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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

github.com/DeepWism/DeepWism-R2 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

Full methodology

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

Member repositories

RepositoryRoleHealth v2
DeepWism/DeepWism-R2main31

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