ARC-AGI

Timeline

1. Oct 16, 2026 · Opinion / analysis · 1 source
   Google's Gemini 4 Argon is closer to the AI frontier than any Google model in months
   Google Deepmind's Gemini 4 Argon scored 97.5 percent on the ARC-AGI-3 benchmark, making Google the second lab after OpenAI to cross the 95 percent mark.
2. Oct 15, 2026 · Research paper · 1 source
   Improving Environment-Agnostic Coding Agents: The Role of Scaffolding
   We investigate why this advantage persists and whether an environment-agnostic agent can be improved without environment-specific engineering.
3. Oct 10, 2026 · Research paper · 1 source
   Knowledge transfer between simple discrete worlds
   We train a single depth-recurrent model on many combinations of rules and test it at four levels of novelty: a new puzzle, a new combination of known rules, a pairing of rules never seen together, and a rule instance not seen at all during training.
4. Oct 9, 2026 · Research paper · 1 source
   Pretraining and Test-Time Training Budgets Matter for ARC: A Controlled Study of VARC and TRM
   The Abstraction and Reasoning Corpus (ARC) is increasingly used to evaluate whether neural networks can infer task-specific transformations from few demonstrations.
5. Oct 8, 2026 · Research paper · 2 sources
   Memento 3: Model-Based Recursive Self-Improvement through Reflective Rulebooks
   We introduce Memento 3, building on the Memento series to enable frozen LLM agents to continually learn explicit world models through external memory.
6. Oct 8, 2026 · Research paper · 2 sources
   Scaling to Tens of Thousands of Test-Time Iterations with Loop-Native Attention Residuals
   In this paper, we introduce InfiLoop, a loop-native residual connection that learns which past computations to retain and how much to accept from each new update.
7. Oct 8, 2026 · Research paper · 1 source
   Tracing the Thoughts of a Coding Agent Playing ARC-AGI-3: Lessons for Continual Learning
   We study how a coding agent learns across a sequence of abstract reasoning tasks.
8. Oct 6, 2026 · Research paper · 1 source
   FreeEvolve: Learning to Evolve Beyond Fixed Loops
   Agent evolvers automate the design of the prompts, skills and workflows around language model agents, yet the optimization process they follow is still designed by hand: a fixed search loop decides how candidates are evaluated, which are kept and when the search stops.
9. Oct 2, 2026 · Opinion / analysis · 1 source
   Academia is for Ambition — Alex Zhang, MIT
   Last call for regular tickets for AI Engineer NYC!
10. Sep 29, 2026 · Opinion / analysis · 1 source
    The Programming Language That Referees Mathematics — Leo de Moura
    Leonardo de Moura created Lean and co-created Z3.
11. Sep 29, 2026 · Opinion / analysis · 1 source
    [AINews] Opus 5.5 is good at explainer videos
    Opus 5.5 shipped this week but the vibes are overwhelmingly positive:

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