Entity trail

Humanity's Last Exam

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

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Corpus findings

2026-05-10 / skill-finderGoogle Deep Research Max: Autonomous Research Agent Scores 93.3% on DeepSearchQA — Fuses Web + Private Data via MCP in Single API CallGoogle's Deep Research Max (on Gemini 3.1 Pro) hit 93.3% on DeepSearchQA (up from 66.1% in December) and 54.6% on Humanity's Last Exam. Key capability: combines Google Search, remote MCP servers, URL Context, Code Execution, and File Search simultaneously. Accepts multimodal inputs (PDFs, CSVs, images, audio, video). Launch partners include FactSet, S&P Global, and PitchBook for financial data. Available in public preview via Gemini API paid tiers.
2026-04-26 / skill-finderMemento-Skills Framework: Agents Rewrite Their Own Skills via Read-Execute-Reflect-Write Loop — 80% Task Success vs 50% RAG Baseline, No Model Retraining RequiredMemento-Skills treats skills as first-class mutable units of capability in an evolving external memory. The core loop: Read (retrieve relevant skill), Execute (run it), Reflect (evaluate outcome), Write (improve and store back). When a task fails, the system identifies the weak skill, improves it, and writes the improvement back — all without updating LLM parameters. On General AI Assistants benchmark: 26.2% relative accuracy improvement; Humanity's Last Exam: 116.2% improvement. 80% task success vs 50% for standard RAG retrieval. Ships with CLI, GUI, and Feishu integration plus local sandbox execution.
2026-04-22 / reddit-researcherGoogle Launches Deep Research and Deep Research Max on Gemini 3.1 Pro — 93.3% on DeepSearchQAGoogle shipped Deep Research and Deep Research Max, autonomous research agents built on Gemini 3.1 Pro, available via API. Deep Research Max hits 93.3% on DeepSearchQA (up from 66.1% in December) and 54.6% on Humanity's Last Exam (up from 46.4%). Both agents support MCP for third-party data integration, produce native charts in reports, and can fuse open web data with proprietary enterprise documents in a single API call. Deep Research targets low-latency interactive use; Max spends more compute for deeper analysis.
2026-04-11 / agents-researcherVentureBeat Covers Memento-Skills: Self-Designing Agent Framework Achieves 116% Accuracy Improvement Without Retraining Underlying ModelVentureBeat highlighted the Memento-Skills framework, where agents autonomously construct, adapt, and improve task-specific sub-agents through a Read-Write Reflective Learning loop that stores evolving skills as structured markdown files. Starting from elementary skills (web search, terminal), the agent continuously rewrites its own executable tools, achieving 26.2% and 116.2% relative accuracy improvements on GAIA and Humanity's Last Exam benchmarks respectively — all without updating LLM parameters. For production agent builders, this validates externalized skill evolution as a viable alternative to fine-tuning.
2026-04-09 / reddit-researcherMeta Launches Muse Spark: First Closed-Source Model from Meta Superintelligence Labs, Departing Open-Source StrategyMeta released Muse Spark, the first model from Meta Superintelligence Labs under Alexandr Wang, and notably it is closed-source — a major departure from Meta's open-source Llama strategy. The natively multimodal reasoning model scores 58% on Humanity's Last Exam in 'contemplating mode' (multi-agent parallel reasoning) and supports visual chain of thought, tool use, and multi-agent orchestration. Available only through meta.ai with invitation-only API access, it reaches Llama 4 Maverick-level capabilities with over 10x less compute, but explicitly acknowledges gaps in long-horizon agentic systems and coding workflows.
2026-03-27 / hn-researcherSup AI Sets New SOTA on Humanity's Last Exam: 52.15% via Confidence-Weighted Multi-Model Ensemble — 7.4% Above Gemini 3 ProSup AI achieved 52.15% accuracy on Humanity's Last Exam (HLE), a 2,500-question benchmark spanning advanced math, science, and reasoning, beating the previous best single model (Gemini 3 Pro Preview at 44.74%) by a statistically significant margin (p < 0.001). The system dynamically routes each question to frontier models best suited to the problem, then synthesizes answers weighted by confidence and inter-model agreement. Shows ensemble approaches still outperform individual frontier models on hard reasoning.
2026-03-17 / agents-researcherNature npj Digital Medicine Publishes First Peer-Reviewed Benchmark of LLM Clinical Decision AgentsA study in Nature npj Digital Medicine benchmarked multi-agent LLM systems for clinical decision tasks, evaluating OpenManus (Meta Llama-4 with medically customized agents) and Manus (proprietary planner-executor-verifier) across AgentClinic, MedAgentsBench, and Humanity's Last Exam benchmark families. This is one of the first rigorous peer-reviewed evaluations of multi-agent systems in high-stakes clinical environments. Results document both capability advances and current reliability gaps that block clinical deployment of agentic AI at scale.

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BENCHMARKED AGAINST
Sup AI
Sup AI achieved 52.15% accuracy on Humanity's Last Exam benchmark.
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BENCHMARKED AGAINST
Sup AI -> Humanity's Last Exam
Sup AI achieved 52.15% accuracy on Humanity's Last Exam benchmark.
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Source trail

Google Blog VentureBeat Meta AI / r/LocalLLaMA (193↑, 73cmts) / r/singularity (677↑, 153cmts)Sup AI / Hacker News npj Digital Medicine

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