OpenRaiser/Intern-Atlas huggingface dataset:
Answering Constraint Path Queries over Graphs
Venue: arXiv cs.DB 2025
Year: 2025

Constraints are powerful declarative constructs that allow users to conveniently restrict variable values that potentially range over an infinite domain. In this paper, we propose a constraint path query language over property graphs, which extends Regular Path Queries (RPQs) with SMT constraints on data attributes in the form of equality constraints and Linear Real Arithmetic (LRA) constraints. We provide efficient algorithms for evaluating such path queries over property graphs, which exploits optimization of macro-states (among others, using theory-specific techniques). In particular, we demonstrate how such an algorithm may effectively utilize highly optimized SMT solvers for resolving such constraints over paths. We implement our algorithm in MillenniumDB, an open-source graph engine supporting property graph queries and GQL. Our extensive empirical evaluation in a real-world setting demonstrates the viability of our approach.
Mofasa: A Step Change in Metal-Organic Framework Generation
Venue: arXiv cs.LG 2025
Year: 2025

Mofasa is an all-atom latent diffusion model with state-of-the-art performance for generating Metal-Organic Frameworks (MOFs). These are highly porous crystalline materials used to harvest water from desert air, capture carbon dioxide, store toxic gases and catalyse chemical reactions. In recognition of their value, the development of MOFs recently received a Nobel Prize in Chemistry. In many ways, MOFs are well-suited for exploiting generative models in chemistry: they are rationally-designable materials with a large combinatorial design space and strong structure-property couplings. And yet, to date, a high performance generative model has been lacking. To fill this gap, we introduce Mofasa, a general-purpose latent diffusion model that jointly samples positions, atom-types and lattice vectors for systems as large as 500 atoms. Mofasa avoids handcrafted assembly algorithms common in the literature, unlocking the simultaneous discovery of metal nodes, linkers and topologies. To help the scientific community build on our work, we release MofasaDB, an annotated library of hundreds of thousands of sampled MOF structures, along with a user-friendly web interface for search and discovery: https://mofux.ai/ .
On the Unreasonable Effectiveness of Last-layer Retraining
Venue: arXiv cs.LG 2025
Year: 2025

Last-layer retraining (LLR) methods -- wherein the last layer of a neural network is reinitialized and retrained on a held-out set following ERM training -- have garnered interest as an efficient approach to rectify dependence on spurious correlations and improve performance on minority groups. Surprisingly, LLR has been found to improve worst-group accuracy even when the held-out set is an imbalanced subset of the training set. We initially hypothesize that this ``unreasonable effectiveness'' of LLR is explained by its ability to mitigate neural collapse through the held-out set, resulting in the implicit bias of gradient descent benefiting robustness. Our empirical investigation does not support this hypothesis. Instead, we present strong evidence for an alternative hypothesis: that the success of LLR is primarily due to better group balance in the held-out set. We conclude by showing how the recent algorithms CB-LLR and AFR perform implicit group-balancing to elicit a robustness improvement.
VideoScoop: A Non-Traditional Domain-Independent Framework For Video Analysis
Venue: arXiv cs.CV 2025
Year: 2025

Automatically understanding video contents is important for several applications in Civic Monitoring (CM), general Surveillance (SL), Assisted Living (AL), etc. Decades of Image and Video Analysis (IVA) research have advanced tasks such as content extraction (e.g., object recognition and tracking). Identifying meaningful activities or situations (e.g., two objects coming closer) remains difficult and cannot be achieved by content extraction alone. Currently, Video Situation Analysis (VSA) is done manually with a human in the loop, which is error-prone and labor-intensive, or through custom algorithms designed for specific video types or situations. These algorithms are not general-purpose and require a new algorithm/software for each new situation or video from a new domain. This report proposes a general-purpose VSA framework that overcomes the above limitations. Video contents are extracted once using state-of-the-art Video Content Extraction technologies. They are represented using two alternative models -- the extended relational model (R++) and graph models. When represented using R++, the extracted contents can be used as data streams, enabling Continuous Query Processing via the proposed Continuous Query Language for Video Analysis. The graph models complement this by enabling the detection of situations that are difficult or impossible to detect using the relational model alone. Existing graph algorithms and newly developed algorithms support a wide variety of situation detection. To support domain independence, primitive situation variants across domains are identified and expressed as parameterized templates. Extensive experiments were conducted across several interesting situations from three domains -- AL, CM, and SL-- to evaluate the accuracy, efficiency, and robustness of the proposed approach using a dataset of videos of varying lengths from these domains.
H-Neurons: On the Existence, Impact, and Origin of Hallucination-Associated Neurons in LLMs
Venue: arXiv cs.AI 2025
Year: 2025

Large language models (LLMs) frequently generate hallucinations -- plausible but factually incorrect outputs -- undermining their reliability. While prior work has examined hallucinations from macroscopic perspectives such as training data and objectives, the underlying neuron-level mechanisms remain largely unexplored. In this paper, we conduct a systematic investigation into hallucination-associated neurons (H-Neurons) in LLMs from three perspectives: identification, behavioral impact, and origins. Regarding their identification, we demonstrate that a remarkably sparse subset of neurons (less than $0.1\%$ of total neurons) can reliably predict hallucination occurrences, with strong generalization across diverse scenarios. In terms of behavioral impact, controlled interventions reveal that these neurons are causally linked to over-compliance behaviors. Concerning their origins, we trace these neurons back to the pre-trained base models and find that these neurons remain predictive for hallucination detection, indicating they emerge during pre-training. Our findings bridge macroscopic behavioral patterns with microscopic neural mechanisms, offering insights for developing more reliable LLMs.
Forget Less, Retain More: A Lightweight Regularizer for Rehearsal-Based Continual Learning
Venue: arXiv cs.LG 2025
Year: 2025

Deep neural networks suffer from catastrophic forgetting, where performance on previous tasks degrades after training on a new task. This issue arises due to the model's tendency to overwrite previously acquired knowledge with new information. We present a novel approach to address this challenge, focusing on the intersection of memory-based methods and regularization approaches. We formulate a regularization strategy, termed Information Maximization (IM) regularizer, for memory-based continual learning methods, which is based exclusively on the expected label distribution, thus making it class-agnostic. As a consequence, IM regularizer can be directly integrated into various rehearsal-based continual learning methods, reducing forgetting and favoring faster convergence. Our empirical validation shows that, across datasets and regardless of the number of tasks, our proposed regularization strategy consistently improves baseline performance at the expense of a minimal computational overhead. The lightweight nature of IM ensures that it remains a practical and scalable solution, making it applicable to real-world continual learning scenarios where efficiency is paramount. Finally, we demonstrate the data-agnostic nature of our regularizer by applying it to video data, which presents additional challenges due to its temporal structure and higher memory requirements. Despite the significant domain gap, our experiments show that IM regularizer also improves the performance of video continual learning methods.
CauSight: Learning to Supersense for Visual Causal Discovery
Venue: arXiv cs.CV 2025
Year: 2025

Causal thinking enables humans to understand not just what is seen, but why it happens. To replicate this capability in modern AI systems, we introduce the task of visual causal discovery. It requires models to infer cause-and-effect relations among visual entities across diverse scenarios instead of merely perceiving their presence. To this end, we first construct the Visual Causal Graph dataset (VCG-32K), a large-scale collection of over 32,000 images annotated with entity-level causal graphs, and further develop CauSight, a novel vision-language model to perform visual causal discovery through causally aware reasoning. Our training recipe integrates three components: (1) training data curation from VCG-32K, (2) Tree-of-Causal-Thought (ToCT) for synthesizing reasoning trajectories, and (3) reinforcement learning with a designed causal reward to refine the reasoning policy. Experiments show that CauSight outperforms GPT-4.1 on visual causal discovery, achieving over a threefold performance boost (21% absolute gain). Our code, model, and dataset are fully open-sourced at project page: https://github.com/OpenCausaLab/CauSight.
Free Tuition, Stratified Pipelines: Four Decades of Administrative Cohorts and Equity in Access to Engineering and Science in an Argentine Public University
Venue: arXiv cs.CY 2025
Year: 2025

Latin American higher education is often portrayed as equitable when tuition is free and access to public universities is formally unrestricted. Yet, growing research shows that massification under tuition-free policies often coexists with strong social and territorial stratification. This article uses four decades of administrative records from a faculty of engineering in north-western Argentina to examine how cohort composition has changed over time. Drawing on 24,133 first-time entrants (1980-2019), we construct a leakage-aware "background census" layer (N1c) harmonising school type, province, and age across legacy systems. We combine descriptive analyses, UMAP+DBSCAN clustering, and a reconstructed macroeconomic panel (inflation, unemployment, poverty, GDP) anchored at entry. All analyses explicitly report structural missingness patterns. Results show that missingness in background variables is historically patterned, declining sharply after the 1990s. Among students with observed data, the share coming from private-especially religious-secondary schools in high-income areas increased from less than half in the 1980s to roughly two-thirds in the 2010s. The catchment area became more local, with the home province gaining weight while distant origins lost ground. Median age at entry remained stable at 19, with persistent right tails of older entrants. Macro-linkage analyses reveal moderate associations between unemployment and older entry age, and between inflation and higher shares of students from interior provinces. We argue that free tuition and open entry have operated within, rather than against, stratified school and residential pipelines. The article illustrates how administrative data can support equity monitoring and discusses implications for upstream school policies and institutional accountability in tuition-free systems.
Unifying Sign and Magnitude for Optimizing Deep Vision Networks via ThermoLion
Venue: arXiv cs.LG 2025
Year: 2025

The training of deep vision models is fundamentally a signal recovery problem amidst high-dimensional stochastic noise. Current optimization paradigms impose a static compromise on information channel capacity. For instance, magnitude-based methods, such as AdamW, operate on the assumption that gradient norms are high-fidelity curvature signals. While this allows for precision in smooth regimes, it leads to catastrophic noise amplification when applied to rugged, non-convex landscapes. Conversely, sign-based methods (e.g., Lion) perform a radical 1-bit quantization of the gradient, which aims to provide robust regularization at the cost of discarding fine-grained descent information. We propose that optimal convergence requires neither static prior, but rather a dynamic modulation of the update bitrate. We introduce ThermoLion, a vision-centric framework that utilizes local Signal-to-Noise Ratio (SNR) gating to autonomously transition parameters between a "low-bit" exploration phase and a "high-precision" exploitation phase. Furthermore, we introduce a Momentum Alignment mechanism that detects constructive interference between historical drift and instantaneous gradients to accelerate convergence during stable trajectories. Empirical benchmarks across 12 diverse vision datasets (including CIFAR, SVHN, and GTSRB) demonstrate that ThermoLion surpasses state-of-the-art optimizers, such as AdamW and Lion, in convergence speed and terminal accuracy.
Improving Phishing Resilience with AI-Generated Training: Evidence on Prompting, Personalization, and Duration
Venue: arXiv cs.CR 2025
Year: 2025

Phishing remains a persistent cybersecurity threat; however, developing scalable and effective user training is labor-intensive and challenging to maintain. Generative Artificial Intelligence offers an interesting opportunity, but empirical evidence on its instructional efficacy remains scarce. This paper provides an experimental validation of Large Language Models (LLMs) as autonomous engines for generating phishing resilience training. Across two controlled studies (N=480), we demonstrate that AI-generated content yields significant pre-post learning gains regardless of the specific prompting strategy employed. Study 1 (N=80) compares four prompting techniques, finding that even a straightforward "direct-profile" strategy--simply embedding user traits into the prompt--produces effective training material. Study 2 (N=400) investigates the scalability of this approach by testing personalization and training duration. Results show that complex psychometric personalization offers no measurable advantage over well-designed generic content, while longer training duration provides a modest boost in accuracy. These findings suggest that organizations can leverage LLMs to generate high-quality, effective training at scale without the need for complex user profiling, relying instead on the inherent capabilities of the model.
Tight Bounds for Feedback Vertex Set Parameterized by Clique-width
Venue: arXiv cs.DS 2025
Year: 2025

We introduce a new notion of acyclicity representation in labeled graphs, and present three applications thereof. Our main result is an algorithm that, given a graph $G$ and a $k$-clique expression of $G$, in time $O(6^kn^c)$ counts modulo $2$ the number of feedback vertex sets of $G$ of each size. We achieve this through an involved subroutine for merging partial solutions at union nodes in the expression. In the usual way this results in a one-sided error Monte-Carlo algorithm for solving the decision problem in the same time. We complement these by a matching lower bound under the Strong Exponential-Time Hypothesis (SETH). This closes an open question that appeared multiple times in the literature [ESA 23, ICALP 24, IPEC 25]. We also present an algorithm that, given a graph $G$ and a tree decomposition of width $k$ of $G$, in time $O(3^kn^c)$ counts modulo $2$ the number of feedback vertex sets of $G$ of each size. This matches the known SETH-tight bound for the decision version, which was obtained using the celebrated cut-and-count technique [FOCS 11, TALG 22]. Unlike other applications of cut-and-count, which use the isolation lemma to reduce a decision problem to counting solutions modulo $2$, this bound was obtained via counting other objects, leaving the complexity of counting solutions modulo $2$ open. Finally, we present a one-sided error Monte-Carlo algorithm that, given a graph $G$ and a $k$-clique expression of $G$, in time $O(18^kn^c)$ decides the existence of a connected feedback vertex set of size $b$ in $G$. We provide a matching lower bound under SETH.
SARL: Spatially-Aware Self-Supervised Representation Learning for Visuo-Tactile Perception
Venue: arXiv cs.CV 2025
Year: 2025

Contact-rich robotic manipulation requires representations that encode local geometry. Vision provides global context but lacks direct measurements of properties such as texture and hardness, whereas touch supplies these cues. Modern visuo-tactile sensors capture both modalities in a single fused image, yielding intrinsically aligned inputs that are well suited to manipulation tasks requiring visual and tactile information. Most self-supervised learning (SSL) frameworks, however, compress feature maps into a global vector, discarding spatial structure and misaligning with the needs of manipulation. To address this, we propose SARL, a spatially-aware SSL framework that augments the Bootstrap Your Own Latent (BYOL) architecture with three map-level objectives, including Saliency Alignment (SAL), Patch-Prototype Distribution Alignment (PPDA), and Region Affinity Matching (RAM), to keep attentional focus, part composition, and geometric relations consistent across views. These losses act on intermediate feature maps, complementing the global objective. SARL consistently outperforms nine SSL baselines across six downstream tasks with fused visual-tactile data. On the geometry-sensitive edge-pose regression task, SARL achieves a Mean Absolute Error (MAE) of 0.3955, a 30% relative improvement over the next-best SSL method (0.5682 MAE) and approaching the supervised upper bound. These findings indicate that, for fused visual-tactile data, the most effective signal is structured spatial equivariance, in which features vary predictably with object geometry, which enables more capable robotic perception.
Pharmacophore-based design by learning on voxel grids
Venue: arXiv cs.LG 2025
Year: 2025

Ligand-based drug discovery (LBDD) relies on making use of known binders to a protein target to find structurally diverse molecules similarly likely to bind. This process typically involves a brute force search of the known binder (query) against a molecular library using some metric of molecular similarity. One popular approach overlays the pharmacophore-shape profile of the known binder to 3D conformations enumerated for each of the library molecules, computes overlaps, and picks a set of diverse library molecules with high overlaps. While this virtual screening workflow has had considerable success in hit diversification, scaffold hopping, and patent busting, it scales poorly with library sizes and restricts candidate generation to existing library compounds. Leveraging recent advances in voxel-based generative modelling, we propose a pharmacophore-based generative model and workflows that address the scaling and fecundity issues of conventional pharmacophore-based virtual screening. We introduce \emph{VoxCap}, a voxel captioning method for generating SMILES strings from voxelised molecular representations. We propose two workflows as practical use cases as well as benchmarks for pharmacophore-based generation: \emph{de-novo} design, in which we aim to generate new molecules with high pharmacophore-shape similarities to query molecules, and fast search, which aims to combine generative design with a cheap 2D substructure similarity search for efficient hit identification. Our results show that VoxCap significantly outperforms previous methods in generating diverse \textit{de-novo} hits. When combined with our fast search workflow, VoxCap reduces computational time by orders of magnitude while returning hits for all query molecules, enabling the search of large libraries that are intractable to search by brute force.
Misalignment of LLM-Generated Personas with Human Perceptions in Low-Resource Settings
Venue: arXiv cs.CY 2025
Year: 2025

Recent advances enable Large Language Models (LLMs) to generate AI personas, yet their lack of deep contextual, cultural, and emotional understanding poses a significant limitation. This study quantitatively compared human responses with those of eight LLM-generated social personas (e.g., Male, Female, Muslim, Political Supporter) within a low-resource environment like Bangladesh, using culturally specific questions. Results show human responses significantly outperform all LLMs in answering questions, and across all matrices of persona perception, with particularly large gaps in empathy and credibility. Furthermore, LLM-generated content exhibited a systematic bias along the lines of the ``Pollyanna Principle'', scoring measurably higher in positive sentiment ($Φ_{avg} = 5.99$ for LLMs vs. $5.60$ for Humans). These findings suggest that LLM personas do not accurately reflect the authentic experience of real people in resource-scarce environments. It is essential to validate LLM personas against real-world human data to ensure their alignment and reliability before deploying them in social science research.
HTG-GCL: Leveraging Hierarchical Topological Granularity from Cellular Complexes for Graph Contrastive Learning
Venue: arXiv cs.LG 2025
Year: 2025

Graph contrastive learning (GCL) aims to learn discriminative semantic invariance by contrasting different views of the same graph that share critical topological patterns. However, existing GCL approaches with structural augmentations often struggle to identify task-relevant topological structures, let alone adapt to the varying coarse-to-fine topological granularities required across different downstream tasks. To remedy this issue, we introduce Hierarchical Topological Granularity Graph Contrastive Learning (HTG-GCL), a novel framework that leverages transformations of the same graph to generate multi-scale ring-based cellular complexes, embodying the concept of topological granularity, thereby generating diverse topological views. Recognizing that a certain granularity may contain misleading semantics, we propose a multi-granularity decoupled contrast and apply a granularity-specific weighting mechanism based on uncertainty estimation. Comprehensive experiments on various benchmarks demonstrate the effectiveness of HTG-GCL, highlighting its superior performance in capturing meaningful graph representations through hierarchical topological information.
Feature Selection Empowered BERT for Detection of Hate Speech with Vocabulary Augmentation
Venue: arXiv cs.CL 2025
Year: 2025

Abusive speech on social media poses a persistent and evolving challenge, driven by the continuous emergence of novel slang and obfuscated terms designed to circumvent detection systems. In this work, we present a data efficient strategy for fine tuning BERT on hate speech classification by significantly reducing training set size without compromising performance. Our approach employs a TF IDF-based sample selection mechanism to retain only the most informative 75 percent of examples, thereby minimizing training overhead. To address the limitations of BERT's native vocabulary in capturing evolving hate speech terminology, we augment the tokenizer with domain-specific slang and lexical variants commonly found in abusive contexts. Experimental results on a widely used hate speech dataset demonstrate that our method achieves competitive performance while improving computational efficiency, highlighting its potential for scalable and adaptive abusive content moderation.