alphaXiv
alphaXiv is a research platform that connects papers, researchers, and organizations and helps AI practitioners discover, understand, and apply the latest research. It combines papers, benchmarks, implementations, AI-assisted discussion and summaries, and research workflows to bridge research and practice.
At a Glance
- AI engineers and applied AI teams
- Academic researchers and research labs
- Industry research teams
- Universities and students
- +1 more
AI Tools by alphaXiv
(1)
OpenResearch
Parallel AI Research Agent Harness
Discussions
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Latest News
Let Your Agents Tinker with Research Papers: alphaXiv described reproducing self-distillation papers with autoresearch agents.
Reinforcing Recursive Language Models: alphaXiv reported RL fine-tuning small models to act as recursive language models.
Evolution Strategies vs GRPO for LLM Fine-Tuning: alphaXiv published an empirical comparison on reasoning tasks.
ArxivQA: Training Retrieval Agents for arXiv Search: alphaXiv described a post-trained multi-hop retrieval agent and research assistant dataset.
Products & Services
Public platform for discovering and following arXiv research, reading AI-generated paper/blog-style summaries, discussing papers, asking questions through AI chat, comparing methods/benchmarks, and interacting with implementations.
AI assistant for research queries over the arXiv corpus, with semantic and keyword search, multi-hop retrieval, paper reading, and grounded answers.
Local-first workspace for parallel research agents and autoresearch. It provides isolated worktrees, experiment trees, reproducible runs, logs and artifacts, agent-harness choice, and execution locally, over SSH, or on supported remote/managed compute.
Compute marketplace and command-line tooling for comparing live GPU/CPU availability, launching and managing instances, connecting existing machines, and running research workloads.
Market Position
alphaXiv positions itself as a bridge from AI research discovery to practical implementation: unlike a paper index alone, it combines papers, benchmarks, implementations, AI-assisted discussion and retrieval, and reproducible/agentic research workflows. It operates in the AI literature discovery and research-assistant category alongside tools such as Semantic Scholar, Elicit, and Consensus, while emphasizing research-to-production and experimentation.
Leadership
Founders
Rehaan Ahmad
Stanford undergraduate and robotics/RL researcher; co-developed Serl, researched at Stanford AI Lab/Stanford ML Group, and previously interned in ML engineering/infrastructure at Anyscale and Meta.
Raj Palleti
Stanford University co-founder and robotics/NLP researcher; he described alphaXiv's Brown Institute Magic Grant as the first funding signal that helped move the project forward.
Daniel Kim
Co-founder; part of the founding team described by alphaXiv as Stanford and Berkeley graduates, and an author of alphaXiv research/engineering blog work on retrieval agents and recursive language models.
Lino Le Van
Berkeley graduate and alphaXiv founder/CTO.
Executive Team
Lino Le Van
Founder and CTO
Berkeley graduate; listed as alphaXiv founder/CTO.
Rehaan Ahmad
Co-Founder
Stanford-trained ML/robotics researcher with prior Anyscale and Meta internships and Stanford AI Lab/ML Group research.
Board of Directors
Founding Story
The project began as an idea for comments on arXiv papers. A Brown Institute Magic Grant award email received in a dorm room on April 30 helped set the project in motion and provided its first funding signal; the team then evolved the prototype into a broader platform for discovering, discussing, and applying research.
Target Markets
- AI engineers and applied AI teams
- Academic researchers and research labs
- Industry research teams
- Universities and students
- Organizations building AI products across industries
- AI engineers finding relevant research and turning it into product features
- Researchers tracking papers, collaborating, and exploring research directions
- Literature review and grounded question answering
- Comparing new methods with leading baselines
- Reproducing papers and running autoresearch experiments
- Parallel hypothesis exploration and experiment management