Christian Claudel, Abduallah Mohamed
Artificial Intelligence
Mingbird: A Local-First Agent Harness Enabling Small Open Models to Complete Real Tasks
Hao Wang and Ting Huang
Small open-weight models (2-9B) run on ordinary laptops, but under cloud-scale
agent harnesses they rarely complete real tasks: tool prefill overflows the context, self-correction diverges, tool demonstrations loop, and tasks are silently abandoned. We present evidence, from a controlled single-machine comparison and one third-party
benchmark, that a substantial share of these failures is attributable to the harness rather than the model. We introduce Mingbird, a local-first
agent harness for Windows and Ollama whose ten mechanisms compensate point-by-point for small-model failure forms, three of them representative: a byte-level net-zero prefill budget, a finish gate that re-reads the task before accepting completion, and signature-level loop detection. On LRAB, a controlled comparison holding machine, models, budgets, and scoring fixed (4 harnesses $\times$ 4 open models (2B-35B) $\times$ 18 real tasks, deterministic artifact scoring), Mingbird reaches 0.886 overall against 0.631 (goose), 0.479 (opencode), and 0.405 (
agent-mini), with all 288
cells published; on $\tau^2$-bench (278 tasks, three arms, one protocol) it totals 0.856 against 0.791 and 0.737; and a frontier-model probe on the same 18 tasks spans 0.997 to 0.478 across harnesses, with well-formed scaffolds staying within 0.072 of each other. A leave-one-mechanism-out ablation is reported as directional only: same-night replications of the same arm move its mean by up to 0.069, the size of every nominal single-trial delta, and the one batch-matched comparison (full mechanism stack versus text re-read alone) gives the executable completion guards a paired +0.10 across three replications. The evidence carries stated limits: a self-built
benchmark, a single machine, and single-trial scoring.
Read the full abstract ▾
Artificial Intelligence
Can AI Oversight Be Zero Knowledge?
Alessandro Chiesa, Ziyi Guan and Burcu Yildiz
AI systems increasingly produce outputs from confidential data, such as a fitness-for-duty assessment from medical records or the predicted properties of a drug candidate from its secret structure. It is important to verify that such outputs are correct without revealing the underlying data. A recent line of work studies verification of AI outputs via interactive proofs and debate for oracle-aided computation, where correctness may depend on an oracle such as human judgment, a physical experiment, or the web. These works focus on verification by a verifier that runs much faster than the computation. However, such efficient verification is impossible for general oracle-aided computation, and these works therefore rely on additional assumptions. We focus instead on
privacy: allowing the verifier to run in time
polynomial in the computation, we ask whether interactive arguments for oracle-aided computation can be zero knowledge, so that the verifier learns nothing about the confidential data beyond the correctness of the output. We prove that, in general, they cannot. In the random oracle model, there are no zero-knowledge proofs for all oracle-aided computations, even if both the prover and the verifier are allowed to run much longer than the computation itself. The impossibility extends to debate, a canonical model for scalable oversight. On the positive side, we show that if the oracle attaches a cryptographic signature to each of its answers, then every oracle-aided computation can be verified in zero knowledge with an efficient prover and verifier, assuming only collision-resistant hash functions. Beyond
privacy, this also gives an alternative approach to scalable oversight that relies neither on an honest opponent, as in debate, nor on the robustness of the computation, as in prior single-prover protocols.
Read the full abstract ▾
Artificial Intelligence
Counterfactual Auditing of Bias in Open-Source Large Language Models for Clinical Triage
Manar Aljohani, Brandon Ho, Kenneth McKinley et al. (+2 more)
Emergency department (ED) triage is a high-stakes prioritization task in which demographic, socioeconomic, and system-context information may improperly influence acuity assignment. Although open-source large
language models (LLMs) are increasingly considered for local and
privacy-preserving clinical decision support, it remains unclear how counterfactual bias varies across model families, sizes, medical-domain models, and domain-adapted models. We present a comparative counterfactual audit of ten open-source LLMs for pediatric Emergency Severity Index (ESI) prediction. Starting from real and handbook-style clinical vignettes, we construct paired counterfactual variants that change only one injected demographic, socioeconomic, healthcare-access, behavioral, social, or system-context variable while holding the clinical presentation fixed. Models include Qwen2.5-7B, Qwen2.5-14B-Instruct, a QLoRA fine-tuned Qwen2.5-7B, MedGemma variants, MedLLaMA2-7B, GPT-OSS-20B, and GPT-OSS-120B. We measure any counterfactual shift, undertriage, overtriage, shifts greater than one ESI level, mean shift, and mean absolute shift. Counterfactual sensitivity varied substantially and did not consistently decrease with larger model size or medical-domain pretraining. The fine-tuned Qwen2.5-7B showed the lowest overall sensitivity, with a 5.27% any-shift rate and mean absolute shift of 0.0534, versus 16.02% and 0.1706 for the base model. Several larger or medical-domain models showed more significant shifts. Stratified and correlation analyses further revealed clinically important directionality and shared failure patterns hidden by aggregate rates. These findings support counterfactual auditing as a lightweight, clinically interpretable framework for comparing
fairness risks in open-source LLMs before clinical deployment.
Read the full abstract ▾
All authors Manar Aljohani, Brandon Ho, Kenneth McKinley, Dennis Ren, Xuan Wang
Artificial Intelligence · Cross-listed from cs.CR
A Hybrid Approach to Malware Detection: Integrating Few-Shot Model-Agnostic Meta-Learning with Autoencoders
Emmanuela Andam, Yasir Abbas Zaidi, Abdelali Hadir et al. (+2 more)
Ransomware has emerged as a major cybersecurity threat, with incidents increasing in frequency and impact across critical sectors. These attacks are typically launched through phishing emails, malicious downloads, or exploitation of software vulnerabilities to gain system access. Once inside, the malware encrypts files and demands a ransom, often in cryptocurrency, for the decryption key. Conventional detection methods often struggle with novel or scarce samples, leaving systems vulnerable. To address these challenges, this paper proposes a hybrid deep learning framework that combines an Autoencoder Feature Extractor (AFE) with a Model Agnostic Meta Learning (MAML) classifier for few shot malware detection. The AFE generates compact latent features that reduce noise and dimensionality, while the MAML classifier rapidly adapts to new threats using limited labeled data. Experiments conducted on the Ransomware
Dataset 2024 demonstrate the effectiveness of the framework in binary classification tasks. Across one to fifty shot settings, the proposed model consistently achieves high accuracy, F1 score, and Matthews Correlation Coefficient values, maintaining reliable classification even under extreme scarcity. These results highlight the model's robustness and effectiveness in adapting to limited data scenarios, demonstrating the potential of combining feature extraction with meta learning to enhance resilience against malware, particularly in sectors such as healthcare, manufacturing, and public infrastructure, where cyberattacks can cause significant operational and financial disruption.
Read the full abstract ▾
All authors Emmanuela Andam, Yasir Abbas Zaidi, Abdelali Hadir, Emmanuel Grant, Naima Kaabouch
Artificial Intelligence · Cross-listed from cs.CL
A rubric landscape for evaluating clinical reasoning in large language models: what exists, what is missing, and what needs to be combined
Zhangshu Joshua Jiang, Zina Ibrahim and James T. Teo
Exam-style accuracy does not establish whether large
language models (LLMs) reason well over clinical records. We define clinical
reasoning as integrating and updating evidence across time and sources to form, revise and justify a patient's problem representation and a defensible plan. This structured narrative review maps three literatures: medical education assessment instruments, clinical LLM
benchmarks published from 2023 onwards, and general-domain methods for evaluating long-form generation. We examine six dimensions: problem representation, temporal synthesis, differential and management
reasoning, counterfactual
reasoning, calibrated uncertainty, and
reasoning faithfulness. Preprints are included and flagged. No single instrument covers all six dimensions. Problem representation and differential or management
reasoning are reasonably covered, although reliability varies by instrument and setting. TIMER-Eval targets temporal synthesis, and ER-Reason assesses sequential diagnostic belief updating. Dedicated uncertainty and counterfactual evaluations are emerging, but their applicability to longitudinal free-text
reasoning remains limited. Factual completeness is well theorised in general-domain evaluation, with early clinical evidence of important omissions. Faithfulness remains the weakest dimension, with one identified clinical causal-ablation study on multiple-choice questions. Existing tools should be combined through binary rubric items, separate completeness and correctness scores, case-specific importance weighting with non-compensable safety caps, temporal order-consistency checks, and chance-corrected reliability reporting. Further design work is needed for calibrated uncertainty, counterfactual
reasoning and faithfulness over longitudinal free-text records. This review provides a design rationale, not a validated instrument.
Read the full abstract ▾
Artificial Intelligence
Rethinking Knowledge Retrieval for Generation: A Survey on RAG Architectures and Applications
Meghana Sunil, Shravya V, Shravan Venkatraman et al. (+1 more)
Large
Language Models (LLMs) have demonstrated remarkable fluency and versatility across natural
language tasks but remain fundamentally limited by their static knowledge and susceptibility to
hallucinations, especially in domains requiring up to date or attribute grounded information.
Retrieval Augmented Generation (RAG) addresses these challenges by integrating external
retrieval mechanisms with generative models, enabling dynamic, context aware generation grounded in verifiable data sources. This survey presents a comprehensive examination of RAG as a modular and evolving paradigm that enhances factual reliability, adaptability, and task
alignment in LLM based systems. We formalize the RAG framework through its three foundational components
retrieval, generation, and augmentation and survey state of the art methods spanning dense and sparse retrievers, fusion strategies,
embedding optimizations, and reinforcement learning based
retrieval policies. Anchored around four emerging axes efficiency, security, user centric interactivity, and complex
reasoning we categorize recent innovations and highlight their implications for scalability, robustness, and personalization. The paper also reviews advances in evaluation protocols, domain specific applications, and architectural variants such as Naïve RAG, Advanced RAG, and Modular RAG. Finally, we identify persistent challenges and outline future directions aimed at advancing the integration of
retrieval with LLMs for more grounded, interpretable, and controllable generation.
Read the full abstract ▾
All authors Meghana Sunil, Shravya V, Shravan Venkatraman, Joe Dhanith PR
Artificial Intelligence · Cross-listed from cs.CL
Cross-Lingual Alignment for Decoder-Only Models using MoE Routers
Lucas Bandarkar, Clark Peng, Ahmed Haj Ahmed et al. (+2 more)
Cross-lingual contrastive learning has been a core component of multilingual encoder training, but the ability to explicitly align representations is not possible in decoder-only LLMs because of varying multilingual tokenization. However, a growing amount of research suggests that even in LLMs, higher cross-lingual representational
alignment leads to improved cross-lingual transfer. In this paper, we propose a novel approach to reimagine cross-lingual contrastive learning given the architectural constraints of modern LLMs. Rather than applying an auxiliary
alignment loss on hidden states, we propose using the outputs of the mixture-of-experts (MoE) routers as the target for
alignment. Router outputs lend themselves better to pooling over many
tokens, enabling more reliable cross-lingual comparisons at the sequence-level. Controlled continual pre-training experiments on four open-source MoEs show that incorporating this routing loss also aligns the underlying hidden representations across
languages. Most importantly, this loss improves multilingual performance on our diverse evaluation suite, demonstrating the potential of cross-lingual MoE router
alignment.
Read the full abstract ▾
All authors Lucas Bandarkar, Clark Peng, Ahmed Haj Ahmed, Aditi Khandelwal, Nanyun Peng
Artificial Intelligence · Cross-listed from cs.CV
MoLE: Mixture of Latent Experts for Complementary Visual Reasoning
Yingcheng Liu, Tianyi Jiang, Yujuan Ding et al. (+5 more)
Latent visual
reasoning equips
vision–
language models with continuous intermediate states that can process visual evidence without explicit textual
reasoning traces or repeated image operations. However, existing methods often allow multiple latent
tokens to access the same visual evidence through shared value projections, providing no mechanism for them to extract complementary visual information; simply increasing the latent budget can therefore yield redundant latent representations. We argue that effective latent
reasoning should encourage different latent
tokens to extract complementary visual information, and thereby act as specialized visual experts. Based on this insight, we propose MoLE, a Mixture of Latent Experts framework that controls both what visual evidence each latent visual expert observes and how it transforms that evidence. MoLE isolates latent visual experts during evidence extraction and uses dedicated latent summary experts to aggregate the complementary representations of latent visual experts. A two-stage training pipeline first forces visual evidence through this latent pathway and then restores direct visual access, requiring neither predefined expert roles nor intermediate visual targets. Across five visual
reasoning benchmarks, MoLE achieves an average score of 78.6, outperforming data-matched supervised fine-tuning by 4.9 and the strongest evaluated latent visual
reasoning baseline at the same latent budget by 3.6. Representation analyses show lower latent-state similarity and more diverse visual
attention, while masking the latent pathway reduces average performance by 9.2. These results demonstrate that specializing latent computation is more effective than merely increasing the number of latent
tokens.
Read the full abstract ▾
All authors Yingcheng Liu, Tianyi Jiang, Yujuan Ding, jiangbo Ai, Xun Jiang, Guoqing Wang, Wei Ye, Yi Bin
Artificial Intelligence · Cross-listed from cs.LG
Asynchronous LLM Post-Training: Group-Mass Capping and Convergence Analysis
Qijia He, Ruinan Jin, Jun Luo et al. (+2 more)
Asynchronous reinforcement learning (RL) improves the efficiency of large
language model post-training but introduces stale rollouts generated by earlier policies. Theoretical understanding of how this staleness affects convergence and how to mitigate its impact remains limited. We derive a convergence bound for GRPO-style
algorithms that explicitly characterizes the tradeoff between the gradient
estimator's second moment and bias. For trajectory-level importance-weighted
estimators, our analysis shows that once the second moment is uniformly controlled, delay enters the bound through the bias introduced by clipping or rescaling. Guided by this insight, we propose a novel group mass capping GRPO (GMC-GRPO) method, which minimizes a ratio-based bias bound within a class of weighted
estimators sharing a common second-moment guarantee. We establish convergence guarantees for asynchronous GMC-GRPO and show that, compared with TIC-GRPO, it improves the threshold dependence of the fourth-order delay term from $O(\epsilon^{-4})$ to $O(\epsilon^{-2})$ as $\epsilon\to0$, where $1+\epsilon$ is the ratio threshold. Under local policy overlap, the delay-dependent term decreases as $G^{-2/5}$ after tuning the step size, where $G$ is the group size. For fixed behavior and current policies, the bias introduced by group rescaling also vanishes as $G\to\infty$, whereas the bias from trajectory-wise clipping can persist. Experiments across Qwen3 models and
reasoning benchmarks demonstrate improved robustness to stale rollouts, with GMC-GRPO achieving the best performance among stable baselines under large rollout delays.
Read the full abstract ▾
All authors Qijia He, Ruinan Jin, Jun Luo, Shaofeng Zou, Yingbin Liang
Artificial Intelligence · Cross-listed from cs.CR
A Structured State Space Sequence Model for Multi-Class Classification of Malware
Emmanuela Andam, Rana Shaaban, Emanuel Grant et al. (+1 more)
By 2030, Internet of Things (IoT) devices are projected to reach 40 billion, with fast-paced technological advancements in fields such as industry, healthcare, agriculture, automobiles, and building/home automation systems. This expansion has created a large attack surface for cybercrime, as the majority of these devices open the door for cybercriminals to exploit vulnerabilities, as they lack adequate built-in security. Cybercriminals launch malware attacks to compromise systems or steal sensitive data, and once a system is compromised, a ransom is typically demanded for its release. Current cybersecurity measures in place are being outpaced by the rapid growth of the IoT, which is accompanied by a subsequent growth in malware variants being created per day. Recognizing this pitfall, this research examines and proposes a novel approach to malware detection and classification to safeguard devices from further attacks and make IoT systems more robust and secure. The framework proposed utilizes a Structured State Space Sequence (S4) model, which discretizes sequences of malware samples in a sequence and captures long-range dependencies, essentially identifying the "cause" and "effect" hidden within malware execution flow. This study presents two novel contributions: the first empirical application of the S4 model for malware analysis, and a comprehensive comparison of its performance against other deep learning architectures, laying the stepping stone for future research in this new paradigm.
Read the full abstract ▾
All authors Emmanuela Andam, Rana Shaaban, Emanuel Grant, Naima Kaabouch
Artificial Intelligence · Cross-listed from cs.LG
Selection-Based Structured Reasoning: Toward Efficient Multimodal Search Agents
Feiyu Gavin Zhu, Xiaoyu Zhu, Jiqi Yang et al. (+9 more)
Multimodal agents commonly generate free-form
reasoning before each action. For small models, limited model capacity can result in lengthy
reasoning that provides little useful guidance for action generation while incurring substantial inference cost. To address this challenge, we introduce Selection-based Structured
Reasoning (SSR), a framework that reformulates
reasoning as selection instead of open-ended generation. SSR represents recurring high-level
reasoning as pre-specified, reusable natural-
language candidates. At each turn, the model selects from these
reasoning candidates based on their likelihoods given the current context, without requiring an auxiliary task head. Using pre-specified
reasoning traces enables parallel scoring, where teacher-forced prefilling computes
token likelihoods concurrently within and across candidates using a shared context KV cache. We evaluate SSR on seven
multimodal search
benchmarks using 2B and 4B models. Across multiple reinforcement learning objectives and supervised fine-tuning, SSR delivers significant efficiency gains without sacrificing task performance. SSR achieves an average success rate competitive with leading search
agents of the same scale, while reducing per-turn
reasoning latency by over 90% and total per-question model inference latency by 28-54%. Project page: https://zfy0314.github.io/ssr-webpage/.
Read the full abstract ▾
All authors Feiyu Gavin Zhu, Xiaoyu Zhu, Jiqi Yang, Rui Yang, Arnab Kumar Mondal, Yancheng Wang, Xinke Deng, Jean Oh, Reid Simmons, Joerg Liebelt, Xiang Kong, Zhongyu Jiang
Artificial Intelligence · Cross-listed from cs.CV
Unsupervised Domain Adaptation for Enhanced Radiometer Image Precipitation Estimation using Conditional Flow Matching
Victor Enescu, Assaad Zeghina, Matthieu Meignin et al. (+2 more)
Deep generative networks have recently achieved unprecedented performance in precise image and
video editing using sophisticated textual
prompts. However, the effectiveness of such models heavily depends on access to very large supervised and annotated image
datasets, which can be very difficult to obtain. This is particularly true for satellite instruments, which very rarely overlap with labelled data, and suffer from domain shifts in the rare occasions they do. In this paper, we investigate the potential of flow matching models for unsupervised domain adaptation of satellite radiometer images. Our main contribution is a novel unsupervised method that achieves precise domain
alignment by leveraging parts of the deterministic ordinary differential equations in flow matching models, conditioned on different satellite instruments. A key strength of our approach is its ability to preserve essential information while adapting across any domains since the perturbations are in theory bijective. Extensive experiments conducted on the GPM-Core constellation show the benefit of our conditional domain adaptation, particularly in improving rain precipitation estimation from radiometer imagery.
Read the full abstract ▾
All authors Victor Enescu, Assaad Zeghina, Matthieu Meignin, Nicolas Viltard, Cécile Mallet
Artificial Intelligence · Cross-listed from cs.LG
Flowing Faster to Coordinate: One-Step Online Multi-Agent Flow Policies
Zhuoran Li, Yunzhan Li, Xun Wang et al. (+2 more)
Multi-
agent reinforcement learning (MARL) provides a powerful framework for learning coordinated behaviors through interactions with the environment. Developing MARL policies requires balancing expressive modeling of complex and
multimodal action distributions with efficient training and execution. Generative policies, particularly diffusionbased policies, can faithfully capture complex and
multimodal behaviors, but costly iterative sampling hinders their scalability in online multi-
agent settings. We propose an Online MARL framework via one-step Flow model (OMAF) that combines expressive generative policies with efficient one-step action generation. OMAF employs a
Transformer-based flow policy to capture complex coordination behaviors, while its approximate path score surrogate provides a principled route to synchronized flow policy optimization. To enable stable and sampleefficient learning, we further develop a joint optimization scheme coupling softmax Q-value estimation with a joint flow policy objective for coordinated policy learning. By eliminating iterative sampling, OMAF dramatically reduces training overhead without sacrificing policy expressiveness. Extensive experiments across 10 standard tasks from MPE and MAMuJoCo show that OMAF consistently achieves superior performance, with up to 3.4x higher returns and 10.5x sample efficiency improvement compared with baseline methods. These results validate the effectiveness of OMAF as an expressive and computationally efficient one-step flow policy paradigm for online MARL.
Read the full abstract ▾
All authors Zhuoran Li, Yunzhan Li, Xun Wang, Yihan Du, Longbo Huang
Artificial Intelligence · Cross-listed from cs.CR
From Network Intrusion Detection to Blockchain-Backed Endpoint Detection and Response: Mapping the Landscape of Decentralized Detection-and-Response Architectures
Yahya Shahsavari, Sara Rouhani and Kaiwen Zhang
While the literature on blockchain-assisted intrusion detection and prevention systems (IDS/IPS) for Internet of Things (IoT) and Industrial Internet of Things (IIoT) networks is mature, existing systematic reviews suffer from two critical limitations: they overlook the structural shift toward modern Endpoint Detection and Response (EDR) and Extended Detection and Response (XDR) architectures, and they conflate blockchain's distinct functional roles into a single monolithic category. This Systematization of Knowledge (SoK) addresses these gaps by proposing a three-axis taxonomy that classifies proposals by detection-system class (NIDS, HIDS, EDR/XDR), blockchain functional role, and response-automation maturity. Synthesizing research published in high-impact venues between 2019 and 2026, we provide a rigorous gap analysis exposing why a genuine per-endpoint blockchain-anchored response loop remains nearly nonexistent due to latency, deployment, and community mismatches. Furthermore, we evaluate structural, cross-cutting challenges persisting across the literature, including consensus latency on constrained devices, post-
quantum cryptographic vulnerability, smart-contract attack surfaces, and the
adversarial vulnerability of evolving LLM-based detection engines. Finally, we outline a comprehensive research agenda centered on hybrid on-chain/off-chain orchestration to bridge the gap between decentralized trust and rapid response automation.
Read the full abstract ▾
Artificial Intelligence · Cross-listed from cs.CR
Walking the Embedding Space: Datastore Extraction from Multimodal RAG
Maria Carmen Jica, Ali Satvaty, Suzan Verberne et al. (+1 more)
Multimodal Retrieval-Augmented Generation (MRAG) has emerged as a reliable and cost-effective technique of grounding the generative capabilities of
Multimodal Large
Language Models (MLLMs) into relevant, up-to-date, external knowledge. Despite presenting several benefits, such as reducing hallucinatory behavior, they also introduce new attack surfaces, including leakage of private information and vulnerabilities against data extraction attacks. In this paper, we introduce $\immrag$, an adaptive and automatic data extraction attack procedure operating in a black box setting against image-returning MRAG, a configuration in which the retrieved visual artifact is itself the response. Each query blends an attacker-held shadow image with an image already recovered from the system, and relevance-weighted resampling steers subsequent queries towards regions of the
embedding space that still yield novel
retrievals. Unlike current extraction attacks that aim to persuade the model towards data leakage by placing a malicious query as a textual
prompt, $\immrag$ embeds the malicious instructions inside a user-given input image. We evaluate $\immrag$ on three plausible and distinct real-world scenarios: medical assistant, document-focused helper and general purpose tool. The experiments involve the study of the effectiveness of the attack on multiple CLIP-family retrievers, as well as the impact of various generators. A single 2500-query run reconstructs up to 611 distinct radiology images, 566 document scans and 416 general-purpose images under local-feature correspondence, and reaches up to $5.6\times$ as many distinct datastore items as a non-adaptive baseline. Our results show the urgent need for safeguards specifically designed for
multimodal data.
Read the full abstract ▾
All authors Maria Carmen Jica, Ali Satvaty, Suzan Verberne, Fatih Turkmen
Artificial Intelligence · Cross-listed from cs.SD
From Isolated Feature to Orbits: Discovering Music Concepts via Multi-SAE Alignment
Liwei Lin and Gus Xia
How can we understand what a music foundation model has learned internally? Most interpretability approaches, such as probing and Sparse Autoencoders (SAEs), focus on identifying individual features with minimal structural assumptions. We argue that many concepts are better understood as structured relations rather than isolated features. This is especially prominent in music, where tonal structures are organized in the space of pitch and time. For example, concepts such as chords or keys are naturally expressed as structured sets (e.g., the 12 transpositions of a chord or the diatonic system within a key), rather than isolated features. In this study, we shift from feature identification to structure-based analysis, asking whether the learned inner representations of music foundation model emerge as organized structures over features. To this end, we introduce a framework that uses pitch transposition as an inductive bias to induce ordered orbits via multi-view SAE
alignment. Concretely, we generate pitch-shifted input pairs and align their SAE representations to discover structured groups of pitch-related features. Experimental results show that this approach recovers orbit structures corresponding to chords, keys, and melodic patterns across two state-of-the-art music foundation models, while requiring only minimal grounding (e.g., a few anchor examples) to interpret entire concept families.
Read the full abstract ▾
Artificial Intelligence
AVSD-Scenes: A Dataset for Audio-Visual Description of Urban Scenes
Dhanunjaya Varma Devalraju, Arshdeep Singh and Mark D. Plumbley
Natural
language descriptions can provide rich semantic representations of audio-visual urban scenes, yet
datasets that jointly describe both auditory and visual information remain limited. In this paper, we introduce AVSD-Scenes, a paired audio-visual scene description
dataset for urban environments. The
dataset contains 12,291 audio-visual scene descriptions generated from the TAU Urban Audio-Visual Scenes
dataset. To construct the
dataset, we first generate audio- and visual-based descriptions using Qwen2-Audio-7B and Qwen2.5-VL-7B, respectively. These modality-specific descriptions are then combined using large
language models, namely Qwen3-14B, Mistral-Small-3.2-24B-Instruct-2506, and Gemma-3-27B-it, to produce
multimodal descriptions that capture complementary information from both modalities. We
benchmark AVSD-Scenes using semantic
alignment, cross-modal
retrieval, scene classification, LLM-as-a-judge evaluation, and human subjective assessment. Results show that
multimodal descriptions improve semantic
alignment and cross-modal
retrieval performance compared with modality-specific descriptions while preserving strong scene-discriminative information. The generated descriptions achieve up to 94.5% accuracy in urban scene classification, while combining audio, visual, and description
embeddings further improves accuracy to 95.4%. Furthermore, the descriptions remain highly scene-discriminative even when scene labels are removed from the prompting instructions, indicating that they capture semantic information derived from the audio-visual content rather than merely reflecting label information.
Read the full abstract ▾
Artificial Intelligence · Cross-listed from cs.SE
Detecting Inconsistencies in Model Specifications with LLM-as-Verifier Reasoning
Zichen Xie, Mrigank Pawagi, Lize Shao et al. (+2 more)
Model specifications define how large
language models (LLMs) should behave, guiding
alignment training, inference-time behavior, and evaluation. Yet these specifications may themselves contain defects: two individually reasonable principles may prescribe incompatible behavior when applied to the same situation, leaving no response that satisfies both. Detecting such inconsistencies is challenging. Formalizing natural-
language specifications risks losing subtle distinctions, while behavior-based testing cannot reliably distinguish specification defects from differences in model behavior. We introduce VeriSpec, the first approach to directly detect inconsistencies in model specifications by auditing the specification text itself. Our key insight is to preserve the specification in natural
language while using an LLM as a verifier. VeriSpec extracts structured, context-aware rules, constructs a topic-guided graph to cluster behaviorally related rules at the same authority level, and applies LLM-as-verifier
reasoning to detect inconsistencies. Applying VeriSpec to the OpenAI Model Spec, we extract 405 rules and manually validate five inconsistencies, all reported to its developers, who responded positively and have initiated internal discussions. Compared with five baselines, VeriSpec identifies the most validated inconsistencies, achieves the highest precision (38.5%), and incurs the lowest cost per validated inconsistency ($11.12). These results establish direct specification auditing as a practical complement to behavioral
alignment evaluation, catching defects at the source before they shape any model. The code is available at https://github.com/HIPREL-Group/VeriSpec.
Read the full abstract ▾
All authors Zichen Xie, Mrigank Pawagi, Lize Shao, Yang Hu, Wenxi Wang
Artificial Intelligence
Temporal-Difference Learning for Dragonchess
Jim O'Connor, Annika Hoag, Sarah Goyette et al. (+1 more)
Our research investigates how two adaptive AI methods, evolutionary transfer learning and TD(lambda), perform in the three-dimensional chess environment Dragonchess. The game challenges players with its unique board structure and computational load, making it an ideal setting to study how adaptive methods can update evaluation heuristics in novel environments. In this work we re-implement the Dragonchess engine, changing it from a PyGame engine to C++. This enables faster gameplay, allowing us to run 10,000 games with confidence intervals and significance tests, rather than a single small tournament. Both adaptive methods outperform all other
agents in the round-robin tournament. Our results showed that there is no significant difference in the performance between the evolved and learned evaluations. This research establishes the efficacy of adaptive methods in structurally complex, novel game domains.
Read the full abstract ▾
All authors Jim O'Connor, Annika Hoag, Sarah Goyette, Gary B. Parker
Artificial Intelligence
On the Divergence of Accuracy and Mechanism Consistency in Time Series World Models
Haochen Zhang, Jiaheng Guo, Zhen Xu et al. (+4 more)
A time series world model (TSWM) predicts a controlled system's state from its observed history and planned actions and exogenous inputs. Current approaches build forecasters with actions as
covariates, trained and evaluated on prediction error under the executed plan. Yet world models compare unexecuted plans, but their responses to changed plans remain untested. We ask which design choices matter and whether accurate forecasters respond to changed plans as real systems do. We address both with a formalization and
benchmark. The formalization separates state, actions and exogenous inputs, distinguishes continuous, mode and event actions, and introduces mechanism consistency, a metric built on declared action-state relations with known directions, such as a vasopressor raising blood pressure: it checks whether shifting an action moves the forecast in the declared direction. The
benchmark consolidates eight public
datasets with real actions from engineered infrastructure and clinical care, varying prediction space, plan fusion and plan encoding across seven backbones and five seeds. First, a frozen latent prediction space lowers MAE by 9.9% over observation space and gated output fusion lowers it by 12.7% over input concatenation on average, with both improving all eight
datasets; temporal plan encoding changes average MAE by at most 2.2%. Second, prediction error and mechanism consistency diverge: the lowest-error configuration is at or below chance in consistency on four of five
datasets with declared mechanisms, and no design choice avoids this. Finally, directional supervision, a loss penalizing the wrong-signed part of the response to a shifted action, significantly raises consistency on penalized mechanisms with no change in MAE. Together they give TSWMs a recipe: a frozen latent space and output-side fusion for accuracy, and a training objective for mechanism consistency.
Read the full abstract ▾
All authors Haochen Zhang, Jiaheng Guo, Zhen Xu, Zachary Plotkin, Nicholas Konz, Zhen Tan, Tianlong Chen
Artificial Intelligence
Code Owns the Simulation, Jev Owns the Evaluation
Yaodong Yang, Hongyao Tang, Yi Ma et al. (+4 more)
Judgment models such as \jev{} return, in a single call and without
reasoning text, a probability for each described option. This makes them attractive as an
agent's action-selection layer, but it is unclear which decisions they can be trusted with. We test \jev{} on reflection tests, one-shot
matrix games, the text game ALFWorld and
robot control, and find a sharp boundary. \jev{} succeeds when the right option can be judged from what the input describes, which we call evaluation. Specifically, it solves 99% of the counterintuitive Cognitive Reflection Test questions. However, it fails when the right option depends on simulation (i.e., predicting something not in the input), such as the opponent's action or the subgoal that must come first. In games, \jev{} plays suboptimally as if its rational opponent acted at random, because the opponent's action is not given. In ALFWorld, \jev{} favors commands that mention an object or place named in the task description. For example, given the task “put a clean knife in the drawer”, \jev{} carries an unwashed knife straight to the drawer instead of first washing it at the sink. Surprisingly, many of these failures are not due to a lack of knowledge. Asked separately what the opponent will do, \jev{} usually answers correctly, and it responds well given the opponent's action. It fails when one call must both perform the simulation and evaluate based on it. This suggests letting code make the prediction or simulation. When code supplies it, such as a lookahead in ALFWorld and physics simulation in
robot control, \jev{} becomes an expert controller through its general evaluation ability.
Read the full abstract ▾
All authors Yaodong Yang, Hongyao Tang, Yi Ma, Xingyu Fan, Weixun Wang, Jinpeng Li, Tianpei Yang
Artificial Intelligence
Continuous Process-Level Evaluation for Evolving Enterprise AI Agent Skills
Ngoc Phuoc An Vo, Aarya Doshi and Vadim Sheinin
Enterprise AI
agent skills evolve as tool APIs, models, and specifications change, yet final-output evaluation can miss process-level behavioral drift. We present a continuous evaluation framework combining outcome-level and process-level checks, applied to Revenue and Productivity variants of a Business Value Determination skill in an enterprise Value Aware Resiliency system. The framework independently computes per-run ground truth, materializes reusable template tests, and evaluates tool selection, arguments, execution order, and database integrity through programmatic checks and a narrowly scoped LLM judge. We evaluate 240 trials across two skills, two specification variants, two
agent harnesses, and three models. Of 175 trials passing all applicable final numerical checks, 162 (92.6 percent; Wilson 95 percent CI: 87.7-95.6 percent) contained another evaluator-detected deviation. Under a broader seven-check final-state definition, 151 of 164 passing runs (92.1 percent; 95 percent CI: 86.9-95.3 percent) still violated a trajectory check. Dependency attribution reduced a mean of 6.34 failed checks per run to 2.65 roots. Specification sensitivity varied by model and harness, with exploratory bootstrap interaction intervals excluding zero for all three Revenue comparisons and one of three Productivity comparisons. Runtime-resolved templates provided reusable regression coverage across the evaluated configurations; longitudinal validation under actual API
evolution remains future work.
Read the full abstract ▾
Artificial Intelligence · Cross-listed from eess.SP
Token Communication-Assisted Collaborative Embodied Artificial Intelligence: Concepts, Framework, and Opportunities
Peng Yi and Ying-Chang Liang
Collaborative embodied artificial intelligence (CEAI) enables multiple physical
agents to perceive, reason, and act cooperatively in dynamic environments. Effective communication is essential for CEAI, yet CEAI
agents must exchange not only large
multimodal observations but also task-relevant insights, intents, and interactive information over long horizons. This article investigates
token communication (TokCom) as a native intelligence interface for CEAI, in which
tokens serve jointly as compact semantic carriers for communication and fundamental inference units for generative foundation models (GFMs). We first discuss how TokCom supports insight sharing, intent
alignment, and interactive control among embodied
agents. We then propose a TokCom-assisted CEAI framework driven by a task-adaptive communication protocol. Comprising a compact codebook, syntax rules, and contextual examples, this protocol guides GFM-based transceivers to distill messages into compact
tokens and reconstruct them after wireless transmission. A case study on collaborative object transport demonstrates that the proposed TokCom framework substantially reduces the source payload bit consumption while preserving task efficiency and showing robustness under noisy channels. Finally, we outline future research directions.
Read the full abstract ▾
Artificial Intelligence
AI-assisted mitotic counting improves reproducibility and efficiency across multiple tumour types
Simon Graham, Mostafa Jahanifar, Quoc Dang Vu et al. (+15 more)
Mitotic counting is an important component of tumour grading, diagnosis and prognostic assessment across several tumour types, but manual assessment is time-consuming and subject to inter-pathologist variability. To help address these challenges, we developed MitPro, an AI tool designed to improve consistency and efficiency by directing pathologists towards regions with the highest predicted mitotic activity and highlighting mitotic figures for review, while retaining pathologist control over region selection and the final count. We evaluated its effect on the reproducibility and efficiency of mitotic counting in a retrospective, non-interventional, paired reader study comprising 385 whole-slide images from 3 centres in 3 countries and 7 tumour types using 3 different scanners. 13 pathologists participated, with each slide assessed independently by 3 pathologists without AI assistance and again with AI assistance after a minimum 2 week washout period. Across all slides, AI-assisted counting increased the intraclass correlation coefficient from 0.589 to 0.949. Mean pathologist-level median assessment time decreased from 286.4 to 127.8 seconds, corresponding to an average saving of 151.8 seconds per assessment. Improvements in agreement and efficiency were also observed in supporting analyses using
HALO AP and Sectra image management systems and in 2 additional tumour types outside the main study population. AI-assisted assessment was associated with a subtle shift towards higher mitotic counts and scores, consistent with identification of more active mitotic hotspots and fewer missed mitotic figures. The frequency of score change between unassisted and AI-assisted assessment was comparable with inter-pathologist variation during routine counting. These findings support the use of MitPro as an assistive tool for more consistent and efficient mitotic assessment in routine practice.
Read the full abstract ▾
All authors Simon Graham, Mostafa Jahanifar, Quoc Dang Vu, Vygante Maskoliunaite, Donatas Petroska, Ruta Barbora Valkiuniene, Ayat Gamal Lashen, Jen Hong Ong, Amede Ogechi Nnorom, Sinclair Couper, Natasha Kardasz, Reshma Agrawal, Brinder Singh Chohan, Jose Luis Solorzano Rendon, Shonali Natu, Arvydas Laurinavicius, Nasir Rajpoot, David Snead
Artificial Intelligence
LineupRL: Verifiable Reinforcement Learning for Time Series Captioning via Caption-to-Series Identification
Haochen Zhang, Laura Yao, Zachary Plotkin et al. (+2 more)
Time series captioning is a fundamental step in time series understanding and can also serve as the bridge between signal and natural
language. Supervised fine-tuning (SFT) relies on a larger model's captions and cannot exceed their quality. Reinforcement learning (RL) can, but its rewards were designed for other modalities and other tasks, and they transfer poorly to open-ended generation in the time series domain. We address this by proposing LineupRL, a reinforcement learning with verifiable rewards (RLVR) pipeline whose reward is caption-to-series identification. The reward model is a frozen large
language model (LLM) verifier that reads the generated caption and the candidate time series as raw values, never the chart, and must pick the described time series from multiple distractors. Matching is a far lighter demand on the verifier than writing questions or judging a caption, so an off-the-shelf LLM can supply the reward. Across two captioning
benchmarks, and on forecasting and reconstruction where the predictor sees only the caption, LineupRL outperforms SFT and RL baselines on every metric. The 3B
vision language model (VLM) trained by LineupRL also outperforms, at 1/24 of the parameters, the 72B VLM whose captions the SFT baseline is distilled from. Our case study shows that LineupRL resists reward hacking, and that the captioner it trains both traces the trend and names the values at key points.
Read the full abstract ▾
All authors Haochen Zhang, Laura Yao, Zachary Plotkin, Gengwei Zhang, Tianlong Chen
Artificial Intelligence · Cross-listed from cs.LG
iADD: Improving Alignment and Diversity in Diffusion Policy Optimization
Ashok Prasad Neupane, Saugat Adhikari, Pramish Paudel et al. (+2 more)
Reinforcement learning based post training of
diffusion models, such as Denoising
Diffusion Policy Optimization (DDPO), optimizes a reverse
diffusion process under a reward function. However, current approaches to reward optimizations do so at the cost of diversity and quality. In this paper, we provide better tradeoffs through careful theoretical considerations and method design. We analyze the theoretical framework and mathematically demonstrate that only-latter timestep updates of
diffusion model may be harmful for diversity contrary to the conclusions presented in a previous work. Additionally, we propose an incremental Feynman-Kac training based on strong theoretical foundations in order to achieve the best-yet
alignment-diversity tradeoffs. We perform extensive experiments and compare our method against related
diffusion policy optimization approaches in three different tasks and also provide strong ablations for each component, thus validating strong performance gains in both
alignment and diversity.
Read the full abstract ▾
All authors Ashok Prasad Neupane, Saugat Adhikari, Pramish Paudel, Ajad Chhatkuli, Danda Pani Paudel
Artificial Intelligence
Not All Experience Belongs in the Weights: Component Routing for Self-Improving GUI Agents
Beining Wu, Zihao Ding and Jun Huang
Self-improving GUI
agents keep the trajectories they produce and return them to the
agent, by fine-tuning or by
retrieval into the
prompt, and studies that compare the two destinations disagree. We attribute this to the unit of experience: a trajectory bundles items with different properties, so a conclusion about the bundle depends on its mix. To address this, (i) we introduce component routing, which splits the experience into locators, procedures, state facts and lessons and sends each component to the context or to the weights, compared on the same items across three backbone families, two environments and three seeds. One pool has two destinations: locators and lessons win in the weights, procedures and state facts in the context. (ii) We fit a rule in two properties measured before any training, recurrence and state-conditionality; it recovers the destination of a held-out backbone family in 24 of 24
cells, two interventions move a component toward the boundary, and routing by the rule beats every whole-trajectory baseline and, by +3.5 points on average, the better single destination of each backbone. (iii) We identify how training and producer-consumer differences change the value of the two destinations: note readout decreases after the same component is written into the weights, most for the items that recur most, context gains increase with the information gap, and weights gains decrease with the policy gap. Code and data will be released.
Read the full abstract ▾