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20232026
most citedStar-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning

2 citations · 9 across the 24 of their papers we have counts for

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6 papers · 1 filter

cs.CL2026

Agentic Routing: The Harness-Native Data Flywheel

Xinchen Liu, Hang Zhou, Yingjie Zong +12

Large language model agents are increasingly executed not by a single model call, but by an execution harness that manages observation, context, control, action, state, and verific…

cs.AI2026

From Question Answering to Task Completion: A Survey on Agent System and Harness Design

Jianyuan Guo, Zhiwei Hao, Chengcheng Wang +14

LLM-based agents mark a shift from passive question answering to active task completion: they perceive environments, invoke tools, maintain state, and act over extended horizons. A…

cs.LG2026

Claw-SWE-Bench: A Benchmark for Evaluating OpenClaw-style Agent Harnesses on Coding Tasks

Mengyu Zheng, Kai Han, Boxun Li +13

The software engineering capabilities of general-purpose agent harnesses remain underexplored, and existing benchmarks offer limited support for comparing these harnesses under con…

cs.LG2026

An Empirical Study of World Model Quantization

Zhongqian Fu, Tianyi Zhao, Kai Han +3

World models learn an internal representation of environment dynamics, enabling agents to simulate and reason about future states within a compact latent space for tasks such as pl…

cs.LG2026

Diffusion In Diffusion: Reclaiming Global Coherence in Semi-Autoregressive Diffusion

Linrui Ma, Yufei Cui, Kai Han +1

One of the most compelling features of global discrete diffusion language models is their global bidirectional contextual capability. However, existing block-based diffusion studie…

cs.CL2026

Top 10 Open Challenges Steering the Future of Diffusion Language Model and Its Variants

Yunhe Wang, Kai Han, Huiling Zhen +13

The paradigm of Large Language Models (LLMs) is currently defined by auto-regressive (AR) architectures, which generate text through a sequential ``brick-by-brick'' process. Despit…