# CXReasonAgent: Evidence-Grounded Diagnostic Reasoning Agent for Chest X-rays

Paper: [arXiv:2602.23276v1](https://arxiv.org/abs/2602.23276v1)

## Core Idea

This paper presents a diagnostic agent that combines an LLM with clinically grounded tools so that its responses are tied to evidence rather than unsupported fluent reasoning. The emphasis is on multi-step, evidence-grounded and verifiable reasoning.

## Why It Matters Here

In high-stakes systems, reasoning is not enough. The system must show what evidence supported the conclusion and give a path for verification.

That idea is directly relevant to `agent_debugger`.

## Key Takeaways For The Repo

### 1. Decision events should be evidence-first

The repo already supports evidence on decisions. This paper reinforces that decision records should clearly show:

- what evidence was available
- which evidence was actually used
- which tool outputs justified the action
- what alternative actions were rejected

### 2. Verifiability should be a product feature

A good agent debugger should help answer:

- why was this action taken
- what tool result supported it
- was the supporting evidence strong or weak
- what evidence was missing

This is better than recording raw chain-of-thought-like text with no grounding.

### 3. Multi-step reasoning should preserve provenance

When decisions depend on prior tools or model outputs, the debugger should preserve provenance across steps:

- this decision depends on these tool results
- this tool call came from this model response
- this checkpoint captures the state after those dependencies

## Concrete Opportunities

- add explicit evidence linkage in the UI
- add "decision provenance" views
- show missing or weak evidence markers on risky actions
- prioritize grounded decision events in importance scoring

## Caution

The paper is medical and safety-critical, so some of its discipline comes from a much higher-stakes environment. The useful lesson is the standard of grounding and auditability, not the specific medical tooling.

## Best Next Experiment

Upgrade one decision view so it answers four questions directly:

1. what was the decision
2. what evidence supported it
3. which upstream events produced that evidence
4. what alternatives were rejected
