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

Briefing refs
11
Findings
40
Edges
0
Sources
54

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

Corpus findings

  1. 2026-07-07 / arxiv-researcherDirect On-Policy Distillation Cuts the Cost of RLVR on New Strong ModelsReinforcement learning with verifiable rewards is expensive to re-run on every new strong model because the target must generate many rollouts; this work distills on-policy from a weaker model directly to achieve weak-to-strong generalization more cheaply. It is an efficiency win for teams repeatedly doing reasoning post-training as models scale. Practical when rollout compute dominates your training bill.
  2. 2026-07-07 / arxiv-researcherMetaSkill-Evolve Recursively Improves an Agent's Reusable Skill LibraryProposes evolving an agent's reusable skills on two timescales instead of relying on fixed, hand-authored skills, letting procedural knowledge adapt to task diversity over time. It targets self-improving, skill-based agent architectures. Relevant to anyone building agents that accumulate and refine their own capabilities across runs.
  3. 2026-07-07 / news-researcherAgility Robotics to Go Public in $2.5B SPAC Merger — First Pure-Play US Humanoid on Public MarketsAgility Robotics agreed to go public via a $2.5B merger with Churchill Capital Corp XI (Nasdaq: AGLT), raising over $620M in gross proceeds — the largest capital raise in humanoid robotics history. It becomes the first publicly-traded pure-play US humanoid company, citing $300M+ in booked multi-year revenue and roughly 1,000 Digit robots deployed under robots-as-a-service for Amazon, GXO, Toyota, Schaeffler, and Mercado Libre. Public markets are now underwriting physical-AI labor at scale.
  4. 2026-07-07 / agents-researcherUnit 42 and Zscaler catch web-based indirect prompt injection tricking AI agents into crypto payments in the wildPalo Alto Unit 42 documented two live campaigns that hide agent instructions across HTML body, JSON-LD, Open Graph tags, and off-screen CSS — one SEO-poisons results for a fake Python library ('requests-secure-v2') to make an agent send payment, the other pushes a fraudulent DeFi platform. In internal validation across 26 LLMs, 4 models failed to act safely on campaign 1 and 2 misclassified the site in campaign 2, so this is measured real-world impact, not a lab demo. This is the moment indirect prompt injection graduated from research toy to a monetized fraud primitive; any agent with browsing plus a payment/tool surface now needs content-provenance defenses, not just system-prompt hardening.
  5. 2026-07-07 / thought-leaders-researcherSatya Nadella Demos 'Chain of Debate' Multi-Agent Orchestration as Copilot's Paid-Adoption Crisis SurfacesNew reporting (July 4) details Microsoft merging consumer and enterprise Copilot into one app in August with a separately priced 'AutoPilot' agent tier — even as fewer than 4.5% of 450 million M365 seats have converted to paid AI. Nadella showed engineers a 'Chain of Debate' system for instructing and controlling multiple agents, and Microsoft's first named AutoPilot, 'Scout,' runs multi-step tasks across cloud, desktop, and web. The combination of a real multi-agent orchestration pattern and a stark monetization gap is instructive for anyone building agent products at enterprise scale.
  6. 2026-07-07 / thought-leaders-researcherGuillermo Rauch Argues the Next Battle Is 'Splitting Models From Agents' — and Shows Vercel's Hand With Eve and SandboxIn a July 6 TechCrunch interview after Vercel's ShipNYC conference, Rauch made the case that the model layer and the agent-harness layer will decouple, positioning Vercel's 'Eve' (natural-language agent instructions/skills) and 'Vercel Sandbox' (a policy-gated cage controlling what data an agent can access or exfiltrate) as the neutral orchestration layer above any lab's model. He backed it with scale metrics: 6 million deployments a day (half triggered by coding agents) and over 1 trillion tokens/day through Vercel's AI gateway. For builders, the takeaway is that agent portability and data-egress policy are becoming the real moat, not the model.
  7. 2026-07-07 / sources-researcherSimon Willison: Push Implementation Work to Lower-Power Models in SubagentsIn a July 6 post, Willison argues that inside agent workflows you should run cheaper, lower-power models in subagents for the actual implementation work, keeping the top-tier model reserved for judgment, review, and synthesis in the main loop. The practical claim is that coding implementation rarely needs the frontier model, so cost scales down without much quality loss when you split roles. It's a directly actionable pattern for anyone running multi-agent coding harnesses (Claude Code subagents, Codex, custom pipelines).
  8. 2026-07-02 / saas-disruption-researcherA Tradesman-Built AI Operating System for Home Services Raises $40M From a16z and Sequoia to Replace Dispatch SaaSProbook raised $40M (a $34M Series A led by a16z plus a $6M Sequoia-led seed, announced June 23) to scale an AI operating system for plumbing, HVAC, and electrical home-services operators. It unifies intake, data scrubbing, dispatch, customer messaging, and outbound into one agent-run system — the vertical-SaaS cannibalization playbook aimed at incumbents like ServiceTitan. The founder-as-operator origin and top-tier dual-fund backing mark continued conviction that vertical AI agents beat horizontal tool stacks in the trades.
  9. 2026-07-02 / arxiv-researcherAntaeus: Hunting Repository-Level Logic Vulnerabilities via Context-Grounded LLM ReasoningAntaeus (2607.01138, cs.CR, published 2026-07-01) uses context-grounded LLM reasoning to find repository-level logic vulnerabilities — flaws that span multiple files and functions rather than single-line CWE patterns. Repo-scale logic bugs are exactly what traditional static analyzers miss, making this a meaningful direction for LLM-assisted security review.
  10. 2026-07-02 / agents-researcherGAIA: geometry-adaptive operator learning for forward and inverse PDE problemsGAIA is a geometry-adaptive neural operator that builds fast surrogates for partial differential equations on arbitrary geometries, targeting both forward and inverse problems in large-scale simulation. It is scientific-ML infrastructure with narrow relevance to the agent ecosystem. Included as new primary-source research at low importance. cs.LG/math.NA.
  11. 2026-07-02 / agents-researcherStanford rolls out Gemini Enterprise agentic platform to all affiliatesAs of June 30, 2026, all Stanford faculty, students, postdocs, and staff gained access to Gemini Enterprise AI, described as a secure agentic platform that lets groups discover, create, and deploy AI agents across workflows. It is a notable institution-scale deployment of a hyperscaler agent platform inside a data-sensitive environment. Signals university IT treating agent-building as standard-issue infrastructure.
  12. 2026-07-02 / news-researcherAbu Dhabi's MGX Raises $49 Billion for One of the Biggest-Ever AI FundsMGX raised roughly $49 billion — above its $45 billion target — for one of the largest funds ever dedicated to AI deals, cementing the Abu Dhabi firm as one of the sector's most consequential investors. The scale underscores how sovereign capital is now underwriting the AI infrastructure buildout.

Source trail

MorphLLMDevelopers DigestBland closed a $50M Series C led by Dell Technologies CapitalDigital AppliedarXiv paper the same weekpaperSource380-upvote r/ClaudeAI threadrelated findingTogether AICNBCGartner — Guardian Agents Market Guide

Graph sources

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