SAGE: Agentic Framework for Interpretable and Clinically Translatable Computational Pathology Biomarker Discovery

Anant Madabhushi; Aniket Ramkrishnan Iyer; Himanshu Maurya; Jincheng Liu; Jinchu Li; Juan Francisco Pesantez Borja; Mohammad Tanvir Hasan; Naoto Tokuyama; Sahar Almahfouz Nasser; Sandeep Manandhar

arxiv: 2602.00953 · v2 · submitted 2026-02-01 · 💻 cs.LG

SAGE: Agentic Framework for Interpretable and Clinically Translatable Computational Pathology Biomarker Discovery

Sahar Almahfouz Nasser , Juan Francisco Pesantez Borja , Jincheng Liu , Sandeep Manandhar , Shikhar Shiromani , Mohammad Tanvir Hasan , Zenghan Wang , Suman Ghosh

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Jinchu Li Xuejian Xu Aniket Ramkrishnan Iyer Naoto Tokuyama Twisha Shah Tilak Pathak Soundharya Kumaresan Yohei Abe Himanshu Maurya Anant Madabhushi

This is my paper

Pith reviewed 2026-05-16 08:28 UTC · model grok-4.3

classification 💻 cs.LG

keywords computational pathologybiomarker discoverymulti-agent systemsknowledge graphshypothesis generationclinical interpretabilityAI validation pipeline

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The pith

SAGE multi-agent framework converts intuition-driven biomarker discovery in pathology into a structured, traceable reasoning process.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents SAGE, a multi-agent system that generates biomarker hypotheses using multi-path reasoning anchored in biological knowledge graphs, evaluates their novelty through agent debates against literature, and automatically validates them via executable analyses on multimodal pathology data. This replaces ad-hoc searches with a process clinicians and researchers can inspect step by step. A sympathetic reader would care because current biomarker work often depends on fragmented literature and personal insight, making results hard to reproduce or trust in clinical settings.

Core claim

SAGE shifts biomarker discovery from an intuition-driven, literature-browsing exercise into a structured, traceable reasoning process that clinicians and researchers can inspect, trust, and build upon, through three mechanisms: knowledge-graph-anchored hypothesis generation via multi-path ontological reasoning, debate-based multi-agent novelty assessment, and an end-to-end automated validation pipeline that translates hypotheses directly into executable analyses on multimodal pathology datasets.

What carries the argument

The SAGE multi-agent framework, which integrates knowledge-graph-anchored multi-path ontological reasoning for hypothesis generation, debate-based novelty assessment against literature, and automated validation pipelines on pathology datasets.

If this is right

Biomarker hypotheses gain explicit traceability to specific ontological paths in the knowledge graph.
Agent debates reduce redundant proposals by systematically stress-testing novelty against published findings.
Validation moves from manual expert effort to direct automated execution on existing multimodal datasets.
The resulting biomarkers become inspectable artifacts that support clinical trust and iterative refinement.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same agentic structure could extend to hypothesis generation in related domains like radiology or oncology genomics where literature is similarly fragmented.
Repeated use might accumulate a growing library of validated, queryable biomarkers that improves with each new dataset.
Incorporating multiple independent knowledge graphs could be tested as a way to cross-check and reduce single-source bias in reasoning paths.

Load-bearing premise

Knowledge-graph-anchored multi-path reasoning and debate-based novelty assessment will reliably produce biologically valid and novel biomarkers without systematic biases from the graphs or agent prompts.

What would settle it

A blinded comparison study in which independent pathologists and biologists validate the biological relevance and clinical utility of biomarkers discovered by SAGE versus those found through standard literature review, measuring success by reproducibility rates on held-out patient cohorts.

read the original abstract

Engineered image-based biomarkers offer a clinically interpretable alternative to black-box AI in computational pathology, yet their discovery remains largely intuition-driven, guided by fragmented literature rather than rigorous biological validation. We introduce SAGE (Structured Agentic system for hypothesis Generation and Evaluation), a multi-agent framework that grounds biomarker discovery in biological evidence through three mechanisms: (i) knowledge-graph-anchored hypothesis generation via multi-path ontological reasoning, (ii) a debate-based multi-agent novelty assessment that stress-tests candidate biomarkers against existing literature, and (iii) an end-to-end automated validation pipeline that translates hypotheses directly into executable analyses on multimodal pathology datasets. Together, these components shift biomarker discovery from an intuition-driven, literature-browsing exercise into a structured, traceable reasoning process that clinicians and researchers can inspect, trust, and build upon.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

SAGE proposes a multi-agent framework for traceable biomarker discovery in pathology but shows only the architecture with zero empirical results or validation.

read the letter

The main takeaway is that this paper describes a new system called SAGE for generating and evaluating biomarkers in computational pathology. It combines knowledge-graph reasoning, multi-agent debate for novelty checks, and an automated validation pipeline. That specific combination is new and not directly in the cited prior work. The motivation is solid: computational pathology still leans on black-box models and scattered literature searches, and the authors lay out a structured alternative that aims for inspectability and biological grounding. The write-up of the three mechanisms is clear enough that a reader can see how the pieces are supposed to fit together. The paper does a decent job framing the problem without overclaiming in the abstract itself. The central weakness is the total lack of any data, runs, ablations, or even example reasoning traces. No datasets are processed, no biomarkers are actually produced and checked, and there is no comparison to simpler literature-based or existing AI methods. This leaves the key assumption—that the graph-anchored paths and agent debates will reliably yield valid, unbiased, novel biomarkers—completely untested. Without at least a proof-of-concept on real pathology slides or a small case study, the claims stay at the level of design intent. The paper is aimed at researchers already working on agentic systems or interpretable AI for medicine who might want to adapt the architecture. Anyone looking for implemented methods, code, or results to cite or build on will find nothing concrete. I would not bring it to a reading group in this state and would not cite it. It does not yet deserve peer review; the authors need to implement the pipeline and show at least preliminary outputs before a referee could evaluate whether the mechanisms deliver what is promised.

Referee Report

2 major / 1 minor

Summary. The manuscript introduces SAGE, a multi-agent framework for interpretable biomarker discovery in computational pathology. It proposes three mechanisms—knowledge-graph-anchored multi-path ontological reasoning for hypothesis generation, debate-based multi-agent novelty assessment against literature, and an end-to-end automated validation pipeline on multimodal datasets—to replace intuition-driven approaches with structured, traceable reasoning.

Significance. If the described mechanisms were shown to reliably produce valid, novel biomarkers without systematic bias, the work could meaningfully advance the field by offering clinicians an inspectable alternative to black-box models and fragmented literature searches. The emphasis on traceability and automated validation addresses a genuine gap, though the significance is currently prospective given the absence of supporting results.

major comments (2)

[Abstract] Abstract and overall manuscript: The central claim that the three mechanisms 'shift biomarker discovery from an intuition-driven... exercise into a structured, traceable reasoning process' rests on untested assertions. No empirical results, error bars, ablation studies, dataset runs, or quantitative comparisons to literature-driven baselines are presented to demonstrate improved validity or novelty.
[Methods] Framework description: No specification of the underlying knowledge graph(s), pseudocode for multi-path reasoning or debate protocols, example reasoning traces, or executed validation pipeline on any pathology dataset is provided, leaving the load-bearing assumption that agent behaviors will deliver biologically valid outputs untested.

minor comments (1)

[Abstract] Notation for the three mechanisms could be clarified with consistent labeling (e.g., Mechanism 1, 2, 3) to aid readability.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed comments, which highlight important areas for improving the clarity and rigor of our manuscript on the SAGE framework. We address each major comment point by point below and describe the revisions we will make.

read point-by-point responses

Referee: [Abstract] Abstract and overall manuscript: The central claim that the three mechanisms 'shift biomarker discovery from an intuition-driven... exercise into a structured, traceable reasoning process' rests on untested assertions. No empirical results, error bars, ablation studies, dataset runs, or quantitative comparisons to literature-driven baselines are presented to demonstrate improved validity or novelty.

Authors: We agree that the manuscript introduces the SAGE framework without new empirical results or quantitative benchmarks against baselines, as its primary contribution is the design of the multi-agent architecture itself. The claim describes the intended structural shift toward traceability (via explicit agent reasoning paths, knowledge grounding, and debate logs) rather than a demonstrated performance improvement. In revision, we will tone down the abstract and introduction to frame SAGE as a proposed framework whose validity and novelty benefits require future empirical validation. We will also add a dedicated section outlining planned experiments, including ablation studies, comparisons to literature-search baselines, and metrics for biomarker validity on pathology datasets. revision: partial
Referee: [Methods] Framework description: No specification of the underlying knowledge graph(s), pseudocode for multi-path reasoning or debate protocols, example reasoning traces, or executed validation pipeline on any pathology dataset is provided, leaving the load-bearing assumption that agent behaviors will deliver biologically valid outputs untested.

Authors: We thank the referee for this precise observation. The revised manuscript will specify the knowledge graphs (e.g., integration of UMLS, Gene Ontology, and pathology-specific resources such as TCGA-derived ontologies). We will include pseudocode for the multi-path ontological reasoning algorithm and the debate protocol, along with concrete example reasoning traces from pilot executions. For the validation pipeline, we will provide a detailed algorithmic description with pseudocode showing how hypotheses are translated into executable analyses on multimodal datasets (e.g., imaging + genomic data from standard cohorts), including sample outputs and logging mechanisms. These additions will make the framework fully specified and reproducible even if large-scale end-to-end biomarker discovery results are reserved for follow-up work. revision: yes

Circularity Check

0 steps flagged

No derivation chain or fitted inputs; framework proposal is self-contained

full rationale

The manuscript proposes a new multi-agent architecture (SAGE) for biomarker discovery using knowledge-graph reasoning, debate-based assessment, and validation pipelines. No equations, parameters, or derivations appear in the abstract or description. The central claims are presented as a novel structured process rather than reductions from prior fitted results or self-citations. The load-bearing assumption (that the agents will produce valid biomarkers) is an untested hypothesis about future behavior, not a circular definition or imported uniqueness theorem. This is a standard non-circular proposal of a new method.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 1 invented entities

The central claim rests on the assumption that existing biological knowledge graphs are sufficiently complete and accurate for hypothesis generation and that multi-agent debate can objectively assess novelty. No free parameters or invented physical entities are introduced; the ledger reflects domain assumptions in the proposal.

axioms (2)

domain assumption Knowledge graphs can accurately anchor biological hypotheses via multi-path ontological reasoning
Invoked in the description of mechanism (i) as the grounding for hypothesis generation.
domain assumption Debate among agents can reliably stress-test novelty against existing literature
Central to mechanism (ii) for novelty assessment.

invented entities (1)

SAGE multi-agent framework no independent evidence
purpose: To structure biomarker discovery as a traceable process
New proposed system whose performance is not yet demonstrated

pith-pipeline@v0.9.0 · 5524 in / 1393 out tokens · 35676 ms · 2026-05-16T08:28:47.350041+00:00 · methodology

discussion (0)

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