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20242026
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cs.CL2026

MiroThinker: Pushing the Performance Boundaries of Open-Source Research Agents via Model, Context, and Interactive Scaling

MiroMind Team, Song Bai, Lidong Bing +52

We present MiroThinker v1.0, an open-source research agent designed to advance tool-augmented reasoning and information-seeking capabilities. Unlike previous agents that only scale…

cs.CL2026

MiroThinker-1.7 & H1: Towards Heavy-Duty Research Agents via Verification

MiroMind Team, S. Bai, L. Bing +39

We present MiroThinker-1.7, a new research agent designed for complex long-horizon reasoning tasks. Building on this foundation, we further introduce MiroThinker-H1, which extends…

cs.CL2026

DeepResearchEval: An Automated Framework for Deep Research Task Construction and Agentic Evaluation

Yibo Wang, Lei Wang, Yue Deng +7

Deep research systems are widely used for multi-step web research, analysis, and cross-source synthesis, yet their evaluation remains challenging. Existing benchmarks often require…

cs.CL2025

Multi-Agent Sampling: Scaling Inference Compute for Data Synthesis with Tree Search-Based Agentic Collaboration

Hai Ye, Mingbao Lin, Hwee Tou Ng +1

Scaling laws for inference compute in multi-agent systems remain under-explored compared to single-agent scenarios. This work aims to bridge this gap by investigating the problem o…

cs.CL2024

Preference-Guided Reflective Sampling for Aligning Language Models

Hai Ye, Hwee Tou Ng

Iterative data generation and model re-training can effectively align large language models(LLMs) to human preferences. The process of data sampling is crucial, as it significantly…

cs.CL2024

Self-Judge: Selective Instruction Following with Alignment Self-Evaluation

Hai Ye, Hwee Tou Ng

Pre-trained large language models (LLMs) can be tailored to adhere to human instructions through instruction tuning. However, due to shifts in the distribution of test-time data, t…