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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: 3 Search mode: keyword RSS

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Team of Thoughts: Efficient Test-time Scaling of Agentic Systems through Orchestrated Tool Calling

Jeffrey T. H. Wong, Zixi Zhang, Junyi Liu, Yiren Zhao · Feb 18, 2026

Citations: 0
Expert Verification Automatic Metrics Multi Agent Coding
  • Existing Multi-Agent Systems (MAS) typically rely on static, homogeneous model configurations, limiting their ability to exploit the distinct strengths of differently post-trained models.
  • To address this, we introduce Team-of-Thoughts, a novel MAS architecture that leverages the complementary capabilities of heterogeneous agents via an orchestrator-tool paradigm.
Duel-Evolve: Reward-Free Test-Time Scaling via LLM Self-Preferences

Sweta Karlekar, Carolina Zheng, Magnus Saebo, Nicolas Beltran-Velez, Shuyang Yu, John Bowlan · Feb 25, 2026

Citations: 0
Pairwise Preference Automatic Metrics MathCoding
  • Building on this observation, we introduce Duel-Evolve, an evolutionary optimization algorithm that replaces external scalar rewards with pairwise preferences elicited from the same LLM used to generate candidates.
  • Results show that pairwise self-preferences provide strong optimization signal for test-time improvement over large, discrete output spaces.
Step 3.5 Flash: Open Frontier-Level Intelligence with 11B Active Parameters

Ailin Huang, Ang Li, Aobo Kong, Bin Wang, Binxing Jiao, Bo Dong · Feb 11, 2026

Citations: 0
Pairwise Preference Simulation Env Tool Use MathCoding
  • We introduce Step 3.5 Flash, a sparse Mixture-of-Experts (MoE) model that bridges frontier-level agentic intelligence and computational efficiency.
  • We focus on what matters most when building agents: sharp reasoning and fast, reliable execution.

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