Hugging Face Blog
DABStep: Data Agent Benchmark for Multi-step Reasoning
Read the original on Hugging Face Blog →The Flow has not summarised this story yet — read it at Hugging Face Blog.
Related stories
arXiv AI
Jun 4
Streaming Communication in Multi-Agent Reasoning
arXiv:2606. 05158v1 Announce Type: cross Abstract: Multi-agent reasoning systems adopt a "generate-then-transfer" paradigm that forces end-to-end latency to scale linearly with pipeline depth.
arXiv AI
Jun 6
Benchmark Everything Everywhere All at Once
arXiv:2606. 06462v1 Announce Type: new Abstract: Benchmarks are fundamental for evaluating and advancing LLMs and MLLMs by providing standardized and explicit measures of performance.
arXiv AI
Jul 1
Agentic-Ideation: Sample Efficient Agentic Trajectories Synthesis for Scientific Ideation Agents
arXiv:2606. 31229v1 Announce Type: new Abstract: Ideation plays a pivotal role in scientific discovery.
arXiv AI
Jun 19
SIGMA: Search-Augmented On-Demand Knowledge Integration for Agentic Mathematical Reasoning
arXiv:2510. 27568v2 Announce Type: replace Abstract: Solving mathematical reasoning problems requires not only accurate access to relevant knowledge but also careful, multi-step thinking.
arXiv AI
Jun 3
Adaptive Latent Agentic Reasoning
arXiv:2606. 02871v1 Announce Type: cross Abstract: Large reasoning models improve performance by generating extended chain-of-thought (CoT) reasoning, but this behavior becomes inefficient when applied to LLM agents.
Hugging Face Trending Papers
Jun 2
Inducing Reasoning Primitives from Agent Traces
ReAct-style LLM agents often rediscover the same reasoning routines across problems, yet leave those routines trapped in transient scratchpads. We introduce Reasoning Primitive Induction, a single-pass method that mines successful ReAct traces, clusters recurrent reasoning moves, and converts the most frequent moves into a compact library of typed pseudo-tools.