A focused feed for RLHF, preference data, rater protocols, agent evaluation, and LLM-as-judge research.
Every paper includes structured metadata for quick triage.
Chenxi Han, Yuheng Min, Zihao Huang, Ao Hong, Hang Liu, Yi Cheng · Feb 13, 2026
Citations: 0
Automatic MetricsLong HorizonGeneral
Recent advances in data-driven reinforcement learning and motion tracking have substantially improved humanoid locomotion, yet critical practical challenges remain.
To address these limitations, we propose the Parameterized Motion Generator (PMG), a real-time motion generator grounded in an analysis of human motion structure that synthesizes reference trajectories using only a compact set of parameteri
Jianke Yang, Ohm Venkatachalam, Mohammad Kianezhad, Sharvaree Vadgama, Rose Yu · Feb 12, 2026
Citations: 0
Automatic MetricsLong HorizonGeneral
We introduce KeplerAgent, an agentic framework that explicitly follows this scientific reasoning process.
The agent coordinates physics-based tools to extract intermediate structure and uses these results to configure symbolic regression engines such as PySINDy and PySR, including their function libraries and structural constraints.
Lai Wei, Liangbo He, Jun Lan, Lingzhong Dong, Yutong Cai, Siyuan Li · Feb 12, 2026
Citations: 0
Automatic MetricsTool UseCoding
To address this, we propose Region-to-Image Distillation, which transforms zooming from an inference-time tool into a training-time primitive, thereby internalizing the benefits of agentic zooming into a single forward pass of an MLLM.
To rigorously evaluate this capability, we further present ZoomBench, a hybrid-annotated benchmark of 845 VQA data spanning six fine-grained perceptual dimensions, together with a dual-view protocol that quantifies the global--regional "zoo
Advances in large language models (LLMs) are driving a shift toward using reinforcement learning (RL) to train agents from iterative, multi-turn interactions across tasks.
By moving search from inference time to the rollout stage of training, TSR provides a simple and general mechanism for stronger multi-turn agent learning, complementary to existing frameworks and rejection-sampling-style selection methods.
Rui Cao, Zhenyun Deng, Yulong Chen, Michael Schlichtkrull, Andreas Vlachos · Feb 11, 2026
Citations: 0
Automatic MetricsWeb BrowsingGeneral
The winning team, HUMANE, achieved an AVerImaTeC score of 0.5455.
This paper provides a detailed description of the shared task, presents the complete evaluation results, and discusses key insights and lessons learned.
Preni Golazizian, Elnaz Rahmati, Jackson Trager, Zhivar Sourati, Nona Ghazizadeh, Georgios Chochlakis · Feb 10, 2026
Citations: 0
Pairwise PreferenceRubric RatingHuman EvalLaw
To this end, we (i) develop a domain-specific evaluation rubric grounded in procedural justice theory, LAPD training materials, and extensive fieldwork; (ii) introduce a rubric-driven preference data construction framework for perspective-c
GUI agents have emerged as a powerful paradigm for automating interactions in digital environments, yet achieving both broad generality and consistently strong task performance remains challenging.
In this report, we present UI-Venus-1.5, a unified, end-to-end GUI Agent designed for robust real-world applications.
In Chomsky's provocative critique "The False Promise of CHATGPT," Large Language Models (LLMs) are characterized as mere pattern predictors that do not acquire languages via intrinsic causal and self-correction structures like humans, there
Yao Xiao, Lei Wang, Yue Deng, Guanzheng Chen, Ziqi Jin, Jung-jae Kim · Feb 9, 2026
Citations: 0
Rubric RatingAutomatic MetricsCoding
However, it often relies on gold-standard answers or explicit evaluation rubrics provided by powerful teacher models or human experts, which are costly and time-consuming.
In this work, we investigate unsupervised approaches to enhance the long-context capabilities of LLMs, eliminating the need for heavy human annotations or teacher models' supervision.
Huatong Song, Lisheng Huang, Shuang Sun, Jinhao Jiang, Ran Le, Daixuan Cheng · Feb 3, 2026
Citations: 0
Simulation EnvLong HorizonCoding
In this technical report, we present SWE-Master, an open-source and fully reproducible post-training framework for building effective software engineering agents.
SWE-Master systematically explores the complete agent development pipeline, including teacher-trajectory synthesis and data curation, long-horizon SFT, RL with real execution feedback, and inference framework design.
Jiliang Ni, Jiachen Pu, Zhongyi Yang, Jingfeng Luo, Conggang Hu · Feb 3, 2026
Citations: 0
Automatic MetricsTool UseGeneral
The proliferation of Large Language Models (LLMs) in function calling is pivotal for creating advanced AI agents, yet their large scale hinders widespread adoption, necessitating transferring their capabilities into smaller ones.
Extensive experiments on challenging and renowned benchmarks demonstrate the effectiveness of our method.
Ahsan Bilal, Ahmed Mohsin, Muhammad Umer, Ali Subhan, Hassan Rizwan, Ayesha Mohsin · Feb 1, 2026
Citations: 0
Automatic MetricsLong HorizonMath
For each problem, the agent runs multiple inference iterations.
Across datasets, our dynamic, PRM-guided approach consistently outperforms direct test-time scaling, yielding large gains on MATH-500 and several-fold improvements on harder benchmarks such as AIME24 and AMO-Bench.
Xiang Zheng, Weiqi Zhai, Wei Wang, Boyu Yang, Wenbo Li, Ruixiang Luo · Jan 31, 2026
Citations: 0
Automatic MetricsMulti AgentMath
Recent large language models (LLMs) achieve near-saturation accuracy on many established mathematical reasoning benchmarks, raising concerns about their ability to diagnose genuine reasoning competence.
To address this gap, we introduce ReasoningMath-Plus, a benchmark of 150 carefully curated problems explicitly designed to evaluate structural reasoning.
