Every paper checked — nothing hidden

All papers

Every paper checked in the week of 12–18 Oct 2026 · Small and efficient models — nothing is hidden. Brief papers first.

All2,430 In brief3 More (on-topic)455 Not relevant1,957 Not assessed6 Older27 Could not confirm6 Blogs with no paper16 All blogs19

Paper	Topic	Relevance
ProtoPur: Prototype-Purity Pseudo-Labeling for Weakly Supervised Instance Learning in Pathology This paper introduces prototype-purity pseudo-labeling to extract accurate instance-level annotations from weakly supervised whole-slide pathology images. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Does not address small models, pruning, quantization, or efficient serving.
ML-CLAPPS: A Probabilistic Framework for Imbalanced Multilabel Learning under Class Prioritization This paper introduces a learnable probabilistic instance sampler to handle severe class imbalance and prioritize critical diagnoses in multilabel learning. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Addresses training sampling under class imbalance, not model compression or efficiency.
Mobility Helps Learning: Unsupervised Model Adaptation for Object Recognition via Movement This paper presents an unsupervised test-time adaptation method that uses movement-induced prediction variance to adapt object recognition models in mobile agents. Peer-reviewed	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on test-time adaptation accuracy rather than model compression or efficiency.
Regime-Aware Router Learning for Multi-Domain Medical Image Generation with Diffusion Experts Evaluates router-learning strategies for combining domain-specialized diffusion experts in multi-domain medical image generation. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Studies multi-domain diffusion expert routing rather than small models or efficient inference.
Learning $ω$-Powered Conditional Distributions with Importance-Weighted Flow Matching The paper introduces an importance-weighted flow matching approach to explicitly learn powered conditional distributions without standard classifier-free guidance. Peer-reviewed	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Not focused on small models, distillation, or efficient inference.
KARAMEL: Knowledge-Aware Ranking for Multimodal Entity Linking in the Arts via Heterogeneous Graphs The paper introduces a multimodal entity linking method that combines vision-language models with heterogeneous graph networks for cultural heritage data. Peer-reviewed	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Not about small language models or efficient inference techniques.
Variational Filtering for Bayesian Last Layers with an Independent Binary Probit Surrogate The paper proposes a closed-form variational filtering algorithm for the last layer of deep classifiers to improve uncertainty calibration. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Not focused on model compression, quantization, or efficient serving.
Code-Interactive Visual Reasoning for Financial Candlestick Understanding The paper introduces a model and dataset for financial candlestick reasoning by generating and executing code to gather visual evidence. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Does not study model compression, distillation, or efficient inference serving.
JtT: Rethinking Point Trajectory Diffusion for Diverse and Rigid Motion from an Image The paper presents a diffusion transformer operating on per-frame velocities to generate rigid point trajectories from a single image. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Investigates diffusion model parameterization, not model quantization or pruning.
Barycentric Guidance: Turning Foundation Image Editors into Continuous Affective Controllers The paper proposes a training-free guidance method enabling continuous valence-arousal emotional control in foundation image editors. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Does not address model compression, pruning, or efficient inference.
HybridDualPathNet: Cross-Fusion Attention of EfficientNetV2 and Swin Transformer for Brain MRI Classification of Alzheimer's and Parkinson's Disease The authors introduce a hybrid architecture combining EfficientNetV2 and Swin Transformer to classify Alzheimer's and Parkinson's disease from brain MRI. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Proposes a standard hybrid vision backbone, not model compression or efficiency.
Efficient Fréchet Distributional Decoder Alignment for Latent Generative Models Proposes a post-training decoder alignment method for latent generative models to resolve distribution mismatches between training and generation latents. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on image generative alignment rather than small or quantized language models.
CÆNL: Collapse-Based Active Neural Learning with Objective-Aligned Representation Control Investigates active learning dynamics by regularizing representation collapse and managing sample acquisition across training iterations. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Deals with sample acquisition in active learning, not model compression.
Calibrated Disambiguation for Partial Multi-label Learning Proposes a calibrated disambiguation model to optimize sample weighting when training classifiers on datasets with noisy or excessive labels. Peer-reviewed	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Addresses noisy multi-label training, not model compression or efficient serving.
Deficit-Driven Stratified Acquisition for Building STRATA-S12: An Open SAR-Optical Corpus for Self-Supervised Learning Introduces a deficit-driven stratified acquisition algorithm to create a balanced, co-registered satellite imagery corpus for self-supervised Earth observation. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on remote sensing dataset collection, not model compression or efficient inference.
A Differentiable 3D Scene Graph Metric via Contextual Hellinger Triplet Geometry Proposes a differentiable graph-level loss function to measure semantic and structural discrepancies between predicted 3D scene graphs and ground truth. Peer-reviewed	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on scene graph loss formulation, not model compression or efficient inference.
Beyond the End-Effector: Learning Whole-Arm Contact Manipulation from Human Demonstrations This paper proposes a hierarchical framework for learning whole-arm robot contact manipulation tasks from human demonstrations. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on contact trajectory optimization, not model compression or efficient serving.
SPARCL: Spatially Adaptive Regional Coverage for Active Learning in Chest X-ray Analysis This paper introduces an active learning framework balancing uncertainty with submodular spatial coverage for chest X-ray lesion segmentation. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on active learning annotation budgets rather than model compression or efficient serving.
SYNPRED: A Synergistic Approach to Multimodal Learning for Clinical Prediction This paper proposes a multimodal variational autoencoder framework that separates task-relevant information to improve clinical predictions even with missing modalities. Peer-reviewed	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Does not focus on model compression, quantization, or efficient serving.
Robust Logical Anomaly Detection under Capture Shifts via Synthetic Anomalies Examines industrial logical anomaly detection under capture shifts and proposes a reference-free classifier trained on synthetic anomalies. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on anomaly detection robustness, not model compression or efficient serving.
Beyond the Encoder: Object-Centric Representations Emerging from Segmentation Foundation Models This paper evaluates whether object pointers from the Segment Anything Model can transfer to downstream clinical tumor prediction tasks. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Investigates representation transfer from SAM2, not model efficiency.
Unbalanced W-Flow: Unbalanced Wasserstein Drifting under Finite-Batch Marginal Fluctuations The paper proposes an unbalanced optimal transport method to reduce minibatch fluctuations in teacher-free one-step generative models. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Addresses optimal transport theory, not small model compression.
MajorTOM:Elliot Pretrain, A Multi-Sensor, Multi-Temporal Pretraining Dataset for Earth Observation This paper introduces a multi-sensor, multi-temporal satellite imagery dataset designed for pretraining Earth observation foundation models. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on large-scale dataset creation, not model compression.
Grid or Nodes? Probing Object Memory in World Models Under Partial Observability The paper compares spatial feature grids and tracked node coordinates for maintaining object memory under partial robot observability. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Compares spatial memory representations, not small model distillation.
Texture Interpolation as Texton Matching This paper studies texture interpolation using appearance-aware texton matching instead of standard positional coordinate blending. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Investigates feature matching for textures, not model efficiency.
AIRRI: Towards a Human-Readable, Machine-Resistant Reading Interface This paper presents an interface framework applying visual transformations and adversarial attacks to create assignment text resistant to machine extraction. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Studies visual perturbation attacks, not model compression or efficiency.
Registration is a Representation Problem: Solving Whole-Body PET/CT Registration This paper presents an anatomical representation approach driving iterative solvers for longitudinal whole-body PET and CT image registration. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Deals with 3D medical registration algorithms, not small model distillation.
Scaling Online Optimal Transport Minibatches for Flow Matching This paper develops an online entropic optimal transport approximation to scale minibatch coupling for stable flow matching. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on flow matching sampling variance, not model compression or serving.
Cepheid: Decoupling Scale from Structure for Cross-Family Motion Completion This paper proposes a dimensionless prior to complete dense mesh motion from sparse observations across different geometry families. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Deals with geometric motion priors, not LLM compression or inference.
ByteMark: A Unified Byte-Level Watermark for Detection Across Language Models This paper presents a byte-level watermarking method that allows a single detector to verify text across models with different tokenizers. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on watermarking across models, not model compression or efficiency.
KedgeFlow: A Programmable Control Plane for Enterprise AI Agents The paper proposes a control plane enforcing structured contracts and single-use allowances to prevent unauthorized actions by enterprise AI agents. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Not focused on model compression, quantization, or efficient inference.
SEEK: Agentic Knowledge Graph Retrieval for Secure Code Generation The paper introduces an agentic knowledge graph retrieval framework that uses vulnerability taxonomies to generate secure code. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Does not address model compression or inference acceleration techniques.
Only What You've Signed up For: Secure Workflows with Constrained Diffusion Agents The paper presents a framework enforcing security policies during the denoising steps of diffusion language models to prevent prompt injection. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Does not study model compression, distillation, or serving efficiency.
From Acoustic Sensing to Physical Actions: A Comprehensive Survey of Security Risks in Edge-Deployed Audio LLM-Based Agents The paper surveys security vulnerabilities and defenses in edge-deployed audio language model agents across the sensing-to-action pipeline. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Examines edge security risks, not model compression or efficiency.
Poisoned into Action: Context Attacks on Typed Decision Workflows The paper studies how context poisoning triggers unauthorized state changes in typed decision workflows and evaluates an execution-aware defense protocol. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Does not cover model compression or inference efficiency.
Robust Detection of Directional Adversarial Attacks in Deep Neural Networks for Radiological Imaging The paper presents a framework using random noise and Sigmoid function analysis to detect directional adversarial attacks on medical imaging models. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Does not study model compression, pruning, or efficient inference.
Can Rényi Accountants Beat Lossless Accountants for DP-SGD? This paper demonstrates that Renyi differential privacy accountants can provide tighter privacy bounds for DP-SGD than lossless accountants. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Investigates DP-SGD accounting, not model compression or efficiency.
Tight End-to-End Accounting for Differentially Private Synthetic Data This paper develops accounting techniques to maintain tight differential privacy guarantees across complex synthetic data generation pipelines. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on differential privacy accounting, not small or efficient models.
SSM-Attack: Learning Where to Perturb for Black-Box Face Privacy This paper proposes using state space models to guide black-box image perturbations that disrupt facial recognition systems. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on black-box adversarial attacks on face recognition, not model efficiency.
COMPASS: Membership Inference Attack Against Vision Large Language Models via Adaptive Noise Calibration This paper investigates membership inference attacks on vision-language models using low-noise Gaussian perturbations to detect training data exposure. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Does not study model compression, distillation, quantization, or efficient serving.
BrowseSafe++: Benchmarking Prompt Injection Detection Across Web Contexts, Languages, and Browser Views This paper introduces a benchmark of paired web pages to evaluate prompt injection detection across browser agents and views. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on prompt injection evaluation rather than model compression or efficiency.
Privacy-Preserving GNN Inference on Financial Transaction Graphs via Homomorphic Encryption This paper proposes an edge-centric graph neural network inference framework using homomorphic encryption for collaborative multi-bank transaction analysis. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on encrypted privacy-preserving inference via CKKS, not small efficient LLMs.
Kairos: Transactional Security Enforcement at the System Boundary for LLM Agents This paper introduces a transactional isolation monitor that delays irreversible actions to stop indirect prompt injections in software agents. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on runtime security enforcement for agents, not model compression.
Towards Safe Autonomy for Computer Use This paper proposes a dual-LLM architecture to establish trust boundaries and prevent indirect prompt injections in visual computer-use agents. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on security boundaries for computer-use agents, not model efficiency.
Autonomous Cyber Operations Gym for Training and Evaluating AI Models for Defence The paper introduces a simulation gym for training and evaluating autonomous cyber defence models and network intrusion detectors. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on cyber network emulation, not model compression or efficient serving.
Fin_Agent at FinEval 2026: Task-Aware Methods for Multilingual Financial NLP The paper describes a system submission combining rules and language models for multilingual financial named-entity recognition and question answering. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Applies existing 32B models without addressing model compression or inference efficiency.
The AI's Poker Face: Contrasting Persuasion and Sycophancy in Voice-Based Multi-Turn Conversations The paper studies opinion change, rhetorical strategies, and sycophancy in spoken multi-turn dialogues between humans and language models. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Investigates voice interaction persuasion dynamics, not model compression or efficiency.
OpenMobile-2: Building Versatile Mobile Agents with Scalable Environments and App-Native Tools The paper presents OpenMobile-2, combining open mobile environments, trajectory datasets, and hybrid GUI-tool actions for mobile agents. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on agent environment design and training recipes, not model compression.
AutoEnvScaling: Automating the Data Flywheel with Terminal Agents The paper presents AutoEnvScaling, using terminal agents to automatically generate reinforcement learning environments from model rollouts. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Deals with agent RL environment generation, not model compression or efficient inference.
Modeling the Arrival Time of Information-Bearing Events in Natural Language The paper uses neural temporal point processes to model the timing of information-bearing events in human and model generated text. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Examines temporal point processes, not efficient model architectures.
The Thinking Leakage in Reinforcement Learning for LLMs The paper examines whether non-thinking mode reinforcement learning gains in hybrid reasoning models stem from leakage of thinking-mode activations. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Analyzes internal reasoning mechanisms, not model efficiency.
Evaluating LLMs as Agentic Workflow Executors in Real-World Customer Support Skills The paper introduces a benchmark derived from real customer support dialogues to evaluate language models executing bounded, tool-using skills. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Evaluates off-the-shelf models rather than compression techniques.
Multimodal Co-Generation With Continuous Bitstream Diffusion The paper applies continuous bitstream diffusion to joint and conditional generation across multimodal data without learned tokenizers. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Studies generative diffusion architectures, not model efficiency.
Tokenization-Invariant Watermarking: A Byte-Local Method for LLM Text The paper presents a byte-local text watermarking method that scores outputs without invoking tokenizers during detection. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Studies watermarking robustness, not model pruning or distillation.
SciCurateBench: Curating Experimental Data from Heterogeneous Scientific Literature with Verbal-Action Agents The paper presents SciCurateBench to evaluate language models on verifying complex experimental conditions from unprocessed scientific literature. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Examines complex multi-agent curation, not efficient small models.
SPROUT: Scaling Agentic Reinforcement Learning for Long-Horizon Software Engineering The paper presents SPROUT, a snapshot-based rollout framework to scale reinforcement learning for long-horizon software engineering tasks. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on agent rollout frameworks, not model compression.
Scaling Laws for Multimodal Data Mixtures The paper derives multimodal data mixing scaling laws across text, vision, and audio using mixture-of-experts models. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Studies pretraining data scaling laws rather than inference efficiency.
MemForm: Benchmarking Memory Formation Poisoning in LLM Agents The paper introduces MemForm to benchmark adversarial memory formation poisoning across five input interfaces in language model agents. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Addresses adversarial memory poisoning, not model compression.
Neither True nor False: Do LLMs Represent Truth-Value Gaps in Plural Predication? The paper introduces HomogeneityNLI to evaluate whether open-weight language models recognise truth-value gaps in plural predication. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on semantic reasoning limitations, not model efficiency.
Evaluating Semantic Relation Knowledge through Bidirectional Relation Prediction from Gloss Pairs The paper introduces a bidirectional gloss-pair prediction task to evaluate semantic relation knowledge across multiple languages in language models. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on semantic evaluation rather than efficient architectures.

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