Skip to content

Researcher Tools

Human Feedback and Eval Paper Explorer

A focused feed for RLHF, preference data, rater protocols, agent evaluation, and LLM-as-judge research. Every paper includes structured metadata for quick triage.

Total papers: 6 Search mode: keyword Shortlist (0) RSS

Featured Papers

Popular high-signal papers with direct links to full protocol pages.

Browse by Topic

Jump directly into tag and hub pages to crawl deeper content clusters.

Popular Tags

Top Protocol Hubs

Weekly Eval Paper Digest

The top RLHF, evaluation, and human feedback papers — curated and summarized every Friday.

No spam. Unsubscribe anytime.

Start Here By Objective

Pick your immediate research objective and jump directly to high-signal pages, not generic search.

Scale Your Evaluation Team

Need human evaluators for your benchmark or preference study? OpenTrain sources pre-vetted domain experts into your annotation pipeline.

Match reason: Keyword overlap 1/1 across title and protocol fields.

Score: 100% High protocol signal Freshness: Warm Status: Ready
Demonstrations Human EvalLlm As Judge Long Horizon General
  • LLM agents fail on the majority of real-world tasks -- GPT-4o succeeds on fewer than 15% of WebArena navigation tasks and below 55% pass@1 on ToolBench (Zhou et al., 2024; Qin et al., 2024) -- yet every failed trajectory is routinely…
  • We introduce AgentHER, a framework that recovers this lost training signal by adapting the Hindsight Experience Replay (HER; Andrychowicz et al., 2017) principle to natural-language agent trajectories for offline data augmentation.
Open paper
Citations: 0

Match reason: Keyword overlap 1/1 across title and protocol fields.

Score: 100% Moderate protocol signal Freshness: Hot Status: Fallback
Llm As Judge Long Horizon General
  • Emerging AI systems in behavioral health and psychiatry use multi-step or multi-agent LLM pipelines for tasks like assessing self-harm risk and screening for depression.
  • We present a statistical framework for multi-agent pipelines structured as directed acyclic graphs (DAGs) that provides an alternative to heuristic voting with principled, adaptive decision-making.
Open paper
PASK: Toward Intent-Aware Proactive Agents with Long-Term Memory

Zhifei Xie, Zongzheng Hu, Fangda Ye, Xin Zhang, Haobo Chai, Zihang Liu · Apr 9, 2026

Citations: 0

Match reason: Keyword overlap 1/1 across title and protocol fields.

Score: 100% High protocol signal Freshness: Warm Status: Fallback
Automatic Metrics Long Horizon General
  • Prior work remains largely confined to laboratory settings, leaving a clear gap in real-world proactive agent: depth, complexity, ambiguity, precision and real-time constraints.
  • We first propose DD-MM-PAS (Demand Detection, Memory Modeling, Proactive Agent System) as a general paradigm for streaming proactive AI agent.
Open paper
AgentSwing: Adaptive Parallel Context Management Routing for Long-Horizon Web Agents

Zhaopeng Feng, Liangcai Su, Zhen Zhang, Xinyu Wang, Xiaotian Zhang, Xiaobin Wang · Mar 29, 2026

Citations: 0

Match reason: Keyword overlap 1/1 across title and protocol fields.

Score: 100% Moderate protocol signal Freshness: Warm Status: Fallback
Automatic Metrics Long Horizon General
  • As large language models (LLMs) evolve into autonomous agents for long-horizon information-seeking, managing finite context capacity has become a critical bottleneck.
  • Building on this perspective, we propose AgentSwing, a state-aware adaptive parallel context management routing framework.
Open paper

Match reason: Keyword overlap 1/1 across title and protocol fields.

Score: 100% Moderate protocol signal Freshness: Warm Status: Fallback
Automatic Metrics Long Horizon General
  • Enterprise AI deploys dozens of autonomous agent nodes across workflows, each acting on the same entities with no shared memory and no common governance.
  • On the LoCoMo benchmark, the architecture achieves 74.8% overall accuracy, confirming that governance and schema enforcement impose no retrieval quality penalty.
Open paper
The Bitter Lesson of Diffusion Language Models for Agentic Workflows: A Comprehensive Reality Check

Qingyu Lu, Liang Ding, Kanjian Zhang, Jinxia Zhang, Dacheng Tao · Jan 19, 2026

Citations: 0

Match reason: Keyword overlap 1/1 across title and protocol fields.

Score: 100% High protocol signal Freshness: Warm Status: Fallback
Automatic Metrics Long Horizon General
  • In this work, we present a comprehensive evaluation of dLLMs (e.g., LLaDA, Dream) across two distinct agentic paradigms: Embodied Agents (requiring long-horizon planning) and Tool-Calling Agents (requiring precise formatting).
  • To assess the potential of dLLMs in agentic workflows, we introduce DiffuAgent, a multi-agent evaluation framework that integrates dLLMs as plug-and-play cognitive cores.
Open paper

Protocol Hubs

Get Started

Join the #1 Platform for AI Training Talent

Where top AI builders and expert AI Trainers connect to build the future of AI.
Self-Service
Post a Job
Post your project and get a shortlist of qualified AI Trainers and Data Labelers. Hire and manage your team in the tools you already use.
Managed Service
For Large Projects
Done-for-You
We recruit, onboard, and manage a dedicated team inside your tools. End-to-end operations for large or complex projects.
For Freelancers
Join as an AI Trainer
Find AI training and data labeling projects across platforms, all in one place. One profile, one application process, more opportunities.