The paper introduces an ability‑residual decoupled framework for affective cognitive diagnosis, which first isolates unmodeled cognitive residuals—such as item calibration bias, concept bias, and student‑concept deviations—using student, item, concept, student‑concept, and low‑rank student‑item components. It then applies an affective module that modulates guess/slip effects, with a Q‑matrix‑constrained concept residual attention mechanism to aggregate only item‑relevant concept residuals. Experiments on multiple datasets and backbones demonstrate improved response prediction and better affect alignment, while ablation and analysis studies show that the residual modeling reduces cognitive contamination in the affective branch and enhances robustness and accuracy.
By Boyuan Zhao, Meng Ye
The paper introduces DSRec, a dual‑interest sequential recommendation model that separates item representations into long‑term and short‑term semantic contexts. Long‑term embeddings capture stable preferences through historical aggregation, while short‑term embeddings focus on local session intent modulated by inter‑click time intervals. Each branch is processed by a distinct State Space Model— a full‑sequence Mamba for long‑term modeling and a time‑modulated SSM for short‑term dynamics— and a residual cross‑fusion mechanism aligns the two granularities while preserving their independence. Experiments on public benchmarks show that DSRec outperforms state‑of‑the‑art methods.
By Shuiying Liao, P. Y. Mok
MIST is a multimodal survival prediction framework that fuses whole-slide images and genomic profiles by representing genomic features as tokens that query histology context tokens derived from a foundation model. The architecture enriches molecular information with histology context before survival prediction, avoiding late-stage merging of separately encoded modalities. Training incorporates discrete-time survival prediction, genomic feature masking, WSI dropout, and contrastive alignment, and demonstrates improved external C-index across colon, renal, lung, and glioblastoma cohorts compared to standard fusion baselines.
By Muhammet Sami Yavuz, Sabri Mustafa Kahya, Richard R. Chen, Jana Lipkova, Benedikt Wiestler
arXiv:2609.21841v1 Announce Type: new
Abstract: Frontier language models now produce professional deliverables that expert graders judge to match human work on a substantial share of economically val...
By Abbas Raza Ali, Muhammad Ajmal Siddiqui, Moona Zahid
The paper introduces the COmmunity-centered Peer Engaged Support (COPES) dataset and a three‑axis evaluation framework to gauge how well Large Language Models (LLMs) align with community perspectives on mental‑health support queries. Experiments show that fine‑tuning LLMs on COPES improves strategy alignment and emotion‑tone alignment by over 50% for general‑purpose models, yet these gains are uneven across subreddits and coping strategies. The study also finds that post‑training shifts the model’s recommendations toward problem‑focused advice while reducing emotion‑focused responses, indicating persistent disparities in performance across different communities and needs.
By Mohit Chandra, Nabin Kim, Eli Min, Aamogh Sawant, Tanmay Sutar, Munmun De Choudhury
arXiv:2609.21227v1 Announce Type: cross
Abstract: Factual hallucination is commonly defined by incorrect factual outputs. We study a paraphrase-induced hallucination setting, where a model answers a...
By Wenhan Yu, Wenxin Wu, Hao Wang, Lei Sha
arXiv:2609.21334v1 Announce Type: cross
Abstract: Effective evaluation of zero-day jamming detectors requires robust adversarial models. However, existing attack models often assume prior knowledge o...
By Ghilas Aissou, R\'emi A. Chou, Taejoon Kim
AtomEgo investigates how to integrate large-scale egocentric human interaction data into embodied foundation model pre‑training. The study uses a curated 2,659‑hour corpus and a scalable data pipeline to evaluate three co‑training paradigms across vision‑language‑action and world‑action architectures. Results show that the benefit of egocentric data depends on both its scale and the quality of alignment with robotic embodiment, offering practical guidance for scalable ego‑robot pre‑training.
By Di Wu, Dongchen Zheng, Junhe Sheng, Zhongxing Wei, Songxin Zhang, Zejian Xie, Xiaoquan Sun, Junyang Zheng, Zhuoyang Song, Jiaxing Zhang, Jiayu Chen
HE-Guardrail is a framework that applies homomorphic encryption to enforce guardrails against jailbreak attacks during encrypted large language model inference. It evaluates guardrail mechanisms—Llama Guard, JBShield, and GradSafe—directly on encrypted data, deciding whether to return the model’s response to the client. The approach preserves confidentiality while closely matching the decisions of plaintext guardrails, offering varied security-efficiency-utility trade‑offs.
By Byeongseo Min, Yongwoo Lee, Young-Sik Kim, Yongjune Kim
TERMon is a lightweight hardware runtime monitor designed for edge AI accelerators in safety‑critical environments. It detects persistent behavioral threats—such as model corruption, distribution shift, and adversarial inputs—by observing inference behavior through hardware‑efficient ternary patterns matched against a thermometer‑encoded fingerprint. Implemented on a PYNQ‑Z2 FPGA, TERMon operates with a two‑cycle decision latency and requires no on‑chip block RAM or DSPs.
By Arish Sateesan, Edlira Dushku
CIBuzzBench is a new benchmark that tests cross‑lingual understanding of Chinese internet buzzwords by providing 3,001 buzzwords with English explanations, equivalents, category labels, and harmfulness annotations. The benchmark defines three tasks—Meaning Explanation, Cross‑lingual Equivalent Matching, and Culturally Grounded Harmfulness Detection—to evaluate how well large language models translate and interpret these culturally nuanced terms. Experiments show that current state‑of‑the‑art LLMs still struggle with fine‑grained non‑literal meanings, robust matching, and accurate harmfulness detection across languages.
By Yifan Wang, Junyu Lu, Qifan Wang, Shun Zhang, Chaozhuo Li, Jiahao Liu, Zhijun Cao, Lingbin Bu, Fanliang Bu
The paper examines whether fine‑tuning large language models (LLMs) with personality‑labelled data improves their ability to act as socially interactive agents. Two small open‑weight LLMs were fine‑tuned on a corpus of personality‑labelled social media posts and dialogues, and the resulting models were evaluated in various social interaction scenarios by independent LLM judges. The findings show that the fine‑tuned models do not outperform their baseline counterparts in role‑playing personalities, though they offer comparable text quality and increased linguistic diversity for the Qwen models; low inter‑rater agreement limits confidence in the results, suggesting future work should focus on training data quality and domain alignment.
By Tim Krabbe, Xiaodan Shi
CRYSTAL is a diagnostic benchmark comprising 6,372 multimodal reasoning instances that assess models through verifiable intermediate steps. It introduces two metrics—Match F1 and Ordered Match F1—to evaluate step-level precision, recall, and order. The benchmark, built via a Delphi-inspired pipeline with four independent MLLMs, reveals systematic failures in current models, such as cherry‑picking and disordered reasoning, and proposes a Causal Process Reward and CPR‑Curriculum to improve reasoning performance.
By Wayner Barrios, SouYoung Jin
arXiv:2609.14744v2 Announce Type: replace
Abstract: By acquiring compute, credentials, accounts, services, and other agents, autonomous AI agents can introduce new authority into a task. Payment, bud...
By Genliang Zhu (Accentrust, Georgia Institute of Technology), Chu Wang (Accentrust, University of Illinois Urbana-Champaign)
The paper introduces CoDRA, a cost-to-disturbance ratio approach for adversarial reinforcement learning that balances controller performance and disturbance exposure without extra penalty terms. CoDRA uses a self‑normalized actor–critic update, scaling value terms by a stop‑gradient normalization constant derived from the current batch. Experiments on MuJoCo pendulum tasks show that CoDRA achieves the lowest cost across a range of forces and masses, outperforming other methods especially on the more challenging InvertedDoublePendulum environment.
By Taeho Lee, Donghwan Lee
The paper introduces a multilingual story moral generation task to evaluate cultural alignment in large language models. Using a dataset of human-written story morals from 14 language‑culture pairs, the authors compare model outputs to human interpretations through semantic similarity, a preference survey, and value categorization. They find that advanced models like GPT‑4o and Gemini produce morally similar and preferred responses but show less cross‑linguistic variation, focusing on a narrower set of shared values, indicating a limitation in capturing the diversity of human narrative understanding.
By Sophie Wu, Andrew Piper
Rhamba is a region‑aware pretraining framework for resting‑state fMRI that combines anatomically guided masking with hybrid Attention‑Mamba architectures. The study pretrained models on the ABIDE dataset using three masking strategies (Any, Majority, Pure) and evaluated four architectural variants, finding that the Mamba‑Attention (MA) hybrid achieved the best average AUROC on downstream schizophrenia and ADHD classification tasks. Explainable AI via Integrated Gradients highlighted that performance depends on the interaction between masking strategy and architecture rather than a single dominant configuration.
By Pankaj Pandey, Ruthwik Reddy Doodipala, Pratheek Eranki, Carolina Torres-Rojas, Manob Jyoti Saikia, Ranganatha Sitaram
arXiv:2606.03963v4 Announce Type: replace-cross
Abstract: Deep reinforcement learning enables autonomous robots to learn complex navigation tasks, but still relies heavily on time consuming manual re...
By Roohan Ahmed Khan, Yasheerah Yaqoot, Amir Atef Habel, Muhammad Ahsan Mustafa, Dzmitry Tsetserukou
The paper introduces an end‑to‑end model‑based reinforcement learning algorithm that synthesises policies satisfying Linear Temporal Logic (LTL) specifications in unknown environments. It synchronises a Limit‑Deterministic Büchi Automaton (LDBA) with a Bayes‑Adaptive Markov Decision Process (BAMDP) and proposes a novel Bayes‑Adaptive Monte‑Carlo Planning (BAMCP) method for approximate Bayes‑optimal strategy synthesis. Experiments on finite and infinite‑horizon tasks show improved property satisfaction and sample efficiency compared to model‑free baselines, and ablation studies confirm the advantage of the new BAMCP over classical variants, including reduced task violations in cautious RL settings.
By Jonathan Hau, Alessandro Abate
arXiv:2609.21190v1 Announce Type: cross
Abstract: Ensuring the correctness of LLM-generated code is a core challenge for modern software engineering. Benchmarks for agentic code generation check corr...
By George Ma, Benjamin Mikek, Haoyu Li, Ferhat Erata, Yuhao Zhang, Zeren Shui, Behrooz Omidvar Tehrani, Jun Huan, Murali Krishna Ramanathan, Somayeh Sojoudi, Hao Zhou, Anoop Deoras