Every paper checked — nothing hidden

All papers

Every paper checked for brief #4 of 12–19 Oct 2026 — nothing is hidden. Brief papers first.

All17,313 In brief24 More (on-topic)3,333 Not relevant13,754 Not assessed87 Older352 Could not confirm93 Blogs with no paper266 All blogs284

Paper	Topic	Relevance
Structure-preserving fidelity diagnostics for partial differential equations The paper introduces diagnostic scores to test whether neural network surrogates respect underlying physical conservation laws rather than numerical errors. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Evaluates physical conservation properties in PDE surrogates, not AI deployment safety.
Structure-preserving fidelity diagnostics for partial differential equations The paper introduces diagnostic scores to test whether neural network surrogates respect underlying physical conservation laws rather than numerical errors. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on physics-informed PDE surrogates rather than small model compression or serving.
Structure-preserving fidelity diagnostics for partial differential equations The paper introduces diagnostic scores to test whether neural network surrogates respect underlying physical conservation laws rather than numerical errors. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Develops physics diagnostic metrics for differential equations rather than LLM benchmarks.
A Time-Synchronous Prequential Evaluation Framework for Next-Week Student Disengagement Prediction from Moodle LMS Logs The paper evaluates online machine learning models within a time-synchronous framework to predict weekly student disengagement from activity logs. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Addresses student disengagement models rather than AI deployment safety or guardrails.
A Time-Synchronous Prequential Evaluation Framework for Next-Week Student Disengagement Prediction from Moodle LMS Logs The paper evaluates online machine learning models within a time-synchronous framework to predict weekly student disengagement from activity logs. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Studies student disengagement prediction rather than language model evaluation or benchmarks.
A Time-Synchronous Prequential Evaluation Framework for Next-Week Student Disengagement Prediction from Moodle LMS Logs The paper evaluates online machine learning models within a time-synchronous framework to predict weekly student disengagement from activity logs. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on educational log classification rather than model compression or efficient serving.
Longitudinal Learner Participation Profiling in Online Learning: Heterogeneous Consensus Clustering under Unequal Observation Windows The paper evaluates clustering methods on learning management system logs to identify learner participation profiles across unequal observation windows. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Covers learner analytics rather than AI deployment safety, guardrails, or hallucination.
Longitudinal Learner Participation Profiling in Online Learning: Heterogeneous Consensus Clustering under Unequal Observation Windows The paper evaluates clustering methods on learning management system logs to identify learner participation profiles across unequal observation windows. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Analyzes student participation clustering rather than language model evaluation or benchmarking.
Longitudinal Learner Participation Profiling in Online Learning: Heterogeneous Consensus Clustering under Unequal Observation Windows The paper evaluates clustering methods on learning management system logs to identify learner participation profiles across unequal observation windows. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Studies student log clustering rather than small, efficient model compression or serving.
Merit: Making every modality earn its contribution in multimodal attention decoding The paper introduces a multimodal auditory attention decoding framework designed to maintain reliance on neural signals alongside behavioural tracking. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Evaluates auditory attention decoding inputs rather than language model benchmarking methods.
Merit: Making every modality earn its contribution in multimodal attention decoding The paper introduces a multimodal auditory attention decoding framework designed to maintain reliance on neural signals alongside behavioural tracking. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Addresses hearing aid decoding methods rather than small model compression or inference.
Merit: Making every modality earn its contribution in multimodal attention decoding The paper introduces a multimodal auditory attention decoding framework designed to maintain reliance on neural signals alongside behavioural tracking. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on auditory signal decoding rather than AI deployment safety or guardrails.
ATTUNE: Agentic Throughput Tuning for LLM Training The paper presents an automated coding agent that tunes large model training configurations for throughput under user-defined compute budgets. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on tuning distributed training throughput rather than LLM evaluation benchmarks.
ATTUNE: Agentic Throughput Tuning for LLM Training The paper presents an automated coding agent that tunes large model training configurations for throughput under user-defined compute budgets. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Addresses training throughput optimization rather than AI deployment safety or guardrails.
Risk-Averse Inference-Time Diffusion Model Alignment with Finite-Sample Guarantees The paper proposes an inference-time alignment method that optimizes expected costs while controlling tail risk in diffusion models. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on diffusion inference alignment rather than model compression or efficient serving.
Risk-Averse Inference-Time Diffusion Model Alignment with Finite-Sample Guarantees The paper proposes an inference-time alignment method that optimizes expected costs while controlling tail risk in diffusion models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Analyzes diffusion alignment algorithms rather than language model evaluation benchmarks.
Training Generalist Navigation Policies via Action Self-Distillation The paper introduces a self-distillation framework where a vision-language model teaches itself language-conditioned robotic navigation policies without human annotations. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Presents a robot navigation policy rather than an LLM evaluation benchmark.
Training Generalist Navigation Policies via Action Self-Distillation The paper introduces a self-distillation framework where a vision-language model teaches itself language-conditioned robotic navigation policies without human annotations. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on general mobile robot navigation rather than industrial manufacturing inspection.
Training Generalist Navigation Policies via Action Self-Distillation The paper introduces a self-distillation framework where a vision-language model teaches itself language-conditioned robotic navigation policies without human annotations. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Uses distillation for robot policy learning rather than model compression or efficient serving.
Training Generalist Navigation Policies via Action Self-Distillation The paper introduces a self-distillation framework where a vision-language model teaches itself language-conditioned robotic navigation policies without human annotations. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on robotic navigation learning rather than AI deployment safety or guardrails.
FedLoRD: Personalized Federated Learning based on Low-Rank Structures of Model Parameter Differences The paper introduces a personalized federated learning method that regularizes local model updates using low-rank difference structures. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Targets federated regularizers rather than small model compression, distillation, or serving.
FedLoRD: Personalized Federated Learning based on Low-Rank Structures of Model Parameter Differences The paper introduces a personalized federated learning method that regularizes local model updates using low-rank difference structures. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on personalized federated learning rather than language model evaluation frameworks.
FedLoRD: Personalized Federated Learning based on Low-Rank Structures of Model Parameter Differences The paper introduces a personalized federated learning method that regularizes local model updates using low-rank difference structures. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Studies federated optimization rather than AI deployment safety, guardrails, or hallucination.
Mixed Precision Scaling Laws for Quantized Pretraining The paper proposes mixed precision scaling laws to model the trade-offs of low-precision quantization during model pretraining. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Studies scaling behavior under quantization rather than general LLM evaluation benchmarks.
Mixed Precision Scaling Laws for Quantized Pretraining The paper proposes mixed precision scaling laws to model the trade-offs of low-precision quantization during model pretraining. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Analyzes quantization scaling behavior rather than AI deployment safety or guardrails.
textGrain watermarking is harder to detect: can sum-tests still do it optimally? The paper analyzes the theoretical detection limits and sum-test efficiency of textGrain watermarking under human text edits. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on watermarking theory rather than model compression or efficient inference methods.
textGrain watermarking is harder to detect: can sum-tests still do it optimally? The paper analyzes the theoretical detection limits and sum-test efficiency of textGrain watermarking under human text edits. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Analyzes statistical watermark detection boundaries rather than general LLM evaluation benchmarks.
Focus on What You Need: Global-to-Local Dynamic Attention for Multimodal Sarcasm Detection The paper introduces a global-to-local dynamic attention model to filter visual noise and improve multimodal sarcasm detection. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on multimodal attention mechanisms rather than model compression or efficient serving.
Focus on What You Need: Global-to-Local Dynamic Attention for Multimodal Sarcasm Detection The paper introduces a global-to-local dynamic attention model to filter visual noise and improve multimodal sarcasm detection. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Studies sarcasm detection rather than AI deployment safety, hallucination, or guardrails.
Focus on What You Need: Global-to-Local Dynamic Attention for Multimodal Sarcasm Detection The paper introduces a global-to-local dynamic attention model to filter visual noise and improve multimodal sarcasm detection. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Presents a multimodal sarcasm classifier rather than an LLM evaluation benchmark.
Confine to the Task, Anchor the Rest: Concept-Guided Retention Regularizer for Parameter-Efficient Fine-Tuning The paper proposes a concept-guided regularizer using sparse autoencoders to reduce catastrophic forgetting during parameter-efficient fine-tuning. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Proposes a regularizer for parameter-efficient tuning rather than an LLM evaluation benchmark.
Confine to the Task, Anchor the Rest: Concept-Guided Retention Regularizer for Parameter-Efficient Fine-Tuning The paper proposes a concept-guided regularizer using sparse autoencoders to reduce catastrophic forgetting during parameter-efficient fine-tuning. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on retaining knowledge during fine-tuning rather than model compression or inference efficiency.
MissPath-FM: Flow Matching with Structured Priors for Partially Observed Time Series The paper develops a flow matching framework with structured priors to model partially observed time series without wasting capacity on observed entries. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Addresses time series flow matching rather than small, efficient model serving or compression.
MissPath-FM: Flow Matching with Structured Priors for Partially Observed Time Series The paper develops a flow matching framework with structured priors to model partially observed time series without wasting capacity on observed entries. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on missing data imputation rather than deployment safety, guardrails, or hallucination.
MissPath-FM: Flow Matching with Structured Priors for Partially Observed Time Series The paper develops a flow matching framework with structured priors to model partially observed time series without wasting capacity on observed entries. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Evaluates time series imputation rather than language model evaluation or benchmarking.
Representation Geometry and Inductive Biases in Quantum Classifiers across Classical Visual Benchmarks The paper investigates the representation geometry and inductive biases of quantum classifiers when applied to classical visual benchmark datasets. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Studies quantum feature mappings rather than AI deployment safety or reliability risks.
Representation Geometry and Inductive Biases in Quantum Classifiers across Classical Visual Benchmarks The paper investigates the representation geometry and inductive biases of quantum classifiers when applied to classical visual benchmark datasets. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Compares quantum classifier inductive biases rather than language model evaluation benchmarks.
Representation Geometry and Inductive Biases in Quantum Classifiers across Classical Visual Benchmarks The paper investigates the representation geometry and inductive biases of quantum classifiers when applied to classical visual benchmark datasets. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Investigates quantum machine learning classification rather than efficient language model compression or inference.
Hybrid Parallel Bessel KAN Model: a Kolmogorov Arnold Network: integrated framework for developing a novel architecture to reconstruct the fused-basis approach via a parameter free quadratic interaction The paper proposes a Kolmogorov-Arnold Network architecture using parallel Bessel branches and quadratic interactions to reduce parameters and conditioning problems. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Develops a specialized neural network architecture rather than methods for small, efficient language models.
Hybrid Parallel Bessel KAN Model: a Kolmogorov Arnold Network: integrated framework for developing a novel architecture to reconstruct the fused-basis approach via a parameter free quadratic interaction The paper proposes a Kolmogorov-Arnold Network architecture using parallel Bessel branches and quadratic interactions to reduce parameters and conditioning problems. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on function approximation architecture rather than AI deployment safety or reliability.
Hybrid Parallel Bessel KAN Model: a Kolmogorov Arnold Network: integrated framework for developing a novel architecture to reconstruct the fused-basis approach via a parameter free quadratic interaction The paper proposes a Kolmogorov-Arnold Network architecture using parallel Bessel branches and quadratic interactions to reduce parameters and conditioning problems. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Evaluates Kolmogorov-Arnold Network variants rather than language model benchmarking methods.
Follow the Perturbed Leader for Linear Bandits: $\sqrt{T}$ Regret over General Convex Sets The paper provides an adversarial linear bandit algorithm achieving sublinear regret over general convex action sets using follow-the-perturbed-leader. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on theoretical linear bandits rather than deployment safety, guardrails, or hallucination.
Follow the Perturbed Leader for Linear Bandits: $\sqrt{T}$ Regret over General Convex Sets The paper provides an adversarial linear bandit algorithm achieving sublinear regret over general convex action sets using follow-the-perturbed-leader. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Analyzes theoretical bandit regret bounds rather than language model evaluation frameworks.
Follow the Perturbed Leader for Linear Bandits: $\sqrt{T}$ Regret over General Convex Sets The paper provides an adversarial linear bandit algorithm achieving sublinear regret over general convex action sets using follow-the-perturbed-leader. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Covers theoretical online optimization rather than small, efficient model architectures or compression.
GeoNIRS: Geometry-Aware Pretraining for Heterogeneous fNIRS Representation Learning The paper presents a geometry-aware pretraining framework that aligns heterogeneous probe layouts for functional near-infrared spectroscopy representation learning. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on brain signal pretraining rather than compact or efficient model architectures.
GeoNIRS: Geometry-Aware Pretraining for Heterogeneous fNIRS Representation Learning The paper presents a geometry-aware pretraining framework that aligns heterogeneous probe layouts for functional near-infrared spectroscopy representation learning. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Studies fNIRS neuroimaging representations rather than language model benchmark design.
GeoNIRS: Geometry-Aware Pretraining for Heterogeneous fNIRS Representation Learning The paper presents a geometry-aware pretraining framework that aligns heterogeneous probe layouts for functional near-infrared spectroscopy representation learning. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Covers hemodynamic representation learning rather than AI safety or deployment guardrails.
Persistence Positional Encoding: A Minimal Transformer Recipe for Persistence Diagrams The paper proposes a minimal positional encoding method using random Fourier features to feed persistence diagrams directly into standard Transformers. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Evaluates topological representation methods on graph tasks rather than LLM benchmarking.
Persistence Positional Encoding: A Minimal Transformer Recipe for Persistence Diagrams The paper proposes a minimal positional encoding method using random Fourier features to feed persistence diagrams directly into standard Transformers. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Addresses topological data representations, not deployment safety or reliability risks.
Persistence Positional Encoding: A Minimal Transformer Recipe for Persistence Diagrams The paper proposes a minimal positional encoding method using random Fourier features to feed persistence diagrams directly into standard Transformers. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Investigates persistence diagram encodings rather than model compression or efficient serving.
Searching for What Matters: Open-Ended Discovery of Multiple Hypotheses with Agentic Data Exploration The paper introduces an open-ended benchmark for evaluating agentic systems on discovering multiple data-supported hypotheses from tabular datasets. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on exploratory hypothesis discovery rather than model compression or inference efficiency.
Searching for What Matters: Open-Ended Discovery of Multiple Hypotheses with Agentic Data Exploration The paper introduces an open-ended benchmark for evaluating agentic systems on discovering multiple data-supported hypotheses from tabular datasets. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Studies scientific hypothesis generation rather than safety, guardrails, or deployment risks.
Deep Mesoscopic Dynamics The paper develops a neural network approach to learn effective mesoscopic field equations directly from microscopic trajectory data. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Addresses physical system simulation, not AI deployment safety or reliability.
Deep Mesoscopic Dynamics The paper develops a neural network approach to learn effective mesoscopic field equations directly from microscopic trajectory data. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on physics PDE surrogate modeling rather than language model evaluation.
Deep Mesoscopic Dynamics The paper develops a neural network approach to learn effective mesoscopic field equations directly from microscopic trajectory data. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Studies mesoscopic physical dynamics rather than small, efficient model compression or serving.
FlashMask-3: Efficient and Expressive Mask-Aware Distributed Attention The paper presents an expressive and distributed attention kernel that compresses structured attention masks to enable efficient context parallelism on Hopper GPUs. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Covers GPU kernel and communication optimizations, not AI safety or guardrails.
FlashMask-3: Efficient and Expressive Mask-Aware Distributed Attention The paper presents an expressive and distributed attention kernel that compresses structured attention masks to enable efficient context parallelism on Hopper GPUs. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Introduces an attention kernel optimization, not an evaluation benchmark.
Stable-Plastic Subspace Construction for Differentially Private Continual LoRA The paper presents a subspace construction method for continual low-rank adaptation to mitigate forgetting under differential privacy constraints. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Addresses private continual fine-tuning methods, not evaluation benchmarks.
Stable-Plastic Subspace Construction for Differentially Private Continual LoRA The paper presents a subspace construction method for continual low-rank adaptation to mitigate forgetting under differential privacy constraints. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on privacy guarantees during adaptation, not model inference efficiency.
NBCLT: Parameter-Agnostic Norm-Ball Clipped Local Training The paper introduces a unified local training framework using norm-ball clipping to improve communication efficiency in distributed and federated learning. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on distributed optimization bounds, not small models or serving.

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