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Progress in legal AI depends on authoritative legal text at scale, yet US local ordinances—a consequential layer of American law—are largely missing from machine-readable corpora. The authors build LOCUS, a corpus of US local ordinances, to broaden legal-AI research data.

Preference-based RL learns reward models from pairwise behavior comparisons, bypassing explicit reward design, but existing methods often rely on passive data collection. UBP2 introduces uncertainty-balanced preference planning to actively select comparisons and learn efficiently from fewer preferences.

As deep learning for physical systems grows, efforts to improve generalizability have focused on architectures embedding physical constraints. This work instead studies optimal scenario design for machine-learning surrogate models of climate, improving generalization and predictive accuracy.

Embodied Vision-Language-Action (VLA) models are typically obtained by fine-tuning powerful pretrained VLMs on robotics data, yet how much commonsense and factual knowledge they retain is unclear. This work measures that retention, revealing how much fine-tuning erodes prior world knowledge.

Text-rich images often hold privacy-sensitive, transactional, or decision-relevant information. As multimodal generators synthesize realistic text and layouts, this work builds a multi-domain benchmark for detecting AI-generated text-rich images from GPT-Image-2, assessing detector reliability.

Automatically generating slide decks from documents is an important LLM application, but existing benchmarks mainly assess completeness and technical depth. X+Slides introduces a benchmark for audience-conditioned slide generation, evaluating how well decks adapt to their intended audience.

Time-series anomaly detection is crucial across applications, and reconstruction-based methods dominate but suffer from over-generalization that reconstructs anomalies too well. SCAN uses multi-scale neighborhood-centered clustering to curb this over-generalization and improve detection.

Existing 3D scene understanding in VLMs relies on complex, model-specific geometry encoders or large training budgets for spatial reasoning. OneCanvas instead uses panoramic reprojection, letting VLMs reason about 3D scenes efficiently without dedicated geometry encoders or heavy training.

Solving the pressure-Poisson equation is the main bottleneck in incompressible unstructured flow solvers, as traditional linear solvers are sensitive to mesh irregularities. This work uses graph neural networks to accelerate an algebraic multigrid pressure solver, improving solve efficiency.

Despite the wide adoption and success of Vision Transformers, understanding of their dimensional and representational geometry remains limited. The Transformer Geometry Observatory (TGO-I) studies ViTs through spectral geometry, observing and analyzing the structure of their representation spaces.

Family members caring for people with Alzheimer's and related dementias form the foundation of long-term care worldwide; in 2023 over 11 million U.S. relatives provided unpaid care. This work presents a taxonomy of caregivers' mental-health and technology needs to guide supportive design.

AI agents promise to accelerate drug discovery by compressing interpretation and decision loops, but deployment needs trusted evaluation on realistic tasks. TxBench-PP is a benchmark analyzing AI agent performance on small-molecule preclinical pharmacology, assessing their practical reliability.

Machine unlearning removes specific data points from trained models without full retraining, but most research targets neural networks. This work studies machine unlearning for the XGBoost gradient-boosted tree model using network intrusion datasets, extending unlearning beyond deep models.

Automatic metrics are the default for evaluating LLM-generated text, yet a metric is quietly asked to do two jobs: tell genuine content alignment from surface coincidence (validity) and discriminate quality. Using open-ended Reddit QA, RECOM analyzes this validity–discrimination trade-off.

Amazon Web Services has begun a public preview of the 'AWS FinOps Agent,' an AI agent that answers questions about AWS costs and investigates the causes of cost anomalies. It targets FinOps operations support. The specific feature scope and accuracy are per the article and announcement, unverified independently.

Abduction explains missing entailments from a knowledge base by proposing a hypothesis that would make them hold. This work studies ABox abduction under repair semantics for the EL description logic, combining multiple properties to produce stronger explanatory hypotheses.

Advances in diffusion models and face-swapping enable highly realistic deepfakes and real-world harm. This work shows AUC can mislead when evaluating detectors under domain shift, and proposes a polarization-aware evaluation that better reflects deepfake detector performance across domains.

The authors introduce Dango, a 1.8B-parameter language model designed for controlled studies of L1-to-L2 (Japanese-to-English) transfer in second language acquisition. By training strictly on L1 only, Dango enables controlled experiments on transfer phenomena that prior SLA model studies could not.

To achieve deeper safety alignment for LLMs, recent work pushes safety interventions earlier into pretraining, mainly by filtering unsafe data or rewriting it into safe forms. Going beyond safe data, this work embeds regular safety reflection during pretraining to instill more fundamental alignment.

Model merging integrates the capabilities of several models fine-tuned from the same pretrained checkpoint into one, enabling multi-task learning. This work proposes Essential Subspace Merging, which extracts and merges each task's essential subspace to reduce interference and preserve multi-task performance.

Audio LLMs can condition speech recognition on textual prompts such as domain descriptions or entity lists, but whether they truly use this context is unclear. IndicContextEval is a benchmark evaluating context utilisation in audio large language models across eight Indic languages.

Transformers' success stems from architectural scaling, which inflates parameter counts and hinders deployment in resource-constrained settings. This work proposes complementary attention head pruning, removing heads so that retained ones stay complementary, preserving accuracy while improving efficiency.

Automated vulnerability discovery in large codebases is hard: static analysis yields high false positives while dynamic methods like fuzzing lack coverage. OpenAnt is an LLM-powered approach combining code decomposition, adversarial verification, and dynamic testing to surface real vulnerabilities.

Dynamical systems are fundamental to modeling the natural world, but modeling them trades off interpretable hand-specified mechanistic models against flexible yet opaque neural ones. OrthoReg introduces orthogonal regularization to disentangle symbolic and neural components in hybrid dynamical systems.

Conversational AI has advanced in language generation, personalization, and long-context interaction, but most methods model social behavior through isolated components. This work offers a formal theory of human-AI coevolution dynamics, explaining how social intelligence emerges through long-term interaction.

Neural Controlled Differential Equations provide a powerful continuous-time framework for time-series forecasting, but standard graph-based extensions struggle to learn spatial structure. INDEQS introduces informed neural controlled differential equations to better capture structure and improve forecasting.

Instruction-based image editing enables complex edits from natural language, but in product-centric scenarios preserving product features and branding is hard. ProductConsistency uses supervised fine-tuning and reinforcement learning to improve product identity preservation during instruction-based editing.

Most learning architectures for dynamical systems rely on generic nonlinear function approximation, often needing high complexity to capture structured behavior. Favoring structure over nonlinearity, this work proposes explicit interaction architectures that model variable interactions directly for efficient dynamical learning.

Chronic disease management relies on regular patient-provider interactions to track progression and control. For Type 2 Diabetes, guidelines prescribe fixed follow-up intervals. This work uses Markov decision processes to optimize follow-up intervals in a context-aware way, tailoring scheduling to each patient.

Widespread parameter-efficient fine-tuning yields ecosystems where one backbone pairs with many task-specialized adapters. ARIADNE provides agnostic routing for inference-time dynamic adapter selection, choosing the right adapter per input without model-specific assumptions.