Every paper checked — nothing hidden

All papers

Every paper checked in the week of 12–18 Oct 2026 · LLM evaluation and benchmarks — nothing is hidden. Brief papers first.

All3,345 In brief3 More (on-topic)587 Not relevant2,721 Not assessed24 Older45 Could not confirm25 Blogs with no paper54 All blogs57

Paper	Topic	Relevance
ANTE: Anticipate, Then Execute -- Learning Internal World Models for Coding Agents from Verified Beliefs This paper introduces a reinforcement learning framework that trains coding agents to anticipate execution outcomes before taking tool actions. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Develops an agent training framework, not an evaluation benchmark.
LiveAnon: Improving Privacy and Utility in Streaming Speaker Anonymization with Codec Language Models This paper proposes a voice-conversion system using codec language models to preserve speech emotion while anonymizing speaker identity in real-time streams. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Speech anonymization paper, not an LLM evaluation benchmark.
Approximation in Contrastive Representation Learning This paper analyzes contrastive representation learning theoretically by framing inner-product scores as spectral approximations of pointwise mutual information. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Theoretical analysis of contrastive learning, not an LLM benchmark.
DC-Flow: Target-Conditioned Cyclic Peptide Design via Delayed-Coupling Flow Matching This paper introduces a flow matching framework that decouples backbone generation from sequence and side-chain design for target-conditioned cyclic peptide synthesis. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on molecular peptide generation, not LLM evaluation.
WUSPruner: Second-Moment-Optimal Transforms for Structured Pruning This paper presents a transform-based framework for structured pruning in language models that optimizes basis selection using activation and weight statistics. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Deals with pruning algorithms, not LLM evaluation benchmarks.
GA-SVD: Generation-aware Singular Value Decomposition for LLM Compression The paper proposes a generation-aware singular value decomposition method to compress large language models while preserving generative output quality. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on compression technique, not benchmarking or evaluation methodology.
Harnessing VLM in Embodied RL: From Reward Regressor to Success Detector via Exploration-Augmented Key-Observation Gating The paper proposes using vision-language models as success detectors rather than reward regressors for embodied reinforcement learning. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on policy reward formulation, not language model evaluation benchmarks.
Riemannian Graph Tokenizer for Structural Knowledge Transfer The paper proposes a Riemannian graph tokenizer that quantizes structural patterns to support cross-domain graph knowledge transfer. Peer-reviewed	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on graph tokenization techniques, not language model evaluation.
Personalized Physiological-Model-Based Neural Network for Glucose Forecasting in Type 1 Diabetes The paper develops a neural network constrained by a personalized four-parameter physiological model to forecast blood glucose levels in diabetes. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Pertains to clinical glucose forecasting, not language model evaluation.
Split Conformal Interval Length under Huber Contamination The paper establishes theoretical bounds on interval lengths for split conformal prediction under Huber data contamination. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Studies statistical conformal prediction interval bounds, not language model benchmarks.
RF Fingerprinting of DSP Implementations using Machine Learning: An Over-the-Air Case Study The paper demonstrates that convolutional neural networks can classify transmitter digital signal processing implementations from over-the-air radio signals. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on RF signal fingerprinting, not language model evaluation.
Revisited Support Vector Machines: A Unified Joint Optimization Framework with Nyström Approximation for Scalable Adaptive Learning The paper presents a continuous joint optimization framework for support vector machines that updates multipliers and kernel hyperparameters together. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on Support Vector Machine optimization, not language model evaluation.
Sensitivity Fidelity Reveals Physical Dependencies Beyond Forecast Accuracy in Deep-Learning Earth-System Models The paper proposes sensitivity fidelity as an evaluation criterion to verify physical consistency in deep-learning Earth-system forecasting models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Addresses Earth-system physical models rather than language model evaluation benchmarks.
Beyond Aggregate Drift: Optimal Client-Specific Windows in Federated Learning The paper presents an accelerated federated learning method that assigns client-specific memory windows based on individual noise and drift rates. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on federated optimization algorithms, not language model evaluation.
Vision-Predictor-Action: Towards Efficient Embodied AI via Neuro-Symbolic JEPA Predictors The paper proposes a robot control framework using predictive architectures and flow matching to generate action chunks without autoregressive decoding. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Presents an embodied AI architecture, not an evaluation framework for language models.
UNISON: A Unified Framework for Learning from Scored Bags The paper presents a unified three-stage framework for multiple instance learning and recommendation from scored bag collections. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on recommender systems and multiple instance learning, not language model evaluation.
Latent-Space Importance Weighting under Covariate Shift The paper proposes a latent-space importance weighting algorithm to improve prediction accuracy under covariate shift. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on statistical covariate shift bounds, not language model benchmarking.
An Attention-Guided Graph Neural Network Surrogate for Boundary Integral Computation in Physical Field Reconstruction The paper introduces an attention-guided graph neural network surrogate to reconstruct missing boundary information in physical field simulations. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on boundary integral reconstruction, not language model benchmarks.
Joint Contrastive-Adversarial Representation Learning for Unsupervised Domain Adaptation The paper establishes theoretical risk bounds for a two-stage unsupervised domain adaptation method combining contrastive learning and adversarial alignment. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on unsupervised domain adaptation bounds, not language model evaluation.
ProML: Progressive Multi-label Learning The paper introduces a progressive multi-label learning framework that decouples direct label fitting from predictor optimization via an auxiliary variable. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Covers multi-label machine learning optimization, not language model evaluation.
Screening Collapse under Recursive Synthetic Data The paper analyzes screening collapse, showing how recursive training on synthetic data degrades feature selection prior to model training. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Analyzes linear variable screening theory rather than language model benchmark suites.
Adaptive Role Assignment and Controlled Feature Reconstruction for Federated Image Semantic Communication The paper introduces an adaptive role-assignment and feature reconstruction framework to improve hybrid federated image communication under wireless constraints. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on federated communication rather than language model evaluation benchmarks.
The Price of Precision: A Calibrated Cost Model for Mixed-Precision LLM Serving The paper presents a hardware-calibrated cost model to optimize mixed-precision serving configurations across varying batch sizes and context lengths. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on serving latency optimization rather than language model evaluation methods.
MERIT: Scaling KG Foundation Models with Relation Transformers The paper presents a transformer-based relation encoder for knowledge graph foundation models to reduce scaling and inference costs. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Evaluates a specific model rather than proposing language model evaluation methodology.
Context-Aware Precision Scheduling for Small Language Models The paper proposes a context-aware mixed-precision scheduling method to balance execution latency and accuracy for small language models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on runtime scheduling, not language model benchmark methodologies.
Testing Central Structure for Complex Networks The paper proposes a hypothesis testing framework using spectral radius to determine if a complex network contains true central structure. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on network centrality tests, not language model evaluation or benchmarks.
Drift, Then Spread: Learning One-Step Generative Policies for Online Reinforcement Learning The paper proposes a one-step generative reinforcement learning policy that balances reward improvement and entropy without iterative sampling. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on online RL policy design, not LLM evaluation benchmarks.
When do explicit penalties improve flow matching? The paper derives geometric and statistical criteria showing when explicit penalties improve sample accuracy in flow matching generative models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Theoretical analysis of flow matching penalties, not LLM evaluation.
Notes on Uniform Policy Score Bounds and Lipschitz Continuity for Truncated Gaussian, Product Cauchy, Boltzmann, and Softmax Policies The paper establishes uniform policy score bounds and Lipschitz continuity conditions across four common reinforcement learning policy parameterizations. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Brief mathematical note on policy bounds, not LLM evaluation.
DRanker: A Transformer-Based Multi-Task Ranking Model for the Disney+ Homepage The paper presents an encoder-only transformer ranking model deployed at Disney+ that uses lightweight debiasing to improve recommendations. Peer-reviewed	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Describes a production recommender model, not LLM evaluation benchmarks.
Adaptive Robust Confidence Sets in High Dimensions This paper analyzes the statistical costs and computational lower bounds of constructing adaptive confidence sets under unknown Huber contamination. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant High-dimensional mathematical statistics paper, not LLM evaluation.
A Dependency-Aware Multi-GPU Training Framework for Temporal Graph Neural Networks This paper presents a multi-GPU training framework for temporal graph neural networks that relaxes event dependencies to reduce synchronization. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Multi-GPU systems paper for graph networks, not LLM evaluation.
Inference on the Value of Policy Improvement in Adaptive Experiments This paper develops an asymptotically normal estimator to measure the value of policy improvement in adaptive experimental settings. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Theoretical econometrics and statistics paper, not LLM evaluation benchmarks.
Grounding Deep Learning Framework Testing in Data Flow This paper introduces a data-flow graph representation and mutation testing technique to uncover bugs in deep learning frameworks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on software testing of DL frameworks, not LLM benchmarks.
Benchmarking Deep Learning Models for Anomaly Detection in Real-World Cloud Telemetry This paper benchmarks deep learning architectures for anomaly detection on real-world cloud telemetry using timing-aware evaluation metrics. Peer-reviewed	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Benchmarks time-series models for cloud telemetry, not LLM evaluation.
Guarded Block Transfers for Polytope Distance: Linear Convergence with Explicit Constants and Gap-Certified Screening for SVM Training This paper presents guarded block transfers for polytope distance computation to accelerate support vector machine training. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Optimization theory paper for SVMs, not LLM evaluation benchmarks.
How Shared Representations Become Specialized: Formation, Maintenance, and Latent Susceptibility This paper analyzes how shared representations specialize among multiple targets and how optimizer state influences this specialization. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Analyzes representation formation in toy neural networks, not LLM benchmarks.
GuidanceNFT: NFT-Based Reward Fine-Tuning of Guided Diffusion Models This paper introduces an online flow matching method to fine-tune guided diffusion models toward specific rewards without losing prompt alignment. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on fine-tuning diffusion models, not LLM benchmark methodologies.
A Hybrid Gaussian Process-Extreme Gradient Boosting Machine Learning Framework for Robust Knockdown Factor Prediction and Reliability-Based Design of Thin Spherical Shell Caps This paper combines Gaussian process regression and gradient boosting to predict buckling knockdown factors for thin spherical shell caps. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Structural engineering application with classical ML, not LLM benchmarking.
Entropy-Regularized Mean-Field Games with Average-Reward Criterion: Span-Seminorm Contractions and Model-Free Learning This paper establishes contraction properties and finite-sample bounds for model-free learning in average-reward entropy-regularized mean-field games. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Theoretical paper on mean-field games, not LLM evaluation benchmarks.
Minimax Rates and Improved Spectral Convergence for Multi-Manifold Clustering This paper establishes minimax rates and improved spectral convergence bounds for multi-manifold clustering using Laplace-Beltrami operator estimators. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Theoretical statistics paper on manifold clustering, not LLM evaluation.
Lightweight Scheduling Policies for Continuously Batched LLM Serving Across GPU Architectures This paper evaluates lightweight scheduling policies for continuous batching in large language model serving across different GPU architectures. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Studies serving scheduler policies rather than establishing LLM evaluation methodologies.
Two Linear Programs are Better than One This paper proves that virtual extension complexity for polytopes can be exponentially smaller than standard extension complexity. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Theoretical math and polyhedral combinatorics paper, not LLM evaluation.
Sample-Optimal Multi-Group Learning with an ERM Oracle This paper introduces an empirical risk minimization oracle algorithm that achieves optimal sample complexity for multi-group learning. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Theoretical statistical learning paper on ERM oracles, not LLM benchmarks.
Fast and Accurate: Improving Personalized LLM Performance by Semantic Concentration Inspired Workload Split between RAG and Fine-Tuning This paper splits personalized language model workloads between parameter-efficient fine-tuning and retrieval based on historical data semantic concentration. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Proposes a system architecture, not a benchmark or evaluation method.
Mesh-Free Convolution: Learned Spectral Attenuation and Transport This paper proposes continuous-space mesh-free convolutions to improve the stability of learned operators for partial differential equations across resolutions. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on PDE operator learning architectures, not LLM evaluation benchmarks.
Error-Based Bidirectional Predictive Coding Enables Deep, 4-Bit Predictive Coding Networks This paper combines bidirectional and error-based predictive coding to enable deep, 4-bit quantized predictive coding networks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on predictive coding architectures, not LLM evaluation benchmarks.
Physics-guided machine learning for saturation control across variable hydraulic conditions in heap leaching This paper develops a physics-guided machine learning surrogate model to predict and control hydraulic saturation profiles in heap leaching. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Presents surrogate modeling for mining hydraulics, not LLM evaluation.
Small area learning This paper develops a semi-supervised learning framework for small area estimation that accommodates covariate and conditional distribution shifts. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Presents small area estimation statistics framework, not LLM evaluation.
Instance-Adaptive Online Multiclass Multicalibration This paper provides an online multiclass multicalibration algorithm with error bounds that adapt to the geometric dimension of outcome means. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Theoretical statistical analysis of multicalibration algorithms, not LLM evaluation.
Harmonic Representations in Symbolic Music Transformers are Localized and Controllable This paper shows that harmonic representations in symbolic music transformers are localized across middle layers and can guide music generation. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Investigates internal representations in music transformers, not LLM evaluation benchmarks.
Using Line Segment Tracking to enable ML-based Track Reconstruction in CMS This paper introduces a transformer-based track reconstruction algorithm using line segment tracking objects for high-pileup particle collisions. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Applies transformers to particle track reconstruction, not LLM evaluation.
Softmax-Free Attention in Spiking Transformers as the Zero-Temperature Limit of Two-Level Systems This paper provides a thermodynamic framework explaining energy efficiency and optimization issues in softmax-free spiking transformers. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Presents thermodynamic theory for spiking attention, not LLM benchmark evaluation.
Probabilistic Forecasting of Wildfire Size Using Conditional Flow Matching This paper proposes a conditional flow matching model to predict the temporal evolution of wildfire acreage from weather and terrain data. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Applies flow matching to wildfire size forecasting, not LLM evaluation.
Dissipative Lagrangian Neural Networks for the Interpretable Modelling of Passive Mechanical Systems This paper presents a neural network method that separately learns conservative and dissipative dynamics to model passive mechanical systems. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Presents Lagrangian neural network physics modeling, not LLM evaluation.
A 4-Qubit Variational Quantum Classifier for Sequence-Specific Classification of DNMT3A and DNMT3B Flanking Methylation Preferences This paper applies a simulated four-qubit variational quantum classifier to distinguish sequence-specific DNA methylation preferences. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Studies quantum classifiers for DNA methylation, not LLM evaluation.
Certified Robustness via Sparse Second-Order Cone Relaxations This paper proposes a hierarchy of sparse second-order cone programming relaxations for certified neural network robustness. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on verification optimization techniques, not LLM benchmarks or evaluation methods.
Limit Theory for Online Expectile Estimation under Short and Long Range Dependence This paper develops asymptotic theory and convergence limits for online expectile estimation under dependent time series data. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Theoretical statistics paper on expectile estimation, not LLM evaluation.
A Nonlinear Time-Series Theory for Constant-step SGD with Momentum This paper provides theoretical convergence and distributional limits for constant-step stochastic gradient descent with momentum viewed as a time series. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Theoretical analysis of optimization algorithms, not LLM benchmark evaluation.
CLNO: Conservative Local Neural Operator for Compressible Flow Prediction This paper proposes a local neural operator for compressible flow prediction that enforces discrete physical conservation across subdomains. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on fluid dynamics neural operators, not language model evaluation.

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