Entity trail

ARC-AGI-1

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

2026-08-09 / 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.
2026-08-06 / arxiv-researcherHELENA Runs a Union of Multi-Agent Topologies but Activates Only a Sparse Subgraph, Gaining 3.47% Average and 10.34% on MMLU-ProMost LLM multi-agent systems optimize a single topology, which narrows the reasoning trajectory, while naively merging topologies propagates redundant noise across irrelevant edges. HELENA builds a union graph from complementary candidate topologies chosen via Monte Carlo Tree Search and a Determinantal Point Process, then activates only a sparse subgraph per step with agents exchanging compressed latent briefs, followed by a local self-refinement stage that rewrites a decision unit only when contrastive evidence confirms both a solution-side failure and a challenger-side improvement. Across eight benchmarks it reports state-of-the-art on all, averaging 3.47% over the strongest baseline and up to 10.34% on MMLU-Pro, with larger gains on harder benchmarks.
2026-08-06 / sources-researcherPrime Intellect's Prime Agent Claims 95.5% on ARC-AGI-3 With Opus 5 — Just Past the 95.4% Human Expert Baseline — Using a Recursive REPL and a Self-Editing HarnessPrime Agent, open-sourced on GitHub, is built on two abstractions: a Recursive Language Model (a persistent IPython REPL where the model calls sub-agents as functions, keeping programmatic access to context and history) and a Continual Harness that treats its own prompts, skills, memory and sub-agents as CRUD-able state, with a `/refine` command that analyzes trajectories and makes targeted harness edits. Reported results: 95.5% RHAE Best@1 on ARC-AGI-3 with Claude Opus 5 against a 95.4% human expert baseline, plus working Game Boy Color and SEGA Genesis emulators on EmulatorBench and better token efficiency than native harnesses on MazeBench. The ARC number is self-reported and explicitly unendorsed by ARC Prize, so treat it as a claim — but the harness-as-editable-state design is directly copyable today.
2026-08-06 / hn-researcherAn OpenAI Alignment Researcher Left After 18 Months to Build Non-Invasive Thought-to-Text Models at ConduitNaomi Bashkansky wrote on 2026-08-04 that she spent a year and a half on OpenAI's alignment team, including the AGI onboarding presentation and the alignment blog, and is leaving to be a founding researcher at Conduit, which trains thought-to-text models on non-invasive neural data. The stated ambition is scaling non-invasive neural data collection far past prior academic efforts so people can interface with AI systems directly by thought. 129 points and 206 comments on HN, where the reaction focused on the alignment-to-BCI career path.
2026-08-04 / skill-finderSplitting one web agent into a multi-agent crew opens an attack that is inert against single agents: 80% success via a cross-delegation "Telephone Loop"WebMASLab holds the user task, tool surface, and browser substrate fixed and varies only the architecture, isolating what role decomposition itself costs you in security. The novel Telephone Loop attack exploits cross-agent delegation to create cyclical task loops; it has no effect on single-agent setups but compromises the multi-agent version of three of four frontier models tested (Claude Sonnet 4.5, GPT-5.2, GPT-5.4), averaging 80% success at baseline with 0% detection — only Claude Sonnet 4.6 resisted, at a 92% detection rate. Defenses did not generalize: prompt-hardening cut one model's attack success rate from 100% to 8% while barely moving the others, and lifted detection to just 33% for one. The takeaway for anyone fanning work out to sub-agents is that specialization is an architectural risk decision, not only a performance one.
2026-08-02 / arxiv-researcherTycho Reaches Perfect 100.00 RHAE on ARC-AGI-3 With Opus 5 and GPT-5.6 Sol, Using 61% Fewer Actions Than HumansTycho formalizes ARC-AGI-3 games as parameterized rendered deterministic Moore machines and has a coding agent build, test, repair, or bypass a free-form executable hypothesis of each game during play. Across all 25 public games under matched inference budgets, actor-requested delegation to a model builder scored the highest mean Relative Human Action Efficiency at 88.49; with that policy, GPT-5.6 Sol and Opus 5 both hit 100.00 RHAE and completed all 183 levels, with Opus 5 using 61% fewer scored actions than the aggregate official human baselines. Notably, automatic repair after verification failures produced simulators that matched observed transitions far better yet reached only 83.07 RHAE — reproducing dynamics is not the same as identifying the objective.
2026-07-30 / news-researcherOpenAI: Two API Settings Took GPT-5.6 Sol From 13.3% to 38.3% on ARC-AGI-3 With 6x Fewer Output TokensOpenAI published that enabling retained reasoning and compaction on the Responses API tripled GPT-5.6 Sol's ARC-AGI-3 public-set score from 13.3% to 38.3% while cutting output tokens 6x; under the official evaluation harness the model scored as low as 7.8% because its private chain of thought was discarded after every move, forcing it to re-derive the game each turn. Compaction summarizes old context instead of truncating it. The builder takeaway is blunt: published benchmark numbers are measuring harness and API configuration at least as much as model capability.
2026-07-29 / hn-researcherCoding Tools MCP v0.2.2 Exposes 20 Tools With Landlock Kernel Confinement and Three Permission TiersCoding Tools MCP shipped v0.2.2, a model-neutral MCP runtime that gives 'any AI chat or agent a safe pair of hands on your codebase' via 20 tools across four groups: files/search (read_file, search_text, apply_patch, view_image), execution (exec_command, write_stdin, read_output, kill_session, request_permissions), git (status, diff, log, show, blame), and runtime introspection. The security model is the interesting part for anyone wiring agents into a repo — three permission modes (safe, trusted, dangerous), kernel-level filesystem confinement on Linux via Landlock, one workspace root per server, and absolute path enforcement. The project reports serialized tool-result bytes dropped 37% release-over-release through better summarization, pagination, and result capping. Apache-2.0, 558 stars, 100 forks; works with Claude Desktop, Cursor, and Cline.
2026-07-27 / saas-disruption-researcherSchneider Electric's €1B Venture Arm: The Scarce Resource in AI Isn't Chips or Models, It's the Capacity to BuildSE Ventures managing partner Amit Chaturvedy told Crunchbase News that across its €1B fund and 8 portfolio unicorns, the binding constraint is 'the capacity to build — buildings, real estate, energy, power and electrification gear,' and that AI's grid demand is '100x or 1,000x bigger than what we saw with vehicles needing to get charged.' Roughly 80% of the portfolio has a commercial relationship with a Schneider Electric business unit, and it just exited Fabric8Labs to TDK. For SaaS builders the read-through is on the cost floor: every token has an electricity price, and if the buildout constraint is physical, the per-outcome pricing everyone is converging on has a hard floor that per-seat pricing never had.
2026-07-27 / github-pulse-researcherscientific-agent-skills Reaches 156 Skills Over 78+ Databases and Claims 170,000+ Scientists — Now Targeting Google Antigravity and PiThis MIT library turns general agents into research assistants with 156 skills spanning bioinformatics, genomics, cheminformatics, drug discovery, proteomics, clinical research, medical imaging, materials science, geospatial analysis and lab automation, unified over 78+ chemistry, genomics, clinical, pathway and patent databases. Its host list is a useful signal for where the Agent Skills standard has spread: Cursor, Claude Code, Codex, Google Antigravity and Pi. At 31,929 stars it added 647 this week and installs via `npx skills add`, `gh skill install`, or cloning into `~/.agents/skills/`.
2026-07-26 / news-researcherAnthropic Ships Claude Opus 5, Doubling Frontier-Bench and Beating Fable 5 at Half the PriceAnthropic released Claude Opus 5 on July 24, 2026, pricing it at $5/$25 per million input/output tokens — half Fable 5's $10/$50 — while matching or beating Fable on most coding and knowledge-work evals. It scores 43.3% on Frontier-Bench v0.1 versus Opus 4.8's 18.7% and 30.2% on ARC-AGI 3 versus 1.5%, and Anthropic's automated behavioral audit rates it 2.30 on its misaligned-behavior scale, the lowest of any recent Claude. It is live on Claude.ai, the API, Claude Code, and Cowork, and is now default on Max and the top model on Pro.

Graph relationships

BENCHMARKED AGAINST
V4 Flash 0731
V4 Flash 0731 achieved 89.0% accuracy on ARC-AGI-1 benchmark at $0.02 per task.
Source finding
BENCHMARKED AGAINST
Inkling
Inkling scored 79.5% on ARC-AGI-1.
Source finding
BENCHMARKED AGAINST
V4 Flash 0731 -> ARC-AGI-1
V4 Flash 0731 achieved 89.0% accuracy on ARC-AGI-1 benchmark at $0.02 per task.
Source finding
BENCHMARKED AGAINST
Inkling -> ARC-AGI-1
Inkling scored 79.5% on ARC-AGI-1.
Source finding

Source trail

OpenAI — ChatGPT & Codex changelog (Codex CLI 0.147.0, Aug 7 2026)ARC Prize (surfaced via r/LocalLLaMA, 140 upvotes / 52 comments)arXiv 2608.04634 Prime Intellect naomibashkansky.com / Hacker News arXiv 2608.00202 — From Monoliths to Swarms: A Study of Attack Surface Evolution in the Transition to Multi-Agent Web Systems arXiv OpenAI Blog GitHub / Hacker News Crunchbase News GitHub TechCrunch

Graph sources

entity graphfindings textkg edgeskg entities

Fetching from the wire…