- Validating Political Position Predictions of Arguments
Jordan Robinson, Angus R. Williams, Katie Atkinson, Anthony G. Cohn · Feb 20, 2026 · Citations: 0
Pairwise Preference Human Eval
Real-world knowledge representation often requires capturing subjective, continuous attributes -- such as political positions -- that conflict with pairwise validation, the widely accepted gold standard for human evaluation.
- SCOPE: Selective Conformal Optimized Pairwise LLM Judging
Sher Badshah, Ali Emami, Hassan Sajjad · Feb 13, 2026 · Citations: 0
Pairwise Preference Automatic Metrics
Large language models (LLMs) are increasingly used as judges to replace costly human preference labels in pairwise evaluation.
- Fast-ThinkAct: Efficient Vision-Language-Action Reasoning via Verbalizable Latent Planning
Chi-Pin Huang, Yunze Man, Zhiding Yu, Min-Hung Chen, Jan Kautz · Jan 14, 2026 · Citations: 0
Pairwise Preference Simulation Env Long Horizon
Fast-ThinkAct learns to reason efficiently with latent CoTs by distilling from a teacher, driven by a preference-guided objective to align manipulation trajectories that transfers both linguistic and visual planning capabilities for embodie
- MemoryArena: Benchmarking Agent Memory in Interdependent Multi-Session Agentic Tasks
Zexue He, Yu Wang, Churan Zhi, Yuanzhe Hu, Tzu-Ping Chen · Feb 18, 2026 · Citations: 0
Pairwise Preference Automatic Metrics Web Browsing
Existing evaluations of agents with memory typically assess memorization and action in isolation.
- Yor-Sarc: A gold-standard dataset for sarcasm detection in a low-resource African language
Toheeb Aduramomi Jimoh, Tabea De Wille, Nikola S. Nikolov · Feb 21, 2026 · Citations: 0
Pairwise Preference Automatic Metrics
This protocol incorporates context-sensitive interpretation and community-informed guidelines and is accompanied by a comprehensive analysis of inter-annotator agreement to support replication in other African languages.
- HEART: A Unified Benchmark for Assessing Humans and LLMs in Emotional Support Dialogue
Laya Iyer, Kriti Aggarwal, Sanmi Koyejo, Gail Heyman, Desmond C. Ong · Jan 9, 2026 · Citations: 0
Pairwise PreferenceRubric Rating Human EvalLlm As Judge
Despite rapid progress in language models, we still lack a clear way to understand how their abilities in these interpersonal domains compare to those of humans.
- RebuttalAgent: Strategic Persuasion in Academic Rebuttal via Theory of Mind
Zhitao He, Zongwei Lyu, Yi R Fung · Jan 22, 2026 · Citations: 0
Pairwise PreferenceCritique Edit Human Eval
In this paper, we introduce RebuttalAgent, the first framework to ground academic rebuttal in Theory of Mind (ToM), operationalized through a ToM-Strategy-Response (TSR) framework that models reviewer mental state, formulates persuasion…
- Same Words, Different Judgments: Modality Effects on Preference Alignment
Aaron Broukhim, Nadir Weibel, Eshin Jolly · Feb 26, 2026 · Citations: 0
Pairwise PreferenceRlaif Or Synthetic Feedback Automatic Metrics
Preference-based reinforcement learning (PbRL) is the dominant framework for aligning AI systems to human preferences, but its application to speech remains underexplored.
- Modeling Distinct Human Interaction in Web Agents
Faria Huq, Zora Zhiruo Wang, Zhanqiu Guo, Venu Arvind Arangarajan, Tianyue Ou · Feb 19, 2026 · Citations: 0
Pairwise Preference Automatic Metrics Web Browsing
In this work, we introduce the task of modeling human intervention to support collaborative web task execution.
- Multi-Agent Comedy Club: Investigating Community Discussion Effects on LLM Humor Generation
Shiwei Hong, Lingyao Li, Ethan Z. Rong, Chenxinran Shen, Zhicong Lu · Feb 16, 2026 · Citations: 0
Pairwise PreferenceRubric Rating Multi Agent
Prior work has explored multi-turn interaction and feedback for LLM writing, but evaluations still largely center on prompts and localized feedback, leaving persistent public reception in online communities underexamined.
- Open Rubric System: Scaling Reinforcement Learning with Pairwise Adaptive Rubric
Ruipeng Jia, Yunyi Yang, Yuxin Wu, Yongbo Gai, Siyuan Tao · Feb 15, 2026 · Citations: 0
Pairwise PreferenceRubric Rating Llm As Judge
To operationalize this view, we present the Open Rubric System (OpenRS), a plug-and-play, rubrics-based LLM-as-a-Judge framework built around Pairwise Adaptive Meta-Rubrics (PAMR) and lightweight Pointwise Verifiable Rubrics (PVRs), which…
- CAMEL: Confidence-Gated Reflection for Reward Modeling
Zirui Zhu, Hailun Xu, Yang Luo, Yong Liu, Kanchan Sarkar · Feb 24, 2026 · Citations: 0
Pairwise PreferenceCritique Edit Automatic Metrics
Building on this insight, we propose CAMEL, a confidence-gated reflection framework that performs a lightweight single-token preference decision first and selectively invokes reflection only for low-confidence instances.
- Who can we trust? LLM-as-a-jury for Comparative Assessment
Mengjie Qian, Guangzhi Sun, Mark J. F. Gales, Kate M. Knill · Feb 18, 2026 · Citations: 0
Pairwise Preference
Large language models (LLMs) are increasingly applied as automatic evaluators for natural language generation assessment often using pairwise comparative judgements.
- Toward Expert Investment Teams:A Multi-Agent LLM System with Fine-Grained Trading Tasks
Kunihiro Miyazaki, Takanobu Kawahara, Stephen Roberts, Stefan Zohren · Feb 26, 2026 · Citations: 0
Pairwise Preference Multi Agent
While mainstream approaches deploy multi-agent systems mimicking analyst and manager roles, they often rely on abstract instructions that overlook the intricacies of real-world workflows, which can lead to degraded inference performance and…
- Decentralized Ranking Aggregation: Gossip Algorithms for Borda and Copeland Consensus
Anna Van Elst, Kerrian Le Caillec, Igor Colin, Stephan Clémençon · Feb 26, 2026 · Citations: 0
Pairwise Preference Multi Agent
The concept of ranking aggregation plays a central role in preference analysis, and numerous algorithms for calculating median rankings, often originating in social choice theory, have been documented in the literature, offering theoretical…
- Balancing Multiple Objectives in Urban Traffic Control with Reinforcement Learning from AI Feedback
Chenyang Zhao, Vinny Cahill, Ivana Dusparic · Feb 24, 2026 · Citations: 0
Pairwise PreferenceRlaif Or Synthetic Feedback Human Eval
Preference-based RL offers an appealing alternative by learning from human preferences over pairs of behavioural outcomes.
- RLHFless: Serverless Computing for Efficient RLHF
Rui Wei, Hanfei Yu, Shubham Jain, Yogarajan Sivakumar, Devesh Tiwari · Feb 26, 2026 · Citations: 0
Pairwise Preference Automatic Metrics
Reinforcement Learning from Human Feedback (RLHF) has been widely applied to Large Language Model (LLM) post-training to align model outputs with human preferences.
- DynamicGTR: Leveraging Graph Topology Representation Preferences to Boost VLM Capabilities on Graph QAs
Yanbin Wei, Jiangyue Yan, Chun Kang, Yang Chen, Hua Liu · Feb 25, 2026 · Citations: 0
Pairwise Preference Automatic Metrics
This ``one-size-fits-all'' strategy often neglects model-specific and task-specific preferences, resulting in inaccurate or over-lengthy responses to graph-related queries.
- Learning to Reason for Multi-Step Retrieval of Personal Context in Personalized Question Answering
Maryam Amirizaniani, Alireza Salemi, Hamed Zamani · Feb 22, 2026 · Citations: 0
Pairwise Preference Long Horizon
Personalization in Question Answering (QA) requires answers that are both accurate and aligned with users' background, preferences, and historical context.
- Hierarchical Reward Design from Language: Enhancing Alignment of Agent Behavior with Human Specifications
Zhiqin Qian, Ryan Diaz, Sangwon Seo, Vaibhav Unhelkar · Feb 20, 2026 · Citations: 0
Pairwise Preference Long Horizon
When training artificial intelligence (AI) to perform tasks, humans often care not only about whether a task is completed but also how it is performed.
- CCCaption: Dual-Reward Reinforcement Learning for Complete and Correct Image Captioning
Zhijiang Tang, Linhua Wang, Jiaxin Qi, Weihao Jiang, Peng Hou · Feb 25, 2026 · Citations: 0
Pairwise Preference Automatic Metrics
Image captioning remains a fundamental task for vision language understanding, yet ground-truth supervision still relies predominantly on human-annotated references.
- Investigation for Relative Voice Impression Estimation
Kenichi Fujita, Yusuke Ijima · Feb 15, 2026 · Citations: 0
Pairwise Preference Automatic Metrics
The estimation target is a low-dimensional vector derived from subjective evaluations, quantifying the perceptual shift of the second utterance relative to the first along an antonymic axis (e.g., ``Dark--Bright'').
- ReCoN-Ipsundrum: An Inspectable Recurrent Persistence Loop Agent with Affect-Coupled Control and Mechanism-Linked Consciousness Indicator Assays
Aishik Sanyal · Feb 26, 2026 · Citations: 0
Pairwise Preference
Inspired by Humphrey's ipsundrum hypothesis, we implement ReCoN-Ipsundrum, an inspectable agent that extends a ReCoN state machine with a recurrent persistence loop over sensory salience Ns and an optional affect proxy reporting…
- HiSAC: Hierarchical Sparse Activation Compression for Ultra-long Sequence Modeling in Recommenders
Kun Yuan, Junyu Bi, Daixuan Cheng, Changfa Wu, Shuwen Xiao · Feb 24, 2026 · Citations: 0
Pairwise Preference
Modern recommender systems leverage ultra-long user behavior sequences to capture dynamic preferences, but end-to-end modeling is infeasible in production due to latency and memory constraints.
- Simplifying Outcomes of Language Model Component Analyses with ELIA
Aaron Louis Eidt, Nils Feldhus · Feb 20, 2026 · Citations: 0
Pairwise Preference
The effectiveness of this approach was empirically validated through a mixed-methods user study, which revealed a clear preference for interactive, explorable interfaces over simpler, static visualizations.
- Synthetic Reader Panels: Tournament-Based Ideation with LLM Personas for Autonomous Publishing
Fred Zimmerman · Feb 16, 2026 · Citations: 0
Pairwise Preference
We present a system for autonomous book ideation that replaces human focus groups with synthetic reader panels -- diverse collections of LLM-instantiated reader personas that evaluate book concepts through structured tournament…
- Reward Modeling from Natural Language Human Feedback
Zongqi Wang, Rui Wang, Yuchuan Wu, Yiyao Yu, Pinyi Zhang · Jan 12, 2026 · Citations: 0
Pairwise PreferenceCritique Edit
To address this issue, we propose Reward Modeling from Natural Language Human Feedback (RM-NLHF), which leverages natural language feedback to obtain process reward signals, thereby mitigating the problem of limited solution space inherent…
- ARGUS: Adaptive Rotation-Invariant Geometric Unsupervised System
Anantha Sharma · Jan 3, 2026 · Citations: 0
Pairwise Preference
Detecting distributional drift in high-dimensional data streams presents fundamental challenges: global comparison methods scale poorly, projection-based approaches lose geometric structure, and re-clustering methods suffer from identity…
- The ASIR Courage Model: A Phase-Dynamic Framework for Truth Transitions in Human and AI Systems
Hyo Jin Kim · Feb 25, 2026 · Citations: 0
Pairwise Preference Automatic Metrics
Although initially formulated for human truth-telling under asymmetric stakes, the same phase-dynamic architecture extends to AI systems operating under policy constraints and alignment filters.
- Probing Graph Neural Network Activation Patterns Through Graph Topology
Floriano Tori, Lorenzo Bini, Marco Sorbi, Stéphane Marchand-Maillet, Vincent Ginis · Feb 24, 2026 · Citations: 0
Pairwise Preference Automatic Metrics
However, it remains unclear how the topology of a graph interacts with the learned preferences of GNNs.
- Moral Preferences of LLMs Under Directed Contextual Influence
Phil Blandfort, Tushar Karayil, Urja Pawar, Robert Graham, Alex McKenzie · Feb 26, 2026 · Citations: 0
Pairwise Preference
Moral benchmarks for LLMs typically use context-free prompts, implicitly assuming stable preferences.
- Alignment-Weighted DPO: A principled reasoning approach to improve safety alignment
Mengxuan Hu, Vivek V. Datla, Anoop Kumar, Zihan Guan, Sheng Li · Feb 24, 2026 · Citations: 0
Pairwise PreferenceRed Team
Recent advances in alignment techniques such as Supervised Fine-Tuning (SFT), Reinforcement Learning from Human Feedback (RLHF), and Direct Preference Optimization (DPO) have improved the safety of large language models (LLMs).
- Differences in Typological Alignment in Language Models' Treatment of Differential Argument Marking
Iskar Deng, Nathalia Xu, Shane Steinert-Threlkeld · Feb 19, 2026 · Citations: 0
Pairwise Preference
Recent work has shown that language models (LMs) trained on synthetic corpora can exhibit typological preferences that resemble cross-linguistic regularities in human languages, particularly for syntactic phenomena such as word order.
- Persona2Web: Benchmarking Personalized Web Agents for Contextual Reasoning with User History
Serin Kim, Sangam Lee, Dongha Lee · Feb 19, 2026 · Citations: 0
Pairwise Preference
Large language models have advanced web agents, yet current agents lack personalization capabilities.
- Learning Personalized Agents from Human Feedback
Kaiqu Liang, Julia Kruk, Shengyi Qian, Xianjun Yang, Shengjie Bi · Feb 18, 2026 · Citations: 0
Pairwise Preference
We introduce Personalized Agents from Human Feedback (PAHF), a framework for continual personalization in which agents learn online from live interaction using explicit per-user memory.
- In Agents We Trust, but Who Do Agents Trust? Latent Source Preferences Steer LLM Generations
Mohammad Aflah Khan, Mahsa Amani, Soumi Das, Bishwamittra Ghosh, Qinyuan Wu · Feb 17, 2026 · Citations: 0
Pairwise Preference
Agents based on Large Language Models (LLMs) are increasingly being deployed as interfaces to information on online platforms.
- How to Train Your Long-Context Visual Document Model
Austin Veselka · Feb 16, 2026 · Citations: 0
Pairwise Preference
We systematically study continued pretraining, supervised finetuning, and preference optimization for 24B and 32B parameter models, backed by extensive LC evaluations and ablations to bridge this gap, and achieve state-of-the-art…
