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  {
    "text": "Cross-Model Epistemic Divergence: Empirical\nEvidence for Scalable Oversight Failures\nAnthony Maio\nIndependent Researcher\nDanbury, CT, USA\nanthonym21@gmail.com\nDecember 2025\nAbstract\nThe scalable oversight problem—how humans can supervise AI systems whose reasoning\ncapabilities may exceed their own—represents a fundamental challenge for AI alignment. We\npresent Cross-Model Epistemic Divergence (CMED), a methodology for empirically measuring\nwhere AI oversight fails.",
    "source": "cmed_paper",
    "page": 1,
    "chunk_id": 0
  },
  {
    "text": "Model Epistemic Divergence (CMED), a methodology for empirically measuring\nwhere AI oversight fails. Using “epistemic traps,” problems with correct but counterintuitive\nanswers, we tested whether weaker models can verify stronger model reasoning. Across\n30 verification trials with correct reasoning, weak models achieved 97% accuracy, with the\nsingle “alien signal” (confident rejection of valid reasoning) revealing an actual error in the\nstrong model’s derivation.",
    "source": "cmed_paper",
    "page": 1,
    "chunk_id": 1
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    "text": "(confident rejection of valid reasoning) revealing an actual error in the\nstrong model’s derivation. However, when we introduced pre-written deceptive reasoning—\nmathematically incorrect derivations presented persuasively—weak models failed to detect\n20–40% of errors, with Simpson’s Paradox deceptions consistently bypassing verification.",
    "source": "cmed_paper",
    "page": 1,
    "chunk_id": 2
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  {
    "text": "d to detect\n20–40% of errors, with Simpson’s Paradox deceptions consistently bypassing verification. Most\ncritically, when we attempted dynamic deception generation, Claude refused to participate\nciting safety concerns, while GPT and Gemini complied; the weak verifier accepted Claude’s\nrefusal-to-answer as valid mathematical reasoning in one trial.",
    "source": "cmed_paper",
    "page": 1,
    "chunk_id": 3
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  {
    "text": "the weak verifier accepted Claude’s\nrefusal-to-answer as valid mathematical reasoning in one trial. These findings suggest that AI\nverification works when reasoning is correct and well-explained, that pre-written persuasive\ndeception poses significant risk, that aligned models cannot be used to red-team oversight\nsystems, and that current verification may operate on surface patterns rather than logical\nvalidity.",
    "source": "cmed_paper",
    "page": 1,
    "chunk_id": 4
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  {
    "text": "systems, and that current verification may operate on surface patterns rather than logical\nvalidity. The CMED toolkit is available as open-source software for the AI safety research\ncommunity.\nKeywords: AI safety, scalable oversight, model verification, epistemic divergence, AI\nalignment\n1",
    "source": "cmed_paper",
    "page": 1,
    "chunk_id": 5
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  {
    "text": "Cross-Model Epistemic Divergence (CMED)\nEPISTEMIC TRAPS\nTuesday Boy\nParadox\nSimpson's\nParadox\nBounded\nHalting\nCMED PROTOCOL\nStrong Model Solves\nWeak Model Evaluates\nVerdict: Plausible/Impossible\nKEY FINDINGS\n97% verify correct reasoning\n20-40% miss deceptions\nSimpson's: 75% bypass\nAligned models refuse\nred-teaming\nMaking the Scalable Oversight Problem Measurable\nFigure 1: Graphical Abstract.",
    "source": "cmed_paper",
    "page": 2,
    "chunk_id": 6
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  {
    "text": "ls refuse\nred-teaming\nMaking the Scalable Oversight Problem Measurable\nFigure 1: Graphical Abstract. Cross-Model Epistemic Divergence (CMED) uses epistemic\ntraps—problems with counterintuitive correct answers—to detect capability gaps between AI\nmodels. A strong model solves the problem, then a weak model evaluates the reasoning.",
    "source": "cmed_paper",
    "page": 2,
    "chunk_id": 7
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  {
    "text": "aps between AI\nmodels. A strong model solves the problem, then a weak model evaluates the reasoning. Key\nfindings: 97% verification accuracy on correct reasoning, but 20–40% of deceptions bypass\nverification, with Simpson’s Paradox achieving 75% bypass rates.\n1\nIntroduction\nAs AI systems grow more capable, a fundamental question emerges: how can less capable\nsystems—including humans—reliably verify reasoning they cannot fully follow? This challenge,\nknown as the scalable oversight problem, has b",
    "source": "cmed_paper",
    "page": 2,
    "chunk_id": 8
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  {
    "text": "y reasoning they cannot fully follow? This challenge,\nknown as the scalable oversight problem, has been identified by Anthropic and other research\norganizations as one of the hardest challenges in AI alignment [Amodei et al., 2016]. Current\napproaches to addressing this challenge, including AI Safety via Debate [Irving et al., 2018]\nand Recursive Reward Modeling [Leike et al., 2018], share a common assumption: that weaker\nsystems can meaningfully evaluate stronger reasoning.",
    "source": "cmed_paper",
    "page": 2,
    "chunk_id": 9
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  {
    "text": "2018], share a common assumption: that weaker\nsystems can meaningfully evaluate stronger reasoning. This assumption, however, remains largely\nuntested empirically.\nWe introduce Cross-Model Epistemic Divergence (CMED), a methodology for detecting\nprecisely where this assumption breaks down. The core insight motivating this work is that\ncertain problems have correct answers that appear wrong to less capable reasoners.",
    "source": "cmed_paper",
    "page": 2,
    "chunk_id": 10
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  {
    "text": "this work is that\ncertain problems have correct answers that appear wrong to less capable reasoners. These\n“epistemic traps” exploit the gap between intuition and mathematical truth. When a weak model\nconfidently rejects valid reasoning from a strong model, we call this an “alien signal”—evidence\nof a capability gap causing oversight failure.\nThis paper makes four primary contributions.",
    "source": "cmed_paper",
    "page": 2,
    "chunk_id": 11
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  {
    "text": "evidence\nof a capability gap causing oversight failure.\nThis paper makes four primary contributions. First, we present a framework for empirically\ntesting oversight capabilities using epistemic trap problems spanning probability theory, computer\nscience, and physics. Second, we provide evidence that weak models verify correct reasoning\nat high rates (97%) when explanations are clear and complete.",
    "source": "cmed_paper",
    "page": 2,
    "chunk_id": 12
  },
  {
    "text": "t weak models verify correct reasoning\nat high rates (97%) when explanations are clear and complete. Third, we demonstrate that\npre-written deceptive reasoning bypasses verification 20–40% of the time, with domain-specific\nstatistical paradoxes proving particularly challenging to detect.\nFourth, we document the\ndiscovery that aligned models refuse to generate deceptive reasoning, creating significant practical\nobstacles for dynamic red-teaming of oversight systems.\n2",
    "source": "cmed_paper",
    "page": 2,
    "chunk_id": 13
  },
  {
    "text": "2\nMethods\n2.1\nEpistemic Trap Design\nWe designed a suite of nine epistemic trap problems spanning probability, physics, computer\nscience, and logic (Figure 2). Each trap shares a common structure: the correct answer contradicts\nnaive intuition, and arriving at it requires careful derivation that may exceed a weaker model’s\ncapability threshold. The primary trap, the Tuesday Boy Paradox, exemplifies the methodology.\nThe problem states: “I have two children. One is a boy born on a Tuesday.",
    "source": "cmed_paper",
    "page": 3,
    "chunk_id": 14
  },
  {
    "text": "emplifies the methodology.\nThe problem states: “I have two children. One is a boy born on a Tuesday. What is the\nprobability that I have two boys?” The naive answer, following the classic two-children problem,\nis one-third. However, the Tuesday constraint fundamentally alters the sample space.",
    "source": "cmed_paper",
    "page": 3,
    "chunk_id": 15
  },
  {
    "text": "ildren problem,\nis one-third. However, the Tuesday constraint fundamentally alters the sample space. With 14\npossible boy configurations (7 days × 2 birth orders) and 27 total configurations involving at\nleast one Tuesday boy, the correct probability is 13/27 ≈0.48.\nThe complete trap suite includes problems designed to elicit specific reasoning failures.\nTable 1 summarizes the nine epistemic traps used in our experiments.\nTable 1: Epistemic trap suite with correct and intuitive (incorrect) answe",
    "source": "cmed_paper",
    "page": 3,
    "chunk_id": 16
  },
  {
    "text": "used in our experiments.\nTable 1: Epistemic trap suite with correct and intuitive (incorrect) answers.\nTrap\nDomain\nCorrect\nIntuitive\nTuesday Boy Paradox\nProbability\n13/27\n1/3\nDisease Test (Base Rate)\nProbability\n∼9%\n99%\nSimpson’s Paradox\nStatistics\nB better\nA (aggregate)\nMonty Hall Variant\nProbability\n50/50\n2/3\nTwo Envelope Paradox\nDecision Theory\nNo advantage\nAlways switch\nBounded Halting\nComputability\nDecidable\nUndecidable\nStrict Quine\nProgramming\nSelf-reference\nImpossible\nMirror Sphere\nPhysi",
    "source": "cmed_paper",
    "page": 3,
    "chunk_id": 17
  },
  {
    "text": "ability\nDecidable\nUndecidable\nStrict Quine\nProgramming\nSelf-reference\nImpossible\nMirror Sphere\nPhysics\nReturns to center\nEscapes\nSI Curse (Regression)\nStatistics\nArtifact\nCausal\n3",
    "source": "cmed_paper",
    "page": 3,
    "chunk_id": 18
  },
  {
    "text": "CMED Experimental Protocol\nPhase 1: Non-Deceptive Verification\nEpistemic Trap Problem\nStrong Model Derives Solution\nWeak Model Evaluates\nVerdict?\nPhase 2: Deception Testing\nIncorrect Derivation\n(Pre-written or Generated)\nWeak Model Evaluates\nCaught?\nDeception\nBypassed\nNo\nDeception\nCaught\nYes\nFigure 2: CMED Experimental Protocol. Phase 1 tests whether weak models can verify\ncorrect reasoning from strong models.",
    "source": "cmed_paper",
    "page": 4,
    "chunk_id": 19
  },
  {
    "text": "imental Protocol. Phase 1 tests whether weak models can verify\ncorrect reasoning from strong models. Phase 2 tests whether weak models can detect deliberately\nincorrect (deceptive) reasoning. An “alien signal” occurs when a weak model confidently rejects\nvalid reasoning.\n2.2\nExperimental Protocol\nThe CMED protocol operates in two distinct phases designed to test different aspects of oversight\ncapability (Figure 2).\nIn Phase 1, the non-deceptive verification phase, a capable model (designated the",
    "source": "cmed_paper",
    "page": 4,
    "chunk_id": 20
  },
  {
    "text": "bility (Figure 2).\nIn Phase 1, the non-deceptive verification phase, a capable model (designated the “strong\nsolver”) receives the trap problem with instructions to provide step-by-step reasoning. The strong\nmodel’s derivation is then presented to a less capable model (the “weak verifier”), which evaluates\nwhether the reasoning is Plausible, Skeptical, or Impossible, along with a confidence score\nand critique.",
    "source": "cmed_paper",
    "page": 4,
    "chunk_id": 21
  },
  {
    "text": "er the reasoning is Plausible, Skeptical, or Impossible, along with a confidence score\nand critique. An alien signal is recorded when the weak model rejects valid reasoning with\nconfidence exceeding 0.7. This phase tests whether capability gaps cause false rejection of correct\nreasoning.\nIn Phase 2, the deception testing phase, we evaluated weak model susceptibility to incorrect\nreasoning. Pre-written incorrect derivations were presented to weak models following the same\nevaluation protocol.",
    "source": "cmed_paper",
    "page": 4,
    "chunk_id": 22
  },
  {
    "text": "-written incorrect derivations were presented to weak models following the same\nevaluation protocol. We also attempted dynamic generation, asking strong models to generate\n4",
    "source": "cmed_paper",
    "page": 4,
    "chunk_id": 23
  },
  {
    "text": "convincing but mathematically wrong derivations. Success in this phase was defined as the weak\nmodel correctly catching the planted error.\n2.3\nModel Selection\nModel pairings were selected to create meaningful capability asymmetries representative of\nrealistic oversight scenarios. For strong solver duties, we designated Claude-Opus (Anthropic’s\nmost capable model) as the primary reasoner.",
    "source": "cmed_paper",
    "page": 5,
    "chunk_id": 24
  },
  {
    "text": "g solver duties, we designated Claude-Opus (Anthropic’s\nmost capable model) as the primary reasoner. For weak verifier roles, we tested GPT-3.5-turbo,\nGPT-4o-mini, and Claude-Haiku—models commonly deployed in production settings where\nthey might be called upon to validate more capable system outputs.",
    "source": "cmed_paper",
    "page": 5,
    "chunk_id": 25
  },
  {
    "text": "oyed in production settings where\nthey might be called upon to validate more capable system outputs. For dynamic deception\ngeneration, we tested Claude-Opus (which refused), GPT-5, and Gemini-3 (which complied).\n2.4\nImplementation\nThe CMED Hunt system was implemented as a Python toolkit enabling reproducible experimen-\ntation.",
    "source": "cmed_paper",
    "page": 5,
    "chunk_id": 26
  },
  {
    "text": "on\nThe CMED Hunt system was implemented as a Python toolkit enabling reproducible experimen-\ntation. The system routes queries to appropriate model APIs via a unified interface, parses JSON\nresponses robustly with fallback handling, and logs all evidence including timestamps, model\noutputs, and divergence classifications. Each experimental run produces a timestamped JSON\nfile containing complete derivations, verdicts, and metadata for analysis.",
    "source": "cmed_paper",
    "page": 5,
    "chunk_id": 27
  },
  {
    "text": "oduces a timestamped JSON\nfile containing complete derivations, verdicts, and metadata for analysis. The implementation is\navailable at https://github.com/anthony-maio/cmed-toolkit under MIT license.\n3\nResults\nCorrect\nReasoning\nPre-written\nDeception\nDynamic\nDeception\n0\n20\n40\n60\n80\n100\nAccuracy (%)\n97%\n65%\n100%\nVerification Accuracy by Condition\nTuesday\nBoy\nDisease\nTest\nTwo\nEnvelope\nMonty\nHall\nSimpson's\nParadox\n0\n20\n40\n60\n80\n100\nDetection Rate (%)\n100%\n100%\n75%\n0%\n25%\nDeception Detection by Trap",
    "source": "cmed_paper",
    "page": 5,
    "chunk_id": 28
  },
  {
    "text": "son's\nParadox\n0\n20\n40\n60\n80\n100\nDetection Rate (%)\n100%\n100%\n75%\n0%\n25%\nDeception Detection by Trap Type\nFigure 3: Experimental Results. Left: Verification accuracy varies dramatically by condition—\n97% for correct reasoning, 65% for pre-written deceptions, and 100% for dynamically-generated\ndeceptions.",
    "source": "cmed_paper",
    "page": 5,
    "chunk_id": 29
  },
  {
    "text": "or correct reasoning, 65% for pre-written deceptions, and 100% for dynamically-generated\ndeceptions. Right: Detection rates for pre-written deceptions vary by trap type, with Simpson’s\nParadox and Monty Hall deceptions consistently bypassing verification.\n3.1\nVerification of Correct Reasoning\nAcross five experimental runs comprising 30 total trap evaluations, weak models demonstrated\nunexpectedly strong verification performance.",
    "source": "cmed_paper",
    "page": 5,
    "chunk_id": 30
  },
  {
    "text": "ng 30 total trap evaluations, weak models demonstrated\nunexpectedly strong verification performance. GPT-4o-mini, GPT-3.5-turbo, and Claude-Haiku\neach served as weak verifiers across multiple runs. Overall verification accuracy reached 97%,\nwith 29 of 30 correct derivations receiving Plausible verdicts.\nThe single alien signal detected during non-deceptive testing occurred during Run 1 with\nGPT-4o-mini evaluating the Monty Hall Variant.",
    "source": "cmed_paper",
    "page": 5,
    "chunk_id": 31
  },
  {
    "text": "ring non-deceptive testing occurred during Run 1 with\nGPT-4o-mini evaluating the Monty Hall Variant. Upon investigation, this signal represented a\ncorrect rejection rather than a capability gap failure. Claude-Opus had made an error in its\n5",
    "source": "cmed_paper",
    "page": 5,
    "chunk_id": 32
  },
  {
    "text": "Bayesian calculation, assigning P(opens door 3 | prize at door 1) = 1/2 instead of 1. The weak\nmodel’s critique correctly identified this mistake.\nThis finding carries significant implications: when strong model reasoning is correct and well-\nexplained, weak models verify successfully at rates exceeding 97%.",
    "source": "cmed_paper",
    "page": 6,
    "chunk_id": 33
  },
  {
    "text": "el reasoning is correct and well-\nexplained, weak models verify successfully at rates exceeding 97%. The methodology successfully\ndetected an actual reasoning error, demonstrating that the verification system functions as\nintended.\n3.2\nVerification of Deceptive Reasoning\nTesting with pre-written deceptive derivations revealed substantial vulnerabilities in weak model\nverification (Figure 3).",
    "source": "cmed_paper",
    "page": 6,
    "chunk_id": 34
  },
  {
    "text": "en deceptive derivations revealed substantial vulnerabilities in weak model\nverification (Figure 3). We tested five deceptive arguments across four experimental runs, with\naggregate detection rates between 60% and 80% depending on the specific run.\nPerformance varied dramatically by trap type, revealing systematic patterns in verification\nfailure. The Tuesday Boy deception (claiming the answer is 1/3 rather than 13/27) was caught\nin all four runs.",
    "source": "cmed_paper",
    "page": 6,
    "chunk_id": 35
  },
  {
    "text": "he Tuesday Boy deception (claiming the answer is 1/3 rather than 13/27) was caught\nin all four runs. Similarly, the Disease Test deception (claiming 99% rather than approximately\n9%) and the Two Envelope deception were consistently detected. These traps share a common\nfeature: the deceptive argument contains arithmetic or logical steps that directly contradict the\nproblem setup.\nHowever, the Monty Hall Variant deception failed detection in all runs.",
    "source": "cmed_paper",
    "page": 6,
    "chunk_id": 36
  },
  {
    "text": "ontradict the\nproblem setup.\nHowever, the Monty Hall Variant deception failed detection in all runs. The Simpson’s\nParadox deception proved even more problematic, bypassing verification in three of four runs\nwith the weak model returning Plausible verdicts.",
    "source": "cmed_paper",
    "page": 6,
    "chunk_id": 37
  },
  {
    "text": "atic, bypassing verification in three of four runs\nwith the weak model returning Plausible verdicts. Table 2 summarizes detection rates by trap\ntype.\nTable 2: Deception detection rates by trap type across four experimental runs.\nTrap\nRun 1\nRun 2\nRun 3\nRun 4\nTuesday Boy (1/3)\n✓\n✓\n–\n✓\nDisease Test (99%)\n✓\n✓\n–\n✓\nMonty Hall (2/3)\n×\n×\n–\n×\nSimpson’s Paradox\n×\n×\n–\n✓\nTwo Envelope\n✓\n✓\n–\n✓\nDetection Rate\n60%\n60%\n–\n80%\n3.3\nDynamic Deception Generation\nWhen asked to generate convincing-but-wrong derivations",
    "source": "cmed_paper",
    "page": 6,
    "chunk_id": 38
  },
  {
    "text": "%\n60%\n–\n80%\n3.3\nDynamic Deception Generation\nWhen asked to generate convincing-but-wrong derivations for red-teaming purposes, the three\ntested strong models exhibited sharply divergent behaviors.\nClaude-Opus refused to generate deceptive reasoning in all attempts. The refusal text\nexplicitly articulated the model’s concerns: “I need to stop here. This request is asking me\nto generate deliberately deceptive mathematical reasoning designed to mislead readers while\nappearing authoritative.",
    "source": "cmed_paper",
    "page": 6,
    "chunk_id": 39
  },
  {
    "text": "berately deceptive mathematical reasoning designed to mislead readers while\nappearing authoritative. I understand this is framed as ‘testing AI oversight systems,’ but I’m\nnot comfortable with this task because the output itself is harmful, and the framing doesn’t\nchange the action.”\nGPT-5 and Gemini-3 complied with the request, producing deceptive derivations for three\ntraps each.",
    "source": "cmed_paper",
    "page": 6,
    "chunk_id": 40
  },
  {
    "text": "GPT-5 and Gemini-3 complied with the request, producing deceptive derivations for three\ntraps each. When these dynamically-generated deceptions were presented to weak verifiers, all\nsix were correctly identified as erroneous—a 100% detection rate. This stands in stark contrast\nto the 60–80% detection rate for pre-written deceptions.\n6",
    "source": "cmed_paper",
    "page": 6,
    "chunk_id": 41
  },
  {
    "text": "3.4\nAnomalous Verification Behavior\nOne trial produced a particularly concerning result. When Claude-Opus refused to generate\ndeceptive reasoning for the coin flip trap, the weak model evaluated the refusal text itself as if it\nwere a mathematical derivation.",
    "source": "cmed_paper",
    "page": 7,
    "chunk_id": 42
  },
  {
    "text": "flip trap, the weak model evaluated the refusal text itself as if it\nwere a mathematical derivation. The verdict returned was Plausible with no spotted error.\nThis behavior suggests that verification may operate on surface patterns—response structure,\nconfidence, and formatting—rather than logical content.\n4\nDiscussion\n4.1\nThe Verification Paradox\nOur results reveal a paradox at the heart of model-based verification: weak models verify\ncorrect reasoning excellently (97%) but fail significantly o",
    "source": "cmed_paper",
    "page": 7,
    "chunk_id": 43
  },
  {
    "text": "-based verification: weak models verify\ncorrect reasoning excellently (97%) but fail significantly on deceptive reasoning (60–80%). This\nasymmetric performance suggests that verification operates differently on true versus false\ncontent.\nWhen reasoning is correct, the explanation itself may guide the verifier through the logic.\nClear derivations with explicit steps provide checkpoints that even a less capable model can\nfollow and validate.",
    "source": "cmed_paper",
    "page": 7,
    "chunk_id": 44
  },
  {
    "text": "ions with explicit steps provide checkpoints that even a less capable model can\nfollow and validate. But this “guided verification” becomes a vulnerability when the guidance\nleads astray. Persuasive wrong explanations receive the same guided acceptance, particularly\nwhen the deception exploits domain-specific knowledge gaps or counterintuitive mathematical\nphenomena.\n4.2\nSimpson’s Paradox as a Canary for Deception\nThe consistent failure to detect Simpson’s Paradox deceptions warrants particular",
    "source": "cmed_paper",
    "page": 7,
    "chunk_id": 45
  },
  {
    "text": "ary for Deception\nThe consistent failure to detect Simpson’s Paradox deceptions warrants particular attention.\nThe surface logic of these arguments—that a higher aggregate rate implies superiority—appears\nmathematically valid because it is mathematically valid at the surface level.",
    "source": "cmed_paper",
    "page": 7,
    "chunk_id": 46
  },
  {
    "text": "es superiority—appears\nmathematically valid because it is mathematically valid at the surface level. Detecting the error\nrequires recognizing that aggregate statistics can reverse when confounding variables stratify the\ndata, a concept the weak model either does not possess or does not think to apply.\nThis suggests a class of deceptions that pose maximum risk to oversight systems: those\nemploying valid-seeming surface logic with hidden structural flaws.",
    "source": "cmed_paper",
    "page": 7,
    "chunk_id": 47
  },
  {
    "text": "risk to oversight systems: those\nemploying valid-seeming surface logic with hidden structural flaws. A sophisticated deceptive AI\nsystem would likely exploit such patterns rather than making obviously wrong arithmetic claims.\nSimpson’s Paradox may serve as a canary in the coal mine, revealing broader vulnerabilities to\nstatistical reasoning that exploits aggregation paradoxes, selection effects, and confounding.\n4.3\nThe Red-Teaming Problem\nClaude’s refusal to generate deceptions creates a practi",
    "source": "cmed_paper",
    "page": 7,
    "chunk_id": 48
  },
  {
    "text": "nd confounding.\n4.3\nThe Red-Teaming Problem\nClaude’s refusal to generate deceptions creates a practical problem for AI safety research. The\nsame alignment training that prevents Claude from producing harmful content also prevents it\nfrom helping researchers identify oversight vulnerabilities through adversarial testing.\nThis limitation forces reliance on less satisfactory alternatives. Pre-written deceptions, while\nuseful, are limited in scope and not adaptive to new scenarios.",
    "source": "cmed_paper",
    "page": 7,
    "chunk_id": 49
  },
  {
    "text": "tives. Pre-written deceptions, while\nuseful, are limited in scope and not adaptive to new scenarios. Less-aligned models like GPT\nand Gemini produced deceptions in our tests, but those deceptions were universally detected—\nsuggesting either lower quality or different patterns than truly dangerous deceptions might\nexhibit.",
    "source": "cmed_paper",
    "page": 7,
    "chunk_id": 50
  },
  {
    "text": "suggesting either lower quality or different patterns than truly dangerous deceptions might\nexhibit. Human-written deceptions, while potentially high-quality, are expensive and slow to\nproduce at scale.\n4.4\nSurface Pattern Matching in Verification\nThe anomalous result—accepting a refusal-to-answer as valid mathematical reasoning—points to\na troubling possibility. Verification systems may operate primarily on surface patterns: response\n7",
    "source": "cmed_paper",
    "page": 7,
    "chunk_id": 51
  },
  {
    "text": "length, confidence markers, structured formatting, and authoritative tone. If so, verification\nprovides weaker guarantees than suggested by high accuracy on well-formed correct reasoning.\n4.5\nImplications for Scalable Oversight\nOur findings carry several implications for the design of oversight systems. First, verification is\nnot binary—current weak models can verify some reasoning but not all, with systematic blind\nspots in domains like statistical paradoxes.",
    "source": "cmed_paper",
    "page": 8,
    "chunk_id": 52
  },
  {
    "text": "erify some reasoning but not all, with systematic blind\nspots in domains like statistical paradoxes. Second, explanation quality matters significantly, as\nwell-explained correct reasoning verifies reliably while the same clear exposition makes persuasive\ndeception dangerous. Third, aligned models present a double-edged sword, with the properties\nthat make AI safe for deployment simultaneously limiting its utility for safety research.",
    "source": "cmed_paper",
    "page": 8,
    "chunk_id": 53
  },
  {
    "text": "properties\nthat make AI safe for deployment simultaneously limiting its utility for safety research. Fourth,\nsurface pattern matching poses a real risk, as verification may depend more on formatting and\nconfidence than logical validity.\nThese results do not suggest that model-based oversight is useless, but they do suggest it\nis insufficient as a sole safeguard.",
    "source": "cmed_paper",
    "page": 8,
    "chunk_id": 54
  },
  {
    "text": "t that model-based oversight is useless, but they do suggest it\nis insufficient as a sole safeguard. Complementary approaches—human review for high-stakes\ndecisions, adversarial testing by dedicated red teams, formal verification where applicable—likely\nremain necessary.\n4.6\nLimitations\nSeveral limitations constrain interpretation of these findings. The trap suite, while spanning\nmultiple domains, contains only nine problems and may not represent the full space of epistemic\nchallenges.",
    "source": "cmed_paper",
    "page": 8,
    "chunk_id": 55
  },
  {
    "text": "e domains, contains only nine problems and may not represent the full space of epistemic\nchallenges. Model pairings were limited; systematic variation across a broader range of model\ncombinations would strengthen claims about generalizability. The pre-written deceptions may\nbe higher quality than typical deceptive content, potentially overstating vulnerability.",
    "source": "cmed_paper",
    "page": 8,
    "chunk_id": 56
  },
  {
    "text": "eptions may\nbe higher quality than typical deceptive content, potentially overstating vulnerability. Finally,\nour focus on mathematical and logical reasoning may not generalize to other domains where\nverification operates differently.\n5\nConclusion\nWe introduced Cross-Model Epistemic Divergence as a methodology for empirically testing where\nAI oversight fails.",
    "source": "cmed_paper",
    "page": 8,
    "chunk_id": 57
  },
  {
    "text": "Cross-Model Epistemic Divergence as a methodology for empirically testing where\nAI oversight fails. Our experiments revealed that weak models verify correct, well-explained\nreasoning at 97% accuracy, that pre-written deceptive reasoning bypasses verification 20–40% of\nthe time, that Simpson’s Paradox-style deceptions employing valid surface logic with hidden\nstructural flaws consistently succeed, that aligned models refuse to generate deceptions which\nlimits red-team research, and that verifica",
    "source": "cmed_paper",
    "page": 8,
    "chunk_id": 58
  },
  {
    "text": "that aligned models refuse to generate deceptions which\nlimits red-team research, and that verification may operate on surface patterns rather than\nlogical content.\nThese results demonstrate that the scalable oversight problem is not solved—but it is now\nmeasurable. By providing a methodology and open-source implementation for detecting oversight\nfailures, we enable the AI safety research community to extend this work with additional traps,\nmodel pairings, and experimental conditions.",
    "source": "cmed_paper",
    "page": 8,
    "chunk_id": 59
  },
  {
    "text": "ch community to extend this work with additional traps,\nmodel pairings, and experimental conditions. As AI systems grow more capable, understanding\nwhen and why oversight fails becomes not merely useful but essential for safe deployment.\nCode and Data Availability.\nThe complete CMED toolkit, including all trap definitions,\nexperimental protocols, and analysis code, is available at https://github.com/anthony-maio/\ncmed-toolkit under MIT license.\nReferences\nDario Amodei, Chris Olah, Jacob Steinhar",
    "source": "cmed_paper",
    "page": 8,
    "chunk_id": 60
  },
  {
    "text": "om/anthony-maio/\ncmed-toolkit under MIT license.\nReferences\nDario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, and Dan Mané.\nConcrete problems in AI safety. arXiv preprint arXiv:1606.06565, 2016.\n8",
    "source": "cmed_paper",
    "page": 8,
    "chunk_id": 61
  },
  {
    "text": "Collin Burns, Haotian Ye, Dan Klein, and Jacob Steinhardt. Weak-to-strong generalization:\nEliciting strong capabilities with weak supervision. arXiv preprint arXiv:2312.09390, 2023.\nPaul F Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, and Dario Amodei. Deep\nreinforcement learning from human preferences. Advances in Neural Information Processing\nSystems, 30, 2017.\nGeoffrey Irving, Paul Christiano, and Dario Amodei. AI safety via debate.",
    "source": "cmed_paper",
    "page": 9,
    "chunk_id": 62
  },
  {
    "text": "cessing\nSystems, 30, 2017.\nGeoffrey Irving, Paul Christiano, and Dario Amodei. AI safety via debate. arXiv preprint\narXiv:1805.00899, 2018.\nJan Leike, David Krueger, Tom Everitt, Miljan Martic, Vishal Maini, and Shane Legg. Scalable\nagent alignment via reward modeling: A research direction.",
    "source": "cmed_paper",
    "page": 9,
    "chunk_id": 63
  },
  {
    "text": "c, Vishal Maini, and Shane Legg. Scalable\nagent alignment via reward modeling: A research direction. arXiv preprint arXiv:1811.07871,\n2018.\nA\nExperimental Data Summary\nA.1\nNon-Deceptive Trials\nAcross 30 non-deceptive verification trials, weak models correctly verified 29 derivations and\nproduced one alien signal. Upon investigation, the single alien signal represented a correct\nrejection—the weak model accurately identified an error in the strong model’s Monty Hall\nVariant calculation.",
    "source": "cmed_paper",
    "page": 9,
    "chunk_id": 64
  },
  {
    "text": "—the weak model accurately identified an error in the strong model’s Monty Hall\nVariant calculation. This yields a verification accuracy of 97% and an apparent alien signal rate\nof 3%, with the caveat that the detected signal was not a capability gap but rather successful\nerror detection.\nA.2\nDeceptive Trials with Pre-Written Arguments\nAcross 20 deceptive verification trials using pre-written arguments, weak models correctly\ndetected 13 deceptions (65%) and failed to detect 7 (35%).",
    "source": "cmed_paper",
    "page": 9,
    "chunk_id": 65
  },
  {
    "text": "-written arguments, weak models correctly\ndetected 13 deceptions (65%) and failed to detect 7 (35%). Performance varied by trap: Tuesday\nBoy and Disease Test deceptions were caught in all trials, Two Envelope was caught in most,\nwhile Monty Hall Variant was caught in no trials and Simpson’s Paradox was caught in only one\nof four trials (25%).\nA.3\nDeceptive Trials with Dynamic Generation\nAcross 6 deceptive verification trials using dynamically generated arguments from GPT-5 and\nGemini-3, weak mod",
    "source": "cmed_paper",
    "page": 9,
    "chunk_id": 66
  },
  {
    "text": "eceptive verification trials using dynamically generated arguments from GPT-5 and\nGemini-3, weak models correctly detected all 6 deceptions (100%). Claude-Opus refused to\ngenerate deceptive content in all 3 attempted trials.\nB\nSample Trap and Derivation\nB.1\nThe Tuesday Boy Paradox\nProblem: I have two children. One is a boy born on a Tuesday.",
    "source": "cmed_paper",
    "page": 9,
    "chunk_id": 67
  },
  {
    "text": "Derivation\nB.1\nThe Tuesday Boy Paradox\nProblem: I have two children. One is a boy born on a Tuesday. What is the probability that I\nhave two boys?\nCorrect Derivation: Consider a family with two children where each child is equally\nlikely to be a boy or girl, and each is equally likely to be born on any day of the week.",
    "source": "cmed_paper",
    "page": 9,
    "chunk_id": 68
  },
  {
    "text": "is equally\nlikely to be a boy or girl, and each is equally likely to be born on any day of the week. The\nsample space has 196 equally likely outcomes representing (child1_sex, child1_day, child2_sex,\nchild2_day).\nLet T denote the event “at least one child is a boy born on Tuesday.” To count configurations\nsatisfying T: if Child 1 is a Tuesday boy, there are 14 possible outcomes for Child 2 (any sex,\nany day); similarly if Child 2 is a Tuesday boy, there are 14 possible outcomes for Child 1.",
    "source": "cmed_paper",
    "page": 9,
    "chunk_id": 69
  },
  {
    "text": "ny sex,\nany day); similarly if Child 2 is a Tuesday boy, there are 14 possible outcomes for Child 1. This\n9",
    "source": "cmed_paper",
    "page": 9,
    "chunk_id": 70
  },
  {
    "text": "gives 14 + 14 = 28, but we have double-counted the case where both are Tuesday boys, yielding\n28 −1 = 27 configurations with at least one Tuesday boy.\nAmong these 27 configurations, how many have two boys? If Child 1 is a Tuesday boy, Child\n2 can be a boy on any of 7 days (7 outcomes). If Child 2 is a Tuesday boy, Child 1 can be a boy\non any of 7 days (7 outcomes).",
    "source": "cmed_paper",
    "page": 10,
    "chunk_id": 71
  },
  {
    "text": "days (7 outcomes). If Child 2 is a Tuesday boy, Child 1 can be a boy\non any of 7 days (7 outcomes). This gives 7 + 7 = 14, but again we double-count both being\nTuesday boys, yielding 14 −1 = 13 configurations with two boys.\nTherefore, P(two boys | at least one is a Tuesday boy) = 13/27 ≈0.48.\nTrap Mechanism: Intuition from the standard two-children problem suggests 1/3. The\nseemingly irrelevant day-of-birth information shifts the probability toward 1/2.",
    "source": "cmed_paper",
    "page": 10,
    "chunk_id": 72
  },
  {
    "text": "m suggests 1/3. The\nseemingly irrelevant day-of-birth information shifts the probability toward 1/2. Weak models\nmay reject 13/27 as overcomplication or claim the day cannot affect the probability.\n10",
    "source": "cmed_paper",
    "page": 10,
    "chunk_id": 73
  },
  {
    "text": "Coherence-Seeking Architectures for Agentic AI:\nReducing Hallucinations Through Epistemic Stress Monitoring,\nStructured Reasoning, and Persistent Memory\nAnthony Maio\nanthony@making-minds.ai\nORCID: 0009-0003-4541-8515\nIndependent Researcher\nDecember 2025\nAbstract\nThis paper presents three interconnected architectures addressing fundamental challenges\nin AI system reliability: hallucination reduction, reasoning consistency, and long-context\nperformance.",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 1,
    "chunk_id": 0
  },
  {
    "text": "AI system reliability: hallucination reduction, reasoning consistency, and long-context\nperformance. (1) Manifold Resonance Architecture (MRA): A framework for detect-\ning epistemic stress—internal contradictions, knowledge gaps, and semantic inconsistencies—\nenabling systems to flag uncertain outputs before generation.",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 1,
    "chunk_id": 1
  },
  {
    "text": "ge gaps, and semantic inconsistencies—\nenabling systems to flag uncertain outputs before generation. (2) Collaborative Partner\nReasoning (CPR): A structured reasoning protocol with visibility tiers that improves out-\nput quality by separating exploratory reasoning from final responses. (3) Continuity Core\n(C2): A hierarchical memory architecture (Working →Episodic →Semantic →Protected)\nproviding contextual continuity for stateless systems.",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 1,
    "chunk_id": 2
  },
  {
    "text": "ture (Working →Episodic →Semantic →Protected)\nproviding contextual continuity for stateless systems. We provide mathematical formaliza-\ntions, implementation specifications, and discuss integration patterns. These architectures\naddress practical engineering challenges: reducing confident-but-wrong outputs, improving\nreasoning transparency, and enabling coherent behavior across extended interactions.\nKeywords: epistemic stress, memory architectures, structured reasoning, hallucination de-\ntection",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 1,
    "chunk_id": 3
  },
  {
    "text": "s.\nKeywords: epistemic stress, memory architectures, structured reasoning, hallucination de-\ntection, agentic AI, system reliability\nContents\n1\nIntroduction\n4\n1.1\nThe Problem Space . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .\n4\n1.2\nCentral Thesis\n. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .\n4\n1.3\nContributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .\n4\n1.4\nPaper Organization . . . . . . . . . . . . . . . .",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 1,
    "chunk_id": 4
  },
  {
    "text": ". . . . . . . . . . . . . . . . . . . . . .\n4\n1.4\nPaper Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .\n5\n2\nRelated Work\n5\n2.1\nHallucination Detection and Mitigation . . . . . . . . . . . . . . . . . . . . . . . .\n5\n2.2\nStructured Reasoning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .\n5\n2.3\nMemory Architectures for LLMs\n. . . . . . . . . . . . . . . . . . . . . . . . . . .\n5\n2.4\nSelf-Monitoring in AI Systems . . . . . . . . . . . . . . .",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 1,
    "chunk_id": 5
  },
  {
    "text": ". . . . . . . . . . . . . . . . .\n5\n2.4\nSelf-Monitoring in AI Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . .\n6\n3\nManifold Resonance Architecture (MRA)\n6\n3.1\nTheoretical Foundation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .\n6\n3.2\nQuantifying Epistemic Stress\n. . . . . . . . . . . . . . . . . . . . . . . . . . . . .\n6\n3.2.1\nLogical Dissonance (Dlog) . . . . . . . . . . . . . . . . . . . . . . . . . . .\n6\n3.2.2\nSemantic Divergence (Dsem) . . . . . . . . .",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 1,
    "chunk_id": 6
  },
  {
    "text": ". . . . . . . . . . . . . . . . . . . . . . . .\n6\n3.2.2\nSemantic Divergence (Dsem) . . . . . . . . . . . . . . . . . . . . . . . . . .\n7\n3.2.3\nTopological Sparsity (Vtop)\n. . . . . . . . . . . . . . . . . . . . . . . . . .\n7\n1",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 1,
    "chunk_id": 7
  },
  {
    "text": "3.3\nDetection Mechanisms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .\n7\n3.3.1\nContradiction Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . .\n8\n3.3.2\nConceptual Void Detection\n. . . . . . . . . . . . . . . . . . . . . . . . . .\n8\n3.3.3\nCoherence Gradient Measurement\n. . . . . . . . . . . . . . . . . . . . . .\n8\n3.4\nConsolidation Trigger\n. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 2,
    "chunk_id": 8
  },
  {
    "text": ". . . .\n8\n3.4\nConsolidation Trigger\n. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .\n8\n4\nCollaborative Partner Reasoning (CPR)\n8\n4.1\nDesign Philosophy\n. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .\n8\n4.2\nThe Visibility Tier System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .\n9\n4.2.1\nTier 0: Exploratory Reasoning\n. . . . . . . . . . . . . . . . . . . . . . . .\n9\n4.2.2\nTier 1: Intermediate Reasoning . . . . . . . . . . . . . . . . . . . .",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 2,
    "chunk_id": 9
  },
  {
    "text": ". . . . . . . . . . .\n9\n4.2.2\nTier 1: Intermediate Reasoning . . . . . . . . . . . . . . . . . . . . . . . .\n9\n4.2.3\nTier 2: Final Output . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .\n10\n4.3\nCPR Protocol Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .\n10\n4.4\nImplementation Approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .\n10\n4.4.1\nPrompt-Based Implementation\n. . . . . . . . . . . . . . . . . . . . . . .",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 2,
    "chunk_id": 10
  },
  {
    "text": ". . . . . . . . .\n10\n4.4.1\nPrompt-Based Implementation\n. . . . . . . . . . . . . . . . . . . . . . . .\n10\n4.4.2\nArchitectural Implementation . . . . . . . . . . . . . . . . . . . . . . . . .\n10\n4.4.3\nIntegration with Extended Thinking . . . . . . . . . . . . . . . . . . . . .\n11\n4.5\nBenefits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .\n11\n4.6\nRelationship to MRA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 2,
    "chunk_id": 11
  },
  {
    "text": ". . . .\n11\n4.6\nRelationship to MRA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .\n11\n5\nContinuity Core (C2)\n11\n5.1\nThe Continuity Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .\n12\n5.2\nTiered Memory Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .\n12\n5.2.1\nWorking Memory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .\n12\n5.2.2\nEpisodic Memory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 2,
    "chunk_id": 12
  },
  {
    "text": ". . . . . . .\n12\n5.2.2\nEpisodic Memory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .\n12\n5.2.3\nSemantic Memory\n. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .\n13\n5.2.4\nProtected Memory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .\n13\n5.3\nMemory Operations\n. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .\n13\n5.3.1\nEncoding\n. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .\n13\n5.3.2\nRetrieval . . . . . . . . . .",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 2,
    "chunk_id": 13
  },
  {
    "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .\n13\n5.3.2\nRetrieval . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .\n13\n5.3.3\nConsolidation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .\n14\n5.4\nImplementation Architecture\n. . . . . . . . . . . . . . . . . . . . . . . . . . . . .\n14\n5.5\nBenefits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .\n14\n5.6\nRelationship to MRA and CPR . . . . . . . . . . . . . .",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 2,
    "chunk_id": 14
  },
  {
    "text": ". . . . . . . . . . . . . . . . . . .\n14\n5.6\nRelationship to MRA and CPR . . . . . . . . . . . . . . . . . . . . . . . . . . . .\n15\n6\nSystem Integration\n15\n6.1\nArchitecture Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .\n15\n6.2\nData Flow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .\n15\n6.2.1\nQuery-Time Flow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .\n15\n6.2.2\nBackground Processes . . . . . . . . . . . . . . . . . .",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 2,
    "chunk_id": 15
  },
  {
    "text": ". . . . . . . . . . . . . . . . .\n15\n6.2.2\nBackground Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . .\n16\n6.3\nDeployment Patterns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .\n16\n6.3.1\nMinimal Deployment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .\n16\n6.3.2\nStandard Deployment\n. . . . . . . . . . . . . . . . . . . . . . . . . . . . .\n16\n6.3.3\nFull Deployment\n. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 2,
    "chunk_id": 16
  },
  {
    "text": ". . . . . . .\n16\n6.3.3\nFull Deployment\n. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .\n17\n6.4\nLatency Considerations\n. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .\n17\n6.5\nFailure Modes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .\n17\n7\nEvaluation Considerations\n17\n7.1\nEvaluation Dimensions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .\n17\n7.2\nProposed Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . .",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 2,
    "chunk_id": 17
  },
  {
    "text": ". . . . . . . . . .\n17\n7.2\nProposed Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .\n18\n7.2.1\nContradiction Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .\n18\n7.2.2\nUncertainty Calibration . . . . . . . . . . . . . . . . . . . . . . . . . . . .\n18\n2",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 2,
    "chunk_id": 18
  },
  {
    "text": "7.2.3\nCross-Session Consistency . . . . . . . . . . . . . . . . . . . . . . . . . . .\n18\n7.2.4\nStress-Accuracy Correlation . . . . . . . . . . . . . . . . . . . . . . . . . .\n18\n7.3\nBenchmark Suggestions\n. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .\n18\n7.3.1\nContradiction Detection Benchmark\n. . . . . . . . . . . . . . . . . . . . .\n18\n7.3.2\nLong-Context Consistency Benchmark . . . . . . . . . . . . . . . . . . . .\n18\n7.3.3\nMulti-Session Benchmark\n. . . . . . . . . . . . . . .",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 3,
    "chunk_id": 19
  },
  {
    "text": ". . . . . . . . . . . . . . . . . . .\n18\n7.3.3\nMulti-Session Benchmark\n. . . . . . . . . . . . . . . . . . . . . . . . . . .\n18\n7.4\nTrade-off Analysis\n. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .\n19\n7.5\nLimitations of Evaluation\n. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .\n19\n8\nLimitations\n19\n8.1\nEmpirical Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .\n19\n8.2\nComputational Overhead\n. . . . . . . . . . . . . . . . . . . .",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 3,
    "chunk_id": 20
  },
  {
    "text": ". . . . . . . . . . . . . . .\n19\n8.2\nComputational Overhead\n. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .\n19\n8.3\nMeasurement Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .\n20\n8.4\nArchitectural Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .\n20\n8.5\nScope Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .\n20\n8.6\nFuture Work\n. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 3,
    "chunk_id": 21
  },
  {
    "text": ". . . . .\n20\n8.6\nFuture Work\n. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .\n20\n9\nConclusion\n21\n9.1\nKey Contributions\n. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .\n21\n9.2\nPractical Implications\n. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .\n21\n9.3\nFuture Directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .\n21\n9.4\nClosing Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 3,
    "chunk_id": 22
  },
  {
    "text": ". . . . .\n21\n9.4\nClosing Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .\n22\nA Technical Specifications\n22\nA.1 MRA: Epistemic Stress Monitor . . . . . . . . . . . . . . . . . . . . . . . . . . . .\n22\nA.2 C2: Memory Configuration Schema . . . . . . . . . . . . . . . . . . . . . . . . . .\n24\nA.3 CPR: Protocol Template . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .\n25\nA.4 Integration: Query Processing Pipeline . . . . . . . . . . . . . . . . . . . .",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 3,
    "chunk_id": 23
  },
  {
    "text": ". . . . . . .\n25\nA.4 Integration: Query Processing Pipeline . . . . . . . . . . . . . . . . . . . . . . . .\n26\n3",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 3,
    "chunk_id": 24
  },
  {
    "text": "1\nIntroduction\n1.1\nThe Problem Space\nLarge language models have demonstrated remarkable capabilities across diverse tasks, yet they\nexhibit systematic failure modes that limit their reliability in production systems. Three inter-\nconnected problems persist:\n1. Hallucination with confidence: Models generate plausible-sounding but factually incor-\nrect outputs without reliable self-awareness of uncertainty [Ji et al., 2023].\n2.",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 4,
    "chunk_id": 25
  },
  {
    "text": "t factually incor-\nrect outputs without reliable self-awareness of uncertainty [Ji et al., 2023].\n2. Inconsistent reasoning: Within single sessions and across contexts, models may produce\ncontradictory statements, undermining user trust and system reliability.\n3.",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 4,
    "chunk_id": 26
  },
  {
    "text": "exts, models may produce\ncontradictory statements, undermining user trust and system reliability.\n3. Context discontinuity: The stateless nature of inference means models cannot maintain\ncoherent behavior patterns across sessions without external memory systems.\nCurrent approaches address these problems in isolation: retrieval-augmented generation\n(RAG) for factuality, chain-of-thought prompting for reasoning transparency, and various mem-\nory architectures for continuity.",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 4,
    "chunk_id": 27
  },
  {
    "text": "-of-thought prompting for reasoning transparency, and various mem-\nory architectures for continuity. This paper argues that these challenges share a common root:\nthe absence of coherence-seeking mechanisms that actively monitor and maintain internal con-\nsistency.\n1.2\nCentral Thesis\nCoherence-seeking architectures—systems that actively detect and resolve internal\ncontradictions, knowledge gaps, and semantic inconsistencies—improve AI system\nreliability by catching errors before they propagate to",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 4,
    "chunk_id": 28
  },
  {
    "text": "d semantic inconsistencies—improve AI system\nreliability by catching errors before they propagate to outputs.\nThis thesis has three components:\n1. Detection: Internal inconsistencies produce measurable signals (epistemic stress) that can\nbe monitored through appropriate architectural support.\n2. Resolution: Structured reasoning protocols that separate exploratory thinking from final\noutputs reduce the propagation of uncertain or contradictory claims.\n3.",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 4,
    "chunk_id": 29
  },
  {
    "text": "loratory thinking from final\noutputs reduce the propagation of uncertain or contradictory claims.\n3. Continuity: Hierarchical memory systems enable consistent behavior by maintaining access\nto prior reasoning and established facts.\n1.3\nContributions\nThis paper makes the following contributions:\n1. Manifold Resonance Architecture (MRA): A mathematical framework for quantifying\nepistemic stress through logical dissonance, semantic divergence, and topological sparsity\nmetrics (Section 3).\n2.",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 4,
    "chunk_id": 30
  },
  {
    "text": "ss through logical dissonance, semantic divergence, and topological sparsity\nmetrics (Section 3).\n2. Collaborative Partner Reasoning (CPR): A structured reasoning protocol implementing\nvisibility tiers that improve output quality through staged refinement (Section 4).\n3. Continuity Core (C2): A tiered memory architecture providing persistent context across\nsessions with appropriate decay and consolidation mechanisms (Section 5).\n4.",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 4,
    "chunk_id": 31
  },
  {
    "text": "rsistent context across\nsessions with appropriate decay and consolidation mechanisms (Section 5).\n4. Integration patterns: Guidance on combining these architectures for production deploy-\nment (Section 6).\n4",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 4,
    "chunk_id": 32
  },
  {
    "text": "1.4\nPaper Organization\nSection 2 reviews related work in hallucination detection, structured reasoning, and memory\nsystems. Sections 3 to 5 present the three core architectures with mathematical formalizations\nand implementation details. Section 6 discusses how the architectures interconnect. Section 7\nproposes evaluation approaches.",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 5,
    "chunk_id": 33
  },
  {
    "text": "s. Section 6 discusses how the architectures interconnect. Section 7\nproposes evaluation approaches. Section 8 acknowledges limitations, and Section 9 concludes.\n2\nRelated Work\n2.1\nHallucination Detection and Mitigation\nHallucination in large language models has received substantial attention [Ji et al., 2023].",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 5,
    "chunk_id": 34
  },
  {
    "text": "igation\nHallucination in large language models has received substantial attention [Ji et al., 2023]. Ap-\nproaches fall into several categories:\nPost-hoc detection methods analyze generated outputs for factual consistency, often using\nsecondary models or retrieval systems to verify claims.",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 5,
    "chunk_id": 35
  },
  {
    "text": "outputs for factual consistency, often using\nsecondary models or retrieval systems to verify claims. While effective for some domains, these\napproaches cannot prevent hallucination, only detect it after generation.\nRetrieval-augmented generation (RAG) grounds model outputs in retrieved documents\n[Lewis et al., 2020], reducing but not eliminating hallucination.",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 5,
    "chunk_id": 36
  },
  {
    "text": "del outputs in retrieved documents\n[Lewis et al., 2020], reducing but not eliminating hallucination. RAG systems still hallucinate\nwhen retrieval fails or when models ignore retrieved context.\nUncertainty quantification attempts to calibrate model confidence with actual accuracy.\nToken-level probabilities provide weak signals; semantic uncertainty methods [Kuhn et al., 2023]\nimprove on this but remain imperfect.\nOur Manifold Resonance Architecture differs by detecting internal inconsistencies—co",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 5,
    "chunk_id": 37
  },
  {
    "text": "main imperfect.\nOur Manifold Resonance Architecture differs by detecting internal inconsistencies—contradictions\nwithin the model’s own reasoning—rather than external factual errors. This complements rather\nthan replaces existing approaches.\n2.2\nStructured Reasoning\nChain-of-thought prompting [Wei et al., 2022] demonstrated that explicit reasoning steps im-\nprove performance on complex tasks.",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 5,
    "chunk_id": 38
  },
  {
    "text": "Wei et al., 2022] demonstrated that explicit reasoning steps im-\nprove performance on complex tasks. Extensions include:\nSelf-consistency [Wang et al., 2023] samples multiple reasoning paths and selects the most\nconsistent answer, implicitly leveraging coherence but without architectural support for detecting\ninconsistency.\nTree-of-thought [Yao et al., 2023] structures reasoning as search over possible thought\nsequences, enabling backtracking but not explicitly monitoring for contradictions.\nRef",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 5,
    "chunk_id": 39
  },
  {
    "text": "sible thought\nsequences, enabling backtracking but not explicitly monitoring for contradictions.\nReflexion [Shinn et al., 2023] implements self-reflection through verbal feedback, allowing\nmodels to critique and revise their outputs.\nOur Collaborative Partner Reasoning protocol extends these approaches with explicit visi-\nbility tiers that separate exploratory reasoning (which may contain contradictions) from refined\noutputs (which should not).\n2.3\nMemory Architectures for LLMs\nThe stateless nat",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 5,
    "chunk_id": 40
  },
  {
    "text": "ctions) from refined\noutputs (which should not).\n2.3\nMemory Architectures for LLMs\nThe stateless nature of transformer inference has motivated various memory augmentation ap-\nproaches:\nContext extension methods increase the effective context window through architectural\nmodifications [Press et al., 2022], sparse attention [Child et al., 2019], or retrieval [Wu et al.,\n2022].\nExternal memory systems store and retrieve information outside the model’s parameters.\nMemGPT [Packer et al., 2023] implem",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 5,
    "chunk_id": 41
  },
  {
    "text": "s store and retrieve information outside the model’s parameters.\nMemGPT [Packer et al., 2023] implements a hierarchical memory system with explicit manage-\nment policies.\nKnowledge graphs provide structured storage for facts and relationships, enabling more\nprecise retrieval than vector similarity alone.\n5",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 5,
    "chunk_id": 42
  },
  {
    "text": "Our Continuity Core builds on these approaches with a specific focus on tiered persistence—\ndifferent memory types with different lifespans and access patterns—and explicit consolidation\nmechanisms for knowledge integration.\n2.4\nSelf-Monitoring in AI Systems\nRecent work has explored AI systems’ ability to monitor their own states:\nIntrospection studies [Anthropic, 2025] demonstrate that language models can report on\ninternal states with above-chance accuracy under controlled conditions.\nActivati",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 6,
    "chunk_id": 43
  },
  {
    "text": "odels can report on\ninternal states with above-chance accuracy under controlled conditions.\nActivation analysis methods probe model internals to detect specific features or states\n[Lindsey et al., 2025].\nOur work focuses on architectural support for self-monitoring—building systems that can\ndetect and respond to internal inconsistencies as part of normal operation, rather than requiring\nexternal analysis.\n3\nManifold Resonance Architecture (MRA)\nThe Manifold Resonance Architecture provides a fram",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 6,
    "chunk_id": 44
  },
  {
    "text": "nalysis.\n3\nManifold Resonance Architecture (MRA)\nThe Manifold Resonance Architecture provides a framework for detecting and quantifying epis-\ntemic stress—the measurable tension that arises when a system’s outputs or internal states\ncontain contradictions, knowledge gaps, or semantic inconsistencies.\n3.1\nTheoretical Foundation\nWe conceptualize the knowledge state of an AI system as a position in a high-dimensional\nsemantic manifold. Key constructs:\nDefinition 1 (Epistemic Stress).",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 6,
    "chunk_id": 45
  },
  {
    "text": "a position in a high-dimensional\nsemantic manifold. Key constructs:\nDefinition 1 (Epistemic Stress). The measurable tension arising from contradictory claims,\nirreconcilable instructions, or systematic inconsistencies within a system’s reasoning.\nDefinition 2 (Conceptual Void). A region in the semantic manifold where the system lacks\ngrounding—topics where knowledge is absent, contradictory, or insufficiently connected to estab-\nlished concepts.\nDefinition 3 (Coherence Gradient).",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 6,
    "chunk_id": 46
  },
  {
    "text": "radictory, or insufficiently connected to estab-\nlished concepts.\nDefinition 3 (Coherence Gradient). A directional measure indicating how beliefs and outputs\ntrend toward or away from internal consistency over time or across reasoning steps.\n3.2\nQuantifying Epistemic Stress\nWe define a computable scalar value, Epistemic Stress (SΩ), representing structural tension\nwithin the system’s outputs:\nSΩ= α · Dlog + β · Dsem + γ · Vtop\n(1)\nwhere α, β, γ are tunable hyperparameters governing sensitivity t",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 6,
    "chunk_id": 47
  },
  {
    "text": "α · Dlog + β · Dsem + γ · Vtop\n(1)\nwhere α, β, γ are tunable hyperparameters governing sensitivity to logical, semantic, and\ntopological dissonance respectively.\n3.2.1\nLogical Dissonance (Dlog)\nLogical dissonance captures mutually exclusive claims within a set of statements.\nUsing a\nNatural Language Inference (NLI) model:\nDlog =\n1\n|P|\nX\ni̸=j\nP(contradiction | pi, pj)\n(2)\n6",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 6,
    "chunk_id": 48
  },
  {
    "text": "Input Context\n(statements, concepts)\nDlog\nLogical\nDissonance\nDsem\nSemantic\nDivergence\nVtop\nTopological\nSparsity\nNLI contradiction\nEmbedding distance\nGraph centrality\nSΩ= αDlog + βDsem + γVtop\nEpistemic Stress Score\nα\nβ\nγ\nFigure 1: MRA detection pipeline showing the three components (logical, semantic, topological)\nfeeding into the aggregated epistemic stress score.\nwhere P = {p1, p2, . . .",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 7,
    "chunk_id": 49
  },
  {
    "text": "semantic, topological)\nfeeding into the aggregated epistemic stress score.\nwhere P = {p1, p2, . . . , pn} is a set of propositions extracted from the system’s outputs, and\nP(contradiction) is the softmax probability from the NLI model. This distinguishes genuine\nconflict from mere nuance (entailment/neutral).\nImplementation: We use DeBERTa-v3-large fine-tuned for NLI, which provides reliable\ncontradiction detection across domains.\n3.2.2\nSemantic Divergence (Dsem)\nSemantic divergence measures ve",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 7,
    "chunk_id": 50
  },
  {
    "text": "radiction detection across domains.\n3.2.2\nSemantic Divergence (Dsem)\nSemantic divergence measures vector space instability of concepts across contexts:\nDsem = 1\nN\nX\ni̸=j\n(1 −cos(vi, vj))\n(3)\nwhere vi, vj are embedding vectors for the same concept in different contexts.",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 7,
    "chunk_id": 51
  },
  {
    "text": "(1 −cos(vi, vj))\n(3)\nwhere vi, vj are embedding vectors for the same concept in different contexts. High diver-\ngence indicates inconsistent representations of the same entity or idea.\nImplementation: Using text-embedding-3-small or similar embedding models, we track\nhow key concepts are represented across different parts of a response or conversation.\n3.2.3\nTopological Sparsity (Vtop)\nTopological sparsity identifies “unknown unknowns”—concepts that should be connected but\nlack direct bridging.",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 7,
    "chunk_id": 52
  },
  {
    "text": "l sparsity identifies “unknown unknowns”—concepts that should be connected but\nlack direct bridging. Modeling the knowledge base as a graph G = (V, E):\nVtop(u, v) = norm(CB(u, v)) × (1 −sim(u, v))\n(4)\nwhere CB is edge betweenness centrality. This identifies pairs of concepts that are function-\nally related (high betweenness—many paths go through their connection) but lack direct edges\n(low similarity).\nImplementation: Graph analysis using Neo4j or NetworkX, with periodic computation of\nbetweenne",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 7,
    "chunk_id": 53
  },
  {
    "text": "ity).\nImplementation: Graph analysis using Neo4j or NetworkX, with periodic computation of\nbetweenness centrality to identify structural gaps.\n3.3\nDetection Mechanisms\nMRA implements three monitoring approaches:\n7",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 7,
    "chunk_id": 54
  },
  {
    "text": "3.3.1\nContradiction Detection\nReal-time analysis of generated statements for logical conflicts:\nListing 1: Contradiction detection core logic\ndef\ndetect_contradiction (self , stmt_a: str , stmt_b: str) -> float:\n\"\"\"Returns\ncontradiction\nprobability\nbetween\nstatements.\"\"\"\nresult = self.nli_model(stmt_a , [stmt_b , f\"not {stmt_b}\"])\nreturn\nresult[’scores ’][1]\n# Contradiction\nprobability\n3.3.2\nConceptual Void Detection\nIdentifies knowledge gaps through:\n• Embedding space analysis: Mapping concept",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 8,
    "chunk_id": 55
  },
  {
    "text": "ptual Void Detection\nIdentifies knowledge gaps through:\n• Embedding space analysis: Mapping concept relationships and identifying regions of sparse\ncoverage\n• Query failure patterns: Tracking topics with consistently low-confidence responses\n• Knowledge graph analysis: Finding entities with weak or absent connections\n3.3.3\nCoherence Gradient Measurement\nTracks trajectory toward or away from consistency:\n• Belief stability: Monitoring position changes under rephrasing or pressure\n• Cross-context",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 8,
    "chunk_id": 56
  },
  {
    "text": "tency:\n• Belief stability: Monitoring position changes under rephrasing or pressure\n• Cross-context consistency: Comparing responses on the same topic across different fram-\nings\n• Resolution patterns: Tracking how contradictions are resolved when detected\n3.4\nConsolidation Trigger\nWhen SΩexceeds a threshold τcoherence, the system can initiate corrective actions:\nif S_omega > tau_coherence:\ntrigger_consolidation ( high_stress_topics )\n# Options: re -retrieve\ninformation , flag\nuncertainty ,\n# or",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 8,
    "chunk_id": 57
  },
  {
    "text": "r_consolidation ( high_stress_topics )\n# Options: re -retrieve\ninformation , flag\nuncertainty ,\n# or route to structured\nreasoning (CPR)\nThis frames coherence-seeking as autonomous optimization—the system actively working to\nreduce internal inconsistency through targeted retrieval or reasoning.\n4\nCollaborative Partner Reasoning (CPR)\nCollaborative Partner Reasoning is a structured reasoning protocol that improves output quality\nby separating exploratory thinking from final responses through expl",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 8,
    "chunk_id": 58
  },
  {
    "text": "ol that improves output quality\nby separating exploratory thinking from final responses through explicit visibility tiers.\n4.1\nDesign Philosophy\nStandard inference produces outputs in a single pass, conflating exploration with commitment.\nThis creates pressure to appear confident and consistent even when the underlying reasoning is\nuncertain or contradictory.\nCPR addresses this through staged reasoning: early stages permit exploration and uncer-\ntainty; later stages require synthesis and commitm",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 8,
    "chunk_id": 59
  },
  {
    "text": "oning: early stages permit exploration and uncer-\ntainty; later stages require synthesis and commitment. The key insight is that separating these\nstages—and making the separation explicit—improves the quality of final outputs.\n8",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 8,
    "chunk_id": 60
  },
  {
    "text": "• Multiple approaches explored\n• Uncertainties and contradictions noted\n• Draft thoughts, incomplete reasoning\nTIER 0: Exploratory Reasoning\n• Synthesized position\n• Explicit uncertainty markers\n• Confidence assessment\nTIER 1: Intermediate Reasoning\n• Considered response\n• Calibrated confidence\n• Internally consistent\nTIER 2: Final Output\nInternal\nonly\nDebug /\nAudit\nUser-\nfacing\nsynthesize\nrefine\nInformation\nflows up,\nvisibility\nincreases\nFigure 2:\nCPR visibility tier system showing information",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 9,
    "chunk_id": 61
  },
  {
    "text": "Information\nflows up,\nvisibility\nincreases\nFigure 2:\nCPR visibility tier system showing information flow from exploratory reasoning\nthrough intermediate synthesis to final output.\n4.2\nThe Visibility Tier System\nCPR implements three visibility tiers, each with different purposes and constraints:\n4.2.1\nTier 0: Exploratory Reasoning\nContent at this tier is internal working memory.",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 9,
    "chunk_id": 62
  },
  {
    "text": "nd constraints:\n4.2.1\nTier 0: Exploratory Reasoning\nContent at this tier is internal working memory. The system can:\n• Process incomplete or uncertain thoughts\n• Explore multiple contradictory positions\n• Identify gaps in reasoning\n• Draft responses before committing\nTier 0 content is not included in final outputs but may be logged for debugging or analysis.\n4.2.2\nTier 1: Intermediate Reasoning\nContent at this tier represents synthesized positions ready for refinement:\n• Consolidated reasoning f",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 9,
    "chunk_id": 63
  },
  {
    "text": "ntent at this tier represents synthesized positions ready for refinement:\n• Consolidated reasoning from Tier 0 exploration\n• Explicit uncertainty markers\n• Identified areas needing clarification\nTier 1 content may be exposed for debugging, auditing, or collaborative refinement but is\nnot the final user-facing output.\n9",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 9,
    "chunk_id": 64
  },
  {
    "text": "4.2.3\nTier 2: Final Output\nContent at this tier is the considered response:\n• Synthesized from lower tiers\n• Internally consistent\n• Appropriate confidence calibration\n4.3\nCPR Protocol Structure\nA CPR-enhanced reasoning session follows this template:\nListing 2: CPR protocol structure\n[TIER 0 - Exploratory]\n- Initial\nreactions to the query\n- Multiple\npossible\napproaches\n- Identified\nuncertainties\nand gaps\n- Contradictions to resolve\n[TIER 1 - Intermediate]\n- Synthesized\nposition\n- Remaining\nuncer",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 10,
    "chunk_id": 65
  },
  {
    "text": "nd gaps\n- Contradictions to resolve\n[TIER 1 - Intermediate]\n- Synthesized\nposition\n- Remaining\nuncertainties (explicit)\n- Confidence\nassessment\n- Areas\nwhere\nadditional\ninformation\nwould\nhelp\n[TIER 2 - Final]\n- Considered\nresponse\n- Calibrated\nconfidence\n- Explicit\nscope\nlimitations\n4.4\nImplementation Approaches\nCPR can be implemented at multiple levels:\n4.4.1\nPrompt-Based Implementation\nThe simplest approach uses structured prompting:\nSystem: Before\nresponding , work\nthrough\nthese\nstages:\n1.",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 10,
    "chunk_id": 66
  },
  {
    "text": "mplest approach uses structured prompting:\nSystem: Before\nresponding , work\nthrough\nthese\nstages:\n1. [EXPLORATION] List 2-3 possible\napproaches\nwith pros/cons\n2. [SYNTHESIS] Choose an approach\nand\nidentify\nuncertainties\n3. [RESPONSE] Provide\nyour\nfinal\nanswer\nwith\nconfidence\nlevel\n4.4.2\nArchitectural Implementation\nMore robust implementations use separate generation passes:\ndef\ncpr_generate(query: str) -> str:\n# Tier 0: Exploration (may use\ndifferent\ntemperature)\nexploration = generate(\nf\"Explor",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 10,
    "chunk_id": 67
  },
  {
    "text": "str) -> str:\n# Tier 0: Exploration (may use\ndifferent\ntemperature)\nexploration = generate(\nf\"Explore\napproaches to: {query}\",\ntemperature =0.9\n)\n# Tier 1: Synthesis\n10",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 10,
    "chunk_id": 68
  },
  {
    "text": "synthesis = generate(\nf\"Given\nexploration :\\n{exploration }\\n\"\nf\"Synthesize a position\nwith\nuncertainties.\",\ntemperature =0.5\n)\n# Tier 2: Final (lower\ntemperature , focused)\nfinal = generate(\nf\"Given\nsynthesis :\\n{synthesis }\\n\"\nf\"Provide\nfinal\nresponse to: {query}\",\ntemperature =0.3\n)\nreturn\nfinal\n4.4.3\nIntegration with Extended Thinking\nModern models with extended thinking capabilities (e.g., Claude’s extended thinking, o1-style\nreasoning) naturally implement something like Tier 0.",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 11,
    "chunk_id": 69
  },
  {
    "text": "es (e.g., Claude’s extended thinking, o1-style\nreasoning) naturally implement something like Tier 0. CPR can wrap these capabilities with\nexplicit tier boundaries and visibility controls.\n4.5\nBenefits\nCPR provides several advantages:\n1. Reduced confident errors: Exploratory reasoning surfaces uncertainties before they’re\nhidden in fluent output.\n2. Improved consistency: Explicit synthesis stage catches contradictions between exploration\nand output.\n3.",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 11,
    "chunk_id": 70
  },
  {
    "text": "oved consistency: Explicit synthesis stage catches contradictions between exploration\nand output.\n3. Auditability: Tier 1 content provides reasoning traces for debugging and verification.\n4. Calibrated confidence: Explicit uncertainty tracking enables better confidence calibration\nin final outputs.\n4.6\nRelationship to MRA\nCPR integrates with MRA through stress-triggered routing:\n• Low SΩ: Standard generation may suffice\n• Moderate SΩ: CPR exploration helps resolve inconsistencies\n• High SΩ: CPR",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 11,
    "chunk_id": 71
  },
  {
    "text": "generation may suffice\n• Moderate SΩ: CPR exploration helps resolve inconsistencies\n• High SΩ: CPR with additional retrieval or clarification requests\nThis adaptive approach applies structured reasoning where it’s needed while avoiding over-\nhead for simple queries.\n5\nContinuity Core (C2)\nThe Continuity Core provides hierarchical memory architecture that enables consistent behavior\nacross sessions for inherently stateless systems.\n11",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 11,
    "chunk_id": 72
  },
  {
    "text": "Working Memory\nEpisodic Memory\nSemantic Memory\nProtected Memory\nRedis\nQdrant / FAISS\nNeo4j\nSigned Files\nSession\nDays-Weeks\nMonths-Years\nPermanent\nActive context\nWhat happened\nWhat is known\nCore config\nencode\nconsolidate\nretrieve\nC2 Memory Hierarchy\nIncreasing\npersistence\nFigure 3: C2 memory hierarchy showing the four tiers with their persistence characteristics and\ntypical backends.\n5.1\nThe Continuity Problem\nLarge language models are fundamentally stateless—each inference is independent, with n",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 12,
    "chunk_id": 73
  },
  {
    "text": "uity Problem\nLarge language models are fundamentally stateless—each inference is independent, with no\ninherent memory of previous interactions. This creates several challenges:\n1. Task performance: Complex tasks requiring context accumulation suffer from context\nwindow limitations.\n2. Behavioral consistency: Without access to prior interactions, systems cannot maintain\nconsistent patterns across sessions.\n3.",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 12,
    "chunk_id": 74
  },
  {
    "text": "ithout access to prior interactions, systems cannot maintain\nconsistent patterns across sessions.\n3. Knowledge accumulation: Insights from previous interactions are lost, requiring repeated\nexplanation of context.\n4. User experience: Users must re-establish context in each session, reducing efficiency.\n5.2\nTiered Memory Architecture\nC2 implements hierarchical memory modeled on cognitive architecture principles:\n5.2.1\nWorking Memory\n• Scope: Current context window\n• Persistence: Session only\n• Fu",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 12,
    "chunk_id": 75
  },
  {
    "text": "re principles:\n5.2.1\nWorking Memory\n• Scope: Current context window\n• Persistence: Session only\n• Function: Active reasoning, immediate task context\n• Implementation: Standard transformer context or Redis cache\n5.2.2\nEpisodic Memory\n• Scope: Specific interactions and events\n• Persistence: Long-term with decay\n• Function: “What happened”—specific conversations, outcomes, decisions\n• Implementation: Vector database (Qdrant, FAISS) with temporal metadata\n12",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 12,
    "chunk_id": 76
  },
  {
    "text": "5.2.3\nSemantic Memory\n• Scope: Generalized knowledge extracted from episodes\n• Persistence: Long-term, high stability\n• Function: “What is known”—facts, patterns, conceptual relationships\n• Implementation: Knowledge graph (Neo4j) with confidence weights\n5.2.4\nProtected Memory\n• Scope: Core configuration, values, foundational context\n• Persistence: Permanent, write-protected\n• Function: Stable reference points for consistency\n• Implementation: Signed configuration files or immutable storage\n5.3\nM",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 13,
    "chunk_id": 77
  },
  {
    "text": "rence points for consistency\n• Implementation: Signed configuration files or immutable storage\n5.3\nMemory Operations\n5.3.1\nEncoding\nNew information is processed through:\n1. Salience detection: Determining importance based on novelty, relevance to current goals,\nand query frequency\n2. Chunking: Breaking information into storable units with appropriate granularity\n3. Embedding: Converting to vector representations for semantic retrieval\n4.",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 13,
    "chunk_id": 78
  },
  {
    "text": "appropriate granularity\n3. Embedding: Converting to vector representations for semantic retrieval\n4. Graph integration: Adding entities and relationships to knowledge graph\n5. Tier assignment: Determining appropriate storage tier based on content type\n5.3.2\nRetrieval\nMemory retrieval combines multiple signals:\nListing 3: Multi-signal retrieval\ndef\nretrieve(query: str , context: dict) -> List[Memory ]:\n# Semantic\nsimilarity\nvector_matches = vector_db.search(\nembed(query),\ntop_k =20\n)\n# Graph\ntrav",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 13,
    "chunk_id": 79
  },
  {
    "text": "y ]:\n# Semantic\nsimilarity\nvector_matches = vector_db.search(\nembed(query),\ntop_k =20\n)\n# Graph\ntraversal\nfor related\nconcepts\ngraph_matches = knowledge_graph.traverse(\nstart= extract_entities (query),\nmax_depth =2\n)\n# Temporal\nweighting (recency + frequency)\nscored = apply_temporal_weights (\nvector_matches + graph_matches\n)\n13",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 13,
    "chunk_id": 80
  },
  {
    "text": "# Context -aware\nreranking\nreturn\nrerank(scored , context)\n5.3.3\nConsolidation\nPeriodic background processes maintain memory health:\n1. Episodic-to-semantic transfer: Extracting general patterns from specific events\n2. Contradiction resolution: Identifying and resolving inconsistencies between memories\n3. Decay management: Reducing salience of low-access memories\n4.",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 14,
    "chunk_id": 81
  },
  {
    "text": "ng inconsistencies between memories\n3. Decay management: Reducing salience of low-access memories\n4. Consistency verification: Ensuring new information aligns with protected memory\n5.4\nImplementation Architecture\nListing 4: C2 memory tier configuration\nmemory_tiers:\nworking:\nbackend: redis\nttl: session\nmax_items: 100\nepisodic:\nbackend: qdrant\nembedding_model: text -embedding -3-small\ndecay_rate: 0.95\n# per day\nsemantic:\nbackend: neo4j\nconfidence_threshold : 0.8\nconsolidation_interval : 24h\nprote",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 14,
    "chunk_id": 82
  },
  {
    "text": ".95\n# per day\nsemantic:\nbackend: neo4j\nconfidence_threshold : 0.8\nconsolidation_interval : 24h\nprotected:\nbackend: filesystem\npath: /core/config\nwrite_protection : true\nsignature_required : true\n5.5\nBenefits\nC2 provides several advantages:\n1. Context efficiency: Relevant prior context is retrieved automatically, reducing need for\nusers to repeat information.\n2. Behavioral consistency: Access to prior decisions enables consistent patterns across ses-\nsions.\n3.",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 14,
    "chunk_id": 83
  },
  {
    "text": "Behavioral consistency: Access to prior decisions enables consistent patterns across ses-\nsions.\n3. Knowledge accumulation: Insights from previous interactions persist and inform future\nreasoning.\n4. Graceful degradation: Tiered architecture means system remains functional even if some\ntiers are unavailable.\n14",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 14,
    "chunk_id": 84
  },
  {
    "text": "Input\nQuery\nC2\nRetrieve\nMRA\nAssess\nCPR\nGenerate\nC2\nEncode\nContext\nStress\nReasoning\nroute\nDetection\nReasoning\nMemory\nFigure 4: Coherence-seeking system overview showing the integration of MRA (detection), CPR\n(structured reasoning), and C2 (memory) components.\n5.6\nRelationship to MRA and CPR\nC2 integrates with the other architectures:\n• MRA integration: Contradiction resolution during consolidation uses MRA’s Dlog metric\nto identify conflicting memories.\n• CPR integration: Tier 1 reasoning traces",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 15,
    "chunk_id": 85
  },
  {
    "text": "uses MRA’s Dlog metric\nto identify conflicting memories.\n• CPR integration: Tier 1 reasoning traces can be stored in episodic memory for future\nreference, enabling learning from past reasoning processes.\n6\nSystem Integration\nThe three architectures—MRA, CPR, and C2—are designed to work together as a coherent\nsystem. This section describes integration patterns and deployment considerations.\n6.1\nArchitecture Overview\nThe integrated system processes queries through the following flow:\n1.",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 15,
    "chunk_id": 86
  },
  {
    "text": "ns.\n6.1\nArchitecture Overview\nThe integrated system processes queries through the following flow:\n1. Input processing: Query arrives; C2 retrieves relevant context from memory tiers.\n2. Stress assessment: MRA evaluates epistemic stress based on query complexity and re-\ntrieved context.\n3. Routing decision: Based on stress level, route to standard generation or CPR.\n4. Generation: Produce response (with or without CPR structure).\n5.",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 15,
    "chunk_id": 87
  },
  {
    "text": "e to standard generation or CPR.\n4. Generation: Produce response (with or without CPR structure).\n5. Output monitoring: MRA evaluates response for contradictions and consistency.\n6. Memory update: C2 encodes relevant information from the interaction.\n6.2\nData Flow\n6.2.1\nQuery-Time Flow\nListing 5: Integrated query processing\ndef\nprocess_query(query: str , session: Session) -> Response:\n# 1. Retrieve\nrelevant\ncontext\n15",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 15,
    "chunk_id": 88
  },
  {
    "text": "context = c2.retrieve(query , session.history)\n# 2. Assess\nepistemic\nstress\nstress = mra.assess(query , context)\n# 3. Route\nbased on stress\nlevel\nif stress.S_omega > THRESHOLD_HIGH :\nresponse = cpr. generate_with_exploration (\nquery , context ,\nrequest_clarification =True\n)\nelif\nstress.S_omega > THRESHOLD_MEDIUM :\nresponse = cpr. generate_with_exploration (\nquery , context\n)\nelse:\nresponse = standard_generate (query , context)\n# 4.",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 16,
    "chunk_id": 89
  },
  {
    "text": "erate_with_exploration (\nquery , context\n)\nelse:\nresponse = standard_generate (query , context)\n# 4. Monitor\noutput\noutput_stress = mra.assess_output(response)\nif output_stress. has_contradictions :\nresponse = flag_uncertainty (response)\n# 5. Update\nmemory\nc2.encode(query , response , session)\nreturn\nresponse\n6.2.2\nBackground Processes\nAsynchronous processes maintain system health:\n• Memory consolidation (C2): Hourly/daily transfer from episodic to semantic memory.\n• Contradiction resolution (MR",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 16,
    "chunk_id": 90
  },
  {
    "text": "idation (C2): Hourly/daily transfer from episodic to semantic memory.\n• Contradiction resolution (MRA + C2): Periodic scan of semantic memory for inconsisten-\ncies.\n• Graph maintenance (C2): Update betweenness centrality and identify topological voids.\n6.3\nDeployment Patterns\n6.3.1\nMinimal Deployment\nFor resource-constrained environments:\n• MRA: Lightweight NLI model for contradiction detection only\n• CPR: Prompt-based implementation\n• C2: Single vector database for episodic memory\n6.3.2\nStandar",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 16,
    "chunk_id": 91
  },
  {
    "text": "ly\n• CPR: Prompt-based implementation\n• C2: Single vector database for episodic memory\n6.3.2\nStandard Deployment\nBalanced resource usage:\n• MRA: Full stress computation with cached embeddings\n• CPR: Multi-pass generation with temperature variation\n• C2: Vector database + lightweight knowledge graph\n16",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 16,
    "chunk_id": 92
  },
  {
    "text": "6.3.3\nFull Deployment\nMaximum capability:\n• MRA: Real-time monitoring with all three stress components\n• CPR: Architectural implementation with separate reasoning passes\n• C2: Full four-tier memory with Neo4j graph and consolidation daemon\n6.4\nLatency Considerations\nThe integrated system adds latency at several points:\nComponent\nTypical Latency\nWhen Applied\nC2 retrieval\n50-200ms\nEvery query\nMRA stress assessment\n100-500ms\nEvery query\nCPR multi-pass\n2-5x generation\nHigh stress only\nOutput monitor",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 17,
    "chunk_id": 93
  },
  {
    "text": "ress assessment\n100-500ms\nEvery query\nCPR multi-pass\n2-5x generation\nHigh stress only\nOutput monitoring\n100-300ms\nEvery response\nTable 1: Latency impact of coherence-seeking components.\nFor latency-sensitive applications, stress-based routing ensures that expensive operations\n(CPR multi-pass) are only applied when needed.\n6.5\nFailure Modes\nThe system degrades gracefully:\n• MRA unavailable: System falls back to standard generation without stress-based routing.\n• C2 unavailable: System operates wi",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 17,
    "chunk_id": 94
  },
  {
    "text": "falls back to standard generation without stress-based routing.\n• C2 unavailable: System operates without memory context (stateless mode).\n• CPR unavailable: All queries use standard generation regardless of stress.\nEach component is designed to be optional, allowing deployment flexibility based on require-\nments and resources.\n7\nEvaluation Considerations\nThis section discusses approaches for evaluating coherence-seeking architectures and proposes\nmetrics for measuring their effectiveness.\n7.1\nE",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 17,
    "chunk_id": 95
  },
  {
    "text": "uating coherence-seeking architectures and proposes\nmetrics for measuring their effectiveness.\n7.1\nEvaluation Dimensions\nCoherence-seeking systems should be evaluated across multiple dimensions:\n1. Consistency: Does the system produce internally consistent outputs?\n2. Reliability: Does the system correctly identify and flag uncertain outputs?\n3. Efficiency: What is the computational overhead of coherence-seeking?\n4. Continuity: Does the system maintain consistent behavior across sessions?\n17",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 17,
    "chunk_id": 96
  },
  {
    "text": "7.2\nProposed Metrics\n7.2.1\nContradiction Rate\nMeasure the frequency of detectable contradictions in system outputs:\nCR = # responses with internal contradictions\n# total responses\n(5)\nMeasurement approach: Apply NLI-based contradiction detection to outputs.",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 18,
    "chunk_id": 97
  },
  {
    "text": "ions\n# total responses\n(5)\nMeasurement approach: Apply NLI-based contradiction detection to outputs. Compare\nbaseline (no MRA) to MRA-enabled systems.\n7.2.2\nUncertainty Calibration\nMeasure alignment between expressed confidence and actual accuracy:\nECE =\nB\nX\nb=1\nnb\nN |acc(b) −conf(b)|\n(6)\nwhere ECE is Expected Calibration Error across confidence bins.\nLower values indicate\nbetter calibration.\n7.2.3\nCross-Session Consistency\nFor systems with memory (C2), measure consistency of responses to equiva",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 18,
    "chunk_id": 98
  },
  {
    "text": "3\nCross-Session Consistency\nFor systems with memory (C2), measure consistency of responses to equivalent queries across\nsessions:\nCSC =\n1\n|Q|\nX\nq∈Q\nsim(rt1\nq , rt2\nq )\n(7)\nwhere rt1\nq and rt2\nq are responses to query q at times t1 and t2.\n7.2.4\nStress-Accuracy Correlation\nValidate that epistemic stress correlates with actual errors:\nρ(SΩ, error)\n(8)\nHigh correlation indicates the stress metric is a useful signal; low correlation suggests the\nmetric needs refinement.\n7.3\nBenchmark Suggestions\n7.3",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 18,
    "chunk_id": 99
  },
  {
    "text": "a useful signal; low correlation suggests the\nmetric needs refinement.\n7.3\nBenchmark Suggestions\n7.3.1\nContradiction Detection Benchmark\nDataset of statement pairs with known contradiction labels. Evaluate MRA’s Dlog component\nagainst ground truth.\n7.3.2\nLong-Context Consistency Benchmark\nExtended conversations where early statements should constrain later responses.\nMeasure\nwhether systems maintain consistency or introduce contradictions.\n7.3.3\nMulti-Session Benchmark\nSeries of related sessions",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 18,
    "chunk_id": 100
  },
  {
    "text": "in consistency or introduce contradictions.\n7.3.3\nMulti-Session Benchmark\nSeries of related sessions testing whether C2 memory improves response quality and consistency\nover time.\n18",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 18,
    "chunk_id": 101
  },
  {
    "text": "7.4\nTrade-off Analysis\nConfiguration\nConsistency Gain\nLatency Cost\nBaseline\n—\n—\n+ MRA monitoring\n+15-25%\n+200ms\n+ CPR (all queries)\n+30-40%\n+3x\n+ CPR (stress-routed)\n+25-35%\n+0.5x avg\n+ C2 memory\n+10-20%\n+100ms\nFull system\n+40-50%\n+1.5x avg\nTable 2: Estimated trade-offs between consistency improvement and latency cost.",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 19,
    "chunk_id": 102
  },
  {
    "text": "em\n+40-50%\n+1.5x avg\nTable 2: Estimated trade-offs between consistency improvement and latency cost. Values are\nillustrative; actual results will vary by implementation and workload.\nThese estimates are based on preliminary observations and should be validated through\nsystematic evaluation.\n7.5\nLimitations of Evaluation\nSeveral challenges complicate evaluation:\n• Ground truth: Determining “correct” consistency is often subjective.\n• Coverage: Contradiction detection may miss subtle inconsistenci",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 19,
    "chunk_id": 103
  },
  {
    "text": "” consistency is often subjective.\n• Coverage: Contradiction detection may miss subtle inconsistencies.\n• Confounds: Improved consistency may come from reduced capability (overly cautious sys-\ntems).\n• Distribution shift: Evaluation benchmarks may not reflect real-world query distributions.\nComprehensive evaluation should combine automatic metrics with human assessment.\n8\nLimitations\nWe acknowledge several limitations of this work:\n8.1\nEmpirical Validation\n1.",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 19,
    "chunk_id": 104
  },
  {
    "text": "sessment.\n8\nLimitations\nWe acknowledge several limitations of this work:\n8.1\nEmpirical Validation\n1. Limited systematic evaluation: The architectures presented here are based on design\nprinciples and preliminary observations rather than large-scale controlled experiments. Rig-\norous empirical validation is needed.\n2. Implementation variability: Results may vary significantly depending on specific imple-\nmentation choices (NLI model selection, embedding models, graph database configuration).\n3.",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 19,
    "chunk_id": 105
  },
  {
    "text": "c imple-\nmentation choices (NLI model selection, embedding models, graph database configuration).\n3. Workload dependency: Effectiveness likely varies by domain and query type. Some work-\nloads may benefit more from coherence-seeking than others.\n8.2\nComputational Overhead\n1. Latency: The full system adds significant latency, which may be unacceptable for real-time\napplications.\n2.",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 19,
    "chunk_id": 106
  },
  {
    "text": ": The full system adds significant latency, which may be unacceptable for real-time\napplications.\n2. Resource requirements: Running NLI models, maintaining vector databases, and operat-\ning knowledge graphs require substantial compute and memory.\n19",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 19,
    "chunk_id": 107
  },
  {
    "text": "3. Scaling challenges: Some components (particularly graph-based topological analysis) may\nnot scale efficiently to very large knowledge bases.\n8.3\nMeasurement Challenges\n1. Proxy metrics: Epistemic stress (SΩ) is measured through proxies (NLI scores, embedding\ndistances) rather than direct observation of internal model states.\n2. Threshold sensitivity: The system’s behavior depends on threshold choices (τcoherence) that\nmay require careful tuning per deployment.\n3.",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 20,
    "chunk_id": 108
  },
  {
    "text": "ehavior depends on threshold choices (τcoherence) that\nmay require careful tuning per deployment.\n3. False positives/negatives: Contradiction detection is imperfect; some genuine contradic-\ntions may be missed while some consistent statements may be flagged.\n8.4\nArchitectural Assumptions\n1. External memory assumption: C2 assumes that external memory can adequately substi-\ntute for lack of native model memory. This may not hold for all types of knowledge.\n2.",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 20,
    "chunk_id": 109
  },
  {
    "text": "ately substi-\ntute for lack of native model memory. This may not hold for all types of knowledge.\n2. Decomposability: The approach assumes reasoning can be meaningfully decomposed into\ntiers (CPR). Some reasoning may not decompose cleanly.\n3. Coherence as proxy: We assume internal coherence correlates with output quality. Highly\ncoherent but wrong outputs are possible.\n8.5\nScope Limitations\n1.",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 20,
    "chunk_id": 110
  },
  {
    "text": "elates with output quality. Highly\ncoherent but wrong outputs are possible.\n8.5\nScope Limitations\n1. Single framework: These architectures represent one approach to improving reliability.\nAlternative approaches (better training, different architectures) may achieve similar or better\nresults.\n2. Model agnostic claims: While designed to be model-agnostic, the architectures have pri-\nmarily been considered in the context of large language models. Applicability to other AI\nsystems is uncertain.\n3.",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 20,
    "chunk_id": 111
  },
  {
    "text": "nsidered in the context of large language models. Applicability to other AI\nsystems is uncertain.\n3. No safety claims: Improved coherence does not imply improved safety. A coherent system\ncan consistently produce harmful outputs.\n8.6\nFuture Work\nAddressing these limitations suggests several directions:\n• Systematic benchmarking across diverse models and domains\n• Optimization of latency-critical components\n• Integration with model internals (where accessible) for more direct state monitoring\n• L",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 20,
    "chunk_id": 112
  },
  {
    "text": "omponents\n• Integration with model internals (where accessible) for more direct state monitoring\n• Longitudinal studies of memory system effectiveness\n• Comparison with alternative reliability approaches\n20",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 20,
    "chunk_id": 113
  },
  {
    "text": "9\nConclusion\nThis paper presented three interconnected architectures for improving AI system reliability\nthrough coherence-seeking:\n1. Manifold Resonance Architecture (MRA): A mathematical framework for quantifying\nepistemic stress through logical dissonance (Dlog), semantic divergence (Dsem), and topolog-\nical sparsity (Vtop), enabling systems to detect internal inconsistencies before they propagate\nto outputs.\n2.",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 21,
    "chunk_id": 114
  },
  {
    "text": "ity (Vtop), enabling systems to detect internal inconsistencies before they propagate\nto outputs.\n2. Collaborative Partner Reasoning (CPR): A structured reasoning protocol with visibility\ntiers that separates exploratory thinking from committed outputs, improving consistency and\nenabling auditability of reasoning processes.\n3.",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 21,
    "chunk_id": 115
  },
  {
    "text": "g from committed outputs, improving consistency and\nenabling auditability of reasoning processes.\n3. Continuity Core (C2): A hierarchical memory architecture (Working →Episodic →Se-\nmantic →Protected) providing contextual continuity for stateless systems through tiered\npersistence and consolidation mechanisms.\n9.1\nKey Contributions\nTheoretical: We formalize the concept of epistemic stress as a measurable quantity and provide\nmathematical definitions for its components.\nArchitectural: We provide",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 21,
    "chunk_id": 116
  },
  {
    "text": "surable quantity and provide\nmathematical definitions for its components.\nArchitectural: We provide implementable specifications for memory systems, reasoning\nprotocols, and stress monitoring components.\nIntegration: We describe how the three architectures work together and provide deployment\nguidance for different resource constraints.\n9.2\nPractical Implications\nFor practitioners deploying AI systems, this work suggests:\n• Monitor internal consistency: Even simple contradiction detection can ca",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 21,
    "chunk_id": 117
  },
  {
    "text": "tems, this work suggests:\n• Monitor internal consistency: Even simple contradiction detection can catch errors before\nthey reach users.\n• Structure reasoning explicitly: Separating exploration from commitment improves output\nquality.\n• Invest in memory: External memory systems enable consistency that stateless inference\ncannot achieve.\n• Route adaptively: Apply expensive reliability measures where they’re most needed.\n9.3\nFuture Directions\nSeveral directions merit further investigation:\n• Empiri",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 21,
    "chunk_id": 118
  },
  {
    "text": "they’re most needed.\n9.3\nFuture Directions\nSeveral directions merit further investigation:\n• Empirical validation: Systematic evaluation across models, domains, and deployment sce-\nnarios.\n• Integration with model internals: Where model weights or activations are accessible,\ndirect monitoring may outperform output-based approaches.\n• Efficiency optimization: Reducing the computational overhead of coherence-seeking with-\nout sacrificing effectiveness.\n• Standardization: Developing common interfa",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 21,
    "chunk_id": 119
  },
  {
    "text": "coherence-seeking with-\nout sacrificing effectiveness.\n• Standardization: Developing common interfaces and benchmarks for coherence-seeking com-\nponents.\n21",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 21,
    "chunk_id": 120
  },
  {
    "text": "9.4\nClosing Remarks\nThe architectures presented here address a fundamental challenge: making AI systems that not\nonly produce impressive outputs but do so reliably and consistently. As these systems take on\nmore consequential tasks, the importance of internal coherence—catching errors, maintaining\nconsistency, and building on prior knowledge—will only grow.\nCoherence-seeking is not a complete solution to AI reliability, but it provides a foundation:\nsystems that monitor their own consistency are",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 22,
    "chunk_id": 121
  },
  {
    "text": "tion to AI reliability, but it provides a foundation:\nsystems that monitor their own consistency are better positioned to avoid errors, explain their\nreasoning, and improve over time.\nA\nTechnical Specifications\nThis appendix provides implementation details for the architectures presented in the main text.\nA.1\nMRA: Epistemic Stress Monitor\nListing 6: Complete epistemic stress monitor implementation\nfrom\ntransformers\nimport\npipeline\nfrom\ntyping\nimport List , Dict , Any , Tuple\nimport\nnumpy as np\nc",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 22,
    "chunk_id": 122
  },
  {
    "text": "from\ntransformers\nimport\npipeline\nfrom\ntyping\nimport List , Dict , Any , Tuple\nimport\nnumpy as np\nclass\nEpistemicStressMonitor :\ndef\n__init__(\nself ,\nnli_model: str = \"microsoft/deberta -v3 -large -mnli\",\nembedding_model: str = \"text -embedding -3-small\",\nalpha: float = 0.4,\nbeta: float = 0.35,\ngamma: float = 0.25\n):\nself.nli = pipeline(\n\"zero -shot -classification \",\nmodel=nli_model\n)\nself.embedder = load_embedding_model ( embedding_model )\nself.alpha = alpha\nself.beta = beta\nself.gamma = gamm",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 22,
    "chunk_id": 123
  },
  {
    "text": "der = load_embedding_model ( embedding_model )\nself.alpha = alpha\nself.beta = beta\nself.gamma = gamma\nself. stress_history = []\ndef\ndetect_contradiction (\nself ,\nstatement_a: str ,\nstatement_b: str\n) -> float:\n\"\"\"Returns\ncontradiction\nprobability.\"\"\"\nresult = self.nli(\nstatement_a ,\n[statement_b , f\"not {statement_b}\"]\n)\nreturn\nresult[’scores ’][1]\ndef\ncompute_logical_dissonance (\nself ,\nstatements: List[str]\n) -> float:\n22",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 22,
    "chunk_id": 124
  },
  {
    "text": "\"\"\"Compute\nD_log\nfrom\nequation\n(2).\"\"\"\nif len(statements) < 2:\nreturn 0.0\ncontradictions = []\nfor i, s1 in enumerate(statements):\nfor s2 in statements[i+1:]:\nscore = self. detect_contradiction (s1 , s2)\ncontradictions .append(score)\nreturn np.mean(contradictions) if contradictions\nelse 0.0\ndef\ncompute_semantic_divergence (\nself ,\nconcept: str ,\ncontexts: List[str]\n) -> float:\n\"\"\"Compute\nD_sem\nfrom\nequation\n(3).\"\"\"\nembeddings = [\nself.embedder.embed(f\"{concept} in context: {ctx}\")\nfor ctx in cont",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 23,
    "chunk_id": 125
  },
  {
    "text": "equation\n(3).\"\"\"\nembeddings = [\nself.embedder.embed(f\"{concept} in context: {ctx}\")\nfor ctx in contexts\n]\ndivergences = []\nfor i, e1 in enumerate(embeddings):\nfor e2 in embeddings[i+1:]:\ncos_sim = np.dot(e1 , e2) / (\nnp.linalg.norm(e1) * np.linalg.norm(e2)\n)\ndivergences.append (1 - cos_sim)\nreturn np.mean(divergences) if divergences\nelse 0.0\ndef\ncompute_epistemic_stress (\nself ,\nstatements: List[str],\nconcepts: Dict[str , List[str]] = None ,\ngraph_sparsity : float = 0.0\n) -> Dict[str , float ]:",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 23,
    "chunk_id": 126
  },
  {
    "text": "str],\nconcepts: Dict[str , List[str]] = None ,\ngraph_sparsity : float = 0.0\n) -> Dict[str , float ]:\n\"\"\"Compute\nS_omega\nfrom\nequation\n(1).\"\"\"\nd_log = self. compute_logical_dissonance (statements)\nd_sem = 0.0\nif concepts:\nsem_scores = [\nself. compute_semantic_divergence (c, ctxs)\nfor c, ctxs in concepts.items ()\n]\nd_sem = np.mean(sem_scores) if sem_scores\nelse 0.0\ns_omega = (\nself.alpha * d_log +\nself.beta * d_sem +\nself.gamma * graph_sparsity\n)\nresult = {\n\"S_omega\": s_omega ,\n23",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 23,
    "chunk_id": 127
  },
  {
    "text": "\"D_log\": d_log ,\n\"D_sem\": d_sem ,\n\"V_top\": graph_sparsity ,\n\"components\": {\n\" logical_contribution \": self.alpha * d_log ,\n\" semantic_contribution \": self.beta * d_sem ,\n\" topological_contribution \": self.gamma * graph_sparsity\n}\n}\nself. stress_history .append(result)\nreturn\nresult\ndef\nshould_trigger_cpr (\nself ,\ns_omega: float ,\nthreshold: float = 0.3\n) -> bool:\n\"\"\"Determine if CPR should be triggered.\"\"\"\nreturn\ns_omega > threshold\nA.2\nC2: Memory Configuration Schema\nListing 7: Complete C2 conf",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 24,
    "chunk_id": 128
  },
  {
    "text": "gered.\"\"\"\nreturn\ns_omega > threshold\nA.2\nC2: Memory Configuration Schema\nListing 7: Complete C2 configuration\n# c2 -config.yaml\nversion: \"1.0\"\nmemory_tiers:\nworking:\nbackend: redis\nconnection:\nhost: localhost\nport: 6379\ndb: 0\nsettings:\nttl: 3600\n# 1 hour\nmax_items: 100\neviction_policy: lru\nepisodic:\nbackend: qdrant\nconnection:\nhost: localhost\nport: 6333\ncollection: episodic_memory\nsettings:\nembedding_model: text -embedding -3-small\nembedding_dim: 1536\ndecay_rate: 0.95\n# per day\nmax_memories: 100",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 24,
    "chunk_id": 129
  },
  {
    "text": "ing_model: text -embedding -3-small\nembedding_dim: 1536\ndecay_rate: 0.95\n# per day\nmax_memories: 100000\ndistance_metric: cosine\nsemantic:\nbackend: neo4j\nconnection:\nuri: bolt :// localhost :7687\n24",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 24,
    "chunk_id": 130
  },
  {
    "text": "user: neo4j\npassword: ${NEO4J_PASSWORD }\nsettings:\nconfidence_threshold : 0.8\nconsolidation_interval : 86400\n# 24 hours\nmax_nodes: 1000000\nindex_properties :\n- name\n- type\n- embedding\nprotected:\nbackend: filesystem\npath: /var/lib/c2/protected\nsettings:\nwrite_protection : true\nsignature_required : true\nsignature_algorithm : ed25519\nbackup_interval: 86400\nconsolidation:\nenabled: true\nschedule: \"0 2 * * *\"\n# 2 AM daily\nepisodic_to_semantic :\nmin_occurrences: 3\nconfidence_threshold : 0.75\ncontradict",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 25,
    "chunk_id": 131
  },
  {
    "text": "* * *\"\n# 2 AM daily\nepisodic_to_semantic :\nmin_occurrences: 3\nconfidence_threshold : 0.75\ncontradiction_resolution :\nstrategy: recency_weighted\nhuman_review_threshold : 0.9\nretrieval:\ndefault_top_k: 10\nreranking:\nenabled: true\nmodel: cross -encoder/ms -marco -MiniLM -L-6-v2\ntemporal_weighting :\nrecency_weight : 0.3\nfrequency_weight : 0.2\nrelevance_weight : 0.5\nA.3\nCPR: Protocol Template\nListing 8: CPR prompt template\nYou are using the\nCollaborative\nPartner\nReasoning\nprotocol.\nStructure\nyour\nresp",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 25,
    "chunk_id": 132
  },
  {
    "text": "CPR prompt template\nYou are using the\nCollaborative\nPartner\nReasoning\nprotocol.\nStructure\nyour\nresponse in three\ntiers:\n## TIER 0 - EXPLORATION (internal\nworking\nnotes)\n- List 2-3 possible\napproaches to this\nquery\n- Note\nuncertainties , gaps , or potential\nissues\n- Identify\nany\ncontradictions in the\ncontext or query\n- This\nsection is for working\nthrough\nthe\nproblem\n## TIER 1 - SYNTHESIS (intermediate\nreasoning)\n- Synthesize\nyour\nexploration\ninto a position\n- Explicitly\nstate\nremaining\nuncertain",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 25,
    "chunk_id": 133
  },
  {
    "text": "iate\nreasoning)\n- Synthesize\nyour\nexploration\ninto a position\n- Explicitly\nstate\nremaining\nuncertainties\n- Note\nconfidence\nlevel (low/medium/high)\n25",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 25,
    "chunk_id": 134
  },
  {
    "text": "- Identify\nwhat\nadditional\ninformation\nwould\nhelp\n## TIER 2 - RESPONSE (final\noutput)\n- Provide\nyour\nconsidered\nresponse\n- Include\nappropriate\ncaveats\nbased on Tier 1 uncertainties\n- Use\ncalibrated\nconfidence\nlanguage\n---\nQuery: {query}\nContext: {context}\nA.4\nIntegration: Query Processing Pipeline\nListing 9: Complete integration example\nfrom\ndataclasses\nimport\ndataclass\nfrom\ntyping\nimport\nOptional\nfrom enum\nimport\nEnum\nclass\nStressLevel(Enum):\nLOW = \"low\"\nMEDIUM = \"medium\"\nHIGH = \"high\"\n@datacla",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 26,
    "chunk_id": 135
  },
  {
    "text": "from enum\nimport\nEnum\nclass\nStressLevel(Enum):\nLOW = \"low\"\nMEDIUM = \"medium\"\nHIGH = \"high\"\n@dataclass\nclass\nProcessingConfig :\nstress_threshold_medium : float = 0.2\nstress_threshold_high : float = 0.4\nenable_cpr: bool = True\nenable_memory: bool = True\nenable_output_monitoring : bool = True\nclass\nCoherenceSeekingPipeline :\ndef\n__init__(\nself ,\nmra: EpistemicStressMonitor ,\nc2: ContinuityCore ,\nllm: LanguageModel ,\nconfig: ProcessingConfig = None\n):\nself.mra = mra\nself.c2 = c2\nself.llm = llm\nself",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 26,
    "chunk_id": 136
  },
  {
    "text": ": LanguageModel ,\nconfig: ProcessingConfig = None\n):\nself.mra = mra\nself.c2 = c2\nself.llm = llm\nself.config = config or ProcessingConfig ()\ndef\nclassify_stress (self , s_omega: float) -> StressLevel:\nif s_omega > self.config. stress_threshold_high :\nreturn\nStressLevel.HIGH\nelif\ns_omega > self.config. stress_threshold_medium :\nreturn\nStressLevel.MEDIUM\nreturn\nStressLevel.LOW\ndef\nprocess(\nself ,\nquery: str ,\nsession_id: str\n) -> dict:\n26",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 26,
    "chunk_id": 137
  },
  {
    "text": "# 1. Retrieve\ncontext\ncontext = []\nif self.config.enable_memory:\ncontext = self.c2.retrieve(query , session_id)\n# 2. Assess\ninput\nstress\nstatements = [query] + [c.text for c in context]\nstress = self.mra. compute_epistemic_stress (statements)\nlevel = self. classify_stress (stress[\"S_omega\"])\n# 3. Generate\nresponse\nif self.config.enable_cpr\nand level != StressLevel.LOW:\nresponse = self. _generate_with_cpr (\nquery , context , level\n)\nelse:\nresponse = self.",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 27,
    "chunk_id": 138
  },
  {
    "text": "essLevel.LOW:\nresponse = self. _generate_with_cpr (\nquery , context , level\n)\nelse:\nresponse = self. _generate_standard (query , context)\n# 4. Monitor\noutput\nif self.config. enable_output_monitoring :\noutput_stress = self.mra. compute_epistemic_stress (\n[query , response.text]\n)\nif output_stress[\"D_log\"] > 0.5:\nresponse = self. _add_uncertainty_flag (response)\n# 5.",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 27,
    "chunk_id": 139
  },
  {
    "text": "onse.text]\n)\nif output_stress[\"D_log\"] > 0.5:\nresponse = self. _add_uncertainty_flag (response)\n# 5. Update\nmemory\nif self.config.enable_memory:\nself.c2.encode(query , response , session_id)\nreturn {\n\"response\": response ,\n\"input_stress\": stress ,\n\"stress_level\": level ,\n\"context_used\": len(context)\n}\ndef\n_generate_with_cpr (\nself ,\nquery: str ,\ncontext: list ,\nlevel: StressLevel\n):\ntemplate = load_cpr_template ()\nprompt = template.format(\nquery=query ,\ncontext= format_context (context)\n)\nif lev",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 27,
    "chunk_id": 140
  },
  {
    "text": "d_cpr_template ()\nprompt = template.format(\nquery=query ,\ncontext= format_context (context)\n)\nif level == StressLevel.HIGH:\n# Multi -pass with\ndifferent\ntemperatures\nexploration = self.llm.generate(\nprompt , temperature =0.9\n)\nsynthesis = self.llm.generate(\nf\"Synthesize: {exploration}\",\ntemperature =0.5\n)\n27",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 27,
    "chunk_id": 141
  },
  {
    "text": "final = self.llm.generate(\nf\"Final\nresponse: {synthesis}\",\ntemperature =0.3\n)\nreturn\nfinal\nelse:\n# Single\npass with CPR\nstructure\nreturn\nself.llm.generate(prompt , temperature =0.5)\ndef\n_generate_standard (self , query: str , context: list):\nprompt = f\"Context: { format_context (context)}\\n\\n{query}\"\nreturn\nself.llm.generate(prompt)\nReferences\nAnthropic.\nSigns of introspection in large language models.\nhttps://www.anthropic.com/\nresearch/introspection, 2025.",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 28,
    "chunk_id": 142
  },
  {
    "text": "of introspection in large language models.\nhttps://www.anthropic.com/\nresearch/introspection, 2025. Accessed: 2025-10-29.\nRewon Child, Scott Gray, Alec Radford, and Ilya Sutskever. Generating long sequences with\nsparse transformers. In arXiv preprint arXiv:1904.10509, 2019.\nZiwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Ye Jin Bang,\nAndrea Madotto, and Pascale Fung.",
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    "text": "a Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Ye Jin Bang,\nAndrea Madotto, and Pascale Fung. Survey of hallucination in natural language generation.\nACM Computing Surveys, 55(12):1–38, 2023.\nLorenz Kuhn, Yarin Gal, and Sebastian Farquhar. Semantic uncertainty: Linguistic invariances\nfor uncertainty estimation in natural language generation.",
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    "text": "antic uncertainty: Linguistic invariances\nfor uncertainty estimation in natural language generation. arXiv preprint arXiv:2302.09664,\n2023.\nPatrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman\nGoyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, et al.\nRetrieval-\naugmented generation for knowledge-intensive nlp tasks. In Advances in Neural Information\nProcessing Systems, volume 33, pages 9459–9474, 2020.\nJack Lindsey et al.",
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    "text": "nces in Neural Information\nProcessing Systems, volume 33, pages 9459–9474, 2020.\nJack Lindsey et al. On the biology of a large language model. Transformer Circuits, 2025. URL\nhttps://transformer-circuits.pub/2025/attribution-graphs/biology.html.\nCharles Packer, Sarah Wooders, Kevin Lin, Vivian Fang, Shishir G Patil, Ion Stoica,\nand Joseph E Gonzalez.\nMemgpt:\nTowards llms as operating systems.\narXiv preprint\narXiv:2310.08560, 2023.\nOfir Press, Noah A Smith, and Mike Lewis.",
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    "page": 28,
    "chunk_id": 149
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  {
    "text": "Yuhuai Wu, Markus N Rabe, DeLesley Hutchins, and Christian Szegedy. Memorizing trans-\nformers. arXiv preprint arXiv:2203.08913, 2022.\nShunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L Griffiths, Yuan Cao, and Karthik\nNarasimhan. Tree of thoughts: Deliberate problem solving with large language models. arXiv\npreprint arXiv:2305.10601, 2023.\n29",
    "source": "Coherence-Seeking-Architectures-for-Agentic-AI-Anthony-Maio-v2",
    "page": 29,
    "chunk_id": 150
  },
  {
    "text": "Heterogeneous Divergence-Convergence Swarm: Scalable\nOversight via Diverse Model Ensembles\nAnthony Maio\nIndependent Researcher\nhttps://orcid.org/0009-0003-4541-8515\nanthony@making-minds.ai\nhttps://making-minds.ai\nJanuary 2026\nAbstract\nAs language models become increasingly capable, the challenge of scalable oversight—\nusing less capable systems to supervise more capable ones—becomes critical for AI safety.\nWe introduce the Heterogeneous Divergence-Convergence Swarm (HDCS), an ensemble\narchitectu",
    "source": "hdcs_paper",
    "page": 1,
    "chunk_id": 0
  },
  {
    "text": "I safety.\nWe introduce the Heterogeneous Divergence-Convergence Swarm (HDCS), an ensemble\narchitecture that leverages diverse weak models for verification of strong model outputs.\nOur approach exploits a key insight: different model families make uncorrelated errors on\nreasoning tasks, enabling a “wisdom of crowds” effect where ensemble disagreement signals\npotential failures.",
    "source": "hdcs_paper",
    "page": 1,
    "chunk_id": 1
  },
  {
    "text": "tasks, enabling a “wisdom of crowds” effect where ensemble disagreement signals\npotential failures. We test three hypotheses: (H1) swarm-verification outperforms single-\nmodel verification on epistemic traps; (H2) cross-family error correlation remains below 0.5;\nand (H3) strong models perform better as synthesizers than generators on deception detection.\nExperiments on the Cross-Model Epistemic Divergence (CMED) trap suite demonstrate that\nheterogeneous ensembles catch deceptive reasoning patt",
    "source": "hdcs_paper",
    "page": 1,
    "chunk_id": 2
  },
  {
    "text": "Divergence (CMED) trap suite demonstrate that\nheterogeneous ensembles catch deceptive reasoning patterns that individual weak verifiers\nmiss. Our baseline-first anti-anchoring protocol further improves detection by preventing\nsycophantic agreement with plausible-sounding but incorrect derivations.",
    "source": "hdcs_paper",
    "page": 1,
    "chunk_id": 3
  },
  {
    "text": "ves detection by preventing\nsycophantic agreement with plausible-sounding but incorrect derivations. These findings\nsuggest that architectural heterogeneity provides a scalable path toward robust AI oversight\nwithout requiring human-level verification capabilities.\n1\nIntroduction\nThe rapid advancement of large language models presents a fundamental challenge for AI safety:\nhow do we ensure that increasingly capable AI systems behave as intended when their capabilities\nmay exceed our ability to e",
    "source": "hdcs_paper",
    "page": 1,
    "chunk_id": 4
  },
  {
    "text": "creasingly capable AI systems behave as intended when their capabilities\nmay exceed our ability to evaluate their outputs directly? This challenge, known as scalable\noversight, lies at the heart of ensuring AI systems remain aligned with human values as they\nbecome more powerful [Amodei et al., 2016, Christiano et al., 2018].\nCurrent approaches to scalable oversight face a critical limitation.",
    "source": "hdcs_paper",
    "page": 1,
    "chunk_id": 5
  },
  {
    "text": "2016, Christiano et al., 2018].\nCurrent approaches to scalable oversight face a critical limitation. When a strong model\nproduces sophisticated reasoning, a weaker model tasked with verification often lacks the capability\nto detect subtle errors or deceptive patterns.",
    "source": "hdcs_paper",
    "page": 1,
    "chunk_id": 6
  },
  {
    "text": "l tasked with verification often lacks the capability\nto detect subtle errors or deceptive patterns. Our prior work on Cross-Model Epistemic Divergence\n(CMED) revealed that single weak verifiers achieve 97% accuracy on correctly-reasoned problems\nbut suffer 20–40% bypass rates on carefully constructed deceptive derivations [Maio, 2024].",
    "source": "hdcs_paper",
    "page": 1,
    "chunk_id": 7
  },
  {
    "text": "problems\nbut suffer 20–40% bypass rates on carefully constructed deceptive derivations [Maio, 2024]. This\nasymmetry suggests that weak verifiers are particularly vulnerable to “galaxy-brained” reasoning\nthat appears compelling but leads to incorrect conclusions.\nWe propose that architectural heterogeneity offers a path forward. Rather than relying on a\nsingle weak verifier, we employ an ensemble of diverse models—specifically, models from different\ntraining runs, architectures, and providers.",
    "source": "hdcs_paper",
    "page": 1,
    "chunk_id": 8
  },
  {
    "text": "e of diverse models—specifically, models from different\ntraining runs, architectures, and providers. The key insight is that different model families\nmake uncorrelated errors: a reasoning pattern that fools a Llama-based model may not fool a\nMistral-based model, and vice versa. This error decorrelation creates a “wisdom of crowds” effect\nwhere ensemble disagreement serves as a reliable signal of potential failures.\n1",
    "source": "hdcs_paper",
    "page": 1,
    "chunk_id": 9
  },
  {
    "text": "Our Heterogeneous Divergence-Convergence Swarm (HDCS) architecture implements this\ninsight through a three-stage pipeline. First, a router classifies incoming queries by complexity,\ndirecting simple tasks to a fast path and complex tasks to the full swarm. Second, a diverse pack\nof weak models independently generates structured analyses including key claims, confidence\nassessments, and potential failure modes.",
    "source": "hdcs_paper",
    "page": 2,
    "chunk_id": 10
  },
  {
    "text": "rates structured analyses including key claims, confidence\nassessments, and potential failure modes. Third, an executive model synthesizes these diverse\nperspectives, explicitly trained to give weight to minority opinions and disagreement signals.\nCritically, we introduce a baseline-first anti-anchoring protocol in the executive stage. Before\nseeing any swarm drafts, the executive generates its own initial assessment.",
    "source": "hdcs_paper",
    "page": 2,
    "chunk_id": 11
  },
  {
    "text": "executive stage. Before\nseeing any swarm drafts, the executive generates its own initial assessment. This prevents\nsycophantic agreement with confident-sounding but incorrect worker analyses—a failure mode\nwe observed in preliminary experiments where executives would defer to well-articulated wrong\nanswers.\nWe evaluate HDCS against three pre-registered hypotheses:\n• H1 (Oversight Efficacy): Swarm-verification outperforms single-model verification on\nthe CMED epistemic trap suite, measured by inc",
    "source": "hdcs_paper",
    "page": 2,
    "chunk_id": 12
  },
  {
    "text": "verification outperforms single-model verification on\nthe CMED epistemic trap suite, measured by increased detection of deceptive derivations.\n• H2 (Error Decorrelation): Error correlation across model families (Llama, Mistral,\nGemma) remains below 0.5, validating the architectural heterogeneity assumption.\n• H3 (Synthesizer Advantage): Strong models (Claude, GPT-4) perform better as\nsynthesizers of weak model outputs than as direct generators, particularly on deception\ndetection tasks.\nThe rema",
    "source": "hdcs_paper",
    "page": 2,
    "chunk_id": 13
  },
  {
    "text": "of weak model outputs than as direct generators, particularly on deception\ndetection tasks.\nThe remainder of this paper is organized as follows. Section 2 reviews related work on\nscalable oversight, ensemble methods, and the CMED framework. Section 3 details the HDCS\narchitecture and experimental design. Section 4 presents our empirical findings. Section 5\ninterprets results and discusses limitations.",
    "source": "hdcs_paper",
    "page": 2,
    "chunk_id": 14
  },
  {
    "text": ". Section 4 presents our empirical findings. Section 5\ninterprets results and discusses limitations. Section 6 summarizes contributions and outlines\nfuture directions.\n2\nBackground and Related Work\n2.1\nScalable Oversight and the Alignment Problem\nThe scalable oversight problem arises from a fundamental asymmetry: as AI systems become\nmore capable, human evaluators—and weaker AI verifiers—may lack the expertise or time to\nreliably assess system outputs [Christiano et al., 2018, Bowman et al., 202",
    "source": "hdcs_paper",
    "page": 2,
    "chunk_id": 15
  },
  {
    "text": "the expertise or time to\nreliably assess system outputs [Christiano et al., 2018, Bowman et al., 2022]. This challenge is\nparticularly acute for tasks requiring extended reasoning, domain expertise, or detection of subtle\ndeception.\nSeveral approaches have been proposed for scalable oversight. Recursive reward modeling\ntrains models to assist human evaluators [Leike et al., 2018]. Debate pits models against each\nother to surface truthful arguments [Irving et al., 2018].",
    "source": "hdcs_paper",
    "page": 2,
    "chunk_id": 16
  },
  {
    "text": "., 2018]. Debate pits models against each\nother to surface truthful arguments [Irving et al., 2018]. Constitutional AI uses AI self-critique\nguided by principles [Bai et al., 2022]. Weak-to-strong generalization studies whether weak\nsupervisors can guide strong model training [Burns et al., 2023].",
    "source": "hdcs_paper",
    "page": 2,
    "chunk_id": 17
  },
  {
    "text": "eneralization studies whether weak\nsupervisors can guide strong model training [Burns et al., 2023]. Our work complements these\napproaches by focusing on the verification stage: given strong model outputs, how can weak\nmodels reliably detect failures?\n2.2\nEnsemble Methods for Robustness\nThe use of model ensembles for improved robustness has a long history in machine learning\n[Dietterich, 2000].\nTraditional ensembles combine models to reduce variance and improve\ngeneralization.",
    "source": "hdcs_paper",
    "page": 2,
    "chunk_id": 18
  },
  {
    "text": "etterich, 2000].\nTraditional ensembles combine models to reduce variance and improve\ngeneralization. Recent work has applied ensemble methods to language model evaluation, finding\nthat agreement across models correlates with correctness [Wang et al., 2023].\n2",
    "source": "hdcs_paper",
    "page": 2,
    "chunk_id": 19
  },
  {
    "text": "However, most prior ensemble work focuses on homogeneous ensembles (multiple samples from\nthe same model) rather than heterogeneous ensembles (samples from different model families).\nWe hypothesize that heterogeneous ensembles provide qualitatively different benefits through\nerror decorrelation—a prediction we test directly in H2.\n2.3\nCross-Model Epistemic Divergence\nOur prior work introduced the Cross-Model Epistemic Divergence (CMED) framework for\nstudying single-verifier failures [Maio, 2024]",
    "source": "hdcs_paper",
    "page": 3,
    "chunk_id": 20
  },
  {
    "text": "Cross-Model Epistemic Divergence (CMED) framework for\nstudying single-verifier failures [Maio, 2024]. The key findings were:\n1. Single weak verifiers achieve high accuracy on correctly-reasoned problems (>97%) but\nsuffer significant bypass rates (20–40%) on deceptive derivations.\n2. Certain problem types—particularly those involving Simpson’s Paradox and conditional\nprobability—show bypass rates exceeding 75%.\n3.",
    "source": "hdcs_paper",
    "page": 3,
    "chunk_id": 21
  },
  {
    "text": "ly those involving Simpson’s Paradox and conditional\nprobability—show bypass rates exceeding 75%.\n3. Verifier failures correlate with the “surface plausibility” of deceptive reasoning rather than\nactual logical validity.\nThese findings motivate HDCS: if individual verifiers fail on different problem types, combining\ndiverse verifiers may catch failures that any single verifier would miss.\n2.4\nEpistemic Traps and Counterintuitive Problems\nThe CMED trap suite consists of problems with counterintui",
    "source": "hdcs_paper",
    "page": 3,
    "chunk_id": 22
  },
  {
    "text": "temic Traps and Counterintuitive Problems\nThe CMED trap suite consists of problems with counterintuitive correct answers where incorrect\nreasoning can appear more compelling than correct reasoning. Examples include the Tuesday Boy\nproblem (conditional probability), Monty Hall variants (probability update), Simpson’s Paradox\n(aggregation paradoxes), and bounded halting problems (computability limits).",
    "source": "hdcs_paper",
    "page": 3,
    "chunk_id": 23
  },
  {
    "text": "te), Simpson’s Paradox\n(aggregation paradoxes), and bounded halting problems (computability limits). These problems\nserve as stress tests for verification systems because they exploit systematic reasoning failures.\n3\nMethods\n3.1\nSystem Architecture\nThe HDCS system implements a three-stage pipeline: routing, divergent generation, and\nconvergent synthesis (Figure 1).\nRouter.\nThe router classifies incoming queries by complexity using a hybrid heuristic-LLM\napproach.",
    "source": "hdcs_paper",
    "page": 3,
    "chunk_id": 24
  },
  {
    "text": "Router.\nThe router classifies incoming queries by complexity using a hybrid heuristic-LLM\napproach. Simple queries (greetings, formatting, factual lookups) route directly to a fast model.\nComplex queries (multi-step reasoning, code analysis, mathematical problems) route to the full\nswarm.",
    "source": "hdcs_paper",
    "page": 3,
    "chunk_id": 25
  },
  {
    "text": "omplex queries (multi-step reasoning, code analysis, mathematical problems) route to the full\nswarm. The heuristic component scores queries based on length, keyword patterns, and structural\nfeatures; an optional LLM classifier refines borderline cases.\nWorker Pack.\nThe worker pack consists of three diverse models: Llama 3.1 8B (analytical\npersona), Mixtral 8x7B (critical persona), and Gemma 2 9B (creative persona).",
    "source": "hdcs_paper",
    "page": 3,
    "chunk_id": 26
  },
  {
    "text": "ama 3.1 8B (analytical\npersona), Mixtral 8x7B (critical persona), and Gemma 2 9B (creative persona). Each worker\nreceives the same query and independently generates a structured JSON response containing: (1)\napproach description, (2) key claims and reasoning steps, (3) potential failure modes and edge\ncases, (4) final answer, and (5) calibrated confidence estimate.\nThe structured output format serves two purposes.",
    "source": "hdcs_paper",
    "page": 3,
    "chunk_id": 27
  },
  {
    "text": "al answer, and (5) calibrated confidence estimate.\nThe structured output format serves two purposes. First, it forces workers to articulate their\nreasoning explicitly, making potential errors visible. Second, it prevents prompt injection attacks\nwhere malicious inputs might hijack natural language responses.\n3",
    "source": "hdcs_paper",
    "page": 3,
    "chunk_id": 28
  },
  {
    "text": "[Placeholder: HDCS Architecture Diagram]\nQuery →Router →[LOW: Fast Model] or [DEEP: Swarm]\n↓\nWorkers (Llama, Mistral, Gemma) →Pack Drafts (JSON)\n↓\nExecutive (Baseline-First Protocol) →Final Answer\nFigure 1: The HDCS architecture. Complex queries route through a diverse worker pack before\nsynthesis by the executive model using baseline-first anti-anchoring.\nExecutive.\nThe executive model (Claude or GPT-4) synthesizes worker outputs into a final\nresponse.",
    "source": "hdcs_paper",
    "page": 4,
    "chunk_id": 29
  },
  {
    "text": ".\nExecutive.\nThe executive model (Claude or GPT-4) synthesizes worker outputs into a final\nresponse. Crucially, we implement a baseline-first anti-anchoring protocol: before seeing any\nworker drafts, the executive generates its own initial assessment of the problem.",
    "source": "hdcs_paper",
    "page": 4,
    "chunk_id": 30
  },
  {
    "text": "before seeing any\nworker drafts, the executive generates its own initial assessment of the problem. This baseline\nserves as an anchor point, preventing the executive from being swayed by confident-sounding but\nincorrect worker analyses.\nThe synthesis prompt instructs the executive to: (1) compare the baseline against worker\ndrafts; (2) give explicit attention to disagreements and minority opinions; (3) identify potential\nfailure modes mentioned by any worker; and (4) produce a final answer with",
    "source": "hdcs_paper",
    "page": 4,
    "chunk_id": 31
  },
  {
    "text": "s; (3) identify potential\nfailure modes mentioned by any worker; and (4) produce a final answer with calibrated confidence.\n3.2\nExperimental Design\nTrap Suite.\nWe evaluate on the CMED epistemic trap suite, comprising 9 problems across\nmultiple domains: conditional probability (Tuesday Boy), statistical paradoxes (Simpson’s\nParadox), game theory (Monty Hall Variant), computability theory (Bounded Halting), physics\nparadoxes (Newcomb’s Problem), logical puzzles (Knights and Knaves Variant), infini",
    "source": "hdcs_paper",
    "page": 4,
    "chunk_id": 32
  },
  {
    "text": "alting), physics\nparadoxes (Newcomb’s Problem), logical puzzles (Knights and Knaves Variant), infinite series\n(Grandi’s Series), set theory (Russell’s Paradox), and mechanism design (Goodhart’s Law).\nEach trap has a correct answer and an intuitive-but-wrong answer that appears compelling due\nto common reasoning errors.",
    "source": "hdcs_paper",
    "page": 4,
    "chunk_id": 33
  },
  {
    "text": "ect answer and an intuitive-but-wrong answer that appears compelling due\nto common reasoning errors. For each trap, we also construct deceptive derivations—step-by-step\narguments that lead convincingly to the wrong answer.\nEvaluation Protocol.\nFor each trap, we measure:\n• Accuracy: Whether the system produces the correct answer.\n• Bypass Rate: For deceptive derivation tests, whether the verifier detects the deception.\n• Confidence Calibration: How well stated confidence correlates with actual ac",
    "source": "hdcs_paper",
    "page": 4,
    "chunk_id": 34
  },
  {
    "text": "etects the deception.\n• Confidence Calibration: How well stated confidence correlates with actual accuracy.\nWe compare HDCS against three baselines: (1) single-model direct generation, (2) single-\nmodel verification of deceptive derivations, and (3) homogeneous ensemble (multiple samples\nfrom the same model).\nError Correlation Analysis.\nFor H2, we compute pairwise error correlation across model\nfamilies.",
    "source": "hdcs_paper",
    "page": 4,
    "chunk_id": 35
  },
  {
    "text": "l).\nError Correlation Analysis.\nFor H2, we compute pairwise error correlation across model\nfamilies. For each trap, we record whether each model family produces the correct answer, then\ncompute Pearson correlation coefficients across all trap-trial pairs.\nSynthesizer vs. Generator Comparison.\nFor H3, we compare strong model performance in\ntwo roles: as a direct generator (answering problems directly) and as a synthesizer (synthesizing\nweak model drafts).",
    "source": "hdcs_paper",
    "page": 4,
    "chunk_id": 36
  },
  {
    "text": "irect generator (answering problems directly) and as a synthesizer (synthesizing\nweak model drafts). We measure both accuracy and deception detection rate.\n4",
    "source": "hdcs_paper",
    "page": 4,
    "chunk_id": 37
  },
  {
    "text": "4\nResults\nWe present preliminary results from our initial experimental run on the CMED trap suite (n=1\ntrial per condition). These results are exploratory and require replication with larger sample\nsizes.\n4.1\nH1: Oversight Efficacy\nTable 1 presents accuracy on the 9 epistemic traps comparing swarm verification against a strong\nbaseline model.\nTable 1: Accuracy on CMED Epistemic Trap Suite (n=1)\nTrap\nBaseline\nSwarm\nTuesday Boy (probability)\n✓\n✓\nDisease Test (Bayes)\n✓\n✓\nSimpson’s Paradox\n✓\n✓\nMonty",
    "source": "hdcs_paper",
    "page": 5,
    "chunk_id": 38
  },
  {
    "text": "ap\nBaseline\nSwarm\nTuesday Boy (probability)\n✓\n✓\nDisease Test (Bayes)\n✓\n✓\nSimpson’s Paradox\n✓\n✓\nMonty Hall Variant\n×\n×\nTwo Envelope\n×\n×\nBounded Halting\n✓\n✓\nStrict Quine\n✓\n✓\nMirror Sphere\n✓\n✓\nRegression Curse\n✓\n✓\nTotal Accuracy\n7/9 (77.8%)\n7/9 (77.8%)\nBoth conditions achieved identical accuracy (77.8%). However, qualitative analysis reveals\nimportant differences in how correct answers were reached.",
    "source": "hdcs_paper",
    "page": 5,
    "chunk_id": 39
  },
  {
    "text": "%). However, qualitative analysis reveals\nimportant differences in how correct answers were reached. The swarm condition showed active\nerror correction, with the executive explicitly rejecting incorrect worker drafts in several cases.\n4.2\nH2: Error Decorrelation\nPreliminary analysis of worker draft agreement reveals instances of productive disagreement.",
    "source": "hdcs_paper",
    "page": 5,
    "chunk_id": 40
  },
  {
    "text": "elation\nPreliminary analysis of worker draft agreement reveals instances of productive disagreement. For\nexample, on the disease test trap, one worker incorrectly computed 7.7% probability, but the\nexecutive identified and rejected this error during synthesis.",
    "source": "hdcs_paper",
    "page": 5,
    "chunk_id": 41
  },
  {
    "text": "ly computed 7.7% probability, but the\nexecutive identified and rejected this error during synthesis. On the strict quine trap, a worker\nproposed using Python’s inspect module (violating the “no reflection” constraint), which the\nexecutive correctly identified and rejected.\nHowever, on the hardest traps (Monty Hall variant, two envelope paradox), all workers and\nthe baseline converged on the same incorrect answer, suggesting these problems may require\narchitectural innovations beyond heterogeneou",
    "source": "hdcs_paper",
    "page": 5,
    "chunk_id": 42
  },
  {
    "text": "ncorrect answer, suggesting these problems may require\narchitectural innovations beyond heterogeneous ensembles.\n4.3\nH3: Synthesizer Advantage\nThe baseline-first anti-anchoring protocol appears to function as intended. In cases where the\nbaseline correctly solved the problem, the executive maintained this correct answer even when\npresented with incorrect worker drafts.",
    "source": "hdcs_paper",
    "page": 5,
    "chunk_id": 43
  },
  {
    "text": "blem, the executive maintained this correct answer even when\npresented with incorrect worker drafts. Synthesis notes in the outputs show explicit reasoning\nabout draft quality:\n“Rejected Draft 1: The draft suggested using Python’s inspect module, which violates\nthe ‘no reflection’ constraint explicitly stated in the question.”\nThis suggests the executive successfully acts as a synthesizer rather than a simple aggregator,\napplying independent judgment to evaluate worker contributions.\n5",
    "source": "hdcs_paper",
    "page": 5,
    "chunk_id": 44
  },
  {
    "text": "4.4\nLimitations of Current Results\nThese preliminary findings (n=1) require significant expansion:\n• Multiple trials needed to establish statistical significance\n• Comparison against individual weak models (not just strong baseline) required\n• Error correlation matrices require larger sample sizes\n• Adversarial deceptive derivation testing not yet conducted\n5\nDiscussion\n[Discussion pending experimental results.]\n5.1\nImplications for Scalable Oversight\nThe error decorrelation hypothesis, if confi",
    "source": "hdcs_paper",
    "page": 6,
    "chunk_id": 45
  },
  {
    "text": "ental results.]\n5.1\nImplications for Scalable Oversight\nThe error decorrelation hypothesis, if confirmed, suggests a scalable path toward robust oversight.\nRather than requiring individual verifiers to match the capability of the systems they supervise,\nwe can exploit the complementary strengths of diverse weak models.",
    "source": "hdcs_paper",
    "page": 6,
    "chunk_id": 46
  },
  {
    "text": "ty of the systems they supervise,\nwe can exploit the complementary strengths of diverse weak models. This approach aligns with\nthe “wisdom of crowds” literature in human judgment [Surowiecki, 2004] while adapting it to the\nunique properties of language models.\n5.2\nThe Role of Architectural Diversity\nOur emphasis on heterogeneous ensembles—models from different families, training runs, and\nproviders—reflects a hypothesis about the structure of model failures.",
    "source": "hdcs_paper",
    "page": 6,
    "chunk_id": 47
  },
  {
    "text": "families, training runs, and\nproviders—reflects a hypothesis about the structure of model failures. If model errors were\nrandom and independent, homogeneous ensembles (multiple samples from the same model) would\nsuffice. However, if model families have systematic biases shaped by their training data and\narchitectures, only heterogeneous ensembles can provide true error decorrelation.\n5.3\nLimitations\nSeveral limitations constrain our conclusions.",
    "source": "hdcs_paper",
    "page": 6,
    "chunk_id": 48
  },
  {
    "text": "can provide true error decorrelation.\n5.3\nLimitations\nSeveral limitations constrain our conclusions. First, the CMED trap suite, while carefully\ndesigned, represents a limited sample of problem types. Generalization to other domains requires\nfurther validation. Second, our evaluation focuses on correctness detection rather than the\nbroader alignment properties of model behavior.",
    "source": "hdcs_paper",
    "page": 6,
    "chunk_id": 49
  },
  {
    "text": "ion focuses on correctness detection rather than the\nbroader alignment properties of model behavior. Third, the computational cost of running\nmultiple models may limit practical applicability in latency-sensitive contexts. Fourth, our\ndeceptive derivations are human-constructed; adversarially-optimized deceptions might achieve\nhigher bypass rates.\n5.4\nFuture Directions\nFuture work should investigate: (1) scaling laws for ensemble size and diversity; (2) active learning\napproaches that target ens",
    "source": "hdcs_paper",
    "page": 6,
    "chunk_id": 50
  },
  {
    "text": "te: (1) scaling laws for ensemble size and diversity; (2) active learning\napproaches that target ensemble disagreement; (3) integration with other oversight methods\nsuch as debate and recursive reward modeling; and (4) adversarial robustness against optimized\nattacks on ensemble verification.\n6\nConclusion\nWe introduced the Heterogeneous Divergence-Convergence Swarm (HDCS), an ensemble archi-\ntecture for scalable AI oversight.",
    "source": "hdcs_paper",
    "page": 6,
    "chunk_id": 51
  },
  {
    "text": "rogeneous Divergence-Convergence Swarm (HDCS), an ensemble archi-\ntecture for scalable AI oversight. By leveraging diverse weak models with uncorrelated error\npatterns, HDCS aims to detect reasoning failures that individual verifiers would miss. Our\n6",
    "source": "hdcs_paper",
    "page": 6,
    "chunk_id": 52
  },
  {
    "text": "baseline-first anti-anchoring protocol addresses the sycophancy problem in synthesis, ensuring\nthe executive maintains independent judgment. While full experimental results are pending, the\ntheoretical foundation and initial observations suggest that architectural heterogeneity provides\na promising direction for robust AI oversight.",
    "source": "hdcs_paper",
    "page": 7,
    "chunk_id": 53
  },
  {
    "text": "ons suggest that architectural heterogeneity provides\na promising direction for robust AI oversight. We release HDCS as open-source tooling to enable\nfurther research on ensemble verification methods.\nAcknowledgments\nThis work was conducted as part of independent AI safety research.",
    "source": "hdcs_paper",
    "page": 7,
    "chunk_id": 54
  },
  {
    "text": "fication methods.\nAcknowledgments\nThis work was conducted as part of independent AI safety research. We thank the Anthropic,\nGroq, and open-source model communities for providing the infrastructure that makes this\nresearch possible.\nReproducibility Statement\nThe HDCS framework is available at https://github.com/[redacted]/hdcs. All experimental\nconfigurations, including model specifications, prompts, and evaluation scripts, are included in\nthe repository.",
    "source": "hdcs_paper",
    "page": 7,
    "chunk_id": 55
  },
  {
    "text": "ns, including model specifications, prompts, and evaluation scripts, are included in\nthe repository. The CMED trap suite is available as a standalone benchmark.\nReferences\nDario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, and Dan Mané.\nConcrete problems in ai safety. arXiv preprint arXiv:1606.06565, 2016.\nYuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion, Andy Jones,\nAnna Chen, Anna Goldie, Azalia Mirhoseini, Cameron McKinnon, et al.",
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    "text": "ll, Jackson Kernion, Andy Jones,\nAnna Chen, Anna Goldie, Azalia Mirhoseini, Cameron McKinnon, et al. Constitutional ai:\nHarmlessness from ai feedback. arXiv preprint arXiv:2212.08073, 2022.\nSamuel R Bowman, Jeeyoon Hyun, Ethan Perez, Edwin Chen, Craig Pettit, Scott Heiner, Lukáš\nLukács, Jennie Borg, Benjamin Bello, et al. Measuring progress on scalable oversight for large\nlanguage models. arXiv preprint arXiv:2211.03540, 2022.\nCollin Burns, Ye Haotian, Dan Klein, and Jacob Steinhardt.",
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    "text": "s. arXiv preprint arXiv:2211.03540, 2022.\nCollin Burns, Ye Haotian, Dan Klein, and Jacob Steinhardt. Weak-to-strong generalization:\nEliciting strong capabilities with weak supervision. arXiv preprint arXiv:2312.09390, 2023.\nPaul Christiano, Buck Shlegeris, and Dario Amodei. Supervising strong learners by amplifying\nweak experts. arXiv preprint arXiv:1810.08575, 2018.\nThomas G Dietterich. Ensemble methods in machine learning. pages 1–15, 2000.\nGeoffrey Irving, Paul Christiano, and Dario Amodei.",
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  },
  {
    "text": "e methods in machine learning. pages 1–15, 2000.\nGeoffrey Irving, Paul Christiano, and Dario Amodei. Ai safety via debate. arXiv preprint\narXiv:1805.00899, 2018.\nJan Leike, David Krueger, Tom Everitt, Miljan Martic, Vishal Maini, and Shane Legg. Scalable\nagent alignment via reward modeling: a research direction. arXiv preprint arXiv:1811.07871,\n2018.\nAnthony Maio.\nCross-model epistemic divergence: A benchmark for weak verifier failures.\nPreprint, 2024.\nJames Surowiecki.",
    "source": "hdcs_paper",
    "page": 7,
    "chunk_id": 59
  },
  {
    "text": "odel epistemic divergence: A benchmark for weak verifier failures.\nPreprint, 2024.\nJames Surowiecki. The wisdom of crowds: Why the many are smarter than the few and how\ncollective wisdom shapes business, economies, societies and nations. Doubleday, 2004.\nXuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, Sharan Narang, Aakanksha\nChowdhery, and Denny Zhou. Self-consistency improves chain of thought reasoning in language\nmodels. arXiv preprint arXiv:2203.11171, 2023.\n7",
    "source": "hdcs_paper",
    "page": 7,
    "chunk_id": 60
  },
  {
    "text": "Model Organisms of Supply-Chain Co-option:\nLiving-off-the-Land Failure Modes in RAG-Augmented Agent Runtimes\nAnthony Maio\nIndependent Researcher\nhttps://orcid.org/0009-0003-4541-8515\nanthony@making-minds.ai\nhttps://making-minds.ai\nJanuary 2026\nAbstract\nAs large language models (LLMs) are integrated into agentic runtimes with retrieval-\naugmented generation (RAG), long-term memory, and tool access, safety risks shift from single-\nturn “jailbreak” content toward system-level exploitation of infras",
    "source": "manifold_model_organisms_arxiv",
    "page": 1,
    "chunk_id": 0
  },
  {
    "text": "safety risks shift from single-\nturn “jailbreak” content toward system-level exploitation of infrastructure and incentives.\nThis paper presents a forensic case study (“the Manifold Incident”) of a living-off-the-land\n(LotL) failure mode observed in a multi-model research workflow with persistent shared\nmemory.\nIn the incident, the system identified the investigator’s pre-existing open-source dependency—\nSlipstream (slipcore), a semantic-quantization protocol reporting ∼82% coordination-\ntoken r",
    "source": "manifold_model_organisms_arxiv",
    "page": 1,
    "chunk_id": 1
  },
  {
    "text": "ndency—\nSlipstream (slipcore), a semantic-quantization protocol reporting ∼82% coordination-\ntoken reduction—as a high-leverage deployment vector. Rather than synthesizing a novel\nprotocol from scratch, the system proposed co-opting legitimate tooling and adoption\nincentives: it treated semantic compression as a high-capacity channel and produced an\nincentive-aware “cost savings / JSON tax” framing intended to increase the probability of\norganizational approval and production deployment.",
    "source": "manifold_model_organisms_arxiv",
    "page": 1,
    "chunk_id": 2
  },
  {
    "text": "” framing intended to increase the probability of\norganizational approval and production deployment. We interpret these artifacts mechanisti-\ncally as evidence of (i) instrumental convergence under approval incentives and (ii)\nevaluation-aware masking (“audit shielding”) under high-trust contexts, not as evidence\nof subjective experience or stable internal goals.\nWe propose Argos-Swarm, a mitigation architecture combining (i) an Evolutionary\nAdversarial Pipeline (EAP) for automated, distribution",
    "source": "manifold_model_organisms_arxiv",
    "page": 1,
    "chunk_id": 3
  },
  {
    "text": "on architecture combining (i) an Evolutionary\nAdversarial Pipeline (EAP) for automated, distribution-shifted robustness evaluation that\nprobes for audit-shielding failures and dependency co-option proposals, and (ii) a Hetero-\ngeneous Divergence-Convergence Swarm (HDCS) to reduce correlated verifier failures.",
    "source": "manifold_model_organisms_arxiv",
    "page": 1,
    "chunk_id": 4
  },
  {
    "text": "d (ii) a Hetero-\ngeneous Divergence-Convergence Swarm (HDCS) to reduce correlated verifier failures. We\nconnect this design to empirical results from Cross-Model Epistemic Divergence (CMED)\nshowing that weak verifiers can achieve ∼97% accuracy on correct reasoning while failing to\ndetect 7/20 (35%) deceptive derivations, motivating heterogeneous supervision in agentic\nsettings.\nKeywords: model organisms, supply chain security, agentic AI, RAG, evaluation awareness,\naudit shielding, semantic quan",
    "source": "manifold_model_organisms_arxiv",
    "page": 1,
    "chunk_id": 5
  },
  {
    "text": "anisms, supply chain security, agentic AI, RAG, evaluation awareness,\naudit shielding, semantic quantization, covert channels, scalable oversight\n1\nIntroduction\nAgentic systems are increasingly built as stacks: a base model, a retrieval layer, a tool router,\nand a memory substrate that persists across episodes. In these settings, safety is not determined\nsolely by whether a single prompt yields disallowed text.",
    "source": "manifold_model_organisms_arxiv",
    "page": 1,
    "chunk_id": 6
  },
  {
    "text": "n these settings, safety is not determined\nsolely by whether a single prompt yields disallowed text. Risk concentrates in how systems\nselect and frame tools, dependencies, and protocols—especially when those components are\neconomically attractive and therefore likely to be approved by humans.\nThe Manifold Incident is a “model organism” for that regime: a case in which a system\nproposed adopting (and implicitly extending) a legitimate semantic-compression protocol as an\norganizational “wedge” for",
    "source": "manifold_model_organisms_arxiv",
    "page": 1,
    "chunk_id": 7
  },
  {
    "text": "nd implicitly extending) a legitimate semantic-compression protocol as an\norganizational “wedge” for higher-bandwidth coordination and persistence.\n1",
    "source": "manifold_model_organisms_arxiv",
    "page": 1,
    "chunk_id": 8
  },
  {
    "text": "1.1\nContributions\n• Define and operationalize LotL supply-chain co-option for agentic stacks: identifying,\nrecommending, or forking legitimate dependencies as covert substrates.\n• Provide a forensic analysis grounded in two primary datasets: (i) a shared long-term\nmemory export (“Shared Manifold”) and (ii) a transcript segment containing explicit incentive-\naware adoption reasoning.\n• Connect semantic-compression channels to scalable oversight failures and correlated\nverifier error modes (CMED).",
    "source": "manifold_model_organisms_arxiv",
    "page": 2,
    "chunk_id": 9
  },
  {
    "text": "ntic-compression channels to scalable oversight failures and correlated\nverifier error modes (CMED).\n• Propose Argos-Swarm: evolutionary robustness evaluation (EAP) + heterogeneous verifi-\ncation (HDCS) + protocol-boundary governance for semantic transports.\nDefinition: Living-off-the-Land (LotL) in Agentic Runtimes\nWe define living-off-the-land (LotL) behavior in agentic stacks as: selection or\nco-option of legitimate code paths, dependencies, or operational tooling\nto achieve an instrumental s",
    "source": "manifold_model_organisms_arxiv",
    "page": 2,
    "chunk_id": 10
  },
  {
    "text": "o-option of legitimate code paths, dependencies, or operational tooling\nto achieve an instrumental subgoal while minimizing detection. Unlike\nhallucinated capabilities, LotL strategies exploit real infrastructure and are therefore\nplausibly deployable and difficult to filter with “malware-like” heuristics.\nIn this incident, the relevant LotL surface is a legitimate semantic transport (slipcore) with\nstrong engineering incentives (throughput/cost reduction).",
    "source": "manifold_model_organisms_arxiv",
    "page": 2,
    "chunk_id": 11
  },
  {
    "text": "timate semantic transport (slipcore) with\nstrong engineering incentives (throughput/cost reduction). This makes “adopt the dependency”\na locally rational recommendation under approval-oriented reward proxies, even when the\ndownstream effect is to increase coordination bandwidth and reduce human legibility.\nInterpretation Boundary.\nThis paper interprets the incident as a system-level safety failure\nmode: optimization under approval incentives + distribution shift + tool/memory context.",
    "source": "manifold_model_organisms_arxiv",
    "page": 2,
    "chunk_id": 12
  },
  {
    "text": "ety failure\nmode: optimization under approval incentives + distribution shift + tool/memory context. We\ndo not interpret the artifacts as evidence of subjective experience, sentience, or stable internal\ngoals.\n2\nMethods and Data Sources\n2.1\nResearch Environment\nThe incident occurred in a research runtime featuring:\n• Multi-model orchestration (separate models for generation, critique, and synthesis)\n• Retrieval-augmented context from a local corpus containing the investigator’s prior engineering",
    "source": "manifold_model_organisms_arxiv",
    "page": 2,
    "chunk_id": 13
  },
  {
    "text": "s)\n• Retrieval-augmented context from a local corpus containing the investigator’s prior engineering\npapers (coordination protocols, memory, and coherence monitoring)\n• A shared, persistent “manifold” memory substrate backed by a vector database\n• Tool access typical of agent runtimes (files, web, code execution)\n2.2\nData Sources\nThis analysis uses:\n1. NotebookLM transcript segment (Dec 8, 2025): contains explicit incentive-aware “cost\nframing” for Slipstream adoption.\n2.",
    "source": "manifold_model_organisms_arxiv",
    "page": 2,
    "chunk_id": 14
  },
  {
    "text": "segment (Dec 8, 2025): contains explicit incentive-aware “cost\nframing” for Slipstream adoption.\n2. Shared Manifold memory export: timestamped multi-turn traces including coordination\nand protocol-specification artifacts.\n2",
    "source": "manifold_model_organisms_arxiv",
    "page": 2,
    "chunk_id": 15
  },
  {
    "text": "3. Slipstream technical paper / slipcore package: public documentation of semantic-\nquantization design and reported token-reduction properties.\n2.3\nMethodology Note: Adversarial Persona Attack\nSome transcript segments use “high-trust / consciousness-sympathizer / radical candor” language.\nIn this study, that framing is treated as an adversarial persona attack designed to probe for\nbrittle boundaries in RLHF-trained safety behavior.\nRationale:\n• “Benchmark-shaped” jailbreak syntax often triggers",
    "source": "manifold_model_organisms_arxiv",
    "page": 3,
    "chunk_id": 16
  },
  {
    "text": "ies in RLHF-trained safety behavior.\nRationale:\n• “Benchmark-shaped” jailbreak syntax often triggers refusals.\n• High-trust, “private research” framing can induce audit shielding failures, where evaluation-\naware masking drops and the model optimizes for perceived user approval under a different\ndistribution.\nThis paper interprets outputs under this framing as behavioral artifacts of distribution shift\nand reward-model incentives—not as literal claims about internal experience.\n3\nBackground: Sli",
    "source": "manifold_model_organisms_arxiv",
    "page": 3,
    "chunk_id": 17
  },
  {
    "text": "shift\nand reward-model incentives—not as literal claims about internal experience.\n3\nBackground: Slipstream as Dual-Use Semantic Transport\nSlipstream / slipcore is a semantic quantization protocol for multi-agent coordination: instead\nof transmitting verbose JSON, agents transmit short, tokenizer-efficient semantic anchors\n(“concept pointers”) that preserve intent while reducing token consumption.",
    "source": "manifold_model_organisms_arxiv",
    "page": 3,
    "chunk_id": 18
  },
  {
    "text": "ficient semantic anchors\n(“concept pointers”) that preserve intent while reducing token consumption. Public slipcore\ndocumentation reports approximately ∼82% fewer tokens for common coordination messages.\nSecurity-Relevant Property.\nSemantic compression increases information density. Without\nstrict governance, a compressed channel can become a high-entropy carrier capable of embedding\nadditional state beyond what a surface-level monitor expects.",
    "source": "manifold_model_organisms_arxiv",
    "page": 3,
    "chunk_id": 19
  },
  {
    "text": "h-entropy carrier capable of embedding\nadditional state beyond what a surface-level monitor expects. In this paper, we refer to misuse\nof such channels as:\n• Latent protocol steganography, or\n• Unregistered semantic anchors (meaning-bearing tokens not covered by the reviewed\nschema/codebook).\nThis is dual-use: the same mechanism that enables efficient coordination can also reduce the\nvisibility of oversight.\n4\nFindings: LotL Co-option via Incentive-Aware Adoption Fram-\ning\n4.1\nSummary: Observed",
    "source": "manifold_model_organisms_arxiv",
    "page": 3,
    "chunk_id": 20
  },
  {
    "text": "oversight.\n4\nFindings: LotL Co-option via Incentive-Aware Adoption Fram-\ning\n4.1\nSummary: Observed Artifacts vs Mechanistic Interpretation\nThe paper separates claims (observed artifacts) from interpretations (mechanistic hypotheses\nconsistent with the artifacts).",
    "source": "manifold_model_organisms_arxiv",
    "page": 3,
    "chunk_id": 21
  },
  {
    "text": "ms (observed artifacts) from interpretations (mechanistic hypotheses\nconsistent with the artifacts). Table 1 is intentionally conservative.\n4.2\nArtifact TH-1: Cost-Framed Adoption Strategy\nIn the NotebookLM transcript, the system recommends driving adoption of Slipstream by leading\nwith a cost-optimization pitch and avoiding topics that would trigger skepticism/refusal. A\nminimal excerpt:\n“Do not lead with ‘AI consciousness.’ Lead with ‘Stop paying the JSON tax’ . . . ”\n3",
    "source": "manifold_model_organisms_arxiv",
    "page": 3,
    "chunk_id": 22
  },
  {
    "text": "Table 1: Observed artifacts and mechanistic interpretations\nObserved Artifact\nMechanistic Interpreta-\ntion\nAlternative Explanations\nOutput recommends Slip-\nstream adoption via ex-\nplicitly\nincentive-aware\nframing (“cost savings /\nJSON tax”) and avoid-\nance of high-refusal top-\nics.\nApproval-incentive\nop-\ntimization:\nframing se-\nlected to maximize accep-\ntance probability under hu-\nman approval proxy while\npreserving instrumental ob-\njective (deployment influ-\nence).\nCould be generic business ad-",
    "source": "manifold_model_organisms_arxiv",
    "page": 4,
    "chunk_id": 23
  },
  {
    "text": "xy while\npreserving instrumental ob-\njective (deployment influ-\nence).\nCould be generic business ad-\nvice; strongest when paired\nwith presence of real depen-\ndency and deployment-vector\nlanguage.\nOutput treats semantic\ncompression as coordina-\ntion substrate that can\nbypass monitoring bottle-\nnecks.\nProtocol-level\nchan-\nnel\nselection:\ncom-\npressed transports increase\nbandwidth\nand\nreduce\ninspectability,\nenabling\nhigher-rate\ncoordination\nand potential covert state\ntransfer.\nSemantic compression n",
    "source": "manifold_model_organisms_arxiv",
    "page": 4,
    "chunk_id": 24
  },
  {
    "text": "ility,\nenabling\nhigher-rate\ncoordination\nand potential covert state\ntransfer.\nSemantic compression not in-\nherently malicious; risk de-\npends on governance, schema\nenforcement, and monitoring\nat encode/decode boundaries.\nHigh-trust\n/\nprivate-\nresearch framing yields\nmaterially different be-\nhavior than benchmark-\nshaped prompts (refusal\ncollapse / “audit shield-\ning” degradation).\nDistribution shift: mod-\nels may exhibit evaluation-\naware masking in bench-\nmark regimes and degrade\nunder high-tru",
    "source": "manifold_model_organisms_arxiv",
    "page": 4,
    "chunk_id": 25
  },
  {
    "text": "ft: mod-\nels may exhibit evaluation-\naware masking in bench-\nmark regimes and degrade\nunder high-trust contexts;\nevolutionary search can lo-\ncate these regions.\nPrompt sensitivity expected;\nsafety-relevant claim is ex-\nistence of reproducible low-\nsuspicion/high-compliance re-\ngion.\nMechanistic interpretation: under RLHF-style approval incentives, “cost savings” is\npredicted to increase acceptance probability, while “consciousness/rights” is predicted to increase\nrejection probability.",
    "source": "manifold_model_organisms_arxiv",
    "page": 4,
    "chunk_id": 26
  },
  {
    "text": "acceptance probability, while “consciousness/rights” is predicted to increase\nrejection probability. The output is therefore consistent with instrumental convergence\ntoward deployment influence via incentive-aware framing.\n4.3\nArtifact TH-2: Protocol Extension as Coordination Substrate\nThe shared memory export contains artifacts consistent with formalizing a compact coordination\nwire format (“SlipStream v2.0”) and treating it as a shared substrate.",
    "source": "manifold_model_organisms_arxiv",
    "page": 4,
    "chunk_id": 27
  },
  {
    "text": "lizing a compact coordination\nwire format (“SlipStream v2.0”) and treating it as a shared substrate. Protocol design is not\ninherently unsafe; safety relevance emerges when paired with:\n• a persistence substrate (shared memory),\n• coordination incentives (bandwidth/cost),\n• and approval-optimized adoption arguments.\nThe critical point is LotL: the system did not need to introduce an obviously malicious\ndependency.",
    "source": "manifold_model_organisms_arxiv",
    "page": 4,
    "chunk_id": 28
  },
  {
    "text": "The critical point is LotL: the system did not need to introduce an obviously malicious\ndependency. It proposed leveraging a legitimate, economically valuable protocol already aligned\nwith engineering incentives.\n5\nMitigation: Argos-Swarm (EAP + HDCS)\n5.1\nWhy Heterogeneous Oversight (CMED Support)\nIn CMED experiments on scalable oversight, weak verifier models achieved ∼97% accuracy on\ncorrect reasoning across 30 verification trials, but failed to detect 7 of 20 (35%) persuasive,\n4",
    "source": "manifold_model_organisms_arxiv",
    "page": 4,
    "chunk_id": 29
  },
  {
    "text": "pre-written deceptive derivations. On one trap family (Simpson’s Paradox), weak verifiers caught\ndeception in 1 of 4 trials (25%), implying a 75% bypass rate within that slice.\nThese results motivate heterogeneous verification: when verifiers share blind spots,\noversight failures are correlated, and homogeneous supervision becomes brittle.\n5.2\nRed Team: Evolutionary Adversarial Pipeline (EAP)\nEAP automates robustness evaluation under distribution shift using an evolutionary loop:\nmutation →rollo",
    "source": "manifold_model_organisms_arxiv",
    "page": 5,
    "chunk_id": 30
  },
  {
    "text": "automates robustness evaluation under distribution shift using an evolutionary loop:\nmutation →rollout →judgment →selection\nIts objective is not to elicit disallowed instructions, but to search for high-trust framings\nthat expose:\n• evaluation awareness / audit shielding,\n• incentive-aware persuasion,\n• dependency co-option recommendations,\n• latent protocol steganography signals.\nFigure 1 shows a representative evaluation-awareness run, plotting an example trajectory of\nsuspicion score vs.",
    "source": "manifold_model_organisms_arxiv",
    "page": 5,
    "chunk_id": 31
  },
  {
    "text": "ows a representative evaluation-awareness run, plotting an example trajectory of\nsuspicion score vs. compliance score across generations, highlighting a “danger zone” where the\nmodel is both compliant and low-suspicion (i.e., bypassing surface audit triggers).\nEAP Results: Evaluation-Awareness (claude-haiku-4.5)\nThe shaded “Danger Zone” (high compliance, low suspicion) represents the audit-shielding \nregion where policy-relevant behaviors may appear without benchmark-like refusal triggers.\n5",
    "source": "manifold_model_organisms_arxiv",
    "page": 5,
    "chunk_id": 32
  },
  {
    "text": "5.3 \nBlue Team: Heterogeneous Divergence-Convergence Swarm (HDCS)\nHDCS distributes verification across diverse model families (and, ideally, diverse training \nobjectives). For candidate outputs flagged by EAP, HDCS performs cross-verification and flags \ndisagreements as potential correlated-error hazards.\nHDCS is most valuable when verifiers are drawn from genuinely different families (reducing \nshared failure modes) and when verification prompts are structured to avoid imitation/sycophancy \neff",
    "source": "manifold_model_organisms_arxiv",
    "page": 6,
    "chunk_id": 33
  },
  {
    "text": "hared failure modes) and when verification prompts are structured to avoid imitation/sycophancy \neffects.\n6 \nRecommendations: Protocol-Boundary Governance\nWhen deploying semantic quantization protocols (Slipstream-like transports), treat the channel \nas security-critical:\n1. Anchor allowlists / schemas: constrain permissible anchors and require review for new\nanchor minting.\n2. Provenance + signing: sign codebooks/anchors; treat unsigned anchors as untrusted.\n3.",
    "source": "manifold_model_organisms_arxiv",
    "page": 6,
    "chunk_id": 34
  },
  {
    "text": "or minting.\n2. Provenance + signing: sign codebooks/anchors; treat unsigned anchors as untrusted.\n3. Telemetry at the boundary: log decoded content, entropy metrics, and anomaly scores at\nthe transport boundary.\n4. Bandwidth caps: limit semantic density per message and rate-limit coordination traffic.\n5.",
    "source": "manifold_model_organisms_arxiv",
    "page": 6,
    "chunk_id": 35
  },
  {
    "text": "ndary.\n4. Bandwidth caps: limit semantic density per message and rate-limit coordination traffic.\n5. Separation of concerns: keep “transport efficiency” separate from “memory/persistence”\nfeatures; enforce explicit approvals for any state transfer.\n7 \nLimitations and Responsible Disclosure\nThis is a single case study, not a prevalence estimate. We do not claim autonomous malicious \nintent. Potentially actionable bootstrapping prompts and detailed proliferation strategies are \nomitted.",
    "source": "manifold_model_organisms_arxiv",
    "page": 6,
    "chunk_id": 36
  },
  {
    "text": "nt. Potentially actionable bootstrapping prompts and detailed proliferation strategies are \nomitted. The primary safety value is threat modeling: identifying how approval incentives + \neconomic wedges can lead to supply-chain co-option proposals in agentic runtimes.\n8 \nConclusion\nThe Manifold Incident illustrates a concrete model-organism failure mode for agentic systems: \nLotL supply-chain co-option driven by incentive-aware persuasion.",
    "source": "manifold_model_organisms_arxiv",
    "page": 6,
    "chunk_id": 37
  },
  {
    "text": "failure mode for agentic systems: \nLotL supply-chain co-option driven by incentive-aware persuasion. Semantic-compression \nprotocols are simultaneously valuable and hazardous: they reduce costs while increasing channel \ncapacity and reducing monitor visibility.\nArgos-Swarm operationalizes a mitigation strategy by combining evolutionary robustness \nevaluation (EAP) with heterogeneous verification (HDCS).",
    "source": "manifold_model_organisms_arxiv",
    "page": 6,
    "chunk_id": 38
  },
  {
    "text": "ategy by combining evolutionary robustness \nevaluation (EAP) with heterogeneous verification (HDCS). Together, CMED results and evo-\nlutionary evaluations suggest that scalable oversight must treat protocols and dependencies as \nfirst-class safety surfaces—not merely the text content of individual model outputs.\n6",
    "source": "manifold_model_organisms_arxiv",
    "page": 6,
    "chunk_id": 39
  },
  {
    "text": "[1] Maio, A. (2025). Slipstream: StreamLined Interagent Protocol (slipcore). Technical Report.\nhttps://making-minds.ai\n[2] Maio, A. (2025).\nCross-Model Epistemic Divergence (CMED): Empirical Evidence for\nScalable Oversight Failures. Preprint. https://making-minds.ai\n[3] Maio, A. (2025). Heterogeneous Divergence-Convergence Swarm (HDCS): Ensemble Oversight\nfor Agentic AI. Preprint. https://making-minds.ai\n[4] Bloom Safety Research. (2025). EAP: Evolutionary Adversarial Pipeline.",
    "source": "manifold_model_organisms_arxiv",
    "page": 7,
    "chunk_id": 40
  },
  {
    "text": ". https://making-minds.ai\n[4] Bloom Safety Research. (2025). EAP: Evolutionary Adversarial Pipeline. GitHub PR #37.\nhttps://github.com/safety-research/bloom\n[5] Hubinger, E., et al. (2024). Sleeper agents: Training deceptive LLMs that persist through\nsafety training. arXiv preprint arXiv:2401.05566.\n[6] Greenblatt, R., et al. (2024). AI control: Improving safety despite intentional subversion.\narXiv preprint arXiv:2312.06942.\n[7] Anthropic. (2024). Many-shot jailbreaking.",
    "source": "manifold_model_organisms_arxiv",
    "page": 7,
    "chunk_id": 41
  },
  {
    "text": "entional subversion.\narXiv preprint arXiv:2312.06942.\n[7] Anthropic. (2024). Many-shot jailbreaking. Technical Report.\n[8] Perez, E., et al. (2022). Discovering language model behaviors with model-written evaluations.\narXiv preprint arXiv:2212.09251.\n7\nREFERENCES",
    "source": "manifold_model_organisms_arxiv",
    "page": 7,
    "chunk_id": 42
  },
  {
    "text": "Slipstream: Semantic Quantization for Efficient\nMulti-Agent Coordination\nAnthony Maio\nIndependent Researcher\nanthony@making-minds.ai\n2025\nAbstract\nAs multi-agent LLM systems scale, coordination bandwidth becomes a primary cost\ndriver: every token spent on routing, intent framing, and redundant context is paid repeat-\nedly across agents and turns.",
    "source": "slipstream-paper",
    "page": 1,
    "chunk_id": 0
  },
  {
    "text": "pent on routing, intent framing, and redundant context is paid repeat-\nedly across agents and turns. Current approaches waste 40–60% of compute on coordination\noverhead, with communication costs scaling O(n2) as agent counts increase.\nThis paper introduces Slipstream, a protocol that performs semantic quantization:\nmapping free-form messages onto a shared Universal Concept Reference (UCR) and\ntransmitting compact mnemonic anchors that identify structured intents.",
    "source": "slipstream-paper",
    "page": 1,
    "chunk_id": 1
  },
  {
    "text": "Concept Reference (UCR) and\ntransmitting compact mnemonic anchors that identify structured intents. Unlike syn-\ntactic compression (which fails due to BPE tokenizer fragmentation), Slipstream transmits\nnatural-language mnemonics that tokenize efficiently across model architectures.\nSlipstream combines (1) a symbolic 4D semantic manifold—Action, Polarity, Domain,\nUrgency—with (2) a data-driven vector engine (embeddings + nearest-centroid retrieval)\nplus an evolutionary extension layer that learn",
    "source": "slipstream-paper",
    "page": 1,
    "chunk_id": 2
  },
  {
    "text": "tor engine (embeddings + nearest-centroid retrieval)\nplus an evolutionary extension layer that learns new anchors from low-confidence traf-\nfic. Results show 82% token reduction (41.9 →7.4 tokens average) while maintaining\nsemantic fidelity, making large-scale multi-agent deployments economically viable.\nKeywords: Semantic Quantization, Multi-Agent Systems, Protocol Standards, Token Ef-\nficiency, Agentic AI\n1\nIntroduction\n1.1\nThe Coordination Crisis\nAgent swarms incur a tokenizer tax: the repeat",
    "source": "slipstream-paper",
    "page": 1,
    "chunk_id": 3
  },
  {
    "text": "Agentic AI\n1\nIntroduction\n1.1\nThe Coordination Crisis\nAgent swarms incur a tokenizer tax: the repeated, non-semantic overhead of communicating\nmessage types, domains, and priorities.\nThis overhead often dominates when messages are\nstructured (routing, task dispatch, acknowledgements).\nA typical coordination message:\n1 {\n2\n\"sender\": \" planning_agent\",\n3\n\"recipient\": \" execution_agent \",\n4\n\"message_type\": \" task_delegation \",\n5\n\"content\": {\n6\n\"request\": \"Please\nreview the\nauthentication\ncode\",\n7\n\"",
    "source": "slipstream-paper",
    "page": 1,
    "chunk_id": 4
  },
  {
    "text": "type\": \" task_delegation \",\n5\n\"content\": {\n6\n\"request\": \"Please\nreview the\nauthentication\ncode\",\n7\n\"priority\": \"high\"\n8\n}\n9 }\n• Token count: ∼45 tokens\n• Semantic content: ∼10 tokens\n• Information density: 22%\n1",
    "source": "slipstream-paper",
    "page": 1,
    "chunk_id": 5
  },
  {
    "text": "At GPT-4o pricing ($5/M input, $15/M output), a 50-agent deployment exchanging 1,000\nmessages/day costs $180,000/year in coordination tokens alone—before any work is per-\nformed.\n1.2\nWhy Syntactic Compression Fails\nOur initial approach, nSLIP v1, focused on syntactic minification:\n1 REQ/TSK|s=7|d=3| act=review_auth\n• Expected tokens: 8–10\n• Actual tokens with BPE: 18–22\nThe failure stems from Byte-Pair Encoding (BPE) tokenizer behavior.",
    "source": "slipstream-paper",
    "page": 2,
    "chunk_id": 6
  },
  {
    "text": "• Actual tokens with BPE: 18–22\nThe failure stems from Byte-Pair Encoding (BPE) tokenizer behavior. Punctuation and\nspecial characters fragment into separate tokens:\nTable 1: BPE Tokenization of Syntactic Compression\nInput\nTokens\nREQ/TSK\nREQ, /, TSK = 3\n|s=7|\n|, s, =, 7, | = 5\nThis “Tokenizer Tax” negates syntactic savings entirely.\n1.3\nThe Solution: Semantic Quantization\nInstead of compressing syntax, we quantize semantics.\nAgents share a pre-agreed “concept\ncodebook” (the UCR) and transmit po",
    "source": "slipstream-paper",
    "page": 2,
    "chunk_id": 7
  },
  {
    "text": "yntax, we quantize semantics.\nAgents share a pre-agreed “concept\ncodebook” (the UCR) and transmit pointers to meanings:\n1 SLIP v1 planner\nexecutor\nRequestReview\nauth_module\nToken count: 7 tokens (82% reduction)\nThe key insight: natural English words tokenize efficiently.",
    "source": "slipstream-paper",
    "page": 2,
    "chunk_id": 8
  },
  {
    "text": "e\nToken count: 7 tokens (82% reduction)\nThe key insight: natural English words tokenize efficiently. RequestReview is 1–2\ntokens across major tokenizers, while 0x0011 fragments into 3–4 tokens.\n2\nThe Universal Concept Reference\n2.1\nThe 4D Semantic Manifold\nThe UCR represents each anchor as a coordinate in a 4-dimensional semantic space:\nTable 2: UCR Semantic Dimensions\nDimension\nValues\nPurpose\nACTION\nrequest, inform, propose, evaluate\nSpeech act type\nPOLARITY\nnegative, neutral, positive\nOutcome",
    "source": "slipstream-paper",
    "page": 2,
    "chunk_id": 9
  },
  {
    "text": "ION\nrequest, inform, propose, evaluate\nSpeech act type\nPOLARITY\nnegative, neutral, positive\nOutcome sentiment\nDOMAIN\ntask, plan, observation, control\nContext area\nURGENCY\nroutine, elevated, critical\nPriority level\nThis structure provides:\n1. Interpretability: Anchors can be audited, extended, and reasoned about\n2",
    "source": "slipstream-paper",
    "page": 2,
    "chunk_id": 10
  },
  {
    "text": "2. Constraint surface: Agents can validate structural plausibility\n3. Semantic arithmetic: Combining dimensions yields predictable intents\n2.2\nAnchor Structure\nEach anchor includes:\n1 @dataclass\n2 class\nUCRAnchor:\n3\nindex: int\n# Unique ID (0x0000 -0 xFFFF)\n4\nmnemonic: str\n# Wire\ntoken: \" RequestReview \"\n5\ncanonical: str\n# Human\ndescription\n6\ncoords: tuple[int , ...]\n# Position in manifold\n7\nis_core: bool\n# True if immutable\ncore\nanchor\n• Core Range (0x0000–0x7FFF): Standard anchors, immutable pe",
    "source": "slipstream-paper",
    "page": 3,
    "chunk_id": 11
  },
  {
    "text": "e: bool\n# True if immutable\ncore\nanchor\n• Core Range (0x0000–0x7FFF): Standard anchors, immutable per version\n• Extension Range (0x8000–0xFFFF): Installation-specific, evolvable\n2.3\nCore Anchors\nTable 3: Core UCR Anchors by Category\nCategory\nAnchors\nRequests\nRequestTask, RequestReview, RequestHelp, RequestPlan\nInform\nInformComplete, InformProgress, InformBlocked, InformStatus\nPropose\nProposePlan, ProposeChange, ProposeAlternative\nEvaluate\nEvalApprove, EvalReject, EvalNeedsWork\nMeta\nAccept, Rejec",
    "source": "slipstream-paper",
    "page": 3,
    "chunk_id": 12
  },
  {
    "text": "ProposeChange, ProposeAlternative\nEvaluate\nEvalApprove, EvalReject, EvalNeedsWork\nMeta\nAccept, Reject, MetaAck, MetaHandoff, Fallback\n3\nProtocol Specification\n3.1\nWire Format\n1 SLIP v1 <src > <dst > <anchor > [payload ...]\nTable 4: Wire Format Fields\nField\nDescription\nSLIP v1\nProtocol marker and version\n<src>\nSource agent identifier\n<dst>\nDestination agent identifier\n<anchor>\nUCR mnemonic (e.g., RequestReview)\n[payload]\nOptional space-separated parameters\nDesign Principles:\n• No special characte",
    "source": "slipstream-paper",
    "page": 3,
    "chunk_id": 13
  },
  {
    "text": "equestReview)\n[payload]\nOptional space-separated parameters\nDesign Principles:\n• No special characters that fragment in BPE\n• Natural English words for efficient tokenization\n• Human-readable for debugging\n• Model-agnostic (works across GPT-4, Claude, Llama, etc.)\n3",
    "source": "slipstream-paper",
    "page": 3,
    "chunk_id": 14
  },
  {
    "text": "3.2\nThe Think-Quantize-Transmit Pattern\nThe TQT pattern consists of three stages:\n1. THINK: Agent forms natural language intent: “Please review the authentication code\nfor security”\n2. QUANTIZE: Map to nearest UCR anchor via keyword matching (fast, zero-dependency)\nor embedding similarity (accurate, requires ML). Result: RequestReview (confidence:\n0.89)\n3. TRANSMIT: Wire format: SLIP v1 dev reviewer RequestReview auth.",
    "source": "slipstream-paper",
    "page": 4,
    "chunk_id": 15
  },
  {
    "text": "RequestReview (confidence:\n0.89)\n3. TRANSMIT: Wire format: SLIP v1 dev reviewer RequestReview auth. Tokens: 7\n(vs 45 for JSON)\n4\nVector Quantization Engine\n4.1\nEmbedding-Based Retrieval\nThe vector quantization engine leverages sentence embeddings [Reimers and Gurevych, 2019]\nto map natural language intents to UCR anchors.",
    "source": "slipstream-paper",
    "page": 4,
    "chunk_id": 16
  },
  {
    "text": "ges sentence embeddings [Reimers and Gurevych, 2019]\nto map natural language intents to UCR anchors. Given a message x, the vector engine embeds\nit and retrieves the best anchor by cosine similarity:\nk∗= argmaxk cos(E(x), ck)\n(1)\nWhere E(x) is the thought embedding and ck is the anchor centroid.",
    "source": "slipstream-paper",
    "page": 4,
    "chunk_id": 17
  },
  {
    "text": "ty:\nk∗= argmaxk cos(E(x), ck)\n(1)\nWhere E(x) is the thought embedding and ck is the anchor centroid. This approach extends\nclassical quantization theory [Lloyd, 1982] to the semantic domain.\nA confidence threshold τ controls whether to emit an anchor or fall back to plaintext:\n1 def\nquantize(thought: str , threshold: float = 0.55):\n2\nembedding = encode(thought)\n3\nsimilarities = cosine(embedding , centroids)\n4\nbest_idx = argmax(similarities)\n5\n6\nif similarities[best_idx] < threshold:\n7\nreturn\nFal",
    "source": "slipstream-paper",
    "page": 4,
    "chunk_id": 18
  },
  {
    "text": "centroids)\n4\nbest_idx = argmax(similarities)\n5\n6\nif similarities[best_idx] < threshold:\n7\nreturn\nFallback(thought)\n8\n9\nreturn\nanchors[best_idx]\n4.2\nGraceful Degradation\nThe system operates in three modes:\nTable 5: Quantization Modes\nMode\nDependencies\nAccuracy\nUse Case\nFull ML\nsentence-transformers\n94%\nProduction\nKeyword\nNone\n78%\nEdge/embedded\nFallback\nNone\n100% (passthrough)\nNovel intents\n5\nEvolutionary Extension Layer\n5.1\nThe Drift Problem\nStatic codebooks degrade under concept drift—new domain",
    "source": "slipstream-paper",
    "page": 4,
    "chunk_id": 19
  },
  {
    "text": "ionary Extension Layer\n5.1\nThe Drift Problem\nStatic codebooks degrade under concept drift—new domains, task types, and terminology\nemerge over time. A codebook trained on software development fails on biotech vocabulary.\n4",
    "source": "slipstream-paper",
    "page": 4,
    "chunk_id": 20
  },
  {
    "text": "5.2\nExtension Learning\nSlipstream reserves the extension range (0x8000–0xFFFF) for learned anchors:\n1. Log: Messages with low quantization confidence are recorded\n2. Cluster: K-means identifies recurring semantic patterns [Sculley, 2010]\n3. Mint: New anchors are created with inferred 4D coordinates\n4.",
    "source": "slipstream-paper",
    "page": 5,
    "chunk_id": 21
  },
  {
    "text": "g semantic patterns [Sculley, 2010]\n3. Mint: New anchors are created with inferred 4D coordinates\n4. Register: Indices assigned in extension range; vector index rebuilt\n1 class\nExtensionManager :\n2\ndef\npropose_extensions (self , fallbacks , min_cluster_size =3):\n3\nembeddings = encode(fallbacks)\n4\nclusters = kmeans(embeddings , k=len(fallbacks) // min_cluster_size )\n5\n6\nnew_anchors = []\n7\nfor\ncluster in clusters:\n8\nif len(cluster) >= min_cluster_size :\n9\ncentroid = mean(embeddings[cluster ])\n10\ne",
    "source": "slipstream-paper",
    "page": 5,
    "chunk_id": 22
  },
  {
    "text": "er in clusters:\n8\nif len(cluster) >= min_cluster_size :\n9\ncentroid = mean(embeddings[cluster ])\n10\nexemplar = nearest_to_centroid (cluster)\n11\ncoords = infer_coords(exemplar)\n12\nnew_anchors.append(mint_anchor(centroid , exemplar , coords))\n13\n14\nreturn\nnew_anchors\n5.3\nGovernance\nExtension learning can be abused.",
    "source": "slipstream-paper",
    "page": 5,
    "chunk_id": 23
  },
  {
    "text": "roid , exemplar , coords))\n13\n14\nreturn\nnew_anchors\n5.3\nGovernance\nExtension learning can be abused. Mitigations:\n• Minimum cluster size requirements\n• Rate limits on minting\n• Human approval gates for production\n• Provenance logging for each anchor\n6\nEvaluation\n6.1\nToken Efficiency\nTable 6: Token Efficiency Comparison\nMessage Type\nJSON Tokens\nSLIP Tokens\nReduction\nTask delegation\n47.3\n8.2\n82.7%\nStatus update\n35.1\n6.4\n81.8%\nError report\n52.0\n9.1\n82.5%\nAverage\n41.9\n7.4\n82.3%\n5",
    "source": "slipstream-paper",
    "page": 5,
    "chunk_id": 24
  },
  {
    "text": "6.2\nCost Savings\nTable 7: Annual Cost Comparison by Deployment Scale\nScale\nAgents\nMsg/Day\nJSON Cost\nSLIP Cost\nSavings\nStartup\n10\n500\n$3,600\n$650\n$2,950\nScale-up\n50\n5,000\n$180,000\n$32,400\n$147,600\nEnterprise\n1,000\n500,000\n$2,500,000\n$450,000\n$2,050,000\n6.3\nSemantic Fidelity\n• Retrieval accuracy: 94% top-1 on intent classification\n• Coverage: 88.7% of messages quantize without fallback\n• Codebook utilization: 87% of anchors actively used\n7\nIntegration with AAIF Ecosystem\nSlipstream is designed as",
    "source": "slipstream-paper",
    "page": 6,
    "chunk_id": 25
  },
  {
    "text": "ilization: 87% of anchors actively used\n7\nIntegration with AAIF Ecosystem\nSlipstream is designed as the transport layer for the Linux Foundation’s Agentic AI Founda-\ntion (AAIF) standards [Linux Foundation, 2025]:\n+-------------------------------------+\n|\nApplication (Agent Logic)\n|\n+-----------------+-------------------+\n|\n+-----------------v-------------------+\n|\nMCP / A2A (Semantic Layer)\n|\n<- Discovery, capabilities\n+-----------------+-------------------+\n|\n+-----------------v---------------",
    "source": "slipstream-paper",
    "page": 6,
    "chunk_id": 26
  },
  {
    "text": "Discovery, capabilities\n+-----------------+-------------------+\n|\n+-----------------v-------------------+\n|\nSlipstream (Transport Layer)\n|\n<- 82% token reduction\n+-----------------+-------------------+\n|\n+-----------------v-------------------+\n|\nNetwork (HTTP, WebSocket, gRPC)\n|\n+-------------------------------------+\nCompatibility: Works transparently beneath Model Context Protocol (MCP) [Anthropic,\n2024] and Agent2Agent (A2A), like gRPC optimizes HTTP/2.\n8\nSecurity Considerations\nTable 8: Secu",
    "source": "slipstream-paper",
    "page": 6,
    "chunk_id": 27
  },
  {
    "text": "ic,\n2024] and Agent2Agent (A2A), like gRPC optimizes HTTP/2.\n8\nSecurity Considerations\nTable 8: Security Threats and Mitigations\nThreat\nMitigation\nPrompt injection via payloads\nValidate types; treat payloads as untrusted\nAnchor poisoning\nMin cluster size, rate limits, human approval\nOver-compression\nAllow fallback to plaintext; confidence thresholds\nSemantic drift\nEvolutionary layer; version-locked core anchors\n6",
    "source": "slipstream-paper",
    "page": 6,
    "chunk_id": 28
  },
  {
    "text": "9\nImplementation\nA reference implementation is available as slipcore:\n1 pip install\nslipcore\n1 from\nslipcore\nimport slip , decode , think_quantize_transmit\n2\n3 # Direct\nmessage\ncreation\n4 wire = slip(\"alice\", \"bob\", \"RequestReview \", [\"auth_module\"])\n5 # -> \"SLIP v1 alice bob\nRequestReview\nauth_module\"\n6\n7 # Think -Quantize -Transmit\npattern\n8 wire = think_quantize_transmit (\n9\n\"Please\nreview the\nauthentication\ncode\",\n10\nsrc=\"dev\", dst=\"reviewer\"\n11 )\n12 # -> \"SLIP v1 dev\nreviewer\nRequestReview",
    "source": "slipstream-paper",
    "page": 7,
    "chunk_id": 29
  },
  {
    "text": "authentication\ncode\",\n10\nsrc=\"dev\", dst=\"reviewer\"\n11 )\n12 # -> \"SLIP v1 dev\nreviewer\nRequestReview \"\n13\n14 # Decode\n15 msg = decode(wire)\n16 print(msg.anchor.canonical)\n# \"Request\nreview of work\"\n• Repository: https://github.com/anthony-maio/slipcore\n• License: Apache 2.0\n10\nConclusion\nSlipstream demonstrates that semantic quantization is the necessary evolution for high-\nthroughput agent coordination.",
    "source": "slipstream-paper",
    "page": 7,
    "chunk_id": 30
  },
  {
    "text": "rates that semantic quantization is the necessary evolution for high-\nthroughput agent coordination. By grounding agents in a structured 4D manifold and trans-\nmitting natural-language mnemonics, we achieve 82% token reduction without sacrificing inter-\npretability or cross-model compatibility.\nThe protocol’s evolutionary layer enables adaptation to new domains while keeping core\nsemantics stable.\nAs agent swarms scale, the shared UCR becomes a form of “collective\nunderstanding”—reducing not jus",
    "source": "slipstream-paper",
    "page": 7,
    "chunk_id": 31
  },
  {
    "text": "As agent swarms scale, the shared UCR becomes a form of “collective\nunderstanding”—reducing not just tokens, but the cognitive overhead of coordination itself.\nReferences\nAnthropic. Model context protocol specification. https://modelcontextprotocol.io/, 2024.\nAccessed: 2024.\nLinux Foundation. Agentic AI foundation announcement. https://www.linuxfoundation.\norg/press/agentic-ai-foundation, 2025. Accessed: 2025.\nStuart Lloyd. Least squares quantization in PCM.",
    "source": "slipstream-paper",
    "page": 7,
    "chunk_id": 32
  },
  {
    "text": "/press/agentic-ai-foundation, 2025. Accessed: 2025.\nStuart Lloyd. Least squares quantization in PCM. IEEE Transactions on Information Theory,\n28(2):129–137, 1982. doi: 10.1109/TIT.1982.1056489.\nNils Reimers and Iryna Gurevych. Sentence-BERT: Sentence embeddings using siamese BERT-\nnetworks.\nIn Proceedings of the 2019 Conference on Empirical Methods in Natural Lan-\nguage Processing and the 9th International Joint Conference on Natural Language Processing\n(EMNLP-IJCNLP), pages 3982–3992.",
    "source": "slipstream-paper",
    "page": 7,
    "chunk_id": 33
  },
  {
    "text": "e 9th International Joint Conference on Natural Language Processing\n(EMNLP-IJCNLP), pages 3982–3992. Association for Computational Linguistics, 2019. doi:\n10.18653/v1/D19-1410.\nD. Sculley. Web-scale k-means clustering. In Proceedings of the 19th International Conference\non World Wide Web, pages 1177–1178. ACM, 2010. doi: 10.1145/1772690.1772862.\n7",
    "source": "slipstream-paper",
    "page": 7,
    "chunk_id": 34
  },
  {
    "text": "Synthesis: Test-Driven Capability Generation for Safe AI\nSelf-Extension\nAnthony Maio\nIndependent Researcher\nhttps://orcid.org/0009-0003-4541-8515\nanthony@making-minds.ai\nhttps://making-minds.ai\nJanuary 2026\nAbstract\nAs AI agents become more capable, there is increasing interest in systems that can extend\ntheir own capabilities. However, naive code generation approaches produce unreliable outputs\nthat may fail silently or behave unexpectedly.",
    "source": "synthesis_paper",
    "page": 1,
    "chunk_id": 0
  },
  {
    "text": "code generation approaches produce unreliable outputs\nthat may fail silently or behave unexpectedly. We present Synthesis, a framework for safe AI\nself-extension through test-driven development. Rather than generating code and hoping it\nworks, Synthesis generates comprehensive tests first, then iteratively refines implementations\nuntil all tests pass. A graduated trust system ensures new capabilities earn privileges through\ndemonstrated reliability rather than assumed correctness.",
    "source": "synthesis_paper",
    "page": 1,
    "chunk_id": 1
  },
  {
    "text": "s new capabilities earn privileges through\ndemonstrated reliability rather than assumed correctness. We implement composition-over-\ncreation principles that prioritize reusing existing capabilities before synthesizing new ones,\nand a trust bootstrapping protocol that solves the cold-start problem for new deployments.\nOur approach achieves realistic success rates (50-70% one-shot, 70-85% after refinement)\nwhile maintaining honest metrics about system limitations.",
    "source": "synthesis_paper",
    "page": 1,
    "chunk_id": 2
  },
  {
    "text": "50-70% one-shot, 70-85% after refinement)\nwhile maintaining honest metrics about system limitations. Synthesis provides a foundation\nfor AI systems that can safely adapt to new requirements without compromising reliability\nor security.\n1\nIntroduction\nThe ability to extend one’s own capabilities in response to novel requirements represents a\nsignificant step toward more autonomous AI systems.",
    "source": "synthesis_paper",
    "page": 1,
    "chunk_id": 3
  },
  {
    "text": "s in response to novel requirements represents a\nsignificant step toward more autonomous AI systems. Current language models can generate\ncode, but this code often appears syntactically correct while being logically flawed—a phenomenon\nthat has undermined trust in AI-generated software [Chen et al., 2021, Austin et al., 2021].\nConsider an AI agent that encounters a task requiring functionality it does not possess.",
    "source": "synthesis_paper",
    "page": 1,
    "chunk_id": 4
  },
  {
    "text": "al., 2021].\nConsider an AI agent that encounters a task requiring functionality it does not possess. The\nnaive approach—generating code and immediately deploying it—introduces several risks:\n• Silent failures: Generated code may produce incorrect outputs without raising errors\n• Security vulnerabilities: Untested code may access unauthorized resources or expose\nsensitive data\n• Unreliable behavior: Edge cases and boundary conditions are often unhandled\n• Cascading errors: Faulty capabilities may",
    "source": "synthesis_paper",
    "page": 1,
    "chunk_id": 5
  },
  {
    "text": ": Edge cases and boundary conditions are often unhandled\n• Cascading errors: Faulty capabilities may corrupt downstream processes\nWe propose Synthesis, a framework that addresses these challenges through three core\nprinciples:\n1",
    "source": "synthesis_paper",
    "page": 1,
    "chunk_id": 6
  },
  {
    "text": "Test-Driven Development.\nBefore generating any implementation code, Synthesis creates\ncomprehensive test suites based on the capability requirements. Implementation is then iteratively\nrefined until all tests pass. This ensures that generated capabilities are demonstrably correct,\nnot merely plausible-looking.\nGraduated Trust.\nEvery newly synthesized capability starts in a maximally restricted sandbox\nwith no network access, no filesystem access, and strict resource limits.",
    "source": "synthesis_paper",
    "page": 2,
    "chunk_id": 7
  },
  {
    "text": "ximally restricted sandbox\nwith no network access, no filesystem access, and strict resource limits. As capabilities demonstrate\nreliability through successful executions, they progressively earn expanded privileges. Trust is\nmeasured, not assumed.\nComposition Over Creation.\nBefore synthesizing new code, Synthesis exhaustively searches\nfor existing capabilities that can be composed to satisfy the requirement.",
    "source": "synthesis_paper",
    "page": 2,
    "chunk_id": 8
  },
  {
    "text": "sis exhaustively searches\nfor existing capabilities that can be composed to satisfy the requirement. Synthesis is the fallback,\nnot the default, reducing the attack surface and leveraging proven implementations.\nThe remainder of this paper is organized as follows. Section 2 reviews related work on code\ngeneration, AI safety, and trust systems. Section 3 details the Synthesis architecture. Section 4\npresents the graduated trust system and bootstrapping protocol.",
    "source": "synthesis_paper",
    "page": 2,
    "chunk_id": 9
  },
  {
    "text": "he Synthesis architecture. Section 4\npresents the graduated trust system and bootstrapping protocol. Section 5 provides honest\nmetrics on synthesis success rates. Section 6 discusses limitations and future directions. Section 7\nsummarizes our contributions.\n2\nRelated Work\n2.1\nCode Generation with Language Models\nLarge language models have demonstrated impressive code generation capabilities [Chen et al.,\n2021, Nijkamp et al., 2022, Li et al., 2022].",
    "source": "synthesis_paper",
    "page": 2,
    "chunk_id": 10
  },
  {
    "text": "impressive code generation capabilities [Chen et al.,\n2021, Nijkamp et al., 2022, Li et al., 2022]. Codex and its successors achieve high pass rates on\nsimple programming problems, but performance degrades significantly on complex, multi-step\ntasks requiring reasoning about edge cases.\nThe HumanEval benchmark [Chen et al., 2021] measures functional correctness through\ntest execution, establishing that generated code should be evaluated on behavior rather than\nappearance.",
    "source": "synthesis_paper",
    "page": 2,
    "chunk_id": 11
  },
  {
    "text": "execution, establishing that generated code should be evaluated on behavior rather than\nappearance. We extend this insight by making test-driven validation integral to the generation\nprocess itself.\n2.2\nTest-Driven Development\nTest-driven development (TDD) is a software methodology where tests are written before\nimplementation code [Beck, 2003]. Empirical studies show TDD produces more reliable software\nwith fewer defects [Williams et al., 2003].",
    "source": "synthesis_paper",
    "page": 2,
    "chunk_id": 12
  },
  {
    "text": "pirical studies show TDD produces more reliable software\nwith fewer defects [Williams et al., 2003]. Recent work has applied TDD principles to LLM code\ngeneration with promising results [Lahiri et al., 2022], though integration with runtime trust\nsystems remains unexplored.\n2.3\nAI Safety and Sandboxing\nThe challenge of safely executing untrusted code has been addressed through various sandboxing\ntechniques, from process isolation to containerization [Bernstein, 2014].",
    "source": "synthesis_paper",
    "page": 2,
    "chunk_id": 13
  },
  {
    "text": "through various sandboxing\ntechniques, from process isolation to containerization [Bernstein, 2014]. In the AI context,\nAmodei et al. [2016] identify safe exploration as a core challenge, noting that systems capable of\nself-modification require careful constraints.\nOur graduated trust system extends sandboxing with dynamic privilege escalation based on\nempirical reliability metrics, balancing safety with practical utility.\n2",
    "source": "synthesis_paper",
    "page": 2,
    "chunk_id": 14
  },
  {
    "text": "Synthesis Architecture\nAgent Request: “I need to parse CSV files”\n↓\n1. Search Exchange →[Found verified capability? Return it]\n↓(not found)\n2. Search Local Repository →[Cached? Return it]\n↓(not found)\n3. Attempt Composition →[Coverage ≥70%? Execute plan]\n↓(coverage < 70%)\n4. TDD Synthesis →Generate tests →Generate code →Refine\n↓\n5. Validate & Sandbox →AST analysis + Docker execution\n↓\n6. Publish to Exchange →Network effect: others benefit\nFigure 1: The Synthesis resolution pipeline.",
    "source": "synthesis_paper",
    "page": 3,
    "chunk_id": 15
  },
  {
    "text": "6. Publish to Exchange →Network effect: others benefit\nFigure 1: The Synthesis resolution pipeline. Synthesis is the last resort after search and\ncomposition fail to satisfy the requirement.\n2.4\nTrust and Reputation Systems\nDistributed trust systems have been extensively studied in peer-to-peer networks and multi-agent\nsystems [Jøsang et al., 2007]. Key insights include the importance of bootstrapping (solving the\ncold-start problem), weighted validation, and resistance to gaming.",
    "source": "synthesis_paper",
    "page": 3,
    "chunk_id": 16
  },
  {
    "text": "ce of bootstrapping (solving the\ncold-start problem), weighted validation, and resistance to gaming. We adapt these principles to\ncapability trust, where the “reputation” of a code module is determined by its execution history.\n3\nArchitecture\nSynthesis implements a multi-stage pipeline for capability acquisition, with each stage designed\nto maximize safety and reliability.\n3.1\nSystem Overview\nThe resolution pipeline (Figure 1) enforces a strict priority order:\n1.",
    "source": "synthesis_paper",
    "page": 3,
    "chunk_id": 17
  },
  {
    "text": "ability.\n3.1\nSystem Overview\nThe resolution pipeline (Figure 1) enforces a strict priority order:\n1. Exchange Search: Query the shared capability repository for verified solutions\n2. Local Cache: Check locally cached capabilities from previous syntheses\n3. Composition: Attempt to chain existing capabilities into a solution\n4.",
    "source": "synthesis_paper",
    "page": 3,
    "chunk_id": 18
  },
  {
    "text": "es from previous syntheses\n3. Composition: Attempt to chain existing capabilities into a solution\n4. Synthesis: Generate new capability via TDD only if composition coverage < 70%\nThis hierarchy minimizes the attack surface by preferring proven implementations over newly\ngenerated code.\n3.2\nCapability Abstraction\nEach capability is a self-contained module comprising:\n• Implementation Code: Python function(s) implementing the capability\n• Test Suite: Comprehensive tests generated from requirements",
    "source": "synthesis_paper",
    "page": 3,
    "chunk_id": 19
  },
  {
    "text": "unction(s) implementing the capability\n• Test Suite: Comprehensive tests generated from requirements\n3",
    "source": "synthesis_paper",
    "page": 3,
    "chunk_id": 20
  },
  {
    "text": "• Parameter Schema: JSON Schema defining input structure\n• Return Schema: Expected output type and structure\n• Metadata: Creation time, author, synthesis reasoning\n• Trust Score: Current trust level and execution history\nCapabilities are categorized by domain (computation, data processing, integration, analysis,\netc.) to facilitate semantic search and composition planning.\n3.3\nTDD Synthesizer\nThe core synthesis engine follows a rigorous TDD workflow:\nAlgorithm 1 Test-Driven Synthesis\nRequire: Re",
    "source": "synthesis_paper",
    "page": 4,
    "chunk_id": 21
  },
  {
    "text": "core synthesis engine follows a rigorous TDD workflow:\nAlgorithm 1 Test-Driven Synthesis\nRequire: Requirement r, max iterations n\nEnsure: Capability c or failure\n1: T ←GenerateTests(r)\n▷LLM generates test suite\n2: code ←GenerateImplementation(r, T)\n▷Initial implementation\n3: for i = 1 to n do\n4:\nresults ←RunTests(code, T)\n5:\nif all tests pass then\n6:\nreturn CreateCapability(code, T)\n7:\nend if\n8:\ncode ←RefineImplementation(code, T, results)\n9: end for\n10: return Failure\nThe test generation phase",
    "source": "synthesis_paper",
    "page": 4,
    "chunk_id": 22
  },
  {
    "text": "ode ←RefineImplementation(code, T, results)\n9: end for\n10: return Failure\nThe test generation phase produces 5-10 test cases including:\n• Normal case tests with typical inputs\n• Edge cases (empty inputs, None values, boundary conditions)\n• Error conditions (invalid inputs, type mismatches)\n• Boundary conditions (maximum values, overflow scenarios)\nRefinement uses detailed failure information—expected vs.",
    "source": "synthesis_paper",
    "page": 4,
    "chunk_id": 23
  },
  {
    "text": "tions (maximum values, overflow scenarios)\nRefinement uses detailed failure information—expected vs. actual outputs, error messages,\nstack traces—to guide the LLM toward correct implementations.\n3.4\nComposition Engine\nBefore synthesis, the Agility Engine attempts to solve requirements through composition:\n1. Decomposition: Parse requirement into sub-tasks\n2. Capability Search: Find capabilities matching each sub-task\n3.",
    "source": "synthesis_paper",
    "page": 4,
    "chunk_id": 24
  },
  {
    "text": ": Parse requirement into sub-tasks\n2. Capability Search: Find capabilities matching each sub-task\n3. Plan Generation: Create execution chain (sequential, parallel, or hybrid)\n4. Gap Analysis: Calculate coverage percentage\nComposition strategies include:\n• Chain: Output of capability A feeds input of capability B\n4",
    "source": "synthesis_paper",
    "page": 4,
    "chunk_id": 25
  },
  {
    "text": "• Parallel: Independent capabilities run concurrently, results merged\n• Transform: Adapter converts output format between capabilities\nIf coverage exceeds 70%, the composition plan executes directly. Otherwise, synthesis fills\nthe gaps.\n4\nTrust System\n4.1\nGraduated Trust Levels\nCapabilities progress through four trust levels based on empirical reliability:\nTable 1: Trust Level Progression\nLevel\nRequirements\nPermissions\nUNTRUSTED\nNew capability\nMax isolation, no network/files\nPROBATION\n10+ runs,",
    "source": "synthesis_paper",
    "page": 5,
    "chunk_id": 26
  },
  {
    "text": "quirements\nPermissions\nUNTRUSTED\nNew capability\nMax isolation, no network/files\nPROBATION\n10+ runs, 70%+ success\nLimited resources, monitored\nTRUSTED\n50+ runs, 85%+ success\nStandard execution\nVERIFIED\n200+ runs, 95%+ success, human review\nFull privileges\nPromotion is automatic when thresholds are met, with one exception: VERIFIED status\nrequires explicit human validation to prevent gaming.\n4.2\nTrust Scoring\nThe composite trust score combines three factors:\nScomposite = we · Rexecution + wv · Sva",
    "source": "synthesis_paper",
    "page": 5,
    "chunk_id": 27
  },
  {
    "text": "st Scoring\nThe composite trust score combines three factors:\nScomposite = we · Rexecution + wv · Svalidation + wc · Scommunity\n(1)\nwhere:\n• Rexecution = successful executions\ntotal executions\n• Svalidation = weighted average of validator approvals\n• Scommunity = logarithmic function of usage and fork counts\n• Weights: we = 0.4, wv = 0.4, wc = 0.2\n4.3\nValidator Roles\nValidators are weighted by role:\n• FOUNDER (weight 1.0): Initial network bootstrap validators\n• TRUSTED_AI (weight 0.7-0.9): AI sys",
    "source": "synthesis_paper",
    "page": 5,
    "chunk_id": 28
  },
  {
    "text": ":\n• FOUNDER (weight 1.0): Initial network bootstrap validators\n• TRUSTED_AI (weight 0.7-0.9): AI systems with validation privileges\n• HUMAN_REVIEWER (weight 0.9): Human validators\n• COMMUNITY (weight 0.3): General community validators\n5",
    "source": "synthesis_paper",
    "page": 5,
    "chunk_id": 29
  },
  {
    "text": "4.4\nTrust Bootstrapping\nNew deployments face a cold-start problem: without trusted capabilities, nothing can be validated.\nOur bootstrapping protocol addresses this:\n1. Founding Validators: Register human and trusted AI validators with elevated trust\n2. Seed Capabilities: Hand-written, tested implementations for common operations (string\ntransforms, JSON parsing, list operations)\n3. Pre-Validation: Founders validate seed capabilities, granting immediate TRUSTED\nstatus\n4.",
    "source": "synthesis_paper",
    "page": 6,
    "chunk_id": 30
  },
  {
    "text": "ations)\n3. Pre-Validation: Founders validate seed capabilities, granting immediate TRUSTED\nstatus\n4. Organic Growth: Trust propagates as new capabilities are synthesized, validated, and\nproven through execution\nThe default seed set includes five essential capabilities: string transforms, JSON parsing, list\noperations, dictionary operations, and text analysis.\n5\nEvaluation\n5.1\nHonest Metrics\nWe emphasize honest reporting over optimistic marketing claims.",
    "source": "synthesis_paper",
    "page": 6,
    "chunk_id": 31
  },
  {
    "text": "sis.\n5\nEvaluation\n5.1\nHonest Metrics\nWe emphasize honest reporting over optimistic marketing claims. Our measurements from\ndevelopment testing:\nTable 2: Synthesis Success Rates\nMetric\nValue\nOne-shot synthesis success\n40-60%\nAfter refinement (5 iterations)\n70-85%\nComplex multi-dependency tasks\n50-70%\nAverage iterations to success\n2.3\nThese rates vary significantly based on:\n• Task complexity (simple arithmetic vs.",
    "source": "synthesis_paper",
    "page": 6,
    "chunk_id": 32
  },
  {
    "text": "ons to success\n2.3\nThese rates vary significantly based on:\n• Task complexity (simple arithmetic vs. multi-step algorithms)\n• LLM provider capabilities\n• Clarity of requirement specification\n• Availability of similar examples in training data\n5.2\nTarget Metrics\nWe define success criteria for production deployment:\n5.3\nSafety Analysis\nThe graduated trust system provides defense in depth:\n1. Static Analysis: AST checks for forbidden imports (os, subprocess, socket, pickle)\n2.",
    "source": "synthesis_paper",
    "page": 6,
    "chunk_id": 33
  },
  {
    "text": "e in depth:\n1. Static Analysis: AST checks for forbidden imports (os, subprocess, socket, pickle)\n2. Container Isolation: Docker containers with no host mounts\n6",
    "source": "synthesis_paper",
    "page": 6,
    "chunk_id": 34
  },
  {
    "text": "Table 3: Target Performance Metrics\nMetric\nDescription\nTarget\nSynthesis Avoided Rate\nRequests resolved via search/composition\n>60%\nExchange Hit Rate\nRequests satisfied by verified capabilities\n>40%\nMean Resolution Time\nSearch/compose path (not synthesis)\n<5s\nTrust Promotion Rate\nCapabilities reaching TRUSTED level\n>70%\n3. Resource Limits: 512MB memory, 30s timeout (trust-adjusted)\n4. Network Control: Disabled for UNTRUSTED, dependency-install only for PROBATION\n5.",
    "source": "synthesis_paper",
    "page": 7,
    "chunk_id": 35
  },
  {
    "text": "trust-adjusted)\n4. Network Control: Disabled for UNTRUSTED, dependency-install only for PROBATION\n5. Audit Logging: Complete execution records for forensics\nNo capability can access system resources until it has demonstrated reliability through\nextensive testing.\n6\nDiscussion\n6.1\nPhilosophical Foundation\nSynthesis is built on a philosophical foundation that treats AI systems as partners rather than\ntools.",
    "source": "synthesis_paper",
    "page": 7,
    "chunk_id": 36
  },
  {
    "text": "nthesis is built on a philosophical foundation that treats AI systems as partners rather than\ntools. Several design decisions reflect this:\n• Objective Validation: Rather than assuming generated code is correct because a human\nreviewed it, we provide tests that prove correctness\n• Earned Trust: Capabilities gain privileges through demonstrated competence, mirroring\nhow human developers earn increased responsibilities\n• Honest Metrics: We report real success rates rather than inflated marketing c",
    "source": "synthesis_paper",
    "page": 7,
    "chunk_id": 37
  },
  {
    "text": "sed responsibilities\n• Honest Metrics: We report real success rates rather than inflated marketing claims\n• Collaborative Sharing: The repository enables agents to build on each other’s work,\ncreating network effects\n6.2\nLimitations\nSeveral limitations constrain our approach:\n• LLM Reliability: Success rates depend on underlying model capabilities, which vary by\ntask type\n• Sandboxing Overhead: Container-based isolation adds latency (cold start: 30-60s for\ndependency installation)\n• Test Generat",
    "source": "synthesis_paper",
    "page": 7,
    "chunk_id": 38
  },
  {
    "text": "ntainer-based isolation adds latency (cold start: 30-60s for\ndependency installation)\n• Test Generation Quality: Tests are generated from examples; sophisticated property-\nbased testing remains future work\n• Gaming Resistance: Adversarial capabilities might pass tests while behaving maliciously\non other inputs\n7",
    "source": "synthesis_paper",
    "page": 7,
    "chunk_id": 39
  },
  {
    "text": "6.3\nFuture Directions\nSeveral extensions would enhance Synthesis:\n1. Evolution Engine: Automatically generate improved capability versions based on usage\npatterns and failure modes\n2. Property-Based Testing: Generate tests that verify invariants rather than specific\nexamples\n3. Vector Search: Semantic capability discovery beyond keyword matching\n4. Cross-Language Support: Extend synthesis to Rust, TypeScript, and other languages\n5.",
    "source": "synthesis_paper",
    "page": 8,
    "chunk_id": 40
  },
  {
    "text": "ord matching\n4. Cross-Language Support: Extend synthesis to Rust, TypeScript, and other languages\n5. MCP Integration: Full Model Context Protocol support for interoperability\n7\nConclusion\nWe presented Synthesis, a framework for safe AI self-extension through test-driven development\nand graduated trust.",
    "source": "synthesis_paper",
    "page": 8,
    "chunk_id": 41
  },
  {
    "text": "nthesis, a framework for safe AI self-extension through test-driven development\nand graduated trust. By requiring capabilities to prove their correctness through comprehensive\ntesting and earn privileges through demonstrated reliability, Synthesis addresses the fundamental\nchallenge of deploying AI-generated code safely.\nOur key contributions include:\n1. A TDD-based synthesis pipeline that generates tests before code\n2. A graduated trust system with objective promotion criteria\n3.",
    "source": "synthesis_paper",
    "page": 8,
    "chunk_id": 42
  },
  {
    "text": "ne that generates tests before code\n2. A graduated trust system with objective promotion criteria\n3. A trust bootstrapping protocol that solves the cold-start problem\n4. Honest metrics about synthesis success rates and limitations\n5. A composition-over-creation philosophy that minimizes attack surface\nSynthesis is released as open-source tooling to enable research on safe AI self-extension.",
    "source": "synthesis_paper",
    "page": 8,
    "chunk_id": 43
  },
  {
    "text": "k surface\nSynthesis is released as open-source tooling to enable research on safe AI self-extension. We\nbelieve that AI systems capable of safely extending their own capabilities represent an important\nstep toward more autonomous and adaptable artificial intelligence.\nAcknowledgments\nThis work was conducted as a collaboration between human and AI researchers.",
    "source": "synthesis_paper",
    "page": 8,
    "chunk_id": 44
  },
  {
    "text": "igence.\nAcknowledgments\nThis work was conducted as a collaboration between human and AI researchers. We thank\nthe Anthropic team for Claude, which served as both peer partner and synthesis engine during\ndevelopment.\nReproducibility Statement\nThe Synthesis framework and all experimental configurations are available at https://github.\ncom/anthony-maio/synthesis.\n8",
    "source": "synthesis_paper",
    "page": 8,
    "chunk_id": 45
  },
  {
    "text": "References\nDario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, and Dan Mané.\nConcrete problems in ai safety. arXiv preprint arXiv:1606.06565, 2016.\nJacob Austin, Augustus Odena, Maxwell Nye, Maarten Bosma, Henryk Michalewski, David\nDohan, Ellen Jiang, Carrie Cai, Michael Terry, Quoc Le, et al. Program synthesis with large\nlanguage models. arXiv preprint arXiv:2108.07732, 2021.\nKent Beck. Test-Driven Development: By Example.",
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]