- Similarity Field Theory: A Mathematical Framework for Intelligence
Kei-Sing Ng · Sep 21, 2025 · Citations: 0
AI systems may be aligned less to safety as such than to human-observable and human-interpretable conceptions of safety, which may not fully determine the underlying safety concept.
- LifeAlign: Lifelong Alignment for Large Language Models with Memory-Augmented Focalized Preference Optimization
Junsong Li, Jie Zhou, Bihao Zhan, Yutao Yang, Qianjun Pan · Sep 21, 2025 · Citations: 0
Pairwise Preference
We introduce LifeAlign, a novel framework for lifelong alignment that enables LLMs to maintain consistent human preference alignment across sequential learning tasks without forgetting previously learned knowledge.
- AirQA: A Comprehensive QA Dataset for AI Research with Instance-Level Evaluation
Tiancheng Huang, Ruisheng Cao, Yuxin Zhang, Zhangyi Kang, Zijian Wang · Sep 21, 2025 · Citations: 0
Long Horizon
While large language models (LLMs) based agents are capable of automating question answering (QA) workflows for scientific papers, there still lacks a comprehensive and realistic benchmark to evaluate their capabilities.
- Can GRPO Boost Complex Multimodal Table Understanding?
Xiaoqiang Kang, Shengen Wu, Zimu Wang, Yilin Liu, Xiaobo Jin · Sep 21, 2025 · Citations: 0
Abstract shows limited direct human-feedback or evaluation-protocol detail; use as adjacent methodological context.
- KANO: Kolmogorov-Arnold Neural Operator
Jin Lee, Ziming Liu, Xinling Yu, Yixuan Wang, Haewon Jeong · Sep 20, 2025 · Citations: 0
In the quantum Hamiltonian learning benchmark, KANO reconstructs ground-truth Hamiltonians in closed-form symbolic representations accurate to the fourth decimal place in coefficients and attains \approx 6\times10^{-6} state infidelity from…
- Distribution-Aligned Decoding for Efficient LLM Task Adaptation
Senkang Hu, Xudong Han, Jinqi Jiang, Yihang Tao, Zihan Fang · Sep 19, 2025 · Citations: 0
- Beyond Words: Enhancing Desire, Emotion, and Sentiment Recognition with Non-Verbal Cues
Wei Chen, Tongguan Wang, Feiyue Xue, Junkai Li, Hui Liu · Sep 19, 2025 · Citations: 0
- Quantifying Genuine Awareness in Hallucination Prediction Beyond Question-Side Shortcuts
Yeongbin Seo, Dongha Lee, Jinyoung Yeo · Sep 18, 2025 · Citations: 0
However, we argue that the reported performance to date reflects not only a model's genuine awareness of its internal information, but also awareness derived purely from question-side information (e.g., benchmark hacking).
- Evolving Language Models without Labels: Majority Drives Selection, Novelty Promotes Variation
Yujun Zhou, Zhenwen Liang, Haolin Liu, Wenhao Yu, Kishan Panaganti · Sep 18, 2025 · Citations: 0
Large language models (LLMs) are increasingly trained with reinforcement learning from verifiable rewards (RLVR), yet real-world deployment demands models that can self-improve without labels or external judges.
- ATTS: Asynchronous Test-Time Scaling via Conformal Prediction
Jing Xiong, Qiujiang Chen, Fanghua Ye, Zhongwei Wan, Chuanyang Zheng · Sep 18, 2025 · Citations: 0
Abstract shows limited direct human-feedback or evaluation-protocol detail; use as adjacent methodological context.
- Llama-Mimi: Exploring the Limits of Flattened Speech Language Modeling
Issa Sugiura, Shuhei Kurita, Yusuke Oda, Ryuichiro Higashinaka · Sep 18, 2025 · Citations: 0
Abstract shows limited direct human-feedback or evaluation-protocol detail; use as adjacent methodological context.
- GeoResponder: Towards Building Geospatial LLMs for Time-Critical Disaster Response
Ahmed El Fekih Zguir, Ferda Ofli, Muhammad Imran · Sep 18, 2025 · Citations: 0
Extensive evaluations across four topologically distinct cities and diverse tasks demonstrate that GeoResponder significantly outperforms both state-of-the-art foundation models and domain-specific baselines.
- Frame Sampling Strategies Matter: A Benchmark for small vision language models
Marija Brkic, Anas Filali Razzouki, Yannis Tevissen, Khalil Guetari, Mounim A. El Yacoubi · Sep 18, 2025 · Citations: 0
- ClearFairy: Capturing Creative Workflows through Decision Structuring, In-Situ Questioning, and Rationale Inference
Kihoon Son, DaEun Choi, Tae Soo Kim, Young-Ho Kim, Sangdoo Yun · Sep 18, 2025 · Citations: 0
Critique Edit
Furthermore, exploratory applications demonstrate that captured steps can enhance generative AI agents in Figma, yielding predictions better aligned with professionals and producing coherent outcomes.
- Bridging Past and Future: Distribution-Aware Alignment for Time Series Forecasting
Yifan Hu, Jie Yang, Tian Zhou, Peiyuan Liu, Yujin Tang · Sep 17, 2025 · Citations: 0
- Reasoning Efficiently Through Adaptive Chain-of-Thought Compression: A Self-Optimizing Framework
Kerui Huang, Shuhan Liu, Xing Hu, Tongtong Xu, Lingfeng Bao · Sep 17, 2025 · Citations: 0
To investigate these trade-offs, we conduct an empirical study based on code generation benchmarks.
- Masked Diffusion Models as Energy Minimization
Sitong Chen, Shen Nie, Jiacheng Sun, Zijin Feng, Zhenguo Li · Sep 17, 2025 · Citations: 0
Experiments on synthetic and real-world benchmarks demonstrate that our energy-inspired schedules outperform hand-crafted baselines, particularly in low-step sampling settings.
- A Simple and Efficient Jailbreak Method Exploiting LLMs' Helpfulness
Xuan Luo, Yue Wang, Zefeng He, Geng Tu, Jing Li · Sep 17, 2025 · Citations: 0
Red Team
This study reveals a critical safety blind spot in modern LLMs: learning-style queries, which closely resemble ordinary educational questions, can reliably elicit harmful responses.
- See, Think, Act: Teaching Multimodal Agents to Effectively Interact with GUI by Identifying Toggles
Zongru Wu, Rui Mao, Zhiyuan Tian, Pengzhou Cheng, Tianjie Ju · Sep 17, 2025 · Citations: 0
To address the challenge, we propose State-aware Reasoning (StaR), a multimodal reasoning method that enables agents to perceive the current toggle state, infer the desired state from the instruction, and act accordingly.
- Linear probes rely on textual evidence: Results from leakage mitigation studies in language models
Gerard Boxo, Aman Neelappa, Shivam Raval · Sep 16, 2025 · Citations: 0
- ReSum: Unlocking Long-Horizon Search Intelligence via Context Summarization
Xixi Wu, Kuan Li, Yida Zhao, Liwen Zhang, Litu Ou · Sep 16, 2025 · Citations: 0
Tool Use
Large Language Model (LLM)-based web agents excel at knowledge-intensive tasks but face a fundamental conflict between the need for extensive exploration and the constraints of limited context windows.
- From Next Token Prediction to (STRIPS) World Models
Carlos Núñez-Molina, Vicenç Gómez, Hector Geffner · Sep 16, 2025 · Citations: 0
- Similarity-Distance-Magnitude Activations
Allen Schmaltz · Sep 16, 2025 · Citations: 0
Abstract shows limited direct human-feedback or evaluation-protocol detail; use as adjacent methodological context.
- DoubleAgents: Human-Agent Alignment in a Socially Embedded Workflow
Tao Long, Xuanming Zhang, Sitong Wang, Zhou Yu, Lydia B Chilton · Sep 16, 2025 · Citations: 0
Pairwise Preference
We present DoubleAgents, a system for human-agent alignment in coordination tasks, grounded in distributed cognition.
- Neural-Quantum-States Impurity Solver for Quantum Embedding Problems
Yinzhanghao Zhou, Tsung-Han Lee, Ao Chen, Nicola Lanatà, Hong Guo · Sep 15, 2025 · Citations: 0
- The AI Memory Gap: Users Misremember What They Created With AI or Without
Tim Zindulka, Sven Goller, Daniela Fernandes, Robin Welsch, Daniel Buschek · Sep 15, 2025 · Citations: 0
Our findings reveal a significant gap in memory: After AI use, the odds of correct attribution dropped, with the steepest decline in mixed human-AI workflows, where either the idea or elaboration was created with AI.
- Collaborative Document Editing with Multiple Users and AI Agents
Florian Lehmann, Krystsina Shauchenka, Daniel Buschek · Sep 15, 2025 · Citations: 0
We propose integrating AI agents directly into collaborative writing environments.
- PeruMedQA: Benchmarking Large Language Models (LLMs) on Peruvian Medical Exams -- Dataset Construction and Evaluation
Rodrigo M. Carrillo-Larco, Jesus Lovón Melgarejo, Manuel Castillo-Cara, Gusseppe Bravo-Rocca · Sep 15, 2025 · Citations: 0
Abstract shows limited direct human-feedback or evaluation-protocol detail; use as adjacent methodological context.
- RAPTOR: A Foundation Policy for Quadrotor Control
Jonas Eschmann, Dario Albani, Giuseppe Loianno · Sep 15, 2025 · Citations: 0
Demonstrations Long Horizon
Humans are remarkably data-efficient when adapting to new unseen conditions, like driving a new car.