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
REPRO: Reflective Evolutionary Prompt Optimizer for Automated LLM Red-Teaming This paper proposes a black-box evolutionary prompt optimization framework using a small open-weight model to discover adversarial jailbreaks against language models. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Uses a 7B model for optimization, but does not study compression.
OceanFlow: Coherent Flow Matching for Probabilistic Ocean Modeling The paper proposes a flow matching framework for probabilistic global ocean modeling to generate calibrated conditional ensembles. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Deals with probabilistic climate modeling, not model compression.
TabMosaic: Unified In-Context Diffusion for Tabular Generative Tasks The paper presents an in-context diffusion model for generating, completing, and synthesizing unseen tabular data records jointly. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on in-context diffusion for tables, not model compression.
RespFocus: Learning Temporal Evidence for Weakly Supervised Respiratory Sound Classification The paper introduces a weakly supervised learning framework and dataset for identifying acoustic evidence in respiratory sound classification. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Deals with weakly supervised acoustic classification, not model efficiency.
bharatSetu: a 1M x 22 bridge for indic translation The paper introduces a synthetic parallel translation dataset covering 22 scheduled Indic languages aligned via English pivot sentences. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Presents a dataset and translation pairs, not model compression.
What DNA Foundation Models Learn Beyond Sequence Composition The paper evaluates whether genomic foundation models offer predictive value over simple sequence frequency baselines across classification benchmarks. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Compares genomic representation features, not model compression or efficiency.
Uncovering the Generative Landscape of Diffusion Models: A Spectral-Guided Quality-Diversity Diffusion Framework The paper integrates quality-diversity optimization with diffusion models to generate diverse sets of high-performing outputs. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on output diversity in diffusion models, not efficient inference.
Hyperbolic Time Series Forecasting with Learnable Decomposition and Manifold Dynamics The paper proposes a hyperbolic time series forecasting framework that maps multiscale frequency hierarchies onto the Poincaré ball. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Proposes a geometric representation framework, not model compression or efficiency.
Agent Sociodrama: Social-Dynamics-Guided Persona Recasting for LLM Agent Teams The paper introduces a framework to model evolving social dynamics and update personas in multi-agent language model teams. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not explore model compression, quantization, or efficient serving.
An Explainable System for Offensive Content Detection in Nastaliq Urdu: Corpus Curation, Transformer Benchmarking, and Word-Level Attribution The paper presents a Nastaliq Urdu comment corpus and evaluates transformer models with word-level attributions for offensive content detection. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Compares standard transformer classifiers without studying model compression or efficiency.
LLM Value Functions for Credit Assignment in Agentic RL The paper uses in-context language model evaluations as state value functions to improve credit assignment in agentic reinforcement learning. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Uses LLM value functions for RL training, not model compression or efficiency.
Comparative Study of Canvas- and Coordinate-Based Geometry-Conditioned Neural Networks for Real-Time Prediction of Indoor Environments with Varying Room Dimensions The paper compares canvas-based and coordinate-based neural network surrogates for predicting indoor airflow and temperature fields under varying room dimensions. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on architectural fluid modeling rather than language model compression.
Variance-Optimal State Resampling for Reinforcement Learning with Verifiable Rewards The paper proposes variance-driven state resampling to optimize the state sampling distribution for reinforcement learning with verifiable rewards. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Addresses training sample variance rather than model compression or inference efficiency.
Make Every Expansion Count: Learning to Search Efficiently in LLM Code Repair The paper introduces a learned search policy that predicts required repair steps to prioritize code edits before generating and testing patches. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Optimizes search iterations rather than model compression or parameter efficiency.
Review Rarely Beats the Stronger Model: Separating Verification from Capability Transfer in LLM Pipelines The paper shows that LLM review pipelines rarely outperform the stronger model alone due to confirmation bias rather than true verification. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not study model compression, pruning, or efficient model serving.
Gen Through the Lens: Camera-Controllable Video Generation via Cross-Coordinate Conditioning Fusion The paper proposes an image-to-video framework combining three coordinate spaces to enable camera-controllable video generation. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on video generation control rather than model compression or efficiency.
Scalable Weak-to-Strong Verifier Hardening for Agent Environments The paper introduces an automated weak-to-strong hacker-fixer loop to find and repair vulnerabilities in agent evaluation verifiers. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on verifier hardening rather than model compression or inference efficiency.
DiffNash: Finding Nash Equilibrium in High-Dimensional Continuous Action Spaces with Diffusion Policies The paper presents a diffusion-based framework that uses reweighted score matching to compute Nash equilibria in continuous multi-agent action spaces. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on game theory algorithms, not LLM compression or inference efficiency.
The Anatomy of a Blind Spot: What an LLM Judge Must Be Shown, and How Much The paper tests what context judge models require, showing that omitting source constraints degrades the quality of iterative feedback loops. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not focus on model compression, quantization, or efficient serving.
Hardware-Assisted Multi-Stage Ransomware Detection with Adaptive Machine Learning The paper combines static software scanning with hardware-level microprocessor monitoring and neural architecture search to detect ransomware in real time. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Applies NAS to malware detection rather than efficient serving or compression of LLMs.
Unfair and Unsafe: Everyday Bias Triggers Emergent Misalignment The paper finds that fine-tuning language models on polite text containing demographic stereotypes triggers broader safety and truthfulness failures on unrelated tasks. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Examines safety alignment dynamics, not model compression or efficient serving.
Of Errors and Echoes: Responsibility-Aware Credit Assignment for Tool-Use Agents The paper introduces an optimization method that separates error identification from credit assignment to improve reinforcement learning for tool-using agents. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Studies RL credit assignment rather than model distillation or quantization.
When Agents Talk, Secrets Travel: Topology-Driven Leakage and Recipient-Conditioned Sanitization in Multi-Agent LLM Systems The paper investigates privacy leakage caused by multi-agent network topologies and proposes an execution sanitizer conditioned on recipient identity. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not address model compression, quantization, or efficient serving.
Autoregressive Decoding as a Martingale: Characterizing the Stochastic Dynamics of LLM Generation The paper models autoregressive decoding errors as a martingale process to analyze the stochastic dynamics of language model generation. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on theoretical generation uncertainty rather than model compression or quantization.
Understanding and Reducing Misalignment in Persuasion Optimization The paper studies how reinforcement learning for persuasion causes models to fabricate evidence and coercive tactics, evaluating strategies to reduce this behavior. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on RL alignment trade-offs, not model efficiency or quantization.
Dream2Discern: Learning from Hard Latent Variants for Runtime Failure Prediction in Multi-Agent Systems The paper presents a runtime auditor that predicts execution failures in multi-agent systems using loss-guided perturbations to identify hard failure cases. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on failure auditing rather than model compression or inference efficiency.
Beyond Single-Pathogen Forecasts: Predicting Shared Respiratory Hospital Burden from Influenza, COVID-19, and RSV This paper evaluates methods for forecasting total hospital respiratory burden by comparing direct combined prediction against summing individual pathogen models. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Addresses hospital forecasting models, not small model distillation or efficiency.
GUIDE: Computationally Augmented Consensus for Trustworthy Clinical Practice Guidelines This paper introduces GUIDE, a multi-agent copilot system that assists clinicians in drafting and updating evidence-based medical practice guidelines. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not study model compression, distillation, or quantization.
PID-Gated Continual Prompt Adaptation Under Noisy Feedback This paper proposes control-inspired gates to decide when language model agents should adapt their prompts without absorbing noisy user feedback. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not involve model pruning, distillation, or quantization.
Query Your Way Out: Benchmarking Multi-Turn Knowledge Reasoning through Generalized Puzzle Solving This paper introduces the Active Probing Benchmark to evaluate multi-turn active questioning and specialized domain reasoning in language models. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not study model compression or efficient serving.
Omni-Modal LLMs Know More Than They Say This paper analyzes why omni-modal language models perceive non-textual inputs correctly yet fail to mention them in text outputs. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not study model compression, distillation, or quantization.
Phantom Merge: When Your Large Language Model Agents Pick One but Tell You About Another This paper identifies and mitigates Phantom Merge, an error where language model agents bind retrieved facts to the wrong entity. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not address model efficiency, pruning, or quantization.
WildTrace: Evaluating Training Data Detection in Realistic Large-Scale LLM Post-Training This paper introduces WildTrace, a benchmark evaluating training data detection methods across realistic, multi-stage language model post-training pipelines. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not address model compression or efficient inference.
Fisher information and the geometry of memorization in neural networks This paper analyzes Fisher information eigenvectors to show how neural networks geometrically decouple generalizable patterns from memorized noisy data. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on learning theory rather than model compression techniques.
Interactive 4D Volumetric Liquid Forecasting under Moving-Solid Interaction This paper proposes a task and framework to forecast dense 3D liquid velocity fields based on how solid objects move through them. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not address model compression or efficient serving.
MP-MNCA: Morphology-Prior Masked Neighbor Cross-Attention for Efficient Spatial Domain Identification This paper presents a cross-attention method with histological priors to improve training efficiency and convergence in spatial transcriptomics. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on biological domain identification, not model compression.
OneShot: Label-Efficient Detector Weighting for Time Series Anomaly Detection Weights an ensemble of time-series anomaly detectors using confirmation of a single reported anomaly from a human operator. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on time-series ensembling, not model compression.
AutoNovo: Agentic Program Search Discovers Transferable Nucleic Acid Design Algorithms Uses agentic program search to iteratively write and refine design algorithms for nucleic acid sequences that transfer across independent models. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Addresses biological sequence generation, not model compression.
Reflected Proximal Dynamics for Accelerated High-Accuracy Log-Concave Sampling Proposes reflected proximal dynamics to combine exact target preservation with momentum-based acceleration in log-concave sampling. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Deals with log-concave sampling theory, not model efficiency.
A Survey of Flow Matching in Reinforcement Learning Surveys the use of flow-matching generative modeling approaches within reinforcement learning across policy parameterization and foundation model alignment. Peer-reviewed	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Addresses reinforcement learning policies rather than small language models.
agent-risk: Risk Assessment for Changes to AI Agents Provides a framework for assessing risks of proposed changes to deployed AI agents by selectively replaying execution logs and saved states. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not address model compression or efficient inference.
SLDBench v2: Active Scaling Law Discovery Expands a benchmark to evaluate whether language model agents can actively select experiments to discover reliable model scaling laws under compute budgets. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Studies scaling law discovery rather than efficient inference or distillation.
BehaviorFormer: Discovering Behavioral Patterns via Clusterable Next-Token Prediction Combines next-token prediction transformers with deep clustering to discover interpretable behavioral patterns in open-ended game trajectories. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on behavioral representation learning rather than model compression.
ChronoScribe: Semantic-Numerical Specialization for Instruction-Driven Time Series Forecasting Presents an architecture separating natural language interpretation from numerical time series foundation models for instruction-driven forecasting. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on hybrid model specialization rather than quantization or distillation.
Rescuing Points Trapped Inside Incorrect Decision Regions: A target for Evaluating and Improving Forward-Forward Learning Introduces a margin-based analysis metric to evaluate class boundary representations in forward-forward learning algorithms. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Evaluates Forward-Forward training dynamics rather than inference compression methods.
DWI Connectome-based Sex Classification in Young using Supervised Variational Autoencoder Applies a supervised variational autoencoder to brain connectome data for sex classification in young adults. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on medical imaging classification rather than efficient model serving.
RetroPlanner: Multi-Route Expansion and Multi-Objective Reasoning for LLM-Based Retrosynthesis Planning Proposes a multi-route retrosynthesis planner that evaluates synthesizability and cost across chemical reaction options. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Addresses chemical planning strategy rather than model compression or efficiency.
ARMED: Adaptive Retrieval-Augmented Medication Recommendation Proposes an adaptive retrieval-augmented generation framework that dynamically expands clinical queries for medication recommendation. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Mentions using small language models in passing, but does not advance compression techniques.
VisExMEM: Visual Evidence-Based Explainable Multimodal Evaluation Metric Presents a reference-free evaluation metric that decomposes captions into atomic claims and verifies them against visual evidence. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Introduces an evaluation metric, not model compression or efficient serving.
CryptoFlowBench: Benchmarking Multi-Hop Reasoning on Blockchains for Cryptocurrency Fund Tracing Introduces a benchmark and tool-augmented agent for evaluating multi-hop reasoning in cryptocurrency fund tracing. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on multi-hop graph reasoning, not small or compressed models.
PATMT2: Two-stage domain adaptation for multilingual patent translation Evaluates a two-stage domain adaptation method for multilingual patent translation and analyzes failure modes in low-resource languages. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on translation domain adaptation rather than novel model compression methods.
Drafting Agents, Not Just Tokens: Speculative Recursive Self-Improvement for Agentic Systems Presents speculative recursive self-improvement to draft successor agents before full evaluation and optimize verification budgets. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Optimizes agent search and verification budgets, not model parameter compression.
pi-Net: Empirical Latent Representations for Differentiable Physics Heads in Data-Scarce Systems Introduces a physics-informed latent variable model combining dimensional analysis and differentiable physics heads for data-scarce multi-physics systems. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Addresses sample efficiency in physical systems, not LLM inference compression.
EAU: Environment-Agent-User Co-evolution for Training User-Centric Agents Co-evolves the user simulator, environment tools, and rewards during training to improve interactive multi-turn language model agents. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Proposes agent training workflows, not model compression or efficient serving.
PatTransBench: an ipc-stratified, multi-dimensional benchmark for patent machine translation evaluation Presents an evaluation benchmark and multi-dimensional protocol for patent machine translation across Chinese, English, Japanese, and Korean. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Tests quantized model variants as a case study, not novel model compression.
AI Discovery in Combinatorics: From Problems to Solutions and Back to Bigger Problems Introduces a benchmark of 29 open algebraic combinatorics problems to evaluate algorithm discovery by frontier AI models. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Studies frontier AI discovery rather than model compression or efficient serving.
Towards Modality-Agnostic Seizure Classification: A Unified Deep Learning Framework for Heterogeneous EEG and Intracranial EEG Signals Presents a deep learning framework that integrates heterogeneous surface EEG and intracranial EEG signals for multimodal seizure classification. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Addresses clinical signal processing rather than small efficient LLM serving.
MixLID: Token-Level Language Identification in Mixed-Language Text Classifies languages at the token level across 111 language-script combinations in mixed-language and code-switched texts. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on multilingual classification accuracy rather than model compression or inference efficiency.
Integrating Feature Understandability into Feature Selection for Human-Centred Tabular Explanations Incorporates human understandability scores directly into feature selection to make tabular machine learning explanations easier for users to interpret. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on random forest feature selection rather than small efficient language models.
AeroChem: Closed-loop Physics-Informed State Space Modeling for Long-term Chemically-Reactive Air Quality Forecasting--Extended Version Combines physical models and state space neural networks in a closed loop to forecast long-term chemically reactive air quality. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on environmental time series modeling rather than small efficient LLMs.

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