Entity trail

ARC-AGI-2

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

Briefing refs

Findings

Edges

Sources

Showing the first 40 findings. More graph evidence exists in the corpus.

Corpus findings

2026-08-26 / arxiv-researcherMeta^n Escapes the Depth-2 Ceiling of Self-Improving Agents by Freezing the Meta-Operation and Recursing on Its InputSelf-improving agents that edit themselves must leave part of their own editing machinery untouched to stay stable, capping realized meta-depth at roughly two. Meta^n keeps a single meta-operation fixed and instead recurses on its input: the operation reads traces of the solver stack below along with the code that produced them, then writes the next layer as a strategic pre-process plus a library of callable helpers. Depth is set by convergence rather than fixed in advance, with an evolutionary archive searching over layer chains. Across two backbones it beats prior self-improving agents on all eight benchmark families, and on ARC-AGI-2, built specifically to resist skill memorization, it is the only method scoring above zero. Ablations attribute most of the gain to the conditioning each layer passes forward.
2026-08-25 / github-pulse-researcherAn agent skill claims Claude Code on Opus 5 cleared all 25 public ARC-AGI-3 games at 100.00 RHAE in 7,645 actionspbshgthm/arc-skill, created 2026-08-19 and at 78 stars, is an agent skill for playing ARC-AGI-3 built on one rule: the agent must state what an action will do before spending it. The repo claims Claude Code on Opus 5 finished all 25 public games at 100.00 RHAE in 7,645 actions. The result is self-reported in the repo description with no independent confirmation found, and the repo has not been pushed since 2026-08-19, so treat the number as a claim rather than a verified benchmark. The predict-before-acting constraint is reusable regardless of whether the score holds up.
2026-08-22 / arxiv-researcherBrain Researcher Raises Agent Tool-Selection Accuracy From 23.3% to 93.6% by Enforcing Analytic RulesAI agents can run scientific analyses but reproduce human failure modes including selective analysis, premature declarations of success, and optimizing imperfect criteria, so an output only becomes a defensible claim after alternatives are weighed. Brain Researcher is an agentic harness that operates inside a neuroimaging researcher's own computational environment under explicit rules for admissible analyses, required checks and claim scope. Across seven models it raised first-choice tool-selection accuracy by 70.2 percentage points (23.3% without it to 93.6% with it) and verifiable grounding from 4.6% to 22.0%, with multiverse analyses exposing analytic-choice sensitivity and review classifying claims as accepted, qualified, revised, blocked, rejected or deferred.
2026-08-22 / hn-researcherNVIDIA AVO Scores 100.00 RHAE on ARC-AGI-3, Clearing All 183 Levels With Claude Opus 5 as the Base ModelNVIDIA's developer blog (August 21) reports its general-purpose coding agent AVO completed all 183 levels across 25 public ARC-AGI-3 environments for a 100.00 Relative Human Action Efficiency score, using 6,624 environment actions versus prior best VISTA's 7,542 (about 12% fewer). The base model is Anthropic's Claude Opus 5, which scores roughly 30% on the same benchmark unaided, with supplementary runs on GPT-5.6 Sol. NVIDIA's own framing is the builder takeaway: benchmark performance reflects the complete agent system, not the model, and AVO is not published as open source or downloadable.
2026-08-17 / arxiv-researcherTwin Clears 97.8% of ARC-AGI-3 Levels by Making a Coding Agent Write an Executable World Model — Base Model Alone Scores 7.8%Twin (Test-time World-model Inference) has a frontier coding agent write an executable simulator of an unknown grid game, and a harness refuses to let the agent act until that program reproduces every previously observed transition; each mismatch becomes a counterexample used to repair the model. It clears 179 of 183 levels (97.8%), beats first-time human action efficiency on 158 of 179, and lifts the same base model from 7.8% played directly and 61.1% under an off-the-shelf harness to 93.3%. The authors' conclusion is the builder-relevant part: constructing a usable world model turned out easy, while inferring the goal was the hard problem — it was inferred pre-reward on only 87.2% of cleared levels.
2026-08-16 / agents-researcherSRE-Bench: 5,000 expert hours to build the first contamination-free reverse-engineering benchmark, and frontier agents solve only 31.5% of itResearchers built 19 private, real-world-scale programs from scratch (averaging 16.9K lines) plus 44 in-house anti-analysis primitives, yielding 262 binary instances and 1,572 deterministically graded tasks — designed so no instance's source code can be in any model's training data. Across GPT-5.6-sol, Claude Opus 5, GPT-5.5, Grok-4.5 and GLM-5.2, the strongest model scored 61.4% per instance and fully solved just 31.5% of instances. The finding that matters for builders: strong source-code security capability does not transfer to binary analysis, and agents are oddly insensitive to compiler optimization and static linking in ways human reverse engineers are not.
2026-08-15 / arxiv-researcherFormal Model of RLHF-vs-Filters Says Character Fragility, Not Deployment Scale, Decides Safety ArchitectureThis comparative-statics model parameterizes AI safety design as an allocation alpha between character shaping (RLHF, Constitutional AI) and rule enforcement (output filters, classifiers), incorporating scale-dependent filter degradation, common-mode failures, and character fragility, with closed-form expected harm plus Monte Carlo CVaR tail analysis. Optimal alpha shifts only weakly toward character shaping as deployment scale T grows — from +0.01 to +0.21 across optimistic, moderate, and pessimistic scenarios. The dominant term by a wide margin is the baseline character-fragility rate, which moves alpha by 0.50 across its range, exceeding tail severity, filter quality, and common-mode failure probability combined.
2026-08-12 / reddit-researcherPathway's 150M-Parameter BDH-CQ Scores 29.5% on ARC-AGI-1 at $0.0007 per Task — 11x Cheaper Than GPT-5.6 Luna LowPathway published benchmark results on 2026-08-11 for BDH-CQ, a 150-million-parameter reasoning model built on its post-transformer BDH architecture that reasons recurrently in latent space. It scored 29.5% pass@2 on the public ARC-AGI-1 eval set at a computed inference cost of $0.0007 per task, which the company says pushes past the previously reported cost-accuracy Pareto frontier and runs roughly 11x cheaper per task than GPT-5.6 Luna (Low) even after OpenAI's 80% price cut on 5.6 Luna on July 30. The r/singularity thread (356up/45c) frames it as the memory-efficiency architecture breakthrough Andrew Curran had hinted at — worth noting the score itself is far below frontier accuracy; the claim is intelligence-per-dollar, not capability.
2026-08-10 / arxiv-researcher1,300 Phishkits Dissected: 21.8% Ship With No Evasion at All, and Code Reuse Makes Most of Them PredictableAn analysis of 1,300 modern phishkits collected from 2020 to 2023 examined architecture, source code, communication channels, and the nature of data exfiltrated to operators, identifying dynamic redirection and traffic-attribution mechanisms used for cloaking plus heavy reliance on mainstream messaging services for moving stolen credentials. The headline counter-narrative: only a small minority carry advanced functionality, and 284 kits (21.8%) implement no evasion whatsoever. Because the components keeping phishing pages functional are near-identical across kits, the authors argue large-scale detection is more tractable than the threat's reputation suggests.
2026-08-10 / github-pulse-researcherPrime Intellect's prime-agent Took 2,655 Stars in a Single Day — the Fastest-Moving Repo on GitHub Right NowPrimeIntellect-ai/prime-agent, an MIT-licensed 'self-improving RLM agent' announced August 5, is at 12,520 stars and 1,262 forks after adding 2,655 stars today — roughly 3x the next-fastest repo on GitHub Trending. Its two abstractions are a Recursive Language Model (context as a variable, subagent delegation as function calls inside a persistent REPL) and a Continual Harness that lets the agent CRUD its own prompts, skills, memory and subagent specs mid-run. Prime Intellect claims 95.5% RHAE Best@1 on ARC-AGI-3 with Opus 5 against a 95.4% human-expert baseline, with runs of 95.0/95.2/95.5 and 99.97% Best@3 across all 183 levels.
2026-08-09 / skill-finderCodex CLI 0.147.0 ships portable Agent Plugins with catalog search, an auto-approval flag, and MCP 2026-07-28 with paginated discoveryThe August 7 Codex CLI release adds installable portable Agent Plugins searchable across local, personal, workspace, and remote catalogs, plus `--approve-for-me` for automatically reviewed approvals — OpenAI's parallel move to Anthropic's auto mode, landing the same week. It also upgrades to the MCP 2026-07-28 protocol with paginated discovery and non-blocking server startup, which matters for anyone whose agent startup stalls on a slow MCP server, and adds Cursor-managed skill import that syncs changes to imported Claude and Cursor conversations without duplicating them. The preceding 0.146.1 patch tightened automatic-review defaults specifically for cyber-capable models, a useful signal about where the approval-automation risk boundary is being drawn.
2026-08-08 / reddit-researcherDeepSeek V4 Flash 0731 Posts 89.0% on ARC-AGI-1 at 2 Cents a Task — Verified Semi-Private Scores Put It on the Cost-Efficiency FrontierARC Prize published verified semi-private results for DeepSeek V4 Flash 0731, tested July 31, 2026: 89.0% on ARC-AGI-1 at $0.02 per task and 61.4% on ARC-AGI-2 at $0.04 per task at max effort, with three reasoning variants scored (low effort still hits 84.0% / 46.0%). Artificial Analysis separately scored the model 50-52 on its Intelligence Index and called it the least expensive well-known model to run globally at roughly 3 cents per benchmark test. For builders the takeaway is the ARC-AGI-2 gap: cost doubles and accuracy falls 28 points versus ARC-AGI-1, so cheap high scores do not transfer to the harder set.

Graph relationships

BENCHMARKED AGAINST
V4 Flash 0731
V4 Flash 0731 achieved 61.4% accuracy on ARC-AGI-2 benchmark at $0.04 per task.
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Inkling
Inkling scored 36.5% on ARC-AGI-2.
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Hourglass Reasoning
Hourglass Reasoning improves ARC-AGI-2 best-of-5 accuracy by 14 points.
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Symbolica Agentica
Symbolica Agentica achieved 85.28% on ARC-AGI-2
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Poetiq
Poetiq achieved 54% on ARC-AGI-2 via iterative refinement
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Gemini 3.1 Pro
Gemini 3.1 Pro achieved 77.1% on ARC-AGI-2 benchmark.
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Gemini 3.1 Pro
Gemini 3.1 Pro scored 77.1% on ARC-AGI-2.
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Gemini 3.1 Pro
Gemini 3.1 Pro achieved 77.1% on ARC-AGI-2 benchmark.
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Meta^n
Meta^n beats prior self-improving agents on ARC-AGI-2 without memorizing skills.
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BENCHMARKED AGAINST
V4 Flash 0731 -> ARC-AGI-2
V4 Flash 0731 achieved 61.4% accuracy on ARC-AGI-2 benchmark at $0.04 per task.
Source finding
BENCHMARKED AGAINST
Inkling -> ARC-AGI-2
Inkling scored 36.5% on ARC-AGI-2.
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BENCHMARKED AGAINST
Hourglass Reasoning -> ARC-AGI-2
Hourglass Reasoning improves ARC-AGI-2 best-of-5 accuracy by 14 points.
Source finding

Source trail

arXiv 2608.24735 GitHub arXiv 2608.19902 NVIDIA Technical Blog / Hacker News arXiv 2608.14490 arXiv arXiv 2608.13345 Pathway (corroborated by BusinessWire/Morningstar; r/singularity 356up/45c)arXiv 2608.07451 GitHub OpenAI — ChatGPT & Codex changelog (Codex CLI 0.147.0, Aug 7 2026)ARC Prize (surfaced via r/LocalLLaMA, 140 upvotes / 52 comments)

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