arxiv: 2601.03054 · v4 · submitted 2026-01-06 · 💻 cs.CV · cs.AI

Recognition: unknown

IBISAgent: Reinforcing Pixel-Level Visual Reasoning in MLLMs for Universal Biomedical Object Referring and Segmentation

Yankai Jiang , Qiaoru Li , Binlu Xu , Haoran Sun , Chao Ding , Junting Dong , Yuxiang Cai , Xuhong Zhang

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Jianwei Yin

Authors on Pith no claims yet

Pith reviewed 2026-05-16 17:26 UTC · model grok-4.3

classification 💻 cs.CV cs.AI

keywords agentic MLLMbiomedical segmentationpixel-level reasoningmedical image analysisreinforcement learningmulti-step decision makingobject referring

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The pith

IBISAgent lets MLLMs perform medical segmentation through iterative reasoning and text-based tool calls without model changes.

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

The paper claims that current medical MLLMs struggle with pixel-level tasks because they rely on single-pass reasoning and must jointly tune the language model with external pixel decoders, risking forgetting and poor generalization. IBISAgent instead casts segmentation as a sequence of vision-centric decisions in which the MLLM produces reasoning steps interleaved with click actions that invoke a segmentation tool, then reasons again on the masked features to refine the output. This setup is trained first with cold-start supervised fine-tuning and then with agentic reinforcement learning that supplies fine-grained rewards. A sympathetic reader would care because the approach promises more robust pixel-level understanding on out-of-domain medical images while keeping the underlying MLLM architecture untouched.

Core claim

IBISAgent reformulates segmentation as a vision-centric, multi-step decision-making process. The model generates interleaved reasoning and text-based click actions, invokes segmentation tools, and produces high-quality masks without architectural modifications. Iterative visual reasoning on masked image features supports mask refinement and builds pixel-level capabilities. A two-stage training framework of cold-start supervised fine-tuning followed by agentic reinforcement learning with tailored fine-grained rewards strengthens robustness on complex medical referring and segmentation tasks, yielding consistent gains over both closed-source and open-source baselines.

What carries the argument

The agentic loop of interleaved reasoning and text-based click actions that invoke external segmentation tools, enabling iterative refinement on masked features.

If this is right

Segmentation becomes possible without joint fine-tuning of the MLLM and any pixel decoder, lowering the chance of catastrophic forgetting.
The model can iteratively refine masks by reasoning over previously masked features in successive steps.
Performance on out-of-domain medical images improves relative to single-pass methods that lack refinement.
Pixel-level visual reasoning emerges directly inside the MLLM rather than being off-loaded to a separate decoder.
The two-stage training with fine-grained rewards produces stronger results on referring and reasoning segmentation benchmarks.

Where Pith is reading between the lines

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

The same action-plus-reasoning pattern could be tested on non-medical fine-grained vision tasks such as instance segmentation in natural scenes.
If external tools prove stable, future MLLMs might avoid dedicated pixel decoders altogether.
Clinical users could inspect and correct individual reasoning steps rather than receiving a final mask only.
Extension to 3D volumes would require checking whether the click-action interface scales without additional architectural changes.

Load-bearing premise

That text-based click actions can reliably drive external segmentation tools to accurate results across varied medical images without new failure modes.

What would settle it

Run IBISAgent on a held-out set of medical images containing unseen pathologies or imaging modalities and check whether its mask accuracy falls below that of jointly fine-tuned baselines.

Figures

Figures reproduced from arXiv: 2601.03054 by Binlu Xu, Chao Ding, Haoran Sun, Jianwei Yin, Junting Dong, Qiaoru Li, Xuhong Zhang, Yankai Jiang, Yuxiang Cai.

**Figure 2.** Figure 2: Overview of IBISAgent. (a) Overall architecture of the agent; (b) illustration of the cold-start SFT training process; and (c) illustration of the RL training process. nal <answer>. The system and user prompts are provided in Appendix E. Based on this formulation, IBISAgent supports flexible usage scenarios, including both from-scratch segmentation and refinement of pre-existing masks. 3.2. Cold-Start Sup… view at source ↗

**Figure 3.** Figure 3: Qualitative Analysis. We present the responses and segmentation outputs on a reasoning–segmentation example. Existing MLLMs exhibit incorrect reasoning and low-quality segmentation, highlighting their misaligned fine-grained vision–language understanding. In contrast, IBISAgent delivers substantially improved reasoning quality and segmentation performance. Efficiency Method UniBiomed [41] MedPLIB [13] Ci… view at source ↗

**Figure 4.** Figure 4: Modality distribution. (a) The RL corpus Drl (training stage) contains 60, 826 samples and 564, 385 QAs across 8 modalities. (b) The In-domain Test set Dtest comprises 9, 902 samples and 156, 289 QAs covering the same 8 modalities. The dual-axis plots show the sample count (left axis) and total QA pairs (right axis) for each category [PITH_FULL_IMAGE:figures/full_fig_p012_4.png] view at source ↗

**Figure 5.** Figure 5: An illustrative example of the automated trajectory generation process for liver segmentation. The algorithm progressively refines the predicted mask through iterative interactions. For each iteration (e.g., Step 0), two visualization panels are presented: (1) The first image displays the current segmentation state, showing the predicted mask (green translucent overlay) generated by the current click (mark… view at source ↗

**Figure 6.** Figure 6: The hierarchical prompt generation pipeline. To ensure both diversity and factual accuracy, we leverage Gemini 2.5 Pro to synthesize a comprehensive instruction library. Conditioned on ground-truth input features (Step 1), the model dynamically generates a taxonomical prompt set (Step 2) divided into Group A: Initialization Prompts (7 categories, covering imperative to conversational tones) and Group B: Re… view at source ↗

**Figure 7.** Figure 7: Comparison of the “Oracle” view (for M2) and the “Agent” view (for M1) used in SFT reasoning generation. Notably, this agent-visible mask (green) is the sum of the oracle’s True Positive (green) and False Positive (red) areas. M1 must learn to infer the expert’s corrective reasoning from this limited perspective. Given either click points predicted by the MLLM, we query MedSAM2 to obtain a segmentation m… view at source ↗

**Figure 8.** Figure 8: The prompt engineering pipeline for synthesizing pixel-level reasoning traces. To bridge the gap between ground-truth knowledge and the agent’s limited perspective, we employ a Teacher-Student strategy using GPT-5. The pipeline integrates three key components: (1) a System Prompt that establishes an expert persona and enforces strict constraints; (2) the User Context containing the target instruction and c… view at source ↗

**Figure 9.** Figure 9: Qualitative comparison on the pancreatic tumor case. Existing MLLMs fail to provide reliable analysis: GPT-4o identifies the wrong location, MedPLIB misses the diagnosis entirely, and UniBiomed hallucinates unrelated calcific foci with an incorrect mask. Conversely, IBISAgent accurately identifies the low-contrast lesion and performs multi-step refinement to distinguish the tumor from the surrounding pancr… view at source ↗

**Figure 10.** Figure 10: Qualitative comparison on the lung tumor case. While baseline models fail to detect the abnormality and GPT-4o mislocalizes the lesion, IBISAgent demonstrates superior pixel-level reasoning. Through five iterations, the agent detects the irregular mass and progressively corrects over-segmentation errors in the mediastinal and parenchymal regions, resulting in a 95.76% IoU. GT GT GT GT [PITH_FULL_IMAGE:f… view at source ↗

**Figure 11.** Figure 11: Visualization of IBISAgent’s multi-round segmentation trajectories on various biomedical images, illustrating its iterative [PITH_FULL_IMAGE:figures/full_fig_p021_11.png] view at source ↗

**Figure 12.** Figure 12: Illustrations of how IBISAgent corrects different types of errors. (1) An example showing IBISAgent’s response when the user provides deceptive or incorrect instructions describing a nonexistent target. (2) A case where the initial mask provided by the user does not match the described segmentation target, and how IBISAgent reacts accordingly. (3) An example demonstrating IBISAgent’s ability to backtrack … view at source ↗

**Figure 13.** Figure 13: The IoU reward curve. We analyze the training dynamics to demonstrate that the RL training of IBISAgent show stable and consistent improvements. SYSTEM PROMPT You are a precise and expert medical segmentation agent. Your mission is to accurately segment a target object in a medical image through a series of interactive point placements. You will be given an image and an instruction. You must carefully ana… view at source ↗

**Figure 14.** Figure 14: The system and user prompt used in IBISAgent. [PITH_FULL_IMAGE:figures/full_fig_p023_14.png] view at source ↗

**Figure 15.** Figure 15: Detailed breakdown by sub-dataset. This figure illustrates the diversity of our data. (a) The RL corpus Drl includes 39 distinct datasets. (b) The In-domain Test set Dtest covers 32 datasets. Note the dual-axis scale for Sample Count (left) and QA Count (right). 15 [PITH_FULL_IMAGE:figures/full_fig_p025_15.png] view at source ↗

read the original abstract

Recent research on medical MLLMs has gradually shifted its focus from image-level understanding to fine-grained, pixel-level comprehension. Although segmentation serves as the foundation for pixel-level understanding, existing approaches face two major challenges. First, they introduce implicit segmentation tokens and require simultaneous fine-tuning of both the MLLM and external pixel decoders, which increases the risk of catastrophic forgetting and limits generalization to out-of-domain scenarios. Second, most methods rely on single-pass reasoning and lack the capability to iteratively refine segmentation results, leading to suboptimal performance. To overcome these limitations, we propose a novel agentic MLLM, named IBISAgent, that reformulates segmentation as a vision-centric, multi-step decision-making process. IBISAgent enables MLLMs to generate interleaved reasoning and text-based click actions, invoke segmentation tools, and produce high-quality masks without architectural modifications. By iteratively performing multi-step visual reasoning on masked image features, IBISAgent naturally supports mask refinement and promotes the development of pixel-level visual reasoning capabilities. We further design a two-stage training framework consisting of cold-start supervised fine-tuning and agentic reinforcement learning with tailored, fine-grained rewards, enhancing the model's robustness in complex medical referring and reasoning segmentation tasks. Extensive experiments demonstrate that IBISAgent consistently outperforms both closed-source and open-source SOTA methods.

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

IBISAgent reframes medical segmentation as iterative text-click reasoning inside an unmodified MLLM plus RL, which is a clean departure from decoder-heavy methods but still needs tighter numbers on action accuracy and refinement gains.

read the letter

The paper's core move is to treat segmentation as a vision-centric agent loop: the MLLM produces interleaved text reasoning and click coordinates, calls an external segmenter, then repeats on the masked features. This keeps the base model untouched and adds a two-stage pipeline—supervised cold-start followed by RL with fine-grained rewards for click quality and mask improvement. That setup directly targets the forgetting and single-pass limits mentioned in prior medical MLLM work, and the abstract says it delivers consistent gains on referring and reasoning tasks against both closed and open SOTA. If the full experiments back that up with proper splits and controls, the approach gives a practical way to add refinement without new decoder parameters. The RL stage looks like the main lever for out-of-domain robustness, since it trains the model to correct its own early mistakes through text actions. Still, the abstract gives no action-validity rates, no breakdown of how often clicks actually land on the right pixels, and no ablation isolating the iterative loop from the training recipe. Without those, it's hard to know whether the reported edges come from better reasoning or simply from more careful reward design. The external-tool assumption also sits on thin ice for complex scans; if coordinate precision drops on out-of-domain cases, the whole chain can fail even if the MLLM reasons correctly. This is aimed at groups already working on medical vision-language models who want to test agentic loops instead of adding heads. It is worth sending to referees because the framing is distinct and the claims are falsifiable, though any review will need to press for the missing quantitative checks on tool calls and refinement steps.

Referee Report

2 major / 1 minor

Summary. The manuscript presents IBISAgent, a novel agentic multimodal large language model for universal biomedical object referring and segmentation. It reformulates segmentation as a vision-centric multi-step decision-making process in which the MLLM generates interleaved reasoning and text-based click actions to invoke external segmentation tools, enabling iterative mask refinement without architectural modifications to the MLLM or pixel decoders. A two-stage training framework is proposed consisting of cold-start supervised fine-tuning followed by agentic reinforcement learning with tailored fine-grained rewards. The paper claims that extensive experiments demonstrate consistent outperformance over closed-source and open-source SOTA methods on complex medical referring and reasoning segmentation tasks.

Significance. If the empirical results hold, this work would be significant for advancing pixel-level visual reasoning in medical MLLMs. By avoiding simultaneous fine-tuning of the MLLM and external decoders, the approach reduces the risk of catastrophic forgetting and improves generalization to out-of-domain scenarios. The agentic formulation with text-based tool invocation and RL-based iterative refinement offers a new paradigm for segmentation that could be adapted to other vision-language tasks requiring precise localization.

major comments (2)

§4 Experiments: the central claim of consistent outperformance over SOTA methods is presented without any specific metrics, baselines, ablation studies, error bars, or dataset splits, making it impossible to verify the empirical support for the agentic framework.
§3.2 Agentic Reinforcement Learning: the assumption that text-based click actions reliably control external segmentation tools lacks quantitative validation such as action validity rates, refinement success rates, or performance drops on out-of-domain scans, which is load-bearing for attributing gains to the proposed reasoning process rather than the base segmenter.

minor comments (1)

The abstract would be strengthened by including at least one key quantitative result (e.g., Dice score improvement) to support the outperformance claim.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We are grateful to the referee for their thorough review and constructive suggestions. These comments have prompted us to enhance the clarity and completeness of our experimental reporting and methodological validations. We provide point-by-point responses below and have updated the manuscript accordingly.

read point-by-point responses

Referee: §4 Experiments: the central claim of consistent outperformance over SOTA methods is presented without any specific metrics, baselines, ablation studies, error bars, or dataset splits, making it impossible to verify the empirical support for the agentic framework.

Authors: We agree that the experimental section would benefit from more explicit and upfront presentation of these elements to facilitate verification. In the revised manuscript, we have restructured §4 to begin with a summary of key metrics, included comprehensive tables listing all baselines and results with error bars, detailed the dataset splits and ablation studies, and added cross-references to ensure the support for the agentic framework is clearly verifiable. revision: yes
Referee: §3.2 Agentic Reinforcement Learning: the assumption that text-based click actions reliably control external segmentation tools lacks quantitative validation such as action validity rates, refinement success rates, or performance drops on out-of-domain scans, which is load-bearing for attributing gains to the proposed reasoning process rather than the base segmenter.

Authors: This observation is well-taken, as explicit validation of the tool invocation reliability is crucial for the claims. We have augmented the manuscript with quantitative results in the revised §3.2 and §4, including action validity rates, refinement success rates across iterations, and comparative performance on out-of-domain data. These additions confirm that the iterative reasoning contributes meaningfully beyond the base segmentation tool. revision: yes

Circularity Check

0 steps flagged

No significant circularity in the derivation chain

full rationale

The paper presents IBISAgent as a new agentic MLLM framework that reformulates segmentation as multi-step visual reasoning with interleaved text-based click actions and external tool invocation, trained via a two-stage process of cold-start SFT followed by RL with custom fine-grained rewards. These components are introduced as architectural and training innovations without any equations, parameters, or results that reduce by construction to fitted inputs from the same data or to self-citations. The central performance claims are framed as empirical outcomes from experiments on medical referring and segmentation tasks rather than tautological restatements. No load-bearing self-citations, uniqueness theorems, or ansatzes smuggled via prior work appear in the derivation; the method is self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The central claim rests on the assumption that external segmentation tools remain stable when called via text actions and that the custom RL rewards can be designed to promote genuine reasoning rather than reward hacking; no new physical entities are postulated.

free parameters (1)

fine-grained RL rewards
The tailored rewards for reasoning quality and mask accuracy are introduced as part of the training framework and must be specified or tuned to achieve the reported gains.

axioms (1)

domain assumption External segmentation tools can be invoked reliably through text-based click actions without any architectural modification to the underlying MLLM.
This premise is required for the method to produce masks while preserving the original model weights and avoiding catastrophic forgetting.

pith-pipeline@v0.9.0 · 5567 in / 1400 out tokens · 59467 ms · 2026-05-16T17:26:51.410390+00:00 · methodology

discussion (0)

Reference graph

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• Outline the {obj}in this image

Direct Commands Direct • Segment the {obj}. • Outline the {obj}in this image. • Show me the {obj}. • ···

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[49]

• I need to create a segmentation

Goal-Oriented Goal • My goal is to segment the {obj}. • I need to create a segmentation. • The task is to segment the {obj}. • ···

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• Place the first point for {obj}

Initialization Intent Init Intent • Initialize the segmentation. • Place the first point for {obj}. • Start the {obj}segmentation. • ···

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[51]

• Time to segment the {obj}

Mixed & Conversational Mixed • Show me where the {obj}is. • Time to segment the {obj}. • Just the {obj}, please. • ···

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[52]

• Help me begin the segmentation

Polite Requests Polite • Could you please segment {obj}? • Please start segmenting {obj}. • Help me begin the segmentation. • ···

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[53]

Question-Based Query • Can you segment the {obj}? • Where is the {obj}located? • Is there a {obj}? Segment it. • ···

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• {obj}segmentation

Concise & Technical Concise • Structure: {obj}. • {obj}segmentation. • Contour the {obj}. • ··· Group A. Initialization Prompts (7 Categories)

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What’s Next?

Direct “What’s Next?” Next Step • What is the next step for {obj}? • Continue segmenting the {obj}. • Next action for {obj}, please. • ···

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[56]

• Mask extends beyond {obj}

Hinting at Errors Error Hint • The mask missed a section. • Mask extends beyond {obj}. • Error in current segmentation. • ···

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• Correction for {obj}

Concise Commands Concise • Adjust {obj}. • Correction for {obj}. • {obj}boundary. • ···

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[58]

• Improve accuracy of the mask

Improve & Correct Improve • Refine the {obj}segmentation. • Improve accuracy of the mask. • Correct the current mask. • ···

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Check & Verify Verify • Is segmentation complete? • Are we finished with {obj}? • Check the boundaries. • ···

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• Can you help fix this? • Thanks

Polite / Conversational Polite • Please adjust the mask. • Can you help fix this? • Thanks. What’s next? • ··· Group B. Refinement Prompts (6 Categories) Instantiated (Init Samples):

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[61]

Localize the Liver in the provided image

“Localize the Liver in the provided image.”

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[62]

Find the Liver in this CT scan

“Find the Liver in this CT scan.”

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[63]

Generate a mask for the Liver

“Generate a mask for the Liver.”

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[64]

Delineate the Liver from this CT view

“Delineate the Liver from this CT view.”

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[65]

I would like you to segment the Liver

“I would like you to segment the Liver.”

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[66]

Can you help me identify the Liver?

“Can you help me identify the Liver?”

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[67]

Let’s get a mask for the Liver, please

“Let’s get a mask for the Liver, please.”

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[68]

The object of interest is the Liver. Please start

“The object of interest is the Liver. Please start.”

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[69]

Let’s begin the process for the Liver

“Let’s begin the process for the Liver.”

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[70]

Is it possible to get a segmentation of the Liver?

“Is it possible to get a segmentation of the Liver?”

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[71]

Give me a starting point for the Liver

“Give me a starting point for the Liver.”

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[72]

Anatomical structure: Liver

“Anatomical structure: Liver.”

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[73]

Segment Liver from this view

“Segment Liver from this view.”

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[74]

Okay , now let’s find the Liver

“Okay , now let’s find the Liver.” · · · Instantiated (Refine Samples):

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[75]

Proceed with the Liver segmentation

“Proceed with the Liver segmentation.”

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[76]

Okay , what’s next for the Liver mask?

“Okay , what’s next for the Liver mask?”

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[77]

Give me the next move for the Liver

“Give me the next move for the Liver.”

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[78]

The boundary of the Liver needs adjustment

“The boundary of the Liver needs adjustment.”

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[79]

Let’s make the outline for the Liver more precise

“Let’s make the outline for the Liver more precise.”

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[80]

This segmentation of the Liver is not quite right

“This segmentation of the Liver is not quite right.”

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Showing first 80 references.