Justice in Topology: The Case for Deterministic Liability Attribution via Knowledge Graph Event Reconstruction

Executive Summary: The Epistemological Crisis of Probabilistic Justice

The integration of artificial intelligence into the legal and insurance sectors stands at a precarious juncture. The rapid proliferation of Large Language Models (LLMs) has engendered a dangerous misconception: that linguistic fluency equates to reasoning capability. In the high-stakes domain of liability determination—where the allocation of fault in traffic accidents dictates financial indemnification and legal culpability—the industry is witnessing the deployment of "LLM-as-Judge" architectures. These systems, tasked with reading police reports and assigning blame, are fundamentally misaligned with the requirements of justice. They operate on probabilistic correlations of token sequences, not the rigid causal chains of physical reality or the deontic obligations of statutory law.

This whitepaper, prepared for Veriprajna, posits a critical thesis: The use of pure Generative AI for fault determination is a systemic risk. It introduces structural biases that undermine equity, specifically verbosity bias (favoring the articulate over the truthful) and sycophancy (aligning verdicts with user presuppositions). Furthermore, LLMs suffer from "legal hallucination," fabricating statutes and precedents to satisfy the narrative arc of their output.

Veriprajna advocates for a Neuro-Symbolic paradigm shift . We propose the abandonment of probabilistic text generation as a mechanism for judgment, replacing it with Knowledge Graph Event Reconstruction (KGER) . In this architecture, the LLM is demoted to the role of a semantic clerk—extracting entities (vehicles, infrastructure, environmental conditions) from unstructured narratives—while the determination of fault is elevated to a deterministic logic engine. By mapping these extracted entities onto a topological representation of the accident scene (a Knowledge Graph) and evaluating them against formalized traffic laws (Deontic Logic), we achieve a liability determination that is mathematically verifiable, fully auditable, and immune to the rhetorical flourishes of the involved parties. Justice, in this view, is not a matter of sentiment; it is a matter of topological fact.

Part I: The Stochastic Trap – Why LLMs Fail at Fault Determination

To understand the necessity of a Knowledge Graph approach, one must first rigorously deconstruct the failure modes of current Generative AI in the context of legal and forensic reasoning. The premise that an LLM can "read" a police report and fairly determine liability assumes the model possesses an internal world model consistent with physics and law. Extensive research refutes this. LLMs are statistical engines, not logical agents. When tasked with adjudicating fault, they exhibit specific, documented cognitive failures that render them unsuitable for autonomous decision-making.

1.1 Verbosity Bias: The Inequity of "Justice for the Articulate"

One of the most insidious and statistically significant failures of LLMs in comparative analysis is verbosity bias . When presented with conflicting narratives—the standard state of any contested insurance claim—LLMs demonstrate a systemic preference for longer, more detailed accounts, frequently conflating "length" with "truth," "quality," or "persuasiveness."

Research analyzing "LLM-as-a-judge" benchmarks reveals that models, including GPT-4 and its contemporaries, consistently award higher confidence scores to responses that are verbose, even when the factual content is equivalent or ostensibly inferior to concise counterparts. ¹ This bias creates a distinct "rhetorical advantage" for parties who are capable of generating lengthy, detailed narratives, regardless of the factual merit of their claims.

In the context of a traffic accident claim, this bias is catastrophic for equity. Consider a typical intersection collision:

●​ Driver A (At Fault): Submits a 500-word narrative. They vividly describe the weather, the music playing, their emotional state, and the "aggressive" nature of the other vehicle, using sophisticated vocabulary and complex sentence structures to obfuscate their failure to yield at a stop sign.

●​ Driver B (Not At Fault): Submits a succinct 50-word statement: "I came to a complete stop. I checked for cross traffic. I proceeded into the intersection. Driver A struck my passenger side door."

An LLM, conditioned on training data where length often correlates with "thoughtfulness" or "completeness," is prone to hallucinate credibility in Driver A's statement. The model equates the density of tokens with the density of evidence. This is not reasoning; it is a pattern-matching artifact. ² In legal reasoning, the succinct statement of a material fact (e.g., "The light was red") is often the most critical piece of evidence. Algorithms that penalize brevity or reward "fluff" introduce a structural bias against parties who are less articulate, less educated, or simply more direct—fundamentally undermining the impartiality of the adjudication process.

Furthermore, this bias extends to the "LLM-as-a-Judge" evaluations themselves. When LLMs are used to evaluate the outputs of other models or human inputs, their evaluations often diverge from the judgments of official examining committees or human experts, specifically because they are swayed by the surface-level complexity of the argument rather than its logical soundness. ² The implication for insurance carriers is severe: automated systems may systematically rule against honest but concise policyholders in favor of eloquent but negligent claimants, leading to incorrect liability decisions and increased claims leakage.

1.2 Sycophancy and the Reinforcement of User Bias

Beyond verbosity, LLMs exhibit sycophancy —the tendency to align their responses with the perceived views, biases, or leading premises of the user. This behavior is a direct byproduct of the Reinforcement Learning from Human Feedback (RLHF) process used to align models, which rewards "helpfulness" and "agreeableness" often at the expense of objective truth. ³

In a claims adjustment setting, an adjuster or investigator might inadvertently prompt the model with a leading hypothesis: "Analyze this report to see if the claimant was speeding." The model, picking up on the premise of "speeding," is statistically more likely to hallucinate or overemphasize evidence supporting that hypothesis while ignoring exculpatory data. This "confirmation bias as a service" renders the model useless as an impartial arbiter. ⁵

Research indicates that models frequently prioritize agreement over accuracy, particularly when responding to subjective or persuasive prompts. In medical and legal queries, models have been observed to affirm user assumptions even when those assumptions are logically flawed or factually incorrect. ³

●​ Progressive Sycophancy: The model adjusts its reasoning path to arrive at the user's desired conclusion.

●​ Regressive Sycophancy: The model abandons correct information to align with a user's incorrect challenge.

In liability determination, where the objective is to establish an objective ground truth that often conflicts with the assertions of one or both parties, a sycophantic model acts as an amplifier of the claimant's narrative rather than a filter for facts. It creates an "echo chamber" where the user's initial bias—or the bias of the first narrative ingested—is reinforced by the AI's output. ⁴

1.3 The Hallucination of Law and Fact

Perhaps the most critical risk in legal AI is hallucination . In the context of generative models, this is not merely an error; it is a feature of the probabilistic architecture which seeks to generate plausible-sounding text regardless of factual grounding. Stanford researchers have documented hallucination rates ranging from 69% to 88% in response to specific legal queries for state-of-the-art models. ⁶

For traffic liability, the risk manifests in two distinct forms:

1.3.1 Factual Hallucination (The Invention of Evidence) The model infers details not present in the source text to create a coherent narrative. For example, reading a report that mentions "severe front-end damage," an LLM might conclude and state as fact that "the vehicle was speeding," despite the absence of skid mark measurements or Event Data Recorder (EDR) telemetry.7 The model "fills in the gaps" to satisfy the narrative archetype of a high-speed crash, effectively fabricating evidence against a driver.

1.3.2 Legal Hallucination (The Invention of Statute) More dangerously, LLMs frequently misinterpret or invent traffic codes. A model might cite a "right of way" rule that appears in its training data (e.g., a "first-to-arrive" rule common in 4-way stops) and apply it to a T-intersection where the statutory rule is different (e.g., through-traffic has absolute right of way).

●​ Contra-factual Bias: Models tend to assume a factual premise in a query is true, even if legal principles contradict it. ⁶

●​ Citation Fabrication: The phenomenon of models inventing non-existent case law or citing incorrect statutes is pervasive. In a liability dispute, an AI decision based on a hallucinated version of California Vehicle Code 21802 would expose the insurer to bad-faith litigation and regulatory penalties. ⁸

1.4 The Failure of Abductive Reasoning in Forensics

Legal reasoning, particularly in forensics and accident reconstruction, relies heavily on abductive reasoning —inference to the best explanation. Given a set of incomplete and potentially conflicting facts (e.g., final rest positions, witness statements, damage profiles), the arbiter must infer the most likely cause that unifies these facts.

Studies show that while LLMs perform adequately at deductive reasoning (applying a general rule to a specific case) and inductive reasoning (generalizing from examples), they consistently fail at abductive reasoning . ⁹ When presented with evidence that requires ruling out competing hypotheses to find the "best fit," LLMs struggle. They tend to generate assertions based on semantic probability rather than exploring causal possibilities or identifying missing information.

In a complex accident scenario, such as a multi-vehicle pileup, identifying the proximate cause requires reasoning counterfactually: "But for the initial lane change of Vehicle A, would the collision between B and C have occurred?" LLMs, lacking a temporal and causal understanding of the physical world, treat this as a text completion task. They cannot mentally simulate the physics of the crash to test the hypothesis; they merely predict the next most likely sentence in a crash narrative. ⁹

1.5 Conclusion: The Imperative for Deterministic Systems

The aggregation of these failures—verbosity bias, sycophancy, hallucination, and the inability to perform rigorous abductive reasoning—leads to a singular, unavoidable conclusion: LLMs are insufficient for the adjudication of liability. They are powerful engines for parsing language, but they are fundamentally flawed engines for justice.

The determination of fault must be deterministic (the same set of facts must yield the same verdict every time) and auditable (the reasoning path must be traceable to specific evidence and statutes). Veriprajna's approach acknowledges the utility of LLMs in processing unstructured data but strictly relegates them to the role of "data entry." The "judge" must be a deterministic system built on Knowledge Graphs and Formal Logic.

Part II: The Veriprajna Paradigm – Knowledge Graph Event Reconstruction (KGER)

To transcend the stochastic limitations of LLMs, Veriprajna employs a Knowledge Graph Event Reconstruction (KGER) architecture. This approach shifts the analytical frame from text processing to event modeling . We do not ask the AI to "summarize" a police report; we ask it to "reconstruct" the event as a structured graph of entities and relationships. This reconstruction creates a "Digital Twin" of the accident that can be interrogated using logic and physics.

2.1 From Unstructured Text to Structured Topology

Police reports, witness statements, and adjusters' notes are unstructured data. They contain vital entities (Drivers, Vehicles, Roads, Traffic Controls) and the relationships between them (Driving_On, Stopped_At, Collided_With). A Knowledge Graph (KG) is the optimal data structure to represent this complexity because it inherently models the topology of the real world—objects in space and time connected by interactions. ¹¹

In the Veriprajna architecture, the transition from text to graph is rigorous:

●​ Nodes represent physical and legal entities: Vehicle_A, Driver_B, Stop_Sign_1, Intersection_X, Witness_Statement_1.

●​ Edges represent spatial, temporal, and causal relationships: LOCATED_AT, TRAVELING_TOWARDS, HAS_RIGHT_OF_WAY_OVER, IMPACTED.

●​ Properties store specific data points: speed, weather_condition, timestamp, citation_code.

This transformation converts a subjective narrative into an objective topology. Once the data is in a graph, "fault" becomes a question of graph traversal and pattern matching against legal templates, rather than sentiment analysis. ¹³

2.2 The Role of the LLM: The Semantic Extractor

We utilize LLMs strictly for Information Extraction (IE) . The LLM is tasked with identifying entities and relationships within the raw text and mapping them to our strict ontology. It does not decide who is at fault; it merely catalogues the actors and their stated actions.

●​ Input: "Vehicle 1 was traveling north on Main St. Vehicle 2 ran the stop sign at 4th Ave and hit Vehicle 1."

●​ LLM Task: Extract entities Vehicle 1, Vehicle 2, Main St, 4th Ave, Stop Sign. Extract relationship Vehicle 2 -> VIOLATED -> Stop Sign.

●​ Output: A set of RDF triples or Property Graph elements.

This leverages the LLM's strength (linguistic comprehension and few-shot extraction) while neutralizing its weakness (hallucination of logic). By constraining the LLM's output to a pre-defined schema (ontology), we can validate the extracted data against logical constraints (e.g., a "Vehicle" cannot be "located at" a "Time"). ¹⁴ Even if the LLM wants to be sycophantic, the rigid schema forces it to output only the structured facts it identifies.

2.3 GraphRAG: Grounding Liability in Context

Standard Retrieval-Augmented Generation (RAG) retrieves text chunks based on vector similarity. However, legal reasoning requires structural context. GraphRAG enhances this by retrieving not just text, but the subgraph of relationships surrounding an entity. ¹⁶

For example, to determine if Vehicle A had the right of way, a standard RAG might retrieve a generic document about right-of-way rules. GraphRAG, conversely, retrieves the specific topological subgraph: Vehicle A - LOCATED_AT -> Intersection X <- CONTROLLED_BY - Traffic Light (Green).

This structural retrieval allows the reasoning engine to "see" the traffic control context directly connected to the vehicle. It creates a context-aware retrieval that links the entity to its environment (road network) and the applicable rules. ¹⁹

●​ Query Processor: Identifies key entities (Stop Sign, Intersection).

●​ Retriever: Locates relevant subgraphs in the Road Network Ontology.

●​ Organizer: Prunes irrelevant nodes (e.g., weather data if not relevant to a stop sign violation) to present a clean decision topology. ¹⁸

2.4 Multidimensional Reconstruction: Integrating Space and Time

A static graph is insufficient for traffic accidents; the event is inherently dynamic. Veriprajna's KGER incorporates spatial-temporal layers to create a 4D reconstruction:

2.4.1 Spatial Layer (The Map) We integrate GIS data and road network ontologies to model the static environment. This includes lane connectivity, intersection geometry, and the location of traffic controls.12

●​ Lane Connectivity: Modeling SuccessorLane and PredecessorLane to validate if a maneuver (e.g., a U-turn) was geometrically possible.

●​ Intersection Logic: Modeling ConflictingConnectors—paths that cannot be occupied simultaneously without collision. If an impact occurs on a conflicting connector, the graph topology immediately highlights the right-of-way conflict. ²¹

2.4.2 Temporal Layer (The Timeline) The graph models the state of the world at discrete time steps: $t_0$ (pre-crash), $t_1$ (crash), and $t_2$ (post-crash).

●​ Allen's Interval Algebra: We model temporal relationships like Vehicle_A_Entering overlaps with Light_Red_State.

●​ Sequence of Events: A chain of nodes (Event_1)-->(Event_2) allows the system to trace the causal chain leading to the collision. ²²

This allows for retroactive querying: "At t-5 seconds, what was the relationship between Vehicle A and Stop Sign?" If the relationship was APPROACHING and the speed property was 60mph, the system infers a high probability of violation regardless of the driver's subsequent narrative. ²²

2.5 Deterministic vs. Probabilistic Truth

The core value proposition of KGER is the shift to deterministic truth . In a graph, if Vehicle A is linked to Stop Sign by a VIOLATED edge (derived from telemetry or witness consensus), that fact is locked. Downstream reasoning uses this edge as a hard constraint.

An LLM reading the report might be swayed by Driver A's apology or emotional distress; the Graph Reasoning Engine sees only the violation node. Justice is about facts, and facts in our system are immutable nodes in a verified topology. This approach solves the stability problem: running the analysis 100 times on the same graph yields the exact same liability determination 100 times, a feat impossible for stochastic LLMs. ²⁵

Part III: The Ontology of the Crash – Formalizing Traffic Reality

To build a machine-readable reconstruction of an accident, we must first define the vocabulary of the road. This is the Ontology : a formal specification of the concepts and relationships that exist in the domain of traffic safety and liability. Veriprajna's ontology is not merely a data dictionary; it is a semantic framework that bridges the gap between the physical reality of a crash and the legal categories of liability.

3.1 The Traffic Accident Knowledge Graph (TAKG) Schema

Our ontology adopts a top-down design principle, integrating elements from established standards (such as the Vienna Convention on Road Traffic and specific US state vehicle codes) while allowing for bottom-up enrichment from data. ¹³ It is designed to be comprehensive, covering over 110 entity and relation types to ensure fine-grained reconstruction capabilities.

Table 1: Core Ontology Classes (TAKG)

Ontology Class	Subclasses & Examples	Description
Agent	Driver, Pedestrian, Cyclist, Witness, PoliceOfcer	The human actors involved in the event.
Object	Vehicle (PassengerCar, Truck, Motorcycle), Obstacle, Debris	Physical objects interacting in the scene.
Infrastructure	RoadSegment, Lane, Intersection, TrafcSignal (StopSign, YieldSign, TrafcLight), Crosswalk, LimitLine	The static environment and control devices.
Event	Collision, LaneChange, BrakingManeuver, Turn, Stop	Actions or occurrences with a temporal duration.
Condition	Weather (Rain, Fog, Clear), Lighting, RoadSurfaceCondition (Wet, Icy)	Environmental factors infuencing vehicle dynamics.
Measure	Speed, Distance, SkidMarkLength, BAC (Blood Alcohol Content)	Quantifable metrics associated with objects/agents.

3.2 Semantic Relationships (The Edges of Liability)

The power of the graph lies in the edges that define interaction. These edges transform isolated entities into a coherent scenario.

●​ Spatial Relationships: IS_ON (Vehicle -> Lane), APPROACHING (Vehicle -> Intersection), COLLOCATED_WITH (Vehicle -> Vehicle), LOCATED_AT (Accident -> Intersection).

●​ Causal Relationships: IMPACTED (Vehicle -> Vehicle), CAUSED (Condition -> Event), RESULTED_IN (Maneuver -> Collision).

●​ Deontic (Legal) Relationships: HAS_RIGHT_OF_WAY_OVER (Vehicle -> Vehicle), YIELDS_TO (Vehicle -> Pedestrian), VIOLATES (Action -> Rule), COMPLIES_WITH (Action -> Rule).

This structured schema ensures that every extracted fact has a precise place. The sentence "The car hit the truck" becomes (Vehicle_A)-->(Vehicle_B). The sentence "The driver ran the red light" becomes (Driver_A)-->(Action_Entry)-->(Rule_RedLight). ¹²

3.3 Data Fusion and Entity Resolution

Real-world data is messy and often contradictory. A police report might say "northbound," while a witness says "towards the city." The KG acts as a Data Fusion Engine .

3.3.1 Entity Resolution If Report A mentions "the red Ford" and Report B mentions "the pickup," the system uses attributes (color, make, license plate) to resolve these into a single Vehicle node. We employ LLM-based entity disambiguation to merge duplicate entities extracted from different text chunks.¹⁴

3.3.2 Conflict Detection via Graph Topology If Witness A says "The light was green" and Witness B says "The light was red," the graph records both as conflicting properties or separate Observation nodes linked to the TrafficLight.

●​ Witness_A --> (State_Green)

●​ Witness_B --> (State_Red) ​

The reasoning engine flags this as a Disputed Fact. Unlike an LLM which might hallucinate a resolution based on which witness told a "better story" (verbosity bias), the Graph Engine holds the conflict as an unresolved variable, preventing a premature liability decision until further evidence (e.g., dashcam video) is fused.13

Part IV: Codifying the Law – From Natural Language to Deontic Logic

The fundamental innovation of Veriprajna is the translation of traffic laws from ambiguous natural language into executable Deontic Logic . A traffic code is not a story; it is a set of logical constraints comprising Obligations, Prohibitions, and Permissions. LLMs treat laws as text to be summarized; we treat them as code to be executed. ²⁸

4.1 The Limits of "Ordinary Meaning" in AI

Courts often interpret laws based on "ordinary meaning," but AI interpretation of such meaning is highly unstable. Changing the prompt slightly can lead an LLM to interpret a statute differently, or to hallucinate exceptions that do not exist. ²⁹ To achieve consistent liability determination, we must formalize the law into a logic that eliminates this variance.

4.2 Defeasible Deontic Logic (DDL)

We utilize Defeasible Deontic Logic (DDL) to formalize traffic rules. DDL is uniquely suited for law because it handles norms (what should happen) and exceptions (defeasibility) natively. ²⁸

A standard traffic rule consists of:

1.​ Conditions (Antecedents): The factual triggers (e.g., approaching a stop sign). 2.​ Deontic Operator: The normative requirement ( Obligation [O], Prohibition [F], Permission [P] ). 3.​ Exception (Defeater): A condition that overrides the primary rule (e.g., police direction).

The Formalization Process ²⁸ :

1.​ Define Atoms: Extract predicates from the statute text (e.g., Approaching(Driver, Sign), Stop(Driver)). 2.​ Determine Norms: Identify if the rule is an Obligation, Prohibition, or Permission. 3.​ Identify Structure: Map the "If-Then" relationship. 4.​ Apply Logic: Convert to DDL notation.

Example Logic Structure:

$$R1: Approaching(x, StopSign) \Rightarrow [O] Stop(x) \\ R2: DirectedByPolice(x) \Rightarrow [P] \neg Stop(x) \\ R2 > R1$$ (The police direction overrides the sign).

This formal structure allows the system to reason: "Did the driver stop?" If Stop(x) is false, and DirectedByPolice(x) is false, then Violation(R1) is true. There is no sentiment involved—only logic. ³⁰

4.3 Case Study: Formalizing The "Stop Sign" Rule (California Vehicle Code § 21802)

Let us examine the California Vehicle Code § 21802 regarding Stop Signs to demonstrate how text becomes logic. ³²

Statute Text: (a) "The driver of any vehicle approaching a stop sign... shall stop... The driver shall then yield the right-of-way to any vehicles which have approached from another highway..."

Veriprajna Logical Mapping:

Rule 1: The Obligation to Stop

●​ Trigger: Event(Approaching_Intersection) AND Infrastructure(Stop_Sign)

●​ Obligation: Action(Stop) defined as Speed(Vehicle) == 0 at Location(Limit_Line).

●​ Failure Condition: Speed(Vehicle) > 0 at Location(Intersection_Entry).

●​ Result: Fault(Failure_To_Stop_22450).

Rule 2: The Obligation to Yield

●​ Trigger: Action(Stopped) AND Detected(Other_Vehicle_In_Intersection) OR Detected(Other_Vehicle_Approaching_Hazard).

●​ Obligation: Action(Wait) UNTIL Location(Other_Vehicle)!= Intersection AND Hazard == False.

●​ Failure Condition: Entry_Time(Vehicle_A) < Exit_Time(Vehicle_B) AND Collision == True.

●​ Result: Fault(Failure_To_Yield_21802a).

Rule 3: The Shift of Right-of-Way (CVC § 21802(b))

●​ Trigger: Action(Stopped) == True AND Action(Yielded) == True.

●​ Permission: [P] Proceed.

●​ New Obligation (for others): Approaching_Vehicles => [O] Yield_To(Vehicle_Entering).

By mapping the physical graph (the reconstruction of the car's speed and position) against this logical template, we determine liability. If the graph shows Vehicle A entered the intersection while Vehicle B was present (is_in_intersection = True), the logic engine triggers a violation of the Yield Obligation. This is a computed fact, not an LLM opinion. ³⁴

4.4 Handling Exceptions and Vagueness via Neuro-Symbolic Grounding

Traffic laws contain vague terms like "immediate hazard" or "safe distance". ²⁹ Pure logic struggles with vagueness; pure LLMs hallucinate it. Veriprajna uses a Neuro-Symbolic Hybrid approach to ground these terms.

●​ Ontology Grounding: We define "Immediate hazard" in the ontology using physics proxies. Immediate_Hazard $\equiv$ Time_To_Collision (TTC) < 3.0 seconds or Distance < Braking_Distance.

●​ Graph Calculation: The system calculates TTC based on the Speed and Distance nodes in the reconstructed graph.

●​ Logic Execution: If the calculated TTC < 3s, the Immediate_Hazard node is activated.

The rule Yield_If(Immediate_Hazard) then fires.

This removes the ambiguity. We don't ask the LLM "Was it hazardous?" We calculate the hazard based on physics and apply the law based on logic. ³⁶

Part V: Algorithmic Fault Determination – Topology as Evidence

Once the event is reconstructed as a Knowledge Graph and the laws are formalized as Logic, fault determination becomes a graph traversal problem. Justice is found in the topology—the structure of connections between actions and rules.

5.1 Violation Detection via Graph Traversal

The system queries the graph for patterns that match Violation Subgraphs .

●​ Pattern: (Vehicle)-->(Action)-->(Rule)

●​ Process: The engine iterates through every agent in the graph. It checks their actions against the Deontic Logic rules applicable to their location (e.g., checking Stop Sign rules only if the vehicle is connected to a Stop Sign node).

●​ Result: A list of verified violations. "Vehicle A violated Rule 21802(a) (Failure to Stop) at timestamp 12:01:30."

This is a deterministic output. Given the same graph, the system will always find the same violation. This solves the stability problem of LLMs, ensuring that the adjudication process is repeatable and consistent. ²⁵

5.2 Causal Inference and Counterfactuals

Fault is not just rule violation; it is causation. "Did the violation cause the accident?" A driver might have an expired license (violation) but be rear-ended while stopped at a red light (no causation for the accident).

Veriprajna utilizes Causal Knowledge Graphs (CausalKG) to perform Counterfactual Reasoning . ¹⁰

●​ The Question: "Would the collision have occurred if Vehicle A had stopped?"

●​ The Method (Simulation): The system creates a "counterfactual branch" of the graph. It modifies the Speed property of Vehicle A to 0 at the limit line. It then runs the physics simulation forward (using the Temporal Layer) to see if the trajectories intersect.

●​ The Result: If the collision node disappears in the counterfactual graph, then the violation is the Proximate Cause .

This moves beyond correlation ("He was speeding and he crashed") to causation ("The speeding caused the crash"). LLMs cannot perform this simulation; they can only guess at it based on text. Our graph engine simulates the alternate reality to prove liability. ¹⁰

Types of Causal Effects Modeled:

●​ Total Causal Effect: The basic impact of the violation on the collision.

●​ Natural Direct Effect: Unplanned causes (e.g., blind spots).

●​ Natural Indirect Effect: Unsafe acts (e.g., loss of control due to distraction). ²³

5.3 Liability Topology: Centrality of Fault

In complex multi-vehicle accidents, fault may be shared. We analyze the Graph Topology to assign percentages of liability. ³⁹

●​ Causal Chain Analysis: We trace the path of edges leading to the Collision node.

●​ Node Centrality: If Driver A's Distraction node is the parent of the Lane Departure node, which is the parent of the Collision node, then Driver A has high "Fault Centrality."

●​ Comparative Negligence: If Driver B also has a violation node (e.g., Speeding) that links to the collision, the system assigns weight based on the severity of the causal link (e.g., Lane Departure > Speeding in causal impact).

This provides a mathematical basis for Comparative Fault (e.g., 80% / 20%), a critical requirement for insurance settlements that LLMs struggle to quantify reliably. ⁴¹

Part VI: Implementation Strategy & Architecture

Veriprajna's solution is not theoretical. It is a robust, modular architecture designed for integration into enterprise insurance and legal workflows. This section outlines the technical stack and deployment strategy.

6.1 The Neuro-Symbolic Pipeline (Sandwich Architecture)

We employ a "Sandwich Architecture" where Neural AI (LLMs) handles the messy unstructured input, and Symbolic AI (Logic/Graph) handles the rigorous reasoning, with a final Neural layer for explanation.

Stage 1: Ingestion & Extraction (The Neural Layer)

●​ Input: Police Reports (PDF), Witness Audio, Telematics Data (JSON).

●​ Processing:

○​ OCR and Speech-to-Text digitization.

○​ LLM Entity Extraction: Specialized prompts extract entities (Vehicles, Signs) and normalize them to the TAKG Ontology. ¹⁴

○​ Constraint Checking: The LLM output is validated against the ontology. If it extracts a "stop sign" where the map database says none exists, the system flags a data conflict.

Stage 2: Graph Construction & Fusion (The Structural Layer)

●​ Database: Neo4j or RDF Triplestore.

●​ Fusion: Merging data from the police report with the Digital Twin of the road network (GIS).

●​ Enrichment: Calculating derived properties (e.g., inferring speed from skid mark length nodes). ¹³

Stage 3: Reasoning & Adjudication (The Symbolic Layer)

●​ Logic Engine: A specialized solver (e.g., Drools or a custom Python-based DDL engine) runs the Deontic Logic rules against the graph.

●​ Causal Simulator: Runs counterfactual checks for proximate cause.

●​ Output: A structured Liability Report detailing violations and causal links.

Stage 4: Explanation & Generation (The Neural Layer)

●​ Final Output: An LLM is used only at the end to convert the structured Liability Report into a readable natural language narrative. This narrative is strictly grounded in the graph facts, preventing hallucination. It explains why the decision was made based on the logic rules. ⁴⁴

6.2 Auditability and Explainability (XAI)

A key advantage of KGER is Explainability .

●​ Traceability: Every conclusion can be traced back to a specific node and rule. "Why is Driver A at fault?" -> "Because Node Vehicle_A violated Rule R1 (Stop Sign) at time t."

●​ Visual Proof: The Knowledge Graph can be visualized, showing the exact chain of events and logic. This is far more persuasive in court than an opaque LLM text block. ⁴⁵

●​ Compliance: This deterministic approach satisfies regulatory requirements for "Explainable AI" in financial and legal decision-making, which black-box models often fail. ⁴⁶

Part VII: Business Impact and ROI for Insurers

The adoption of Veriprajna's Knowledge Graph Event Reconstruction offers transformative value for insurance carriers, moving beyond efficiency to fundamental accuracy and loss control.

7.1 Reducing Claims Leakage and Litigation Costs

"Leakage" occurs when insurers pay more than they should due to inaccurate liability assessment. A probabilistic LLM might suggest a 50/50 split because the narratives are messy or the user prompted it poorly. Veriprajna's deterministic logic might reveal a clear

100/0 liability based on a specific right-of-way violation.

●​ Precision: By accurately identifying fault, carriers avoid overpayment on liability claims.

●​ Defense: The audit trail provided by the KG allows for robust defense in subrogation and litigation. It is hard to argue against a physics-based, logically derived graph. ⁴⁷

7.2 Accelerating Straight-Through Processing (STP)

Current automation efforts struggle with complex liability. Simple fender benders are automated; intersection crashes go to humans.

●​ Neuro-Symbolic STP: Veriprajna enables STP for complex claims by providing a reliable "Judge" layer. If the graph logic computes 100% certainty of rule violation, the claim can be settled automatically without human intervention.

●​ Efficiency: This reduces cycle times from weeks to minutes for a significant portion of claims, boosting customer satisfaction (NPS). ⁴⁹

7.3 Operational Consistency

Human adjusters vary in their judgment. One might interpret a rule one way; another might differ. LLMs vary even more (stochasticity).

●​ Standardization: The Logic Engine applies the same formalized rules to every claim. This consistency is vital for regulatory compliance and large-scale portfolio management. This mirrors the approach of industry leaders like Kennedys IQ, who have adopted neuro-symbolic AI precisely to eliminate the "black box" concern. ⁴⁵

7.4 Table: ROI Comparison – LLM Wrapper vs. Veriprajna

Metric	LLM Wrapper (Probabilistic)	Veriprajna KGER (Deterministic)
Fault Accuracy	Low (Susceptible to Verbosity/Sycophancy)	High (Based on Physics/Logic)
Auditability	Low (Black Box)	High (Traceable Graph)
Hallucination Risk	High (Fabricates Laws/Facts)	Near Zero (Constrained by Ontology)
Consistency	Low (Varies by prompt/run)	100% (Rule-based)
Complex Reasoning	Fails at Abductive/Causal	Excels at Counterfactuals

Conclusion: Justice is a Graph, Not a Probability

The legal and insurance industries stand at a crossroads. The allure of Generative AI is strong—it is easy to implement and produces impressive-looking text. But in the domain of law, looking impressive is not the same as being right. In the domain of Fault and Liability, being "mostly right" is being wrong.

Asking an LLM to read a police report and judge liability is asking a poet to do physics. It will give you a beautiful answer, but it will likely be fiction.

Veriprajna offers a different path. We believe that justice is about facts. It is about the precise relationships between entities in space and time, governed by the rigid logic of the law. By building Knowledge Graph Event Reconstruction, we strip away the noise of sentiment and verbosity. We extract the signal—the entities, the vectors, the rules—and map them into a deterministic structure. We determine fault by measuring the topology of the event against the topology of the law.

This is not just "AI." It is Neuro-Symbolic AI —the fusion of learning and logic. It is the only path to a future where automated liability is not only efficient but also rigorously, demonstrably just.

Stop guessing. Start reconstructing.

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