Automatic Image-Level Morphological Trait Annotation for Organismal Images
Authors: Vardaan Pahuja
Samuel Stevens
Alyson East
Sydne Record
Yu Su
Venue: ICLR 2026 Poster (2026)
Keywords: morphological traits
morphological trait annotation
ecology
trait description generation
Primary area: datasets and benchmarks
TL;DR: We develop a trait annotation pipeline that leverages sparse autoencoders to generate interpretable morphological trait descriptions from ecological images.

Abstract: Morphological traits are physical characteristics of biological organisms that provide vital clues on how organisms interact with their environment. Yet extracting these traits remains a slow, expert-driven process, limiting their use in large-scale ecological studies. A major bottleneck is the absence of high-quality datasets linking biological images to trait-level annotations. In this work, we demonstrate that sparse autoencoders trained on foundation-model features yield monosemantic, spatially grounded neurons that consistently activate on meaningful morphological parts. Leveraging this property, we introduce a trait annotation pipeline that localizes salient regions and uses vision-language prompting to generate interpretable trait descriptions. Using this approach, we construct Bioscan-Traits, a dataset of 80K trait annotations spanning 19K insect images from BIOSCAN-5M. Human evaluation confirms the biological plausibility of the generated morphological descriptions. We assess design sensitivity through a comprehensive ablation study, systematically varying key design choices and measuring their impact on the quality of the resulting trait descriptions. By annotating traits with a modular pipeline rather than prohibitively expensive manual efforts, we offer a scalable way to inject biologically meaningful supervision into foundation models, enable large-scale morphological analyses, and bridge the gap between ecological relevance and machine-learning practicality.

Published as a conference paper at ICLR 2026
AUTOMATIC IMAGE-LEVEL MORPHOLOGICAL TRAIT ANNOTATION FOR ORGANISMAL IMAGES
Vardaan Pahuja' Samuel Stevens'
Alyson East Sydne Record?
Yu Su'
'The Ohio State University UUiversitty of Maine
pahuja. ouosu. edu
ABSTRACT
Morphological traits are physical characteristics of biological organisms that pro- vide vital clues on how organisms interact with their environment. Yet extracting these traits remains slow, expert-driven process, limiting their use in large-scale ecological studies. A major bottleneck the absence of high-quality datasets linking biological images to trait-level annotations. In this work, demonstrate that sparse autoencoders trained on foundation model features yield monosemantic, spatially grounded neurons that consistently activate meaningful morphological parts. Leveraging this property, we introduce a trait annotation pipeline that local- izes salient regions and uses vision-language prompting generate interpretable DASCAN-TRAITI a dataset
trait descriptions. Using this approach, we construct of 80K trait annotations spanning 19K insect images from BIOSCAN-5M. Human evaluation confirms the biological plausibility of the generated morphological descriptions. We assess design sensitivity through a comprehensive ablation study, systematically varying key design choices and measuring their impact on the qual- ity of the resulting trait descriptions. By annotating traits with a modular pipeline rather than prohibitively expensive manual efforts, we offer scalable way to inject biologically meaningful supervision into foundation models, enable large-scale morphological analyses, and bridge the gap between ecological relevance and machine- learning practicality!
INTRODUCTION
The accelerating biodiversity crisis demands rapid advancement in our understanding of ecosystem function and species' responses to environmental change. While taxonomic identification answers the question "what species is this?", it fails to explain why organisms succeed or fail under changing conditions. Morphological traits (the measurable physical characteristics of organisms) provide this critical mechanistic link, predicting with remarkable accuracy how species interact with their environment (Diaz al., 2016 Kennedy et al.) 2020) McGill et al. 2006). Morphological traits can predict species' ecological niches and functions with up to 85% accuracy (Pigot et al. 2020), offering insights into resource utilization and potential responses to disturbance. Despite their paramount importance, trait data remains trapped in an analog bottleneck: millions of biological specimens and images exist in collections worldwide, but extracting standardized trait measurements requires painstaking manual work by domain experts (Violle et al. 2007), rendering large-scale trait-based ecology virtually impossible.
Measuring even simple characters such as body length tibia ratio still takes minutes per specimen despite modern digitization techniques (Hardisty et al., 2022). Natural-history institutions curate 3B+ specimens, so a full trait census would consume person-centuries of expert labour (Nelson & Ellis] 2019). Protocols differ by taxon (wing chord for birds, elytral lengths for beetles, sepal length for plants, etc) and this heterogeneity. combined with observer subjectivity, introduces systematic bias that complicates data synthesis (Heberling) 2022). Even when traits are quantified, they often remain in notebooks image captions, invisible machine pipelines, leaving a global "trait data desert" that blocks large-scale trait ecology studies.
Cooee and data available gi thub. http:///wwg...eeeee////aaaattaaa

---

Published as a conference paper at ICLR 2026
Prompt
Unlabeled specimen images
Bounding over monosemantic features
Filtered Traits
Pipeline
Multimodal LLM
VIT
Patchilevel activations
SAE
Potch Activation Vectors
"Wing: Transparent, thin, elongated, with visible veins."
"The anternae thin, elongated, segmented and dark brown."
"Phorex: segmented, dark Drown, smooth texture"
Grounded, image-trait pairs
Figure Given an input specimen image, we first compute dense visual representations using an off-the-shelf backbone (e.g., DINOv2). These features are passed through pre-trained sparse autoencoder (SAE), which identifies high-activation latent units corresponding to semantically meaningful regions (Algorithm T). We extract the spatial masks associated with these activations and overlay them on the original image to localize trait-relevant boxes. Finally, a multimodal language model (MILM) is prompted with the annotated image to generate fine-grained morphological trait descriptions. This results in a large-scale, automatically labeled image-level trait dataset.
Automating trait mining pushes ML into worst-case regime. First, biology's cross-taxon hetero- geneity means the feature manifold warps whenever one moves from, say, angiosperm leaves to wasp antennae: He et al. (2024) lists this taxonomic domain shift as the single largest unsolved barrier to reliable pipelines. Digitized specimens further exhibit uncontrolled pose, preservation artifacts, and background clutter, factors that amplify distribution shift and explode the sample complexity demanded by supervised learning. Second, systematic review of 50+ herbarium-vision papers finds that apparently "simple" tasks (leaf area, margin type) still need bespoke augmentation recipes and hyper-parameter sweeps for every dataset, with method transferring cleanly across collections (Hussein et al. 2022). Third, even mature semi-automated tools, such as Inselect (Hudson et al. 2015) for drawer segmentation, end up handing users GUT for redrawing boxes; human operators spent 108 seconds per image correcting model outputs. Together, these observations show that standard supervised learning struggles when labels are scarce, morphology non-stationary, and objects occupy only tiny, variable parts of the frame-precisell the conditions that trait ecology presents.
Our key insight is recognizing that sparse autoencoders (SAEs) be used interpretable part-detectors for trait extraction. A sparse autoencoder learns, from unlabeled data, a dictionary of latent units that can linearly reconstruct frozen foundation-mode embeddings while enforcing two pressures: (i) sparsity: only a few units fire for any image, and (ii) non-negativiry activations cannot cancel each other. These constraints push each latent unit toward single, reusable visual cause rather than mixture of unrelated cues. In practice, training an SAE over pre-trained image features produces units whose activations map back onto tight, spatially coherent regions such as "hind-leg femur band," "dorsal eye stripe," or "apical leaf tip." (see $4.4 example visualizations) After training, we can (1) isolate just the pixels that define a candidate trait, (2) visually indicate the relevant area, and (3) describe those areas with a vision- language model. To focus truly diagnostic parts, we introduce a species-contee contrastive ranking: a unit is valuable when it fires strongly for a target species but remains almost silent for closely related species. High-ranked units, therefore, highlight
2

---

Published as a conference paper at ICLR 2026
precisely the salient, fine-scale structures that taxonomists record as traits, making the SAE an ideal front end for our trait- ditt-distillaiion pipeline.
We instantiate these ideas in three-step, concrete, trait-labeling pipeline (Figure 1 and apply it to the BIOSCAN-SM insect corpus (Gharaee et al. 2024):
1. We rank SAE units by a species- contrasscvn score that privileges activations that are strong for focal species yet weak for its congeners.
2. High-score masks are boxed into tight patches.
3. Each patch is prompted to a large multimodal large language model (Qwen2.5-V1L-725) with a lightweight template.
Because the SAE provides locality and taxonomic focus before any language model is consulted, the MLLM's task is far easier: "describe this part" rather than "describe the whole scene", which sharply reduces hallucinations and background leakage. While BIOSCAN-5M provides the large-scale, species-labeled data for our experiments, the pipeline itself only requires images with taxonomic labels. Such supervision is widely available in many other domains (e.g.. iNaturalist (Homn et al. 2018), TreeOfLife (Stevens et al. 2024), Caltech-UCSD Birds-200-2011 (Wah et al.) 2011)). These resources span plants, birds, fungi, and other taxa, making our approach broadly applicable for converting labeled image repositories into rich, interpretable trait annotations. Using the best- performing configuration, we label 19K images with 80K morphological trait descriptions (averaging 4.2 traits per image), yiclding the BIOSCAN-TRAITS dataset. To evaluate robustness and design sensitivity, conduct comprehensive ablation study, systematically examining how individual design choices influence the quality of the resulting trait descriptions. As an initial validation, we fine-tune BioCLIP (Stevens al., 20244] Gu al. 2025). a biologically grounded vision-language foundation model on BIOSCAN- TRAITS and observe improved zero-shot species classification on an in-the-wild benchmark, highlighting the downstream potential of trait-level supervision.
In summary, we contribute (i) a species-contrastive unsupervised images into high-fidelity, spatially grounded trait labels and (ii) BIOSCAN-TRAITE large, open, image-trait dataset, and (iii) an initial downstream evaluation showing that fine-tuning a foundation model on BIOSCAN-TRAIT improves zero-shot species classification on an on-the-wile benchmark. By using a modular pipeline for trait annotation instead of expensive manual labeling, we provide a scalable way to incorporate biologically meaningful supervision into foundation models, support large-scale morphological analyses, and narrow the gap between ecological relevance and practical machine-learnine workflows.
-basedd algorithm that turns
2 RELATED WORK
Sparse Autoencoders. Sparse autoencoders (SAEs) (Makhzani & Frey 2014: 2015) have proven effective for uncovering disentangled and human-interpretable latent factors in high-dimensional representations (Stevens et al. 2025). Prior work has shown the utility of SAEs to learn improved image (Makhzami & Frey 2014 2013) and word representations (Subramanian et al. 2018) J To en- hance feature disentanglement and interpretability. several architectural variants have been proposed, including top-k activation mechanisms (Bussmann et al. (024) and multi-layer Matryoshka encoders designed to promote hierarchical concept structure (Bussmann al. (025). SAES have also been applied to the internal activations of transformer-base units aligned with semantically meaningful and interpretable concepts (Yun al.] [02T Bricken
language models, where they reveal latent
et al. 2023 Gao et al. 2025 Templeton et al. 2024). Recent work demonstrates that, when traindd on embeddings from large pretrained models, SAES can produce monosemantic features, latent units that respond consistently to a single semantic concept (Templeton et al. (024]] Pach et al. 2025). In
this work, we extend these insights to the domain of biological vision, using SAFs to construct a dataset of fine-grained morphological traits from organismal images.
Fine-grained Visual Recognition. Fine-grained visual recognition (FGVR) (Lin et al. 2015) aims to distinguish subordinate categories with small inter-class variation but large intra-class variation, where the most discriminative cues are often subtle and localized (e.g., texture, shape, or color patterns). As result, FGVR models particularly vulnerable to background correlations, viewpoint and pose changes, and the scarcity of expert annotations (Beery et al.] 20018). A major line of work therefore seeks to localize discriminative regions without dense part supervision, using either weak
3

---

Published as a conference paper at ICLR 2026
supervision (Hu al. 2019) self-supervised consistency signals (Huang et al. 2020 Wu et al. 2022). In real-world settings, FGVR must further contend with distribution shifts across environments, motivating benchmarks and methods that emphasize out-of-distribution (OOD) generalization (Beery et al. 2020 Koh et al., 2021 Pahuja et al. 2024). More recently, language has emerged as a useful interface for fine-grained semantics: models extract or generate part-level attributes and leverage MLLM reasoning to better align visual evidence with fine-grained category names (Liu et al., (2024). In this context, our work uses sparse autoencoders to automatically extract morphological traits, providing trait-level supervision that improves fine-graie fine-grained visual recognition.
Morphological Trait Extraction. Traditionally. morphological analysis has relied on manual measurements and qualitative trait descriptions process that is labor-intensive, time-consuming and dependent on domain expertise (Hunt & Pedersen, (025). While these methods offer valuable insights, they are inherently difficult to scale to large datasets. Recent approaches have begun to automate trait extraction by leveraging representation learning. For instance, |Hoyal Cuthill al. (2019) used convolutional triplet network to map images into phenotypic embedding space, enabling quantitative similarity measures and phenotypic tree reconstruction from purely visual data. More recent work has pushed further: deep models that segment relevant image regions (e.g., in herbarium scans (Ariouat et al.) 2025)) or learn latent representations (e.g., via VAEs (Tsutsumi et al. 2023)) show that rich morphological information can be captured without hand- engineered features. A key challenge, however, is developing models that remain robust to digitization artifacts and background clutter. while also offering interpretability so that ecologists can identify which morphological features drive predictions. Our work leverages SAEs to automatically extract morphological traits in BIOSCAN (Gharaee et al., 2024) specimen images. We posit that such trait-level supervision can enhance the robustness and generalizability of MLLMs for fine-graing grained taxonomic classification.
3 METHODOLOGY
3.1 BACKGROUND
Sparse autoencoders (SAEs) transform dense representations into sparse encodings, where each unit ideally corresponds an interpretable latent factor. Given a dense input vector z Rd from an intermediate layer of vision transformer, the autoencoder maps z to a high-dimensional sparse representation g=)., from which is subsequently reconstructed. This decomposition reveals structured latent factors while preserving the original information content, We use ReL.U autoencoders (Bricken al. 2023 Templeton et al. 2024) for our experiments.
u Welz ba) + be: g(z) ReL.Uu)) Σ Wag(z) + ba:
where W. Raxd b R", Wa E _dxx and ba E Rd. Here, W, Rad denotes the SAE encoder matrix that maps the dense backbone representation z E Rd to the pre-activation latent vector u R", and Wa E Raxn denotes the decoder matrix that maps the sparse code back to the reconstructed representation & E Rd. The encoder and decoder also include bias terms: b E R and ba E Rdd respectively.
The training objective minimizes the reconstruction enror while encouraging sparsity in the latent representation:
= 1z
2 R(g(2))))
where R denotes the sparsity regularizer and the sparsity coefficient (a) controls the trade-off between sparsity and reconstruction. We use DINOv2-base (Oquab et al., 2024) as the feature backbone to extract dense visual representations from specimen images (see ablations in Appendix E
4

---

Published as a conference paper at ICLR 2026
Algorithm 1: Salient Trait Extraction from Sparse Autoencoder Activations
Input:
Species-labeled dataset D = ( U1)
Trained sparse autoencoder fo Activation threshold factivation
Normalized frequency threshold frreq Output: Set of salient traits Tdissinct for each species
1 Initialize counters Cspecies and Cgenus as empty maps;
2 foreach (Iz, 84) E D do
3
2 - (o(
// Sparse latent vector
4
2, {zj =< lactivation
5 foreach trait do
Ospecios
[s][a]
Σ E:ye
1[z E Zd]
Ogenus [g|[2]]
Σ
1 Z,]
i-genus( (1r )
7 foreach species and iss genus do
8 foreach trait do
fa(z) - E., Cypecia [7]
[+]][]
10
[s(2) - E. Creen =
Camul
11 Initialize Taistinct <- -3:
12 foreach species with genus g do
13
Ta 1 {2 (x() > ffrog A fz(=)
frrog A Af((2) > fz(2)):
Tdistiner [8] Tsi
15
return Teisninet
3.2 DATASET GENERATION
We use the high-activation latents (with values above a certain threshold factivation) ) to generate descriptions of salient morphological traits in species images. The trait extraction procedure consists of the following steps:
1. Sparse Activation Computation: For each image in the BIOSCAN-SM dataset annotated the species level, we compute its sparse latent representation using the trained autoencoder.
2. Trait Selection via Activation Thresholding: From the full set of activated latent features for a given sample, we retain only those whose activation values exceed predefined threshold (denoted by factivation), indicating salient trait expression.
3. Taxonomic Trait Aggregation: We then compute the frequency distribution of activated traits both the species and genus levels across the dataset.
4. Trait Filtering by Prevalence: Within each taxonomic rank, we retain only those traits whose normalized frequency, computed as the ratio of trait occurrences the total number of trait activations for the taxon, exceeds predefined threshold (denoted by (fre)) This filtering step mitigates noise and retains consistently expressed traits.
5. Salient Trait Identification: We identify salient morphological traits for species the ones expressed in a significantly higher proportion within that species than across its corresponding genus, indicating taxon-specific salience.
Algorithm iilustrates the procedure for selection of salient traits in detail. Given these traits, we prompt multimodal language models to query the morphological trait descriptions (Figure Prompt templates are provided in Appendix C and dataset statistics are in Table D.5] Additional dataset examples shown in Appendix G We also discuss ecology applications in Appendix
5

---

Published as a conference paper at ICLR 2026
BIOSCAN- TRAITS
Grad-CAM
Trait Descriptions
BIOSCAN- TRAITS: Leg: Thin, elongated, light brown, segmented.
Grad-CAM: Head: Highlights the cephalothorex, emphasizing its rounded shape and the presence sensory organs. Legs: Focuses the anterior legs, showcasing their robust structure and segmentation, Body: Emphasizes the abdomen, noting its segmented appearance and the presence of spinnerets at the posterior end.
Figure Comparison of trait localization for Thymoites guanicae. BIOSCAN-TRAITS (left) generates interpretable trait descriptions that are tied to clear, specific anatomical structures. In contrast, Grad-CAM (center) produces diffuse heatmaps that highlight broad body areas without species-level disentanglement.
EXPERIMENTS
4.1 SPARSE AUTOENCODER TRAINING
We use the BIOSCAN-5M dataset (Gharaee et al. 2024) for our experiments. BIOSCAN-5M is a comprchensive dataset of insect specimens with multple modalities, including images, DNA barcodes, taxonomic, geographic, and size information. It contains insect images annotated at different levels of the taxonomic hierarchy, with 9.2% of the samples annotated at species-level. While our experiments
BIOSCAN-5M as the large-scale, species-labeled dataset, the method itself only needs image collections paired with taxonomic labels--superisioon that is common across many repositories (e.g.. iNaturalist (Horn et al. 2018) and TrecOfLife (Stevens al., 2024)). ) Such datasets cover plants, birds, fungi. and many other groups: therefore, the pipeline is broadly applicable and can scale transform species- or genus-labeled biological image archives into rich, interpretable trait-level annotations.
We train the sparse autoencoder on the entire of images in BIOSCAN-5M, while the trait generation pipeline uses the subset with species-level labels. The complete hyperparameter setup is given in Table D.4 in the Appendix
4.2 COMPARISON WITH GRAD-CAM
We compare our pipeline to using traditional feature visualization approaches like Grad-CAM (Selvaraju et al. 2017) for obtaining saliency maps and then forwarding to the MLLM for trait generation. While Grad-CAM can highlight salient regions for a given class label, it lacks trait-level disentanglement, i.e., its heatmaps typically blend multiple anatomical cues, making it difficult for an MILMM to generate precise, interpretable trait descriptions. Moreover, Grad-CAM activations are not species-diss disrresdiicrimiiative,, often capturing features shared across related taxa (genus or family level), whereas our SAE-based approach explicitly isolates species-specific, monosemantic neurons tied to fine-grained traits.
4.3 DATASET ABLATIONS
We conduct a series of ablation studies to evaluate the impact of key design choices on the accuracy and plausibility of trait annotations. For each configuration, we randomly sample 30 trait descriptions and evaluate them using five-point rubric. Three domain experts independently rated the samples. We apply per-rater mean normalization to ratings, rescaling each annotator's s scores so that their personal mean equals the global mean (Riley al. 2024 Kirk et al. 2024). This ensures that differences in individual scale usage (e.g., consistently harsh or lenient raters) do not skew aggregated results. The evaluation rubric is given in Appendix
Comparison with MLLM-only baseline. As baseline, prompt a multimodal large language model (MLLM) with just the specimen image(s) without the trait localization and request a description of salient morphological traits (Figure 3) We compare this our SAE-guided trait extraction pipeline,

---

Published as a conference paper at ICLR 2026
(a)
(b)
(c)
Common Morphological Traits All Three Images: Head:- Shape: Rounded. Color: Dark
black. Texture: Appears smooth
distinct markings. 2. Thorax: Shape: Oval
slightly elongated. Color: Lighter than the head, ranging from light brown beige. Texture:
Smooth, with visible segmentation. Abdomen: Shape: Rounded and bulbous. Color: Dark brown or black, similar the head. Texture: Smooth, with distinct markings. 4. Legs: Number: Eight legs visible in all images. Shape: Thin and segmented.. Color: Light brown, matching the thorax.
MLLM SAE: Leg: Thin, elongated, light brown, segmented.
Figure 3: Comparison of salient morphological trait description generation using just MLLM vs.
MLLM + SAE (Iireg = 1e 2) for Agyneta straminicola. Each red box highlights region selected by SAE neurons with high activation, indicating regions used for prompting the MLLM + SAE. The use of SAE helps MLLMs focus on salient morphological traits rather than general descriptions of all body parts.
Table Incorporating latent-specific patches significantly improves the quality trait descriptions. Including multiple images in the prompt encourages MILMs to focus on the traits common across all images, at the cost more tokens per query. Using multiple images with SAE-extracted bounding boxes leads to improved precision, better ratings indicate. We report both raw and normalzzd ratings. The experimental setup uses Qwen2.5-VL-72 as MLLM, a normalized frequency threshold of (fdee)) 30 3, and 1,000 input images.
Method
# Images
# Tokens
# Images
# Traits
Avg, Raw Rating
Avg. Rating
MLLM MILM
413
3.01
3.00
3
940
3.12
3.15
MLLM SAE
1
411
460
9,435
3.92
3.84
MLLM SAE
1,072
460
7,897
4.01
3.91
which localizes trait-relevant regions via sparse latent activations (Table T}. Incorporating latent- specific patches leads to a substantial improvement in description quality: the average human rating increases from 3.15 to 3.91 the multi mullti-image setting, highlighting the benefits of spatial grounding provided by the sparse autoencoder for fine- mmeggrained trait extraction.
Multiple vs. Single Image per Latent. We investigate the effect of varying the number of input images trait quality by comparing single-image against 3-image prompts the multimodal language model (T. Providing multiple images of the same species encourages the model to focus on consistent, shared morphological features while suppressing spurious or image maage-specific traits. This consensus-drives trait extraction leads to improved precision, as reflected by increase the average human rating from 3.84 to 3.91, albeit the cost of higher token usage per query. similar trend holds for the MLLM-only bascline.
Additionally, we do a qualitative analysis the morphological trait descriptions generated by both approaches (Figure 4. Using single image often leads to trait descriptions that overfit to idiosyncratic visual details of that instance, frequently summarizing multiple anatomical regions, as seen in the example where both the legs and abdomen described together. This broad coverage can dilute trait precision and obscure what is taxonomically distinctive. In contrast, prompting the model both with multiple images and latent-specific regions encourages extract traits that
7

---

Published as a conference paper at ICLR 2026
(a)
(b)
(c)
(a) Single image: The highlighted area appears to encompass the main body (thorax and abdomen) of the insect. Observable Morphological Features: Shape: The body has an elongated oval shape.
Size: occupies a signíficant portion the image but cannot quantified without scale. Color: The body primarily brown with some darker and lighter shading, suggesting segmentation texture. Texture: The surface appears somewhat smoath but with fine details that could indicate hairs scales, though is entirely clear. Distinct Markings: There highlighted area; the coloration relatively uniform.
prominent markings vislble within the
(a, b, c) Multiple images: Body: Brown, oval-shaped, segmented exoskeleton.
Figure 4: Comparison of salient morphological trait description generation using a single image vs. three images for Contacyphon ochraceus. Each red box highlights region selected by SAE neurons with high activation, indicating regions used for prompting the MLLM + SAE. The use of multiple images yields concise and taxonomically meaningful output, isolating traits with clearer morphological grounding.
Normalized Activation Strength
Cecidomylidae
Stenomacrus
Cecidomylidae
Stenomacrus
Bethylidae
Soythrididae
Figitidae
Belytinae
Belytinae
Bethylidae
Figure 5: Neurons 4852 and 13860 in SAE get activated at the wings and antennae of insects, respectively. The labels denote the highest annotated taxonomic level. Additional examples are shown in Appendix
visually consistent specimens. This consensus constraint filters incidental details and leads to more focused, high-precision descriptions (e.g., isolating just the leg features). As shown in Figure the multi-image setup yields concise and taxonomically meaningful output, isolating traits with clearer morphological grounding and higher inter-image agreement.
SAE Quality. We investigate the sparse autoencoder's s inherent tradeoff between reconstruction error and sparsity and its downstream impact on morphological trait generation (Table Specifically, we compare performance across varying values of the sparsity regularization coefficient (a), which controls the Lo-saarsity of the latent representation. We observe that lower sparsity (i.e., smaller a, larger Lp) consistently yields better performance across both values the normalized frequency threshold free (Figure(6). This setting results in lower input reconstruction. Importantly, reduced sparsity increases the number of activated latents per image, thereby improving trait coverage and recall in the final description set.
squared error (MSE), indicating improved
8

---

Published as a conference paper at ICLR 2026
Table 2: SAEs often trade off between reconstruction error (MSE) and sparsity (Lo). We investigate the effect of choosing between different balances of these errors. We find that lower sparsity performs better for both values frequency threshold (ftre)). A lower value of the sparsity coefficient (a) leads to lower MSE and thus better reconstruction. It improves the coverage of latents, leading to better recall. The experimental setup uses an input dataset of 1,000 images.
Method
Q
SAE MSE
SAE Lo
# Images
# Traits
Avg. Rating
MLLM+SAE
2e 1
1e-2
8.8e
1,081.1
60
60
3.84
MLLM+SAE
4e-4
3e-3
2. 2-7e-2
690.4
460
7,897
3.91
MLLM+SAB
de-4
1e-2
2.7e-2 2
690.4
20
20
3.58
MLLM+SAB Se 3e-3
5.4e-2
242.2
458
3,060
3.87
3.9
Table 3: Effect of normalized frequency threshold ((rea)) on trait selection. We analyze how varying freeq. which controls the minimum intra-species normalized frequency required to retain a latent feature, impacts trait extraction. Lower thresholds include all activated traits, while higher thresholds restrict output to only the most consistently ex- pressed traits. Increasing ffroq improves precision but reduces the number of extracted traits, reflect- ing tra between coverage and specificity.
Avg. Rating
3.8
3.7
3.6
2 x 10-4
4 10
8 x 10
Sparsity Coefficient a
Avg. Rating (Itmq 3 x 10-9)
Avg. Rating (ftreq
10 7)
Method
troog
# Images
# Traits
MLLM+SAE 3e-3
460
7,897
Figure 6: Variation of rating with different lev- els of SAE sparsity. A lower level of sparsity performs better for both values of frequency threshold free.
MILM+SAE
6e-3
322
785
MLLM+SAE
1e-2
20
20
SAE Filtering. We analyze the effect of the normalized frequency threshold tireq the trait through- put using 1,000 input images and sparsity coefficient (a) = 4e 4. We observe that increasing Liey leads progressive reduction in the number of retained latent features (Table This results in the selection of only the more consistently activated latents across a taxon, effectively narrowing the subset of input images that contribute trait descriptions. Thus, ffee acts as a precision-recall knob: lower values yield broader trait coverage but more noise, while higher values emphasize dominant, tuxonomically stable traits.
MILM Quality. We compare Qwen2.5-VL and Qwen2.5-ViL VLLLL22B (Wang et al. 2024) for trait generation from latent-indexed patches. The larger 72B model yields higher human evaluation scores and better spatial grounding, avoiding false positive traits; see Appendix B for details.
4.4 NEURON ACTIVATION ANALYSIS
We analyze the top-activating neurons (or latent dimensions) in the SAE to investigate whether they correspond meaningful morphological traits. Representative examples are shown in Figure 5 Notably, neuron 4852 consistently activates on insect wings, while neuron 13860 responds to antennae, suggesting that specific neurons the sparse representation are aligned with semantically coherent, interpretable, and biologically plausible traits.
4.5 COST-OF-USE ANALYSIS
We next quantify the efficiency and cost of our pipeline and examine how well the SAE- -uiiedd prompting strategy transfers across different MLLMs. Table 4 reports runtime and throughput on the DOSCAN--RRIIT workload using two NVIDIA H100 80GB GPUs. The SAE introduces only a small overhead: DINOv2 activation computation and the SAE forward pass together take

---

benchmark. Published as a conference paper at ICLR 2026
Table Runtime and throughput of the proposed pipeline, measured on two NVIDIA H100 80GB GPUs. Times are averaged over the BIOSCAN-TRAID workload.
Task
Time
Remarks
Activation computation (1 image)
2.74 ms
DINOv2 backhone Sparse Autoencoder
SAE forward (1 image)
4.53 ms
Total preprocessing (1 image)
7.26 ms
Feature extraction + SAE
MLLM inference (3 images annotation)
4.62
Qwen2.5-VL-72B
Throughput (2 NVIDIA H100 80GB GPUs) 208.9 annotations/h/GPU
7.26 ms per image, whereas MILMM inference (conditioning on three SAE-selected patches per image) dominates the budget at 4.62 per annotation. A cost-of-use analysis comparing public API pricing (Qwen2.5-VL-72B vs. GPT-5-mini) is provided in Appendix
4.6 FINE-TUNING WITH TRAIT SUPERVISION
Table 5: Zero-shot species classification accuracy (%) on the Insects (Ullah et al. 2022)
Incorporating trait-level supervision yields clear gains over the baseline pretrained model. BioCLIP is pretrained on BIOSCAN-5M; therefore, we evaluate it directly under trait-level supervision.
Model
BioCLIP
BioCLIP
Baseline
34.8
55.3
Baseline + speciees-evel fine-tuning CITOSCAN-TRAITE
39.6
Baseline + trait-level fine-tuning TRAITS)-TRAIIS)
39.9
56.23
To assess the utilityy of our morphological trait description dataset, fine-tuned BioCLIP (Stevens ct al. 2024; Gu et al. 2025). a biologically grounded vision-language foundation model on this Insects (Ullah al., 2022). volunteer-labeled, in-the-wild benchmark, this yielded significant gain zero-shot species classification over the pre-trained model. This provides initial evidence that trait-level supervision supports better generalization, underscoring the potential of our dataset for training biologically grounded foundation models.
dataset. When evaluated
Notably, sparse autoencoders disentangle foreground from background by activating distinct neuron subsets. By aggregating consistent traits across multiple images per species, our pipeline further improves robustness real-world noise. As result, models fine-tuned on SAE-derived trait descriptions generalize more effectively to challenging, in-the-wild imagery.
5 CONCLUSION
We present a novel pipeline for distilling morphological traits into high-fidelity, natural language descriptions by leveraging sparse autoencoders and multimodal language models. Applied to the BIOSCAN-5M dataset, our method produces a large-scale corpus of over 80K trait descriptions across 19K insect images, constituting one of the first datasets to provide structured, interpretable trait-level supervision at scale. BIOSCAN-TRR TRAITS can support ecology applications such as scaling trait databases and enabling morphology-euvironnee environment analyses from existing image repositories. Through extensive analysis, we examine the impact of key design factors, including the use of multiple images for trait verbalization, trait frequency thresholds, sparsity levels the autoencoder, and the choice of MILM backbone, on the precision and accuracy of generated traits. Integrating trait-level supervision improves generalization in downstream tasks such as fine-grained species classification, underscoring the utility of our proposed pipeline-generated datasets for biologically grounded learning. We discuss the limitations of our approach in Appendix A Looking forward, we aim to extend this pipeline construct large-scale datasets across diverse biological domains and across multiple taxonomic levels, enabling domain-specifii vision-languase models with improved robustness, interpretability, and ecological relevance for large-scale biodiversity applications.
10

---

Published as a conference paper at ICLR 2026
ETHICS STATEMENT
This work advances global biodiversity conservation by introducing scalable trait annotation pipeline for generating image-to-trait datasets, which can support the development of biologically grounded foundation models. Such models have the potential to improve species recognition, facilitate un- derstanding of evolutionary patterns, and inform conservation strategies the context of climate change. By reducing reliance on expert-curated annotations, our approach democratizes access to morphological data and empowers under-resourced institutions and citizen science efforts with automated analysis tools. However, errors in trait interpretation, such as those arising from hallucina- tion or domain shift, may propagate into downstream applications, including species classification and conservation decision decision-making It is therefore essential that these tools be deployed in close collaboration with domain experts to ensure reliability and accuracy.
REPRODUCIBILITY STATEMENT
Our code is available at giggtthhhb.g.. com OSS NLP Grouproouueeeeeeeeeee MLP-Group/sae-tree
dataset can be accessed at huggingface co/ trrouppsae-traittt anaittaanotttion and The
hyperparameter settings are listed in Table the trait generation pipeline outlined in Algorithm and prompt templates are provided in Appendix C H100 GPUs.
[]
All experiments were performed NVIDIA
ACKNOWLEDGEMENTS
We thank colleagues in the OSU NLP group for valuable feedback. This research was supported in part by NSF CAREER #2443149, NSF OAC 21 18240, and an Alfred P. Sloan Foundation Fellowship. We also acknowledge computational resources provided by the Ohio Supercomputer Center Center (1987).

---

Published as a conference paper at ICLR 2026
REFERENCES
Hanane Ariouat, Youcef Sklab, Edi Prifti, Jean-Daniel Zucker, and Eric Chenin. Enhancing plant morphological trait identification in herbarium collections through deep learning-based segmen- tation. Applications in Plant Sciences, 2025. URL :ttptts::/bsspuue
onlinelibrary
wiley.come 1.lley..commoo//a////00000000
11t.1111..000222 1002/ aps3. 20000000 70000?af=R,
Sara Beery, Grant Van Horn, and Pietro Perona. Recognition in terra incognita. In Proceedings of ECCV, 2018.
030 01270 0_28
Sara Beery, Elijah Cole, and Arvi Gioka. The iwildcam 2020 competition dataset. arXiv preprint arXiv:2004. 10340, 2020.
10040 10340
Trenton Bricken, Adly Templeton, Joshua Batson, Brian Chen, Adam Jermyn, Tom Conerly, Nick Turner, Cem Anil, Carson Denison, Amanda Askell, Robert Lasenby, Yifan Wu, Shauna Kravec, Nicholas Schiefer, Tim Maxwell, Nicholas Joseph, 7ac Hatfield-Dodds, Alex Tamkin, Karina Nguyen, Brayden McLean, Josiah E Burke, Tristan Hume, Shan Carter, Tom Henighan, and Christopher Olah. Towards monosemanticity; Decomposing language models with dictionary learning. Transformer Circuits Thread, 2023. URL http::: /Rtttt:t///taaa www.rr--iieuiitt....-e.--------e pub/20233 http////ooosmmmaccceaaaattteeeee
hte///ndexxx.. html
Bart Bussmann, Patrick Leask, and Neel Nanda. Batchtopk sparse autoencoders. In Proceedings of NewrlPS 2024 Workshop Scientific Methods for Understanding Deep Learning, 2024. URL
https:://pennevvieys wtwp:r/wwwwee..eeeooouteieieeee forum? [orumm?dddddttooaaaa
Bart Bussmann, Noa Nabeshima, Adam Karvonen, and Neel Nanda. Learning multi-level fea- tures with matryoshka sparse autoencoders. In Proceedings of ICML, 2025. URL https
net. htt..nneeetoooommmmmmmm32311111
Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin. Emerging properties in self-supervised vision transformers. In Proceedings of ICCV, 2021.
10. 1111/111111111111 2021.
Ohio Supercomputer Center. Ohio supercomputer center,, 1987.
Sandra Díaz, Jens Kattge, Johannes HC Cornelissen, Ian Wright, Sandra Lavorel, Stéphane Dray, Björn Reu, Michael Kleyer, Christian Wirth, Colin Prentice, et al. The global spectrum of plant form and function. Nature, 2016. URL httpp:://www.atat// www/erticleeaeeeeeeeeeeeeees
naturel66 16889
Leo Gao, Tom Dupre Tour, Henk Tillman, Gabriel Goh, Rajan Troll, Alec Radford, Ilya Sutskever, Jan Leike, and Jeffrey Wu. Scaling and evaluating sparse autoencoders. In Proceedings of ICLR, 2025. URL
Zahra Gharaee, Scott C. Lowe, ZeMing Gong, Pablo Millan Arias, Nicholas Pellegrino, Austin Wang. Joakim Bruslund Haurum, Iuliia Eyriay, Lila Kari, Dirk Steinke, Graham W. Taylor, Paul W. Fieguth, and Angel X. Chang. Bioscan-5m:: multimodal dataset for insect biodiversity. In
Proceedings of NeurlPS, 2024. URL /
http:/paprrfffesspppeee
and Benchmarks Track. html
Janyang Gu, Samuel Stevens, Elizabeth G Campolongo, Matthew J Thompson, Net Zhang, Jiaman Wu, Andrei Kopanev, Zheda Mai, Alexander E. White, James Balhoff, Wasila Dahdul, Daniel Rubenstein, Hilmar Lapp. Tanya Berger-Wolf: Wei-Lun Chao, and Yu Su. BioCLIP 2: Emergent properties from scaling hierarchical contrastive learning. In Proceedings of NeurlPS, 2025. URL
https://openrevi.. net hot.
Alex R. Hardisty, Paul Brack, Carole A. Goble, Laurence Livermore, Ben Scott, Quentin Groom, Stuart Owen, and Stian Soiland-Reyy Reyes. The specimen data refinery: A canonical workflow framework and FAIR digital object approach to speeding up digital mobilisation of natural history collections. Data Intell., 2022. URI //doi org/ 0. 11 62. /i nt 00134
12

---

Published as a conference paper at ICLR 2026
Yichen He, James M Mulqueeney, Emily C Watt, Arianna Salili-James, Nicole S Barber, Marco Ca- maiti, Eloise SE Hunt, Oliver Kippax-Chui, Andrew Knapp, Agnese Lanzetti, et al. Opportunities and challenges in applying ai to evolutionary morphology. Integrative Organismal Biology, 2024. //academ C. oup. http://///...aalaoooooo/////////
J Mason Heberling. Herbaria as big data sources of plant traits. International Journal of Plant Sci- ences, 2022. URL https: www. wwwp:///wwwwww..a.oooooooooo uchi cago.
10867
717623
Grant Van Horn, Oisin Mac Aodha, Yang Song, Yin Cui, Chen Sun, Alexander Shepard, Hartwig Adam, Pietro Perona, and Serge J. Belongie. The inatural- ist species classification and detection dataset. In Proceedings of CVPR, 2018.
URL htt.:/ttppp//ppacccsssssss httppppwwwaaccsss..s..eeeeeeeeee com catm//ooteet__tccceeeeeee _vprr_eppp_. cevpr_20180 2010//ttmm /Van_ Horn html
The INatura
Jennifer Hoyal Cuthill, Nicholas Guttenberg, Sophie Ledger, Robyn Crowther, and Blanca Huertas. Deep learning butterfly phenotypes tests evolution's oldest mathematical model. Science ad- vances, 2019, URL https::///// wwwwwwwwwww......w..w..... www.scciiicc.........
doi
111226//ccidi 4774967
Tao Hu, Honggang Qi, Qingming Huang, and Yan Lu. See better before looking closer: Weakly supervised data augmentation network for fine-grained visual classification. arXiv preprint 17X1v91901.09891, 2019. hRthstts:///axt
org. hbsp/ab//// 1901. 09891
Zeyi Huang, Yang Zou, B. K. Vijaya Kumar, and Dong Huang. Comprehensive attention self-distillation for weakly-supervised object detection. In Proceedings of NeurlPS, 2020. URL https::/prrcsedii/ Abstract .tml 25555eeaaf231tooccccd0Ceeee
neurips cc/ papp://p20/aaahh
Lawrence N Hudson, Vladimir Blagoderov, Alice Heaton, Pieter Holtzhausen, Laurence Livermore, Benjamin W Price. Stéfan van der Walt, and Vincent S Smith. Inselect: automating the digitization of natural history collections. PLoS one, 2015. URL /tltps://jounnae plos org/ pone. 0143402
Roberta Hunt and Kim Steenstrup Pedersen. The phantom of the elytra- Alyrra-phylogenttie trait ex- traction from images of rove beetles using deep learning-is the mask enough? arXiv preprint 041::25002.04441, 2025. UR///ttps://aaxvvoorr/////
04541
Burhan Rashid Hussein, Owais Ahmed Malik, Wee-Hong Ong. and Johan Willem Frederik Slik. Applications of computer vision and machine learning techniques for digitized herbar- ium specimens: A systematic literature review. Ecological Informatics, 2022. URL https
7 w/wwwscciencedieet rectt com http://ccco..cccooooooooo//.ooaa
Subhash Kantamneni, Joshua Engels, Senthooran Rajamanoharan, Max Tegmark, and Neel Nanda. Are sparse autoencoders useful? case study in sparse probing. In Proceedings of ICML, 2025.
URL [RRl/tttpps://peeevviee http:/ewwwwwiwweeeeeeeeeeeee fttt:/oormmiiddeeem
Jonathan D Kennedy, Petter 2 Marki, Jon Fjeldsä, and Carsten Rahbek. The association between morphological and ecological characters across a global passerine radiation. Journal of Ani- mal Ecology, 2020.
onlionniiiinnn www.eeooonnennnnnnneeeeeeeeeaaae wetaarrwwwllyy. wiley. com/ doi/
Full/ Eull/10.1110 1t1////.11111111--655533
Hannah Rose Kirk, Alexander Whitefield, Paul Röttger, Andrew M. Bean, Katerina Margatina, Rafael Mosquera Gómez, Juan Ciro, Max Bartolo, Adina Williams, He He, Bertie Vidgen, and Scott Hale. The PRISM alignment dataset: What participatory, representative and individualised hu- man feedback reveals about the subjective and multicultraa alignment of large language models. In Proceedings of NewrIPS, 2024. URL httt::ttpppprrrst//// nips cc wwwprrappppppeeee http://pppppppeeeeaeeeeeepeesee
org.aeeeeeeooaaaeeeeeeeeeeaeeaar fearrr_fllepppppe /aper/
and Benchmarks Track black.hte html
Pang Wei Koh, Shiori Sagawa, Henrik Marklund, Sang Michael Xie, Marvin Zhang, Akshay Bal- subramani, Weihua Hu, Michihiro Yasunaga, Richard Lanas Phillips, Irena Gao, Tony Lee, Eti- enne David, Ian Stavness, Wei Guo, Berton Eamshaw, Imran S. Haque, Sara M. Beery, Jure
13

---

Published as a conference paper at ICLR 2026
Leskovec, Anshul Kundaje, Emma Pierson, Sergey Levine, Chelsea Finn, and Percy Liang. WILDS: A benchmark of in-the wild distribution shifts. In Proceedings of ICML, 2021. URL
http::/ppoceeddin
mll.. pwwss/v139/koh222222 http////hh2ahaahhh..a
Tsung- Yu Lin, Aruni RoyChowdhury, and Subhransu Maji. Bilinear enn models for fine-grained visual recognition. In Proceedings of ICCV, 2015. URL httpp::/6bii.rr//00...
ICCV. 2DGV.2015.11
Mingxuan Liu, Subhankar Roy, Wenjing Li, Zhun Zhong, Nicu Sebe, and Elisa Ricci. Democratizing fine-grained visual recognition with large language models. In Proceedings of ICLR, 2024. URL
https:://penrevieww net forum dd=ccDDDD111g
Alireza Makhzani and Brendan Frey. K-sparse autoencoders. Proceedings of ICLR, 2014. URL
https: org/ www./oggg//./ooo//./tt..tt...e.t 1312. 5663
Alireza Makhzani and Brendan J. Frey.
Winner-take-all autoencoders. In Pro-
ceedings of NeurIPS, 2015.
URL http://papezs. www.sessssiiiccooo..m cttpsspppppccccpeee///eepppppp
Brian J McGill, Brian J Enquist, Evan Weiher, and Mark Westoby. Rebuilding community
ecology from functional traits. Trends in ecology & evolution, 2006. URL https::///// www
sciencedirect com/ http:////e//attte/////////111111
Gil Nelson and Shari Ellis. The history and impact of digitization and digital data mobilization on biodiversity research. Philosophical Transactions of the Royal Sociery B, 2019. URL https
//royal soc www.et ishi ng www.g/ooo/g/eoooeeeoooeeee 1098/
wwt.///ttbb.eeeee rstb. 2017 0391
Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy V. Vo, Mare Szafraniec, Vasil Khali- dov, Pierre Fernandez, Daniel HAZIZA, Francisco Massa, Alaaeldin El-Nouby, Mido Ass- ran, Nicolas Ballas, Wojciech Galuba, Russell Howes, Po-Yao Huang, Shang-Wen Li, Is- han Misra, Michael Rabbat, Vasu Sharma, Gabriel Synnaeve, Hu Xu, Herve Jegou, Julien Mairal, Patrick Labatut, Armand Joulin, and Piotr Bojanowski. DINOv2: Learning robust vi- sual features without supervision. Transactions Machine Learning Research, 2024. URL htttps://openreviit htteeeewwwwweeeeeeeeeeeeeeeeeeee
net. da 6
Mateusz Pach, Shyamgopal Karthik, Quentin Bouniot, Serge Belongie, and Zeynep Akata. Sparse autoencoders learn monosemantic features vision- models. In Proceedings of NeurIPS, 2025. URLhttps htttt:::///penerevieiie
net htt.://www.ett.rrrrmooaoaaaaaaaa
Vardaan Pahuja, Weidi Luo, Yu Gu, Cheng-Hac Hao Tu, Hong- You Chen, Tanya Y. Berger- Wolf, Charles V. Stewart, Song Gao, Wei-Lun Chao, and Yu Su. Reviving the context: Camera trap species classification link prediction on multimodal knowledge graphs. In Proceedings of CIKM, 2024.
URL httpttttss:/ddo..rrrgg (ttpg//11111151226666611 3679545)
Alex L Pigot, Catherine Sheard, Eliot T Miller, Tom Bregman, Benjamin G Freeman, Uri Roll, Nathalie Seddon, Christopher H Trisos, Brian C Weeks, and Joseph A Tobias. Macroevolutionary convergence connects morphological form to ecological function birds. Nature Ecology & Evolution, 2020. URL httpt:::///// //wps:///wwww.. nature. cww. www.rricclle/eeo htt.://aaaa.cceeeeeeeee5555111 wet.ee////1991111-111111
Parker Riley, Daniel Deutsch, George F. Foster, Viresh Ramakkar Ali Dabirmoghaddam, and Markus Freitag. Finding replicable human evaluations via stable ranking probability. In Proceedings of NAACL, 2024. URL htt.thtts:///ee http:/:::://di.oo//1...11.
naacl-long 275
Ramprasaath Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra. Grad-cam: Visual explanations from deep networks via gradient-based based localiza- tion. In Proceedings of ICCV, 2017. (tt.httss:///ett httttps:://doi.og/111.1 huttt::://bll.org//00111//////oo 2017.
Samuel Stevens, Jiaman Wu, Matthew Thompson, Elizabeth G Campolongo, Chan Hee Song, David Edward Carlyn, Li Dong, Wasila M Dahdul, Charles Stewart, Tanya Berger-War Wolf. Wei-Lun Chao, and Yu Su. Bioclip: A vision foundation model for the tree life. In Proceedings of CVPR, 2024. ht http:///ppppccccce/////// theevf. com CVPR P wttpw/wwww.o/ooeoteetteeeoeeoeee content CV2p2024222222/2/// hhtml/ html
Stevens BIoCLIP A Vision Foundation,
Model_fao for the Tree of Life
14

---

Published as a conference paper at ICLR 2026
Samuel Stevens, Wei-Lun Chao, Tanya Berger- Wolf, and Yu Su. Sparse autoencoders for scientifically rigorous interpretation of vision models. arXiv preprint arXiv:2502.06755, 2025. URL https
//arxiv otg/ abs 200../202..0 06755
Anant Subramanian, Danish Pruthi, Harsh Jhamtani, Taylor Berg-Kirkpatrick, and Eduard H. Hovy. Spine: Sparse interpretable neural embeddings. In Proceedings of AAAI, 2018. URL https
www.. wwt.oo////ooooo hewps/iidxx..... wwtpddee.ppp/peeeeee TADp/AAAI/AAA
Adly Templeton, Tom Conerly, Jonathan Marcus, Jack Lindsey, Trenton Bricken, Brian Chen, Adam Pearce, Craig Citro, Emmanuel Ameisen, Andy Jones, Hoagy Cunningham, Nicholas L Turner, Callum McDougall, Monte MacDiarmid, C. Daniel Freeman, Theodore R. Sumers, Edward Rees, Joshua Batson, Adam Jermyn, Shan Carter, Chris Olah, and Tom Henighan. Scaling monosemanticity: Extracting interpretable features from claude 3 sonnet. Trans- rcu former Circuits Thread, 2024. URL htttps::/trrassforrmet pubbss.2222222
pub/20247
scal iig-inggmon ing.- html
Masato Tsutsumi, Nen Saito, Daisuke Koyabu, and Chikara Furusawa. A deep learning approach for morphological feature extraction based on variational auto-encoder: an application to mandible
shape. NPJ systems biology and applications, 2023. URL https: www nature. com/ articles/ 0eeeclees/115500020002222220
Ihsan Ullah, Dustin Carrión- -jeidn Sergio Escalera, Isabelle Guyon, Mike Huisman, Felix Mohr, Jan N. van Rijn. Haozhe Sun, Joaquin Vanschoren, and Phan Anh Vu. Meta- album: Multi-domain meta-dataset for few-shot image classification. In Proceedings of NeurlPS, 2022. URL httt::/ppppers..n .nps wwtp://wppppp.ppp...eepeeepeeeee filperrrrffl_eeeeeeeeeepeeeeeeee www.ll///ppp//////// 2022/ -atasstss_a
Benchmarks..tg
Cyrille Violle, Marie-Laure Navas, Denis Vile, Elena Kazakou, Claire Fortunel, Irène Hummel, and Eric Garnier. Let the concept of trait be functional! Oikos, 2007. URL http:: /
//wo:///aaaooooonasssss inlinelibrary... wiley www/cooooooooooo doi /ttp//o.i/111111111 5.0030 1299 2293001992.200015559..
Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge Belongie. The caltech- uesd birds-200-2011 dataset. 2011. URL
www.lribrr..... www.ech..tccccceooeooo..ee.....e cachh eduu
reecords/cvm3y-55h12
Peng Wang, Shuai Bai, Sinan Tan, Shijie Wang, Zhihao Fan, Jinze Bai, Keqin Chen, Xuejing Liu, Jialin Wang. Wenbin Ge, Yang Fan, Kai Dang, Mengfei Du, Xuancheng Ren, Rui Men, Dayiheng Liu, Chang Zhou, Jingren Zhou, and Junyang Lin. Qwen2-vl: Enhancing vision- language model's perception of the world at any resolution. arXiv preprint arXiv:2409.12191, 2024. httltttss//axii..ooo
/ww..///////........//// 12191
Di Wu, Siyuan Li, Zelin Zang. and Stan Li. Exploring localization for self-supervised fine-grained contrastive learning. In Proceedings of BMVC, 2022. (RL//tttps///mmcc2222......
mpq. mpp..e//22. dww.:da//22....
pddf
Zhengxuan Wu, Aryaman Arora, Atticus Geiger, Zheng Wang, Jing Huang, Dan Jurafsky, Christo- pher D Manning, and Christopher Potts. Axbench: Steering LLMs? even simple baselines outperform sparse autoencoders. In Proceedings of ICML, 2025. /
net//oorum?id=x2Cckz O
Zeyu Yun, Yubei Chen, Bruno Olshausen, and Yann LeCun. Transformer visualization via dictionary learning: contextualized embedding linear superposition of transformer factors. In Proceed- ings of Deep Learning Inside Out (DeeLIO): The 2nd Workshop on Knowledge Extraction and Integration for Deep Learning Architectures, 2021. URL https/////aaaaanololoo.oooo 2021. del..eeeello1.11
15

---

APPENDICES
This supplementary material provides additional details omitted the main text.
Contents
Appendices
16
Limitations
17
Comprehensive Results
17
System Prompts
18
D Experimental Setup
20
D.I Hyperparameter Configuration
20
D.2 Downstream Evaluation
20
Feature Detector Ablations
20
Crowdsourcing Details
21
Dataset Examples
21
Annotation Cost Analysis
21
Ecology Applications
24
J
Additional Neuron Activation Analysis
24
K
Additional Dataset Ablation Examples
25
K.1
MLLM + SAE vs. MLLM-ont baseline
25
K.2
2L vs. Qwen-2.3-VL-7225
25
K.3 Multiple vs. Single Image per Latent
29
LLM Usage Details
29
16

---

Published as a conference paper at ICLR 2026
A LIMITATIONS
We assume that the dense features from the backbone image foundation model encode morphology- relevant signals. If these representations are biased toward generic visual concepts, important biological traits may be underrepresented. The SAE discovers latent factors that are spatially and semantically coherent, but some latents might correspond to multiple co-oceurring traits (e.g., "elon- gated + thin"). This can make it difficult to disentangle fine-graine grained trait attributes or compositional traits. Trait descriptions generated with smaller MILMs like Qwen-2.5-VL-775 are susceptible to hallucination, particularly when prompted with noisy or background-dominal ating trait correctness at scale remains challenge due to the absence of ground-truth morphological trait annotations.
patches. Also, evalu-
Recent work (Kantamneni et al. 2025 Wu et al., 2025) has highlighted the limitations of SAEs, showing that they do not consistently outperform simpler baselines on downstream tasks. However, we do not use SAEs for steering or sparse probing in LLMs, but rather as a pragmatic tool for proposing spatially localized, candidate part detectors in DINOv2 features that can be grounded to image patches and then described by an MILM. We mitigate some known SAE limitations by (i) applying species-contrastive ranking and frequency thresholds to filter spurious latents, (ii) enforcing multi-image consistency (traits must recur across many instances of the same species), and (iii) evaluating the resulting traits both via expert ratings and via downstream transfer to in-the-wild Insects classification. In other words, we do not assume that SAE features are the true underlying traits; instead, we treat them as a useful decomposition that is subsequently empirically validated and filtered.
B COMPREHENSIVE RESULTS
The comprehensive results with standard deviation for ratings for various ablations are given in Table B.I
Table B.2, and Table B.3 respectively.
Table B.I: Incorporating latent-specific patches significantly improves the quality of trait descriptions. Including multiple images in the prompt encourages MLLMs focus the traits common across all images, at the cost of more tokens per query. Using multiple images with SAE-extracted bounding boxes leads to improved precision, as better ratings indicate. The experimental setup Qwen2.5- VL-72B as MLLM, a normalized frequency threshold of (freg) = 3e-3, 3, and 1,000 input images.
Method
Images
Tokens
# Images
Traits
Avg. Rating
MLLM MLLM
413
3,00 (#0.71)
3
940
3.15 (#0.54)
MILMM + SAF
411
460
9,435
3.84 (1.063)
MLLM + SAE
3
1.072
460
7,897
3.91 (#0.922
Table B.2: SAFs often trade off between reconstruction error (MSE) and sparsity (Tn). We investigate the effect of choosing between different balances of these errors. We find that lower sparsity performs better for both values of frequency threshold (tireq). A lower value of the sparsity coefficient (a) leads to lower MSE and thus better reconstruction. It improves the coverage of latents, leading to better recall. The experimental setup uses an input dataset of 1,000 images.
Method
ttrog
SAE MSE
SAE Lo
# Images
# Traits
Avg. Rating
MLLM+SAE
2e-4
le-2
8.8e-3
1,081.1
60
60
3.84 (+0.77)
MLLM+SAE
4e-4
3
2.7e-2
690.4
460
7,897
3.91 (+0.92)
MLLM+SAE
4e-4
le-2
2.7e-2
690.4
20
20
3.58 (+1.05)
MLLM+SAE
8e-4
3
5.4e-2
242.2
458
3,060
3.87 (+:0.83
MILM Quality Ablations. To evaluate the impact of model scale on morphological trait generation. compare descriptions produced by Qwen2.5-VI.-7B and Owwen2.5VVL-72B (Wang al. 2024
17

---

Published as a conference paper at ICLR 2026
Table B.3: We investigate the effect of the verbalizer MILM for morphological trait extraction for both the MLLM-only and MLLM SAE models. We observe that GPT---mini achieves the highest average rating, outperforming both open Qwen-2.5- VL variants by a substantial margin. The larger Qwen-2.5-VL-72B model (Wang et al., 2024) consistently obtains better ratings than its 7B counterpart.
note that GPT-5 mini and Qwen-2.5-VL-77 models might lead to false positives due to hallucination while extracting common traits in three input SAE-annotated images (Figure B.I . contrast, the Qwen2.5-VL- VL-72B model demonstrates improved robustness, avoiding such hallucinations and yielding more accurate trait descriptions. The experimental setup input dataset of 20K images and fizg 1e-2
Method
MILM
# Images
Traits
Avg. Rating
MLLM MLLM
Qwen-2.5 7B
476
536
2.85 (#0.67)
Qwen-2.5 VL 72B
371
411
3.15 (#0.54)
MLLM SAE
Qwen-2.5
478
538
2.90 (1:1.99)
MLLM + SAE
Qwen-2.5 VL 72B
358
370
3.58 (1:1.05)
MILM SAE
GPT-5 mini
478
538
4.04 (10.455
(a)
(b)
(c)
Twon2.5-VL-7B: Wing: Transparent, delicate, slightly curved, light brownish color, fine texture. GPT-5 mini: Wing: translucent, pale, elongated, Legs: dark, slender, jointed, Body (thorax/abdomeen): dark brown, rounded, glossy. Qwen2.5-VL-72B: None: No shared visible traits across all three regions.
Figure B.1: Comparison of morphological trait description quality between @wwn2.55VL-7B GPT-5 mini, and Qwen2.5-VL-72B for Diplonevra nitidula. Each red box highlights region selected by SAE neurons with high activation, indicating regions used for prompting the MLLM + SAE. The Qww-2.5-VLL-2BB
VL-72B model correctly recognizes the background context and refrains from hallucinating visible traits, suggesting improved spatial grounding.
when prompted with latent-indexed image patches (Table B.3). The larger 72B model consistently receives higher human evaluation scores than its 7B counterpart. In one illustrative example, Qwen2.5- VI.-2BB correctly identifies a red-boxed region as background, while the 7B model incorrectly hallucinates body part description (Figure B.I). These results suggest that larger models exhibit improved spatial grounding and are more reliable in avoiding false positive trait attributions.
C SYSTEM PROMPTS
The prompts used for the MLLM + SAE model are shown in Figure C.2 and Figure C.3 corresponding to the multi-in multi-image and single- image settings, respectively. For comparison, the prompts for the MLLM-onlly baseline are provided in Figure C.4 (multi-image) and Figure C.5 (single-image).
18

---

Published as a conference paper at ICLR 2026
You are given three images of insects, each with multiple red bounding boxes highlighting specific regions.
For each image:
1. For every highlighted region, determine whether it contains visible insect body part or just background. If it is mostly background, respond with "background".
2. it contains visible body part, identify which part is (e.g,, leg, wing, antenna), and describe its visible morphological traits: shape, size, color, texture, and any distinct markings. Use only the visual information present the image. After analyzing three highlighted regions images:
3. Identify and list the morphological traits that are common across three regions, solely based on what is visible in all images.
Important Instructions:
Do not infer or assume information that is not directly observable. Avoid adding external knowledge.
Use only what is clearly visible.
Be concise. Limit the total response to under 200 tokens.
Output Format:
mmage 1]:
[Body Part] IVisible trait]
Image 21:
[Body Part] (Visible trait]
[Tmage 3]:
[Body Part] IVisible trait]
[Common Traita Across A11 Three Images]:
[Body Part] [Shared visible trait]
Figure C.2: Prompt for MILM + SAE (multiple images)
You are given an image of an insect with multiple red bounding boxes overlaid on it, highlighting a specific region.
1. Determine whether the highlighted region contains visible body part of the insect or only the background. If appears to background, respond with "background".
2. it contains visible body part, identify which part is. Then, briefly describe the observable morphological features such as shape, size, color, texture, or distinct markings based solely on what is visible in the image.
IMPORTANT: Do not infer or assume information that is not directly observable, Avoid adding external knowledge.
Figure C.3: Prompt for MILM SAE (single image)
19

---

Published as a conference paper at ICLR 2026
You are given three images insects. Your task is to visually analyze them extract observable morphological traits.
1. Identify the visible body parts of the insect (e.g., head, thorax, abdomen, legs, wings, antennae), common in all three images.
each part, identify its morphological features markings.
shape, size, color, texture, or distinct
3. After analyzing all three images individually, list the morphological traits that common across
all three insects. Only output traits that are visibly consistent across all images.
IMPORTANT: Do infer assume information that is not directly observable. Avoid adding external knowledge.
Figure C.4: Prompt for MLLM-only baseline (multiple images)
You are given an image of an insect specimen. Your task is to visually examine the insect and describe its observable morphological traits.
1. Identify the visible body parts of the insect (e.g., head, thorax, abdomen, legs, wings, antennae). each part, briefly describe the observable morphological features such shape, size, color, texture, or distinct markings based solely on what is visible in the image.
IMPORTANT:
1. Do not infer assume information that is not directly observable. Avoid adding external knowledge.
Keep your response concise under tokens.
Figure C.5: Prompt for MLLM-only baseline (single image)
D EXPERIMENTAL SETUP
D.1 HYPERPARAMETER CONFIGURATION
Table D.4 summarizes all hyperparameters used for SAE training and dataset generation. We experiment with different learning rate values and choose 1c-3 based on qualitative inspection of learned traits. All experiments were conducted on NVIDIA H100 GPUs. SAE training required approximately 11 hours, while the dataset generation took 193 hours using a single process on GPUs.
D.2 DOWNSTREAM EVALUATION
For downstream evaluation, field photos of live insects interacting with flowers and foliage, often partially occluded. in diverse poses, backgrounds, and viewing distances. This introduces multiple distribution shifts (background clutter, illumination, pose, occlusion, and scale) beyond the lab setting. We fine-tune BioCLIP in standard image-text contrastive manner, where the text input a caption that concatenates the species name with the trait description. Concretely, we use prompts of the form "A photo of <species-name> with prraitdessrriptionz.
the Insects dataset (Ollah et al. 2022), which consists of volunteer
E FEATURE DETECTOR ABLATIONS
We use DINOv2-base (ViT-B/14) (Oquab et al. 2024
our feature extractor, motivated by prior
work showing its effectiveness in producing quahitulity SAE representations (Stevens et al. 2025 Pach al. 2025). To validate this choice, conducted preliminary experiments on a 1000-species benchmark derived from BIOSCAN-5M (20 train 30 test images per species), comparing CLIP VIT-B/16 (Caron et al. 2021 and DINOv2-base features (Table E.6).
20

---

Published as a conference paper at ICLR 2026
Table D.4: Hyperparameters for SAE training, filtering, and downstream tine-tuning
Hyperparameter
Value
Hidden Width
24,576 (32x expansion)
Sparsity Coefficient
12e- 4, 4e-4. Se-4]
Sparsity Coefficient Warmup
500 steps
Batch Size
16,384
Learning Rate !)
(5e 4, 1e-3]
Learning Rate Warmup
500 steps
Activation threshold Factivation
0.9
VIT layer ID
10
BioCLIP FT Learning Rate
3e-4
BioCLIP FT Warmup
2,000 steps
BioCLIP FT Learning Rate
2e-5
BioCLIP FT Warmup
2, 2,000 steps
Table D.5: Dataset statisties. average, each image associated with 4.2 trait samples.
Metric
Value
Species
736
Genera
417
Unique images
19.1K
Samples
80.8K
We observed that DINOv2- babse substantially outperforms CLIP ViT-B/16, using the kNN classifier. Based on these results, we selected DINOv2-base as our backbone. Following prior work (Stevens) et al. 2025), we extract features from the penultimate layer of the VIT for SAE training.
Table E.6: Species classification accuracy 1000-species benchmark derived from BIOSCAN-5M (20 train / 30 test images per species). DINOv2-base substantially outperforms CLIP ViT-B/16, using the kNN classifier.
Model
Top1 Accuracy (%)
CLIP ViT-B/16, ANN
24.57
SigCLIP VIT-B/16, &NN
29.68
DINOv2-base ANN
41.28
F CROWDSOURCING DETAILS
All trait description ratings performed solely by the authors of this paper. who voluntarily participated in the evaluation. The IRB indicated that our research is exempt and does not require approval. The evaluation rubric is shown in Table E7
G DATASET EXAMPLES
We use the BIOSCAN-5M GGarace et al. 2024) for training the SAE models and for dataset generation. It is licensed under the Creative Commons Attribution 3.0 Unported license, which permits its use for academic research. Trait annotation examples from BIOSCAN-TRAIE shown in Figures G.
H ANNOTATION COST ANALYSIS
Table H.8 summarizes the cost-of-use when calling Qwen2.5-VL-722 and GPT-5-mini via public APIs. Closed models such GPT-5-mini offer stronger performance at lower marginal API cost. In
21

---

Published as a conference paper at ICLR 2026
Table F.7: Example-based rubric for evaluating trait descriptions.
Score Example Image
Evaluation Criteria
5
Completely Correct Body part correctly identified; all traits visibly match (color, texture, shape, size); no hallucina- tions.
Example: "[Leg]: Thin, elongated, light brown, segmented."
Mostly Correct - Body part is correct; Most traits are ac- curate, one minor imprecision.
Example: "Leg: Thin, segmented, translucent; jointed struc- ture" (leg is not translucent, but other traits are correct)
Partially Correct - Body part correct; 1-2 traits vague incorrect.
Example: "[Leg]:
Thick, black, elongated." (body part is correct, but leg is thin and brown.)
2
Mostly Incorrect
Incorrect body part major trait mis-
matches.
Example: "[Segmented]: All parts are visibly segmented"
(the body part is missing, the segmented trait is correct though)
Completely Incorrect Hallucinated wrong body part. Example: "[Antennae]: dark brown." (No antennae are visi- ble)
22

---

Published as a conference paper at ICLR 2026
Figure G.6: Example 1 from BIOSCAN-TRAITS: "- Wing: Transparent, elongated, with visible veins.
Antenna: Thin, segmented, light brown".
Figure G.7: Example 2 from COOSAN--RAAITS [Leg]: Thin, elongated, light brown, segmented".
Figure G.8: Example 3 from BIOSCAN-TRAITS: ". Wing: Transparent, elongated, with visible veins. Antenna: Thin, segmented, dark brown".
Figure G.9: Example from BIOSCAN-TRAITS: " WWin:: Brown, translucent, folded, with visible
veins".
23

---

Published as a conference paper at ICLR 2026
Figure G.10: Example 5 from BIOSCAN-TRAITS: "- Antenna: Thin, elongated, segmented, dark color".
Table H.8: Cost-of-use analysis for generating trait annotations with Swen2.5-VL-72B (together.ai) and GPT-5-mini APIs, reported as average cost per annotation and extrapolated total cost for pro- cessing 100K images. The cost averaged over the BIOSCAN-TRAITS workload, with 1,072 input tokens and 250 output tokens per annotation.
Model
Cost per annotation
Approx. cost for 100K images
Qwen2.5-VL-72B API (together.ai) GPT-5-mini API
$4.1x10 /annotation
$410
$8x10 18x10- /annotation
$80
practice, the open-source Swen2.5-VL-72. can be hosted in-house, shifting cost from per-call API pricing to amortized compute, and allowing users with goteenoovenencee constraints to keep images on-premises.
I ECOLOGY APPLICATIONS
Below, we outline several concrete ways in which ecologists can leverage the proposed trait- nait-generation pipeline:
Expanding trait databases: Building trait databases by hand using domain experts is time- consuming. An automated tool can quickly add thousands of traits from existing images, populating databases or filling gaps. This helps ecologists who rely on traits (for example, to model species' niches or ecosystem roles) by providing many more data points.
study cor- relations between morphology and environment or behavior at scale. For instance, you could analyze how wing shapes vary across climates, link body color patterns predation risk. Traits explain ecological patterns better than just species names, and an automated pipeline makes these analyses feasible large collections.
Enabling new analyses: With rich trait labels attached to images, researchers
Boosting identification tools: As shown with BioCLIP, trait-annotated images can improve automatic pnecies-identificatee models. Models trained on trait captions learn more nuanced visual cues, making them more robust to new specimens or image conditions.
Overall, our pipeline provides scalable way to inject expert-like knowledge (descriptions of body parts) into machine learning without manual annotation. By turning images into meaningful trait statements, it bridges the gap between digitized specimens and quantitative trait databases, supporting wide range of biodiversity and ecological research.
J ADDITIONAL NEURON ACTIVATION ANALYSIS
Similar to Section 4.3, we analyze additional cases of the top-activating neurons (or latent dimensions) the SAE to investigate whether they correspond to meaningful morphological traits (Figurep.TI) For instance, observe that within the SAF, neuron 4040 consistently activates the thorax, while
24

---

Published as a conference paper at ICLR 2026
neuron 16584 responds to the leg-body junction, highlighting spatially grounded morphological regions.
Anthomyidae
Hymenoptera
Bethylidae
Chrysididae
Bethylidae
Normalized Aclivation Strength
Bradysia
Sciaridae
Ceratopogonidae
Soydmaeninae
Ceratopogonidae
Pholeomyia
Chironomidae
Metopina
Drosophilidae
Ceratopogonidae
Frankliniella invasor
Bemisia tabaci
Chironomidae
Chironomidae
Cecidomylidae
Figure J.11: Neurons 4040, 16584, 13433, and 14153 SAE get activated the thorax, at the place where the leg attaches to the body, eyes, and the abdomen, respectively. The labels denote the highest annotated taxonomic level.
K
ADDITIONAL DATASET ABLATION EXAMPLES
K.1 MLLM + SAE MLLM-ONLY BASELINE
Figure K.12 4.12K.15 K.15 present additional examples comparing the salient morphological trait descriptions generated by the MLLM-only baseline versus MLLM SAE.
K.2 QWEN-2.5-VL-7B vs. QWEN-2.5-VLL-72
present additional examples comparing the salient morphological trait descriptions generated by -.
Figure K.16 K.17
Owen-2.5-VL-72BB the backbone MLLM for MLLM SAE. The larger Sowen.55VLL-722 model accurately identifies the insect's body parts and avoids the hallucinations observed in its 7B counterpart.
25

---

Published as a conference paper at ICLR 2026
(8)
(b)
MLLM: Common Morphological Trails Across All Three Images: Head: Shape: Rounded, slightly elongated.- Cextrrexxtur:: Light-calored with darker speckled pattems.- Distinct Markings: Dark spots patches scallered
Shape: Small and compact, merging seamlessly with the head. Color/exture: Similar light coloration as the head, with dark speckles. Dislinct Markings: Speckled pattem continues from head. Abdomen: Shape: Oval-shaped, larger the head and thorax combined. ColorTexture:: Light color with dense covering of dark spackles.- Distinat Markings: Dark speckles form mottled appearance.
MLLM SAE: [Thin, elongated structure]: Segmented appearance, brownishidark coloration.
Figure K.12: Comparison of salient morphological trait description generation using just MILMM vs. MLLM + SAE for Scytodes intricata. Each red box highlights a region selected by SAE neurons with high activation, indicating regions used for prompting the MLLM + SAE.
(a)
(b)
(c)
MLLM:
Common Morphological Traits Across All Three Images: Head Shape: Rounded and slightly
flattened. Size: Small relative to the body. Color: Light brown or belge. Texture: Appears smooth
no distinct markings. Thorax Shape: Slightly elongated and oval. Size: Medium-sized, connecting the head and abdomen. Color: Light brown beige, similar to the head. Texture:
Smooth, no distinct markings. 3. Abdomen Shape: Large, rounded, and bulbous. Size: Significantly Color:
larger
and Smooth, with a glossy appearance.
compared
Texture:
SAE: [Leg]: Thin, elongated, brown, segmented.
Figure K.13: Comparison of salient morphological trait description generation using just MILM vs. MLLM + SAE for Erigone psychrophila. Each red box highlights a region selected by SAE neurons with high activation, indicating regions used for prompting the MLLM + SAE.
26

---

Published as a conference paper at ICLR 2026
(a)
(b)
(c)
MLLM: Common Morphological Traits Across All Three Images: Body Segmentation: Shape: The body segmented into multiple rounded sediins.. Size: The segments appear uniform size
relative to one another. Color: The segments are dark purple or maroon. Texture: The surface appears rough slightly smalle maller than the other segments and is located at one end the body. Color: The head
bumps
segment. Head: Shape:
matches the dark purple or marbon color of the rest of the body. Distinat Markings: lighter-colored visible
front head, possibly cating sensory organs mouthparts. Antennae: Shape: Two short, thin antennae are visible extending from the head.
MLLM SAE: Body; Dark brown, textured surface; irregular shape.
Figure K.14: Comparison of salient morphological trait description generation using just MLLM
vs. MILM + SAE for Morulina thulensis. Each red box highlights region selected by SAE neurons with high activation, indicating regions used for prompting the MILM + SAF.
(a)
(b)
(c)
Marphological Traits
Images: Body Segmentation: Shape:
body segmented into muliple rounded sections. Size: The segments appear uniform in size
relative to one another. Color: The segments are dark purple or maroon. Texture: The surface appears rough with small protrusions bumps slightly smaller than the other segments and is located at end of the body. - Color. The head matches the dark purple maroon color the
segment. Head: Shape: The head is
body Distinct Markings: colored area visible near the front of the head, possibly indicating sensory organs mouthparts. Antennae: Shape: Two short, thin antennae are visible extending from head.
MLLM SAE: Body: Dark brown, textured surface; irregular shape.
Figure K.15: Comparison of salient morphological trait description generation using just MLLM vs. MILM + SAE for Islandiana cristata. Each red box highlights region selected by SAE neurons with high activation, indicating regions used for prompting the MLLM + SAE.
27

---

Published as a conference paper at ICLR 2026
<wen2.5.VL-7B: [Body Part]: Wing Shared visible trait: Large, translucent, with slightly darker
margin, smooth texture.
Qwen2.5-VL-728. [Leg]: Thin, elongated, segmented. light brown.
Figure K.16: Comparison of morphological trait description quality between Qwen2.5-VL-7B and Owen2.5-VL-721 for Agyneta straminicola. Each red box highlights a region selected by SAE neurons with high activation, indicating regions used for prompting the MLLM SAE. The larger model correctly identifies the highlighted body part of the insect.
(b)
(c)
Sween.55VV-77B Wing (Shared visible trait): Translucent, with darker margin
Qwen2. Swwnn.5-VL.-2B: [Leg]: Thin, elongated, dark brown, segmented. [Antenna]: Thin, long, dark brown,
Figure K.17: Comparison of morphological trait description quality between Qwen2.5-VL-7B and Qwen2.5-VLL-7 V-L for Erigone psychrophila. Each red box highlights a region selected by SAE neurons with high activation, indicating regions used for prompting the MLLM SAE. The larger model correctly identifies the highlighted body part of the insect.
28

---

Published as a conference paper at ICLR 2026
K.3 MULTIPLE vs. SINGLE IMAGE PER LATENT
Figure K. K.19 present additional examples comparing the salient morphological trait descriptions generated using single image versus multiple images for MLLM SAE. This consensus-drinn driven trait extraction encourages the model to focus on consistent traits and leads to improved precision.
(a)
(b)
(c)
(a) Single image: (a, c): highlighted appears encompass
part
abdomen of the insect. Observable Morphological Features: Shape: The thorax is somewhat
rectangular connects relatively large compared to the head and legs, while the abdomen slongated. Color: The thorax and abdomen have dark brown coloration with lighter, possialy yellowish or beige, pattems or markings.- Texture: The surface appears have fine
abdomen, which tapers slightly towards the end.
thorax
giving slightly textured look. Distinct Markings: There are ighter. irregularly shaped markings on the darker background of the thorax
(a. b, Multiple images: Fine Hairs: Present on legs and body, small and
Figure K.18: Comparison of salient morphological trait description generation using single image vs. three images for Deltocephalus fuscinervosus. Each red box highlights region selected by SAE neurons with high activation, indicating regions used for prompting the MLLM SAE. Using multiple images with SAE-extracted bounding boxes leads to dramatically improved precision.
(c)
The highlighted area encompasses the legs and part of the thorax of the insect. Observable Morphological Features: Shape: legs elongated and segmented, visible at various points along their length. The thorak is partially visible and appears rounded. Size: The legs are relatively thin compared the overall size of the insect. The thorax section within highlighted area broader than the legs. Color: The legs thorax appear black color, Texture: The surface of the legs seems somewhat smooth but with fine hairs or setae markings patterns
(a) Single image:
joints
dark brown
upon the legs or thorax within the highlighted area. (a, b, c) Multiple images: [Leg]: Elongated, brown, segmented.
inspection.. Distinct Markings:
Figure K. 19: Comparison of salient morphological trait description generation using single image vs. three images for Erigone arctophylacis. Each red box highlights a region selected by SAE neurons with high activation, indicating regions used for prompting the MLLM SAE. Using multiple images with SAt-extracted bounding boxes leads to dramatically improved precision.
L LLM USAGE DETAILS
We utilized large language models (LLMs) to aid in the writing and editing of this paper. Their role within our trait-generation pipeline, specifically the use of multimodal LLMs (MLLMs), is described in Section 3
29


Reviews:
[{"review_id": "7mY33OOn75", "reviewer": "Reviewer_dwm8", "summary": "This work uses sparse autoencoders to generate trait descriptions of insect images from BIOSCAN-5M. The authors select a 19k image subset of the dataset labeled to the species level and generate trait captions. They perform a series of ablations to explore how the train description vary as a function of workflow components. Three domain experts provided ratings to assess quality. The authors investigate improvements in a zero-shot setting when using their dataset for fine tuning BioCLIP.", "questions": "- Few of the terms in the equations in section 3.1 are defined. What do they represent?\n- Line 200: what proportion of BIOSCAN-5M is annotated to the species level? \n- How is $t_{activation}$ set? How does varying the threshold impact trait selection? \n- Line 318: What is the rubric used for human quality rating? The scores are stated without a range so it is difficult to contextualize improvements. \n- In section 4.5, is 'in-the-wild' meant as natural images vs. the preserved samples in BIOSCAN-5m? Or something else? There are number of other distribution shifts inherent in the case of the former that worth expanding upon. \n- How were the traits encoded in when fine-tuning BioCLIP? \n- What is the species overlap with between BIOSCAN-5m and the BioCLIP training sets?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "- The combination of SAE and MLLM is a new approach to auto-generating trait captions for biological images. \n- The authors provide a number of ablations, as reviewed by domain experts, to assess the quality of the resulting annotations as a function of different elements of their pipeline. \n- Overall writing clarity is ok, with a few confusing sections as noted below. \n- The annotation pipeline is the most significant part of the work thought it will need lots more testing in different domains, even of biodiversity imagery.", "weaknesses": "- The ablation study appears quite robust, but is missing an exploration of what $t_{activation}$ does. \n- The explanation of the zero-shot experiments need some clarification.\n- The authors could spend some time discussing how their labeling strategy might impact a hierarchical framing of the classification problem. It seems they significantly reduce the amount of data available by requiring images to be drawn from the same taxonomic level.", "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": "gJ4fOnefR0", "reviewer": "Reviewer_npDF", "summary": "The paper proposes a pipeline for automatically annotating morphological traits at the image level from organismal images. The method (i) extracts dense visual features (DINOv2) and passes them through a sparse autoencoder (SAE) to obtain monosemantic, spatially grounded latent units; (ii) converts high-activation latents into localized boxes/masks; and (iii) prompts a multimodal LLM (here, Qwen2.5-VL-72B) to produce natural-language trait descriptions for those regions. Applying this to BIOSCAN-5M, the authors release BIOSCAN-TRAITS: ~80k trait annotations over ~19k insect images. Human evaluation indicates these SAE-guided descriptions are more precise than MLLM-only and Grad-CAM baselines. Fine-tuning BioCLIP on BIOSCAN-TRAITS improves zero-shot species classification on the Insects benchmark (+2.9% absolute), suggesting trait-level supervision boosts downstream generalization.", "questions": "**Why not BioCLIP2?** You fine-tune BioCLIP and show +2.9% on Insects. If BioCLIP2 is available, can you (a) replicate the gain, and (b) test whether trait-level supervision compounds improvements already present in BioCLIP2? Reporting both would isolate the incremental value of trait supervision from backbone/dataset size advances.\n\n**Other LMMs (open vs. closed) and robustness**: Beyond Qwen2.5-VL (7B/72B), have you tried LLaVA-Next, Gemini-Vision, or GPT-4o-mini for trait generation? A compact matrix (quality, hallucination rate, token usage, throughput) would reveal portability, licensing trade-offs, and whether SAE-guided prompting narrows the gap between open and closed models. You already compared Qwen 7B vs. 72B; extending that table to 2–3 additional LMMs would be very informative.\n\n**Cost-of-use analysis:**  Could you add: Per-image cost & time: preprocessing (feature extraction + SAE forward), #patches, tokens, MLLM seconds, and total $ at typical API/on-prem pricing; Throughput under batching and GPU memory constraints for open-weights models. This would let institutions forecast the budget for, say, 100k images.\n\n**How do you envision end-users using it?**  It would help to spell out a reference workflow for curators/biologists\nEven a short “Operations” section or a demo video link would greatly increase practical adoption.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 4, "presentation": 3, "contribution": 4, "strengths": "This is a well-written paper on a topic of significant importance. \n\n*Clear, modular pipeline with interpretability baked in*: SAEs yield localized, monosemantic units (e.g., “wing”, “antenna”) that ground the text prompts and reduce hallucination. The pipeline is easy to reason about and replicate. \n\n*Substantive dataset contribution*: BIOSCAN-TRAITS (80k traits / 19k images) fills a gap in large-scale, trait-level supervision for biodiversity ML.\n\n*Downstream utility*: Fine-tuning BioCLIP with trait-level labels improves zero-shot accuracy on an in-the-wild benchmark, underscoring practical value.", "weaknesses": "*Model coverage of VLMs/LMMs is narrow*: Most experiments rely on Qwen2.5-VL (7B vs. 72B); broader open/closed LMM comparisons (and robustness under domain shift) would strengthen the paper.\n\n*Cost/latency unquantified beyond tokens*: While the paper reports token counts per setting, it lacks a wall-clock + $-cost analysis per annotated trait/image across model sizes and batching regimes. This will be very useful for labs and museums planning large runs.\n\n*BioCLIP only (no BioCLIP2):* Downstream tests fine-tune BioCLIP; given rapid progress in biologically grounded VLMs, results may be stronger (and more current) with BioCLIP2 if available. \n\n*End-user pathways are implicit*. The paper could more concretely describe how curators or ecologists operate the system (inputs, thresholds, QA loop, export formats) and how to integrate trait text with existing collection databases.", "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": "rw8LGKtf8m", "reviewer": "Reviewer_nY1L", "summary": "Morphological traits (the measurable physical characteristics of organisms) accurately predict how species interact with their environments, but extracting these traits remains a slow, expert-driven process. This paper contends that sparse autoencoders trained on foundation-model features yield monosemantic, spatially grounded neurons that consistently activate on meaningful morphological parts. Leveraging this property, we introduce a trait annotation pipeline that localizes salient regions and uses vision-language prompting to generate interpretable trait descriptions. Using this approach, we construct BIOSCAN-TRAITS, a dataset of 80K trait annotations spanning 19K insect images from BIOSCAN-5M. Human evaluation confirms the biological plausibility of the generated morphological descriptions. When used to fine-tune BioCLIP, a biologically grounded vision-language model, BIOSCAN-TRAITS improves zero-shot species classification on the in-the-wild Insects benchmark.\n\nAuthors' contributions: The recognition that sparse autoencoders (SAEs) can be used as interpretable part-detectors for trait extraction. In practice, training an SAE over pre-trained image features produces units whose activations map back onto tight, spatially coherent regions, and a method taking advantage of SAEs as trait detectors. A new dataset, BIOSCAN-TRAITS, and some experiments showing that it is useful for downstream classification tasks.", "questions": "* Are the images for the main figures cherry-picked? If so, the authors should say so.\n* What are the limitations and drawbacks of the authors' approach? Under what circumstances will it be most effective?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 4, "contribution": 3, "strengths": "* This is an unusually well-written paper. I very much appreciate the clear dual expertise of the authors, which allows them to efficiently and accurately describe the mechanism by which SAEs operate as well as the challenges of Morphological Trait Extraction. The Related Works section of this paper, while not long, is good.\n* The topic is important, and understudied; I'm very happy to see more work in this area.\n* Table 4 is a nice contribution; it's a nice finding that BIOSCAN-TRAITS works better than species-only fine-tuning.", "weaknesses": "I do have some concerns about the authors overclaiming particularly in the introduction, and the results not being as comprehensive as I would like in certain places.\n\n* The paper spends a long time on introduction and related work; we only get to the actual experiments on page 4. This is not a stylistic problem, as I recognize this paper probably needs more introduction than most, but it does mean there isn't as much room for actual description of the method and experiments. I think the second half of the introduction could be compacted, especially considering it contains many bold statements which aren't all that well supported.\n* The method ablations are unusually weak for an empirical paper; only two backbones considered, DINOV2 and ViT-B-16 CLIP, and no ablations on the choice of SAE. A wider range of self-supervised and contrastive backbones would have been nice to see here, e.g. DINOV1, DINOV3, SigLIP, etc, and the decision to use an SAE is absolutely central to the method and should have been ablated.\n* Personally I think the Algorithm 1 is not that important for understanding the work and should go to the appendix; I would rather see more core experiments (or ablations) here and the algorithm in the appendix.\n* GradCAM is fine as a (weak) initial baseline; a more realistic baseline would be semi-supervised object detection based on target traits similar to (https://acsess.onlinelibrary.wiley.com/doi/10.1002/ppj2.20107), would also be nice to see how PCA, L1-regularized linear models, and Random projections do. These are extremely cheap to run, you don't even need a GPU.\n* The authors write that the procedure is fully unsupervised; this is true only if you don't count the backbone -- DINO uses various kinds of weak and self supervision during pretraining, I think it would be clearer to include a mention of this. Furthermore, species-contrastive ranking sounds very much like it would require supervised / labeled data.\n* BIOSCAN-5M is an unusually friendly dataset for this type of mapping because the insects are dead and presented mostly centered in the image, and well lit on plates, and there is always exactly 1 insect per image. It would have been nice to have seen an ablation on how well SAEs work on in-the-wild insect data, but failing that, I think the claim should be softened accordingly.\n* SAEs are not nearly as robust as the paper's claims would indicate. Sometimes they work well, but often they're no better than much simpler, older approaches. https://arxiv.org/html/2501.16615v1, https://arxiv.org/html/2505.11756, https://arxiv.org/abs/2501.17148, https://arxiv.org/abs/2502.16681v1. Max Tegmark, Neel Nanda, Christopher Manning, Christopher Potts are all doubters of SAEs at this point. I think this should be mentioned as a significant limitation.\n\nIf these concerns can be addressed during the rebuttal, I think the paper is worthy of acceptance.", "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)