A focused feed for RLHF, preference data, rater protocols, agent evaluation, and LLM-as-judge research.
Every paper includes structured metadata for quick triage.
Jingzhuo Wu, Jiajun Zhang, Keyan Jin, Dehua Ma, Junbo Wang · Feb 23, 2026
Citations: 0
Automatic MetricsMulti AgentMultilingual
This limitation stems from the inability of current single-model and static multi-agent systems to perceive and adapt to stylistic variations.
To address this, we introduce the Style-Adaptive Multi-Agent System (SAMAS), a novel framework that treats style preservation as a signal processing task.
Yue Pan, Xingyao Wang, Hanyue Zhang, Liwei Liu, Changxin Li, Gang Yang · Feb 23, 2026
Citations: 0
Automatic MetricsLong HorizonMedicineCoding
The model's high sensitivity was further corroborated by additional follow-up data, confirming its efficacy in predicting HF deterioration and its potential to secure patient safety in remote, home-based settings.
Chongyang Gao, Diji Yang, Shuyan Zhou, Xichen Yan, Luchuan Song, Shuo Li · Feb 23, 2026
Citations: 0
Automatic MetricsLong HorizonCoding
We introduce \CFE{} (\textbf{C}lassroom \textbf{F}inal \textbf{E}xam), a multimodal benchmark for evaluating the reasoning capabilities of large language models across more than 20 STEM domains.
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.
The suite uses versioned tracks that invite researchers to contribute new benchmark datasets.
Dongming Jiang, Yi Li, Songtao Wei, Jinxin Yang, Ayushi Kishore, Alysa Zhao · Feb 22, 2026
Citations: 0
Automatic MetricsLong HorizonGeneral
Agentic memory systems enable large language model (LLM) agents to maintain state across long interactions, supporting long-horizon reasoning and personalization beyond fixed context windows.
Despite rapid architectural development, the empirical foundations of these systems remain fragile: existing benchmarks are often underscaled, evaluation metrics are misaligned with semantic utility, performance varies significantly across
Personalization in Question Answering (QA) requires answers that are both accurate and aligned with users' background, preferences, and historical context.
By optimizing multi-turn reasoning trajectories under a personalized reward function, the framework reinforces reasoning paths that better align with user-specific preferences and contextual signals reflected by the reward model.
Diogo Glória-Silva, David Semedo, João Maglhães · Feb 22, 2026
Citations: 0
Automatic MetricsLong HorizonGeneral
Our evaluation shows that VIGiA outperforms existing state-of-the-art models on all tasks in a conversational plan guidance setting, reaching over 90\% accuracy on plan-aware VQA.
With the rapid advancement of agent-based methods in recent years, Agentic RAG has undoubtedly become an important research direction.
However, existing benchmarks typically provide only final questions and answers, while lacking the intermediate hop-level questions that gradually connect atomic questions to the final multi-hop query.
Why do language agents fail on tasks they are capable of solving?
Every well-defined tool-use task imposes a canonical solution path (i.e., a convergent set of tool invocations shared across successful runs) and agent success depends critically on whether a trajectory stays within this path's operating en
Yonathan Ron, Shiri Gilboa, Tammuz Dubnov · Feb 21, 2026
Citations: 0
Automatic MetricsMulti AgentLawCoding
We introduce Whisper: Courtside Edition, a novel multi-agent large language model (LLM) pipeline that enhances Whisper transcriptions without retraining.
The pipeline intercepts Whisper's initial transcript, applies specialized LLM agents for domain context identification, named entity recognition, and jargon detection, and generates compact prompts that guide Whisper's decoder.
Toheeb Aduramomi Jimoh, Tabea De Wille, Nikola S. Nikolov · Feb 21, 2026
Citations: 0
Pairwise PreferenceHuman EvalGeneral
One annotator pair achieved almost perfect agreement ($κ= 0.8743$; $93.8\%$ raw agreement), exceeding a number of reported benchmarks for English sarcasm research works.
Abraham Paul Elenjical, Vivek Hruday Kavuri, Vasudeva Varma · Feb 21, 2026
Citations: 0
Pairwise PreferenceHuman EvalMathMedicine
We introduce a psychologically grounded metacognitive framework that operationalizes Ann Brown's regulatory cycle (Planning, Monitoring, and Evaluation) as a structured prompting architecture, and study its integration within a lightweight
Across diverse reasoning and diagnostic benchmarks (GSM8K, CRUXEval, MBPP, AIME, CorrectBench, and TruthfulQA) using Llama-3 and Qwen-3 (8B), explicit regulatory structuring substantially improves error diagnosis and yields a threefold incr
To overcome this limitation, we reformulate RAG as a cooperative multi-agent decision-making problem and propose Cooperative Retrieval-Augmented Generation (CoRAG), a framework in which the reranker and the generator act as peer decision-ma
Wenlong Meng, Chen Gong, Terry Yue Zhuo, Fan Zhang, Kecen Li, Zheng Liu · Feb 21, 2026
Citations: 0
Automatic MetricsLong HorizonMathCoding
LLM agents rely heavily on high-quality trajectory data to guide their problem-solving behaviors, yet producing such data requires substantial task design, high-capacity model generation, and manual filtering.
Despite the high cost of creating these datasets, existing literature has overlooked copyright protection for LLM agent trajectories.
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.
As AI agents tackle increasingly complex tasks, aligning their behavior with human-provided specifications becomes critical for responsible AI deployment.
