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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: 6 Search mode: keyword Shortlist (0) RSS

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Agent Q-Mix: Selecting the Right Action for LLM Multi-Agent Systems through Reinforcement Learning

Eric Hanchen Jiang, Levina Li, Rui Sun, Xiao Liang, Yubei Li, Yuchen Wu · Apr 1, 2026

Citations: 0

Match reason: Keyword overlap 2/2 across title and protocol fields.

Score: 100% High protocol signal Freshness: Hot Status: Fallback
Automatic Metrics Multi Agent MathLaw
  • In this paper, we propose Agent Q-Mix, a reinforcement learning framework that reformulates topology selection as a cooperative Multi-Agent Reinforcement Learning (MARL) problem.
  • Across seven core benchmarks in coding, reasoning, and mathematics, Agent Q-Mix achieves the highest average accuracy compared to existing methods while demonstrating superior token efficiency and robustness against agent failure.
Open paper
TRIMS: Trajectory-Ranked Instruction Masked Supervision for Diffusion Language Models

Lingjie Chen, Ruizhong Qiu, Yuyu Fan, Yanjun Zhao, Hanghang Tong · Apr 1, 2026

Citations: 0

Match reason: Keyword overlap 1/2 across title and protocol fields.

Score: 88% Moderate protocol signal Freshness: Hot Status: Fallback
Automatic Metrics Long Horizon MathCoding
  • Experiments on LLaDA and Dream across math and coding benchmarks show that TRIMS significantly improves the accuracy-parallelism trade-off over both standard MDLM training and train-free acceleration baselines, while achieving competitive…
Open paper
Cache What Lasts: Token Retention for Memory-Bounded KV Cache in LLMs

Ngoc Bui, Shubham Sharma, Simran Lamba, Saumitra Mishra, Rex Ying · Dec 3, 2025

Citations: 0

Match reason: Keyword overlap 2/2 across title and protocol fields.

Score: 98% High protocol signal Freshness: Cold Status: Fallback
Automatic Metrics Long Horizon Math
  • Across mathematical reasoning (GSM8K, MATH-500, AIME24), procedural generation (LongProc), conversational long-memory benchmarks (LongMemEval), and long-context understanding (LongBenchV2 and SCBench), TRIM-KV consistently outperforms…
  • Qualitative analyses further reveal that learned retention scores align with human intuition, naturally recovering heuristics such as sink tokens, sliding windows, and gist compression without explicit design.
Open paper
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

Match reason: Keyword overlap 1/2 across title and protocol fields.

Score: 81% High protocol signal Freshness: Warm Status: Ready
Pairwise Preference 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.
  • Step 3.5 Flash demonstrates strong performance across agent, coding, and math tasks, achieving 85.4% on IMO-AnswerBench, 86.4% on LiveCodeBench-v6 (2024.08-2025.05), 88.2% on tau2-Bench, 69.0% on BrowseComp (with context management), and…
Open paper

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