BudgetSchemaBench is a diagnostic tool for evaluating how different schema‑context budgets affect text‑to‑SQL systems. It automatically derives relevance labels from gold SQL, tests four budgets across 80 databases, and compares three schema representations while keeping table rankings fixed. The study shows that increasing the budget improves execution accuracy, especially for lexical retrieval, and that dense retrieval already captures most needed tables at low budgets.
By Chen Shen
The article surveys safety concerns for self‑evolving agents that continually update their internal state, such as model parameters and memories, from new interactions. It introduces the SAVER framework, which tracks reusable influence, adaptation, violations, exposure, and response to assess whether safety properties persist as agents evolve. The survey finds that legitimate state can become unsafe when its persistence, authority, or scope expands beyond its original conditions, and highlights gaps in current research on descendant repair and longitudinal evaluation.
By Jiahao Chen, Zhou Feng, Oubo Ma, Yichen Yan, Ruixiao Lin, Hangtao Zhang, Linkang Du, Hengyu An, Yong Yang, Jun Liu, Junhao Li, Naen Xu, Chunyi Zhou, Yuan Su, Zehao Jin, Qianli Ma, Leyi Qi, Yiming Wang, Zhe Ma, Yuwen Pu, Mengyao Du, Yuanyi Song, Enhao Huang, Zhihui Fu, Jun Wang, Jinfeng Li, Yuefeng Chen, Hui Xue, Yiming Li, Tianyu Du, Shouling Ji
The study investigates how large language model (LLM) advisors affect complex problem‑solving in a simulated clothing‑factory setting. Two preregistered experiments (N=200 and N=198) found that participants with AI support reported higher confidence and understanding, expended less effort, and in some cases achieved better performance or avoided bankruptcy. Within the AI‑supported group, more frequent changes to the AI’s recommendations were linked to improved unaided performance and knowledge.
By Robin Welsch
The study reports that white men lacking an undergraduate degree receive the lowest average ratings from large language models across credit, hiring, and rental application scenarios. Using 18 models and 32 demographic profiles, the researchers found that these individuals scored 75.87 in credit, 92.71 in hiring, and 86.62 in rental housing, while black women with degrees scored highest. The findings show that white men without degrees consistently rank lowest or second‑lowest in most model‑scenario combinations, underscoring a distinct disadvantage not captured by broader racial or gender categories.
By Maxim Chupilkin
The paper introduces GRDisaster, a multi-task geospatial reasoning framework that leverages vision‑language models to interpret, geolocalize, and assess damage in crowdsourced disaster imagery. It builds on a new benchmark dataset of 26,340 images from PhotoMappers, linking volunteer geographic information, street‑view imagery, and remote sensing data across multiple disaster events from 2018 to 2024. GRDisaster combines deterministic and probabilistic cross‑view geolocalization with multi‑view fusion, and introduces spatial reasoning indicators to validate cross‑view matches and quantify disaster severity using expert‑verified annotations.
By Wenping Yin, Fabian Desuer, Ziqi Liu, Naixia Mou, Weijia Li, Pedram Ghamisi, Xiao Xiang Zhu, Hao Li
Proof‑Gated Signing (PGS) is a method for securing on‑chain AI agents that control wallets by simulating proposed transactions, extracting their effects, and using an SMT solver to verify a declarative value‑and‑permission policy across a range of oracle‑uncertainty prices. PGS then compiles on‑chain post‑conditions—such as wallet balance bounds, payee receipts, allowance caps, and ownership checks—ensuring that any execution satisfying these conditions also satisfies the policy, even when the chain state drifts due to front‑running, contract upgrades, or token‑parameter changes. In a testbed of 260 scenarios, PGS prevented 93.6 % of harmful cases and passed 97.5 % of benign ones, outperforming simulation‑only checking and static allowlists, while incurring about 41 k gas and 0.1–0.2 s per check.
By Bravish Ghosh
This survey reviews deep learning methods for detecting anomalies in railway systems, organizing them by taxonomy of anomaly location, data representation, sensing modality, and temporal traits. It categorizes approaches—convolutional, recurrent, attention-based, autoencoders, GANs, transformers—into classification, prediction, reconstruction, and hybrid paradigms, and discusses data challenges, evaluation, metrics, and deployment issues such as edge‑cloud architectures and hardware constraints. The paper also offers a decision‑oriented framework linking anomaly characteristics, data properties, and operational constraints to guide the selection and deployment of suitable detection solutions.
By Ammar Bouketta, Smail Niar, Hamza Ouarnoughi
The paper introduces a comparative explainability framework for auditing DeBERTa‑v3 in zero‑shot medical abstract classification. It evaluates five explanation methods—SHAP, LIME, occlusion, Input × Gradient, and Attention × Gradient—using a natural language inference engine on a balanced corpus of 1,000 abstracts per diagnostic category. The study finds that explanatory stability aligns with predictive certainty, identifies three systemic failure mechanisms, and recommends combining multiple explanation methods and quantitative agreement metrics for transformer‑based medical text classifiers.
By Javier Diaz Esteban-Herreros, David Mu\~noz-Valero, Raquel Mart\'inez-Espa\~na, Jose M. Juarez, Juan Moreno-Garcia
VisionQ is a new benchmark for qualitative analysis in computer vision that evaluates vision‑language models (VLMs) on criterion‑conditioned visual discrimination. It is built from over 1,800 peer‑reviewed comparison figures in CVPR and ICCV papers, linking each image crop to author‑stated visual claims through 3,911 hand‑annotated data points. The benchmark includes a 51‑leaf taxonomy of visual criteria, a protocol that hides method identities and reports accuracy per criterion, and a DPO‑tuned Gemma‑4‑E4B judge that improves accuracy on a held‑out test set.
By Vu Dinh Xuan, Duc-Hai Nguyen, Minh-Dung Dao, Vu Quynh Giao, Quang Hong Nguyen, Binh-Son Hua, Barry O'Sullivan, David Murphy, Hoang D. Nguyen
The paper investigates whether code large language models (CodeLLMs) inadvertently reproduce proprietary or sensitive code by evaluating seven state‑of‑the‑art training data detection (TDD) methods on eight CodeLLMs. It introduces CodeSnitch, a benchmark of 9,000 function‑level code samples across three languages, each labeled as included or excluded from training data, and applies mutation strategies based on the Type‑1 to Type‑4 code clone taxonomy to test TDD robustness. The study offers a systematic assessment of current TDD techniques for code and suggests directions for developing more effective detection methods.
By Tianlin Li, Yunxiang Wei, Zhiming Li, Aishan Liu, Qing Guo, Xianglong Liu, Dongning Sun, Yang Liu
PixelDense introduces a dual‑stream representation alignment for pixel diffusion, separating semantic and geometric teachers (DINOv2, SAM2, Depth Anything v2, Metric3D v2) into distinct projection spaces with an orthogonality penalty. The method improves dense‑prediction benchmarks, boosting PixelGen‑XXL’s GenEval score from 0.7927 to 0.8093, achieving significant gains in panoptic quality and depth accuracy, and accelerating training from random initialization. It also enhances SDEdit editing by preserving background structure and increasing PSNR.
By Lehan Yang, Daiqing Qi, Wenhao Zhang, Avery Li, Yiqing Yang, Yifan Li, Yu Kong, Haitian Zheng, Zhifei Zhang, Zhe Lin, Varun Jampani, Sheng Li
PhysVista is a new benchmark that evaluates physical intelligence in Vision‑Language Models (VLMs) by integrating perception, reasoning, and plausibility assessment into a closed cognitive loop. It distinguishes between event‑level and scale‑level reasoning and tests models on both real‑world and AI‑generated videos to provide a holistic, fine‑grained analysis of physical understanding. Experiments show significant gaps in VLMs’ physical reasoning and plausibility assessment, underscoring the need for more principled, physically grounded multimodal designs.
By Xinge Peng, Yiting Lu, Tianwu Zhi, Wen Wen, Jianzhao Liu, Xin Li, Zhibo Chen
Omni-Embed-Mini is a 0.9B‑parameter model that embeds text, speech, audio, images, video, and visually‑rich documents into a single shared cosine space without updating any text‑side parameters. It uses a dense cascaded caption as a teacher signal, allowing the teacher and student to share identical backbone weights and requiring only lightweight projectors and phased LoRA adapters for alignment. The model achieves strong text retrieval performance (49.57 nDCG@10 on MTEB‑v2 BEIR‑8) while extending to five additional modalities and is significantly smaller than other open omni‑modal embedders.
By Mohammed Irfan Kurpath, Jaseel Muhammad Kaithakkodan, Sahal Shaji Mullappilly, Ivan Laptev, Hisham Cholakkal
The paper introduces FaV-A, a multimodal agent that generates positive and negative space compositions in a staged manner. It first creates a base object, analyzes its shape to identify negative‑space semantics, and then produces compositional instructions for the final image. Experiments show that this approach yields more visually coherent and semantically aligned compositions than direct zero‑shot multimodal large language model baselines.
By Shiwen Wang, Jian Yang, Xu Wang, Xincan Wang, Weiming Dong
The paper proposes a new architecture for large language model (LLM) agents that enhances spatial understanding by combining geometrical tools with an LLM orchestrator in grid‑world environments. It first gathers geodesic trajectories, vector‑quantizes them to create a representative subset, and then has the LLM label each trajectory with a natural language description, turning them into reusable tools. During operation, the LLM selects the appropriate tool based on the current state and goal, while low‑level control executes the chosen trajectory, enabling efficient decision‑making in a partially observable 2D grid setting.
By Gabriel Turinici
MemFit is a long‑term memory system designed for conversational agents that stores each dialogue turn verbatim in an append‑only store, enabling near‑instantaneous, LLM‑free insertion. It indexes turns using segment summaries and employs an LLM‑free, multi‑path retrieval strategy that blends lexical and semantic signals with cross‑encoder reranking over caption‑augmented episodes. Experiments on LoCoMo, MemGallery, and LongMemEval‑S demonstrate state‑of‑the‑art performance while drastically reducing memory construction time and cost.
By Mitchell Piehl, Muchao Ye
The paper highlights that as Large Language Models grow in capability and prevalence, their environmental footprint is increasing, yet the machine learning community lacks standardized carbon accounting practices. An automated review of 5,285 NeurIPS 2025 papers shows almost no reporting of environmental impact. To address this, the authors propose standardized sustainability metrics for training efficiency, heuristics for estimating inference carbon costs, a software tool called carbonbenchmark for tracking emissions, and the SMAJ framework to encourage prioritizing computational efficiency and environmental accountability over marginal accuracy gains.
By Lachlan McGinness, Dan Pagendam, Robert Offner
Laurito et al. (PNAS 2025) found that large language models prefer model‑written descriptions over human‑written ones. In this study, the authors reconstructed the 5×5 generator‑by‑selector matrices from the original data and tested for an “own‑model premium” using a two‑way fixed‑effects model and permutation tests. The results show no statistically significant preference for a selector’s own model across products, paper abstracts, or films, with effect sizes near zero and confidence intervals that rule out a premium larger than about 0.04 share points.
By Dmitrij \.Zatuchin
The paper investigates how massive activations—large residual-stream coordinates that act as attention sinks in transformers—emerge, grow, and consolidate during training. It shows that these activations stabilize early, then concentrate onto a few redundant channels as surrounding ones erode, and that their collective scale is governed by weight decay. A balance model demonstrates that AdamW preconditioning drives growth while weight decay limits peak magnitude, with peak timing and size scaling predictably with the decay coefficient.
By S. Aaron McClendon, Jorge Gallego-Feliciano, Antonios Saravanos
The paper investigates how well large language models specify the software environments needed to run AI-generated code. Using a new agent protocol and a three‑layer dependency framework, the authors evaluate three coding agents across four languages and fifty tasks, finding that dependency specifications are often inconsistent, redundant, or incomplete. Agreement on dependency sets is as low as 7% for identical tasks, and newer agents show no improvement, indicating that environment specification remains a significant, unaddressed challenge in code generation.
By Bhanu Prakash Vangala, Tanu Malik