This study presents a transfer learning framework for digital image forgery detection that combines compression-aware feature enhancement with CNNs. A hybrid input representation (RGB + FDIFF) and Youden Index-based threshold optimization are proposed. Evaluated on CASIA v2.0 with six architectures, DenseNet121 achieved highest accuracy and AUC, while ResNet50 had the best MCC, emphasizing that accuracy alone is insufficient for forensic applications.

This study presents a whole-body tumor segmentation method developed for the AutoPET III challenge using the nnU-Net framework with a ResNet encoder. It systematically investigates training strategies including intensity normalization, batch dice optimization, and CraveMix augmentation, achieving a Dice score of up to 0.80 and a third-place ranking. The code is publicly available.

WATCH is a framework for month-level change-event localization on PlanetScope satellite mosaics (2017-2024, 4.7 m/px) supporting three complementary scoring approaches: Temporal Embedding Distance (TED), Self-Supervised Change Detection (SSCD), and Weakly Supervised (WS) temporal localization. Benchmarked on 1,943 archaeological sites in Afghanistan, unsupervised methods (TED, SSCD) consistently outperform WS. TED with SatMAE achieves 55% exact-month recall, while TED with GeoRSCLIP, CLIP, or Satlas-Pretrain reaches 92.5% within a three-month tolerance. Directional margin analysis reveals temporal biases: SSCD offers early warning, while TED favors confirmation-oriented detection.

VT-Bench is the first unified benchmark standardizing vision-tabular discriminative prediction and generative reasoning tasks. It aggregates 14 datasets across 9 domains (over 756K samples) and evaluates 23 representative models, highlighting substantial challenges in visual-tabular learning.

Current vision language models face hallucination and robustness issues against ambiguous or corrupted modalities. We hypothesize that these issues can be addressed by exploiting the shared information between modalities to compensate for the impaired one. To this end, we analyze multimodal interactions -- redundant (shared), unique (exclusive), and synergistic (emergent) task-relevant information provided by the modalities -- to determine their impacts on model reliability. Specifically, amplifying redundant interactions would increase this exploitable shared information to resolve these issues; yet, modern instruction datasets often eliminate redundancies to prioritize visual grounding. We bridge this gap through a self-captioning workflow featuring a Multimodal Interaction Gate: a mech…

A study benchmarks ResNet backbones in RT-DETR for round object detection under environmental variations. ResNet50 excels under illumination changes (accuracy near 1.00, confidence up to 0.869, latency ~0.058-0.059 ms), while ResNet34 performs best under background changes (confidence up to 0.887). Results show optimal architecture depends on the environmental variation, with intermediate-depth models offering the best balance.

Researchers propose VLADriver-RAG, a framework that enhances vision-language-action models for autonomous driving by using retrieval-augmented generation to improve generalization in long-tail scenarios. It abstracts sensory inputs into spatiotemporal semantic graphs to filter noise and uses a scenario-aligned embedding model with graph-DTW metric alignment to prioritize topological consistency. Achieves a driving score of 89.12 on Bench2Drive, setting a new state-of-the-art.

Magis-Bench is a new benchmark evaluating LLMs on magistrate-level legal writing tasks, derived from Brazilian judicial exams. It includes 74 questions across 8 exams (2023–2025). 23 LLMs were evaluated using an LLM-as-a-judge methodology with four frontier models. Gemini-3-Pro-Preview achieved the highest average score (6.97/10), but even top models scored below 70%, indicating judicial reasoning remains challenging for current LLMs.

This paper introduces Sem-ECE, a framework for evaluating calibration of large language models in open-ended question answering. It samples answers, groups them into semantic classes, and uses frequencies as confidence. The framework outperforms existing methods and provides diagnostics for question difficulty.

This research proposes a computational model that uses LLMs to convert explanations into action plans (policy prior and value map) for planning under partial observability. Through four preregistered experiments with 1,200 explanations on 24 maps, higher-scored explanations are judged more helpful and improve navigation performance. Results show procedural explanation as utility-guided communication shaped by grounding language into action under uncertainty.

This study explores the use of large language models (LLMs) with remote sensing imagery for smart city tasks, including design suggestions, constructability assessment, land use patterns, and risk identification. It evaluates multi-scale imagery inputs and compares models like InternVL and Qwen, demonstrating the potential for AI-assisted decision-making.

AIPO is an enhanced reinforcement learning framework that improves LLM reasoning by enabling the policy model to proactively consult three collaborative agents (Verify, Knowledge, and Reasoning) during exploration, expanding its capability boundary. Experiments show consistent gains on AIME, MATH500, GPQA-Diamond, and LiveCodeBench.

Jina AI introduces jina-embeddings-v5-omni, a suite of multimodal embedding models capable of encoding text, image, audio, and video into a single semantic space. Using frozen-tower composition, only 0.35% of total weights are trained, achieving state-of-the-art performance with high efficiency.

This study uses large language models to analyze debates on Reddit's r/ChangeMyView forum, finding that expressions of concession or empathetic alignment significantly increase the likelihood of belief revision, while frontal refutation, credibility attacks, and topic deflection diminish it. The findings highlight the importance of relational framing alongside evidential content in effective public reasoning.

A study using edge attribution patching across six tasks and seven models measures circuit reuse, consistency, and specificity. It finds high within-task reuse and necessity, but circuits lack task-specificity: ablating one task's circuit harms another task's performance as much as its own. Cross-task overlap is substantial, limiting targeted intervention.

New research introduces controlled-invariance tests (Force and Remove) to determine whether hallucination detection methods evaluate reasoning or exploit answer-level artifacts. The study also presents TRACT, a lightweight lexical trajectory scorer that achieves robust detection without complex learned representations.

SalesSim is a framework for evaluating multimodal LLMs in simulating realistic customer behavior in retail conversations. Benchmarks reveal low lexical diversity and persona drift. The proposed UserGRPO method improves decision alignment by 13.8%.

This study empirically tests the observability of Tian (2025)'s feature repulsion theorem in two-layer network grokking. The predicted sign rule holds robustly but the spectral signature is strongly activation-dependent: with squared activation, a clear rank-2 spectral lock-in signal appears; with ReLU, the spectrum remains rank-1 and the detector never fires. This reveals a structure-mechanism dissociation, highlighting the critical role of the activation derivative.

This paper proposes the Safety-Aware Denoiser (SAD), an inference-time framework for integrating safety constraints into text diffusion models. SAD modifies the iterative denoising process to steer final text samples toward provably safe regions, avoiding costly retraining. Experiments show SAD substantially reduces unsafe generations while preserving quality, diversity, and fluency, outperforming existing methods.

This paper analyzes three KV cache quantization schemes (KV, KQV, QKQV) under a fair bit budget. Using the Beta distribution on the hypersphere, it traces how QJL inflates inner product variance by π/2, which softmax amplifies nonlinearly via Jensen's inequality. Empirical findings show that at n=4 KQV wins on all measures; K-V asymmetry is unconditional; and a budget-dependent crossover exists. A sufficient condition is presented explaining the Jensen mechanism as the cause of the crossover.

This paper evaluates geospatial foundation model embeddings (Prithvi-EO-1.0-100M and ViT-Base) for cross-country maize yield prediction using a leave-one-country-out cross-validation on 6,404 fields from five African countries. Results show a generalisation gap: all feature sets perform poorly under cross-country testing with negative R². Frozen Prithvi-EO embeddings offer no advantage over traditional spectral features. The main limitation is yield distribution shift between countries.

A new framework TTCD uses Transformer integration and reconstruction-guided causal signal distillation for causal discovery in non-stationary time series, outperforming baselines.

BaLoRA is a Bayesian extension of LoRA that uses input-adaptive Bayesian parameterization to add minimal parameters and compute, providing well-calibrated uncertainty estimates and significantly improving prediction accuracy, narrowing the gap with full fine-tuning. Applied to band gap prediction in metal-organic frameworks, its zero-shot uncertainty estimates correlate more strongly with model error than a trained ensemble of LoRA models.

Researchers developed a deep learning model to predict particle lift forces in inertial microfluidic devices without explicit geometric parameters, enabling generalization to unseen channel geometries. The model speeds up simulations and matches literature results.

This paper introduces C-DSAC, a distributional reinforcement learning algorithm that minimizes the squared Cramér distance to learn state-action value distributions. Results on robotic benchmarks show it outperforms SAC and other distributional methods, especially in complex environments, due to confidence-driven updates that mitigate overestimation.

PathBoost is a gradient tree boosting method for graph-level classification and regression that learns discriminative path-based features directly from the input graph structure. It introduces three extensions: binary classification via logistic loss, incorporation of multiple node and edge attributes through prefix-based decomposition, and automatic anchor node selection. Compared to graph neural networks and graph kernel methods on several benchmarks, PathBoost achieves better results in half and comparable results in the rest, especially on graphs with larger node counts.

A new framework, RL-Kirigami, combines optimal-transport conditional flow matching with reinforcement learning to enable inverse design of kirigami structures, achieving high accuracy and rapid laser cutting of prototypes.

This study probes how LLMs learn in-context using a toy graph random-walk task. Evidence from PCA and causal interventions suggests a dual-mechanism account: genuine structure inference and induction circuits operate in parallel, rather than pure pattern-matching or structure inference alone.

CoCoDA is a framework that co-evolves the planner and tool library through a compositional code DAG, enabling scalable tool use within a fixed context budget. It uses typed DAG retrieval to prune candidates efficiently and folds successful trajectories into new composite tools during training. Experiments show an 8B student model matches or exceeds a 32B teacher on GSM8K and MATH.

A new arXiv paper introduces PLACO, a multi-stage framework to optimize cost-effective performance in human-AI teams for classification tasks. It combines human and model outputs using Bayes' rule under conditional independence, leveraging calibrated probabilities from both sides to improve decision reliability.