Gen-Verse

Excited to introduce DiscoveryRSI! One model that recursively learns to discover valuable hypotheses, and gets sharper with every research round. Let's RSI Discovery! 📄 Paper: arxiv.org 💻 Code: Gen-Verse/DiscoveryRSI #RSI #AIforScience #AIAgents #ScientificDiscovery

Can research agents get better at science by learning from the research they do? Introducing DiscoveryRSI 🧬: a framework that turns hypothesis-driven research into a recursive self-improvement loop. 📄 Paper: arxiv.org 💻 Code: Gen-Verse/DiscoveryRSI 🔴 Live Arena: foundry-arena.rsi-arena.workers.dev 🔹 What is a valuable hypothesis? We define it as a joint object H = (claim, investigation plan, decision criteria, alternative explanations). A hypothesis is valuable if resolving it would change a research decision. It is testable if the plan can produce evidence that supports, weakens, or bounds the claim. 🔹 How it works - Lead, Generation, Evolution, Critique and Progress agents grow hypothesis trees through evolve / repair / crossover / restart - Every version stays linked to its evidence, data, assessments and contribution records - Selected plans compile into executable Harbor tasks 🔹 Three self-improvement mechanisms ① Hypothesis credit optimization: blinded TrueSkill assessments assign bounded credit to the agent steps that actually contributed, and DiscoveryRSI-27B is trained only on those steps (Qwen3.8-27B init, 256K context; 2,397 of 15,792 trajectories kept; 2.12% of tokens supervised) ② Harness replay: diagnose recurring failures such as repeated exploration, failed tool use, lost artifacts and premature closure, then refine the workflow ③ Rediscovery: run the accepted model and harness updates on fresh questions 🔹 Results (DFM-Bench, 4 life-science domains) - Hypothesis quality 4.86 → 5.18 → 5.39 over two rounds - Falsifiability 4.00 → 6.20, alternative explanations 4.44 → 5.98, data resolvability 3.73 → 4.99 - Evolve/repair share 77.5% → 91.7%, restarts 21.7% → 5.0%: the agent learns to keep developing an idea instead of starting over - Caveat: importance and novelty dip slightly. The model learns to design better tests, not yet to choose bolder questions. 🔹 Expert review 27 physicians and bioscience researchers scored 292 anonymized proposals: - 41% of DiscoveryRSI-27B revisions improve quality vs 31% (GPT-5.6 Sol) and 25% (GPT-6 Luna) - Only 12% make things worse vs 32% and 31% 🔹 Does it anticipate real science? Against 16,440 publications filtered by each model's cutoff, 6.3% of DiscoveryRSI-27B's hypotheses connect to later published research (16.9% in therapeutic targets). Comparison: DeepSeek 5.4%, GLM 2.8%, GPT 2.5%, Claude 2.3%. Watch six models do research in real time in DiscoveryRSI Arena, and follow every proposal, critique and compile step 👇 foundry-arena.rsi-arena.workers.dev Co-led by Jianyuan Zhong, Chenwei Wu & Zhaochen Yu, with wonderful collaborators across PhAI Labs, CUHK, Stanford, MIT, Harvard Medical School, Oxford, Princeton, PKU and more. Grateful to @james_y_zou @Jiaru_Zou @Charles_Y_Wu @zhenfei_yin_yzf 🙏

🚀 Introducing DiscoveryRSI: Recursively Learning to Discover Valuable Hypotheses 6.3% of DiscoveryRSI-27B's hypotheses match later published work, vs 2.5% GPT-6 & 2.3% Claude Opus 5. 📄 arxiv.org 💻 Gen-Verse/DiscoveryRSI 🔴 foundry-arena.rsi-arena.workers.dev

DiscoveryRSI: Recursively Learning to Discover Valuable Hypotheses Scientific discovery requires developing worthwhile hypotheses into practical investigations, yet learning from this evolving research process remains challenging. We introduce an operational definition of valuable and testable research hypotheses: evidence-grounded claims about consequential uncertainties, coupled with feasible investigations, explicit decision criteria, and alternative explanations. Our DiscoveryRSI framework develops hypothesis trees through multi-agent evolutionary search, compiles selected investigations into executable Harbor tasks, and recursively learns through three mechanisms: hypothesis credit optimization attributes scientific assessments to documented research contributions; harness replay identifies productive strategies and recurring failures to refine research workflows; and rediscovery applies accepted improvements to new investigations, generating further experience for subsequent learning. We also introduce DFM-Bench, which provides comprehensive assessment of hypothesis quality and cumulative research progress at the levels of hypotheses, revisions, development paths and research runs. Across four life-science domains, automated evaluation shows that hypothesis quality rises from 4.86 for the base model to 5.39 after two rounds of self-improvement, with the large gains in falsifiability and data resolvability. We further validate hypothesis quality through expert review, comparing our DiscoveryRSI-27B with other research agents. Preliminary human assessments show that DiscoveryRSI-27B scores higher than GPT-6 Luna on hypothesis importance and whether claims are plausible and evidence-bounded. DiscoveryRSI-27B achieved a 6.3% rate of hypothesis correspondence with research published after the model cutoff, largely outperform GPT-6 with 2.5% and Claude Opus 5 with 2.3%, while delivering greater human-rated improvement along research paths. Comments: Code: Gen-Verse/DiscoveryRSI

"RSI-Anything is a domain-general framework that turns world-model predictions and interaction feedback into recursive self-improvement. Built on JEPA-Anything, it connects candidate generation, prediction-guided selection, external evaluation and learning in a shared process." ➡️ any interest? Gen-Verse/RSI-Anything Community Overview: lifehubber.com Resources are shared for discovery and are not independently vetted—please do your own due diligence. New resources are added regularly — feel free to join the sub for updates. Full searchable archive of all resources posted so far on our community site, LifeHubber: lifehubber.com 300+ open-ish AI models, agents, tools, datasets, and related resources, with filtering and sorting.

"RSI-Anything is a domain-general framework that turns world-model predictions and interaction feedback into recursive self-improvement. Built on JEPA-Anything, it connects candidate generation, prediction-guided selection, external evaluation and learning in a shared process." ➡️ any interest? Gen-Verse/RSI-Anything Community Overview: lifehubber.com Resources are shared for discovery and are not independently vetted—please do your own due diligence. New resources are added regularly — feel free to join the sub for updates. Full searchable archive of all resources posted so far on our community site, LifeHubber: lifehubber.com 300+ open-ish AI models, agents, tools, datasets, and related resources, with filtering and sorting.

The AI Agent that trains itself on code and GUIs 🤖 ⭐ GitHub Repository: Gen-Verse/RSI-Anything In this video, we explore RSI-Anything, an open-source project that rSI-Anything is a domain-general framework that leverages world-model predictions and interaction feedback to drive recursive self-improvement across code repair, GUI agents, and scientific planning.. #github #coding #opensource #programming #developer

Show HN: RSI-Anything – JEPA world models for self-improving agents Discussion

Show HN: RSI-Anything – JEPA world models for self-improving agents

RSI-Anything! One RSI model that works across coding, GUI, planning, or even Science tasks. 📄 Paper: arxiv.org 💻 Code: Gen-Verse/RSI-Anything