OpenRaiser/Intern-Atlas huggingface dataset:
To Defend Against Cyber Attacks, We Must Teach AI Agents to Hack
Venue: arXiv cs.CR 2026
Year: 2026

For over a decade, cybersecurity has relied on human labor scarcity to limit attackers to high-value targets manually or generic automated attacks at scale. Building sophisticated exploits requires deep expertise and manual effort, leading defenders to assume adversaries cannot afford tailored attacks at scale. AI agents break this balance by automating vulnerability discovery and exploitation across thousands of targets, needing only small success rates to remain profitable. Current developers focus on preventing misuse through data filtering, safety alignment, and output guardrails. Such protections fail against adversaries who control open-weight models, bypass safety controls, or develop offensive capabilities independently. We argue that AI-agent-driven cyber attacks are inevitable, requiring a fundamental shift in defensive strategy. In this position paper, we identify why existing defenses cannot stop adaptive adversaries and demonstrate that defenders must develop offensive security intelligence. We propose three actions for building frontier offensive AI capabilities responsibly. First, construct comprehensive benchmarks covering the full attack lifecycle. Second, advance from workflow-based to trained agents for discovering in-wild vulnerabilities at scale. Third, implement governance restricting offensive agents to audited cyber ranges, staging release by capability tier, and distilling findings into safe defensive-only agents. We strongly recommend treating offensive AI capabilities as essential defensive infrastructure, as containing cybersecurity risks requires mastering them in controlled settings before adversaries do.
CaST: Causal Discovery via Spatio-Temporal Graphs in Disaster Tweets
Venue: arXiv cs.SI 2026
Year: 2026

Understanding causality between real-world events from social media is essential for situational awareness, yet existing causal discovery methods often overlook the interplay between semantic, spatial, and temporal contexts. We propose CaST: Causal Discovery via Spatio-Temporal Graphs, a unified framework for causal discovery in disaster domain that integrates semantic similarity and spatio-temporal proximity using Large Language Models (LLMs) pretrained on disaster datasets. CaST constructs an event graph for each window of tweets. Each event extracted from tweets is represented as a node embedding enriched with its contextual semantics, geographic coordinates, and temporal features. These event nodes are then connected to form a spatio-temporal event graph, which is processed using a multi-head Graph Attention Network (GAT) \cite{gat} to learn directed causal relationships. We construct an in-house dataset of approximately 167K disaster-related tweets collected during Hurricane Harvey and annotated following the MAVEN-ERE schema. Experimental results show that CaST achieves superior performance over both traditional and state-of-the-art methods. Ablation studies further confirm that incorporating spatial and temporal signals substantially improves both recall and stability during training. Overall, CaST demonstrates that integrating spatio-temporal reasoning into event graphs enables more robust and interpretable causal discovery in disaster-related social media text.
Recommender system in X inadvertently profiles ideological positions of users
Venue: arXiv cs.SI 2026
Year: 2026

Studies on recommendations in social media have mainly analyzed the quality of recommended items (e.g., their diversity or biases) and the impact of recommendation policies (e.g., in comparison with purely chronological policies). We use a data donation program, collecting more than 2.5 million friend recommendations made to 682 volunteers on X over a year, to study instead how real-world recommenders learn, represent and process political and social attributes of users inside the so-called black boxes of AI systems. Using publicly available knowledge on the architecture of the recommender, we inferred the positions of recommended users in its embedding space. Leveraging ideology scaling calibrated with political survey data, we analyzed the political position of users in our study (N=26,509 among volunteers and recommended contacts) among several attributes, including age and gender. Our results show that the platform's recommender system produces a spatial ordering of users that is highly correlated with their Left-Right positions (Pearson rho=0.887, p-value < 0.0001), and that cannot be explained by socio-demographic attributes. These results open new possibilities for studying the interaction between human and AI systems. They also raise important questions linked to the legal definition of algorithmic profiling in data privacy regulation by blurring the line between active and passive profiling. We explore new constrained recommendation methods enabled by our results, limiting the political information in the recommender as a potential tool for privacy compliance capable of preserving recommendation relevance.
InfMem: Learning System-2 Memory Control for Long-Context Agent
Venue: arXiv cs.CL 2026
Year: 2026

Reasoning over ultra-long documents requires synthesizing sparse evidence scattered across distant segments under strict memory constraints. While streaming agents enable scalable processing, their passive memory update strategy often fails to preserve low-salience bridging evidence required for multi-hop reasoning. We propose InfMem, a control-centric agent that instantiates System-2-style control via a PreThink-Retrieve-Write protocol. InfMem actively monitors evidence sufficiency, performs targeted in-document retrieval, and applies evidence-aware joint compression to update a bounded memory. To ensure reliable control, we introduce a practical SFT-to-RL training recipe that aligns retrieval, writing, and stopping decisions with end-task correctness. On ultra-long QA benchmarks from 32k to 1M tokens, InfMem consistently outperforms MemAgent across backbones. Specifically, InfMem improves average absolute accuracy by +10.17, +11.84, and +8.23 points on Qwen3-1.7B, Qwen3-4B, and Qwen2.5-7B, respectively, while reducing inference time by $3.9\times$ on average (up to $5.1\times$) via adaptive early stopping.
Every Bit Counts: A Theoretical Study of Precision-Expressivity Tradeoffs in Quantized Transformers
Venue: arXiv cs.LG 2026
Year: 2026

Quantization reduces the numerical precision of Transformer computations and is widely used to accelerate inference, yet its effect on expressivity remains poorly characterized. We demonstrate a fine-grained theoretical tradeoff between expressivity and precision: For every p we exhibit a function Γ, inspired by the equality function, and prove that a one-layer softmax Transformer can compute Γ, with p bits of precision, but not with p-1 bits of precision. This result concretely explains the widely observed phenomenon of empirical loss of expressivity when quantization is used. Practically, it suggests that tasks requiring equality-like comparisons (exact match, membership, etc.) are especially sensitive to quantization. Dropping even one bit can cross a threshold where the model cannot represent the needed comparison reliably. Thus, it paves the way for developing heuristics that will help practitioners choose how much quantization is possible: the precision should be chosen as a function of the length of equality to be checked for the specific task. Our proofs combine explicit finite-precision Transformer constructions with communication-complexity lower bounds, yielding a tight "one-bit" threshold.
On the Feasibility of Hybrid Homomorphic Encryption for Intelligent Transportation Systems
Venue: arXiv cs.CR 2026
Year: 2026

Many Intelligent Transportation Systems (ITS) applications require strong privacy guarantees for both users and their data. Homomorphic encryption (HE) enables computation directly on encrypted messages and thus offers a compelling approach to privacy-preserving data processing in ITS. However, practical HE schemes incur substantial ciphertext expansion and communication overhead, which limits their suitability for time-critical transportation systems. Hybrid homomorphic encryption (HHE) addresses this challenge by combining a homomorphic encryption scheme with a symmetric cipher, enabling efficient encrypted computation while dramatically reducing communication cost. In this paper, we develop theoretical models of representative ITS applications that integrate HHE to protect sensitive vehicular data. We then perform a parameter-based evaluation of the HHE scheme Rubato to estimate ciphertext sizes and communication overhead under realistic ITS workloads. Our results show that HHE achieves orders-of-magnitude reductions in ciphertext size compared with conventional HE while maintaining cryptographic security, making it significantly more practical for latency-constrained ITS communication.
Hierarchical Entity-centric Reinforcement Learning with Factored Subgoal Diffusion
Venue: arXiv cs.LG 2026
Year: 2026

We propose a hierarchical entity-centric framework for offline Goal-Conditioned Reinforcement Learning (GCRL) that combines subgoal decomposition with factored structure to solve long-horizon tasks in domains with multiple entities. Achieving long-horizon goals in complex environments remains a core challenge in Reinforcement Learning (RL). Domains with multiple entities are particularly difficult due to their combinatorial complexity. GCRL facilitates generalization across goals and the use of subgoal structure, but struggles with high-dimensional observations and combinatorial state-spaces, especially under sparse reward. We employ a two-level hierarchy composed of a value-based GCRL agent and a factored subgoal-generating conditional diffusion model. The RL agent and subgoal generator are trained independently and composed post hoc through selective subgoal generation based on the value function, making the approach modular and compatible with existing GCRL algorithms. We introduce new variations to benchmark tasks that highlight the challenges of multi-entity domains, and show that our method consistently boosts performance of the underlying RL agent on image-based long-horizon tasks with sparse rewards, achieving over 150% higher success rates on the hardest task in our suite and generalizing to increasing horizons and numbers of entities. Rollout videos are provided at: https://sites.google.com/view/hecrl
AROLA: A Modular Layered Architecture for Scaled Autonomous Racing
Venue: arXiv cs.RO 2026
Year: 2026

Autonomous racing has advanced rapidly, particularly on scaled platforms, and software stacks must evolve accordingly. In this work, AROLA is introduced as a modular, layered software architecture in which fragmented and monolithic designs are reorganized into interchangeable layers and components connected through standardized ROS 2 interfaces. The autonomous-driving pipeline is decomposed into sensing, pre-processing, perception, localization and mapping, planning, behavior, control, and actuation, enabling rapid module replacement and objective benchmarking without reliance on custom message definitions. To support consistent performance evaluation, a Race Monitor framework is introduced as a lightweight system through which lap timing, trajectory quality, and computational load are logged in real time and standardized post-race analyses are generated. AROLA is validated in simulation and on hardware using the RoboRacer platform, including deployment at the 2025 RoboRacer IV25 competition. Together, AROLA and Race Monitor demonstrate that modularity, transparent interfaces, and systematic evaluation can accelerate development and improve reproducibility in scaled autonomous racing.
Time-Critical Multimodal Medical Transportation: Organs, Patients, and Medical Supplies
Venue: arXiv cs.CL 2026
Year: 2026

Timely transportation of organs, patients, and medical supplies is critical to modern healthcare, particularly in emergencies and transplant scenarios where even short delays can severely impact outcomes. Traditional ground-based vehicles such as ambulances are often hindered by traffic congestion; while air vehicles such as helicopters are faster but costly. Emerging air vehicles -- Unmanned Aerial Vehicles and electric vertical take-off and landing aircraft -- have lower operating costs, but remain limited by range and susceptibility to weather conditions. A multimodal transportation system that integrates both air and ground vehicles can leverage the strengths of each to enhance overall transportation efficiency. This study introduces a constructive greedy heuristic algorithm for multimodal vehicle dispatching for medical transportation. Four different fleet configurations were tested: (i) ambulances only, (ii) ambulances with Unmanned Aerial Vehicles, (iii) ambulances with electric vertical take-off and landing aircraft, and (iv) a fully integrated fleet of ambulances, Unmanned Aerial Vehicles, and electric vertical take-off and landing aircraft. The algorithm incorporates payload consolidation across compatible routes, accounts for traffic congestion in ground operations and weather conditions in aerial operations, while enabling rapid vehicle dispatching compared to computationally intensive optimization models. Using a common set of conditions, we evaluate all four fleet types to identify the most effective configurations for fulfilling medical transportation needs while minimizing operating costs, recharging/fuel costs, and total transportation time.
Entropy-Guided Dynamic Tokens for Graph-LLM Alignment in Molecular Understanding
Venue: arXiv cs.LG 2026
Year: 2026

Molecular understanding is central to advancing areas such as scientific discovery, yet Large Language Models (LLMs) struggle to understand molecular graphs effectively. Existing graph-LLM bridges often adapt the Q-Former-style connector with fixed-length static tokens, which is originally designed for vision tasks. These designs overlook stereochemistry and substructural context and typically require costly LLM-backbone fine-tuning, limiting efficiency and generalization. We introduce EDT-Former, an Entropy-guided Dynamic Token Transformer that generates tokens aligned with informative molecular patches, thereby preserving both local and global structural features for molecular graph understanding. Beyond prior approaches, EDT-Former enables alignment between frozen graph encoders and LLMs without tuning the LLM backbone (excluding the embedding layer), resulting in computationally efficient finetuning, and achieves stateof-the-art results on MoleculeQA, Molecule-oriented Mol-Instructions, and property prediction benchmarks (TDC, MoleculeNet), underscoring its effectiveness for scalable and generalizable multimodal molecular understanding
Exploring Collaborative Immersive Visualization & Analytics for High-Dimensional Scientific Data through Domain Expert Perspectives
Venue: arXiv cs.HC 2026
Year: 2026

Cross-disciplinary teams increasingly work with high-dimensional scientific datasets, yet fragmented toolchains and limited support for shared exploration hinder collaboration. Prior immersive visualization and analytics research has emphasized individual interaction, leaving open how multi-user collaboration can be supported at scale. To fill this critical gap, we conduct semi-structured interviews with 20 domain experts from diverse academic, government, and industry backgrounds. Using deductive-inductive hybrid thematic analysis, we identify four collaboration-focused themes: workflow challenges, adoption perceptions, prospective features, and anticipated usability and ethical risks. These findings show how current ecosystems disrupt coordination and shared understanding, while highlighting opportunities for effective multi-user engagement. Our study contributes empirical insights into collaboration practices for high-dimensional scientific data visualization and analysis, offering design implications to enhance coordination, mutual awareness, and equitable participation in next-generation collaborative immersive platforms. These contributions point toward future environments enabling distributed, cross-device teamwork on high-dimensional scientific data.
A Parametrized Complexity View on Robust Scheduling with Budgeted Uncertainty
Venue: arXiv cs.DM 2026
Year: 2026

In this study, we investigate a robust single-machine scheduling problem under processing time uncertainty. The uncertainty is modeled using the budgeted approach, where each job has a nominal and deviation processing time, and the number of deviations is bounded by Gamma. The objective is to minimize the maximum number of tardy jobs over all possible scenarios. Since the problem is NP-hard in general, we focus on analyzing its tractability under the assumption that some natural parameter of the problem is bounded by a constant. We consider three parameters: the robustness parameter Gamma, the number of distinct due dates in the instance, and the number of jobs with nonzero deviations. Using parametrized-complexity theory, we prove that the problem is W[1]-hard with respect to Gamma, but can be solved in XP time with respect to the same parameter. With respect to the number of different due dates, we establish a stronger hardness result by showing that the problem remains NP-hard even when there are only two different due dates and is solvable in pseudo-polynomial time when the number of due dates is upper bounded by a constant. To complement these results, we show that the case of a common (single) due date, reduces to a robust binary knapsack problem with equal item profits, which we prove to be solvable in polynomial time. Finally, we prove that the problem is solvable in FPT time with respect to the number of nonzero deviations.
AmharicStoryQA: A Multicultural Story Question Answering Benchmark in Amharic
Venue: arXiv cs.CL 2026
Year: 2026

With the growing emphasis on multilingual and cultural evaluation benchmarks for large language models, language and culture are often treated as synonymous, and performance is commonly used as a proxy for a models understanding of a given language. In this work, we argue that such evaluations overlook meaningful cultural variation that exists within a single language. We address this gap by focusing on narratives from different regions of Ethiopia and demonstrate that, despite shared linguistic characteristics, region-specific and domain-specific content substantially influences language evaluation outcomes. To this end, we introduce \textbf{\textit{AmharicStoryQA}}, a long-sequence story question answering benchmark grounded in culturally diverse narratives from Amharic-speaking regions. Using this benchmark, we reveal a significant narrative understanding gap in existing LLMs, highlight pronounced regional differences in evaluation results, and show that supervised fine-tuning yields uneven improvements across regions and evaluation settings. Our findings emphasize the need for culturally grounded benchmarks that go beyond language-level evaluation to more accurately assess and improve narrative understanding in low-resource languages.
Smell with Genji: Rediscovering Human Perception through an Olfactory Game with AI
Venue: arXiv cs.HC 2026
Year: 2026

Olfaction plays an important role in human perception, yet its subjective and ephemeral nature makes it difficult to articulate, compare, and share across individuals. Traditional practices like the Japanese incense game Genji-ko offer one way to structure olfactory experience through shared interpretation. In this work, we present Smell with Genji, an AI-mediated olfactory interaction system that reinterprets Genji-ko as a collaborative human-AI sensory experience. By integrating a game setup, a mobile application, and an LLM-powered co-smelling partner equipped with olfactory sensing and LLM-based conversation, the system invites participants to compare scents and construct Genji-mon patterns, fostering reflection through a dialogue that highlights the alignment and discrepancies between human and machine perception. This work illustrates how sensing-enabled AI can participate in olfactory experience alongside users, pointing toward new possibilities for AI-supported sensory interaction and reflection in HCI.
Simulating Human Audiovisual Search Behavior
Venue: arXiv cs.HC 2026
Year: 2026

Locating a target based on auditory and visual cues$\unicode{x2013}$such as finding a car in a crowded parking lot or identifying a speaker in a virtual meeting$\unicode{x2013}$requires balancing effort, time, and accuracy under uncertainty. Existing models of audiovisual search often treat perception and action in isolation, overlooking how people adaptively coordinate movement and sensory strategies. We present Sensonaut, a computational model of embodied audiovisual search. The core assumption is that people deploy their body and sensory systems in ways they believe will most efficiently improve their chances of locating a target, trading off time and effort under perceptual constraints. Our model formulates this as a resource-rational decision-making problem under partial observability. We validate the model against newly collected human data, showing that it reproduces both adaptive scaling of search time and effort under task complexity, occlusion, and distraction, and characteristic human errors. Our simulation of human-like resource-rational search informs the design of audiovisual interfaces that minimize search cost and cognitive load.
Reshaping Perception Through Technology: From Ancient Script to Large Language Models
Venue: arXiv cs.CY 2026
Year: 2026

As large language models reshape how we create and access information, questions arise about how to frame their role in human creative and cognitive life. We argue that AI is best understood not as artificial intelligence but as a new medium -- one that, like writing before it, reshapes perception and enables novel forms of creativity. Drawing on Marshall McLuhan's insight that "the medium is the massage," we trace a lineage of technologies -- from DNA and the nervous system to symbols, writing, and now LLMs -- that mold cognition through a shared logic of flexible unfolding and co-creation. We observe that as technologies become more externalized and decoupled from physiology, they introduce both greater creative potential and greater risk of inauthenticity and manipulation. This tension is acute with LLMs, but not unprecedented: ancient responses to writing reveal a recurring human tendency to project intelligence onto powerful new media. Rather than viewing AI as a competitor, we propose framing it as a medium that foregrounds artistic skills: aesthetic judgment, curation, and the articulation of vision. We discuss implications for education, creative practice, and how society might adapt to this new medium as it did to writing.
Joint Learning of Hierarchical Neural Options and Abstract World Model
Venue: arXiv cs.LG 2026
Year: 2026

Building agents that can perform new skills by composing existing skills is a long-standing goal of AI agent research. Towards this end, we investigate how to efficiently acquire a sequence of skills, formalized as hierarchical neural options. However, existing model-free hierarchical reinforcement algorithms need a lot of data. We propose a novel method, which we call AgentOWL (Option and World model Learning Agent), that jointly learns -- in a sample efficient way -- an abstract world model (abstracting across both states and time) and a set of hierarchical neural options. We show, on a subset of Object-Centric Atari games, that our method can learn more skills using much less data than baseline methods.