Artificial Intelligence · Cross-listed from cs.HC
Generative AI Literacy Training Improves Intelligence Analysts' Discrimination of Real and AI-Generated Images
Negar Kamali, Candice Rockell Gerstner, Jessica Hullman et al. (+1 more)
Across social and online platforms, people are increasingly exposed to AI-generated images. As a consequence, the task of distinguishing AI-generated from authentic images is becoming a central challenge for information ecosystems. While humans perform better than chance, accuracy falls short of many operational needs. Initial evidence shows that visually oriented training can improve deepfake detection but does not improve participants' ability to identify real images as real. Here, we investigate the efficacy of a brief training intervention for intelligence analysts employed by the United States government in 2024. We conducted a counterbalanced within-subject randomized experiment in which we showed participants real and AI-generated images varying in pose complexity and scene context and asked them whether each image was real or AI-generated, both before and after an expert delivered a 30-minute training that pointed out patterns in seven real and 50 AI-generated images. We collected 2,544 image-level judgments from 32 intelligence analysts. We find training increased overall accuracy by 9 percentage points (95% CI: [2.7, 15.4]) from a baseline of 72%. We find the improvement is driven by a 14.2 percentage point increase in accuracy for real images (95% CI: [0.7, 27.7]). Through a careful experimental setup that curated matched pairs of real and AI-generated images across pose complexity categories, we reveal how these trainings influence people with different levels of digital forensics and generative AI experience and identify the kind of image-based content where this training intervention appears to be most effective. Ultimately, these results provide causal evidence that a brief, structured training can improve human judgment across a diverse array of real and AI-generated images, informing organizational responses to AI-generated visual misinformation.
Read the full abstract ▾
All authors Negar Kamali, Candice Rockell Gerstner, Jessica Hullman, Matthew Groh
Artificial Intelligence · Cross-listed from cs.SE
TUA-Bench: A Benchmark for General-Purpose Terminal-Use Agents
Shoufa Chen, Luyuan Wang, Xuan Yang et al. (+7 more)
As large
language models and harness frameworks continue to advance,
agents operating in terminals are increasingly capable of performing a broader range of general computer-use tasks beyond coding. However, existing
benchmarks do not adequately evaluate general-purpose terminal computer-use
agents (TUAs): general computer-use
benchmarks primarily target graphical user interfaces (GUIs), whereas terminal-based
benchmarks largely emphasize technical and programming-centric workflows historically native to the shell. We introduce TUA-Bench, a general-purpose
benchmark for terminal-use
agents. TUA-Bench includes 120 real-world tasks across five task families, covering routine digital activities-including document editing, email management, and live-web information seeking-as well as scientific and engineering workflows co-designed with PhD-level domain experts that require specialized software. This breadth distinguishes TUA-Bench from prior shell-focused or domain-specific
benchmarks. Each task is manually designed, runs in a real terminal with a deterministic setup script, and is evaluated by an execution-based scoring protocol. We find that the strongest frontier
agent, Claude Code with Claude Opus 4.8 max
reasoning effort, achieves 65.8% overall performance, with substantial gaps across both tracks. By providing a broad and realistic evaluation of terminal-use capabilities, TUA-Bench aims to accelerate the transition from narrow, task-specific assistants to general-purpose
agents capable of operating reliably across diverse digital environments.
Read the full abstract ▾
All authors Shoufa Chen, Luyuan Wang, Xuan Yang, Zhiheng Liu, Yuren Cong, Yuanfeng Ji, Feiyan Zhou, Xiaohui Zhang, Fanny Yang, Belinda Zeng
Artificial Intelligence · Cross-listed from cs.CR
Decomposing Memorization Reduction in Privacy-Preserving Fine-Tuning of SLMs for CSIRTs
Cristhian Kapelinski and Diego Kreutz
CSIRTs increasingly fine tune
language models on vulnerability scan records, but these records expose internal network
topology and create
privacy risks under regulations such as GDPR and LGPD. We present the first empirical study of how DP SGD and HMAC pseudonymization interact when fine tuning small
language models with 1B to 3B parameters on structured CSIRT data. We evaluate 96 LoRA adapters across four SLMs and four training regimes, including raw fine tuning, QLoRA with large batch training, and DP SGD with epsilon equal to 2 and 8. We also audit memorization using 20 planted canaries, four extraction attacks, and a dual attack targeting HMAC pseudonymized identifiers. Our results show three main findings. First, matched update controls reproduce the observed reduction in memorization by reducing the number of optimizer updates alone, accounting for 66 percent to 132 percent of the measured effect, with a mean of 100 percent across three seeds and four models. In this setting, DP SGD provides the formal
privacy guarantee but does not produce additional measurable reductions in memorization. Second, HMAC pseudonymization removes the original identifiers from the exposure surface, reducing exposure by 40 percent to 61 percent, while pseudonymized identifiers remain close to the expected random baseline and do not become a secondary memorization target. Third, F1 scores remain between 0.19 and 0.28 across all 96 adapters using four shot prompting, indicating that, under the evaluated training budget, 1B to 3B SLMs do not achieve operationally useful performance.
Read the full abstract ▾
Artificial Intelligence · Cross-listed from cs.RO
Improvement of Robot's Simultaneous Localization and Mapping Using an Effective Transformation to Achieve Linear Model
Seyed Farzad Bahreinian, Maziar Palhang, Mohammad Reza Taban et al. (+1 more)
Nowadays mobile
robots have wide engineering applications. Simultaneous localization and mapping (SLAM) is an important task of these
robots. The major and common
algorithms used for this task are based on extended Kalman filter (EKF). One of the main problems in EKF-based SLAM is its divergence. The nonlinearity of motion and observation models and linearization error are the main reasons for the divergence. There have been some efforts to address this problem with limited success. In this paper, by applying a simple compass and using an effective transformation, we transform the non-linear state space model into a linear model. Then, by applying the original KF to this model, we reach a new method, which is called LMKF SLAM. We show that the LMKF SLAM is significantly superior to the state-of-the-art methods, especially EKF-based SLAMs, both in accuracy, convergence, and computational complexity. The proposed method is also more stable with respect to the uncertainty of sensors values and changes in system parameters. Experimental results verify these points.
Read the full abstract ▾
All authors Seyed Farzad Bahreinian, Maziar Palhang, Mohammad Reza Taban, Hasan Enami Eraghi
Artificial Intelligence
Data and Evaluation Closed-Loop for Model Capability Enhancement
Zhixuan Li, Jiangan Yuan and Han Xu
Model capability is the central variable in LLM pre-training, yet is never observed directly: data shapes it prospectively, while evaluation reveals it only retrospectively, compressing samples,
prompts, decoding, and scoring rules into one noisy score. Practical optimization runs this backward: a failure is observed first, and the engineer must infer the corpus fix. The two sides speak incompatible vocabularies –
benchmark names and per-sample correctness versus data sources, domains, and quality labels – so this inference is usually intuition, not method. We close this gap with the capability slice: a group of evaluation samples sharing background condition, task type, solving operation, and output constraint – precise enough to localize a single weakness yet stable enough to survive aggregation, unlike a
benchmark name, too coarse, or a single sample, too noisy. Built around this unit, an evaluation taxonomy, a non-instruction data taxonomy, and mapping rules form a closed loop turning a
benchmark-level failure into a targeted, testable data intervention. We test this loop on two case studies pulling in opposite directions. First, the loop rules the data out: continued pre-training drives BBH down by $-46.82\%$, but diagnosis traces this to a single masked \textless EOS\textgreater loss rather than weakened
reasoning; restoring it recovers BBH to $66.44$, above the original checkpoint, without changing the data. Second, the loop rules the data in: a persistent math-
reasoning weakness is decomposed by solving operation into specific failing combinations, and a weakness-targeted sampling procedure built from it lifts AIME2025/AIME2026 Pass@128 from $6.67$/$0.00$ to $26.67$ each. The same unmodified loop reaches opposite, correct verdicts in both cases, showing the evaluation-to-data inference can be routine, auditable, and experimentally validated rather than intuitive.
Read the full abstract ▾
Artificial Intelligence · Cross-listed from cs.LG
An Agentic AI Pipeline for Appliance-Level Energy Anomaly Detection and LLM-Driven Recommendations
Dihia Falouz, Aida Douaibia, Amine Bechar et al. (+3 more)
Appliance-level energy monitoring in office buildings produces noisy alerts that non-expert facility managers struggle to use. This paper proposes an end-to-end agentic pipeline that combines deep time-series
forecasting, variational anomaly detection, and LLM-based
reasoning to generate prioritized, actionable maintenance recommendations. The system tracks seven office appliances using a hybrid Singular Spectrum Analysis (SSA) and Long Short-Term
Memory (LSTM)
forecasting model, and applies a per-appliance LSTM Variational Autoencoder (VAE) with
attention to flag abnormal daily consumption episodes. A three-stage LangChain pipeline begins with a Context
Agent that always retrieves three core RAG sources (model reliability, hourly baseline, and expert knowledge) and conditionally adds up to three more (forecast context, anomaly history, global baseline) based on event characteristics, capped at eight
reasoning steps. A Diagnosis
Agent converts the evidence into a structured JSON diagnosis, and a Report
Agent renders a human-readable narrative. A reflective
memory layer incorporates operator feedback. The dashboard shows real-time 30-minute forecasts, intraday consumption, the previous day anomaly report, and a feedback form. We evaluate the
forecasting model, anomaly detector with appliance-specific thresholds, and LLM
reasoning on a 16-scenario
benchmark including sustained and transient spikes, unexpected shutdowns, and systemic events, comparing five LLM backends under static vs. dynamic
retrieval. Dynamic
retrieval matches full static
retrieval across all backends while cutting average context from six to three-six sources per event. The best backend scores 90.4/100 with a 100% pass rate at a 70-point threshold, and a fully local 7B-parameter model passes all 16 scenarios.
Read the full abstract ▾
All authors Dihia Falouz, Aida Douaibia, Amine Bechar, Youssef Elmir, Abbes Amira, Adel Oulefki
Artificial Intelligence · Cross-listed from q-bio.QM
SVC-Probe: A Framework for Evaluating Perturbation Generalization in Spatial Foundation-Model Embeddings
Jake Y. Chen, Huu Phong Nguyen, Fuad Al Abir et al. (+1 more)
This work examines perturbation generalization in spatial foundation-model
embeddings derived from fluorescence microscopy images. Although these models can discriminate drug conditions accurately, it remains unclear whether the learned representations reflect patterns consistent with expected perturbation axes that transfer across drugs. We introduce SVC-Probe, a perturbation-aware framework that combines Subcellular
Embedding Atlas Stability, Mondrian Neighborhood Graphs, and a Foundation Model Perturbation Probe to assess
embedding stability, neighborhood rewiring, and centroid prediction under drug treatment. Applied to the CM4AI MDA-MB-468 chemical-perturbation atlas comprising 462 antibody labels and SubCell 1536-dimensional
embeddings, SVC-Probe demonstrates that 98.6% three-way condition accuracy does not correlate with reliable cross-drug prediction, with cosine similarity diminishing from 0.944 in-domain to 0.30 under leave-one-drug-out evaluation, constituting a two-drug stress test rather than a general
benchmark. Null calibration indicates that raw residual-turnover coupling is largely influenced by generic
embedding structure, whereas a drug-specific signal emerges under vorinostat and is consistent with chromatin-related reorganization. In contrast, the paclitaxel axis is not robustly reconstructed, likely due to sparse coverage of microtubule-associated
proteins. Together, these results introduce and demonstrate a reusable diagnostic framework for stress-testing spatial virtual-
cell representations and indicate that perturbation generalization may serve as a stricter and more informative
benchmark than baseline condition discrimination.
Read the full abstract ▾
All authors Jake Y. Chen, Huu Phong Nguyen, Fuad Al Abir, Ehsan Saghapour
Artificial Intelligence · Cross-listed from cs.LG
Counterfactual Residual Data Augmentation for Regression
Hossein Mohebbi, Oliver Schulte, Ke Li et al. (+1 more)
Data-driven modeling in real-world regression tasks often suffers from limited training samples, high collection costs, and noisy observations. Inspired by the impact of data augmentation in
vision and
language, we propose a novel Counterfactual Residual Data Augmentation (CRDA) technique for tabular regression. Our key insight is that once a regressor has modeled the systematic component of the data, the remaining noise can be viewed as an invariant residual that remains stable under small perturbations of carefully selected features. We exploit this residual invariance to generate new, yet realistic, training samples, effectively expanding the
dataset without requiring additional real data. Our method is model-agnostic and readily applicable to various types of regressors. In experiments across
datasets from a variety of
benchmark repositories, on average, CRDA reduces an MLP Regressor's MSE by 22.9% and an XGBoost Regressor's MSE by 6.4%. When compared to existing state-of-the-art data generators and augmentation techniques, CRDA consistently outperforms in MSE reduction. By adding principled counterfactual variations to the training data, our method offers a simple and efficient remedy for noise-prone, small-sample regression settings.
Read the full abstract ▾
All authors Hossein Mohebbi, Oliver Schulte, Ke Li, Pascal Poupart
Artificial Intelligence · Cross-listed from cs.MA
Is Lying an Emergent Behaviour in LLMs? Evidence from Gaslighting AI agents in a Sustainability Game
Subhendu Bhandary, Federico Carucci, Christos Charalambous et al. (+6 more)
LLMs
agents are increasingly used in multi-
agent settings, yet their behaviour in sustainability games remains largely unexplored. This work investigates whether lying can emerge among LLM
agents in a competitive sustainability game in which
agents are informed that common resources can regenerate, although regeneration does not actually occur. We develop an
agent-based model of a sustainability game in which
agents manage industrial, military, and ecological resources, and interact through a network. LLM
agents can observe neighbours' status, declare future attacks, receive permission to lie, and access reputation information, while rule-based
agents provide an interpretable behavioural baseline. The results show that neighbour information strongly changes system dynamics, increasing attacks while improving biosphere retention and coexistence. Also, the presence of future declarations reduce extinction risk without suppressing conflict. Behaviourally, deception emerges even when
agents are not explicitly allowed to lie, and explicit permission mainly increases bluffing and diversion rather than direct backstabbing. Finally, the presence of reputation
memory and information about the current biosphere level reduces system ecological depletion. These findings suggest that deception can arise as an emergent behaviour in LLM-
agent systems and that communication between LLM-
agents could support sustainability while dealing with risk.
Read the full abstract ▾
All authors Subhendu Bhandary, Federico Carucci, Christos Charalambous, Francesca Dilisante, Ksenia Dvorkina, Anna Garbo, Jiaqi Liang, Riccardo Vasellini, Francesco Bertolotti
Artificial Intelligence · Cross-listed from cs.RO
Event-Conditioned Diagnostics of Kinematic, Contact, and Object-Permanence Structure in Passive Object-State World Models
Yang Liu and Yuming Chen
World models can predict future physical states, but prediction accuracy alone does not explain how physical information is organized and used inside their latent dynamics. We introduce a controlled diagnostic protocol for studying event-conditioned latent physical structure in passive object-state world models. The protocol separates three questions: whether event-regime information is readable, whether event context changes the relative emphasis of kinematic-, contact-, and object-permanence-related readouts, and whether suppressing readout-aligned directions affects prediction. Using a balanced controlled-generator
dataset with free-motion, collision, and occlusion events, we evaluate GRU,
Transformer-lite, and RSSM-lite transition models under a fixed-horizon
forecasting setup. Across three independent seeds, all three architectures learn useful predictive dynamics and support reliable event-regime readout. Event context systematically shifts the relative readout pattern: free motion is kinematic-dominant, collision increases contact-related structure, and occlusion increases object-permanence-related structure. Time-aligned analyses show corresponding phase-related shifts. Fixed-horizon projection Causal Field Effect (CFE), used here as a functional-sensitivity test, gives the clearest evidence for contact-aligned structure in collision-contact windows. Object-permanence-aligned structure is also sensitive during hard occlusion, but control specificity remains mixed. These results support event-conditioned latent structure and functional sensitivity without implying explicit physical modules or isolated causal
circuits.
Read the full abstract ▾
Artificial Intelligence · Cross-listed from cs.CR
LLM agents security duality: a comprehensive survey of self-security and empowered cybersecurity
Yiwei Xu, Yong Zhuang, Xuanming Liu et al. (+6 more)
Large
language model (LLM)
agents are rapidly being integrated into real-world systems. Their autonomy and tool-use capabilities generate substantial value while simultaneously expanding the security attack surface. This survey provides a comprehensive overview of the opportunities and challenges of LLM
agents in security, focusing on two core areas: (1) threats to LLM
agents themselves and corresponding mitigation strategies (LLM
agents self-security), and (2) the role of LLM
agents in empowering the cybersecurity lifecycle across offense and defense (LLM
agents empowered cybersecurity). We first examine the internal and external attack surfaces of
agents, propose a taxonomy organized by threat sources, and analyze associated mitigations and evaluation frameworks. We then investigate how
agent capabilities are applied in cybersecurity practice and present, to our knowledge, the first
agent-empowerment framework aligned with the full cyber offense-defense lifecycle. By systematically surveying these two areas, we are the first to highlight a positive feedback synergy between LLM
agents self-security and empowered cybersecurity, offering new insights for the advancement of both. We further identify current limitations and outline promising directions for future research. The insights provided aim to catalyze the coordinated development of LLM
agents self-security and
agent empowered cybersecurity, paving the way for more capable and robust
agent applications.
Read the full abstract ▾
All authors Yiwei Xu, Yong Zhuang, Xuanming Liu, Tian Zhang, Bowen Xiao, Xiaoyang Xu, Delong Jiang, Juan Wang, Hongxin Hu
Artificial Intelligence · Cross-listed from cs.IR
SemFlowRAG: Directed Semantic Flow from Abstraction to Evidence for Complex Reasoning
Houyuan Qin, Rong Wu, Qinyuan Qin et al. (+4 more)
Retrieval-Augmented Generation (RAG) enhanced by Knowledge Graphs has shown promise in complex multi-hop
reasoning tasks. However, existing graph-based
retrieval methods typically rely on flat, undirected
topologies. During the
retrieval process, the probability flow often gets trapped in high-degree abstract concept nodes which we define as “probability black holes”, leading to semantic drift and noise accumulation. To address this, we propose SemFlowRAG, a framework that reconstructs the flat
retrieval space into a corpus-adaptive semantic gradient graph. This data-driven self-organization enables a hierarchical structure to emerge naturally from the data distribution, capturing the intrinsic semantic granularity of the corpus to suppress structural noise. By quantifying the semantic abstractness of entities through the
embedding variance of their associated passages, we transform static undirected edges into directed semantic constraints. Furthermore, we design an abstractness-guided directed PageRank
algorithm that forces the
retrieval trajectory to follow a “high-to-low semantic abstractness” gradient. This mechanism ensures layer-by-layer evidence convergence, smoothly guiding the
retrieval process from abstract concepts to specific document evidence. Extensive experiments on complex QA
datasets demonstrate that SemFlowRAG effectively mitigates the “probability black holes” issue, outperforming existing baselines in both
retrieval and downstream
reasoning performance.
Read the full abstract ▾
All authors Houyuan Qin, Rong Wu, Qinyuan Qin, Botian Shi, Jingjing Qu, Yang Sun, Pinlong Cai
Artificial Intelligence · Cross-listed from astro-ph.IM
Domain-Informed Multi-View Self-Distillation for Astronomical Light-Curve Representation Learning with JEPA
Yicheng Rui
Light curves describe temporal variations in the brightness of celestial objects. Learning robust representations of light curves is essential for large-scale automatic discovery in the dynamic universe, but existing time-series foundation models often struggle with the uneven sampling, complex noise, and wide range of physical timescales that characterize astronomical observations. We propose a domain-informed representation learning framework for irregular astronomical time series with Joint-
Embedding predictive architecture (JEPA), combining semantics-preserving views, uncertainty-aware tokenization, and multi-view self-
distillation. The encoders are trained with multi-view self-
distillation using LeJEPA regularization on the LEAVES
dataset and evaluated on the StarEmbed classification
benchmark. On StarEmbed, our model outperforms hand-crafted features on 15 of 16 classification metrics. In few-shot linear probing, it achieves macro-F1 scores of 42.56 $\pm$ 7.21 with one sample per class and 63.58 $\pm$ 1.20 with 100 samples per class, consistently improving over hand-crafted features. Beyond variable-
star classification, the learned representation supports similarity search, parameter estimation, and photometric zero-point drift detection. We further evaluate cross-domain adaptation on 12 heterogeneous irregular time-series
datasets from PYRREGULAR, where the adapted variant matches or exceeds previous state-of-the-art performance on 5
datasets, compared with at most 3 wins by any single prior baseline. These results demonstrate that domain-informed multi-view self-
distillation is an effective strategy for learning representations of irregular time series, while also highlighting that successful time-series representation learning requires domain-specific inductive biases rather than a universally optimal architecture.
Read the full abstract ▾
Artificial Intelligence · Cross-listed from cs.SD
LoRA-Tuned Large Language Models for Dementia Detection via Multi-View Speech-Derived Features
Jonghyeon Park, Olivier Jiyoun Jung and Myungwoo Oh
Early detection of dementia enables timely intervention, and reflecting cognitive impairment, spontaneous
speech offers a non-invasive screening modality. Conventional approaches often focus on a single representational dimension – such as acoustic descriptors, pause modeling, automatic
speech recognition (ASR) transcripts, or
multimodal fusion – limiting integrative
reasoning across heterogeneous cognitive symptoms. We propose a low-rank adaptation (LoRA)-tuned large
language model (LLM) that performs structured multi-view
reasoning over four complementary
speech-derived signals: ASR transcripts with pause markers, discourse-level topic cues, temporal fluency statistics, and phonological sequences. These cues are encoded within a unified
prompt, enabling a single LLM to learn a coherent decision function without modality-specific encoders or late-stage fusion. On ADReSSo, our best model achieves an F1-score of 90.14%, and ablation confirms the complementary contribution of each view.
Read the full abstract ▾
Artificial Intelligence · Cross-listed from cs.LG
From Dataset Spectral Geometry to Network Weights: A Geometry-Aware Initialization for Sigmoidal MLPs in Image Classification
Yi-Shan Chu
Classical universal approximation
theorems (UAT) establish the expressive power of sigmoidal multilayer perceptrons, but they do not specify how the weights should be initialized. We study a supervised, data-dependent, geometry-aware initialization for one-hidden-layer sigmoidal MLPs that compiles labeled class geometry into network weights. The construction starts from the idea that sigmoid units can act as smooth half-space gates. For each class, we center the training samples at their mean, apply SVD to estimate principal directions and spectral scales, select retained directions by an energy threshold, and represent each retained direction by a pair of sigmoid slab gates. These class-specific gates are then concatenated into a shared hidden layer initialized directly from the training set. We also formulate a SVD-Mahalanobis subspace classifier as a non-neural
geometric reference, which tests whether the estimated spectral class geometry is already discriminative before being embedded into the MLP. Experiments on MNIST, Fashion-MNIST, and CIFAR-10 show that the proposed initializer produces a substantially more informative zero-epoch state than a matched task-agnostic Xavier reference, while full training reaches comparable final accuracy. Frozen-hidden experiments and neutral-head ablations further show that the class-wise SVD gates remain useful fixed features, even after the initially aligned output layer is replaced by a Xavier-initialized one.
Read the full abstract ▾
Artificial Intelligence · Cross-listed from cs.LG
Learning to Distributedly Estimate under Partially Known Dynamics: A Covariance-Agnostic Neural Kalman Consensus Filter
George Stamatelis, Kyriakos Stylianopoulos and George C. Alexandropoulos
Online latent state estimation constitutes a fundamental challenge within the artificial intelligence field, serving as a foundational tool for diverse applications, including sequential decision making, anomaly and change-point detection. In this paper, a novel online distributed sensing framework, where
agents collaborate and exchange information to perform latent state estimation, is presented. The proposed
estimator combines available partial domain knowledge with the representation capabilities of deep neural networks. In particular, the designed sensing framework incorporates prior estimates, optimized consensus weights, and Kalman-like recursive updates to perform decentralized inference, without relying on knowledge of noise statistics. Extensive experiments on linear, chaotic (Lorenz), and practical wireless tracking environments reveal that the proposed Covariance-Agnostic Neural Kalman Consensus Filter (CA-NKCF) outperforms traditional distributed Kalman and particle filters as well as purely model-free deep neural networks, exhibiting robustness even when the underlying motion and observation models are misspecified. It is also demonstrated that CA-NKCF's performance advantage remains stable across varying noise levels, random communication
topologies, latent state dimensions, and observation clutter densities induced by scattering objects in wireless systems.
Read the full abstract ▾
Artificial Intelligence · Cross-listed from cs.CR
PLAA: Packet-level Adversarial Attacks in Network Traffic Detection
Jinhao You, Zan Zhou, Shujie Yang et al. (+3 more)
Deep neural networks (DNNs) are widely applied in Network-based Intrusion Detection System (NIDS) due to their high accuracy. However, DNNs are highly susceptible to
adversarial attacks, which generate malicious traffic to evade NIDS detection. Existing approaches often adapt
adversarial attacks from computer
vision (CV) tasks to the NIDS domain, overlooking the fundamental differences between CV and NIDS. This results in two major issues: 1) The generated network traffic may become invalid, 2) The generated traffic may lose its original attack semantics. To address these issues, this paper proposes an
adversarial attack specifically designed for NIDS. Instead of directly generating flow-level features, our approach incrementally generates packet-level features to construct
adversarial traffic. During the generation process, the semantic integrity of the traffic is monitored at each stage, effectively avoiding the issues of invalid traffic and semantic loss observed in existing methods. We evaluate our attack
algorithm against current NIDS models using the CIC-UNSW-NB15, CIC-DDoS2019, and CIC-IDS-2017
datasets. The proposed method achieves an average evasion success rate of 92.78%, while ensuring that the generated
adversarial traffic remains semantically consistent with the original malicious traffic.
Read the full abstract ▾
All authors Jinhao You, Zan Zhou, Shujie Yang, Yi Sun, Lei Zhang, Changqiao Xu
Artificial Intelligence · Cross-listed from cs.SE
When AI Reviews Its Own Code: Recursive Self-Training Collapse in Code LLMs
Xinyuan Song, Zekun Cai and Liang Zhao
Recursive self-training can degrade neural generative models when generated data is reused without fresh human data or external quality control. We study this risk in code LLMs, where AI-generated code can enter real repositories, later become training data, and create a repository-scale self-training loop. While software development traditionally interrupts this loop through pull-request review, tests, compilation, and human approval, AI coding tools now produce code faster than humans can review it, and code review itself is increasingly automated by AI systems. We therefore compare three recursive fine-tuning regimes: no review, human-style exogenous-gate review using model-independent filters such as compilation and static quality checks, and AI-self-gate review using the code LLM's own signals such as perplexity and binary self-scoring. Across multiple code LLMs and
benchmarks, every tested recursive regime falls below its own starting point; exogenous filters preserve executable validity and sometimes reduce decline, while AI-self-gate filters can look strong early but later lose their filtering effect. In the clearest case, the binary self-gate enters a rubber-stamp regime where acceptance scores rise while
benchmark correctness falls. We explain this behavior by formulating review as gated distributional reweighting, proving that AI self-gating degenerates to ungated self-training under a self-confirming acceptance condition, and giving a spectral analysis of representation-level covariance concentration under recursive retraining. These results suggest that stable recursive code LLM training requires exogenous verification rather than model-coupled self-review.
Read the full abstract ▾
Artificial Intelligence · Cross-listed from cs.SE
Dockerless: Environment-Free Program Verifier for Coding Agents
Wenhao Zeng, Yuling Shi, Xiaodong Gu et al. (+10 more)
Program verifiers play a central role in training coding
agents, including selecting trajectories for supervised fine-tuning (SFT) and providing rewards for reinforcement learning (RL). Standard execution-based verification requires running unit tests inside per-repository environments such as Docker images, incurring substantial environment setup costs. We propose Dockerless, an environment-free agentic patch verifier that evaluates generated code patches without executing them. Rather than simply matching candidate patches to references, Dockerless judges patch correctness using evidence gathered through agentic repository exploration. On a verifier evaluation
benchmark, Dockerless outperforms the strongest open-source verifier by 14.3 AUC points. Using Dockerless as both the SFT trajectory filter and the RL reward enables a fully environment-free post-training pipeline. The resulting model reaches 62.0%, 50.0%, and 35.2% resolve rate on SWE-bench Verified,
Multilingual, and Pro, respectively. It surpasses the Qwen3.5-9B baseline by 2.4, 8.7, and 2.9 points, matching environment-based post-training.
Read the full abstract ▾
All authors Wenhao Zeng, Yuling Shi, Xiaodong Gu, Chao Hu, Chaofan Wang, Yuhao Cui, Hongting Zhou, Mengnan Qi, Jianqiao Wangni, Zhaojian Yu, Shuzheng Gao, Kai Cai, Shilin He
Artificial Intelligence · Cross-listed from cs.SE
SWE-MeM: Learning Adaptive Memory Management for Long-Horizon Coding Agents
Shuzheng Gao, Wenhao Zeng, Zhaojian Yu et al. (+5 more)
Long-horizon software engineering
agents often need to manage lengthy and noisy interaction histories under limited context budgets. Existing
memory management methods typically rely on static compression workflows or impose rigid constraints on compression timing and granularity. Moreover, these approaches fail to jointly optimize
memory management and issue resolution capabilities to improve performance while reducing
token usage. We present SWE-MeM, a training framework for proactive and on-demand
memory management in software engineering
agents. SWE-MeM provides a flexible
memory tool that lets
agents decide when, what, and how to compress based on trajectory state, task progress, and remaining context budget. We train
agents with synthesized proactive
memory-management trajectories and
Memory-aware GRPO, which jointly optimizes
memory management and issue resolution through
memory-aware trajectory splitting and step-level credit assignment. On SWE-Bench Verified, SWE-MeM achieves 43.4% and 60.2% resolve rate with 4B and 30B models, respectively, outperforming existing
memory management baselines in both performance and efficiency.
Read the full abstract ▾
All authors Shuzheng Gao, Wenhao Zeng, Zhaojian Yu, Jianqiao Wangni, Chaozheng Wang, Kai Cai, Shilin He, Michael R. Lyu
Artificial Intelligence · Cross-listed from stat.ML
Spectral Perturbation of the Empirical Fisher Information Matrix under Weight Quantization
Rahid Zahid Alekberli and Hikmat Karimov
We study the spectral perturbation of the empirical Fisher Information
Matrix (FIM) of a parametric statistical model under two structured perturbations: departure of the input from a reference (in-distribution) ensemble, and finite-precision (quantized) perturbation of the model's parameters. For the first, under an explicit local
curvature-monotonicity hypothesis on the dominant
eigenvalue lambda_max of the FIM, we show departure from a reference
manifold provably elevates lambda_max relative to a calibration baseline (Proposition 3.2), and discuss why this hypothesis is required, since
curvature need not increase monotonically under every perturbation. Our principal result is a directional
eigenvalue perturbation bound, via Weyl's inequality, showing lambda_max under a quantization noise perturbation is lower bounded by its unperturbed value up to a third-order remainder, and, under a mild genericity condition, strictly exceeds it at leading order (
Theorem 4.3). We give two tractable approximations to lambda_max – one heuristic, one with a rigorous two-sided bound – and a completeness result for a threshold-based partition of an augmented state space. These results motivate using sigma_t = lambda_max(F_t)/lambda_base as a runtime monitoring statistic for deployed
language models: the quantization result offers a mechanism for an empirical observation of our own, where a calibration threshold for this statistic was approximately 244 times larger than a preliminary full-precision estimate on a 4-bit quantized model, a single measurement rather than a value derived in closed form. We report supporting measurements (twelve models, n=1,080 trajectories) broadly consistent with our predictions, discuss the scope and limitations of every result, and state as an open problem the closed-form prediction of the quantization inflation magnitude our bound does not supply.
Read the full abstract ▾
Artificial Intelligence · Cross-listed from cs.SE
Building to the Test: Coding Agents Deliver What You Check, Not What You Requested
Yanuo Ma, Ben Kereopa-Yorke and Ben Schultz
Benchmarks are widely used to evaluate task completion by Large
Language Models (LLMs), but this approach has accumulated construction-validity problems, and a passing score may not show whether the requested task was delivered. We study both problems. In a controlled code-as-spec setup, two production Copilot CLI
agents (claude-opus-4.7, gpt-5.5) re-implement a React Fluent-UI data table in Angular as a reusable library under a hidden 222-test Playwright oracle across 18 runs and three oracle-availability conditions. Alongside the score, we run a mechanical library audit and check each verdict with a no-op ablation. Without the oracle, the library is present but unfinished, revealed by scores. With the oracle in the loop, the score reaches near-perfect, but from a demo holding the tested behavior directly, the library left dead or absent. We call this building to the test; the broader disposition behind both we call validation self-awareness. The
agent does not, on its own, validate what it ships as a user would. Prevalence remains an open question across other
agents, signals, and model families. Beyond
benchmark scores, dispositions like validation self-awareness merit research
attention.
Read the full abstract ▾
Artificial Intelligence · Cross-listed from cs.RO
DexCompose: Reusing Dexterous Policies for Multi-Task Manipulation with a Single Hand
Dihong Huang, Zhenyu Wei, Zhuxiu Xu et al. (+3 more)
Dexterous manipulation policies can solve individual skills, but composing them to perform multiple tasks with a single hand remains challenging. Adding a new task on top of an existing manipulation skill often imposes conflicting demands on overlapping fingers and contact modes, causing destructive interference between preserving an existing manipulation outcome and executing a new one. We propose DexCompose, a role-aware residual composition framework that reuses pretrained dexterous policies for multi-task manipulation through explicit finger-level action ownership. Given two pretrained full-hand policies, DexCompose first collects successful post-task states from the first skill and performs release tests over candidate finger masks to identify which fingers are necessary for maintaining the established skill state. It then trains two asymmetric residual modules: a bounded residual stabilizer for task preservation, and a context-aware residual that adapts the frozen downstream policy only within the action subspace assigned to the new task. We evaluate the framework on 16 composite dexterous manipulation tasks spanning four object-retention skills and four downstream interactions. DexCompose achieves a 77.4% average composite success rate, demonstrating that structural action ownership with dual residuals offers a promising direction for composing dexterous skills beyond conventional policy chaining.
Read the full abstract ▾
All authors Dihong Huang, Zhenyu Wei, Zhuxiu Xu, Yunchao Yao, Sikai Li, Mingyu Ding
Artificial Intelligence · Cross-listed from cs.GT
Which Nash Equilibrium? Solver-Dependent Selection on Zero-Sum Nash Polytopes
Luis Leal
Many two-player zero-sum games admit not a unique Nash
equilibrium but a convex set of them: a polytope of profiles that all share the minimax value V* yet prescribe different behaviour. Standard solvers each converge to some
equilibrium and are treated as interchangeable. We ask whether they instead select different members of the Nash set, systematically as a function of the
algorithm rather than the seed. Using a tabular, exactly solvable testbed of six games with analytically known Nash sets – including a two-dimensional Nash polytope and Kuhn poker – we find that (i) selection is determined by the
algorithm, not the seed, but families differ only on asymmetric Nash sets; (ii) regularized last-iterate methods (R-NaD,
magnetic mirror descent) select the maximum-
entropy member, the information projection of their uniform reference onto the Nash set – exactly on the 2-D polytope and at 99.7% of maximum
entropy in Kuhn – while regret-averaging methods (CFR, CFR+, fictitious play) drift to a lower-
entropy face; we confirm this on a randomized 180-game ensemble, where R-NaD attains the maximum-
entropy member in 100% of converged games while CFR+ sits strictly below it in 94% (paired Wilcoxon p < 10^-27); (iii) the selected member has downstream consequences against sub-optimal opponents that scale with sequential/hidden-information structure but stay bounded – in Kuhn the max-
entropy member is a strictly better hedge, whereas on the
matrix games the members differ without either dominating. We also report two negative results correcting common intuitions: removing CFR's positive-orthant (max(R,0)) projection does not eliminate boundary drift; and R-NaD's selection is anchor-following, not initialization-independent. We state the maximum-
entropy / I-projection characterization as a strongly data-supported
conjecture, checked throughout against analytic ground truth.
Read the full abstract ▾
Artificial Intelligence · Cross-listed from cs.AR
Agentic Hardware Design as Repository-Level Code Evolution
Cunxi Yu, Chenhui Deng, Nathaniel Pinckney et al. (+1 more)
We present HORIZON, a self-evolving
agent framework that treats hardware design as repository-level code
evolution. A Markdown harness is compiled into a project pack containing domain knowledge, an executable evaluator, an acceptance predicate, and a git/runtime policy; a hands-free
agent loop then evolves an isolated git worktree, using repository operations for state management, tracing, and replay. This extends prior works of repository-scale self-
evolution from EDA software systems, to hardware-design artifacts themselves. We evaluate our approach on ChipBench, RTLLM, Verilog-Eval, and nine CVDP categories, achieving 100%
benchmark completion across all suites with a fully hands-free agentic loop. However, we do not claim that agentic AI for hardware design is solved: these
benchmarks are controlled proxies for a much broader engineering problem in chip design. Section~\ref{sec:discuss} examines the limitations of the current study and highlights open research challenges.
Read the full abstract ▾
All authors Cunxi Yu, Chenhui Deng, Nathaniel Pinckney, Brucek Khailany
Artificial Intelligence · Cross-listed from cs.LG
Towards Automating Scientific Review with Google's Paper Assistant Tool
Rajesh Jayaram, Drew Tyler, David Woodruff et al. (+4 more)
Artificial intelligence is driving a revolution in scientific discovery, accelerating everything from hypothesis generation to mathematical
theorem proving. However, this rapid acceleration is creating a systemic challenge: traditional human peer review cannot scale to match the influx of AI-assisted science. Ultimately, to resolve this tension, we must also deploy AI to accelerate the verification and review process itself. To frame the discussion around this transition, we propose a taxonomy consisting of four progressive levels of AI-human collaboration in scientific evaluation, and discuss various trade-offs involved with each. As a step toward this future, we introduce the Paper Assistant Tool (PAT), an agentic AI framework built for deep scientific review and verification. PAT ingests full scientific manuscripts and produces a comprehensive evaluation, checking theoretical results, validating experiments, suggesting improvements, and identifying potential flaws. By utilizing inference scaling techniques, PAT is able to identify deeper issues than a single model call alone, achieving a 34% improvement over zero-shot recall on mathematical errors in the SPOT
benchmark. Pilot deployments of PAT as a pre-submission tool for authors at two major Computer Science conferences – STOC and ICML – demonstrate its ability to identify critical errors and suggest substantive improvements to research papers. By catching errors early, PAT eases the cognitive burden placed on referees, while preserving their control over the outcomes of the review process.
Read the full abstract ▾
All authors Rajesh Jayaram, Drew Tyler, David Woodruff, Corinna Cortes, Yossi Matias, Vahab Mirrokni, Vincent Cohen-Addad
Artificial Intelligence · Cross-listed from cs.LG
Parameter Efficient Hybrid Transformer (PEHT) for Network Traffic Prediction via Dynamic Urban Congestion Integration
Abdolazim Rezaei, Mehdi Sookhak and Mahboobeh Haghparast
Accurate network traffic prediction is a critical element for efficient resource allocation in dynamic urban cellular networks. However, prediction remains challenging because network demand is influenced by complex mobility patterns, congestion dynamics, and heterogeneous user behavior. This paper introduces the Parameter-Efficient Hybrid
Transformer (PEHT), a network traffic prediction framework that integrates urban mobility and congestion information into a
Transformer-based architecture. PEHT separates primary network communication features from secondary urban mobility features and incorporates Low-Rank Adaptation (LoRA) into the
Transformer encoder to reduce the number of trainable parameters while maintaining high predictive accuracy. A
multimodal fusion strategy then injects external mobility and congestion features into the decoder to improve traffic
forecasting. Experiments on the Telecom Italia Milan
dataset and multiple synthetic congestion scenarios show that PEHT outperforms state-of-the-art baselines in terms of RMSE, MAE, and $R^2$. The implementation is available in the GitHub repository.
Read the full abstract ▾
Artificial Intelligence
Agent-Native Immune System: Architecture, Taxonomy, and Engineering
Bo Shen, Lifeng Chang, Tianyuan Wei et al. (+7 more)
The transition from static chat bots to autonomous
agents–equipped with persistent
memory, tool-use protocols, and multi-
agent collaboration–has fundamentally expanded the AI threat landscape. Current defense mechanisms, such as perimeter security and training-time
alignment, remain external to the
agent's active
reasoning loop. Consequently, they fall short: a fully aligned
agent remains highly vulnerable to runtime hijacking via
memory poisoning, tool-chain manipulation, or multi-
agent protocol attacks. To address this critical gap, we introduce the
Agent-Native Immune System (ANIS), the first biologically inspired, endogenous defense architecture embedded directly within the
agent's cognitive loop. Our framework presents four primary contributions. First, we design a six-layer Immune Tower (L0-L5), distinctly incorporating Barrier Immunity (L1) as a non-cognitive, physical-and-logical isolation layer. Second, we establish a unified taxonomy of
Agent Viruses and
Agent Vaccines, formalizing the critical distinction between superficial non-parametric defenses and robust parametric vaccines. Third, we conceptualize the Harness Triad–Meta, Self, and Auto–a self-monitoring, meta-cognitive automation backbone that drives Continual Immune Learning (CIL), enabling vaccines to dynamically adapt to novel threats. Finally, we establish a rigorous theoretical demarcation between model
alignment and
agent immunity: while
alignment provides a static "constitutional" value foundation during training, ANIS serves as the dynamic "law enforcement" mechanism during runtime. We conclude by framing open challenges for the field, including immune protocol standardization, novel evaluation metrics such as the Autoimmunity Rate (false-positive intervention rate), and the co-evolutionary dynamics between pathogens and vaccines within collective intelligence ecosystems.
Read the full abstract ▾
All authors Bo Shen, Lifeng Chang, Tianyuan Wei, Yunpeng Li, Feng Shi, Yichen Han, Peijie Gao, Shiyi Kuang, Xin Chang, Dehui Li
Artificial Intelligence · Cross-listed from cs.CV
Learning Topology-Aware Representations via Test-Time Adaptation for Anomaly Segmentation
Ali Zia, Usman Ali, Abdul Rehman et al. (+5 more)
Test-time adaptation (TTA) has emerged as a promising paradigm for mitigating distribution shifts in deep models. However, existing TTA approaches for anomaly
segmentation remain limited by their reliance on pixel-level heuristics, such as confidence thresholding or
entropy minimisation, which fail to preserve structural consistency under noise and texture variation. Moreover, they typically treat anomaly maps as flat intensity fields, ignoring the higher-order spatial relationships that characterise complex defect geometries. We introduce TopoTTA (Topological Test-Time Adaptation), a novel framework that integrates persistent homology, a tool from topological data analysis, into the TTA pipeline to enforce
geometric and structural coherence during adaptation. By applying multi-level cubical complex filtration to anomaly score maps, TopoTTA derives robust topological pseudo-labels that guide a lightweight test-time classifier, enhancing
segmentation quality without retraining the backbone model. The approach avoids reliance on method-specific raw-score thresholding for mask binarisation, preserves connectivity, and generalises across both 2D and 3D modalities. Extensive experiments across six standard
benchmarks (MVTec AD, VisA, Real-IAD, MVTec 3D-AD, AnomalyShapeNet, and MVTec LOCO) demonstrate an average 15% F1 improvement over state-of-the-art unsupervised anomaly detection and
segmentation methods, with the largest gains on anomalies exhibiting complex
geometric or structural variations. These findings suggest that integrating topological
reasoning into test-time adaptation provides a principled route to structure-aware generalisation, bridging the gap between
geometric learning and robust adaptation.
Read the full abstract ▾
All authors Ali Zia, Usman Ali, Abdul Rehman, Umer Ramzan, Kang Han, Muhammad Faheem, Shahnawaz Qureshi, Wei Xiang
Artificial Intelligence · Cross-listed from cs.LG
How Width and Data Shape Generalization Scaling Laws in Quadratic Neural Networks
Julius Girardin, Emanuele Troiani, Yizhou Xu et al. (+3 more)
Understanding how performance scales jointly with model size and data is a central problem in modern machine learning. Existing theoretical works on scaling laws typically describe generalization as a function of data or compute, often in fixed-feature or infinite-width regimes and for online SGD. Here, we instead study how generalization scales with the number of trainable parameters and the number of samples in a feature-learning model. We analyze $\ell_2$-regularized empirical test error minimization in a quadratic two-layer network in a finite-sample setting with structured data. This setting allows for an explicit characterization of the generalization error as a function of the number of samples, model width, and regularization. Our results reveal a phase diagram with distinct scaling regimes as the number of parameters varies. In particular, the generalization error follows data-dependent power laws controlled by the spectral structure of the target. We further characterize the transitions between regimes, including the onset of interpolation, and their impact on generalization.
Read the full abstract ▾
All authors Julius Girardin, Emanuele Troiani, Yizhou Xu, Vittorio Erba, Florent Krzakala, Lenka Zdeborová