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
Does Location Matter? A Black-Box Causal Audit Reveals Model-Dependent Audio Grounding in LALM Hallucination Audits audio-language model hallucination mitigations by testing localized acoustic evidence ablation against baseline methods across model architectures. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Focuses on causal grounding and contrastive decoding, not model compression or efficient serving.
Locate, Judge, Read: Role-Specialized Tool Agents over a Knowledge-Point Index for Open-Domain Multimodal Document QA Designs a modular multi-agent pipeline separating search, verification, and reading for multimodal document question answering. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Uses a 35B model with 3B active parameters, but efficiency is not the primary research focus.
Looking Ahead and Back: Anticipatory Generative Modeling for Road-Network Trajectories Introduces an anticipatory generative modeling approach for road networks to improve realistic next-transition selection in trajectory generation. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Does not address model compression, quantization, distillation, or efficient LLM serving.
JANUS: Enhancing Long-Horizon Autonomous Research with Co-Evolving World Models Proposes JANUS, using a co-evolving world model to predict, verify, and guide exploration paths in autonomous scientific research. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Does not cover model compression, distillation, quantization, or efficient deployment.
How Reliable are AMR Parser Outputs? Uncertainty Estimation for AMR Parsing Studies uncertainty estimation for abstract meaning representation parsers using top-k beam candidates and prediction rejection ratios. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Does not address quantization, distillation, or efficient model compression.
Agreement Without Alignment: Label-Free Rubric Clarification Increases LLM-Judge Consensus but Not Agreement with Humans Evaluates a label-free rubric clarification method, finding it increases consensus among LLM judges without improving agreement with human evaluations. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Does not address model efficiency, distillation, quantization, or compact architectures.
Masked Diffusion Language Agents for Tool-Integrated Chemical Reasoning Introduces ChemDiffAgent, applying masked diffusion to plan tool use and reasoning trajectories bidirectionally for chemical problem-solving. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Does not investigate small language models, quantization, pruning, or efficient inference techniques.
AgentSWE: Can Coding Agents Build the Agent You Actually Want? Introduces AgentSWE, a 25-task benchmark evaluating whether coding agents can build, edit, and optimize other software agents against user requirements. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Does not study model compression, distillation, quantization, or efficient on-device serving.
ProgramGym: Scaling Training Environments for Long-Horizon Program Reproduction Agents Introduces ProgramGym, a framework providing verifiable sandbox environments and black-box test feedback for training long-horizon program reproduction agents. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Focuses on code reproduction environments rather than model compression or inference efficiency.
In-Context Learning for System Identification: Probabilistic Formulation, Long-Sequence Handling, and Benchmark Validation Develops a probabilistic transformer meta-learning architecture to perform zero-shot system identification directly from long training sequence contexts. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Addresses meta-learning for dynamical systems rather than model compression, distillation, or lightweight serving.
SqueezeBench: Saturation-Resilient Evaluation of Mathematical Capability Introduces SqueezeBench, a saturation-resilient benchmark for formal mathematical reasoning where new accepted proofs dynamically raise the target bounds. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Studies mathematical capability benchmarking, not model compression, distillation, or efficient serving.
Beyond Temporal Proximity: Explanatory World Models for Credit Assignment in Reinforcement Learning Presents Explanatory World Models, using language model causal explanations to redistribute reinforcement learning rewards across distant actions. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Addresses RL sample efficiency in recommenders rather than small language models or model compression.
Retrieval-Augmented Multimodal Language Model with Heterogeneous Evidence Integration for Clinical Prognosis Proposes PRISM, a multimodal clinical prognostic model combining patient records with retrieved population-level embedding memory banks. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Focuses on medical multimodal integration rather than model compression, quantization, or serving efficiency.
SelectArena: A Modern Benchmark for Feature Selection on Real-world Tabular Data Introduces SelectArena, a benchmark designed to evaluate tabular feature selection methods across real-world domain datasets and contemporary models. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Covers feature selection for tabular data rather than small language models or LLM compression.
Pretraining and Test-Time Training Budgets Matter for ARC: A Controlled Study of VARC and TRM Evaluates how pretraining and test-time training computational budgets affect ARC benchmark performance across two distinct reasoning model architectures. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Studies training and test-time budget effects on ARC rather than model compression or quantization.
A Multi-Agent LLM System for Automated Tuning of Specialized Neural Network Models Presents a multi-agent LLM framework that automatically tunes specific internal algorithm components while preserving overall neural network structures. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Does not focus on model compression, quantization, distillation, or small language models.
Inject a Turn, Not a Token: Learning Where to Redirect LLM Reasoning for Self-Correction Introduces UPTURN, a framework that trains a small classifier to inject redirection prompts as new user turns during reasoning failures. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Focuses on reasoning accuracy via prompt injection rather than model compression or efficient serving.
Variance-Reduced Policy Gradient is Nearly Minimax-Optimal Introduces Loopless Variance-Reduced Policy Gradient, proving near-optimal sample complexity under weak gradient domination using first-order updates. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Analyzes policy gradient theoretical bounds rather than efficient model inference, serving, or compression.
Calibration in One Go: Jointly Learning Accurate and Calibrated Neural Networks Employs a bilevel Stackelberg formulation to jointly optimize classification accuracy and confidence calibration without separate calibration datasets. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Focuses on prediction calibration rather than model compression, quantization, or inference acceleration.
Amortized Counterfactual Prediction with Graph Neural Networks Presents a graph neural process framework to answer counterfactual queries directly from observational data without prior causal graph discovery. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Does not address model compression, quantization, distillation, or efficient inference serving.
SafeDiSTL: Decomposing Joint Constraints for Safe Multi-Agent Reinforcement Learning Decomposes joint temporal logic constraint violations to allocate credit among agents in cooperative reinforcement learning. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Focuses on constrained reinforcement learning rather than small or quantized models.
Matched-Context Group Policy Optimization for LLM Agent Training Constructs context-matched preference comparisons from sibling rollouts to improve credit assignment during language model agent training. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Focuses on agent policy optimization rather than model quantization or inference efficiency.
When Edge Independence Fails: Joint Graph Diffusion with Latent Sociability Priors Introduces a latent sociability prior to mitigate edge-independence issues in discrete graph diffusion models. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Deals with graph generation quality rather than model efficiency or compression.
UkrMeme: Multimodal Explainable Persuasion Detection in Ukrainian Memes Introduces a Ukrainian meme dataset and evaluates vision-language models on detecting persuasive and propaganda techniques. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Does not address small models, model compression, or inference optimization.
Your Discrete Graph Diffusion Model is Secretly Memorizing Demonstrates that discrete graph diffusion models exhibit conditional memorization when reverse trajectories are conditioned on corrupted training graphs. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Deals with graph generation theory, not model compression or efficient serving.
FORTE: A Production Framework for Feature Evaluation and Deprecation in Industrial Recommender Systems Describes an industrial framework for evaluating, ablating, and deprecating features in production recommender systems without performance regressions. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Focuses on feature lifecycle in recommender pipelines, not model compression or small models.
Studying Comics at Scale with Artificial Intelligence: The PAGE Framework and Three Studies of American Comics and Japanese Manga Applies vision-language models and structured annotation to parse and analyze historical comic books and manga at scale. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Does not address model compression, quantization, or efficient inference serving.
Task Context Shapes Component Roles in Language Models Investigates how changing tasks redistributes attention head influence and alters internal activations in open-weight large language models. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Studies internal representations rather than model compression or inference efficiency.
Revisiting Tabular Deep Regression: Objectives and the Limits of Global Early Stopping Evaluates regression objective functions and global early stopping limits across neural network backbones and established tabular datasets. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Examines tabular regression loss functions rather than model compression or efficient LLM inference.
Hide and Seek: HIDE Concepts to SEEK explanations for black box models Introduces a model-agnostic, concept-level attribution method for black-box vision models by masking concepts located with vision-language models. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Does not address model compression, efficient serving, or small language models.

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