GraphiMind Interactive Novelty Assessment

• The paper presents an advanced system that integrates LLMs with graph-based models to extract and assess novelty in research papers and dynamic agent environments.
• It leverages structured graph representations and semantic metrics to provide evidence-based novelty scores and rationales for adaptive model updates.
• The methodology combines API-driven literature retrieval, LLM-based extraction, and logical reasoning to offer transparent, interactive workflows for novelty assessment.

GraphiMind for Interactive Novelty Assessment is an advanced system that facilitates the evaluation and adaptation to novelty in scientific discovery and agent-based reasoning. It integrates LLMs, structured graph representations, semantic retrieval, and logical reasoning modules to enable users and autonomous systems to detect, characterize, and accommodate novelty in research papers and dynamic environments. This platform supports transparent, evidence-based novelty assessment workflows for both academic peer review and open-world agent modeling, as defined in (Silva et al., 17 Oct 2025) and (Thai et al., 2023).

1. System Architecture and Functional Modules

GraphiMind operates as a two-tier web-based tool with a TypeScript frontend and a Python FastAPI backend. Its architecture encapsulates major modules for literature novelty assessment (Silva et al., 17 Oct 2025) and agent-driven novelty detection (Thai et al., 2023):

Component	Function	Primary Technologies
Annotation Module	LLM-powered extraction of claims, methods, experiments from papers	GPT-4o, Gemini 2.0 Flash
Graph Construction Module	Converts extracted elements to typed, directed graphs	Node-edge model, visualization
Retrieval Module	API-based citation and semantic neighbor retrieval; embedding computation	arXiv, Semantic Scholar, SentenceTransformers
Novelty Scoring Module	LLM-based classification, novelty score aggregation, structured rationale generation	LLM inference, prompt engineering
Novelty Engine (Agents)	Detection and characterization of environmental and task-level novelties	ASP, statistical filters
Adaptive Model Builder	Automated update of knowledge/model base in response to detected novelties	Graph DBs (Neo4j), atomic updates
User Interface	Interactive visualization, evidence inspection, user-in-the-loop adaptation	Frontend streaming, real-time dashboards

The data flows in this system enable users to search/upload papers, initiate novelty evaluation, and interactively explore and refine extracted evidence and updated models.

2. Graph-Based Representation and Semantic Metrics

Scientific papers and agent knowledge bases are encoded as directed, typed graphs, supporting both micro- and macro-level analysis:

• Scientific Paper Graphs: Each manuscript is mapped onto a graph $G=(V,E)$, where $V$ contains claims ($C$), methods ($M$), and experiments ($X$). Edges represent logical relationships: "validated-by" ($C \to M$), "evaluated-by" ($M \to X$), "supports" ($C \to X$). Citation and semantic similarity edges form a secondary, relational layer, labeled as Supporting (+), Contrasting (–), Background, or Target (Silva et al., 17 Oct 2025).
• Knowledge Graphs for Agents: The world model is captured as $M = \langle S, A, T, R, \gamma \rangle$, with logical predicates over fluents and actions. Novelties are encoded as symbolic deltas $$h = (\Delta A, \Delta\delta, \Delta\beta, \Delta T, \Delta R)$$, which compactly track model adaptation events (Thai et al., 2023).
• Semantic Similarity: Relations rely on vector embeddings. Semantic similarity $\mathrm{sim}(p, q)$ is the cosine similarity $\frac{v_p \cdot v_q}{\|v_p\|\|v_q\|}$ (Silva et al., 17 Oct 2025). Filtering and ranking of citations and recommended works depend on similarity granularity (background vs. target).

A plausible implication is that these graph models enable direct traceability and rationalization of novelty judgments, supporting transparent assessment and real-time model adaptation.

3. Novelty Detection, Characterization, and Scoring

GraphiMind employs both deterministic logical mechanisms and LLM-powered reasoning to detect, characterize, and score novelty:

• LLM Extraction and Annotation: Papers are ingested via arXiv API, parsed to Markdown, and processed by LLMs which extract labeled elements and their interconnections, producing a JSON graph (Silva et al., 17 Oct 2025).
• Novelty Score Computation: The system aggregates novelty votes over $N$ LLM runs to yield $S$, the percentage of “Novel” classifications:

$$S = \frac{1}{N}\sum_{i=1}^N \mathbf{1}\{\text{novel}^{(i)}\} \times 100\%$$

• Agent Novelty Detection: Discrepancies are flagged via logical comparison of predicted and observed fluents:

$$\mathrm{disc}(F, t) = (\text{holds}(F, t) \wedge \neg\text{obs}(F, t)) \vee (\neg\text{holds}(F, t) \wedge \text{obs}(F, t))$$

Statistical novelty score

$$\delta(o_t) = -\log P_M(o_t | h_{t-1}) \quad \text{or} \quad \frac{1}{|F|}\sum_{F}\mathbf{1}(\mathrm{disc}(F, t))$$

exceeding threshold $\tau$ flags a novelty event (Thai et al., 2023).

• Characterization & Model Adaptation: Upon detection, hypotheses are spawned testing for new actions, changed preconditions/effects. Updates are performed atomically on the knowledge graph.

This approach allows combined macro (citation/semantic network) and micro (element extraction and logic) novelty analysis.

4. Retrieval Pipeline and API Integration

GraphiMind interfaces with arXiv and Semantic Scholar for retrieval and literature comparison:

• arXiv Integration: Uses REST API to fetch metadata and full texts, parses LaTeX to Markdown for LLM processing (Silva et al., 17 Oct 2025).
• Semantic Scholar Integration: Pulls citation and “recommended” papers, extracts citation contexts, and batches requests with rate limiting.
• Retrieval Workflow:

1. Bibliography parsing for candidate citations.
2. Filtering by embedding similarity.
3. Stance classification (Supporting/Contrasting) via LLM.
4. Semantic neighbor retrieval and ranking.
5. Pairwise embedding similarity computations and evidence aggregation.

A plausible implication is that this multi-source, multi-modal retrieval accelerates the traceability and repeatability of novelty assessments and supports robust comparison across fields and modalities.

5. Interactive User Experience and Workflow

GraphiMind's frontend provides multi-stage interactive workflows for scientific discovery and agent modeling:

• Search and Configuration: Users access precomputed library papers or dynamically query arXiv, configuring citation/neighbor breadth, LLM model, and filters.
• Novelty Assessment Dashboard: Visualizes metadata, evaluated novelty score, evidence snippets, interactive structured graphs, and related papers tables.
• Interactive Adaptation: In agent environments, the UI visualizes knowledge graph updates on novelty detection, highlights $\Delta$ subgraphs, presents alerts and hypotheses, and enables user-driven acceptance/refinement of adaptations (Thai et al., 2023).
• Export and Reporting: Structured novelty reports can be exported as PDF or Markdown for further analysis or peer review (Silva et al., 17 Oct 2025).
• Sequence of Events: Example workflows include searching manuscripts, configuring retrieval, streaming novelty assessment, inspecting evidence, exporting results, and (in agent scenarios) guiding model adaptation in real time.

This high-transparency, evidence-rich interface supports critical assessment and rapid feedback loops.

6. Limitations and Extensibility Considerations

Known limitations of GraphiMind and agent novelty modules include:

• API Dependence: Operation is constrained by external API availability, rate limits, and evolving formats.
• LLM Hallucination and Domain Bias: Extracted element accuracy and citation stance classification may require manual validation; domain-specialized content may degrade performance.
• Retrieval Latency/Scale: Current retrieval depends on external APIs; future work aims to build large in-house databases for lower latency and broader coverage.
• Metric Formalization: While structured novelty scores are operational, embedding-based formulae with tunable hyperparameters are not yet fully implemented (Silva et al., 17 Oct 2025).
• User Feedback Integration: Planned updates include feedback loops to refine extraction and ranking, and extending to non-arXiv sources.

Extensibility plans comprise broader literature coverage, database scale-out, formal metric improvements, and enhanced interactive logic refinements.

7. Demonstrations and Case Scenarios

• Scientific Discovery: Internal deployment on an ICLR 2024 submission ("Graph-Based Causal Inference") highlighted GraphMind's ability to surface missing references, uncover novel decomposition steps, and deliver an 80% 'Creative' novelty score, validated by structured evidence (Silva et al., 17 Oct 2025).
• Agent Domain Example: In Monopoly-style agent environments, the system detected rule changes (e.g., jail-fine reductions), characterized novelty with ASP logic, performed adaptive model updates, and triggered planner re-rollouts, with UI-mediated user approval and inspection (Thai et al., 2023).

This suggests that the framework provides rigorous, interactive mechanisms for both scientific and agent-based novelty assessment, uniting deep reasoning, evidence traceability, and real-time adaptation.

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For further details, source code and live demonstration links, refer to (Silva et al., 17 Oct 2025) and (Thai et al., 2023).