- When Users Change Their Mind: Evaluating Interruptible Agents in Long-Horizon Web Navigation
Henry Peng Zou, Chunyu Miao, Wei-Chieh Huang, Yankai Chen, Yue Zhou · Apr 1, 2026 · Citations: 0
Critique Edit Simulation Env Long Horizon
As LLM agents transition from short, static problem solving to executing complex, long-horizon tasks in dynamic environments, the ability to handle user interruptions, such as adding requirement or revising goals, during mid-task execution…
- IntelliAsk: Learning to Ask High-Quality Research Questions via RLVR
Karun Sharma, Vidushee Vats, Shengzhi Li, Yuxiang Wang, Zhongtian Sun · Jan 23, 2026 · Citations: 0
Pairwise PreferenceExpert Verification Human Eval
Peer review relies on substantive, evidence-based questions, yet current LLMs generate surface-level queries that perform worse than human reviewer questions in expert evaluation.
- From Pixels to Policies: Reinforcing Spatial Reasoning in Language Models for Content-Aware Layout Design
Sha Li, Stefano Petrangeli, Yu Shen, Xiang Chen · Feb 14, 2026 · Citations: 0
Critique Edit Simulation Env
We introduce LaySPA, a reinforcement learning framework that equips large language models (LLMs) with explicit and interpretable spatial reasoning for content-aware graphic layout design.
- XtraGPT: Context-Aware and Controllable Academic Paper Revision via Human-AI Collaboration
Nuo Chen, Andre Lin HuiKai, Jiaying Wu, Junyi Hou, Zining Zhang · May 16, 2025 · Citations: 0
Pairwise PreferenceCritique Edit Human Eval
To address these scenarios, we propose a human-AI collaboration framework for academic paper revision, centered on criteria-guided intent alignment and context-aware modeling.
- Cross-Cultural Expert-Level Art Critique Evaluation with Vision-Language Models
Haorui Yu, Xuehang Wen, Fengrui Zhang, Qiufeng Yi · Jan 12, 2026 · Citations: 0
Rubric RatingCritique Edit
Existing benchmarks assess perception without interpretation, and common evaluation proxies, such as automated metrics and LLM-judge averaging, are unreliable for culturally sensitive generative tasks.
- Can Large Language Models Replace Human Coders? Introducing ContentBench
Michael Haman · Feb 23, 2026 · Citations: 0
Critique Edit Automatic Metrics
This paper introduces ContentBench, a public benchmark suite that helps answer this replacement question by tracking how much agreement low-cost LLMs achieve and what they cost on the same interpretive coding tasks.
- MARS: toward more efficient multi-agent collaboration for LLM reasoning
Xiao Wang, Jia Wang, Yijie Wang, Pengtao Dang, Sha Cao · Sep 24, 2025 · Citations: 0
Critique Edit Automatic Metrics Multi Agent
Large language models (LLMs) have achieved impressive results in natural language understanding, yet their reasoning capabilities remain limited when operating as single agents.
- MAS-ZERO: Designing Multi-Agent Systems with Zero Supervision
Zixuan Ke, Austin Xu, Yifei Ming, Xuan-Phi Nguyen, Ryan Chin · May 21, 2025 · Citations: 0
Critique Edit Automatic Metrics Multi Agent
Multi-agent systems (MAS) leveraging the impressive capabilities of Large Language Models (LLMs) hold significant potential for tackling complex tasks.
- The Ultimate Tutorial for AI-driven Scale Development in Generative Psychometrics: Releasing AIGENIE from its Bottle
Lara Russell-Lasalandra, Hudson Golino, Luis Eduardo Garrido, Alexander P. Christensen · Mar 30, 2026 · Citations: 0
Critique Edit Tool Use
Psychological scale development has traditionally required extensive expert involvement, iterative revision, and large-scale pilot testing before psychometric evaluation can begin.
- Unlocking Reasoning Capability on Machine Translation in Large Language Models
Sara Rajaee, Sebastian Vincent, Alexandre Berard, Marzieh Fadaee, Kelly Marchisio · Feb 16, 2026 · Citations: 0
Critique Edit Long Horizon
We systematically evaluate several open- and closed-weights RLMs on the WMT24++ benchmark and find that enabling explicit reasoning consistently degrades translation quality across languages and models.
- Critique-Coder: Enhancing Coder Models by Critique Reinforcement Learning
Chi Ruan, Dongfu Jiang, Yubo Wang, Wenhu Chen · Sep 26, 2025 · Citations: 0
Critique Edit
We fine-tune multiple models (Critique-Coder) and evaluate them on different benchmarks to show their advantages over RL-only models.
- Bootstrapping Exploration with Group-Level Natural Language Feedback in Reinforcement Learning
Lei Huang, Xiang Cheng, Chenxiao Zhao, Guobin Shen, Junjie Yang · Mar 4, 2026 · Citations: 0
Critique Edit
Experiments on both verifiable and non-verifiable benchmarks show that GOLF achieves superior performance and exploration efficiency, achieving 2.2\times improvements in sample efficiency compared to RL methods trained solely on scalar…
- From Hallucination to Structure Snowballing: The Alignment Tax of Constrained Decoding in LLM Reflection
Hongxu Zhou · Apr 7, 2026 · Citations: 0
Critique Edit
While structured feedback can mitigate this issue, existing approaches often rely on externally trained critics or symbolic tools, reducing agent autonomy.
- The Self Driving Portfolio: Agentic Architecture for Institutional Asset Management
Andrew Ang, Nazym Azimbayev, Andrey Kim · Apr 2, 2026 · Citations: 0
Critique Edit
Agentic AI shifts the investor's role from analytical execution to oversight.
- Revision or Re-Solving? Decomposing Second-Pass Gains in Multi-LLM Pipelines
Jingjie Ning, Xueqi Li, Chengyu Yu · Apr 1, 2026 · Citations: 0
Critique Edit
We evaluate this design across two model pairs on three benchmarks spanning knowledge-intensive MCQ and competitive programming.
- Understanding Teacher Revisions of Large Language Model-Generated Feedback
Conrad Borchers, Luiz Rodrigues, Newarney Torrezão da Costa, Cleon Xavier, Rafael Ferreira Mello · Mar 29, 2026 · Citations: 0
Critique EditRlaif Or Synthetic Feedback
First, we find that teachers accept AI feedback without modification in about 80% of cases, while edited feedback tends to be significantly longer and subsequently shortened by teachers.
- SAHOO: Safeguarded Alignment for High-Order Optimization Objectives in Recursive Self-Improvement
Subramanyam Sahoo, Aman Chadha, Vinija Jain, Divya Chaudhary · Mar 6, 2026 · Citations: 0
Critique Edit
We introduce SAHOO, a practical framework to monitor and control drift through three safeguards: (i) the Goal Drift Index (GDI), a learned multi-signal detector combining semantic, lexical, structural, and distributional measures; (ii)…
- Natural Language Declarative Prompting (NLD-P): A Modular Governance Method for Prompt Design Under Model Drift
Hyunwoo Kim, Hanau Yi, Jaehee Bae, Yumin Kim · Feb 26, 2026 · Citations: 0
Critique Edit
NLD-P is formalized as a modular control abstraction that separates provenance, constraint logic, task content, and post-generation evaluation, encoded directly in natural language without reliance on external orchestration code.
- REA-RL: Reflection-Aware Online Reinforcement Learning for Efficient Reasoning
Hexuan Deng, Wenxiang Jiao, Xuebo Liu, Jun Rao, Min Zhang · May 26, 2025 · Citations: 0
Critique Edit
To address these issues, we propose REA-RL, which introduces a small reflection model for efficient scaling in online training, offering both parallel sampling and sequential revision.