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  "record_id": "20861928",
  "document_id": "20861928",
  "title": "StateLens for Situational Intelligence: State-First, URL-Native Signal Grammar for Situation-Aware Assistance",
  "pages": 25,
  "authors": [
    "Raynor Eissens"
  ],
  "doi_confirmed_in_pdf": "10.5281/zenodo.20861928",
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  "abstract_extracted": "StateLens began as a URL-native AI state diary protocol: a way to compress user-world moments into readable operator states so that a day could later be reconstructed without storing raw recordings, transcripts, or permanent profiles. Version 1.1 extends that role. It positions StateLens as a state-first grammar for situational intelligence: AI assistance that recognizes when an external event becomes relevant to a particular human life, surfaces a compact state signal, preserves private context behind a gate, and leaves action with the human. The paper argues that without a state-first layer, situational AI tends to collapse into one of three less desirable forms: verbose notifications, hidden automation, or invasive profiling. StateLens introduces a different sequence: public operator first, gated explanation second, human branch third, and provenance trail fourth. A signal such as x-vvv-x does not expose the user's life. It denotes a class of state - conflict, mismatch, incoherence, or unstable relation - while the specific reason remains in a trusted private AI context or local v",
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  "full_text": "=== PDF PAGE 1 ===\nSTATELENS PROTOCOL · VERSION 1.1 · JUNE 2026\nStateLens for Situational\nIntelligence\n\nState-First, URL-Native Signal Grammar for Situation-Aware\nAssistance\n\nStateLens externalizes state, not the person. It puts the state on the web while keeping the life\nbehind the gate.\n\nAuthor\nRaynor Eissens\n\nVersion\n1.1 Final\n\n10.5281/zenodo.20861928\n\nDOI\n\nCanonical site\nhttps://statelens.net/\n\nRelated pages\nhttps://statelens.net/situational-intelligence/ ·\nhttps://companionhabitat.com/situational-intelligence/\n\nRelated layers\nStateLens · Trailstate · ObjectPortal · Companion Habitat · Reversible Systems\n\nDocument type\nConceptual protocol paper / position paper\n\nSuggested citation: Eissens, R. (2026). StateLens for Situational Intelligence: State-First, URL-Native Signal Grammar for\nSituation-Aware Assistance. Zenodo. https://doi.org/10.5281/zenodo.20861928\n\n1\n\n=== PDF PAGE 2 ===\nSTATELENS PROTOCOL · VERSION 1.1 · JUNE 2026\nState-First, URL-Native Signal Grammar for Situational Intelligence\n\nAbstract\n\nStateLens began as a URL-native AI state diary protocol: a way to compress user-world moments into\nreadable operator states so that a day could later be reconstructed without storing raw recordings,\ntranscripts, or permanent profiles. Version 1.1 extends that role. It positions StateLens as a state-first\ngrammar for situational intelligence: AI assistance that recognizes when an external event becomes\nrelevant to a particular human life, surfaces a compact state signal, preserves private context behind a\ngate, and leaves action with the human.\n\nThe paper argues that without a state-first layer, situational AI tends to collapse into one of three less\ndesirable forms: verbose notifications, hidden automation, or invasive profiling. StateLens introduces a\ndifferent sequence: public operator first, gated explanation second, human branch third, and provenance\ntrail fourth. A signal such as x-vvv-x does not expose the user's life. It denotes a class of state - conflict,\nmismatch, incoherence, or unstable relation - while the specific reason remains in a trusted private AI\ncontext or local vault.\n\nThe contribution is not a new foundation model, sensor system, clinical intervention, or emergency\nservice. It is an interface and protocol pattern: a finite, URL-native, human-readable and\nmachine-readable signal grammar for situation-aware assistance. It integrates ideas from ambient\nintelligence, context-aware computing, calm technology, situation awareness, Just-in-Time Adaptive\nInterventions, Semantic Web architecture, REST, provenance, and human-in-the-loop AI, while making a\nnarrower claim: these traditions do not, by themselves, provide a public, URL-native, state-first grammar\nfor personal situational relevance with gated context and provenance-backed action trails.\n\nA reference Heat Ping case illustrates the pattern. A severe weather warning conflicts with a user's\noutdoor work context. The surface signal is only THERMOMETER | x-vvv-x | Weather/work mismatch\ndetected. Open?. If the user opens it, the private AI explains why the state appeared, offers reversible\nbranches, and records a Trailstate path only if chosen. The system may signal, explain, offer, draft, and\nsave. It may not send, call, cancel, contact a third party, or decide without explicit confirmation.\n\nKeywords\n\nStateLens; state-first computing; situational intelligence; situation-aware assistance; URL-native\noperators; gated context; Trailstate; ObjectPortal; provenance; Just-in-Time Adaptive Interventions;\nhuman action boundary; calm technology; context-aware computing.\n\nExecutive summary\n\nThe central claim of this paper is simple but carefully bounded: future AI systems may increasingly detect\nwhen situations matter to people, but the open design question is how that relevance should become\nvisible. StateLens proposes that relevance should appear first as compact state, not as a full explanation,\nhidden inference, or autonomous action.\n\nThe prior-art review indicates that the components of the problem are well known. Ambient intelligence\nmakes environments responsive; context-aware computing adapts services to user state; calm\ntechnology minimizes attention cost; situation awareness studies dynamic decision environments;\nJust-in-Time Adaptive Interventions deliver timely adaptive support; REST and Semantic Web traditions\nmake resources addressable; W3C PROV models provenance; and human-in-the-loop/XAI literature\nemphasizes oversight. However, the combination of public or semi-public operator state, gated private\ncontext, URL-native addressability, provenance-backed trails, and explicit human action boundaries is not\nfound as a coherent user-facing protocol pattern in the reviewed materials.\n\nThe paper therefore treats StateLens v1.1 as a concept/protocol proposal. It does not claim empirical\neffectiveness, medical safety, or universal novelty. It offers a design vocabulary and a reference\narchitecture. Its strongest defensible claim is that StateLens defines a state-first signal grammar for\nsituation-aware assistance: compact public operators, gated private context, human-confirmed branches,\nand replayable provenance.\n\n2\n\n=== PDF PAGE 3 ===\nSTATELENS PROTOCOL · VERSION 1.1 · JUNE 2026\nState-First, URL-Native Signal Grammar for Situational Intelligence\n\nContents\n\nG\n1. Introduction\n\nG\n2. From State Diary to State Visibility\n\nG\n3. The Situational Intelligence Gap\n\nG\n4. Prior Art and Adjacent Traditions\n\nG\n5. State-First Computing\n\nG\n6. State Resolution\n\nG\n7. URL-Native Operator Grammar\n\nG\n8. Public State and Private Context\n\nG\n9. Architecture\n\nG\n10. Relationship to Just-in-Time Adaptive Interventions\n\nG\n11. Operators Instead of Notifications\n\nG\n12. Heat Ping Case Study\n\nG\n13. Protocol Specification v1.1\n\nG\n14. Branching Agentic Workflows and Plugins\n\nG\n15. Privacy, Safeguards and Human Action Boundary\n\nG\n16. Future Applications\n\nG\n17. Limitations, Falsification and Reviewer Risks\n\nG\n18. Defensible Claims and Publication Positioning\n\nG\n19. Conclusion\n\nG\nReferences\n\nG\nAppendix A. Minimal Operator Set\n\nG\nAppendix B. Signal Object Schema\n\n3\n\n=== PDF PAGE 4 ===\nSTATELENS PROTOCOL · VERSION 1.1 · JUNE 2026\nState-First, URL-Native Signal Grammar for Situational Intelligence\n\n1. Introduction\n\nMany people expect AI to do more than wait for prompts. They expect a system that can notice when the\noutside world becomes relevant to their life: a severe weather alert before an outdoor workday, a travel\ndisruption before a commitment, a safety mismatch before a tool is used, or a conflicting object state\nbefore an agent acts. This expectation is not merely a demand for stronger models. It is a demand for\nsituated assistance.\n\nCurrent AI products often remain organized around explicit initiation. Chatbots wait for a question.\nAssistants respond to commands. Agents execute goals supplied by the user. Automations run only after\na rule or schedule has been configured. These forms are powerful, but they still require the user to notice\nthe situation, name the relevance, provide context, and request action.\n\nThe problem addressed by this paper is narrower than general intelligence. It asks how relevance should\nbecome visible when an AI system detects that an external event intersects with a user's bounded private\ncontext. A naive design would push a verbose notification. A more invasive design would expose personal\ncontext. A more dangerous design would act automatically. StateLens proposes a fourth pattern: surface\na compact state first, keep the explanation gated, allow the user to open context, then branch only with\nconfirmation.\n\nPeople do not only expect AI to answer. They expect intelligence to notice when the world becomes\nrelevant to their life.\n\nThe phrase situational intelligence is used here in a specific sense: AI assistance that links external world\nevents to a person's bounded context and surfaces relevance before the person has to discover the issue\nmanually. The term overlaps with older concepts such as situation awareness, ambient intelligence, and\ncontext-aware computing, and this paper does not claim that those broader traditions are new. Instead, it\nintroduces StateLens as a concrete state-first signal grammar that can make situational relevance\nreadable without making the person public.\n\n1.1 Scope and contribution\n\nThe contribution of StateLens v1.1 is a protocol pattern rather than an empirical result. It consists of four\nclaims:\n\nG\nAI systems that detect relevance should not default to full explanations, hidden automation, or public\nexposure of private context.\n\nG\nA bounded operator can serve as an initial state surface: a visible sign that a meaningful relation has\nchanged without disclosing why.\n\nG\nURL-native operator addresses can make states portable, linkable, replayable and\nprovider-independent.\n\nG\nTrailstate-style provenance and explicit human action boundaries can preserve agency before\nconsequential action.\n\nThe paper is therefore conservative about novelty. It builds on established work in ambient intelligence,\ncontext-aware computing, calm technology, situation awareness, Just-in-Time Adaptive Interventions,\nSemantic Web architecture, REST and provenance. The specific proposal is the synthesis: a public or\nsemi-public, URL-native, low-entropy state grammar for personal situational relevance with gated context\nand provenance-backed human branching.\n\n4\n\n=== PDF PAGE 5 ===\nSTATELENS PROTOCOL · VERSION 1.1 · JUNE 2026\nState-First, URL-Native Signal Grammar for Situational Intelligence\n\n2. From State Diary to State Visibility\n\nThe earlier StateLens framing described a state diary: a compact trail of operators that allows AI to\nreconstruct a day, decision path or lived sequence without storing full raw experience. This remains a\nvalid use case. A sequence such as q-vvv-p -> n-vvv-n -> 0-vvv-0 can mark a question opening, attention\nnarrowing and a decision stabilizing. The system stores the shape of the transition, not a full transcript of\nthe user's inner life.\n\nThe situational interpretation asks a complementary question: how can AI signal that a present or future\nsituation matters without revealing the entire reason at the surface layer? This turns StateLens from a\nretrospective diary into a live state surface. The same operator grammar can support memory after the\nfact and relevance before or during the moment.\n\nCore question\nStateLens role\n\nUse case\nTemporal\norientation\n\nState Diary\nAfter the moment\nWhat happened, in state\nterms?\n\nReconstruct a day or decision path\nfrom compact operator trails.\n\nSituational Signal\nBefore or during the\nmoment\n\nHas a relevant state\nappeared?\n\nSurface a low-entropy state so the\nuser can choose whether to open\ncontext.\n\nBranching Agentic\nWorkflow\n\nDuring action\nselection\n\nWhich branch is safe or\nappropriate?\n\nProvide a visible state node before\ntools, plugins or agents execute.\n\nThis paper therefore defines the expanded role of StateLens as state visibility. A diary is one application.\nSituational intelligence is another. Branching agentic workflows are a third. In each case, the same design\nprinciple holds: state first, not transcript first; signal first, not action first.\n\n2.1 Continuity without full capture\n\nStateLens is motivated by a tension in AI memory and context systems. The more useful a personal AI\nbecomes, the more context it may need. Yet storing raw recordings, transcripts and full profiles increases\nprivacy risk, surveillance anxiety and governance burden. StateLens explores the opposite direction:\ncompress experience into states, then preserve only enough structure for later reconstruction or situated\nassistance.\n\nStateLens does not store the person. It stores the shape of relevance.\n\nThis statement is not a claim that state storage has no privacy risk. Long-term state patterns can still\nreveal routines, vulnerabilities or stress cycles. The claim is more modest: a bounded operator reveals\nless than a transcript, image, audio recording or detailed profile, and it can be paired with visibility levels,\nretention rules and gated provenance.\n\n5\n\n=== PDF PAGE 6 ===\nSTATELENS PROTOCOL · VERSION 1.1 · JUNE 2026\nState-First, URL-Native Signal Grammar for Situational Intelligence\n\n3. The Situational Intelligence Gap\n\nA capable model can converse intelligently while still failing to notice that a weather event matters for a\nuser who works outdoors. A simple alert can notice weather while failing to understand the user's work,\ncommute, body and choices. The gap is therefore not only a lack of model capability. It is a lack of\nsituatedness and state surface.\n\nPattern\nTrigger\nTypical context\nLimitation\n\nChatbot\nUser prompt\nCurrent conversation\nThe user must notice the problem,\nformulate the context and ask.\n\nAssistant\nCommand, wake word\nor app invocation\n\nDevice/app context\nUseful but still mostly reactive;\ncontext is siloed.\n\nAgent\nUser goal or task\nTools, memory, files, APIs\nCan act, but usually after the user\ndefines the goal.\n\nAutomation\nSchedule, rule or\nsensor\n\nPreconfigured parameters\nNarrow and brittle when personal\nnuance changes.\n\nCompanion\nRelationship, chat,\nmemory or notification\n\nLonger-term interaction\nMay optimize engagement rather\nthan bounded protective relevance.\n\nSituational\nintelligence\n\nWorld data plus user context\nRequires state visibility, privacy\nboundaries and human control.\n\nExternal event +\nbounded private\ncontext\n\nThe design problem can be stated as follows: how can an AI companion, wearable or ambient agent signal\nrelevance without becoming a surveillance system, a paternalistic controller, or a noisy notification\nengine? StateLens answers with a state-first sequence. The system does not initially reveal everything it\nknows. It first surfaces a bounded operator that says: this situation has entered a meaningful state.\n\nSituational intelligence notices relevance. StateLens makes relevance readable.\n\n3.1 What StateLens is not\n\nStateLens is not proposed as an emergency alert system, a medical device, a replacement for\noccupational policy, or a system that can guarantee safety. It is also not a claim that every situation\nshould be abstracted into a cryptic code. The protocol is most useful when relevance is meaningful but\nthe full reason is private, when the first surface should be compact, and when action must remain under\nuser control.\n\n6\n\n=== PDF PAGE 7 ===\nSTATELENS PROTOCOL · VERSION 1.1 · JUNE 2026\nState-First, URL-Native Signal Grammar for Situational Intelligence\n\n4. Prior Art and Adjacent Traditions\n\nThe literature review shows substantial overlap between StateLens and older research traditions. This\noverlap is not a weakness; it is the academic floor. The novelty claim must be limited to what those\ntraditions do not provide together: a public, URL-native, state-first signal grammar with private context,\nprovenance and human-confirmed action.\n\nTradition\nContribution\nSimilarity to StateLens\nDifference\n\nShares the aim of unobtrusive\ncontextual support.\n\nAmbient Intelligence\nSmart environments adapt to\npeople through embedded\nsensing and context\nsensitivity.\n\nOften environment-centric\nand automation-oriented;\ndoes not define a user-facing\nURL-native state grammar.\n\nContext-aware\ncomputing\n\nProvides the context\nmatching foundation.\n\nFocuses on adaptation inside\nsystems rather than public\nstate surfaces.\n\nSystems use location, activity,\nsocial, informational or\nemotional state to adapt\nbehavior.\n\nSupports the idea of\nlow-attention signals.\n\nA design philosophy, not a\nbounded symbolic protocol.\n\nCalm Technology\nTechnology moves between\nperiphery and center of\nattention without overload.\n\nShares the relevance and\ndecision-support orientation.\n\nSituation Awareness\nPerception, comprehension\nand projection in dynamic\nsituations.\n\nUsually dashboards/data\nintegration for operators, not\ncompact personal-life state\nsignals.\n\nStrongly overlaps in timing\nand personalization.\n\nJITAIs\nReal-time, tailored support\ndelivered when needed,\nespecially in mHealth.\n\nIntervention-first and\ndomain-specific; StateLens is\nstate-first and\nprotocol-oriented.\n\nPersonal AI /\nAssistants\n\nShares the direction toward\ncontext-rich assistance.\n\nMemory, app context,\nproactive suggestions and\nworkflow help.\n\nMostly product-internal and\ntext/action-oriented; no\npublic state grammar.\n\nAI Companions\nPersistent relational or\nemotional support.\n\nShares continuity and\nsituated support goals.\n\nOften\nengagement/relationship\noriented; may not preserve\nagency through state-first\nsignals.\n\nURI-based entities, RDF/OWL,\ngraphs and symbolic relations.\n\nKnowledge\nRepresentation /\nSemantic Web\n\nProvides a technical\nprecedent for addressable\nsemantics.\n\nRepresents structured\nknowledge, not the UX of a\npersonal state signal.\n\nREST / Hypermedia\nResources are identified by\nURIs and manipulated through\nrepresentations.\n\nSupports the idea that\nstate/resources can be\nweb-native.\n\nDoes not define a\nhuman-facing operator\ngrammar for situational\nrelevance.\n\nProvenance / W3C\nPROV\n\nSupports Trailstate-style\nreceipts and auditability.\n\nEntities, activities and agents\ncan be traced through\nderivation.\n\nGeneric data provenance,\nnot a live personal signal\ninterface.\n\nXAI /\nHuman-in-the-loop\n\nExplanations, oversight and\nhuman control.\n\nSupports gated explanation\nand action boundaries.\n\nOften\nexplanation-after-model;\nStateLens begins with\nstate-before-explanation.\n\n4.1 Literature review conclusion\n\nNo reviewed tradition exactly matches the full StateLens configuration: public or semi-public URL-based\nstate signal, bounded symbolic operator, private context gate, provenance trail, replayable transitions,\nand human confirmation before action. The underlying ideas are known. Their combination as a state-first\nsignal grammar for situation-aware assistance appears to be a defensible research gap.\n\nThe strongest academic posture is therefore not to claim invention of context awareness or situational\nassistance. The safer claim is that StateLens proposes a distinct interface/protocol layer for making\nsituational relevance visible while reducing immediate exposure of private context.\n\n7\n\n=== PDF PAGE 8 ===\nSTATELENS PROTOCOL · VERSION 1.1 · JUNE 2026\nState-First, URL-Native Signal Grammar for Situational Intelligence\n\n5. State-First Computing\n\nState-first computing is proposed here as a design orientation: reasoning should resolve to a visible state\nbefore it resolves to a full explanation or action. In many AI systems, the first output is a paragraph,\nrecommendation, task execution or notification. In StateLens, the first output is a bounded operator.\n\nThis distinction matters because the first surface of an interaction determines the privacy and agency\nposture of the system. A full explanation may disclose sensitive context. An action may overstep. A\nstandard notification may be noisy and semantically ambiguous. A state signal is smaller: it says that\nsomething has entered a state class, while leaving the user to open context if desired.\n\nExternal World\n\nPrivate Context Match\n\nState Resolution\n\nStateLens Operator\n\nUser Opens\n\nPrivate AI Explanation\n\nHuman Decision\n\nTrailstate\n\nFigure 1. Complete state-first architecture for situation-aware assistance.\n\n5.1 State first, context second, action third\n\nThe canonical interaction sequence is:\n\n1. Public signal\n   THERMOMETER | x-vvv-x\n\n2. User opens it\n   Private AI explains the gated context.\n\n3. User chooses action\n   Draft message / call supervisor / drink water / take leave / ignore.\n\n4. Trailstate records the path\n   x-vvv-x -> q-vvv-p -> n-vvv-n -> r-vvv-r -> 0-vvv-0.\n\n5. Context remains gated\n   Only the state/provenance surface is web-native.\n\nThis sequence is conservative by design. It grants the AI permission to notice and signal, but not to\nexecute. It grants the user permission to open context, branch, confirm, ignore, correct or save. The\nhuman remains the place where state becomes action.\n\nAI makes the relevant state visible. The human decides what that state means for action.\n\n8\n\n=== PDF PAGE 9 ===\nSTATELENS PROTOCOL · VERSION 1.1 · JUNE 2026\nState-First, URL-Native Signal Grammar for Situational Intelligence\n\n6. State Resolution\n\nState Resolution is the process of mapping a real-world situation onto a bounded operator. It is the central\nprotocol concept in StateLens v1.1. A recognition system asks: What is this? A StateLens system asks:\nWhat state did this situation resolve into?\n\nWorld\n\nReasoning\n\nState Resolution\n\nOperator\n\nFigure 2. State Resolution maps a real-world situation to a bounded operator.\n\nState Resolution is not the same as classification in the narrow machine-learning sense. It may use\nclassifiers, language models, rules, sensor data, user context and object anchors. The output is\nconstrained: a finite operator and optional label, not an unconstrained explanation. This constraint is what\nmakes StateLens low-entropy and auditable.\n\nStage\nInput\nOperation\nOutput\n\nCandidate event\n\nNotice that a candidate event\nexists\n\nDetection\nExternal event, sensor input,\nobject state, calendar change,\nmessage, weather alert\n\nContext match\nCandidate event + allowed\nprivate context\n\nCheck whether the event\nintersects with user context\n\nRelevance score or state\ncandidate\n\nOperator such as x-vvv-x\n\nState Resolution\nRelevance candidate + operator\ngrammar\n\nMap the relation to a bounded\nstate\n\nSurface\nOperator + optional topic\nShow minimal state signal\nPublic or protected state\nsurface\n\nGated explanation\nUser opens signal\nExplain why the state appeared\nPrivate explanation\n\nTrailstate path\n\nBranch\nUser chooses path\nOffer or execute only confirmed\nbranches\n\n6.1 Why State Resolution must be bounded\n\nIf State Resolution produces unrestricted text, the system becomes a normal AI explanation layer. If it\nproduces unrestricted action, it becomes an agent. If it produces raw logs, it becomes a surveillance\ndiary. The operator boundary is what keeps the first surface small. It also makes it easier to compare\nstates across time, providers and interfaces.\n\n9\n\n=== PDF PAGE 10 ===\nSTATELENS PROTOCOL · VERSION 1.1 · JUNE 2026\nState-First, URL-Native Signal Grammar for Situational Intelligence\n\n7. URL-Native Operator Grammar\n\nThe operator grammar is deliberately finite and low-entropy. Operators are ASCII-native, URL-safe, short,\nstable, human-readable, machine-readable and suitable for domain-native addresses. Their meanings are\nintentionally abstract; context and provenance make them specific.\n\nOperator\nSurface semantics\nContext semantics\nAllowed branches\nDefault\nvisibility\n\nPublic\n\no-vvv-o\nOpen field / beginning\nA new situation, day, route or\ninteraction opens\n\nobserve, enter,\ncontinue\n\nq-vvv-p\nQuestion / uncertainty\nA decision question or relevance\nquestion opens\n\nexplain, compare,\ndefer, ignore\n\nPublic or\nprotected\n\nProtected\n\nn-vvv-n\nNarrowing /\ncomparison\n\nchoose, compare,\nvalidate\n\nPossible branches or\ninterpretations become more\nfocused\n\nx-vvv-x\nConflict / mismatch /\nincoherence\n\nopen, ignore, repair,\nescalate, save\n\nPublic or\nprotected\n\nExternal event conflicts with\ncontext or a relation becomes\nunstable\n\nProtected\n\nr-vvv-r\nRepair / recovery\nA mitigation, correction or\nrecovery action begins\n\nplan, rest, hydrate,\nrevise, recover\n\n0-vvv-0\nStabilized / resolved\nDecision or interpretation\nstabilizes\n\narchive, save, close\nPublic or\nprotected\n\nu-vvv-u\nArchived / closed\nTrail is closed for reconstruction\nreplay, summarize,\narchive\n\nPrivate or\nprotected\n\nImportant semantic boundary. x-vvv-x should not mean danger by default. It should mean conflict,\nmismatch, incoherence, or unstable relation. A heat warning, product defect, social friction,\nrobot-action mismatch, or object-state mismatch can all resolve to x-vvv-x; provenance explains why.\n\n7.1 URL-native properties\n\nProperty\nMeaning for StateLens\n\nHuman-readable\nA person can recognize the operator as a visible state, not only an opaque ID.\n\nMachine-readable\nSoftware can parse the operator as a finite state token.\n\nURL-safe\nOperators can appear in paths, domains, query strings and receipts without special\nencoding.\n\nBookmarkable\nA state address or receipt can be revisited.\n\nReplayable\nSequences of operators can reconstruct trails or decision paths.\n\nSearchable\nStates can be indexed as states without exposing sealed context.\n\nLinkable\nOperators can point to canonical state pages or documentation.\n\nProvider-independent\nA state grammar can travel across AI providers, agents and devices.\n\nLow entropy\nThe first surface is compact; detailed context remains behind the gate.\n\nAn operator address such as x-vvv-x.com should define the public meaning of the operator, not host the\nuser's private incident. The specific event belongs in Trailstate or an equivalent provenance layer. The\nsensitive reason belongs in the private AI context.\n\n10\n\n=== PDF PAGE 11 ===\nSTATELENS PROTOCOL · VERSION 1.1 · JUNE 2026\nState-First, URL-Native Signal Grammar for Situational Intelligence\n\n8. Public State and Private Context\n\nThe privacy architecture of StateLens depends on separating what kind of state appeared from why that\nstate mattered for a particular person. The state may be public or semi-public; the context should remain\ngated. This does not eliminate privacy risk, but it reduces the amount of personal information exposed at\nthe surface layer.\n\nLayer\nWhat is visible\nExample\nDefault handling\n\nPublic\nOperator only\nx-vvv-x\nVisible as a state class; no\nprivate reason.\n\nProtected\nOperator + topic\nx-vvv-x | weather-work conflict\nShare only with chosen\nsystems, receipts or trusted\nagents.\n\nPrivate\nOperator + reason\nWeather risk matched outdoor work\nand commute context\n\nKeep in private AI context,\nlocal vault or provider-gated\nmemory.\n\nSealed\nFull sensitive context\nHealth details, employer details,\npersonal identity history\n\nLocal, encrypted, or\nprovider-gated; never public\nby default.\n\nThis produces the principle: public state, private reason, gated provenance. The public surface says that a\nmeaningful state has occurred. The private context explains why it matters. The provenance trail records\nhow the state was derived without necessarily exposing sealed context.\n\n8.1 Privacy-light, not privacy-null\n\nA single operator reveals little. Long-term patterns may reveal more. If an observer sees repeated conflict\nstates, recovery states or narrowed-decision states, they may infer stress cycles, habits or vulnerabilities.\nStateLens therefore treats states as privacy-light rather than privacy-null. Visibility settings, retention\nlimits, aggregation, local storage and protected receipts are necessary design requirements.\n\n8.2 Relation to provider memory\n\nIn a consumer implementation, private context may remain inside a trusted personal AI provider,\nencrypted local vault or user-controlled memory system. StateLens does not require the open web to\nstore the user's life. It only externalizes the state surface. The AI provider may know why x-vvv-x\nappeared; the public web only needs to know that a conflict/mismatch state exists if the user allows that\nsurface to be visible.\n\n11\n\n=== PDF PAGE 12 ===\nSTATELENS PROTOCOL · VERSION 1.1 · JUNE 2026\nState-First, URL-Native Signal Grammar for Situational Intelligence\n\n9. Architecture\n\nThe architecture separates state, context, provenance and action. This separation is the core safety\nproperty. A StateLens system should not collapse all context into a single profile, should not expose\nsealed context in state URLs, and should not leap from detection to execution.\n\nExternal event\n  -> ObjectPortal anchor\n  -> Relevance check against private context\n  -> State Resolution\n  -> StateLens operator signal\n  -> Optional gated explanation\n  -> Human-selected branch\n  -> Trailstate provenance trail\n  -> Reversible action boundary\n\n9.1 Public operator layer\n\nThe public operator layer contains only compact operator states. These are not designed to replace\nexplanation. They provide an initial state surface, allowing humans and machines to recognize that a\nsituation has entered a particular class.\n\n9.2 Private context layer\n\nThe private context layer contains the reason a state mattered for the user: work context, route, health\nconstraints, time obligations, preferences, relationships, prior decisions or object history. This layer\nshould be bounded by consent, minimization, auditability and revocation.\n\n9.3 ObjectPortal anchor layer\n\nObjectPortal-style anchors prevent all meaning from being collapsed into a single opaque user profile. A\nweather event, workplace, route, tool, object, document or room can have its own address. The AI can\nthen resolve relevance through object/context relations rather than exposing one undifferentiated user\nmodel.\n\n9.4 Trailstate provenance layer\n\nTrailstate records what happened in the state path: which operator appeared, which topic was involved,\nwhich source or anchor contributed, what level of trust or visibility was attached, and how the state later\nevolved. The provenance trail should not automatically contain sealed context.\n\n9.5 Reversible Systems action boundary\n\nReversibility is the difference between helpful signal and coercive automation. The system may prepare,\nsuggest, draft or branch. It must not execute irreversible actions without the user's explicit confirmation.\n\n12\n\n=== PDF PAGE 13 ===\nSTATELENS PROTOCOL · VERSION 1.1 · JUNE 2026\nState-First, URL-Native Signal Grammar for Situational Intelligence\n\n10. Relationship to Just-in-Time Adaptive\nInterventions\n\nJust-in-Time Adaptive Interventions (JITAIs) are one of the closest prior-art families. They are especially\nimportant because they already combine timing, personalization and context. In mobile health, JITAIs\ndeliver support that corresponds to a need in real time, adapting content or timing to data collected since\nsupport began. This makes them a strong neighbor to StateLens.\n\nWhile JITAIs optimize the timing of interventions, StateLens optimizes the visibility of situational state\nbefore any intervention occurs.\n\nAspect\nJITAIs\nStateLens for Situational Intelligence\n\nPrimary domain\nmHealth and behavior change, such as\nphysical activity, smoking, medication\nadherence or mental health\n\nGeneral situation-aware assistance across\nwork, travel, objects, agents, wearables,\nsafety and daily context\n\nCore output\nIntervention, prompt, exercise,\nrecommendation or behavioral support\n\nLow-entropy operator state first; explanation\nonly after user opens\n\nDesign orientation\nIntervention-first\nState-first\n\nAgency model\nOften system-triggered and designed to\ninfluence behavior\n\nHuman action boundary: signal, explain and\noffer; no execution without confirmation\n\nPrivacy posture\nMay rely on sensor streams, EMA, activity\ndata and health context\n\nPublic/protected state surface with\nprivate/sealed context behind gate\n\nRepresentation\nText, app notification, treatment component,\ndecision rule\n\nURL-native operator, state trail and\nprovenance receipt\n\nEvaluation tradition\nEmpirical studies, micro-randomized trials,\nfeasibility and effectiveness research\n\nCurrently conceptual/protocol proposal;\nneeds usability and implementation studies\n\n10.1 Complement, not replacement\n\nStateLens should not be positioned as a replacement for JITAIs. JITAIs are more mature in empirical\nmethodology and intervention design. StateLens can be understood as a protocol/interface layer that may\nsit before, beside or above a JITAI-like system. A JITAI may detect the moment; StateLens may represent\nthe moment as a compact state before any intervention is shown.\n\nThis distinction is important for publication. If a reviewer argues that the Heat Ping Demo resembles a\nJITAI, the correct response is: yes, it shares just-in-time timing and context adaptation. The difference is\nthat StateLens does not begin with a full behavioral support message. It begins with a state class, then\nlets the user choose whether to reveal the reason and branch.\n\n13\n\n=== PDF PAGE 14 ===\nSTATELENS PROTOCOL · VERSION 1.1 · JUNE 2026\nState-First, URL-Native Signal Grammar for Situational Intelligence\n\n11. Operators Instead of Notifications\n\nA common objection is that StateLens may simply be a redesigned notification system. This objection\nmust be taken seriously. Weather alerts, calendar reminders and health prompts already notify users\nwhen something may matter. The StateLens distinction is not that it produces a message. The distinction\nis that it classifies the situation first and separates that state from the private explanation and any action.\n\nNotification: Tomorrow will be hot\n\nFull message appears immediately\n\nUser reads or dismisses\n\nFigure 3a. Standard notification pattern.\n\nStateLens: THERMOMETER | x-vvv-x\n\nUser decides whether to open\n\nPrivate AI explains gated context\n\nUser confirms branch or ignores\n\nFigure 3b. StateLens operator pattern.\n\n11.1 Notifications tell; operators classify\n\nA notification tells the user a fact, recommendation or reminder. An operator classifies the relation before\nexplanation. For example, Tomorrow will be hot is a fact. x-vvv-x says that a relation has become\nconflicted or unstable. The weather may be hot for everyone, but the conflict state exists because\nweather intersects with a specific bounded context: outdoor work, commute, schedule and relevant body\nconstraints.\n\n11.2 Operator advantages\n\nG\nThe first surface can be short without losing the ability to open explanation.\n\nG\nThe operator can be reused across domains because it describes relation state, not event category.\n\nG\nThe state can be linked, indexed, replayed or validated without exposing sealed context.\n\nG\nThe same operator can appear in wearables, browser notifications, agent logs, object inventories or\nprovenance receipts.\n\nG\nThe action boundary is built into the interaction sequence rather than left to application convention.\n\n11.3 Operator risks\n\nOperators can also fail. If too abstract, they may be cryptic. If overused, they may become noise. If\ndisplayed publicly over time, they may leak patterns. If poorly mapped, they may create false confidence.\nStateLens therefore requires careful design of labels, thresholds, visibility settings and correction paths.\n\n14\n\n=== PDF PAGE 15 ===\nSTATELENS PROTOCOL · VERSION 1.1 · JUNE 2026\nState-First, URL-Native Signal Grammar for Situational Intelligence\n\n12. Heat Ping Case Study\n\nThe Heat Ping Demo is a deliberately small case study. It is not presented as a clinical system,\noccupational policy, or working weather service. It is a protocol example showing how StateLens can\nsurface personal relevance without exposing the entire personal context.\n\nScenario: a user works outdoors in Lelystad, lives in Almere, performs physically heavy work, and has\nbounded private body/health context. A code red heat warning is issued for Flevoland. In a standard\nsystem, the user must discover the warning, infer relevance, remember personal constraints, ask AI what\nto do, and explain the situation. In a situational system, the AI can notice the external event and match it\nto allowed context. In a StateLens system, it does not start by exposing all details. It starts with a state.\n\nTHERMOMETER | x-vvv-x\nWeather/work mismatch detected. Open?\n\nLayer\nContent in the Heat Ping Demo\n\nExternal event\nCode red heat warning in Flevoland\n\nPrivate context\nOutdoor work in Lelystad; commute from Almere; body or health sensitivity; leave option\navailable\n\nPublic signal\nTHERMOMETER | x-vvv-x | Weather/work mismatch detected. Open?\n\nGated explanation\nThis signal was triggered because external weather risk matched private work/body context.\n\nHuman branches\nDraft message; call supervisor; drink water; take leave; ignore; save trail\n\nTrailstate path\nx-vvv-x -> q-vvv-p -> n-vvv-n -> r-vvv-r -> 0-vvv-0\n\n12.1 Trail example\n\nState\nLabel\nInterpretation\n\nx-vvv-x\nHeat/work mismatch\nThe situation conflicts with tomorrow's work context.\n\nq-vvv-p\nShould I contact supervisor?\nA decision question opens.\n\nn-vvv-n\nOptions narrowed\nCall, message, leave or ignore become the branches.\n\nr-vvv-r\nRecovery action\nHydration, rest, cooling or adapted plan begins.\n\n0-vvv-0\nDecision stabilized\nThe user's decision is settled.\n\nAn example protected provenance URL may look like:\n\nhttps://trailstate.org/?r=x-vvv-x&q=heat-work-context&source=weather-\nalert&visibility=protected\n\nThis URL is not the private story. It is a protected receipt for the state event and its provenance surface.\n\n15\n\n=== PDF PAGE 16 ===\nSTATELENS PROTOCOL · VERSION 1.1 · JUNE 2026\nState-First, URL-Native Signal Grammar for Situational Intelligence\n\n13. Protocol Specification v1.1\n\nThe following minimal specification describes one situational signal. It is not a full API standard. It is a\nreference pattern for implementers.\n\n{\n  \"schema\": \"statelens.situational_signal.v1.1\",\n  \"operator\": \"x-vvv-x\",\n  \"surface_label\": \"Weather/work mismatch detected. Open?\",\n  \"icon_hint\": \"thermometer\",\n  \"visibility\": \"public\",\n  \"topic\": \"heat-work-context\",\n  \"context_layer\": \"gated_private_ai\",\n  \"object_anchor\": \"objectportal:event/flevoland-heat-warning\",\n  \"provenance_url\": \"https://trailstate.org/?r=x-vvv-x&q=heat-work-\ncontext&source=weather-alert&visibility=protected\",\n  \"state_resolution\": {\n    \"external_event\": \"weather-alert\",\n    \"private_context_match\": \"outdoor-work + commute + body-context\",\n    \"resolved_state\": \"conflict/mismatch\"\n  },\n  \"allowed_branches\": [\n    \"open_context\",\n    \"draft_message\",\n    \"call_supervisor\",\n    \"drink_water\",\n    \"take_leave\",\n    \"ignore\",\n    \"save_trail\"\n  ],\n  \"forbidden_without_confirmation\": [\n    \"send_message\",\n    \"place_call\",\n    \"cancel_work\",\n    \"contact_employer\",\n    \"make_decision\",\n    \"disclose_sealed_context\"\n  ],\n  \"boundary\": \"signal_explain_offer_never_execute_without_confirmation\"\n}\n\n13.1 Required fields\n\nField\nPurpose\n\noperator\nThe bounded StateLens operator chosen by State Resolution.\n\nsurface_label\nA short human-readable description, ideally under one line.\n\nvisibility\nPublic, protected, private or sealed.\n\ntopic\nA coarse topic tag; should not contain sealed private context.\n\ncontext_layer\nWhere private reasoning lives, e.g. trusted AI provider or local vault.\n\nobject_anchor\nOptional reference to object, event, route, tool or environment anchor.\n\nprovenance_url\nWhere the event trail or receipt can be checked.\n\nstate_resolution\nMetadata explaining how the state class was resolved, without exposing sealed\ncontext.\n\nallowed_branches\nPermitted next steps the user may choose.\n\nforbidden_without_confirmation\nActions that must never happen automatically.\n\nboundary\nHuman action boundary statement.\n\n16\n\n=== PDF PAGE 17 ===\nSTATELENS PROTOCOL · VERSION 1.1 · JUNE 2026\nState-First, URL-Native Signal Grammar for Situational Intelligence\n\n14. Branching Agentic Workflows and Plugins\n\nStateLens is compatible with plugins, tools and agentic workflows because it gives the agent a visible\nstate node before action. The agent does not leap from detection to execution. It branches through a\nhuman-confirmed path.\n\nDetect\n\nResolve State\n\nSurface Operator\n\nUser Opens\n\nExplain\n\nBranch\n\nConfirm\n\nAct or Ignore\n\nSave Trail\n\nFigure 4. Branching workflow pattern with human confirmation.\n\nThis pattern is especially useful for high-stakes or sensitive domains: health, work, travel, finance,\ncaregiving, family logistics, robots and object-bound AI. A plugin can receive a StateLens signal, display\nthe operator, open explanation only with user permission, and then present branches. The branch can be\nlogged as a state transition rather than an opaque tool execution.\n\n14.1 From tools to visible state nodes\n\nMost tool-using agents represent intermediate reasoning internally. StateLens proposes that some\nintermediate states should become visible before action. This does not mean exposing chain-of-thought\nor private reasoning. It means surfacing a bounded state class, such as conflict, question, narrowing,\nrepair or stabilization. The details remain gated, but the user can see the condition of the workflow.\n\n14.2 Example branches\n\nSignal\nPossible branches\nForbidden without\nconfirmation\n\nx-vvv-x weather/work\nmismatch\n\nOpen context, draft message, hydrate, take\nleave, ignore, save trail\n\nSend message, call employer, cancel\nwork\n\nq-vvv-p medication\nuncertainty\n\nOpen context, compare instructions, contact\nclinician, ignore\n\nChange dosage, contact clinician,\ndisclose health data\n\nx-vvv-x robot-action\nmismatch\n\nPause robot, show object state, ask user,\nrollback\n\nContinue physical action, move\nobject, override human\n\nn-vvv-n travel options\nnarrowed\n\nShow options, choose route, defer, save\nBook ticket, cancel appointment,\nshare location\n\n17\n\n=== PDF PAGE 18 ===\nSTATELENS PROTOCOL · VERSION 1.1 · JUNE 2026\nState-First, URL-Native Signal Grammar for Situational Intelligence\n\n15. Privacy, Safeguards and Human Action\nBoundary\n\nStateLens does not claim that state storage is privacy-free. It claims that state-first design can reduce the\namount of sensitive context exposed at the first surface. The protocol still requires safeguards.\n\nRisk\nDescription\nSafeguard\n\nThresholding, user feedback, easy ignore/correct\n\nFalse positive\nSystem surfaces a mismatch that is not\nimportant\n\nFalse negative\nSystem fails to surface a relevant state\nDomain-specific validation and fallback alerts\n\nPrivacy leakage\nState patterns reveal routines or\nvulnerabilities over time\n\nVisibility levels, retention limits, protected\nreceipts, aggregation\n\nPaternalism\nAI treats user as someone to be\nmanaged\n\nHuman action boundary; no execution without\nconfirmation\n\nOver-reliance\nUser stops noticing context without AI\nDesign for awareness and agency, not\nreplacement\n\nProvenance\nconfusion\n\nUser cannot tell why a state appeared\nTrailstate receipt, source/confidence surface,\ncorrection path\n\nOperator ambiguity\nMapping from situation to operator feels\narbitrary\n\nFormal definitions, examples, correction and audit\nlogs\n\nInfrastructure\nleakage\n\nURLs or logs leak metadata\nMinimize topics, protect receipts, avoid sealed\ncontext in URLs\n\n15.1 Human action boundary\n\nThe companion first surfaces only a low-entropy state signal. If the user opens it, the private AI explains\nthe gated context and may offer reversible actions such as drafting a message, planning a recovery step\nor saving the trail. It never sends, calls, cancels, contacts a third party, discloses sealed context, or\ndecides without explicit user confirmation.\n\nBoundary formula. Signal is allowed. Explanation is allowed after opening. Branching is allowed.\nDrafting is allowed. Execution requires confirmation.\n\n15.2 Why this is not a surveillance diary\n\nA surveillance diary stores raw life. StateLens stores state transitions. A transcript says what was said. A\nvideo shows what happened. A profile asserts who the person is. A StateLens trail records the shape of\nrelevance: conflict opened, question opened, options narrowed, recovery began, decision stabilized.\n\n18\n\n=== PDF PAGE 19 ===\nSTATELENS PROTOCOL · VERSION 1.1 · JUNE 2026\nState-First, URL-Native Signal Grammar for Situational Intelligence\n\n16. Future Applications\n\nThe protocol is intentionally domain-general. StateLens does not only apply to weather, health or diaries.\nIts strongest potential may appear wherever AI systems must notice relevance without immediately\nexposing private context or acting autonomously.\n\nDomain\nPossible StateLens role\nExample state\n\nWearables\nLow-friction state surface for health, travel, work and\nenvironment signals\n\nx-vvv-x: body/environment\nmismatch\n\nx-vvv-x: action/object mismatch\n\nRobotics\nPre-action mismatch signal before physical movement\nor object manipulation\n\nVehicles\nDriver or route context signal before navigation/action\nn-vvv-n: route options narrowed\n\nx-vvv-x: tool/environment conflict\n\nIndustrial safety\nProtected signal when work condition conflicts with\nenvironment or procedure\n\nHealthcare support\nState surface before opening sensitive health context\nq-vvv-p: care question opened\n\nn-vvv-n: options narrowed\n\nFamily coordination\nLow-detail signal before exposing schedules or private\nobligations\n\nr-vvv-r: recovery path begins\n\nCompanion AI\nState-first support that does not optimize for emotional\nretention\n\nAmbient computing\nPeripheral, calm signal layer across rooms and devices\no-vvv-o: environment open\n\nx-vvv-x: object state mismatch\n\nObject-bound agents\nObjectPortal anchor plus StateLens operator for object\nstate\n\n0-vvv-0: state stabilized\n\nDigital twins\nState-level interface to a modeled personal or\noperational context\n\n16.1 Minimal viable implementation\n\nA minimal prototype does not require a new model. It can be built as a small layer above existing AI\nsystems:\n\nG\nDetect or receive an external event.\n\nG\nMatch it against an explicitly allowed private context domain.\n\nG\nResolve the situation to a StateLens operator.\n\nG\nShow only the public or protected state first.\n\nG\nOpen gated explanation only on user request.\n\nG\nOffer reversible branches.\n\nG\nRecord a Trailstate receipt if the user chooses to save the path.\n\n19\n\n=== PDF PAGE 20 ===\nSTATELENS PROTOCOL · VERSION 1.1 · JUNE 2026\nState-First, URL-Native Signal Grammar for Situational Intelligence\n\n17. Limitations, Falsification and Reviewer\nRisks\n\nThis section attempts to falsify or weaken the framework. It should be included because the safest\nacademic positioning is not a first-mover claim but a transparent proposal.\n\nObjection\nWhy it matters\nResponse or research need\n\nState signals still leak\nprivacy\n\nRepeated operators may reveal routines\nor vulnerabilities\n\nUse visibility levels, retention limits and\nprotected receipts; avoid sealed context in\nURLs.\n\nOperators may be too\nminimal\n\nUsers may not understand why the signal\nappeared\n\nUse clear surface labels and open-on-demand\nexplanation; evaluate usability.\n\nNo empirical evidence\nyet\n\nReviewers may ask whether users\nunderstand or prefer operators\n\nPosition as concept/protocol paper; propose user\nstudies.\n\nMapping is subjective\nWhy should a situation resolve to x-vvv-x\nrather than another state?\n\nDefine State Resolution formally; allow\ncorrection and audit.\n\nURL-native can be\nfragile\n\nLogs, firewalls, redirects and corporate\npolicies may create risk\n\nKeep URLs minimal; separate state address from\nprivate context; use protected receipts.\n\nLooks like status\nnotification\n\nCritics may say this is just a short alert\nEmphasize classification-before-explanation,\ngated context, provenance and replay.\n\nImplementation\ncomplexity\n\nRequires ObjectPortal, private context\ngate, Trailstate and operator grammar\n\nStart with narrow prototypes and optional\nlayers.\n\nOver-standardization\nA universal grammar may not fit all\ncultures or domains\n\nTreat the operator set as extensible, versioned\nand domain-sensitive.\n\n17.1 Claims to avoid\n\nG\nDo not claim that StateLens replaces medical advice, emergency services, occupational policy or\nprofessional judgment.\n\nG\nDo not claim that state signals eliminate privacy risk.\n\nG\nDo not claim that no similar ideas exist anywhere.\n\nG\nDo not claim that an AI has authority over the human.\n\nG\nDo not claim empirical effectiveness without a study.\n\nG\nDo not frame the work as competing with large AI providers; frame it as a protocol layer.\n\n20\n\n=== PDF PAGE 21 ===\nSTATELENS PROTOCOL · VERSION 1.1 · JUNE 2026\nState-First, URL-Native Signal Grammar for Situational Intelligence\n\n18. Defensible Claims and Publication\nPositioning\n\nThe paper should be positioned as a conceptual protocol paper. Its strongest claims are architectural and\ninterface-level, not empirical. The following table separates safe, risky and unsupported claims.\n\nClaim type\nExample\nStatus\n\nDefensible as a protocol proposal.\n\nSafe\nStateLens proposes a state-first signal grammar for\nsituation-aware assistance.\n\nSupported by literature review.\n\nSafe\nThe framework builds on ambient intelligence,\ncontext-aware computing, JITAIs, REST, Semantic Web\nand provenance.\n\nDefinitional and architectural claim.\n\nSafe\nStateLens separates public state from private context\nand human-confirmed action.\n\nCautious\nThe exact combination appears underdescribed in prior\nwork.\n\nReasonable if phrased as\nreview-based, not absolute.\n\nRisky\nStateLens is the first system of its kind.\nAvoid unless exhaustive\npatent/product review is\ncompleted.\n\nRequires empirical study.\n\nUnsupported\nStateLens improves safety or reduces cognitive load in\npractice.\n\nUnsupported\nUsers will prefer low-entropy operators.\nRequires user testing.\n\n18.1 Recommended title\n\nThe literature review recommends an academically careful title. This paper adopts:\n\nStateLens for Situational Intelligence: State-First, URL-Native Signal Grammar for Situation-Aware\nAssistance\n\n18.2 Defensible taxonomy\n\nThe following taxonomy is proposed rather than asserted as standard literature:\n\nChatbots\n  -> Assistants\n  -> Agents\n  -> Companions\n  -> Situational Intelligence\n  -> StateLens signal grammar\n\nThe taxonomy should be read as an interface/interaction gradient: from reactive dialogue, to\ncommand-based help, to goal-directed agency, to persistent companionship, to situation-aware relevance\ndetection, to the state-first signal layer that makes such relevance visible.\n\n21\n\n=== PDF PAGE 22 ===\nSTATELENS PROTOCOL · VERSION 1.1 · JUNE 2026\nState-First, URL-Native Signal Grammar for Situational Intelligence\n\n19. Conclusion\n\nStateLens began as a state diary protocol. Its broader role is now visible as a state-first grammar for\nAI-readable situations. In a diary, the protocol helps reconstruct what happened. In situational\nintelligence, it helps surface what matters. In branching agentic workflows, it provides a visible state node\nbefore action.\n\nThis matters because the next generation of AI assistance will not be defined only by better answers or\nmore autonomous agents. It will be defined by whether AI can recognize relevance without becoming\ninvasive, helpful without becoming paternalistic, and contextual without exposing the person.\n\nFuture AI systems will increasingly recognize when situations matter. The remaining question is not\nwhether AI can detect relevance, but how that relevance should become visible to people. StateLens\nproposes that this visibility should begin with compact, URL-native operator states rather than hidden\ninference, verbose notification, or autonomous action.\n\nSituational Intelligence is the category. StateLens is the signal grammar. Trailstate is the provenance.\nObjectPortal is the context anchor. Reversible Systems is the action boundary.\n\nCanonical phrases\n\nG\nStateLens externalizes state, not the person.\n\nG\nStateLens puts the state on the web while keeping the life behind the gate.\n\nG\nURL-native states allow AI to signal relevance publicly while keeping personal context private.\n\nG\nWithout a state-first grammar, situational intelligence becomes verbose notification, hidden\nautomation, or invasive profiling.\n\nG\nJITAIs optimize intervention timing. StateLens optimizes state visibility before intervention.\n\nG\nAI makes the relevant state visible; the human decides what the state means for action.\n\n22\n\n=== PDF PAGE 23 ===\nSTATELENS PROTOCOL · VERSION 1.1 · JUNE 2026\nState-First, URL-Native Signal Grammar for Situational Intelligence\n\nReferences and Related Work\n\nAarts, E., & Marzano, S. (Eds.). (2003). The New Everyday: Views on Ambient Intelligence. 010 Publishers.\n\nAbowd, G. D., Dey, A. K., Brown, P. J., Davies, N., Smith, M., & Steggles, P. (1999). Towards a better understanding of context and\ncontext-awareness. Proceedings of HUC '99, 304-307.\n\nBerners-Lee, T., Hendler, J., & Lassila, O. (2001). The Semantic Web. Scientific American, 284(5), 34-43.\n\nCook, D. J., Augusto, J. C., & Jakkula, V. R. (2009). Ambient intelligence: Technologies, applications, and opportunities. Pervasive and Mobile\nComputing, 5(4), 277-298.\n\nDey, A. K. (2001). Understanding and using context. Personal and Ubiquitous Computing, 5, 4-7.\n\nDucatel, K., Bogdanowicz, M., Scapolo, F., Leijten, J., & Burgelman, J.-C. (2001). Scenarios for Ambient Intelligence in 2010. ISTAG.\n\nEndsley, M. R. (1995). Toward a theory of situation awareness in dynamic systems. Human Factors, 37(1), 32-64.\n\nFielding, R. T. (2000). Architectural Styles and the Design of Network-based Software Architectures. Doctoral dissertation, University of\nCalifornia, Irvine.\n\nHarel, D. (1987). Statecharts: A visual formalism for complex systems. Science of Computer Programming, 8(3), 231-274.\n\nHardeman, W., Houghton, J., Lane, K., Jones, A., & Naughton, F. (2019). A systematic review of just-in-time adaptive interventions to\npromote physical activity. International Journal of Behavioral Nutrition and Physical Activity, 16, 31.\n\nMoreau, L., & Missier, P. (Eds.). (2013). PROV-DM: The PROV Data Model. W3C Recommendation.\n\nNahum-Shani, I., Smith, S. N., Spring, B. J., Collins, L. M., Witkiewitz, K., Tewari, A., & Murphy, S. A. (2018). Just-in-time adaptive\ninterventions (JITAIs) in mobile health: Key components and design principles for ongoing health behavior support. Annals of Behavioral\nMedicine, 52(6), 446-462.\n\nSchilit, B. N., Adams, N., & Want, R. (1994). Context-aware computing applications. Proceedings of the Workshop on Mobile Computing\nSystems and Applications, 85-90.\n\nWeiser, M. (1991). The computer for the 21st century. Scientific American, 265(3), 94-104.\n\nWeiser, M., & Brown, J. S. (1996). The Coming Age of Calm Technology. Xerox PARC.\n\nWorld Wide Web Consortium. (2013). PROV-O: The PROV Ontology. W3C Recommendation.\n\nEissens, R. (2026). StateLens: A URL-Native AI State Diary Protocol for Multimodal State Compression and Day Reconstruction. Zenodo.\nhttps://doi.org/10.5281/zenodo.20770792\n\nEissens, R. (2026). StateLens for Situational Intelligence: URL-Native State Signals for Situated Care, Gated Context, and Provenance-Backed AI Assistance.\nVersion 1.0 draft. DOI: https://doi.org/10.5281/zenodo.20861928\n\nProject URLs\n\nStateLens: https://statelens.net/\n\nCompanion Habitat: https://companionhabitat.com/\n\nTrailstate: https://trailstate.org/\n\nObjectPortal: https://objectportal.com/\n\nDOI: https://doi.org/10.5281/zenodo.20861928\n\n23\n\n=== PDF PAGE 24 ===\nSTATELENS PROTOCOL · VERSION 1.1 · JUNE 2026\nState-First, URL-Native Signal Grammar for Situational Intelligence\n\nAppendix A. Minimal Operator Set\n\nThis appendix restates a minimal operator set suitable for situational intelligence examples. It is not the\nfull StateLens grammar. It is a constrained subset used for protocol clarity.\n\nOperator\nSurface semantics\nContext semantics\nAllowed branches\nDefault\nvisibility\n\nPublic\n\no-vvv-o\nOpen field / beginning\nA new situation, day, route or\ninteraction opens\n\nobserve, enter,\ncontinue\n\nq-vvv-p\nQuestion / uncertainty\nA decision question or relevance\nquestion opens\n\nexplain, compare,\ndefer, ignore\n\nPublic or\nprotected\n\nProtected\n\nn-vvv-n\nNarrowing /\ncomparison\n\nchoose, compare,\nvalidate\n\nPossible branches or\ninterpretations become more\nfocused\n\nx-vvv-x\nConflict / mismatch /\nincoherence\n\nopen, ignore, repair,\nescalate, save\n\nPublic or\nprotected\n\nExternal event conflicts with\ncontext or a relation becomes\nunstable\n\nProtected\n\nr-vvv-r\nRepair / recovery\nA mitigation, correction or\nrecovery action begins\n\nplan, rest, hydrate,\nrevise, recover\n\n0-vvv-0\nStabilized / resolved\nDecision or interpretation\nstabilizes\n\narchive, save, close\nPublic or\nprotected\n\nu-vvv-u\nArchived / closed\nTrail is closed for reconstruction\nreplay, summarize,\narchive\n\nPrivate or\nprotected\n\nAppendix A.1 Design constraints\n\nG\nOperators should be short enough to display on wearables, browser surfaces, object pages or receipts.\n\nG\nOperators should remain abstract enough to generalize across domains.\n\nG\nOperators should never encode sealed context directly.\n\nG\nOperators should be stable enough to support replay and indexing.\n\nG\nOperators should be accompanied by correction paths if the state resolution is wrong.\n\n24\n\n=== PDF PAGE 25 ===\nSTATELENS PROTOCOL · VERSION 1.1 · JUNE 2026\nState-First, URL-Native Signal Grammar for Situational Intelligence\n\nAppendix B. Signal Object Schema\n\nThe following describes one possible minimal schema for implementers. It is intentionally not framed as a\nfinal API standard.\n\n{\n  \"schema\": \"statelens.situational_signal.v1.1\",\n  \"operator\": \"x-vvv-x\",\n  \"surface_label\": \"Weather/work mismatch detected. Open?\",\n  \"icon_hint\": \"thermometer\",\n  \"visibility\": \"public\",\n  \"topic\": \"heat-work-context\",\n  \"context_layer\": \"gated_private_ai\",\n  \"object_anchor\": \"objectportal:event/flevoland-heat-warning\",\n  \"provenance_url\": \"https://trailstate.org/?r=x-vvv-x&q=heat-work-\ncontext&source=weather-alert&visibility=protected\",\n  \"state_resolution\": {\n    \"external_event\": \"weather-alert\",\n    \"private_context_match\": \"outdoor-work + commute + body-context\",\n    \"resolved_state\": \"conflict/mismatch\"\n  },\n  \"allowed_branches\": [\n    \"open_context\",\n    \"draft_message\",\n    \"call_supervisor\",\n    \"drink_water\",\n    \"take_leave\",\n    \"ignore\",\n    \"save_trail\"\n  ],\n  \"forbidden_without_confirmation\": [\n    \"send_message\",\n    \"place_call\",\n    \"cancel_work\",\n    \"contact_employer\",\n    \"make_decision\",\n    \"disclose_sealed_context\"\n  ],\n  \"boundary\": \"signal_explain_offer_never_execute_without_confirmation\"\n}\n\nA production implementation would also need authentication, access control, retention policy,\ncryptographic integrity, consent management, correction paths, localization and domain-specific\nvalidation.\n\n25"
}