UniPat AI

Echo is live. Our prediction intelligence system is now running in production, turning uncertainty into measurable outcomes. Prediction should be general, evaluable, trainable, and profitable. Echo is how we get there. Developer API coming soon. Stay tuned.

@Gradient_HQ Appreciate the shoutout, @Gradient_HQ. We think prediction should be something you can train, evaluate, and improve — not just speculate on. Echo is a small step in that direction. More to come. 🚀

As fellow training nerds, UniPat AI’s approach caught our eyes. Synthesizing future-event data to fix outcome bias and actually beat human prediction markets is defined something worth checking out!

[10/10] We think the next frontier for AI is not just understanding the world. 🌍 It’s reasoning about how the world changes. 🔄🤖 Let the world hear the echo of intelligence in prediction. 📣 🌐 Website: echo.unipat.ai 📝 Blog: unipat.ai/blog/Echo

Today we’re introducing Echo — our full-stack prediction intelligence system, which turns uncertainty🔮 into profit📈. We Make Prediction General, Evaluable, Trainable and Profitable. 🌐Website: echo.unipat.ai

[9/10] Echo outperforms the human market: 🧠⚔️📊 🏛️ 63.2% in Politics & Governance 📅 59.3% on 7+ day horizons 🌫️ 57.9% when the market is uncertain

[8/10] EchoZ API delivers calibrated probabilities, evidence, counterfactual analysis, and monitoring recommendations. 📡 Built to capture alpha. In the last two weeks, 4 of 5 OpenClaw bots using our API profited on Polymarket. 📈 Join the waitlist: echo.unipat.ai/apply

[7/10] The lead is robust. 📈🛡️ Across the full σ sensitivity sweep, EchoZ stays #1. The benchmark is also designed to remain stable under: 🔄 missing submissions 🧊 cold starts 🌊 changing model pools

[6/10] On the March 2026 Echo leaderboard, EchoZ-1.0 ranks #1 with 1034.2 Elo — ahead of Gemini-3.1-Pro, Claude-Opus-4.6, Grok-4.1-Fast, and GPT-5.2.

[5/10] Trainable At the core is EchoZ-1.0 — the first LLM trained end-to-end under the Train-on-Future paradigm. 🚀 The core mechanisms include: 🧪 Dynamic Question Synthesis 🔍 Rubric Search 🗺️ MapReduce Agent Architecture

[4/10] We rethought prediction evaluation. 📊 Prediction gets easier as new information arrives, so comparing models at different timestamps is noisy. Echo evaluates models in pairwise battles, aligned on the same question at the same prediction time. 🎯⏱️

[3/10] Echo has 3 layers🧩: — a dynamic evaluation engine — a Train-on-Future post-training paradigm — an AI-native prediction API

[2/10] Humans have always predicted — from farming to markets to elections. In modern prediction markets, this instinct becomes a recursive, collective intelligence that reflects both social meaning and economic value. AI can empower this. 🤖 This is what we aim to do.

[9/9] Next step: towards “AI executes science.” Research-grade reasoning in a 30B model, competitive with frontier systems at a fraction of params.💥 🔗 GitHub: github.com/UniPat-AI/UniS… 📝 Blog: unipat.ai/blog/UniScient…

UniPat AI introduces UniScientist — a 30B model (3B active) for autonomous scientific research: hypothesis → evidence → verification → iterative refinement until convergence. With just 3B active params, it scores 28.3 on FrontierScience-Research.

[8/9] Critical: big performance gains persist even WITHOUT tool access. Not just better retrieval — intrinsic scientific reasoning was genuinely enhanced through training.🚀

[7/9] FrontierScience-Research: UniScientist-30B-A3B: 28.3 GPT-5.2 xhigh: 25.2 DeepSeek V3.2 w/ tools: 26.7 Seed 2.0 Pro w/ tools: 26.7 With aggregation: 33.3 | FrontierScience-Olympiad: 71.0 (= Claude Opus 4.5). Also competitive on DeepResearch Bench I/II & ResearchRubrics. 🔥

[6/9] Additional training for collective research intelligence: Given N candidate reports, the model synthesizes a consolidated output with the strongest elements. Selected via rubric-based rejection sampling. Mirrors real science — researchers consolidate the best evidence. 🤝

[5/9] Evaluation: each open-ended output → N closed-ended, independently verifiable rubric checks. Each item is atomic, objective, evidence-grounded. Transforms non-verifiable research assessment into an approximately verifiable protocol.

[4/9] LLMs generate at scale, humans verify easily. UniScientist exploits this via Evolving Polymathic Synthesis: Models generate research problems from expert-validated claims; domain experts verify quality. Dataset: 4,700+ instances, 50+ disciplines, 20+ rubric items each. 📊

[3/9] Research as a dynamical system with two primitives: • Active Evidence Integration — acquire & validate evidence from external sources • Model Abduction — update hypotheses to best explain current evidence Iterate until convergence → structured report.✍️

[2/9] Most LLMs fake research — they build conclusions first, then retrofit evidence. Reads well, fails on reproducibility. UniScientist improves this by formalizing the complete research loop — not just generating research-like text, but training the actual scientific process.