DeepWism/DeepWism-R2
DeepWism R2 is a next-generation AGI system built on the T3CEDS framework (Thin-Thick-Thin Crowd Entropy Dynamics System), which redefines intelligence as a process of entropy reduction rather than attention modeling. observed · 2026-08-28
Health v2 · maintenance only
31/100
- Activity 28
- Release rhythm 35
- Longevity 31
Flags: no_releases no_license
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 436
- days_rel: n/a
- days_push: 432
- n_releases_24m: 0
Adoption not part of the score
1017 stars · 154 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
DeepWism R2 is an AI 'Research&Report' system presented as a next-generation AGI agents framework built on a Thin-Thick-Thin Crowd Entropy Dynamics System (T3CEDS), claiming entropy reduction rather than attention modeling as its core mechanism. It offers a chat interface for complex reasoning, deep retrieval, and automated research report generation, with self-reported benchmark results on Humanity's Last Exam and xbench.
Use cases
- generate a deep research report on a complex topic automatically
- AI agent that performs multi-step web research and answers hard science questions
- chat assistant for deep search and reasoning across multiple domains
- explore an entropy-based alternative to attention-based AI architectures
- agent for multi-domain question answering across science, retrieval, and logic
- research agent benchmarked against OpenAI DeepResearch
When to choose
- You want an autonomous research-and-report agent that combines deep retrieval with structured reasoning and produces reports
- You want to experiment with an alternative architecture built on entropy reduction and crowd-intelligence mechanisms
- You need a chat interface for complex science QA and deep search tasks
When to avoid
- You need production software with a clear license - the repository has no license and an unknown primary language
- You require independently verified performance - the claimed HLE and xbench scores are self-published with limited methodological detail
- You need documented, inspectable implementation code - the README offers mostly high-level marketing claims about the T3CEDS architecture
- You prefer standard, well-supported LLM and agent tooling with active community and vendor support
Facets
application · maturity experimental
agent-framework rag chatbot nlp artificial-intelligence chatbots large-language-models cloud deep-research research-agent agi entropy-reduction crowd-intelligence t3ceds research-reports deep-search self-reported-benchmarks ai-agents retrieval-augmented-generation natural-language-processing web-server
1 source
- readme: https://github.com/DeepWism/DeepWism-R2 · fetched 2026-08-28 · 31c7382a8445
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| DeepWism/DeepWism-R2 | main | 31 |
For agents
markdown · JSON · MCP: product_card(name="DeepWism/DeepWism-R2")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem