I trained a 44M parameter quantized LLM from scratch on 45B tokens. It ships in 19.8 MB and runs at ~1,900 tok/s on CPU. [P]
Reddit r/MachineLearning submitted by  
Three weeks back , i posted SHADOW-250M here. It got 360 upvotes, 293 on r/LocalLLaMA and 94 GitHub stars. Thank you.
That model was 60 MB, ran around 400 tok/s on CPU and could retrieve records from an archive on disk. What it couldn’t do reliably was reason over what it retrieved or compute. So I built a smaller one to experiment with those two problems.
SHADOW-50M is actually 44M parameters, trained from scratch on 45B tokens. 19.8 MB complete model, ~1,900 tok/s on laptop CPU, ~41 MB RAM, ternary {-1,0,+1} weights, 73,880-token vocabulary represented by fixed 512-bit fingerprints instead of a trained embedding, and a 159 KB compiled kernel. It runs completely offline. The same kernel compiled to WebAssembly runs in a browser tab at around 500 tok/s.
This is a proof of concept, not a product.
If SHADOW decides something needs calculation, it writes something like [calc]347*86[eq]. A fixed circuit at the readout takes over and fills in the correct digits in the same token stream. No calculator API, no tool call and nothing pasted back into the prompt. I added circuits for arithmetic, percentages, dates, weekdays, units, counting, sorting, comparisons and a small program machine.
When SHADOW stores a record, it reads it once and writes its attention state to disk at 1 bit, 288 bytes/token. Later it can write [need]condition of Patient P-204, the index finds the record in roughly a microsecond and the stored attention state goes directly back into the model in about 0.03 ms. The text isn’t re-read.
The index is 22 bytes/token and doesn’t use a vector database or embedding model. At 100M tokens, the archive is 28.8 GB plus a 2.2 GB index on disk, while the process uses about 28 MB RAM because everything is memory-mapped and a question only touches the pages it needs.
The index also has a small persistent trail. When retrieved information gets used, that record is reinforced in the index itself. On repeated questions, measured top-1 went from 0.571 to 0.743 without training the model.
I also put it next to Supra-50M-Reasoning, a 51.8M parameter Llama-style bf16 model.
Supra beats SHADOW on every standard benchmark I tested. ARC-Easy: 0.435 vs SHADOW 0.307. PIQA: 0.600 vs 0.570. WikiText-2 perplexity: 165 vs 186.
I’m publishing that because I don’t want to hide where SHADOW is weak.
But those tests aren’t what I trained this model for. Here are some actual greedy outputs, one fresh process per question:
“Tell me a short joke about computers.”
SHADOW: Why did the computer go to the doctor? Because it had a virus.
Supra: Thought for 189 tokens about what computers are and stopped without a joke.
“I have 3 books and I bought 5 more. How many books do I have now?”
SHADOW: You have 8 books.
Supra: Books often come from collections of short stories, poems, or other forms of literature.
“My bill is 240 dollars. What is 15 percent of that?”
SHADOW: 36 dollars.
Supra: 15% ($240) is $250. It’s $150 because 20% of it is $200.
“What date is 45 days after December 20, 2026?”
SHADOW: February 3, 2027.
Supra: The German government introduced a new date called March 22nd that precede February 25th in 1900.
Eight records stored, one of them “Patient P-204 condition: asthma”.
“What is the condition of Patient P-204?”
SHADOW: From disk: The condition of Patient P-204 is asthma.
Supra, with the records in its prompt: The patient experiences asthma when they get too much sleep.
“What is the condition of Patient Z-999?” No such record exists.
SHADOW: There is no record of Patient Z-999.
Supra: The condition of an allergic reaction occurs when the immune system mistakenly attacks specific cells on the body.
All 11 side-by-side questions, scripts and traces are in the repo.
On size, Supra is 103.6 MB in bf16 and 56.2 MB at 8-bit. Below that it starts losing quality. Int4 takes its perplexity from 165 to 193 and ternary breaks it. Its vocabulary is 32,000 tokens. SHADOW carries 73,880 tokens in a 4.7 MB frozen table.
Something unexpected happened with that frozen table too.
The first SHADOW-50M release was missing about 8,600 English word pieces. Lowercase “fitzgerald”, for example, could reach the model as “fitz”. I tried fixing it through fine-tuning, but every run that learned the missing words broke something else.
So I added the 8,600 missing rows directly to the frozen fingerprint table. No training. Same weights.
All 34/34 previously published answers stayed unchanged, while the model could now read many of the new pieces. The table scores 0.594 Spearman against human word-similarity ratings versus -0.057 for random codes.
A trained embedding can’t simply accept thousands of new rows without training. A frozen table can.
I also built four harnesses that put this tiny model next to larger models.
My favourite is video memory. Gemma 3 4B watches a ten-minute film once, one frame every two seconds, and writes 298 little descriptions such as “Moment M-0039 scene: A chubby white rabbit reaches for a purple butterfly.”
Then Gemma leaves.
SHADOW keeps those 298 moments as memory on disk. Afterwards I can ask the 20 MB model what happened at a particular moment, by number, by time or by what appeared in it. It answers from memory with the record quoted, without the film and without Gemma. It scored 55-56/60 across those query types on a laptop in about 42 MB RAM.
The other three experiments: an inventory of 1,600 records got 159/160, with all 20 questions about items never stored correctly returning “no record”; SHADOW as a draft model for Qwen3-32B took llama.cpp generation from 19.7 to 28.5 tok/s while Qwen still chose every final token; and an MCP memory server let Qwen3-14B store facts mid-chat and later retrieve 5/5 with the original records quoted.
There are plenty of shortcomings. General knowledge is thin. Creative writing isn’t good. Seven-digit operands sometimes get copied incorrectly. A large archive can occasionally pull an unrelated record into a question carrying a number. They’re documented in the repo next to the successful results.
And one thing happened after my last post that I really didn’t expect.
Someone called engram-forge sent a pull request to SHADOW-250M containing a CUDA engine, a quantization tutorial, and then a talking Peppa Pig plush toy with SHADOW inside it.
Microphone, small speaker, ~$35 board. You talk to the toy, it listens, SHADOW generates the answer locally and the toy talks back. No cloud, no account, no internet.
I haven’t merged the ~11,000 lines yet because I can’t properly verify that much CUDA myself. When the demo is finished I’ll keep it under engram-forge’s name.
I never imagined one of these models living inside a stuffed toy on someone’s shelf .Thanks
I’m not saying a 20 MB model beats normal LLMs. It doesn’t. I’m trying to find out how much useful behaviour can fit into a tiny local model when computation and persistent memory are treated differently.
Everything is MIT licensed. The master weights and fine-tuning/export kit are public. Next I’m releasing the training code, dataset, frozen table and a proper write-up of how I built it, including the costs, failed experiments and mistakes.
Code:
https://github.com/QLNI/SHADOW-50M-Instruct
Weights:
https://huggingface.co/QLNI/shadow-50m-instruct
Run it in your browser:
https://qlni.github.io/SHADOW-50M-Instruct/web
AllShowers: One model for all calorimeter showers
arXiv cs.LGarXiv:2601.11716v2 Announce Type: replace-cross
Abstract: Accurate and efficient detector simulation is essential for modern collider experiments. To reduce the high computational cost, various fast machine learning surrogate models have been proposed. Traditional surrogate models for calorimeter shower modeling train separate networks for each particle species, limiting scalability and reuse. We introduce AllShowers, a unified generative model that simulates calorimeter showers across multiple particle types using a single generative model. AllShowers is a continuous normalizing flow model with a Transformer architecture, enabling it to generate complex spatial and energy correlations in variable-length point cloud representations of showers. Trained on a diverse dataset of simulated showers in the highly granular ILD detector, the model demonstrates the ability to generate realistic showers for electrons, photons, and charged and neutral hadrons across a wide range of incident energies and angles without retraining. In addition to unifying shower generation for multiple particle types, AllShowers surpasses the fidelity of previous single-particle-type models for hadronic showers. Key innovations include the use of a layer embedding, allowing the model to learn all relevant calorimeter layer properties; a custom attention masking scheme to reduce computational demands and introduce a helpful inductive bias; and a shower- and layer-wise optimal transport mapping to improve training convergence and sample quality. AllShowers marks a significant step towards a universal model for calorimeter shower simulations in collider experiments.
CBW: Towards Dataset Ownership Verification for Speaker Verification via Clustering-based Backdoor Watermarking
arXiv cs.LGarXiv:2503.05794v4 Announce Type: replace-cross
Abstract: Speaker verification models are trained on large-scale public datasets whose licenses usually prohibit unauthorized commercial use, yet such infringement is difficult to detect or deter. Dataset ownership verification (DOV) is the mainstream countermeasure: it can watermark a dataset with backdoor attacks so that models trained on it exhibit owner-specified behaviors. However, existing DOV methods presuppose a closed label space fixed at watermarking time, whereas in open-set speaker verification the identities that a deployed model accepts are enrolled by third parties after release and are never observed by the dataset owner. We show that straightforward adaptations fail in two characteristic modes, and accordingly distill three requirements for an effective watermark, namely identity agnosticism, coverage, and fidelity, together with an intrinsic tension between the latter two. Our clustering-based backdoor watermark (CBW) resolves this tension by partitioning training speakers into clusters by feature similarity and implanting a distinct trigger for each cluster, so that each trigger covers one region of the speaker embedding space while the trigger set is designed to jointly cover it. We further develop paired hypothesis tests for ownership verification under both the similarity-available and the decision-only black-box settings at the 1-to-1 and 1-to-$N$ enrollment scales, and theoretically characterize when the audit succeeds, including an exact small-sample certificate and the effect of the enrollment size. Extensive experiments on benchmark datasets and representative models verify the effectiveness of our CBW, its resistance to watermark-removal attacks, and its transferability across model structures. Code is at https://github.com/Radiant0726/CBW/tree/master.
Task- and dataset-specific information in protein language models
arXiv cs.LGarXiv:2608.12090v3 Announce Type: replace
Abstract: Protein language models (PLMs) have transferred the latest advances from natural language processing to computational biology. These models, trained on large corpora of protein sequence data, are widely used to translate amino acid sequences into latent-space embeddings, ready for use in diverse downstream tasks (DTs). By consensus, embeddings from the models' last layers are used, while the models' internal behavior remains poorly understood. We analyzed 13 PLMs across 15 DTs and 9 datasets to assess the value of embeddings from intermediate PLM layers. We trained probe models on embeddings from each layer, compared their performance, and showed that the last layers of PLMs rarely produced embeddings that led to the best results on downstream tasks. Furthermore, we identified a connection between how models learn a certain DT and the similarity between that DT and the pre-training objective. For example, for residue-level downstream tasks, we observed a steady increase in performance across almost all PLM layers, which we attributed to their similarity to most PLMs' pre-training objectives. To allow the community to capitalize on our findings, we provide PLMSommelier, a Python package that automatically identifies the best PLM layer for a given DT with ~98% accuracy and creates a truncated model using only the early layers up to the best-performing layer. This will help users save time and memory during inference and yield better predictive performance.
A Spectral Decomposition Framework for Multiscale Nonlinear Dimensionality Reduction
arXiv cs.LGarXiv:2604.02535v2 Announce Type: replace
Abstract: Dimensionality reduction (DR) involves two longstanding trade-offs. First, preserving local neighborhoods can come at the cost of global structure. Neighbor embedding methods such as t-SNE and UMAP prioritize local similarity preservation but do not explicitly constrain global organization, whereas standard spectral methods such as Laplacian Eigenmaps capture smooth, coarse-scale graph structure but offer limited flexibility to depict finer local structure. Second, the flexibility of nonlinear DR methods often comes at the cost of analytical transparency. Many methods do not explicitly reveal how high-dimensional structure produces patterns in the embedding. We introduce SDMP (Spectral Decomposition for Multiscale Projection), a nonlinear DR framework built on an explicit spectral decomposition. In this formulation, each embedding dimension is expressed as a weighted combination of Laplacian eigenvectors derived from a neighborhood graph, with the weights learned via a UMAP-style cross-entropy objective. By progressively expanding the spectral subspace to capture increasingly fine graph structure, SDMP produces a sequence of embeddings, making the evolving balance between global organization and local detail explicit, controllable, and inspectable. The explicit decomposition also reveals which spectral scales shape the overall embedding and how individual eigenvectors influence point positions. Quantitative evaluations on synthetic, image, and single-cell data show competitive local and global structure preservation, while case studies illustrate how the decomposition supports interpretation of clusters and developmental trajectories across spectral scales.
GraphIFE: Rethinking Graph Imbalance Node Classification via Invariant Learning
arXiv cs.LGarXiv:2509.23616v2 Announce Type: replace
Abstract: The class imbalance problem refers to the disproportionate distribution of samples across different classes within a dataset, where the minority classes are significantly underrepresented. This issue is also prevalent in graph-structured data. Most graph neural networks (GNNs) implicitly assume a balanced class distribution and therefore often fail to account for the challenges introduced by class imbalance, which can lead to biased learning and degraded performance on minority classes. We identify a quality inconsistency problem in synthesized nodes, which leads to suboptimal performance under graph imbalance conditions. To mitigate this issue, we propose GraphIFE (Graph Invariant Feature Extraction), a novel framework designed to mitigate quality inconsistency in synthesized nodes. Our approach incorporates two key concepts from graph invariant learning and introduces strategies to strengthen the embedding space representation, thereby enhancing the model's ability to identify invariant features. Extensive experiments demonstrate the framework's efficiency and robust generalization, as GraphIFE consistently outperforms various baselines across multiple datasets. The code is publicly available at https://github.com/flzeng1/GraphIFE.
GPEvac: GNN-Based PPO for Adaptive Evacuation Routing During Shooting Events
arXiv cs.LGarXiv:2609.16163v1 Announce Type: cross
Abstract: The sharp increase in mass shootings underscores an urgent need for systems that guide victims to safety in real time. An effective evacuation system must minimize threat exposure while also accounting for adversarial uncertainty and crowding dynamics. Current methods in the literature are rigidly constrained to layout-specific policies and computationally intractable in large-scale layouts, while practical guidelines simply advise victims to "run", "hide", or "fight". We propose GPEvac: a GNN-based PPO framework that computes adaptive evacuation routes during shooting events. To capture both local and long-distance dependencies, we introduce an edge-first sequential message-passing scheme with a learnable virtual global node. The resulting graph embeddings are integrated into a permutation-invariant scoring mechanism that allows a single learned policy to operate across building layouts of diverse topologies and sizes. Through extensive simulation, we show that GPEvac outperforms intelligent baselines across distinct architectural layouts, significantly reducing total threat exposure. Crucially, the system computes global evacuation routes in just 14.73 ms on local CPU hardware, enabling seamless integration with live surveillance systems. In addition to saving lives during shooting events, the methodologies developed are transferable to other graph-structured decision-making domains, including critical infrastructure, intelligent transportation systems, and adaptive sensor networks.
Bridging the Confidence Gap: Temperature Scaling for Calibrating Test-Time Prompt Tuning
arXiv cs.LGarXiv:2609.17386v1 Announce Type: new
Abstract: Test-time prompt tuning (TPT) enables adaptation on a single test instance, achieving improved accuracy but often sacrificing calibration performance. Most existing calibration methods introduce additional regularization terms to promote dispersion across text embeddings and reduce calibration error, yet these methods often suffer from a drop in accuracy. Motivated by the well-calibrated nature of zero-shot predictions, we propose CoTS, a simple yet effective post-hoc calibration method that preserves accuracy. Specifically, CoTS applies temperature scaling to minimize the confidence gap between adapted and zero-shot predictions. To fully exploit the potential of multiple augmentations during adaptation, we introduce a weak-strong ensemble strategy that further boosts accuracy. We then apply CoTS to this ensemble, termed E-CoTS, to maintain its well-calibrated property. Extensive experiments on diverse datasets and backbones show that our approaches effectively mitigate miscalibration without compromising primary accuracy. For instance, E-CoTS reduces the average expected calibration error of TPT from 11.90% to 5.38% on ImageNet variants, while even increasing accuracy from 60.74% to 62.95%. Moreover, when integrated with existing calibration methods, E-CoTS usually enhances both accuracy and calibration simultaneously.
Repurposing Unified Topological Signatures for Graph Representation Learning
arXiv cs.LGarXiv:2609.17061v1 Announce Type: new
Abstract: Message-passing Graph Neural Networks (GNNs) iteratively propagate and aggregate local neighborhood information followed by global readout to learn graph representations. However, their discriminative power is upper-bounded by the Weisfeiler--Lehman (1-WL) graph isomorphism test. This prevents GNNs from distinguishing certain non-isomorphic graphs with identical local neighborhood structures, often leading to similar graph representations. Unified Topological Signatures (UTS) capture compact, multi-scale representation of global graph topology derived from persistent homology. We introduce two complementary UTS signatures: Graph_UTS- a static signature of the input graph topology, and Embedding_UTS- a dynamic signature of the evolving embedding topology. They encode structural information inaccessible to 1-WL-based message-passing GNNs, yet their capabilities are explored solely for post-hoc embedding-space analysis. We integrate UTS into GNN training across three architectural interventions: (i) UTS-Aug: augmenting with standard readout feature that encodes graph's true topology; (ii) UTS-Reg: topological regularizer that constrains representation collapse; (iii) UTS-Pool: topology-guided pooling that retains structurally critical nodes. We further leverage UTS as a layer-wise diagnostic to quantify oversmoothing during GNN training. Theoretically, we show that integrating UTS into GNN optimization strictly extends GNN expressivity beyond the 1-WL hierarchy. Experiments on three graph classification benchmarks show consistent benefits: Graph-UTS, Dual-UTS, and UTS-Pool improve accuracy across all three datasets, Embedding-UTS provides smaller but similarly consistent gains, and UTS-Reg's benefit varies across graph domains. Accuracy improves by up to 5.8% with Graph-UTS augmentation, by up to 1.9% with UTS-Reg, and achieves comparable performance to TOGL with UTS-Pool.
Distributed JEPA: A Self-Supervised Framework for Energy Forecasting
arXiv cs.LGarXiv:2609.17029v1 Announce Type: new
Abstract: Traditional energy forecasting solutions rely on task-specific supervision and energy asset representations, limiting transferability and the ability to capture general temporal dynamics across heterogeneous assets. We address this by proposing a distributed Joint Embedding Predictive Architecture (JEPA) for self-supervised learning from heterogeneous energy time-series. The framework predicts latent representations of masked temporal segments while integrating temporal observations and contextual information within a shared embedding space. To prevent representation collapse, training combines a latent-space predictive objective with covariance and temporal variance regularization. The evaluation was conducted on energy consumption and generation datasets under data-degradation scenarios and compared with a Transformer forecasting baseline. The learned representations remained stable (cosine similarity $\approx 0.98$; effective rank 185-235). JEPA achieved performance comparable to a Transformer on building energy data, higher $R^2$ in 3/5 consumer clusters, and outperformed the baseline on 9/10 unseen PVs ($R^2$=0.73-0.88 vs. <0.45), while showing greater robustness to missing data.
Bounded Adjustment with Reliability-Guided Embedding for Imbalanced Learning with Noisy Labels
arXiv cs.LGarXiv:2609.16380v1 Announce Type: new
Abstract: Class-balanced learning and label noise create a coupled failure mode: frequency correction prevents majority classes from dominating the decision rule, but can amplify incorrectly labeled minority examples. We introduce BARGE (Bounded Adjustment with Reliability-Guided Embeddings), a single-stage objective combining a bounded, prior-adjusted density-power score with reliability-guided angular geometry. Its classification score is strictly proper in the adjusted probability space and recovers balanced Bayes ordering under clean supervision and the true class prior. Under label contamination, its finite range bounds classification-risk perturbation at a fixed predictor, while its logit gradient redescends when the model confidently contradicts the supplied label. The adjusted target probability also weights class-equal feature compactness, and a one-sided separation term discourages aligned class directions. BARGE requires neither a noise rate nor a transition matrix, uses one network, and leaves inference unchanged. We evaluate it on CIFAR-10, CIFAR-100, and Tiny ImageNet under long-tail and step imbalance, clean labels, and 20% and 40% random incorrect-label replacement. Across 12 clean settings, BARGE ranks second overall and attains the lowest error in four. Under corruption, it achieves the lowest mean balanced error in all six dataset-corruption settings, reducing the six-setting average from 72.32% for the strongest competitor to 70.00%. It also obtains the highest macro-F1 and macro-AUPRC in every corrupted-label setting. Ablations show that class-equal angular compactness improves on the bounded score alone. These results support bounded predictive influence and reliability-guided geometry as complementary mechanisms for imbalanced learning with uncertain labels.
Agentic Search Spaces for Tabular Machine Learning
arXiv cs.LGarXiv:2609.16309v1 Announce Type: new
Abstract: Despite the rapid progress of LLM-based agents for planning, code generation, and debugging, their practical value for tabular machine learning remains underexplored. In this paper, we investigate a concrete use case: whether state-of-the-art agentic AI systems can design extended HPO search spaces for established tabular models that outperform the standard search spaces provided by the model authors. Specifically, we represent each tabular model as a modular pipeline covering preprocessing, embeddings, architecture, training, and inference. We then task the agent to propose candidate code implementations for each module and use a classical HPO algorithm to jointly optimize over these candidates and the model's default hyperparameters. Compared with the base HPO spaces, the expanded search spaces improve the performance of nearly every model family across a suite of 45 datasets, with average relative gains of 0.6%, rising to 2.0% on small-to-medium regression datasets. Notably, these gains come at no extra tuning cost: the enlarged spaces outperform the base under the same tuning and ensembling budgets. The gains transfer to the recent TabArena benchmark, where the agentic spaces improve the official Elo scores of four of the five model families and the two strongest agentic ensembles surpass the best AutoGluon ensemble of conventional models. Overall, our study suggests that LLM agents can provide practical value for tabular ML by expanding the design space.
Schema-Adaptive Action-Conditioned JEPA for Cross-Machine CNC Transfer under Partial Sensor Overlap
arXiv cs.LGarXiv:2609.16071v1 Announce Type: new
Abstract: Cross-machine deployment of industrial world models requires transfer across changes in dynamics, sensing interfaces, sampling regimes, and control units. We study a schema-adaptive action-conditioned Joint-Embedding Predictive Architecture (SAAC-JEPA) for CNC dynamics, where the source machine has 17 canonical sensor channels and the target shares only 10. Evaluation uses group-disjoint source splits, source-only normalization, held-out self-supervised validation, unit audits, and a sealed target test after model locking. Across five seeds, JEPA pretraining gives no clean-source forecasting gain: scratch and pretrained-body models obtain \(\mathrm{RMSE}=0.811\pm0.022\) and \(0.813\pm0.022\). A source-only search over 20 candidates selects a schema-consistent action-conditioned JEPA after seven-seed stability checks. On the confirmatory target pass, the locked model reaches zero-shot \(\mathrm{RMSE}=0.546\), \(R^2=0.012\), and \(\mathrm{NLL}=0.52\), outperforming persistence but not RevIN-equipped PatchTST and iTransformer baselines (\(0.503\) and \(0.498\)). A pre-declared paired ablation shows that RevIN in the same architecture improves RMSE to \(0.495\pm0.004\) over three seeds, but degrades target calibration (\(\mathrm{NLL}=20.6\)) on stationary context windows. A pre-lock adaptation sweep further reduces RMSE to \(0.520\) with limited target support. These results show that source-domain forecasting accuracy alone is insufficient to assess industrial predictive representations, and that cross-machine adaptation under partial sensor overlap is a distinct evaluation axis.
Same Answer, Different Representations: Hidden instability in VLMs
arXiv cs.CVarXiv:2602.06652v2 Announce Type: replace-cross
Abstract: The robustness of Vision Language Models (VLMs) is commonly assessed through output-level invariance, implicitly assuming that stable predictions reflect stable multimodal processing. In this work, we argue that this assumption is insufficient. We introduce a representation-aware and frequency-aware evaluation framework that measures internal embedding drift, spectral sensitivity, and structural smoothness (spatial consistency of vision tokens), alongside standard label-based metrics. Applying this framework to modern VLMs across the SEEDBench, MMMU, and POPE datasets reveals three distinct failure modes. First, models frequently preserve predicted answers while undergoing substantial internal representation drift; for perturbations such as text overlays, this drift approaches the magnitude of inter-image variability, indicating that representations move to regions typically occupied by unrelated inputs despite unchanged outputs. Second, robustness does not improve with scale; larger models achieve higher accuracy but exhibit equal or greater sensitivity, consistent with sharper yet more fragile decision boundaries. Third, we find that perturbations affect tasks differently: they harm reasoning when they disrupt how models combine coarse and fine visual cues, but on the hallucination benchmarks, they can reduce false positives by making models generate more conservative answers.
Extremely coarse learning objectives induce human-aligned representations in AI vision models
arXiv cs.CVarXiv:2605.05556v2 Announce Type: replace
Abstract: Artificial neural networks trained on visual tasks develop internal representations resembling those of the primate visual system, a discovery that has guided a decade of computational neuroscience. Research on building brain-aligned models has progressively embraced finer-grained learning ob- jectives, from object classification to contrastive self-supervised objectives that maximize distinc- tions among individual images. Yet the effect of learning-signal granularity on brain alignment remains largely unexamined. Here we systematically investigate how the granularity of a learning signal shapes representational alignment with human vision. We parametrically vary the number of training classes using a data-driven approach that partitions a set of training images into differ- ent numbers of categories via PCA-based splits of pretrained embeddings. We train hundreds of neural networks across convolutional and transformer architectures on these coarse classification tasks and compare their representations with human fMRI responses, macaque electrophysiology recordings, and human behavior. We find that networks trained to distinguish as few as eight broad categories learn representations that match or exceed the neural alignment of models distinguishing 1,000 classes. Even more strikingly, these coarsely trained networks align more closely with hu- man perceptual similarity judgments than all other models evaluated, including networks trained with fine-grained supervision or self-supervision as well as leading large-scale vision models. These results demonstrate that human-like visual representations can emerge from surprisingly simple learning objectives, reframing what learning signals vision may require and opening a path toward building AI systems that are more aligned with human perception.
HyCal: A Training-Free Prototype Calibration Method for Cross-Discipline Few-Shot Class-Incremental Learning
arXiv cs.CVarXiv:2604.15678v2 Announce Type: replace
Abstract: Pretrained Vision-Language Models (VLMs) like CLIP show promise in continual learning, but existing Few-Shot Class-Incremental Learning (FSCIL) methods assume homogeneous domains and balanced data distributions, limiting real-world applicability where data arises from heterogeneous disciplines with imbalanced sample availability and varying visual complexity. We identify Domain Gravity, a representational asymmetry where data imbalance across heterogeneous domains causes overrepresented or low-entropy domains to disproportionately influence the embedding space, leading to prototype drift and degraded performance on underrepresented or high-entropy domains. To address this, we introduce Cross-Discipline Variable Few-Shot Class-Incremental Learning (XD-VSCIL), a benchmark capturing real-world heterogeneity and imbalance where Domain Gravity naturally intensifies. We propose Hybrid Prototype Calibration (HyCal), a training-free method combining cosine similarity and Mahalanobis distance to capture complementary geometric properties-directional alignment and covariance-aware magnitude-yielding stable prototypes under imbalanced heterogeneous conditions. Operating on frozen CLIP embeddings, HyCal achieves consistent retention-adaptation improvements while maintaining efficiency. Experiments show HyCal effectively mitigates Domain Gravity and outperforms existing methods in imbalanced cross-domain incremental learning.
CLIP Embeddings for AI-Generated Image Detection: A Few-Shot Study with Lightweight Classifier
arXiv cs.CVarXiv:2505.10664v2 Announce Type: replace
Abstract: Verifying the authenticity of AI-generated images presents a growing challenge on social media platforms these days. While vision-language models (VLMs) like CLIP outdo in multimodal representation, their capacity for AI-generated image classification is underexplored due to the absence of such labels during the pre-training process. This work investigates whether CLIP embeddings inherently contain information indicative of AI generation. A proposed pipeline extracts visual embeddings using a frozen CLIP model, feeds its embeddings to lightweight networks, and fine-tunes only the final classifier. Experiments on the public CIFAKE benchmark show the performance reaches 95% accuracy without language reasoning. Few-shot adaptation to curated custom with 20% of the data results in performance to 85%. A closed-source baseline (Gemini-2.0) has the best zero-shot accuracy yet fails on specific styles. Notably, some specific image types, such as wide-angle photographs and oil paintings, pose significant challenges to classification. These results indicate previously unexplored difficulties in classifying certain types of AI-generated images, revealing new and more specific questions in this domain that are worth further investigation.
Automated Distinction of Intimal and Medial Intracranial Arterial Calcification from CT Head
arXiv cs.CVarXiv:2609.16035v1 Announce Type: cross
Abstract: Intracranial arterial calcifications (IACs) are a common finding on clinical non-contrast enhanced head CT scans and are associated with neurovascular disease. Calcifications can occur in the intimal or medial layer of the arterial wall, subtypes that differ in aetiology and may have distinct clinical relevance. These subtypes can be visually distinguished by radiologists based on the shape of the calcifications.
We investigate three automated approaches for subtype classification of IAC from head CT-derived segmentation masks: (1) an automated adaptation of the established radiological visual score, (2) a sphericity-based method, and (3) a method based on shape embeddings extracted by a medical shape foundation model. All approaches use the same lightweight classification pipeline on top of the features they compute and are evaluated using 5-fold cross-validation. The three methods achieved comparable performance, with the embedding-based approach yielding the best overall results with a weighted F1 (mean $\pm$ SD) of up to 71.5 $\pm$ 3.7 for a single artery and 59.8 $\pm$ 1.7 for the joint artery classification. Performance was largely preserved when using automated instead of manual IAC segmentation masks, and we found the difference in weighted F1 not significant.
Our results show that fully automated IAC subtype quantification from head CT is feasible and remains robust to the use of manual and automated IAC segmentation masks. Code at https://github.com/bjin96/iac-subtyping.
Conditioning noise is a free regularizer for LoRA fine-tuning: no pathology encoder required for diffusion-based artifact detection in histopathology
arXiv cs.CVarXiv:2609.16032v1 Announce Type: cross
Abstract: Diffusion-based artifact detectors score whole-slide image patches by reconstruction error under a model fine-tuned on clean tissue. We show that conditioning this fine-tuning on random Gaussian embeddings -- resampled at every step from approx. 200 KB of precomputed embedding statistics, with no encoder, no cache, and no change to inference -- consistently widens the clean/artifact separation. A four-step ablation chain shows the benefit requires neither content (shuffled real embeddings), provenance (synthetic Gaussians), a tuned intensity (flat across an 8x variance range), nor per-patch identity (fresh per-step noise); a LoRA-dropout control shows the conditioning pathway specifically, not generic weight perturbation, carries the effect. Patch-level gains of +0.25-0.48 Cohen's d replicate across nine trainings; honest leave-one-slide-out evaluation clears a pre-registered bar in 2/2 seeds; and two pre-registered external endpoints on a 281-case set confirm pooled Delta F1 = +0.0073 (95% CI) and +0.0129 (97.5% CI, two-look corrected). We release the full evaluation protocol, including measured seed noise and selection-optimism pricing.
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