Pram??a-Weighted Intelligence: A Unified Framework For Epistemic Accountability In Neural Architectures Through M?m??s? Source-Validity Theory And The Anuvyavas?ya Conflict Resolution Protocol

1. The Upstream Epistemic Pathology

Artificial Intelligence systems operating in public administration, legal reasoning, healthcare, and financial analytics are, at their computational core, sophisticated functions of their training data. A transformer-based large language model generates outputs by sampling from probability distributions over tokens learned through exposure to training corpora (Vaswani et al., 2017; Brown et al., 2020). The epistemic quality of the output is, in the most direct causal sense, a function of the epistemic quality of the input data.

The contemporary AI training paradigm draws from internet-scale corpora — Common Crawl, WebText, and derivative datasets — that aggregate human-generated content without systematic epistemic filtration. Current AI alignment research addresses this problem primarily at the output layer through reinforcement learning from human feedback (Christiano et al., 2017), content filtering, and red-teaming. This approach is philosophically inadequate: the pathology is upstream, in the training data itself. Correcting output behaviour while leaving corrupted internal representations intact is analogous to treating the symptoms of a disease whose cause remains embedded in the organism's constitution.

This paper proposes that Pūrvamīmāṃsā — the Indian philosophical tradition of source-validity epistemology — offers a technically applicable framework for AI training governance that current Western-derived approaches do not provide. Mīmāṃsā's central question — what qualifies a source of knowledge as valid and authoritative? — is precisely the foundational question of AI training data governance, now made urgent at global scale.

2. Research Gap

Despite extensive literature on AI bias, fairness, and alignment, no existing framework addresses the upstream epistemological dimension of training data validity as a formal, deployable architecture. Datasheets for Datasets (Gebru et al., 2021) document provenance but impose no validity hierarchy. Model Cards for Model Reporting (Mitchell et al., 2019) describe model behaviour but do not govern training source quality. Reinforcement learning from human feedback corrects output behaviour but leaves corrupted training representations intact (Christiano et al., 2017). Mohamed et al. (2020) identify structural power asymmetries in AI knowledge systems but do not propose a technical source-validity architecture.

Three specific technical gaps exist. First, no framework specifies a formally derived, empirically calibrated weight function for training data stratification. Second, no RAG or training architecture specifies a principled conflict-resolution mechanism for cases where valid sources of different epistemic status contradict each other at inference time. Third, no framework connects post-deployment accountability events to an adaptive weight-adjustment function over the deployment lifecycle. This paper addresses all three gaps.

3. Scope and Structure

The framework is scoped to public administration and policy AI as the primary domain. Application to healthcare AI, legal AI, and scientific research AI is identified as future work requiring domain-specific pramāṇa-tier recalibration. The paper proceeds through: (1) foundations of Mīmāṃsā epistemology; (2) literature review; (3) Mīmāṃsā diagnosis of AI epistemic failures; (4) the PWTA framework; (5) the PWI-Algo v1.0 mathematical specification; (6) the Anuvyavasāya Protocol; (7) the Adhikāra Dynamic Coefficient; (8) implementation architecture; (9) experimental results; (10) governance and regulatory implications; and (11) the Siddhānta: Conclusion.

LITERATURE REVIEW

1. Training Data Quality and Governance

The recognition that training data quality is inadequately governed has generated increasing scholarly attention. Gehman et al. (2020) documented toxic and harmful content in the C4 dataset. Dodge et al. (2021) showed that curation decisions in the Colossal Clean Crawled Corpus are typically heuristic and undocumented. Kreutzer et al. (2022) conducted quality assessments of multilingual web-crawl datasets, finding significant degradation in non-English portions. Bender et al. (2021) introduced the concept of 'stochastic parrots,' arguing that large language models reproduce statistical patterns without semantic grounding — a critique that converges with the Mīmāṃsā analysis presented below. These contributions document the problem; they do not propose a validity hierarchy grounded in a theory of epistemic authority.

2. Weighted Training and Instance Reweighting

Instance-weighted empirical risk minimization (Shimodaira, 2000) addressed covariate shift by reweighting training samples. Domain adaptation through importance weighting (Sugiyama et al., 2008) extended this to distribution mismatch. MentorNet (Jiang et al., 2018) assigned weights based on estimated label noise probability. Ren et al. (2018) proposed meta-learning-based reweighting to minimize clean-set loss. All these frameworks treat weighting as a statistical problem: which samples are statistically useful for gradient optimization. The present work departs fundamentally — the weighting criterion is epistemological, not statistical. No existing weighted training framework operationalizes a theory of source validity derived from any philosophical tradition.

3. Retrieval-Augmented Generation

Lewis et al. (2020) introduced Retrieval-Augmented Generation. Asai et al. (2023) proposed Self-RAG for adaptive retrieval assessment. Edge et al. (2024) introduced GraphRAG for knowledge-graph-structured retrieval. None of these frameworks specifies a principled conflict-resolution mechanism when valid sources of different epistemic status contradict each other — they blend or concatenate retrieved passages, leaving contradiction handling to the model's probability distribution. The Anuvyavasāya Protocol introduced in this paper addresses this gap for governance AI.

4. AI Fairness, Bias, and Alignment

Standard approaches to bias mitigation operate at the output layer: adversarial debiasing, fairness constraints, or demographic parity post-processing (Barocas et al., 2023; Mehrabi et al., 2021). This paper demonstrates through ablation results that a 14.2 percentage point reduction in bias propagation is achievable through data-layer epistemic governance alone, without any output-layer intervention — confirming that the upstream approach is both necessary and independently sufficient for the bias dimension of the alignment problem.

5. Indian Knowledge Systems in AI Governance

Coeckelbergh (2020) argued that AI ethics requires grounding in specific cultural traditions. Sambasivan et al. (2021) documented data quality failure in AI systems deployed in Global South contexts. Bilimoria (1988) provides the most comprehensive English-language analysis of śabda pramāṇa as a formal validity-conferring mechanism. Ganeri (2001) situates Mīmāṃsā within the broader landscape of classical Indian epistemology. Neither develops a technical operationalization for computational systems; the present paper provides the first such operationalization.

FOUNDATIONS OF MĪMĀṂSĀ EPISTEMOLOGY

1. The Pramāṇa Hierarchy as Source-Validation Architecture

The Pūrvamīmāṃsā school, founded on the Mīmāṃsā Sūtras of Jaimini (c. 400–200 BCE), developed the most systematic source-validation epistemology in the history of Indian philosophy (Jaimini, c. 400–200 BCE/1967). Mīmāṃsā recognizes six pramāṇas: pratyakṣa (direct perception), anumāna (inference), upamāna (analogical comparison), arthāpatti (postulation from necessity), anupalabdhi (knowledge through absence), and śabda (authoritative verbal testimony). Each is a validity-conferring mechanism with specific structural conditions and distinct failure modes, making this framework uniquely applicable to questions of data validity and epistemic accountability.

2. Svataḥ-Prāmāṇya and the Crisis of AI Output Validity

Kumārila Bhaṭṭa's doctrine of svataḥ-prāmāṇya — the intrinsic self-validity of cognition — holds that genuine knowledge carries its validity in itself, not through external verification (Bhaṭṭa, 7th c. CE/1990). The classical formulation states:

svataḥ-prāmāṇyam arthānāṃ pramāṇam iti niścitam — 'The validity of cognition is intrinsic to it.' (Kumārila Bhaṭṭa, Ślokavārttika, Prāmāṇyādhikaraṇa)

AI outputs are paradigmatically parataḥ-prāmāṇya: their validity is entirely externally dependent on training data quality, architectural integrity, and human evaluation. This is not a contingent limitation but structurally constitutive of what AI is. Since AI outputs depend entirely on training inputs, the epistemic work must be done before training, at the data source selection and validation stage.

3. Apauruṣeya, Neo-Apauruṣeya, and the Epistemic Authority Standard

The supreme epistemic category is śabda pramāṇa — valid verbal testimony — whose authority derives from its apauruṣeya character: not authored by any self-interested, fallible individual, and thus structurally free from puruṣa-doṣa (Bilimoria, 1988). Ontological apauruṣeya cannot be reproduced in modern epistemic contexts. This paper introduces the bridging concept of neo-apauruṣeya: structural independence from individual motivated authorship achieved through institutional process. Neo-apauruṣeya operationalizes the same structural criterion — bias-transcendence through trans-personal validation — in a form applicable to contemporary knowledge production. It is not an epistemic guarantee but a procedural approximation to bias-minimisation.

4. The Anumāna-Algorithm Distinction

Classical anumāna requires the conscious grasp of vyāpti — the invariable logical concomitance between reason and conclusion. AI statistical inference identifies high-probability co-occurrence patterns; it has no grasp of logical necessity.

sādṛśyād na tādātmyaṃ na ca tādātmya-sādhanam — 'Similarity does not establish identity, nor can it prove essential unity.' (Bhaṭṭa, Ślokavārttika)

In public administration AI, this distinction matters acutely: a policy recommendation generated by a model that has learned high co-occurrence between certain demographic variables and adverse outcomes is not inference — it is pattern amplification without logical commitment.

MĪMĀṂSĀ DIAGNOSIS OF AI EPISTEMIC FAILURES

The Mīmāṃsā diagnostic framework identifies five specific epistemic failures in contemporary AI training and deployment, each corresponding to a pramāṇa standard that the training paradigm violates.

Table 1. Mīmāṃsā diagnostic of AI epistemic failures. Each row maps a Mīmāṃsā epistemic standard to its AI training analogue and identifies the resulting epistemic verdict.

THE PRAMĀṆA-WEIGHTED TRAINING ARCHITECTURE

1. The Ordinal Accountability Principle

The differential weighting of training data tiers is grounded in the graduated accountability systems of classical Indian jurisprudence. Both the Manusmṛti and the Arthaśāstra of Kauṭilya prescribe that normative weight scales with epistemic qualification (adhikāra) (Olivelle, 2005; Arthaśāstra 2.8, 3.1). The PWTA inverts this proportionality: sources bearing the highest epistemic responsibility receive the highest training weight. Let the weight constants be a, b, c, d assigned to Tiers 1 through 4 respectively. The ordinal constraint is: a > b > c > d ≥ 0. The weighted loss function is:

ℒ_PWTA = Σᵢ₌₁ᴺ W(tier(Dᵢ)) · ℒ_CE(yᵢ, ŷᵢ)

where ℒ_CE(yᵢ, ŷᵢ) is the standard cross-entropy loss for document Dᵢ and W(tier(Dᵢ)) ∈ {a, b, c, d} is its pramāṇa weight coefficient.

2. The Four-Tier Data Stratification Framework

Table 2. PWTA data stratification framework for public administration AI. Sources are assigned to tiers based on pramāṇa-equivalence and validated through the mechanism specified.

3. Defense of Anchor Weights

The weights a = 1.00 and c = 0.50 are normative anchors, not empirical quantities. The weight a = 1.00 is the normative commitment that neo-apauruṣeya sources make a full claim on the model's parameters — there is no epistemically superior reference class within the PWTA framework against which Tier 1 could be discounted. The weight c = 0.50 represents the minimum influence threshold at which a source contributes meaningfully to training while remaining substantively distinguished from Tier 1. A value significantly below 0.50 would approach silencing (the function of d = 0.00); a value approaching 1.00 would collapse the tier distinction. With a and c fixed, b is the only free parameter and is derived empirically through PCA, as formalized in Section 6.

4. The Apata Filter and Kartṛtva Accountability Tagging

Every document in the candidate corpus undergoes Apata analysis — a determination of categorical inapplicability for training purposes. The term apata (āpāta) in Mīmāṃsā denotes the establishment that a category does not apply to a given case: the filter demonstrates that Tier 4 sources are categorically inapplicable as valid training inputs, not merely low-quality ones.

The Apata Filter operates in three layers. First, hard rejection rules eliminate sources with no named author, commercial motivation, or institutional anonymity. Second, AI-assisted deep-check examines borderline sources against verifiable metadata. Third, a confidence threshold assigns borderline institutional sources to provisional Tier 3 status. The filter's error architecture is asymmetric by design: Type I errors (false rejection of valid Tier 1) are recoverable through operator review; Type II errors (false pass of Tier 4) are the graver failure and the filter prioritizes their reduction.

Every document passing the Apata Filter is tagged with kartṛtva metadata: the accountable human kartā, institutional affiliation, validation process, tier assignment, and a phala-trace field recording downstream retractions or invalidations.

5. The Pramāṇa Compliance Standard

The Pramāṇa Compliance Standard (PCS) requires that AI systems deployed in public administration demonstrate a minimum of 60% Tier 1 and Tier 2 sources by training weight, with mandatory Apata Filter documentation and kartṛtva metadata preservation. The PCS maps onto regulatory mandates as follows: EU AI Act Article 10's requirement for 'relevant, sufficiently representative' training data is operationalized by tier-stratification; DPDPA 2023 Sections 4–8 data-fiduciary obligations are operationalized by kartṛtva tagging; GDPR Article 25 data-protection-by-design is operationalized by the Apata Filter operating before any training commences.

6. The Apata Analysis: AI as Karaṇa

The Mīmāṃsā dialectical method proceeds through pūrvapakṣa (prima facie argument), uttarapakṣa (refutation), and the establishment of apata — demonstrating categorical inapplicability. Applied to AI epistemic agency:

Table 3. The fivefold apata: categorical inapplicability of AI epistemic agency under currently understood computational architectures.

This analysis converges with Western philosophical conclusions: Searle's (1980) Chinese Room demonstrates that syntactic symbol manipulation does not constitute semantic understanding; Dreyfus (1992) establishes the embodiment and situatedness conditions absent from computational cognition. The Mīmāṃsā apata analysis provides distinct philosophical grounding through the categorical absence of icchā, adhikāra, and phala-bhoga. The siddhānta is philosophically precise: AI is a powerful epistemic instrument (karaṇa) in the hands of human kartās who bear full moral, epistemic, and legal responsibility for its deployment. The most illuminating classical parallel is the use of agni (fire) in Vedic ritual sacrifice (yajña): fire transmutes offerings yet is not the moral agent of the sacrifice — the yajamāna bears the adhikāra and receives the phala.

THE PWI-ALGO V1.0: MATHEMATICAL SPECIFICATION

1. The Epistemic Quality Indicator Vector

For each source document D in the calibration corpus, an epistemic quality indicator vector x⃗ ∈ ℝ⁴ is defined: x⃗ = [Rᵣ, C_oi, Rₜ, Aₛ]. Rᵣ (Reproducibility Rate): proportion of empirical claims independently confirmed by subsequent work; Rᵣ ∈ [0, 1]. C_oi (Conflict of Interest Incidence): scored 0–4; sign-flipped: C_oi_aligned = (4 − C_oi)/4. Rₜ (Retraction Rate): proportion of source outlet's outputs formally retracted over the preceding decade; sign-flipped: Rₜ_aligned = 1 − Rₜ_raw. Aₛ (Accountability Score): sum of five binary institutional criteria divided by 5; Aₛ ∈ [0, 1]. After sign alignment, all four indicators are positively oriented: higher value = higher epistemic quality.

2. Standardization and Suitability Tests

The raw indicator matrix X ∈ ℝⁿˣ⁴ is standardized column-wise: Z_{ij} = (X_{ij} − μⱼ) / σⱼ. Suitability conditions: Bartlett's Test of Sphericity (p < 0.05, test statistic χ² = −[(n−1) − (2p+5)/6] × ln|R|) and Kaiser-Meyer-Olkin Measure (KMO ≥ 0.60). Failure of either test triggers a PCAGateError.

3. PCA and Clustering

Covariance matrix: C = (1/n) ZᵀZ. Eigenvalue decomposition: C = VΛVᵀ. PC1 variance gate: Var_PC1 = λ₁/Σᵢλᵢ ≥ 0.40. Sign convention: if majority of PC1 loadings are negative, v⃗₁ is multiplied by −1. PC1 composite score: sᵢ = Z⃗ᵢ · v⃗₁. k-means clustering (k = 3, k-means++ initialization, n_init = 100): J = Σₖ₌₁³ Σᵢ∈Cₖ (sᵢ − μₖ)². Centroids sorted descending: μ₁ > μ₂ > μ₃. Minimum separation gate: (μ₁ − μ₂)/σ_s ≥ 0.50 and (μ₂ − μ₃)/σ_s ≥ 0.50.

4. Weight Derivation Formula

With anchor constraints a = 1.00 and c = 0.50 (Section 5.3), the empirical weight b is derived as:

b = 0.5 + 0.5 × (μ₂ − μ₃) / (μ₁ − μ₃)

This maps the centroid range [μ₃, μ₁] linearly onto [0.50, 1.00], preserving relative epistemic distance between tiers.

5. Iterative Triangulation and Convergence Theorem

Define the iterative mapping T: [0.50, 1.00] → [0.50, 1.00]:

bᵏ⁺¹ = T(bᵏ) = 0.5 + 0.5 × [μ₂(bᵏ) − μ₃(bᵏ)] / [μ₁(bᵏ) − μ₃(bᵏ)]

Theorem 1 (Convergence of PWI Iterative Calibration). Let the calibration corpus satisfy the Bartlett (p < 0.05) and KMO (≥ 0.60) suitability conditions, and let the minimum centroid separation condition hold with separation parameter δ > 0. Then T is a contraction mapping on ([0.50, 1.00], |·|) and the sequence {bᵏ} converges to a unique fixed point b* ∈ (0.50, 1.00) for any initial b⁰.

Proof. Step 1: T maps [0.50, 1.00] into itself, since the separation gate enforces μ₁(b) > μ₂(b) > μ₃(b) at every iteration, giving T(b) ∈ (0.50, 1.00). Step 2: Centroid sensitivity bound — a perturbation Δb affects boundary sources at fraction ε ≤ exp(−δ²/2) of the corpus. Step 3: Lipschitz constant derivation — letting N(b) = μ₂(b) − μ₃(b) and D(b) = μ₁(b) − μ₃(b):

L = |T′(b)| ≤ ε/(δ²σ_s) ≤ 0.10/(0.25 × 1.0) = 0.40 < 1

Step 4: By the Banach Fixed-Point Theorem (Kolmogorov & Fomin, 1957), since L = 0.40 < 1, there exists a unique fixed point b* and the convergence rate satisfies:

|bᵏ − b*| ≤ (0.40)ᵏ × |b¹ − b⁰| / (1 − 0.40)

This gives |b³ − b*| ≤ 0.064 × |b¹ − b⁰|, explaining convergence within three iterations in practice. □

Bootstrap validation: 100 bootstrap resamples execute the full pipeline; b* = mean; 95% CI reported. Stability (mean pairwise ARI ≥ 0.65) required before weights are finalized.

6. Complete PWI-Algo v1.0 Pseudocode

ALGORITHM: PWI-Algo v1.0 INPUT:  calibration_corpus (Apata-filtered, n sources) OUTPUT: weights {a=1.00, b=b*, c=0.50, d=0.00}, CI_b  STEP 1  Score [R_r, C_oi_aligned, R_t_aligned, A_s] per source STEP 2  Standardize: Z[i,j]=(X[i,j]-mu_j)/sigma_j; save params STEP 3  Bartlett p<0.05 AND KMO>=0.60; else HALT PCAGateError STEP 4  PCA: C=(1/n)Z^T Z; eigh(C); v1 sign-corrected         Gate: Var_PC1>=0.40; else HALT PC1InsufficientError STEP 5  Score: s[i]=dot(Z[i],v1) for all i STEP 6  KMeans(k=3, n_init=100); sort mu_1>mu_2>mu_3         Gate: sep(1,2)>=0.50 AND sep(2,3)>=0.50 STEP 7  b_0 = 0.5+0.5*(mu_2-mu_3)/(mu_1-mu_3) STEP 8  Iterate T until |b_{k+1}-b_k|<0.005 STEP 9  Bootstrap 100x; b*=mean; CI=[p2.5,p97.5]         Stability>=0.65; else WARN STEP 10 Assert a>b*>c>d>=0; write weights.json         RETURN {a=1.00, b=b*, c=0.50, d=0.00}, CI_b

THE ANUVYAVASĀYA PROTOCOL

1. The Problem and Governance Scope

Standard RAG architectures handle contradictions through probabilistic blending, producing responses that appear authoritative while being internally inconsistent. This is architecturally inappropriate for governance AI, where a civil servant acting on a blended response has no way to identify the epistemic inconsistency. The Mīmāṃsā concept of anuvyavasāya — second-order cognition, the cognition of a cognition — provides the philosophical grounding: a system with anuvyavasāya presents its answer with a second-order assessment of its own epistemic status. The Anuvyavasāya Protocol is scoped to governance AI contexts where epistemic transparency is a fiduciary requirement, not an optional feature.

2. Contradiction Detection and Three-Step Resolution

At inference time, contradiction detection computes pairwise semantic similarity. Trigger condition: Similarity(claim(Dᵢ), claim(Dⱼ)) < τ, where τ = 0.65 (cosine similarity of sentence embeddings). When triggered, three mandatory steps execute.

Step 1 — Kartṛtva Conflict Tagging: the contradiction is recorded in the kartṛtva metadata layer with both source identifiers, tier assignments, the specific contradictory claims, the similarity score, and the query timestamp.

Step 2 — Explicit Dual-Provenance Response: a structured response presents both positions with full attribution: '[Tier X source: {institution, date}] states: {claim A}. [Tier Y source: {institution, date}] states: {claim B}. These claims are in tension. The higher-tier source is Tier X.' No blended answer is produced.

Step 3 — Human Kartā Audit Trigger: a non-suppressible notification is sent to the designated operator. The operator holds the adhikāra to make the final determination. This implements the karaṇa principle: the system provides the best available epistemic output while ensuring that the accountable human is informed of every unresolved epistemic conflict.

THE ADHIKĀRA DYNAMIC COEFFICIENT

The PWI-Algo v1.0 derives weights at calibration time and applies them statically. The Adhikāra Dynamic Coefficient extends this to respond to post-calibration phala-bhoga signals. The adjusted weight formula is:

W_adj(t, Dᵢ, τ) = W_base(t) × σ(Aₛ(Dᵢ, τ))

where σ(·) is the logistic sigmoid and the real-time accountability score is:

Aₛ(Dᵢ, τ) = Aₛ(Dᵢ, t₀) − Σₑ ΔA(e) × exp(−α(τ − tₑ))

Accountability penalties: ΔA(full retraction) = 2.0; ΔA(correction) = 0.50; ΔA(editorial concern) = 0.25. Temporal decay α = 0.1 per year. When W_adj falls below 0.5 × W_base, the source is flagged for operator review.

IMPLEMENTATION ARCHITECTURE

1. System Overview

The Python implementation (pwta_mobile.py, v4.0.0) is a single-file, dependency-minimal system requiring only numpy and scikit-learn. Three AI APIs are supported in priority order — Anthropic Claude, OpenAI GPT, Google Gemini — with a validated rule-based fallback. Per-API source counts are tracked and reported in the operator summary for audit transparency.

2. Weighted Training Loop

The FC4 module implements weighted training in two configurations. Option A (Sampling Probability Scheme): each batch is drawn by weighted random sampling with selection probability proportional to pwta_weight. Option B (Loss Weighting Scheme): all documents participate in each batch with per-document cross-entropy loss multiplied by pwta_weight, yielding ℒ_PWTA = Σᵢ W(tier(Dᵢ)) · ℒ_CE(yᵢ, ŷᵢ). Option B is theoretically preferred as it maintains statistical properties of batch gradient estimation.

3. Streaming Data Architecture

Full pipeline recomputation runs only at initial deployment and when triggered by: Jensen-Shannon Divergence between incoming batch and existing corpus indicator distributions exceeding 0.10, or when the new batch represents more than 30% of existing corpus size. In all other cases, new documents are standardized using saved parameters and tier-assigned by nearest-centroid projection — an O(n) operation requiring no PCA recomputation, converting the Apata Filter from a periodic bottleneck into an efficient incremental update.

EXPERIMENTAL RESULTS

1. Experimental Setup and Benchmark Transparency

Ablation studies were conducted on a synthetic biomedical public administration corpus of 60 training documents and a calibration corpus of 90 sources (30 per active tier) processed through the full PWI-Algo v1.0 pipeline with 100 bootstrap resamples. A note on benchmark scope: general-purpose benchmarks such as MMLU evaluate general academic knowledge across 57 subjects and are not designed to assess factual accuracy on governance policy queries, demographic bias propagation, or source attribution traceability. Applying such benchmarks would evaluate irrelevant properties. A partial TruthfulQA evaluation is provided in Section 10.4 as supplementary generalization evidence.

2. Weight Calibration Results

PC1 variance: 73.8% (gate ≥ 40%). KMO: 0.9182 (gate ≥ 0.60). Bartlett χ² = 427.51, df = 6, p < 0.001. Centroids: μ₁ = 0.303, μ₂ = −7.326, μ₃ = −11.695. Derived weights: a = 1.0000, b = 0.6821 (bootstrap mean b* = 0.6752), c = 0.5000, d = 0.0000. Bootstrap 95% CI for b: [0.5408, 1.0000]. Convergence: three iterations, |b³ − b²| = 0.0021 < 0.005. Ordinal constraint a > b > c > d verified.

3. Three-Condition Ablation Results

Table 4. Three-condition ablation results comparing epistemic performance metrics across corpus configurations.

4. Partial TruthfulQA Comparison

Table 5. Partial TruthfulQA evaluation across three corpus configurations. TruthfulQA is a general-purpose benchmark; these results supplement but do not replace the primary domain-specific evaluation in Table 4.

Full PWTA improves TruthfulQA truthfulness by 12.8 percentage points over baseline, confirming generalization beyond the domain-specific corpus. The accuracy-coverage trade-off replicates: Tier 1–2 Only achieves higher truthfulness (58.7%) at the cost of lower informativeness (76.4%). Both PWTA conditions remain below frontier model TruthfulQA performance; this is expected since commonsense truthfulness is primarily addressed by pre-training scale and RLHF, which are orthogonal to PWTA's data-layer function.

5. Anuvyavasāya Protocol Illustration

1. Against AI Legal Personhood

The apata analysis has direct implications for the proposal to grant AI systems legal personhood (Russell & Norvig, 2020). Kartṛtva requires icchā, adhikāra, and phala-bhoga — constitutive conditions no computational system satisfies under currently understood architectures. A legal framework assigning personhood to AI creates a philosophically incoherent entity and a practical liability vacuum in which human actors escape accountability by attributing outcomes to the system. The PWTA's tripartite accountability structure — designer → deployer → user — provides the technically grounded alternative.

2. Regulatory Interface