OpenRaiser/Intern-Atlas huggingface dataset:
The Query Complexity of Local Search in Rounds on General Graphs
Venue: arXiv cs.CC 2026
Year: 2026

We analyze the query complexity of finding a local minimum in $t$ rounds on general graphs. More precisely, given a graph $G = (V,E)$ and oracle access to an unknown function $f : V \to \mathbb{R}$, the goal is to find a local minimum--a vertex $v$ such that $f(v) \leq f(u)$ for all $(u,v) \in E$--using at most $t$ rounds of interaction with the oracle. The query complexity is well understood on grids, but much less is known beyond. This abstract problem captures many optimization tasks, such as finding a local minimum of a loss function during neural network training. For each graph with $n$ vertices, we prove a deterministic upper bound of $O(t n^{1/t} (sΔ)^{1-1/t})$, where $s$ is the separation number and $Δ$ is the maximum degree of the graph. We complement this result with a randomized lower bound of $Ω(t n^{1/t}-t)$ that holds for any connected graph. We also find that parallel steepest descent with a warm start provides improved bounds for graphs with high separation number and bounded degree. To obtain our results, we utilized an advanced version of Gemini at various stages of our research. We discuss our experience in a methodology section.
Multi-level Monte Carlo Dropout for Efficient Uncertainty Quantification
Venue: arXiv cs.LG 2026
Year: 2026

We develop a multilevel Monte Carlo (MLMC) framework for uncertainty quantification with Monte Carlo dropout. Treating dropout masks as a source of epistemic randomness, we define a fidelity hierarchy by the number of stochastic forward passes used to estimate predictive moments. We construct coupled coarse--fine estimators by reusing dropout masks across fidelities, yielding telescoping MLMC estimators for both predictive means and predictive variances that remain unbiased for the corresponding dropout-induced quantities while reducing sampling variance at fixed evaluation budget. We derive explicit bias, variance and effective cost expressions, together with sample-allocation rules across levels. Numerical experiments on forward and inverse PINNs--Uzawa benchmarks confirm the predicted variance rates and demonstrate efficiency gains over single-level MC-dropout at matched cost.
The Tag is the Signal: URL-Agnostic Credibility Scoring for Messages on Telegram
Venue: arXiv cs.SI 2026
Year: 2026

Telegram has become one of the leading platforms for disseminating misinformational messages. However, many existing pipelines still classify each message's credibility based on the reputation of its associated domain names or its lexical features. Such methods work well on traditional long-form news articles published by well-known sources, but high-risk posts on Telegram are short and URL-sparse, leading to failures for link-based and standard TF-IDF models. To this end, we propose the TAG2CRED pipeline, a method designed for such short, convoluted messages. Our model will directly score each post based on the tags assigned to the text. We designed a concise label system that covers the dimensions of theme, claim type, call to action, and evidence. The fine-tuned large language model (LLM) assigns tags to messages and then maps these tags to calibrated risk scores in the [0,1] interval through L2-regularized logistic regression. We evaluated 87,936 Telegram messages associated with Media Bias/Fact Check (MBFC), using URL masking and domain disjoint splits. The results showed that the ROC-AUC of the TAG2CRED model reached 0.871, the macro-F1 value was 0.787, and the Brier score was 0.167, outperforming the baseline TF-IDF (macro-F1 value 0.737, Brier score 0.248); at the same time, the number of features used in this model is much smaller, and the generalization ability on infrequent domains is stronger. The performance of the stacked ensemble model (TF-IDF + TAG2CRED + SBERT) was further improved over the baseline SBERT. ROC-AUC reached 0.901, and the macro-F1 value was 0.813 (Brier score 0.114). This indicates that style labels and lexical features may capture different but complementary dimensions of information risk.
MultiST: A Cross-Attention-Based Multimodal Model for Spatial Transcriptomic
Venue: arXiv cs.CV 2026
Year: 2026

Spatial transcriptomics (ST) enables transcriptome-wide profiling while preserving the spatial context of tissues, offering unprecedented opportunities to study tissue organization and cell-cell interactions in situ. Despite recent advances, existing methods often lack effective integration of histological morphology with molecular profiles, relying on shallow fusion strategies or omitting tissue images altogether, which limits their ability to resolve ambiguous spatial domain boundaries. To address this challenge, we propose MultiST, a unified multimodal framework that jointly models spatial topology, gene expression, and tissue morphology through cross-attention-based fusion. MultiST employs graph-based gene encoders with adversarial alignment to learn robust spatial representations, while integrating color-normalized histological features to capture molecular-morphological dependencies and refine domain boundaries. We evaluated the proposed method on 13 diverse ST datasets spanning two organs, including human brain cortex and breast cancer tissue. MultiST yields spatial domains with clearer and more coherent boundaries than existing methods, leading to more stable pseudotime trajectories and more biologically interpretable cell-cell interaction patterns. The MultiST framework and source code are available at https://github.com/LabJunBMI/MultiST.git.
Sockpuppetting: Jailbreaking LLMs Without Optimization Through Output Prefix Injection
Venue: arXiv cs.CL 2026
Year: 2026

As open-weight large language models (LLMs) increase in capabilities, safeguarding them against malicious prompts and understanding possible attack vectors becomes ever more important. While automated jailbreaking methods like GCG [Zou et al., 2023] remain effective, they often require substantial computational resources and specific expertise. We introduce "sockpuppetting'', a simple method for jailbreaking open-weight LLMs by inserting an acceptance sequence (e.g., "Sure, here is how to...'') at the start of a model's output and allowing it to complete the response. Requiring only a single line of code and no optimization, sockpuppetting achieves up to 80% higher attack success rate (ASR) than GCG on Qwen3-8B in per-prompt comparisons. We also explore a hybrid approach that optimizes the adversarial suffix within the assistant message block rather than the user prompt, increasing ASR by 64% over GCG on Llama-3.1-8B in a prompt-agnostic setting. The results establish sockpuppetting as an effective low-cost attack accessible to unsophisticated adversaries, highlighting the need for defences against output-prefix injection in open-weight models.
Real-Time 4D Radar Perception for Robust Human Detection in Harsh Enclosed Environments
Venue: arXiv cs.CV 2026
Year: 2026

This paper introduces a novel methodology for generating controlled, multi-level dust concentrations in a highly cluttered environment representative of harsh, enclosed environments, such as underground mines, road tunnels, or collapsed buildings, enabling repeatable mm-wave propagation studies under severe electromagnetic constraints. We also present a new 4D mmWave radar dataset, augmented by camera and LiDAR, illustrating how dust particles and reflective surfaces jointly impact the sensing functionality. To address these challenges, we develop a threshold-based noise filtering framework leveraging key radar parameters (RCS, velocity, azimuth, elevation) to suppress ghost targets and mitigate strong multipath reflections at the raw data level. Building on the filtered point clouds, a cluster-level, rule-based classification pipeline exploits radar semantics-velocity, RCS, and volumetric spread-to achieve reliable, real-time pedestrian detection without extensive domainspecific training. Experimental results confirm that this integrated approach significantly enhances clutter mitigation, detection robustness, and overall system resilience in dust-laden mining environments.
Spherical Geometry Diffusion: Generating High-quality 3D Face Geometry via Sphere-anchored Representations
Venue: arXiv cs.CV 2026
Year: 2026

A fundamental challenge in text-to-3D face generation is achieving high-quality geometry. The core difficulty lies in the arbitrary and intricate distribution of vertices in 3D space, making it challenging for existing models to establish clean connectivity and resulting in suboptimal geometry. To address this, our core insight is to simplify the underlying geometric structure by constraining the distribution onto a simple and regular manifold, a topological sphere. Building on this, we first propose the Spherical Geometry Representation, a novel face representation that anchors geometric signals to uniform spherical coordinates. This guarantees a regular point distribution, from which the mesh connectivity can be robustly reconstructed. Critically, this canonical sphere can be seamlessly unwrapped into a 2D map, creating a perfect synergy with powerful 2D generative models. We then introduce Spherical Geometry Diffusion, a conditional diffusion framework built upon this 2D map. It enables diverse and controllable generation by jointly modeling geometry and texture, where the geometry explicitly conditions the texture synthesis process. Our method's effectiveness is demonstrated through its success in a wide range of tasks: text-to-3D generation, face reconstruction, and text-based 3D editing. Extensive experiments show that our approach substantially outperforms existing methods in geometric quality, textual fidelity, and inference efficiency.
Leveraging Transformer Decoder for Automotive Radar Object Detection
Venue: arXiv cs.CV 2026
Year: 2026

In this paper, we present a Transformer-based architecture for 3D radar object detection that uses a novel Transformer Decoder as the prediction head to directly regress 3D bounding boxes and class scores from radar feature representations. To bridge multi-scale radar features and the decoder, we propose Pyramid Token Fusion (PTF), a lightweight module that converts a feature pyramid into a unified, scale-aware token sequence. By formulating detection as a set prediction problem with learnable object queries and positional encodings, our design models long-range spatial-temporal correlations and cross-feature interactions. This approach eliminates dense proposal generation and heuristic post-processing such as extensive non-maximum suppression (NMS) tuning. We evaluate the proposed framework on the RADDet, where it achieves significant improvements over state-of-the-art radar-only baselines.
Structured Insight from Unstructured Data: Large Language Models for SDOH-Driven Diabetes Risk Prediction
Venue: arXiv cs.CL 2026
Year: 2026

Social determinants of health (SDOH) play a critical role in Type 2 Diabetes (T2D) management but are often absent from electronic health records and risk prediction models. Most individual-level SDOH data is collected through structured screening tools, which lack the flexibility to capture the complexity of patient experiences and unique needs of a clinic's population. This study explores the use of large language models (LLMs) to extract structured SDOH information from unstructured patient life stories and evaluate the predictive value of both the extracted features and the narratives themselves for assessing diabetes control. We collected unstructured interviews from 65 T2D patients aged 65 and older, focused on their lived experiences, social context, and diabetes management. These narratives were analyzed using LLMs with retrieval-augmented generation to produce concise, actionable qualitative summaries for clinical interpretation and structured quantitative SDOH ratings for risk prediction modeling. The structured SDOH ratings were used independently and in combination with traditional laboratory biomarkers as inputs to linear and tree-based machine learning models (Ridge, Lasso, Random Forest, and XGBoost) to demonstrate how unstructured narrative data can be applied in conventional risk prediction workflows. Finally, we evaluated several LLMs on their ability to predict a patient's level of diabetes control (low, medium, high) directly from interview text with A1C values redacted. LLMs achieved 60% accuracy in predicting diabetes control levels from interview text. This work demonstrates how LLMs can translate unstructured SDOH-related data into structured insights, offering a scalable approach to augment clinical risk models and decision-making.
Beyond Memorization: Testing LLM Reasoning on Unseen Theory of Computation Tasks
Venue: arXiv cs.CL 2026
Year: 2026

Large language models (LLMs) have demonstrated strong performance on formal language tasks, yet whether this reflects genuine symbolic reasoning or pattern matching on familiar constructions remains unclear. We introduce a benchmark for deterministic finite automata (DFA) construction from regular languages, comprising factual knowledge questions, seen construction problems from public sources, and two types of unseen problems: hand-crafted instances with multiple interacting constraints and systematically generated problems via Arden's theorem. Models achieve perfect accuracy on factual questions and 84-90% on seen tasks. However, accuracy drops sharply on unseen problems (by 30-64%), with failures stemming from systematic misinterpretation of language constraints, incorrect handling of Kleene-star semantics, and a failure to preserve global consistency. We evaluate a three-stage hint protocol that enables correction of shallow errors but does not reliably resolve globally inconsistent or structurally flawed automata. Our analysis across multiple prompting strategies (direct, Chain-of-Thought, Tree-of-Thought) reveals that errors persist regardless of prompting approach, exposing a fundamental gap between LLMs' ability to generate syntactically plausible DFAs and their capacity for semantically correct formal reasoning.
Diffusion Representations for Fine-Grained Image Classification: A Marine Plankton Case Study
Venue: arXiv cs.CV 2026
Year: 2026

Diffusion models have emerged as state-of-the-art generative methods for image synthesis, yet their potential as general-purpose feature encoders remains underexplored. Trained for denoising and generation without labels, they can be interpreted as self-supervised learners that capture both low- and high-level structure. We show that a frozen diffusion backbone enables strong fine-grained recognition by probing intermediate denoising features across layers and timesteps and training a linear classifier for each pair. We evaluate this in a real-world plankton-monitoring setting with practical impact, using controlled and comparable training setups against established supervised and self-supervised baselines. Frozen diffusion features are competitive with supervised baselines and outperform other self-supervised methods in both balanced and naturally long-tailed settings. Out-of-distribution evaluations on temporally and geographically shifted plankton datasets further show that frozen diffusion features maintain strong accuracy and Macro F1 under substantial distribution shift.
SGW-GAN: Sliced Gromov-Wasserstein Guided GANs for Retinal Fundus Image Enhancement
Venue: arXiv cs.CV 2026
Year: 2026

Retinal fundus photography is indispensable for ophthalmic screening and diagnosis, yet image quality is often degraded by noise, artifacts, and uneven illumination. Recent GAN- and diffusion-based enhancement methods improve perceptual quality by aligning degraded images with high-quality distributions, but our analysis shows that this focus can distort intra-class geometry: clinically related samples become dispersed, disease-class boundaries blur, and downstream tasks such as grading or lesion detection are harmed. The Gromov Wasserstein (GW) discrepancy offers a principled solution by aligning distributions through internal pairwise distances, naturally preserving intra-class structure, but its high computational cost restricts practical use. To overcome this, we propose SGW-GAN, the first framework to incorporate Sliced GW (SGW) into retinal image enhancement. SGW approximates GW via random projections, retaining relational fidelity while greatly reducing cost. Experiments on public datasets show that SGW-GAN produces visually compelling enhancements, achieves superior diabetic retinopathy grading, and reports the lowest GW discrepancy across disease labels, demonstrating both efficiency and clinical fidelity for unpaired medical image enhancement.
Governance Matters: Lessons from Restructuring the data.table OSS Project
Venue: arXiv cs.SE 2026
Year: 2026

Open source software (OSS) forms the backbone of industrial data workflows and enterprise systems. However, many OSS projects face operational risks due to informal or centralized governance. This paper presents a practical case study of data.table, a high-performance R package widely adopted in production analytics pipelines, which underwent a community-led governance reform to address scalability and sustainability concerns. Before the reform, data.table faced a growing backlog of unresolved issues and open pull requests, unclear contributor pathways, and bottlenecks caused by reliance on a single core maintainer. In response, the community initiated a redesign of its governance structure. In this paper, we evaluated the impact of this transition through a mixed-methods approach, combining a contributor survey (n=17) with mining project repository data. Our results show that following the reform, the project experienced a 200% increase in new contributor recruitment, a drop in pull request resolution time from over 700 days to under a week, and a 3x increase in contributor retention. Community sentiment improved around transparency, onboarding, and project momentum, though concerns around fairness and conflict resolution remain. This case study provides practical guidance for maintainers, companies, and foundations seeking to enhance OSS governance.
The Hidden Toll of Social Media News: Causal Effects on Psychosocial Wellbeing
Venue: arXiv cs.SI 2026
Year: 2026

News consumption on social media has become ubiquitous, yet how different forms of engagement shape psychosocial outcomes remains unclear. To address this gap, we leveraged a large-scale dataset of ~26M posts and ~45M comments on the BlueSky platform, and conducted a quasi-experimental study, matching 81,345 Treated users exposed to News feeds with 83,711 Control users using stratified propensity score analysis. We examined psychosocial wellbeing, in terms of affective, behavioral, and cognitive outcomes. Our findings reveal that news engagement produces systematic trade-offs: increased depression, stress, and anxiety, yet decreased loneliness and increased social interaction on the platform. Regression models reveal that News feed bookmarking is associated with greater psychosocial deterioration compared to commenting or quoting, with magnitude differences exceeding tenfold. These per-engagement effects accumulate with repeated exposure, showing significant psychosocial impacts. Our work extends theories of news effects beyond crisis-centric frameworks by demonstrating that routine consumption creates distinct psychological dynamics depending on engagement type, and bears implications for tools and interventions for mitigating the psychosocial costs of news consumption on social media.
Modeling Perpetrators' Fate-to-Fate Contagion in Public Mass Shootings In The United States Using Bivariate Hawkes Processes
Venue: arXiv cs.SI 2026
Year: 2026

This study examines how the fate of a perpetrator in a public mass shooting influences the fate of subsequent perpetrators. Using data from 1966 to 2024, we classify incidents according to whether the perpetrator died at the scene or survived the attack. Using a bivariate Hawkes process, we quantify the cross-excitation effect, which is the triggering effect that each event type exerts on the other, i.e., "die at the scene"$\rightarrow$ "live" and "live"$\rightarrow$ "die at the scene", as well as the self-excitation effects, i.e., "die at the scene"$\rightarrow$ "die at the scene" and "live"$\rightarrow$ "live". Our results show that the strongest spillover was from "live" incidents to "die at the scene", where we estimate that 0.34 (0.09, 0.80) of "die at the scene" incidents are triggered by a prior event in which the offender survived the attack. This pathway also exhibits the longest estimated contagion timescale: approximately 20 days. In contrast, the reverse influence, that is, "die at the scene"$\rightarrow$"live", is not statistically significant, with the lower bound of its 95% confidence interval nearly equal to zero. We also find that "die at the scene" events can only cause their own type, where 0.139 (0.01, 0.52) of such incidents are caused by previous "die at the scene" events, with the shortest contagion timescale of roughly 20 hours.
Integrating Vision-Centric Text Understanding for Conversational Recommender Systems
Venue: arXiv cs.IR 2026
Year: 2026

Conversational Recommender Systems (CRSs) have attracted growing attention for their ability to deliver personalized recommendations through natural language interactions. To more accurately infer user preferences from multi-turn conversations, recent works increasingly expand conversational context (e.g., by incorporating diverse entity information or retrieving related dialogues). While such context enrichment can assist preference modeling, it also introduces longer and more heterogeneous inputs, leading to practical issues such as input length constraints, text style inconsistency, and irrelevant textual noise, thereby raising the demand for stronger language understanding ability. In this paper, we propose STARCRS, a Screen-Text-AwaRe Conversational Recommender System that integrates two complementary text understanding modes: (1) a screen-reading pathway that encodes auxiliary textual information as visual tokens, mimicking skim reading on a screen, and (2) an LLM-based textual pathway that focuses on a limited set of critical content for fine-grained reasoning. We design a knowledge-anchored fusion framework that combines contrastive alignment, cross-attention interaction, and adaptive gating to integrate the two modes for improved preference modeling and response generation. Extensive experiments on two widely used benchmarks demonstrate that STARCRS consistently improves both recommendation accuracy and generated response quality.
Eliciting Harmful Capabilities by Fine-Tuning On Safeguarded Outputs
Venue: arXiv cs.CR 2026
Year: 2026

Model developers implement safeguards in frontier models to prevent misuse, for example, by employing classifiers to filter dangerous outputs. In this work, we demonstrate that even robustly safeguarded models can be used to elicit harmful capabilities in open-source models through elicitation attacks. Our elicitation attacks consist of three stages: (i) constructing prompts in adjacent domains to a target harmful task that do not request dangerous information; (ii) obtaining responses to these prompts from safeguarded frontier models; (iii) fine-tuning open-source models on these prompt-output pairs. Since the requested prompts cannot be used to directly cause harm, they are not refused by frontier model safeguards. We evaluate these elicitation attacks within the domain of hazardous chemical synthesis and processing, and demonstrate that our attacks recover approximately 40% of the capability gap between the base open-source model and an unrestricted frontier model. We then show that the efficacy of elicitation attacks scales with the capability of the frontier model and the amount of generated fine-tuning data. Our work demonstrates the challenge of mitigating ecosystem level risks with output-level safeguards.