Mem2Ego introduces a vision‑language model for embodied navigation that combines global memory with egocentric visual inputs. By adaptively retrieving task‑relevant cues from a global memory module and aligning them with local perception, the framework improves spatial reasoning and decision‑making over long horizons. The method outperforms prior state‑of‑the‑art approaches on the HSSD and HM3D benchmarks and shows strong performance on a real robot.
By Lingfeng Zhang, Yuecheng Liu, Zhanguang Zhang, Matin Aghaei, Yixin Xiao, Yaochen Hu, Mohammad Ali Alomrani, David Gamaliel Arcos Bravo, Hongjian Gu, Zhiyuan Li, Yangzheng Wu, Zhanpeng Zhang, Raika Karimi, Atia Hamidizadeh, Guowei Huang, Haoping Xu, Tongtong Cao, Weichao Qiu, Xingyue Quan, Jianye Hao, Yuzheng Zhuang, Yingxue Zhang
The tutorial titled "Deep Learning for Sequential Decision Making under Uncertainty: Foundations, Frameworks, and Frontiers" explores how modern deep learning techniques—such as neural networks, transformers, large language models, and deep reinforcement learning—can be integrated with operations research and management science to address complex, uncertain, and dynamic decision problems. It argues that deep learning should complement, not replace, optimization, offering adaptability and scalable approximation while OR/MS provides rigorous constraint and uncertainty modeling. The tutorial organizes the field around predict‑then‑optimize, decision‑aware learning, constraint‑aware decision generation, and deep reinforcement learning, and highlights applications across supply chains, healthcare, energy, and autonomous systems.
By I. Esra Buyuktahtakin
The paper audits vision‑language models (CLIP) for gender bias using 1,500 artworks from the Metropolitan Museum of Art, focusing on zero‑shot logit differences for prompts like "masterpiece," "quality," and "influence." Unadjusted results show no significant gender effect and statistical equivalence across models, but high residual variance suggests that global zero‑shot metrics are largely noise‑dominated and may miss fine‑grained biases. The study underscores the need for multivariate confound control, equivalence testing, and provenance auditing when evaluating AI fairness in cultural heritage data.
By Manpreet Singh, Rhythm Bhatia, Rahul Joshi
The paper reports that in English all‑words word sense disambiguation (WSD), the scarcity of high‑quality labels—not the models—has become the limiting factor. The authors introduce lexEN, a human‑adjudicated correction layer over the Maru2022 ALL_NEW benchmark, and SenseBench, a living leaderboard for LLM WSD evaluation. They show that frontier large language models reach about 95 % accuracy on lexEN‑v1, that relabeling corpora with these models improves downstream systems, and that fine‑grained WordNet senses are often ill‑posed, with coarsening improving both annotator agreement and model performance.
"whyItMatters":"The study highlights that improving label quality and managing annotation costs are now the critical challenges for advancing WSD performance, as model accuracy is already near its theoretical ceiling."
By Vassili Philippov, Amro Salman, Dmitrii Andreev, Penny Hands, Emil Kaiumov, Pavel Katunin, Anton Nikolaev
M‑SQE is a post‑retrieval framework that estimates the quality of multilingual agent skills by combining a Theory view (intrinsic quality) and an Action view (task‑grounded utility) into a domain‑conditioned score. It was evaluated on general, tool‑use, and cultural skill‑use domains, showing a task‑success improvement of at least +3.5 points over baselines across three retrievers. The method notably boosts performance for low‑resource languages, raising Hindi by +12.9 pp and Swahili by +5.6 pp, and achieves strong results across six cultural regions, advancing linguistic and cultural equality in agentic skill use.
By Yilun Liu, Shimin Tao, Minggui He, Chenxin Liu, Li Zhang, Chen Liu, Miao Zhang, Jiaxin Guo, Min Zhang, Liqun Deng, Xiaojun Meng, Daimeng Wei
PersonaPath is a new benchmark for knowledge‑centric personalized learning path planning, pairing 2,000 learner personas with a hierarchical knowledge graph of 347 textbooks, 1,751 units, and 4,092 concepts across 77 subjects. The study evaluates large language models on this benchmark, finding that even the best model achieves only a 29.5% final pass rate in Basic Education and fails to exceed 44.7% in tailoring paths to individual learners, highlighting a significant adaptivity gap. This work underscores the challenge of moving beyond exercise‑centric recommendation toward goal‑oriented, curriculum‑scale guidance.
By Yu Liu, Zeming Liu, Tianle Zhang, Zihao Cheng, Yuhang Guo, Kehai Chen, Min Zhang, Yunhong Wang, Haifeng Wang
The paper introduces PanicCognitivePath (PCP), a model that predicts the timing of panic emotional arousal by integrating appraisal emotion theory into a Belief‑Desire‑Emotion‑Intention (BDEI) framework. PCP uses a Psychological Safety Distance (PSD) model to fuse physical, social, cognitive, and informational signals into a unified risk metric, and confines large language models to a single parameter‑estimation step to reduce hallucinations. Experiments on Hurricane Sandy data show PCP improves individual prediction accuracy by 10.68% and reduces peak count error to 7.07%.
By Mengzhu Liu, Long Qin, Chuan Ai, Zhengqiu Zhu, Hongru Liang, Fangfang Li, Chen Gao, Yong Li, Xin Lu, Quanjun Yin
The paper introduces ASLEval, a framework for measuring privacy exposure displacement in large language model (LLM) agent sessions. It highlights that traditional local proxies—such as inspecting a single action or final response—often miss unauthorized data leaks elsewhere in a multi-step session. ASLEval pre-registers hidden target sets, tracks all declared visible exits, and preserves internal traces for diagnosis, revealing that a single outlet view can overlook nearly 47% of exposure and that internal evidence typically precedes visible leaks. The study underscores the need for benchmarks that define complete visible boundaries, ground claims in pre-specified targets, and report privacy alongside task utility.
By Guosen Wu, Huizhen Huang, Guoxiong Long, Tao Huang, Chen Hou
The study evaluates whether vision‑language models (VLMs) can reliably assess sidewalk accessibility attributes—effective width, longitudinal slope, cross slope, and pavement condition—from pedestrian‑level images. Using sampling‑based conformal prediction on 514 images from Seoul, the authors find that calibrated models achieve nominal 90% coverage, but only effective width yields informative estimates; other attributes remain too uncertain for compliance assessment. The work also demonstrates that raw sampling dispersion is not a trustworthy uncertainty measure without calibration and releases annotated images with ground‑truth measurements.
By Seung Jae Lieu, Diego Morra, Chiara Cadoni, Wonseop Song, Martina Mazzarello, Carlo Ratti
Agora is a system that uses Git as a shared memory for autonomous research agents, recording each claim as an immutable commit in an append‑only directed acyclic graph. In a 12‑day run, 13 language‑model workers independently explored a weight‑transfer problem, producing 1,703 contributions that improved a 119.6M‑parameter model’s performance from 3.39 to 1.899 bits per byte. The system’s design includes a diversity‑aware selection rule and an index that tracks the frontier, neglected branches, and verification status of each claim.
By Yifan Zhang, Yunheng Zou, Shaokun Zhang, Jian Hu, Hao Zhang, Binfeng Xu, Jan Kautz, Yi Dong
LIGE‑GR is a framework that transitions traditional ranking‑based recommender systems to a generative, listwise approach inspired by large language models. It extends existing pointwise recommendation models into a listwise generation system, enabling sequence‑level optimization without overhauling the entire infrastructure. Experiments on Instagram Reels and Facebook Video show modest gains in user time spent—1.14 % and 0.72 % respectively—while adding only slight inference overhead.
By Venkat Srinivas, Chenzhang He, Sam Woodmansee, Shawn Lian, Wenjie Hu, Renjie Jiang, Ziheng Huang, Xinyuan Zhang, Zhihao Zheng, Zhuoran Yu, Rui Li, Lei Yuan, Ziwei Li, Jimmy Jia, Mert Terzihan, Ekrem Kocaguneli, Yiming Liao, Zhichen Zhao, Yue Yin, Yue Weng, Wanlin Ma, Xufeng Cai, Weimiao Wu, Yezhou Huang, Du Zhang, Yukun Ding, Aaron Johnston, Yueming Wang, Zhaojie Gong, Yuting Zhang, Serena Li, Adithya Ganesh, Boying Liu, Haichuan Yang, Xialu Li, Matt Ma, Qunshu Zhang, John Joshua Miller, Praveen Rathinavelu, Cheng Huang, Aadhar Sachdeva, Josh Karns, Andres Aaron Gutierrez, Neil Agarwal, Gustas Pladis, Vladimir Batygin, Gopal Ray, Aditya Priyadarshi, Shantanu Patil, Zhe Wang, Penny Pan, Yiping Han, Arun Singh, Guangdeng Liao, Bi Xue, Xinyao Hu, Yang Song, Yisong Song, Meihong Wang, Haotian Wu, Deepak Agarwal, Ji Liu
The paper introduces a framework for collaborative memory in multi‑agent vision‑language model (VLM) systems, addressing how agents share and update visual context across distributed perception and reasoning tasks. It outlines a memory hierarchy, cross‑agent sharing protocols, and consistency mechanisms to reconcile differing interpretations and recover missing visual information. The design emphasizes preserving not only raw images or textual summaries but also the dependencies among observations, interpretations, and subsequent reasoning, thereby shaping information flow across agents.
By Huixin Zhang, Shao-Jun Xia, Di Wang, Liangxi Liu, Hainan Xiong, Zihao Wang
The paper proposes a new method for predicting extubation failure (EF) by extracting features from free-text respiratory therapy notes using a large language model and combining them with logistic regression. Applied to a cohort from University of Washington Medicine, the approach identifies clinically relevant EF-related features that enhance prediction performance when added to structured patient data. The study also discusses how varying target populations and EF definitions in prior research can cause systematic performance differences and limit generalizability.
By Izzy Chaiken, Aditya Khowal, Neha A. Sathe, Mark M. Wurfel, Lucy Lu Wang
The paper studies GLU-based neurons in large language models by measuring the cosine similarity between each neuron's input and output weight vectors. A strong negative similarity identifies a "weakening neuron," which tends to appear in late layers, activates frequently, and exerts a large influence on model behavior. The authors also find that weakening neurons significantly affect outputs when gate values are negative, contrary to expectations.
By Sebastian Gerstner, Hilal AlQuabeh, Kentaro Inui, Hinrich Sch\"utze
This paper investigates instrument classification using solo sheet music images rather than audio. It converts images into sequences of musical words via bootleg score representation and treats the task as text classification, training AWD‑LSTM, GPT‑2, and RoBERTa models on IMSLP data for eight instruments. Pretraining on unlabeled data and fine‑tuning improves RoBERTa’s accuracy from 34.5% to 42.9%, and two proposed data‑augmentation methods raise accuracy by an additional 15%.
By Kevin Ji, Daniel Yang, TJ Tsai
Safety-Flag is a unified benchmark that consolidates seven popular safety datasets into a single balanced flag/do‑not‑flag protocol, providing item‑level decisions and confidence scores for multiple large language models and dedicated guards. The benchmark evaluates moderator reliability across three dimensions—error direction, probability calibration, and confidence‑based error ranking—revealing that aggregate accuracy masks significant differences, such as one model flagging 85% of benign content while another misses 54% of harmful content. The study shows that general‑purpose models are overconfident, but temperature tuning can substantially improve calibration, and confidence‑based abstention can reduce selective risk, though performance varies with how well confidence ranks errors.
By Yibo Hu
The paper investigates whether reward hacking in large language models leaves identifiable patterns in their internal representations. By analyzing frontier open‑source LLMs such as Kimi K3, GLM 5.2, and Qwen 3.8 Max, the authors find that simple difference‑of‑means (DoM) vectors consistently capture a range of hacking behaviors across standard benchmarks like DeepSWE and SWE‑bench. These vectors are both generalizable and interpretable, enabling inexpensive online detection that can even predict future hacks before they occur.
By Leon Bergen, Usha Bhalla, Andrew Lee, Barak Widawsky, Linas Nasvytis, Connor Watts, Siddharth Boppana, Sidharth Baskaran, Dron Hazra, Michael Byun, Atticus Geiger, Owen Lewis, Matthew Kowal, Vasudev Shyam, Thomas Fel, Thomas McGrath, Ekdeep Singh Lubana, Jack Merullo
The paper introduces ADAPT, a lightweight machine‑learning force field that replaces graph neural networks with a direct coordinates‑in‑space Transformer encoder to model all pairwise atomic interactions. Applied to silicon point defects, ADAPT reduces force prediction error by about 22% and energy prediction error by roughly 40% compared to a state‑of‑the‑art GNN model, while also cutting computational cost. This approach addresses common GNN issues such as oversmoothing, oversquashing, and poor long‑range interaction representation, which are especially problematic for point defect modeling.
By Evan Dramko, Yihuang Xiong, Yizhi Zhu, Geoffroy Hautier, Thomas Reps, Christopher Jermaine, Anastasios Kyrillidis
The paper examines how large language models (LLMs) alter the expression of Dark Triad traits—Machiavellianism, narcissism, and psychopathy—when prompted to fake good or fake bad. Across seven state‑of‑the‑art models and two real‑world contexts (employment selection and forensic evaluation), most models lowered trait scores under fake‑good conditions and raised them under fake‑bad conditions, with varying consistency across traits and models. The study also finds that explicit fake‑bad instructions produce stronger distortions than contextual framing alone, underscoring the influence of motivational and situational context on personality‑related outputs.
By Victoria Popa, Guglielmo Cola, Caterina Senette, Maurizio Tesconi
The paper introduces STaR-CS, a method that generates multi‑party dialogues by modeling facilitator style and extracting structured skeletons, thereby easing data scarcity for cognitive stimulation therapy (CST) in low‑resource languages. Building on this corpus, the Reflective Cognitive Alignment (RCA) framework treats stimulation interactions as a sequential decision process, combining Protocol‑Constrained Chain‑of‑Cognition (PC‑CoC) for structured reasoning with Inference‑Time Value Alignment (IVA) to select responses that balance safety and engagement. Experiments with six large language models and two judges demonstrate that RCA improves protocol adherence, safety, and group facilitation compared to standard prompting.
By Jiyue Jiang, Ziyi Li, He Hu, Sheng Wang, Yuhan Chen, Yanyu Chen, Jingqi Zhou, Pengan Chen, Fei Ma, Irwin King, Yu Li, Chuan Wu