IC-Seg: Versatile Segmentation Methods

• IC-Seg is a learning paradigm where segmentation is conditioned on a few labeled examples, enabling zero-shot and few-shot performance by leveraging a frozen vision model.
• IC-Seg also denotes an interactive framework that clarifies ambiguous referring queries through multi-turn dialogue, improving mask accuracy.
• IC-Seg further applies to automated segmentation of integrated-circuit metal lines from SEM imagery, using topology-aware losses to preserve electrical connectivity.

Searching arXiv for the specified papers to ground the article. IC-Seg is not a single, universally fixed term in the arXiv literature. It has been used in at least three closely related but technically distinct senses: as shorthand for in-context segmentation, a learning paradigm in which segmentation is conditioned on a few labeled examples at inference time; as the name of an agentic referring segmentation framework that resolves ambiguous natural-language queries through multi-turn clarification; and, in hardware assurance, as shorthand for integrated-circuit segmentation, specifically the segmentation of IC metal lines from SEM imagery. These usages share a concern with task-conditioned mask prediction, but they differ in supervision signals, interaction protocols, backbone assumptions, and evaluation criteria (Meng et al., 2023, Yang et al., 17 May 2026, Gehrmann et al., 17 Mar 2026).

1. Terminological scope and disambiguation

Recent work uses “IC-Seg” in three distinct research contexts.

Usage	Definition in the literature	Representative paper
In-context segmentation	A segmentation model solves a new, task-specific segmentation problem on a target image by conditioning on a few labeled example images without retraining the backbone	"SEGIC: Unleashing the Emergent Correspondence for In-Context Segmentation" (Meng et al., 2023)
Agentic clarification for referring segmentation	A framework that proactively clarifies user intent through multi-turn conversation before segmentation	"$\textit{Don't Guess, Just Ask}$: Resolving Ambiguity in Referring Segmentation via Multi-turn Clarification" (Yang et al., 17 May 2026)
Integrated-circuit segmentation	Automated segmentation of integrated circuit metal lines from scanning electron microscope imagery	"SAMSEM -- A Generic and Scalable Approach for IC Metal Line Segmentation" (Gehrmann et al., 17 Mar 2026)

The first usage is conceptual: IC-Seg denotes a learning paradigm that parallels in-context learning in NLP. The second is nominal: IC-Seg is the explicit name of a system for ambiguous referring segmentation. The third is domain-specific shorthand, where IC-Seg refers to segmentation in the integrated-circuit reverse-engineering and verification pipeline. A plausible implication is that the acronym now functions more as a family resemblance across segmentation settings than as a uniquely identifying term.

2. IC-Seg as in-context segmentation

In-context segmentation is formulated as predicting a binary mask $y \in \{0,1\}^{H \times W}$ for a target image $I \in \mathbb{R}^{3 \times H \times W}$ conditioned on $K$ labeled in-context examples $\{x^r\}^K = \{(I^r, y^r)\}^K$, without task-specific fine-tuning of the backbone (Meng et al., 2023). The model extracts general-purpose features from a frozen vision foundation model, builds dense correspondences between the target and the examples, and distills example information into explicit instructions that condition a lightweight mask decoder. This differs from standard supervised segmentation, which requires many labeled samples from the target task and full training, and from few-shot segmentation, which typically fine-tunes or retrains task-specific parameters on support sets (Meng et al., 2023).

The paper "SEGIC: Unleashing the Emergent Correspondence for In-Context Segmentation" introduces an end-to-end segment-in-context framework built on a single frozen VFM, using DINOv2 as its default VFM with ViT-B/L/G backbones (Meng et al., 2023). The framework comprises four stages: feature extraction, dense correspondence discovery, instruction extraction, and mask decoding. For a target image $I$ and reference image $I^r$, SEGIC computes frozen features

$f = F(I), \qquad f^r = F(I^r),$

then obtains a pixel-level correspondence matrix

$C = \mathrm{Upsample}(\mathrm{Dist}(f, f^r)),$

where $\mathrm{Dist}$ computes pairwise cosine distances or similarities between patch tokens across the two images (Meng et al., 2023).

From these correspondences, SEGIC extracts three instruction types. Geometric instructions propagate the reference mask to the target,

$y \in \{0,1\}^{H \times W}$0

and derive sparse anchors via top-$y \in \{0,1\}^{H \times W}$1 positional encodings $y \in \{0,1\}^{H \times W}$2. Visual instructions pool reference features under the mask,

$y \in \{0,1\}^{H \times W}$3

Meta instructions encode language hints with a pre-trained CLIP text encoder,

$y \in \{0,1\}^{H \times W}$4

The instruction set is $y \in \{0,1\}^{H \times W}$5 (Meng et al., 2023).

The mask decoder is query-based and uses a single learnable object query $y \in \{0,1\}^{H \times W}$6. Instructions are projected into decoder latent spaces by dedicated modules:

$y \in \{0,1\}^{H \times W}$7

followed by fusion

$y \in \{0,1\}^{H \times W}$8

Internally, decoding alternates query self-attention, image-to-query cross-attention, query-to-image cross-attention, and an output head that predicts the final mask from the enhanced image feature and the first element of the query sequence (Meng et al., 2023).

Training freezes the VFM and optimizes only the mask decoder with

$y \in \{0,1\}^{H \times W}$9

computed on $I \in \mathbb{R}^{3 \times H \times W}$0 selected points via importance sampling, specifically 12544 points per mask (Meng et al., 2023). Two robustness strategies are introduced: context reversion, which randomly swaps target and reference during training, and negative entity augmentation, in which non-target entities in the same image serve as negatives in settings with multiple exclusive entities (Meng et al., 2023).

SEGIC is trained jointly on COCO, ADE20k, and LVIS, with datasets sampled uniformly at 160K samples per epoch in the main experiments, using PyTorch on $I \in \mathbb{R}^{3 \times H \times W}$1V100 GPUs, batch size 32, AdamW with weight decay $I \in \mathbb{R}^{3 \times H \times W}$2, initial learning rate $I \in \mathbb{R}^{3 \times H \times W}$3 decayed by $I \in \mathbb{R}^{3 \times H \times W}$4 at epoch 10, and inputs cropped or padded to $I \in \mathbb{R}^{3 \times H \times W}$5 (Meng et al., 2023). The framework is described as extremely memory efficient due to the frozen backbone, typically using less than 10 GB (Meng et al., 2023).

Empirically, SEGIC sets state of the art on one-shot segmentation benchmarks and generalizes to video object segmentation and open-vocabulary segmentation (Meng et al., 2023). Reported results include COCO-$I \in \mathbb{R}^{3 \times H \times W}$6 mean mIoU of 76.1 for SEGIC-L and 74.5 for SEGIC-G, LVIS-$I \in \mathbb{R}^{3 \times H \times W}$7 mIoU of 44.6 for L and 47.8 for G, and FSS-1000 zero-shot mIoU of 86.8 for L and 88.4 for G (Meng et al., 2023). Zero-shot VOS performance reaches DAVIS-17 $I \in \mathbb{R}^{3 \times H \times W}$8 of 71.4 for L and 73.7 for G, and YouTube-VOS-18 $I \in \mathbb{R}^{3 \times H \times W}$9 of 62.7 for L and 65.4 for G (Meng et al., 2023). Ablations show that geometric and visual instructions are crucial for out-of-domain generalization, while meta instructions help in-domain performance; combining all three performs best across tasks (Meng et al., 2023).

This formulation makes dense correspondence a first-class primitive in visual in-context learning. The paper explicitly argues that models with better zero-shot semantic correspondence yield better IC-Seg performance, and that high or multi-resolution support is essential; models lacking it, such as CLIP ViT-B and MAE ViT-B in the frozen setting, yield trivial IC-Seg performance (Meng et al., 2023).

3. IC-Seg as an agentic framework for ambiguous referring segmentation

A second usage of IC-Seg refers to a framework for referring image and video segmentation under ambiguous natural-language queries. Here the core problem is not few-shot conditioning from labeled exemplars, but intent clarification: a single query $K$0 may match multiple candidate objects in the scene, although only one is intended by the user (Yang et al., 17 May 2026). For images, the input is a single frame $K$1 and query $K$2; for videos, a frame sequence $K$3 and query $K$4. The system must first interact to clarify intent and then produce a segmentation of the intended object, namely a mask $K$5 for images or a per-frame sequence $K$6 for videos (Yang et al., 17 May 2026).

The architecture consists of a policy MLLM $K$7 based on Qwen3-VL-Instruct-4B or 8B, a dialogue manager and intent memory, a constraint or attribute extractor, a frozen SAM2-Large segmentation executor, and a user simulator $K$8 based on Qwen3-VL-32B (Yang et al., 17 May 2026). The policy detects ambiguity, asks targeted clarifying questions, decides when to stop asking, selects a keyframe, and emits a 2D bounding box and an interior point. A frozen SAM2 then propagates the mask to all frames. The action space includes AskQuestion, RequestConfirmation, TriggerSegmentation, and Stop, with a hard cap of 5 turns during both training and inference (Yang et al., 17 May 2026).

The decision process is explicitly multi-turn. The policy reads the frames and query, detects ambiguity, proposes the most discriminative question if ambiguity exists, consumes the user answer, updates intent memory, reassesses ambiguity, and repeats until a unique target is identified or the turn limit is reached; it then selects a keyframe, outputs a bounding box and point, and invokes SAM2 (Yang et al., 17 May 2026). The framework is thus “clarification-first,” contrasting with non-interactive systems that commit to a guess from the initial query alone.

Optimization is based on Hierarchical Group Relative Policy Optimization (Hi-GRPO), introduced to address the sparse credit-assignment problem of multi-turn agentic interaction (Yang et al., 17 May 2026). The GRPO objective is given as

$K$9

with sampled group trajectories $\{x^r\}^K = \{(I^r, y^r)\}^K$0 and importance ratio $\{x^r\}^K = \{(I^r, y^r)\}^K$1 as printed in the paper (Yang et al., 17 May 2026).

Hi-GRPO injects supervision at three levels. The trajectory-level reward is

$\{x^r\}^K = \{(I^r, y^r)\}^K$2

where IoU and box are binary constraints, point rewards validity relative to the ground-truth box and point, and keyframe rewards choosing a salient frame (Yang et al., 17 May 2026). The turn-level reward is

$\{x^r\}^K = \{(I^r, y^r)\}^K$3

with entropy reduction

$\{x^r\}^K = \{(I^r, y^r)\}^K$4

and interaction efficiency

$\{x^r\}^K = \{(I^r, y^r)\}^K$5

where $\{x^r\}^K = \{(I^r, y^r)\}^K$6 is approximated as $\{x^r\}^K = \{(I^r, y^r)\}^K$7 under a uniform prior over the remaining candidate count $\{x^r\}^K = \{(I^r, y^r)\}^K$8 (Yang et al., 17 May 2026). The step-level guidance uses the privileged or student probability ratio

$\{x^r\}^K = \{(I^r, y^r)\}^K$9

where $I$0 is expert diagnostic guidance from $I$1 (Yang et al., 17 May 2026).

The hierarchical advantage is defined in the paper as

$I$2

with $I$3 weighting the turn-level objective, $I$4 and $I$5 the group mean and standard deviation, $I$6 the step-level mixing coefficient, and $I$7 indicating direction (Yang et al., 17 May 2026). The paper states that additional KL and entropy regularization are disabled, although PPO-style clipping remains inside GRPO (Yang et al., 17 May 2026).

The benchmark for this setting is Ambi-RVOS, constructed from ReVOS and MeViS to contain ambiguous user queries in dense scenes (Yang et al., 17 May 2026). It contains 1500 curated test samples and three difficulty buckets: 400 simple samples with exactly two candidates, 600 medium samples with three to five candidates, and 500 difficult samples with six or more candidates (Yang et al., 17 May 2026). Evaluation for video uses the DAVIS-style metrics region similarity $I$8, contour accuracy $I$9, and average $I^r$0; image tasks use gIoU and cIoU (Yang et al., 17 May 2026).

On Ambi-RVOS, IC-Seg-8B achieves 55.1 $I^r$1, with 51.9 $I^r$2 and 58.3 $I^r$3, compared with 36.5 $I^r$4 for Qwen3-VL-8B*; the 4B variant achieves 52.0 versus 30.4 for Qwen3-VL-4B* (Yang et al., 17 May 2026). Across difficulty levels, IC-Seg-8B reports 63.7 on simple, 57.4 on medium, and 45.5 on difficult (Yang et al., 17 May 2026). On ReasonSeg, IC-Seg-8B achieves validation gIoU 72.3 and cIoU 74.6, and test gIoU 66.7 and cIoU 60.2; on ReasonVOS it reaches 60.9 $I^r$5, indicating that interactive training does not degrade standard reasoning segmentation performance (Yang et al., 17 May 2026).

Ablation results attribute the gain primarily to the hierarchical reward design. For IC-Seg-4B on Ambi-RVOS, trajectory-only training yields 46.4 $I^r$6, while the full combination of $I^r$7, $I^r$8, and step-level guidance yields 52.0 (Yang et al., 17 May 2026). Efficiency analysis reports an average of 2.5 clarification turns, 2.2 seconds per sample, and a training cost of 333.5 seconds per step for IC-Seg; removing the step-level or turn-level components increases turns and latency while reducing accuracy (Yang et al., 17 May 2026).

This system redefines “IC-Seg” around interaction rather than static conditioning. The central design claim is that ambiguous referring segmentation should ask rather than guess, and the empirical evidence is organized around dialogue quality, ambiguity reduction, and final grounding fidelity rather than solely mask quality (Yang et al., 17 May 2026).

4. IC-Seg as integrated-circuit metal line segmentation

In hardware assurance and reverse engineering, IC-Seg refers to the automated segmentation of integrated-circuit metal lines from scanning electron microscope imagery (Gehrmann et al., 17 Mar 2026). The task is defined on a grayscale SEM image $I^r$9 of a metal layer: segment the set of pixels belonging to metal lines $f = F(I), \qquad f^r = F(I^r),$0 while suppressing the background $f = F(I), \qquad f^r = F(I^r),$1, producing a binary mask $f = F(I), \qquad f^r = F(I^r),$2 of the same size as $f = F(I), \qquad f^r = F(I^r),$3 (Gehrmann et al., 17 Mar 2026). The motivation is explicitly electrical rather than merely visual: the relevant downstream quantities are connectivity preservation, netlist recovery, and detection of malicious or unintended circuitry. As the paper emphasizes, pixel-level segmentation accuracy is useful but insufficient; hardware assurance depends on electrical correctness, measured in terms of opens, shorts, false positives, and false negatives per metal line (Gehrmann et al., 17 Mar 2026).

The domain challenges are substantial. The dataset spans technology nodes from approximately 200 nm down to approximately 20 nm, varied manufacturing materials and processes, layer-specific topology, vias that introduce local brightness and shadow artifacts, sample-preparation defects, and SEM heterogeneity arising from detector types, magnification, dwell time, resolution, and acceleration voltage (Gehrmann et al., 17 Mar 2026). Original SEM images often exceed $f = F(I), \qquad f^r = F(I^r),$4 pixels, so naive downscaling can fuse closely spaced lines and create shorts (Gehrmann et al., 17 Mar 2026).

"SAMSEM -- A Generic and Scalable Approach for IC Metal Line Segmentation" adapts SAM2 to this setting (Gehrmann et al., 17 Mar 2026). It uses the SAM2 large checkpoint and fine-tunes the image encoder, mask decoder, and prompt encoder; ablations show that the image encoder has the largest impact, reducing ESD to 0.8% when fine-tuned alone, whereas prompt encoder tuning alone has little impact at 66.1% ESD (Gehrmann et al., 17 Mar 2026). The best in-distribution result is obtained by fine-tuning all three components, reaching 0.7% ESD with negligible training-time overhead compared to image-encoder-only fine-tuning (Gehrmann et al., 17 Mar 2026).

SAMSEM is explicitly multi-scale. Model BoxOne operates on full images resized to $f = F(I), \qquad f^r = F(I^r),$5, preserving global context but risking line fusion under downscaling. Model BoxTwo operates on $f = F(I), \qquad f^r = F(I^r),$6 patches from the original image with at least 10% overlap, then upsamples each patch to $f = F(I), \qquad f^r = F(I^r),$7 before encoding, improving fine-structure separation but sometimes hallucinating spurious lines in low-context regions (Gehrmann et al., 17 Mar 2026). For each image, both masks are computed and compared patchwise. Speckle counting treats a patch as speckled if it contains at least 50 connected components of size at most 16 pixels. Two patches are said to agree if at least 60% of their pixels are identical (Gehrmann et al., 17 Mar 2026). The decision logic then selects BoxOne or BoxTwo per patch, or flags manual inspection when both are speckled (Gehrmann et al., 17 Mar 2026).

The loss combines pixel-level and topology-aware terms. The pixel loss is

$f = F(I), \qquad f^r = F(I^r),$8

and the full segmentation loss is

$f = F(I), \qquad f^r = F(I^r),$9

with selected hyperparameters $C = \mathrm{Upsample}(\mathrm{Dist}(f, f^r)),$0 and $C = \mathrm{Upsample}(\mathrm{Dist}(f, f^r)),$1 (Gehrmann et al., 17 Mar 2026). The topology term is a Betti matching loss implemented through the topolosses package, using filtration type sublevel, barcode length threshold 0.345, and push_unmatched_to_1_0 = True (Gehrmann et al., 17 Mar 2026). The purpose is to favor electrical connectivity correctness by discouraging short and open circuits rather than optimizing only pixel overlap (Gehrmann et al., 17 Mar 2026).

The dataset comprises 48 metal layers across 14 ICs (Gehrmann et al., 17 Mar 2026). For ICs 1–7, the split is 70% train, 10% validation, and 20% test. ICs 8–14 are reserved for out-of-distribution testing only. A final model is trained on 90% of the images from all 14 ICs and evaluated on the remaining 10% (Gehrmann et al., 17 Mar 2026). Most of the dataset cannot be released for legal reasons, although one IC dataset published by Rothaug et al. is publicly available (Gehrmann et al., 17 Mar 2026).

Training uses PyTorch v2.7.1, CUDA 13.0, Optuna with a Hyperband pruner, AdamW, batch size 12, and H100 GPUs. Fine-tuning on the full dataset takes approximately 72 hours on a single H100 (Gehrmann et al., 17 Mar 2026). A two-stage HPO procedure is used: a pixel-only stage with 56 trials, of which 49 are pruned, followed by a topology stage with 75 trials, of which 58 are pruned (Gehrmann et al., 17 Mar 2026). Selected augmentation hyperparameters include an augmentation probability of 38.5%, intensity 0.61, and a transform set containing RandomResizedCrop, GaussianBlur, ColorJitter, RandomVerticalFlip, RandomHorizontalFlip, and GaussianNoise (Gehrmann et al., 17 Mar 2026).

The principal evaluation metric is the ESD error rate, described as the relative number of opens, shorts, false positives, and false negatives per metal line (Gehrmann et al., 17 Mar 2026). In-distribution on ICs 1–7, across 36,413 lines, SAMSEM reports 263 ESD errors, or 0.72%, with breakdown 27 opens, 146 shorts, 59 false positives, and 31 false negatives; pixel metrics are PA 0.972, Dice 0.960, and IoU 0.925 (Gehrmann et al., 17 Mar 2026). Out-of-distribution on ICs 8–14, across 15,153 lines, it reports 838 errors, or 5.53%, with PA 0.959, Dice 0.939, and IoU 0.888 (Gehrmann et al., 17 Mar 2026). When fine-tuned on all 14 ICs, it reaches 0.62% ESD error rate over 19,088 lines, with PA 0.971, Dice 0.960, and IoU 0.924 (Gehrmann et al., 17 Mar 2026).

The paper’s comparison to baselines is notable because pixel metrics alone can be misleading. In-distribution, SAMSEM achieves approximately six times fewer ESD errors than the next-best baseline, U-Net, while maintaining comparable pixel metrics (Gehrmann et al., 17 Mar 2026). Out-of-distribution, SAMSEM reduces unseen-IC ESD error rate to approximately 5.5%, compared with 24.77% for DeepLabV3, 27.16% for SAMIC, 38.38% for FCN, and 44.90% for U-Net (Gehrmann et al., 17 Mar 2026). This suggests that topology-aware optimization and multi-scale decision logic are better aligned with electrically meaningful correctness than conventional pixel-centric objectives alone.

5. Comparative methodological themes

Although the three usages of IC-Seg arise in different subfields, they exhibit a common architectural pattern: a large frozen or partly frozen foundation component is paired with a lightweight or task-specialized control mechanism.

In SEGIC, the frozen component is a vision foundation model, specifically DINOv2, and the trainable component is a lightweight query-based mask decoder conditioned on geometric, visual, and meta instructions (Meng et al., 2023). In the referring-segmentation IC-Seg system, the frozen component is SAM2-Large, while the trainable policy is a Qwen3-VL-based MLLM optimized for ambiguity detection, questioning, and final localization (Yang et al., 17 May 2026). In SAMSEM, the base model is SAM2 large, but the contribution lies in targeted fine-tuning plus a multi-scale inference wrapper and a topology-aware loss (Gehrmann et al., 17 Mar 2026).

A second shared theme is the replacement of undifferentiated end prediction with structured intermediate representations. SEGIC uses explicit instruction types $C = \mathrm{Upsample}(\mathrm{Dist}(f, f^r)),$2 distilled from correspondence and text (Meng et al., 2023). The ambiguity-resolving IC-Seg uses dialogue history, intent memory, candidate-set size estimates, and explicit action primitives such as AskQuestion and TriggerSegmentation (Yang et al., 17 May 2026). SAMSEM introduces patch-level speckle statistics, agreement heuristics, and electrical-significance-aware losses to guide mask assembly (Gehrmann et al., 17 Mar 2026).

A third theme is the use of evaluation protocols tailored to the actual deployment objective rather than to generic segmentation overlap alone. SEGIC reports mIoU and VOS metrics because its aim is generalization across one-shot, video, and open-vocabulary segmentation (Meng et al., 2023). The interactive IC-Seg system adds dialogue efficiency, ambiguity reduction, and user-simulator-based evaluation on Ambi-RVOS, while still reporting $C = \mathrm{Upsample}(\mathrm{Dist}(f, f^r)),$3, $C = \mathrm{Upsample}(\mathrm{Dist}(f, f^r)),$4, gIoU, and cIoU (Yang et al., 17 May 2026). SAMSEM centers ESD error rates because opens and shorts directly affect netlist extraction fidelity and assurance analyses, even when PA or IoU remain high (Gehrmann et al., 17 Mar 2026).

These parallels do not imply methodological equivalence. A plausible implication is instead that “IC-Seg” labels a broader shift toward segmentation systems that are conditioned, interactive, or constraint-aware, rather than purely feed-forward predictors of per-pixel labels.

6. Limitations, failure modes, and open directions

Each line of work identifies distinct limitations. SEGIC currently uses one in-context example per entity and leaves multi-example aggregation for future work (Meng et al., 2023). Its performance depends on the chosen VFM’s correspondence quality and its ability to process high or multi-resolution inputs; poor correspondences or severe domain shifts can degrade geometric label propagation (Meng et al., 2023). The paper also notes sensitivity to the number and quality of top-$C = \mathrm{Upsample}(\mathrm{Dist}(f, f^r)),$5 points, masking noise, and class imbalance, and that meta instructions can hurt out-of-domain tasks if textual cues misalign with visual domains (Meng et al., 2023).

The agentic IC-Seg system is limited by its current reliance on text-based interaction (Yang et al., 17 May 2026). The paper identifies multimodal interaction such as pointing gestures, audio cues, and clicks as a future direction, and notes that domain shifts or noisy simulators can affect entropy estimation and turn-efficiency rewards (Yang et al., 17 May 2026). Scalability is also identified as an issue: while the reported inference latency and turn count are moderate, broader deployment may require more sample-efficient training and further optimization (Yang et al., 17 May 2026). Ethical considerations explicitly include preventing over-questioning, ensuring accessibility, and preserving privacy in interactive scenarios (Yang et al., 17 May 2026).

SAMSEM is constrained by ambiguous ground truth, limited exploration of alternative topology losses due to compute cost, and restricted dataset availability because most of the underlying IC imagery cannot be released (Gehrmann et al., 17 Mar 2026). Reported failure cases include image-to-ground-truth misalignment, via shadows and missing vias, and delayering defects that leave only outlines and obscure true connectivity (Gehrmann et al., 17 Mar 2026). The paper also notes that topology loss improves in-distribution ESD but does not strictly dominate pixel-only loss out of distribution, where pixel-only training yields 5.21% ESD versus 5.53% for the blended loss, with different tradeoffs among shorts, false positives, and opens (Gehrmann et al., 17 Mar 2026).

Across all three usages, the central open problem is not merely better mask prediction. In SEGIC it is broader visual in-context learning from limited exemplars; in the agentic system it is robust intent disambiguation under realistic user behavior; in SAMSEM it is electrically faithful segmentation under severe domain heterogeneity. This suggests that the continuing evolution of IC-Seg will depend less on a single architecture family than on how segmentation systems represent task context, uncertainty, and deployment-specific correctness criteria.