RegionReasoner: Region-Grounded Multi-Round Visual Reasoning
Authors: Wenfang Sun
Hao Chen
Yingjun Du
Yefeng Zheng
Cees G. M. Snoek
Venue: ICLR 2026 Poster (2026)
Keywords: large vision-language models  multi-round visual reasoning
Primary area: foundation or frontier models, including LLMs
TL;DR: We propose RegionReasoner, a reinforcement learning framework that enforces grounded reasoning by requiring each reasoning trace to explicitly cite the corresponding reference bounding boxes.

Abstract: Large vision-language models have achieved remarkable progress in visual reasoning, yet most existing systems rely on single-step or text-only reasoning, limiting their ability to iteratively refine understanding across multiple visual contexts.
To address this limitation, we introduce a new multi-round visual reasoning benchmark with training and test sets spanning both detection and segmentation tasks, enabling systematic evaluation under iterative reasoning scenarios.
We further propose RegionReasoner, a reinforcement learning framework that enforces grounded reasoning by requiring each reasoning trace to explicitly cite the corresponding reference bounding boxes, while maintaining semantic coherence via a global–local consistency reward.
This reward extracts key objects and nouns from both global scene captions and region-level captions, aligning them with the reasoning trace to ensure consistency across reasoning steps.
RegionReasoner is optimized with structured rewards combining grounding fidelity and global–local semantic alignment.
Experiments on detection and segmentation tasks show that RegionReasoner-7B, together with our newly introduced benchmark RegionDial-Bench, considerably improves multi-round reasoning accuracy, spatial grounding precision, and global–local consistency, establishing a strong baseline for this emerging research direction.

Published as a conference paper at ICLR 2026
REGIONREASONER: REGION-GROUNDED MULTI- ROUND VISUAL REASONING
Wenfang Sun".
Hao Chen
Yingjun Du
Yefeng Zhengl
Cees G. M. Snoek'
Uniieessity of Amsterdam
Annui University
Westlake University
Equul contribution.
'Corresponding author.
ABSTRACT
Large vision- vision-language models have achieved remarkable progress in visual reason- ing, yet most existing systems rely on single-step or text-only reasoning, limiting their ability to iteratively refine understanding across multiple visual contexts. To address this limitation, we introduce a new multi-round visual reasoning benchmark with training and test sets spanning both detection and segmentation tasks, enabling systematic evaluation under iterative reasoning scenarios. We further propose Re- gionReasoner, reinforcement learning framework that enforces grounded reason- ing by requiring each reasoning trace to explicitly cite the corresponding reference bounding boxes, while maintaining semantic coherence via elobal-local consis- lency reward. This reward extracts key objects and nouns from both global scene captions and region-level captions, aligning them with the reasoning trace ensure consistency across reasoning steps. RegionReasoneer is optimized with structured rewards combining grounding fidelity and global-local semantic alignment. Exper- iments on detection and segmentation tasks show that RegionReusoner-78 together with our newly introduced benchmark RegionDial-Bened multi-round reasoning accuracy, spatial grounding precision, and global-local consistency, establishing a strong baseline for this emerging research direction. Our code is available at RegionReasoner.
considerably improves
INTRODUCTION
Recent advances in large Vision-Language Models have led remarkable progress in multimodal reasoning tasks. Leading systems such as OpenAl GPT-40/GPT-ol (Hurst et al., 2024; Jaech et al., 2024), Gemini-2.5 (Gemini Team et al., 2023), DeepSeek (DeepSeek-AL et al., 2025; Wu et al., 2024) and VL-Rethinker (Wang et al., 2025a) have achieved state-of-the-ard results on benchmarks including Math (Lu et al., 2024), MMMU (Yue et al., 2024), and MEGA-Bench (Chen et al., 2025). These methods follow a common paradigm: they first process multimodal inputs, extract textual cues, and then perform chain-of-thie of-thought reasoning (Wei et al., 2022) exclusively the text space. Within the vision community, two particularly relevant lines have pushed the field forward. VisionReasoner (Liu et al., 2025b) showed that structured perception-reasone shaping (c.g., format and geometric rewards) yields robust single-turn grounding and interpretable trajectories. SegLLM (Wang et al., 2025b) demonstrated that multi-round interaction is beneficial for challenging referring segmentation, organizing dialogue-style supervision and evaluation across turns.
with explicit output tags and reward
VisionReasoner (Liu al., 2025b) establishes strong single-turn paradigm with structured tags and base rewards (format and geometry). However, when naively stacked into a multi-round protocol, two issues arise: (i) the framework does not require the reasoning to explicitly cite regions grounded in previous turns, so reference propagation across rounds is brittle-credi credit assignment becomes ambiguous and coordinate hallucinations are hard to detect; and (ii) its reward shaping primarily targets the final outputs (boxes/points) and tag validity, providing little signal to stabilize the reasoning trace itself as dialogue context accumulates, which leads to semantic drift between global descriptions and local evidence deeper rounds. Conversely, SegLLM (Wang al.. 2025b) brings multi-round interaction into referring segmentation, but it does not model a thinking process: there no explicit, verifiable reasoning trace check whether references are truly used, mechanism to enforce

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Published as a conference paper at ICLR 2026
Hi RegionReasoner, please find the player wearing jersey number
Output: R1
cscena: The ineoe depints a banebell game, A batter at the canter of the scens, hoiding a bet and weoring whihe unforr Sevend other players white jerseya are sttncing in the degout bahind him. ollenn-
siteeds 1 first look at di the playert Mast then ore the degeui the player the viddle foregroundd is adtivaly butting and Ne jo rwy desrit thowe the sumber 18. officee
Regis
Great, then please locating the standing spectator behind the R1 on the left.
Output: R2
domeoo The image depicts tsseball ganw baher of center seans, holding bui standing
players
cboua> raforence blow- 22, St1, 412) cormaspnnc satter waaring Jersay holding bust image.
cliida The query specifies loncle slunding spet idtor sekinl the boundlos, sil, 4:00 the kell. apply systid tegget
dugorn.
whits
ght balind butteurs oftiiito
Reyler
Excellent, Identify another spectator next to R2.
Output: R3
while usfor n. Scerrl ofher players white jrress standing digur behind sin ciiene
correpponds stoecing spectstor jansey checus
batter, loceted the lafh
every specifis Mlentify spectalor rekthen the asother shending parson in wa te Jerney viliie c/rinds
opply the reforenced spertsher the Inmodiatt
ReglerRostnerr
Figure I: Regionneesoner in a three-round, region-grounded dialogue. At round I, the user query may refer to region localized earlier (R1/2)). For each turn, RegionReasoner produces structured trajectory: <scene> (global context), <focus> (caption restricted to the referenced region with serialized coordinates, c.g., bbox- [x1, Y1, X2, y22 ), <think> (reasoning that explicitly cites the reference and the required spatial relation), and <answer> (final localization). The example shows correct citation and stable multi-round grounding for "behind the R1 on the left" and "next to the R2", illustrating how explicit reference use and coherent global-local descriptions support consistent localization the dialogue deepens.
global-local semantic coherence, and no learning signal to shape intermediate steps; the supervision remains mask-centric and does not naturally extend to detection. These gaps motivate our design in Fig. I: each round produces structured trajectory (<scene>, <focus>, <think>, <answer>) with reference reference--rounded thinking and a global-local consistency signal; rewards act on the reasoning trace and the final prediction, enabling interpretable and verifiable multi-round grounding.
Building on these insights, we present RegionReasoner, a reinforcement -ottiing--ptimizedd frame- work that extends VisionReasoner's structured outputs to the multi-round setting studied by SegI.LM and directly addresses the limitations above. First, we introduce reasoning step must explicitly cite the required reference bounding boxes in <t <inin>> A dedicated citation reward and a penalty for missing or hallucinated citations make evidence use verifiable and stabilize reference propagation across rounds. Second, we propose a global-locus consistency reward that aligns keywords from the global scene caption (<scene>) and region-level captions (<focus>) with the reasoning trace (<think>); lightweight paatial/compparisoo/ooolaaaazzai encourages explicit relational language and reduces semantic drift as context accumulates. Third, we assemble RegionDial- -inch, a multi-round benchmark spanning detection and segmentation with per-turn metrics and train/evaluation splits constructed from public referring datasets, enabling quanti- tative assessment of reasoning accuracy, grounding fidelity, and global- local alignment under iterative interaction. Taken together, these contributions complement VisionReasoner's structured, reward- shaped formulation and SegLLM's multi-round protocol by explicitly modeling and reinforcing the reasoning process across turns.
thinking: every
lexicon further
Our RegionReasoner is trained with reinforcement learning using structured rewards that target grounding fidelity, global-local semantic alignment, and task correctness. On Region BinceBBehh,,
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RegionReasoner consistently outperforms strong Vision- Language Models and task-specific baselines on both referring segmentation and detection. Two empirical patterns emerge: (i) gains are most pronouneed at later turns, reflecting slower emror accumulation and more stable reference propagation; and (ii) the signals act complementarily--reeee reefeeeeel citation chiefly reduces coordinate hallucinations and improves reuse/refinement of prior regions, while global-local consistency stabilizes the seman- tics of the reasoning trace in scenes with weak spatial cues. Ablations corroborate these trends, with the combined signals delivering the strongest multi-round performance and qualitative trajectories showing verifiable citations and coherent scene-region descriptions across turns.
2 RELATED WORK
Post-training for vision-language models. Post-training techniques, including instruction tuning and reinforcement learning (RL), have become essential for adapting large Laagua-Ln Models (VLMs) to complex multimodal reasoning tasks. Early efforts such as LL (Liu et al., 2023), LLaVA-OV (Li et al., 2024), Infinit--MM (Gu et al., 2024), MAmmo'TH-Vi (Guo al., 2025). LISA (Lai et al., 2024), PixeILM (Ren et al., 2024), and GLAMM (Rasheed et al., 2024) demonstrate that scaling instruction-tune tununng datasets and diversifying task formats can significantly improve generalization across multimodal benchmarks. More recent work, such as VL-Rethinker (Wang et al., 2025a), further explores post-training for reasoning, introducing techniques like selective sample replay to address instability in RL optimization. Unlike these approaches, which mainly focus on single-pass or text-only reasoning, our work enforces explicit spatial grounding and global-local consistency within multi-round visual reasoning.
Reinforcement learning for multimodal reasoning. RL has emerged as a powerful tool for enhancing the reasoning and decision-makinn of VLMs. Vision-RI (Huang et al., 2025) and Video- RI (Feng et al., 2025) integrate RL to improve spatial grounding and temporal reasoning. respectively, while VLM-R1 (Shen et al., 2025) applies RL to fine-grained grounding tasks. Pixel Reasoner (Su et al., 2025) further incentivizes pixel-space reasoning with curiosity-drie driven exploration. Visionary- R1 (Xia al., 2025) mitigates shorteut behaviors in visual reasoning with explicit RL signals, and the Self-Rewarding VLM (Li et al., 2025) adopts a reasoning-decompositte
strategy where the model first generates image captions before deriving answers. Other efforts, such as Open VLThinker (Deng et al., 2025) and LMM-RI (Peng et al., 2025), adopt policy optimization methods like PPO (Schulman al., 2017) to train VLMs as interactive decision-maked approaches focus on single-pas: reasoning or rely on textualized visual inputs, limiting their ability to enforce explicit spatial grounding or multi-step consistency. In contrast, RegionReasoner leverages RL to jointly optimize multi-round reasoning accuracy, region-level grounding fidelity, and global-local semantic alignment, providing a more structured training signal than prior RL-based methods.
Despite these advances, most RL-based
Multi-round visual understanding. SegLIM (Wang et al., 2025b) explores multi-round interaction for referring segmentation and shows the value of dialogue-styl supervision and evaluation, but it does not model explicit reasoning trajectories incorporate RL signals, making it difficult to verify evidence use or enforce global-local semantic coherence. VisionReasoner (Liu et al., 2025b) provides structured, swardd-hapedd perception prreepiio-reasoonnng in single-tur setting without reference propagation across rounds. In this context, SegLLM also releases a multi-round segmentation benchmark; our RegionDial-Benie complements it by adding explicit reasoning-oriented design and per-turn evaluation for both referring detection and referring segmentation, enabling analysis of reasoning accuracy, grounding fidelity, and global- local alignment under iterative interaction.
PROBLEM FORMULATION WITH REGIONDIAL-BENCE
Multi-round region-grounded reasoning, Given image I and dialogue T turns with queries {ge YT=1> a model interacts with the visual scene over multiple turns. Each turn t may include a set of reference boxes Bref {[x,, {[1,,91, 22, Ø2l} that are propagated from earlier turns externally provided, specifying regions that subsequent queries should condition on. Let M,- denote the dialogue memory up to turn t-1 (e.g., previously localized regions or textual context). A policy me produces a turn-level output
02 ~: I, Mt Brel Mr-1),
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where or instantiates the task-specific prediction at turn é (e.g., 2D bounding box for detection, point/mask for segmentation, or count). The memory is updated as M, = M oz)} to enable reference propagation across turns. An episode ends at T; evaluation is conducted per turn and aggregated over the dialogue.
Tasks: detection and segmentation. We consider two instantiations of o (i) referring detection, where is a 2D box for the referred region; and (ii) referring segmentation, where Or is a sparse point or mask for the referred region. Later turns may refer to regions predicted earlier via B For detection, we report per-turn AP at IoU= 0.5 (APsn) and the average across turns. For segmentation, we report per-turn generalized IoU (gloU) averaged over images and then over turns.
ReggionDialBennchmare To operationalize this setting, we construct a multi-round benchmark, dubbed RegionDial-Bence from the public dffering--expresiioin datasets RefCOCO+ and Ref- COCOg. These corpora are built on the MSCOCO image backbone and provide (i) high-quality instance-level bounding boxes and segmentation masks, (ii) wiratewritten referring expressions that are tightly aligned with individual objects, and (iii) multiple expressions per image. This combi- nation makes them particularly well-suited for constructing dialogue-style multi-round grounding tasks without introducing new annotations or relying on synthetic text. In RegionDial-Bencee consolidate image-wise related expressions into dialogues and rewrite later turns to include explicit references to previously localized boxes. Concretely, our resource contains RefCOCO+ Multi-turn (715 images, 2355 turns) and RefCOCOg Multi-turn (1,580 images, 4405 turns). Training dialogues generated by decomposing multi-object instructions and propagating ground-truth references to later turns; test dialogues use model-pe model-predicted references, so errors made at early turns can propagate through the dialogue. Construction rules, spatial-relation templates, statistics, and examples are detailed in Appendix B, which also discusses how the same procedure can be extended to other referring expression referiingeepressioon datasets with sufficiently dense annotations.
we
REGIONREASONER
In this section, we present RegionReasoner and its reinforcement learning framework for multi- round visual reasoning. We first formalize the end-to-end pipeline ($4.1), then describe the model architecture and structured I/O design ($4.2). We next detail the reference- grounded and global-local consistency rewards ($4.3), and finally outline the training procedure ($4.4). An overview of the complete framework is provided in Appendix Figure D.
4.1 PIPELINE FORMULATION
Inputs and state. At turn f, the agent observes the image I, the current textual query qr, optional (k) (k) (k)
set reference boxes Bret {[ 1/1 .T2 1 (propagated or newly provided), and memory M,- that stores structured outputs from previous turns. We serialize Bref and M into the prompt to make them available to the model.
Policy and action space. RegionReasoner is an auto-regressive VLM policy Te that generates structured text action composed of four tagged blocks y/ (st, ft. h, a.) with tags <scene>, <focus>, <think>, <answer>, Let = (w.,....., Wr.N.) ...N..) denote the token sequence for the whole action; then
Nr Td We. 1,4,, Bret (e-1,,Ween))
To(ye gt. Byet M = II
(1)
n=1
Constrained decoding enforces the tag schema and JSON validity for <answer>, while allowing free-form natural language in <scene>, <focus>, and <think>.
Turn update and termination. After decoding finishes (upon emitting the end token or the closing < answer>> tag), we parse a to obtain task outputs (e.g., 2D boxes or points) and update the memory:
M, M U {(St, fi, he, at)).
(2)
multi-round episode consists of T turns (fixed or query-driven). The per-turn reward R(t) is computed from (St, Jt, h at) and aggregated across turns (Sec. 4.3, 4.4).
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Compact notation for the loop. For brevity, we denote the one-tum transition produced by the policy as
(st, he, as) TT(- ge, B MM-1), Mr t Ma-1 U au
(3)
4.2 REGIONREASONER MODEL
Unified perception-reasoning backbone. RegionReasoner extends the unified perception-reasoning framework of VisionReasoner (Liu et al., 2025b) to multi-round setting, where each turn emits a structured and verifiable trajectory. The model is initialized from a large VI.M backbone and reasoning purely in text, while remaining explicitly grounded to image
performs chain-of-thouge regions through serialized bounding-box references. Each turn-t output is organized into four tagged blocks: a global scene caption St (<scene>), localized caption fa tied to a provided reference box (<focus>, optional), a reasoning trace hgg (<think>), and a JSON answer a (<answer>). Constrained decoding with schema and tag guards ensures format validity, supports automatic post-hoc parsing, and prevents untagged content from leaking into <answer>.
Reference-grounder thinking. To improve verifiability and reduce free-form hallucination, Re- gionReasoner requires that reasoning must cite evidence. When a query specifies references, the prompt encodes the set a {[={") (k) (&)
222 , 1) in canonical textual form and instructs the model to reason with verbatim coordinate mentions inside <t>ink>. The same coordinates injected in q: so attention aligns with the intended regions across turns. During decoding, he must explicitly reference the used boxes and, when relevant, name spatial relations (e.g., "to the right of bbox [x1, y/, 22, "2l""). This design yields causal chain from evidence to conclusion that is parsable into cited coordinates S (h,, and directly comparable to Bi enabling automatic grounding checks and precise credit assignment RL. In multi-round interaction, previously cited boxes can be re-used or refined; the explicit citation acts as a stable interface across turns, which improves temporal coherence of the reasoning trajectory and curbs region drift.
Global-local semantic consistency. Iterative reasoning often breaks down when global descriptions and local evidence diverge; to prevent this, RegionReasoner jointly produces 8, (global) and fe (localized to the reference) before generating l2,, and then enforces that the semantics of s, and fo are reflected within ft. Concretely, a lightweight deterministic pipeline extracts keyword sets KC(::). K(ff), and K((h)) (lowercasing, stop-wort removal, lemmatization, and noun/object filter). We later compute asymmetric overlaps Ov(s,, hu) and Ov(fi, hạ) as part of the reward (Sec. 4.3), pushing the model propagate entities and relations from the global and local captions into the reasoning itself. Making <think> the aligmment nexus-rather than correcting only at the final answer-yields finer-grained RL signals, better consistency across turns, and improved spatial reasoning, especially when h2 is encouraged to include localization lexicon (c.g., leffrrighhinssdeevverrrpppp to) together with explicit box mentions.
Task output without extra heads. Detection and segmentation are expressed directly through the ISON <answer> without introducing task-specific heads. For segmentation, we use sparse point_28 2d outputs to probe masks following our benchmark protocol; evaluation employs IoU/Dice or point-based matching as appropriate. This head-free design keeps the learning signal unified: structural validity and geometric precision are attributed to <answer>, while grounding fidelity and global-local agreement are attributed to <think> in conjunction with <scene> and <focus>, The result is closed loop where interpretable trajectories, verifiable references, and final predictions are optimized jointly under multi-round supervision.
4.3 REWARD FUNCTIONS
We optimize RegionReasoner with reinforcement learning, shaping both intermediate reasoning and final predictions. Besides the base rewards inherited from prior work (Liu et al., 2025b), Thinking Formal, Answer Formal, Non-Repeat, Bboxes loU, Bboxes LI, and Points LI, we introduce two multi-round objectives that explicitly encode (i) citation of required references inside the reasoning trace and (ii) semantic alignment between global and local evidence.
Notation. At turn t, the model outputs s, (<scene>), ft (<focus> if any), h2 (<think>), and a, (<answer>). Required references are Brel (W(e) (possibly empty). A lightweight extractor KC(-)
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returns keyword sets (lowercasing, stop-word removal, lemmatization, noun/object filter). We parse bbox mentions from hy as S(1 and use kw kw(h,) {0,1} to flag bbox-related tokens.
Reference citation reward. To make the reasoning verifiable and grounded, the trace must explicitly cite the referenced boxes when they are required. We reward correct citation and penalize hallucinated coordinates:
B Ø
In Reet(t), S(he) \ B3 # 0, [Rrr((),
Ree
A Akw(h)) + (S(h)) O 1 max (IS(h))): 1)
Best (1)
otherwise.
otherwise.
(4)
with = μ 1.0, = 0.5, and clipping (ter(t) [0.
Global-local consistency reward. To keep the reasoning coherent with both global scene context and localized evidence, we align he with 8; and (when present) ft. Let the asymmetric keyword overlap be
Ov(X, [K(X)
(5)
max (IC(X)), 1)
We also include a light logic prior E(he) E [o, 1] counting spataalcoompariso/loooaaaaaaaaaa terms (capped 1). The consistency reward is
Reons (t) w. Ov(st, h1) + w Hf [B #o] Ov(fe,h) + we E(he);
(6)
with W. = 1.0, = 0.6, we = 0.4, clipped to [0.
Total per-turn objective and episode return. Let Rhase (t) denote the base rewards from (Liu et al., 2025b) (Thinking/Answer Format, Non-Repeat, Bboxes loU/LI, Points LI). The per-turn reward aggregates as
R(t) = Roase (t) + a (ter(t) + Reons (t),
(7)
where a = 3 = 1 by default. Each component is normalized to j0, 2] prior to aggregation to balance scales, and the episode return is E, RR(t) over turns. Compared to basclines, these rewards are used only as internal training signals; all evaluation metrics remain purely geometry -aaeed (AP and gloU) and are computed identically for all models.
4.4 TRAINING
We optimize the policy Ta with GRPO (Shao et al., 2024) over multi-turn rollouts. For each batch, the model generates structured actions yt = (st. he, ax) at turns f I... condi- tioned (L, qt, Bret M,-1) as defined in Sec. 4.1. Per-turn rewards follow the decomposition in Sec. 4.3 Rbase, Riet, Reous- with componentwise normalization to j0, 2]; the episode return is Σ R(t).
Objective. We optimize the clipped policy objective GRPO (Shao al., 2024) on the autoregressive likelihood of the structured action (cf. equation 1):
To(u 1, qr, Bref Mr- 1) Taaia (v: 1, It. Bret ,M-1))
Leip (0) -E min (p)(0) A., clip(p:(0), -)) pr(0)
+e)+
Advantage estimation and value targets. Let 8 =1, gt, Broe .M--1) denote the turn-t state and r the per-turn reward. We use learned value head Vo(s) and compute advantages with GAE:
T-t
& = + Vo(5 Vo(s). A, = Σ (G2)) 8141-
1=0
Each dialogue a finite episode; the last turn T' is terminal, so we set
(8($441) = 0.
The value target is A. and the critic is trained with Leatne = (Vo(s.) Á. We add a small entropy bonus to encourage exploration and, optionally a KL penalty to frozen reference policy for stability:
Luntal
Letip + Cv fralue ce H (-(s=)) 3 (TT(((())ss))) mee((-1s)))

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Table I: Detection on RegionDial-Benci with 7-round dialogues. Columns report per-round AP (R1-R7) and the mean across turns for RefCOCO+ Multi-turn and RefCOCOg Multi-turn. RegionReasoner-77 achieves the top averages on both splits and maintains larger margins later rounds, reflecting stronger robustness to error accumulation.
RefCOCO+
(AP
RefCOCOg Mulii-turn (AP
Method
R1
R2
R3
R4
R5
R6
R7
Avg
R1
R2
R3
R4
RS
R6
R7
Avg.
Owen2 VL--BB
8.5
6.5
5.4
7.5
3.6
3.5
6.7
7.8
6.2
3.5
3.5
5.6
4.0
6.1
Qwen2.5 VL-7B 7B
65.5
49.0
48.1
36.5
30.0
38.2
25.9
49.9
63.9
43.7
39.0
37.9
42.2
43.2
33.8
49.7
Seg. Sag--ero-BB
90.5
73.6
59.6
48.8
58.2
73.1
61.8
70.0
68.8
VisionReasomer-78
88.1
74.7
75.8
64.2
56.3
57.3
47.0
74.8
85.0
65.8
66.8
69.3
68.3
75.2
72.5
73.6
RegionReasoner
89.3
75.0
75.0
sliding memory M preserves prior turns under context budget, and a light turn-depth cur- riculum gradually increases the maximum T early in training. Constrained decoding enforces tag/schema and JSON validity so that rewards are well-defined both for intermediate reasoning (<scene>/< foos>>/occ>>>/h/hh nn>) and final outputs (<answer>). Compared to SegLI.M (Wang et al., 2025b), which performs multi-round segmentation without explicit reasoning traces or RI., our training aligns interpretable, reference-grounder thinking with global-local consistency under unified multi-round objective.
EXPERIMENTS
5.1 EXPERIMENTAL SETTINGS
Benchmark and protocol. We evaluate under the multi-round setting in Sec. on RegionDial- (RefCOCO+ / RefCOCOg Multi-turn). Detailed descriptions of the dataset construction procedure, together with quantitative statistics, are provided in Appendix B. In addition, following the evaluation protocol of VisionReasoner (Liu et al., 2025b), we also report results under the single-round setting.
Base model. RegionReasoner-7B initialized from Owen2.5-VL-771 (Bai et al., 2025) (7B parame- ters). We keep the vision-language backbone intact and optimize it end-to-end with reinforcement learning; no additional tpsc-spec heads are introduced.
Implementation details. RegionReasonerr 7B is trained with GRPO (Shao et al., 2024) using the rewards in Sec. 4.3. Constrained decoding enforces tag/schema validity and JSON correctness. We the backbone's vision tokenizer and input resolution; the maximum turn depth T matches the dialogue length. Training uses a global batch size of 16 with K=8 rollout samples per prompt (per step). The initial learning rate is 1 x 1x10 with weight decay 0.01. All experiments run 4x NVIDIA H100 GPUs; total training time is about 10 hours. Unless noted, we fix random seeds and use identical multi-turn contexts and references across methods; shared evaluation scripts ensure consistent aggregation.
Baselines. We compare RegionReasoner-7B with strong VLMs and task-specialized models: Owen2.5-VL-77 (Bai et al., 2025) and Qwen2-VL-7B (Wang et al., 2024); Seg-Zero-7B (Liu al., 2025a)
inpeenttationcentric. VisionReasoner- (Liu et al., 2025b) (structured pereep- tion- reasonegn in a single-tur setting); and SegLLM (Wang et al., 2025b) (multi-round segmentation without explicit thinking or RL). methods are evaluated under the same multi-turn protocol with reference propagation; for models without structured reasoning, we adapt prompts to accept referenced boxes.
5.2 MAIN RESULTS
Referring detection under multi-round interaction. Table reports AP on AagionniialBeence RegionReasoner-7B attains the highest turn-average on both splits, improving over VisionReasoner- 7B by 5.9 points RefCOCO+ (80.7 vs. 74.8) and 4.6 points on RefCOCOg (78.2 vs. 73.6). Against Seg-Zero-7BB the gains are 7.6 (RefCOCO+) and 7.1 (RefCOCOg) points. Late-turn improvements pronounced: RefCOCO+ the margins R5/R6/R7 are +5.6/+11.88 1188+17..7 over VisionReasonerr 7B. These results indicate that explicit reference citation and global local consistency preserve localization quality dialogue context deepens.
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Table Segmentation on RegionDial-Bence with 7-round dialogues. Columns report per-round gloU (R1-R7) and the mean across turns for RefCOCO+ Multi-turn and RefCOCOg Multi-turn. RegionReasoner-77 attains the highest averages on both splits and sustains larger gains later rounds, indicating stronger robustness to enror accumulation in multi-round settings.
RefCOCO+ Multi-tunn (gloU
RefCOCOg Mulli-tura (gloU
Method
R1
R2
R3
R4
R5
R6
R7
Avg.
R1
R2
R3
R4
RS
R6
R7
Avg.
Qwen2 VL--BB
12.2
9.0
6.5
5.3
6.3
6.3
9.5
9.4
8.5
11.6
8.8
10.0
7.0
6.4
4.4
9.3
Qwen2.5 VL-70 7B
56.5
43.3
41.4
34,5
23.4
33.6
24.9
43.6
53.8
36.3
35.5
31.6
37.3
36.8
28.6
42.1
Seg-Zero-7B
78.6
51.6
42.4
50.8
64.0
72.3
52.3
53.5
59.4
58.1
LM
71.1
71.7
70.4
58.7
41.9
39.2
30.3
60.7
68.9
55.3
50.5
47.7
47.3
37.8
25.4
56.7
VisionReusoner- 7B
75.6
65.9
54.9
52.7
57.8
57.6
ReglonReasoner -7B
76.4
73.1
72.0
58.8
51.3
59.4
54.6
69.6 73.9
62.9
60.7
58.9
64.4
66.8
63.3
66.5
Table 3: Ablation on Region components for detection. Left: components toggled. Right: Single-Round Multi- Base rewards follow Liu et al. (2025b). "Ref-cite" enforces explicit bbox citation in <think>; "Consist." is the keyword-overlay consistency reward; "Logic" is the lightweight ppatiallomppariionooccaaaatt yields additional gains, and the full model provides the strongest multi-round AP.
prior. Ref-cite and Consist. both help, their combination
Toggles
Single-Round
Mouti-Round
Components
Ref-cite
Consist.
Logic
RefCOCO+
RefCOCOg
RefCOCO+
RefCOCOg
Base only (no signals)
87.9
87.5
74,8
73.6
+
Ref-cite only
88.6
88.4
78.9
77.1
+ Ref-cite Consist.
88.1
80.2
77.6
Ref-cite Consist. Logic
87.7
87.9
80.7
78.2
Table 4: Ablation on Region components for segmentation. Same toggles as Table 3. Overall, either Ref-cite or Consist. improves over the base, their combination brings further gains, and the full model attains the best multi-round performance.
Toggles
Single-Round
Multi-Round
Components
Ref-cite
Consist.
Logic
RefCOCO+
RefCOCOg
RefCOCO+
ReiCOCOg
Base only signals)
74.9
71.3
64.3
59.9
Ref-cite only
76.9
74.4
67.9
63.6
Ref-cite + Consist.
74.0
70.9
68.3
65.8
Ref--itte Consist. Logic
74.1
71.2
69.6
66.5
Referring segmentation under multi-round interaction. Table summarizes gloU on RigionDial- Bench. RegionReasoner -7B attains the highest turn-average on both RefCOCO+ and RefCOCOg and exceeds all baselines across most rounds. Relative to VisionRReasone-77. the average gains are 5.3 points on RefCOCO+ and 6.6 points on RefCOCOg: RegionReasoner also improves over SegLLM by about 8.9 and 9.8 points on RefCOCO+ and RefCOCOg, respectively. The gap widens deeper turns (R7), indicating that explicit reference citation together with global-local consistency mitigates error accumulation and preserves spatial fidelity as dialogue context grows. Representative trajectories are shown in Fig. where RegionReasoner explicitly cites referenced boxes <think>, maintains agreement between scene- and region-level descriptions, and resists nearby distractors, while VesionReasoner tends to drift at later turns.
5.3 ABLATION ANALYSIS
We study the contribution of each signal using Tables and 4, which report single- and multi-round results on RefCOCO+ and RefCOCOg.
Effect of reference citation (Ref-cite). Enforcing explicit citation of the referenced box in <think> consistently boosts multi-round performance for both tasks, with the largest gains at later turns where error carryover is strongest. Citation turns cross-turn dependence into verifiable evidence use: the policy learns to reuse or refine previously grounded coordinates, which curbs drift and avoids spurious boxes. In the single-round protocol, a nontrivial subset of queries still provides a reference region (from our seetial-relation templates), so Ryef is active and yields measurable improvements by tying the reasoning trace to the given coordinates and aligning <think> with <answer>; when no reference is provided, this term is neutral. By contrast, the consistency and logic signals chiefly stabilize semantics and relational language across turns, hence their effects are most visible in the multi-round setting.
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Round1 Label
Round2 Label
Round3 Label
Input
Roundt:
Highlight the person in row 2 with glasses and head turned.
<thinlc> The task is ta gighight the person row with glasses and head turned. The image shows a crowe peopls, and we need to ioently the second row from the botem. m. The persen the second row with glacsos and their head tumed located tawards the left side of he image. The person is wear suit and s8, and their head turned di gndyy to the sido, locking at something or someone comeraa <thinko
VislonReasoner
The
logether, gathering many background.
lies.
<thinko first the turned
from Among them, lock
the side the there dark <thinto-
RegionReasoner
Round2: Focus who talking
<thinik klentiyy talking person (00,300,388)
The reference seams fiuur purple Likelyy
crowd, appears to main subject possibly being
vague,
VisionReeasones VisionReasoner
image large cameras background
browwst) 301) comeeponde
glasses.
image,
right. offccus
query asks person
referenced man[176, 388), right, person directly facing
RegionReasoner
Round:: Recognize the person directly in front RZ.
<thinkss The taok to recognize the panson directy in front woman in the purple jacket near the center foreground The person directly frant her appears to be man wearing sunglasses and dark sull, holding a phone in hts hands. wthhiko-
VisionReasoner
-ccee>> image large cameras
comresponde background. mage. looking engaged
postioned bbox
<thinko query recuires dentilynng person directly
man(232, 303), long hair, wearing
RegionReesoner
Observing spataal layout.
sunglasses purple Her directy alinnd
Each panel shows RegionReasoner vs. VisionReasoner. Blue boxes mark the ground-tree truth reference regions for each round. Green boxes denote predicted detection boxes, while red masks denote predicted segmen- tation outputs. Checkmarks and crosses indicate prediction correctness. RegionReasoner consistently cites the reference coordinates inside <t.hink nk> and aligns its reasoning with global (<scene>) and local (<focus>) descriptions, yielding stable localization in later rounds. VisionReasoner,, lacking explicit citation, is prone to semantic drift or neighbor confusion when context accumulates.
Figure 2: Qualitative multi-round trajectories (RI-R3) on our RegionDial-
Effect of global-local consistency (Consist.), Aligning keywords between global scene descriptions and localized region captions strengthens the reasoning trace, with particularly clear benefits on RefCOCO+ where spatial hints in the query are weak. The key effect semantic anchoring: nouns and objects echoed in <t <think> keep the trajectory focused on the same entities across turns, which limits off-topic attention and stabilizes segmentation quality in cluttered scenes.
Effect of the logic prior. Adding the lightweight spatia//compariion/ccaaiiiiaoooo lexicon yields small yet persistent gains, most visible at deeper turns. Encouraging phrases such as inside, next to, left of increase reward density for partially correct reasoning and nudges the model to articulate relations explicitly. This makes the trace casier to verify and helps the policy recover when two candidates are visually similar.

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Depth robustness and single- vs. multi-round difficulty. Across datasets and tasks, single-round results (Round 1) are consistently higher than their multi-round counterparts, which reflects an intrinsic difficulty gap rather than artifact of particular model. In the single-round setting, the policy only needs to resolve one query against the image. In contrast, later rounds must both interpret the current query and correctly reuse and propagate previously predicted boxes references. Any localization error at an early turn is carried forward and compounds over subsequent turns, so the effective difficulty increases with turn depth. All compared methods exhibit this depth-dependent degradation in Tables 3 and 4, highlighting multi-turn error accumulation and robust reference propagation as central challenges for grounded dialogue. The full RegionReasoner configuration degrades more slowly with turn index than any variant without citation or without consistency: its trajectories remain parseable and self-consistent, which limits the accumulation of small localization enrors over long dialogues. For all ablations, we keep schema and JSON checks enabled to isolate learning effects from parsing noise.
CONCLUSIONS
We introduced multi-round visual reasoning and presented RegionReasoner, a areinforcement-leanin framework that couples interpretable, reference-grounded thinking with global-local semantic align- ment. The model emits structured trajectories, and is optimized with two targeted rewards: a ciarenccecitattinn signal that enforces explicit grounding to cited boxes and consistency signal that aligns global and region-level captions with the reasoning trace. To enable systematic evaluation, we released RegionDial-Bencll multi-turn training and testing resources spanning detection and seg- mentation. Experiments on RefCOCO+ and RefCOCOg under multi-round protocols show consistent improvements, especially at deeper turns where cascading errors typically degrade performance.
Ethics statement. This work proposes RegionReasoner and RegionDial-Bench for multi-round visual reasoning. We do collect new human data or elicit sensitive attributes. All images and annotations used to build RegionDial-Benu BennhhhhlBeehheeee are derived from public referring datasets (RefCOCO+, RefCOCOg) under their licenses; our multi-turn dialogues are programmatic reformulations of existing annotations, with no additional human labeling. We do not attempt to infer demographics, identities, or other sensitive information. Potential misuse includes applying the method to private imagery without consent or deploying it in settings that require privacy guarantees; discourage such uses and recommend adherence to data-governance policies and applicable licenses.
Reproducibility statement. All compared models (e.g.. Qwen2.5-VL-7B, Seg-Zero-7B. VisionReassnne-7B. SegLLM) and datasets are publicly accessible. Methodology, reward design, and training procedure are detailed in Sections 4 and 4.4; benchmark construction, evaluation protocols, and baselines are in Section 5. To facilitate replication. we will release code, RegionDial- conversion scripts, prompts, reward configurations, and evaluation scripts upon acceptance. Compute details: RegionReasoner--7 is trained with policy-gradient RI. on 4x NVIDIA H100 GPUs for approximately 10 hours; batch size, optimizer settings, and other hyperparameters are reported in Section 5. We will provide random seeds and exact checkpoints to ensure reproducibility.
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ACKNOWLEDGMENTS
This work was supported by the European Union's Horizon Europe research and innovation pro- gramme under grant agreement number 101214398 (ELLIOT).
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A LLM USAGE STATEMENT
We used a large language model (ChatGPT) solely for gramiar checking and language polishing of the manuscript text. It did not contribute to research ideation, method design, experiments, data analysis, or result generation; all technical content was authored and verified by the authors.
B MULTI-ROUND BENCHMARKS
Training construction. We extend the ~7k single-turn samples from VisionReasoner (Liu et al., 2025b) into 100 dialogue samples. The expansion comes from decomposing multi-object instructions into sequential sub-queries, such that single original sample may yield multiple turns. Later rounds are explicitly grounded to the bounding boxes predicted in earlier rounds, while single- object queries remain in single-turn form without references.
For example, the instruction "a black and white dog laying down, looking away from the camera" and "standing dog" is reformulated into: (1) "a black and white dog laying down, looking away Here, the coordinates 10,457.374.677 .7.,7,2722 denote the ground-trute -ouun bounding box of the "a black and white dog laying down" from Round injected into Round
from the camera"; (2) "find the standing dog, next to bbox=(0,457.374.61
reference bounding box. An illustration of this reformulation process is shown in Figure 3. This process increases the total number of training samples to about 10k, though not all samples involve reference propagation.
Figure 3: Example of training data construction. Round 1 localizes the "laying dog" (red box). Round reformulates the query into "standing dog, bbox=(0.457,374.672) (blue box).
To diversify spatial interactions, introduce eight spatial relation templates covering adjacency, directional, containment, and overlap/contact relations (Table 5).
Test set construction. RegionDial- is constructed entirely from the public referring expression benchmarks RefCOCO+ and ReíCOCOg, using only their official test splits. We reuse the original images, human-written written referring expressions, and ground-truth bounding boxes/masks without introducing any new images annotations. In the original datasets, each test sample is single-ture example consisting one query and one target region, but many such samples share the same underlying image.
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Table 5: Eight spatial relation templates used to construct multi-round dialogues. They cover four categories of spatial interactions: adjacency (next to), directional (above, below, left, right), containment (inside), and contact/overlap (overlapping with, touching).
Relation Type
Template
Adjacency
next to ybox= above
Directional (above)
Directional (below)
below bbox=(x1,y1,x2, to the left of bbox=
Directional (left)
Directional (right)
to the right of
Containment
inside bbox= (xi, x2,
Overlap
overlapping with [box=(x1, y2] touching bbox- [x1, x2,
Touching
We first group all RefCOCO+/g test samples by image and then merge the queries associated with the same image into coherent multi-round dialogues. As illustrated in Figure 4, Round localizes the "man in blue shirt" (red box) with ground-trutt box 107,,03,,466 we deterministically inject the bounding box predicted an earlier round (or the ground-truth box during training) into the query an explicit reference token (e.g.. "bbox=[47,10) (1/4441,007330 177,003,4666"" while keeping the original target labels unchanged. This procedure yields two multi-turn evaluation sets: RefCOCO+ Multi-turn (715 images, 2355 dialogue turns) and RefCOCOg Multi-turn (1,580 images, 4405 dialogue turns), with dialogue lengths ranging from to 7 rounds. Table 6 reports the per-round sample counts and resulting dialogue-lenge length distribution. Object categories strictly follow those in the original RefCOCO+/g datasets (COCO-styy categories for RefCOCO+, with testA dominated by the "person" class, and 78 categories for RefCOCOg).
For each subsequent round,
Dataset choice. Our goal is to study multi-round referring grounding with both detection and segmentation, under a protocol that requires: (i) high-quality instance-level masks and bounding boxes, (ii) human- -witten referring expressions aligned with specific objects, and (iii) multiple expressions per image to support dialogue-style construction. RefCOCO+ and RefCOCOg jointly satisfy all these requirements. Both datasets are built on the MSCOCO dataset (Lin et al., 2014), and therefore inherit its large-scale instance segmentation and detection annotations with well-established train/val/test splits. Crucially, they are explicitly designed for referiin--cprresion grounding, offering clean natural- llnuual- queries that correspond to individual object instances. Furthermore, many images contain several distinct referring expressions, which is essential for forming coherent multi- round dialogues over the same scene.
expression
Using MSCOCO alone would require generating mining referring expressions a prepro- cessing step, introducing an additional modeling component orthogonal to our focus on multi-round grounding. Visual Genome (Krishna et al., 2017) provides rich relational annotations and region descriptions, but its instance segmentation masks are sparse and less consistent, making the link between text and fine- fine-grained segmentation less reliable. For our setting-wher where each turn requires an accurate region mask or bounding box as reference- thire mismatch becomes a serious limitation.
Within the RefCOCO family, choose RefCOCO+ and RefCOCOg rather than including RefCOCo itself. Although they share the same underlying MSCOCO images, the linguistic design differs: Ref- COCO+ forbids location words, yielding appearance- centric expressions, while RefCOCOg contains longer and more descriptive queries covering 78 categories. Using RefCOCO+ and RefCOCOg thus provides a diverse combination of concise and rich expressions without introducing near pear-duplicate supervision from ResCOCO, whose differences stem primarily from annotation rules rather than visual content.
We refer to these resources collectively as RegionDial-Bench the first manually curated multi-round benchmark for reference reference-grounded reasoning. Unlike prior multi-roumt resources constructed via GPT-style automatic rewriting. RegionDial Bench is built from human-authored referring expressions combined with deterministic reference propagation from ground -tuth boxes, avoiding LLM-iinuced artifacts and yielding more reliable evaluation.
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Figure 4: Example from RefCOCO+ Multi-turn illustrating the construction pipeline in RigionDial- Bench. Round localizes the "man in blue shirt" (red box) with ground truth box 177,107,303,460 This box then injected into Round as an explicit reference, reformulating the query into "Who next to 100x
to localize the "man in white shirt" (blue box).
Table 6: Per-round dialog-turn statistics for DigiionDial-eenaee Dialogue lengths range from 1 to 7 rounds; the bottom row reports the total number of dialogue turns in each multi-turn test set.
Round
RefCOCO+ Multi-tum (dialog turns)
RefCOCOg Multi Multi-turn (dialog turns)
715
1,580
2
715
1,580
310
570
4
260
290
160
180
110
125
85
80
Total
2,355
4,405
C INSTRUCTION SCHEMA
To guide the policy model toward producing structured reasoning trajectories, we design unified instruction schema for training in Table 7. At inference time, we use a unified instruction schema in Table which shared by all baseline methods to ensure fair comparison. This schema specifies how user queries, reference bounding boxes, and reasoning steps are serialized into consistent prompt format, inspired by prior approaches (Liu al., 2025b; Wang al., 2025b).
D REGIONREASONER FRAMEWORK
Figure 5 illustrates the overall framework of RegionReasoner.. The model is built upon the Qwen2.5- VL-7B backbone and is optimized with two reinforcement learning objectives: the reference citation reward, which enforces explicit grounding to previously localized objects, and the global-local consistency reward, which aligns holistic scene understanding with reference-based reasoning. This framework summarizes how user instructions, reference propagation, and reward shaping are inte- grated enable coherent multi-round reasoning.
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Table 7: Instruction schema used during training.
Training Instruction Schema
<image>
Task: "Please find "(Question)" with bboxs and points."
Reference guidance: a reference bbox is provided (e.g., above/ below/ to the left of/ to the right of/ inside/ overlapping with/ touching bbox=[x1, y1, x2,))))))) use it as spatial guidance.
Steps: 1) In <scene> </scene>, give concise global scene description.
2) In <focus> </ focuss,, describe what is visible inside the reference bbox (if provided). (do not output the final answer or target label here). 3) In <think> </thi kk>,
the whole image by combining the global scene and the reference bbox relation. Explicitly state which spatial relation from the question you apply (e.g.. "target above the reference"), and use it to constrain the search over the scene to locate the target object(s). If multiple candidates exist, compare them and pick the closest match.
4) In <answer> < /answrr>, output the bbox(es) and point(s) for the target object(s) in JSON.
Format: <scene> global description of the image here </scene>
<Éocus> description of reference bbox content here < focus> <think> thinking process here </think> <answer> (Answer) </answe>>
Input
QwenVL2.55
player
number
Reference cistion reward
Round
locating sanding spocatur
comsistancy
&
Figure 5: Framework of Region The model processes multi-round queries with Qwen2.5- VL-7B, guided by two complementary reward signals: (1) the reference citation reward, ensuring explicit grounding to previously predicted objects, and (2) the global-locat consistency reward. enforcing alignment between holistic and reference-based reasoning.
E ADDITIONAL QUALITATIVE RESULTS
To complement the quantitative results the main paper, we provide additional qualitative visualiza- tions in Figure 6. These examples illustrate how our model performs multi-roum iffference-rruundee reasoning on challenging cases from RegionDial- ability to propagate references across dialogue turns and maintain consistent localization. Beyond
In particular, they highlight the model's
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Table Instruction schema used during inference.
Inference Instruction Schema
<image>
Task: "Please find "[Question]" with bboxs and points."
Reference guidance: If a reference bbox is provided (e.g., above/ below/ to the left of/ to the right of/ inside/ overlapp ng with/ touching bbox= yll,yy,,xxe x2, y21), use it as spatial guidance only. (11,
Steps: 1) In <scene> </scene>, give concise global scene description. 2) If reference bbox exists, <focus> </focus> describe ONLY what is visible inside that bbox (do not output the final answer target label here).
3) In <think> </think>, reason over the whole image by combining the global scene and the reference bbox relation. Explicitly state which spatial relation from the question you apply (e.g., "target above the reference"), and use it constrain the search over the candidates exist, compare them and pick the closest match.
locate the target object(s). If multiple
4) In <answer> < answer>, output the bbox(es) and point(s) for the target object(s) in JSON.
Format: <scene> global scene description </scene>
<focus> description of reference bbox content (if provided bbox=[xl.y1.x2.p22 </focus>
<think> reasoning that applies the spatial relation to the scene and narrows to the final target(s) < </think>
<answer> [Answer) </answer>
the three-turn examples shown above, we also include cases with longer dialogue chains. Figure illustrates four-turn dialogue from RegionDial-Bench demonstrating how model propagates references across multiple levels reasoning.
F GENERALIZATION TO EXTERNAL BENCHMARK
To assess whether RegionReasoner generalizes beyond RegionDial-Bench. we further evaluate the model the V* benchmark (Wu & Xie, 2024), which explicitly targets attribute-level and spatial visual search in multimodal LLMs. We follow the official V* evaluation protocol and compare RegionBee with GPT-4V, SEAL (Wu Xie, 2024) (the method proposed in V" ), Qwen2.5-VL--B, and VisionReasoner-7B. The quantitative results are shown in Table 9, SEAL achieves the highest overall score because it incorporates an explicit visual-search mechanism specifically engineered for the V* benchmark and tightly coupled to the LLaVA architecture, making incompatible with the Qwen2.5-VI family without substantial re-engineering. Within the Qwen2.5- VL family, RegionReasoner attains the strongest overall performance among all models withour a dedicated visual-search module. RegionReasoner demonstrates particularly large gains on the Spatial dimension (+7.9 over Qwen2.5-VL and +7.9 over VisionReasoner). indicating that reference- grounded reasoning and global- consistency rewards improve spatial localization and visual search in way that transfers beyond proposed benchmark. Note that RegionReasoner is trained exclusively on RegionDial Dial-Bnhnh Bench and never on V*, further confirming the generalizability of our approach.
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Hi RegionReasoner, is the green shirt?
Output: R1
aseno The image showe neisurert satting where several people are attinng table, Two pieces are plaas plans, bee chuikoo Leeking the inege, there it dely one perton weating premn ckirt we socted of the certer of imags bahind pimta dinae no
culary napkin arranged, background
grean shir is presam, targat muet on parnen.
Which pizza R1 about to eat?
oicanas The image showa rastourant actting whare saveral papp are aitting a tobte Twoo plazes plazed on plates, beur
Output: R2
the and coffery naptirs orcer god. tuckground
decut busid the raftranca blox (2AL, 472, 166), men i green ahirt vadia holding fort,
rivvess The query osko "which sicee pizca is 1841, are, 165) shoul to waitt. The reference thow 1641, I. divectly postiened from rnfermnce
green
the pizza from kim. sporii maietie "the pizca loothed the plane frrst of this man the one hat about eus, othesso
ResleR
Who is the person next to R1?
Gceno The image showe reitzrrort setting where severd people are atting of table, Tho piccos are plicet Hanee, beer
Output: R3
ourlery nepkin craagee. bacagroudd
dizaa traide reforarce bben (241, 472, MA1) thene is man in great shirt atting the with
diilo The query odko "Wh perten rest to (8l1, i. 423, wagh The reference thew 1841, 1, 472 cormupores
crangament greas Looking with long hair inimndisttll laft Thersfore, the persce next to the reference is this veman chhisto
The shows
ReglerRasteer
Figure 6: Multi-round qualitative example from RegnehDial-Bench. The dialogue contains three
rounds: (1) "Who is the man in the green shirt?" + localized as the bounding box 1,472, 165]. (2) "Which slice pizza is RI about to eat?" - where RI refers to the bounding box predicted in Round 1, and the model localizes the corresponding pizza slice. (3) "Who is the person next to RI?" + again using the bounding box from Round as a reference, the model identifies the adjacent person.
Table 9: Evaluation the V benchmark. RegionReasoner achieves the best performance among models based on the Qwen2.5-VL backbone and shows strong generalization to attribute-level and spatial visual search without using specialized visual-search module.
Model
Attribute
Spatial
Overall f
Visual Search Mechanism
GPT-4V
51.30
60.52
54.97
no yes
SEAL
74.78
76.31
75.39
Qwen2.5-VL.-BB
72.17
60.52
67.53
no
VisionReasoner-78
75.62
60.52
69.63
no
RegionRea
75.65
68.42
72.77
n1
G STANDARD SINGLE-ROUND REC AND RES RESULTS
We report standard single-round referring expression comprehension (REC; detection) and referring expression segmentation (RES) results the RefCOCO+ and RefCOCOg benchmarks. In this conventional setting, each referring expression is evaluated independently, without any multi-round dependencies. As shown in Table 10, the model achieves strong performance both REC and RES in the standard single-round setting across RefCOCO+ and RefCOCOg. These results demonstrate that the model maintains solid grounding capability under the conventional single-turn protocol.
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RegionReasoner, which person wearing pink jacket?
Output: R1
acs1p image snoee weeder with seand anjacts indudiig og lopteps, mags popern, this chilcc
lable,
imge show mime people artingg Among thems person disrtly liseiffes officies
Regleraaanee
Which computer R1 using?
doeene the shand wooden vide with several ects inckciigg tapips, liee typeartter Around tekls, mahgle people cfting and working d'seane
Output: R2
clooas The reference biox 1808, a SAL, 147) coriains the person in piat jacket seated of haka cfltnao
cliido The query sake "Wirih competer [108, sal, 347) using" the reference blen 1108, S41, 1417) cor reppodd person piak jocket Looking image, this person sected diractly behind laptop
they o/tiido
Regice
The black computer next to R2.
werten with sevel skietts incked megi, hypartter Anound the tobls malt pie people are cttinng and working. </iceta
Output:
<focat referince sirs jackat offecue
"The blace
1800, 10, 1965" referceco troo
192) cormuponce to the taptop fooe of the perser wearing the pirk jacket Looking on the soomal energement
legnop nefamente
taploy,
RegiceRessoeer
Who using computer
siceo The image shove woodan table neveral objects inclading laptops, maga popera, tike
Output: R4
deeas rafersnoo Mae (241,
a6d] cutaiis brack laplop. ofocus
oltvits The every skke: "Whe using the computer 1861, 122, 50%, 260" The reference bbox (2A), U2, 506. 26c) surresponds is black laptap lsshedd
blue hypour sinae the lophep draccty fruet the parsan unting bakind typantter; user this computer individiaal positioned hpawrileer cthido
Regler
Figure 7: Four-turn qualitative example from RegionDial-Bench. The dialogue proceeds follows:
(1) "Which person wearing a pink jacket?" - localized as bounding box RI. (2) "Which computer is R1 using?" + model grounds the computer associated with R1, denoted bounding box R2. (3) "The black computer next to R2." - model localizes the black computer adjacent to R2, denoted as bounding box R3. (4) "Who is using the computer R3?" - finally, the model grounds the user of the black computer R3.
Table 10: Standard single-round REC (detection AP) and RES (segmentation gloU) on RefCOCO+ and RefCOCOg test sets.
Model
Seg. RefCOCO+
Seg. RefCOCOg
Det. RefCOCO+
Det. RefCOCOg
Qwen2-VLL VL-7B
65.7
63.5
76.5
78.2
Qwen2.5-VL-
76.8
72.8
88.2
85.7
VisionReasoner:7B
74.9
71.3
87.9
87.5
Region
76.9
74.4
88.6
88.4
H SENSITIVITY STUDY OF REWARD WEIGHTS a AND 3
To examine the sensitivity of the per-turn reward
R(1) Phase (1) + Ree((!) Reons (1);
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Table 11: Sensitivity of RegionReasoner variations in reward weights and B. Metrics are averaged over multi-turn detection (Det) and segmentation (Seg) on the RefCOCO+ and ReiCOCOg benchmarks.
a/ Setting
RefCOCO+ Det
RefCOCOg Det
RefCOCO+ Seg
RefCOCOg Seg
a= 1.0, / = 0.5
79.7
77.6
68.1
65.7
0.5, _ = 1.0
79,9
77.7
68.4
65.9
1.5, 8= 1.0
80.4
78.1
69.2
66.2
1.0, 1.5
80.2
78.0
68.9
66.0
a 1.0, B 1.0 (default)
80.7
78.2
69.6
66.5
we conduct small-scale study varying the coefficients and 3 around the default setting used throughout the main paper (a = 1.0). All reward components are normalized to the range [0, 2). so setting both coefficients to 1 1.0 provides balanced weighting between reference-citation fidelity and global-local semantic consistency.
Table 11 reports performance when either coefficient is halved or increased by 50% while holding the other fixed. Across all four cetrics-ddetcctoe and segmentation on RefCOCO+ and RefCOCOg- the overall trends remain stable. Increasing a slightly improves robustness at deeper turns by strengthening reference grounding, while increasing 3 slightly improves performance scenes with weaker spatial cues. The balanced setting B 1.0 offers the best trade-off across datasets and metrics, without requiring dataset-specific tuning. The results indicate that RegionReasoner robust to moderate changes in reward weighting, and the default balanced configuration is effective choice across all benchmarks.
I LIMITATIONS
Our consistency reward relies on lightweight keyword extraction and a hand-crafted logic prior, which may miss paraphrases subtle relations. Grounding enforced via boxes and points rather than full masks, and our constrained schema may introduce sensitivity to formatting. Extend- ing RegionReasoner richer relation graphs, mask-level grounding, longer dialogues and videos, and learnable entailment-based consistency is a promising direction. In the meantime, we hope RegionDial-Bor -Bacch and RegionReasoner establish strong baseline that spurs further research on interpretable, setrence-grounder multi-round visual reasoning.
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Reviews:
[{"review_id": "DCcsb4dm9w", "reviewer": "Reviewer_5kz9", "summary": "This paper investigates the problem of grounding visual referents in multi-turn dialogues for vision-language models (VLMs). They introduce RegionDial-Bench, a benchmark for evaluating multi-round question-answering where each response must be grounded in a specific object instance within the image, annotated via bounding boxes. Alongside the benchmark, they propose RegionReasoner-7B, a model trained using a GRPO-based reinforcement learning approach. The reward function incorporates three key objectives: correctness of the object grounding, global-local semantic consistency, and answer accuracy. Experimental results on RegionDial-Bench demonstrate the effectiveness of the proposed method.", "questions": "1. How was RegionDial-Bench constructed? should detail the data sources, annotation protocols, and key statistics.\n\n2. Does RegionReasoner generalize to other VQA benchmarks beyond RegionDial-Bench? Evaluation on established benchmarks (e.g., V*) is needed to verify its broader applicability.\n\n3. Why does multi-round conversation performance consistently lag behind single-round? Furthermore, what explains the outlier for RefCOCOg Multi-turn (R6) in Table 1?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. This paper introduces RegionDial-Bench, a new benchmark designed to study multi-round conversational reasoning in VLMs, with a specific focus on the groundedness of evidential objects in each dialogue turn.\n\n2. The authors propose a GRPO-based training framework that rewards models for accurate object grounding, global-local semantic consistency, and answer correctness. Experimental results demonstrate the effectiveness of their resulting model, RegionReasoner, on the proposed benchmark.", "weaknesses": "1. The creation process of RegionDial-Benchmark, which constitutes a major contribution of this work, is not sufficiently detailed in the paper. The authors should include a clear description of the benchmark construction methodology， such as data sources, annotation protocols, and key statistics (e.g., number of dialogues, turns, and object categories)，to  facilitate wider adoption.\n\n2. The evaluation of RegionReasoner is currently limited to the proposed RegionDial-Bench. To better assess the generalizability of the method, it is important to also report performance on established benchmarks such as V*. Without such results, it remains unclear whether the improvements are specific to the proposed benchmark or reflect broader applicability.\n\n3. The multi-round conversation results in Table 1 are notably lower than those in the single-round setting, which appears strange. Furthermore, the result for RefCOCOg Multi-turn (R6) stands out as an outlier, being significantly higher than those of R5 and R7. These inconsistencies warrant further analysis and explanation.\n\n4. As shown in Table 3, the model consistently performs better in single-round settings compared to multi-round scenarios across multiple metrics. This recurring pattern suggests a systematic challenge in handling multi-turn grounded dialogues, which should be explicitly discussed in the paper.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "pxgmn3us9p", "reviewer": "Reviewer_y9ox", "summary": "This paper presents a multi-round visual reasoning benchmark for detection and segmentation. They propose a grounded reasoning method, RegionReasoner, which incorporates reinforcement learning and a global-local consistency reward to enhance semantic coherence. On RegionDial-Bench, the proposed method achieves improvement compared to other VLMs, especially in the later turns.", "questions": "- How did you generate the questions, or did you use the templates in Table 5?\n- In the later turns, do you provide the ground truth box of the previous object? \n- Could you compare your methods on standard REC and RES benchmarks?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- This paper presents an interesting reasoning task that integrates QA, referring expression in a multi-turn manner. \n- They propose new reward functions for the new task. They propose a global-local consistency reward to align keywords from the global and local context.", "weaknesses": "- The way they expand the referring expression to multiple turns is confusing and may not be natural. In Appendix B, they illustrate how to simply use a preposition + bbox coordinates in the later turns. A natural referring expression considers the composition between objects. However, in the qualitative examples, they have more complicated and natural questions, such as \"Which slice of pizza is R1 about to eat\"? \"Who is the person next to R1\"? They mention that those GPT-style questions used in the previous paper may hallucinate, but it is unclear how they convert the question to this.\n- The task setting may not be challenging enough or fair. 1) If the latter turn is based on the ground-truth previous turn (as Appendix B), then the task is essentially a regular single-turn QA, which is not novel. 2) If the latter turn is based on the previous turn, then the reason other models can not achieve good performance is that they are not trained on these templates. If we feed the ground-truth in the question, RegionReasoner may not perform much better than previous methods. It would be nice to see the comparison with the provided ground-truth of the previous step object.\n- It is unclear if the new training data affects the performance on standard REC and RES benchmarks.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "hyVdjqNW8l", "reviewer": "Reviewer_qvqc", "summary": "This paper extends a previous work VisionReasoner by adapting to the multi-round setting. They present RegionReasoner, which is a reinforcement learning framework that uses SegLLM to bring multi-round interactions. To validate RegionReasoner, the authors also introduce a new benchmark RegionDial-Bench, which is designed to test the multi-round reasoning ability. The main tasks focus on detection and segmentation. In each round of reasoning, the model can refer to information such as box coordinates of previous rounds, and thus provide more grounded reasoning. In each round, RegionReasoner generates a structured text action includes scene, focus, think and answer, and the memory is updated accordingly. RegionReasoner forces the reasoning to cite evidence to reduce the hallucination by adding the reference citation reward. RegionReasoner-7B outperforms VisionReasoner-7B and other VLMs such as QwenVL-7B in multi-round detection and segmentation tasks on RegionDial-Bench.", "questions": "Some of the figures should be polished. For example, the text in Figure 2 is not clear enough when zooming in.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "(1) RegionReasoner extends a strong previous single-round model VisionReasoner and adapts to the challenging multi-round setting. Results on the proposed benchmark show the validity of RegionReasoner. \n\n(2) The benchmark itself can be used later form multi-round vision reasoning studies. The motivation of referring to object locations is direct and clear.", "weaknesses": "(1) The paper claims \"RegionReasoner consistently outperforms strong Vision-Language Models and task-specific baselines on both referring segmentation and detection.\". Previous benchmarks focus on single-round detection/segmentation, but in the main table 1 and table 2, the results are shown on the proposed multi-round benchmark. I think it would be reasonable to add the table to show some \"task-specific baselines\" for the previous single-round benchmarks. \n\n(2) Also, the proposed benchmark uses RefCOCO+ and RefCOCOg, but there are also other benchmarks such as MSCOCO and Visual Genome, which are diverse and have boxes and segmentations masks. Have the authors tried to use other datasets to construct the benchmark? Why RefCOCO+ and RefCOCOg are selected here?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "esDMKF714D", "reviewer": "Reviewer_9M2T", "summary": "The paper introduces RegionReasoner, an RL-trained vision–language policy that emits structured per-turn trajectories for multi-round, region-grounded visual reasoning. \nTwo novel reward components are proposed: (1) an explicit reference-citation reward that forces <think> to verbatim-cite bbox coordinates and penalizes hallucinated citations, and (2) a global–local semantic consistency reward that aligns keywords across <scene>, <focus>, and <think>. \nThe authors also present RegionDial-Bench, a multi-turn benchmark built from RefCOCO+/RefCOCOg, and show that RegionReasoner-7B improves multi-turn detection and segmentation metrics, especially at later turns.", "questions": "1. How about the ablations with different reward weight hyperparameters $\\alpha$, $\\beta$?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "1. Reward design — The reference-citation and global–local consistency rewards are intuitive, easily implementable, and well tied to the structured output format. They provide fine-grained shaping for intermediate (reasoning trace) tokens rather than only final outputs.\n2. Results in Tables 1 and 2 show consistent improvements, and the claim that improvements compound at later turns (robustness to error accumulation) is supported by both quantitative and qualitative examples.", "weaknesses": "1. The authors state that test dialogues reformulate later queries to explicitly cite bounding boxes predicted in earlier rounds. If the test references are model-predicted boxes (rather than strictly ground truth), the evaluation can be sensitive to the upstream model used to generate them. This raises two issues:  inconsistent comparison if different baselines consume different predicted references, and potential leakage effects. Please clarify exactly how test references are created and ensure all methods receive the same predicted references (or show oracle vs. predicted-reference performance). \n\n2. The global–local consistency reward depends on a handcrafted lightweight keyword extractor + lemmatizer + noun filter. This may be brittle: paraphrases, synonyms, pronouns, coreference, or longer expressions are likely missed. More importantly, if baselines do not produce structured <scene>/<focus> text, how is the comparison fair? Forcing <think> to repeat the same noun form may advantage RL-trained models.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)