Every paper checked — nothing hidden

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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
Chunk-wise Model-based Policy Optimization The paper introduces a chunk-wise model-based reinforcement learning approach that uses single-pass rollouts to optimize policies without recurrent backpropagation through time. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Addresses robot locomotion control, not AI safety or deployment guardrails.
Chunk-wise Model-based Policy Optimization The paper introduces a chunk-wise model-based reinforcement learning approach that uses single-pass rollouts to optimize policies without recurrent backpropagation through time. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on reinforcement learning for robotics, not industrial computer vision.
Chunk-wise Model-based Policy Optimization The paper introduces a chunk-wise model-based reinforcement learning approach that uses single-pass rollouts to optimize policies without recurrent backpropagation through time. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Introduces an RL algorithm, not LLM evaluation or benchmarks.
Strong Gaussian Approximation of Constant-Learning-Rate SGD: Bias and Trajectory Inference The paper derives strong Gaussian approximations for constant learning rate stochastic gradient descent under heavy-tailed noise to enable trajectory-level inference. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Theoretical optimization paper unrelated to operational AI safety or reliability.
Strong Gaussian Approximation of Constant-Learning-Rate SGD: Bias and Trajectory Inference The paper derives strong Gaussian approximations for constant learning rate stochastic gradient descent under heavy-tailed noise to enable trajectory-level inference. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Theoretical paper on SGD approximations, not LLM evaluation methods.
Strong Gaussian Approximation of Constant-Learning-Rate SGD: Bias and Trajectory Inference The paper derives strong Gaussian approximations for constant learning rate stochastic gradient descent under heavy-tailed noise to enable trajectory-level inference. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on theoretical SGD trajectory bounds, not model efficiency techniques.
Dissecting the Carbon-SLO Tension in Geo-Distributed LLM Serving The paper evaluates carbon-aware scheduling for geographically distributed language model serving under strict latency service-level objectives using an empirical simulator. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Addresses carbon-aware inference scheduling, not safety guardrails or robustness.
Dissecting the Carbon-SLO Tension in Geo-Distributed LLM Serving The paper evaluates carbon-aware scheduling for geographically distributed language model serving under strict latency service-level objectives using an empirical simulator. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Studies serving latency and carbon tradeoffs, not LLM evaluation benchmarks.
LLM-Augmented Bayesian Optimization with Adaptive Policies for Chiplet Design Space Exploration The paper combines language models with Bayesian optimization to guide architectural design exploration for heterogeneous chiplet systems. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Covers Bayesian optimization for chiplets, not AI safety or reliability.
LLM-Augmented Bayesian Optimization with Adaptive Policies for Chiplet Design Space Exploration The paper combines language models with Bayesian optimization to guide architectural design exploration for heterogeneous chiplet systems. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Optimizes chiplet hardware layout rather than model efficiency or serving.
LLM-Augmented Bayesian Optimization with Adaptive Policies for Chiplet Design Space Exploration The paper combines language models with Bayesian optimization to guide architectural design exploration for heterogeneous chiplet systems. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Applies LLMs to chiplet design, not LLM evaluation or benchmarking.
From Rainfall to Road-Network Functionality: Transferable Graph Forecasting for Flood-Disrupted Transportation Networks The paper proposes a transferable spatiotemporal graph learning framework to forecast road network disruption caused by extreme rainfall across unseen regions. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Studies flood transportation resilience, not AI safety, guardrails, or hallucination.
From Rainfall to Road-Network Functionality: Transferable Graph Forecasting for Flood-Disrupted Transportation Networks The paper proposes a transferable spatiotemporal graph learning framework to forecast road network disruption caused by extreme rainfall across unseen regions. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Addresses spatiotemporal graph learning, not efficient model compression or inference.
From Rainfall to Road-Network Functionality: Transferable Graph Forecasting for Flood-Disrupted Transportation Networks The paper proposes a transferable spatiotemporal graph learning framework to forecast road network disruption caused by extreme rainfall across unseen regions. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on flood road forecasting, not LLM benchmarks or evaluation.
TagBO: LLM-Driven Task-Aware Graph Bayesian Optimization for Scientific Discovery The paper introduces a Bayesian optimization method that combines language model relational priors with structural descriptors for mixed-variable scientific discovery. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on scientific search spaces, not efficient serving or quantization.
TagBO: LLM-Driven Task-Aware Graph Bayesian Optimization for Scientific Discovery The paper introduces a Bayesian optimization method that combines language model relational priors with structural descriptors for mixed-variable scientific discovery. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Applies LLMs to Bayesian optimization, not LLM benchmarking or testing.
TagBO: LLM-Driven Task-Aware Graph Bayesian Optimization for Scientific Discovery The paper introduces a Bayesian optimization method that combines language model relational priors with structural descriptors for mixed-variable scientific discovery. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Integrates LLM priors into optimization, not AI safety or alignment.
Ascent Conditions for Length-Normalized Single-Rollout Policy Updates The paper analyzes policy gradient updates that divide trajectory scores by length, identifying mathematical conditions under which expected returns can decrease. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Covers policy update mathematical theory, not model efficiency or compression.
Ascent Conditions for Length-Normalized Single-Rollout Policy Updates The paper analyzes policy gradient updates that divide trajectory scores by length, identifying mathematical conditions under which expected returns can decrease. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Analyzes reinforcement learning theoretical bounds, not LLM benchmark evaluation.
Ascent Conditions for Length-Normalized Single-Rollout Policy Updates The paper analyzes policy gradient updates that divide trajectory scores by length, identifying mathematical conditions under which expected returns can decrease. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on RL ascent conditions, not deployment safety or guardrails.
Triage-PiEvo: Evidence Admission for Principle-Evolving Scientific Agents The paper presents an evidence admission framework that verifies anomalies before updating the persistent principles of scientific language model agents. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Deals with autonomous scientific agents, not model compression or efficiency.
Triage-PiEvo: Evidence Admission for Principle-Evolving Scientific Agents The paper presents an evidence admission framework that verifies anomalies before updating the persistent principles of scientific language model agents. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Proposes an evidence admission agent framework, not benchmark evaluation.
One-Way Coupling via Patchwise Adaptive Normalization in Transformer Surrogates The paper proposes patchwise adaptive normalization to represent one-way coupled exogenous fields in transformer surrogates for partial differential equations. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Deals with scientific PDE surrogates, not general language model compression.
One-Way Coupling via Patchwise Adaptive Normalization in Transformer Surrogates The paper proposes patchwise adaptive normalization to represent one-way coupled exogenous fields in transformer surrogates for partial differential equations. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Addresses physics boundary compliance, not AI deployment safety or guardrails.
One-Way Coupling via Patchwise Adaptive Normalization in Transformer Surrogates The paper proposes patchwise adaptive normalization to represent one-way coupled exogenous fields in transformer surrogates for partial differential equations. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Studies PDE surrogate modeling architectures, not LLM benchmarking methods.
Accelerating Cross-Model LLM Inference via Attention Map Reuse This paper presents a system that speeds up multi-model language model serving by translating and reusing attention maps across models. Peer-reviewed	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Presents an inference serving system, not an LLM benchmark.
Accelerating Cross-Model LLM Inference via Attention Map Reuse This paper presents a system that speeds up multi-model language model serving by translating and reusing attention maps across models. Peer-reviewed	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on serving latency reduction, not safety guardrails or alignment.
Understanding Hidden Spatial Preferences in Bayesian Optimization This paper identifies spatial boundary biases in Bayesian optimization and modifies exploration using parameterized spatial weight fields. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Concerns acquisition function behavior, not deployment safety or red-teaming.
Understanding Hidden Spatial Preferences in Bayesian Optimization This paper identifies spatial boundary biases in Bayesian optimization and modifies exploration using parameterized spatial weight fields. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Studies spatial preferences in Bayesian optimization, not model compression.
Understanding Hidden Spatial Preferences in Bayesian Optimization This paper identifies spatial boundary biases in Bayesian optimization and modifies exploration using parameterized spatial weight fields. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on Bayesian optimization exploration, not LLM evaluation methods.
Linear-Time Non-Local Codec Placement for Machine Learning Model Compression This paper introduces a linear-time algorithm to place codec operators in computational graphs to compress operand traffic during on-device inference. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on codec operator placement algorithms, not LLM benchmarking.
Linear-Time Non-Local Codec Placement for Machine Learning Model Compression This paper introduces a linear-time algorithm to place codec operators in computational graphs to compress operand traffic during on-device inference. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Addresses computational graph optimization, not AI safety or deployment guardrails.
DeKoR: Decomposed Knowledge Routing for Efficient GNN--LLM Fusion This paper proposes a decomposed routing framework that distills large language model knowledge into lightweight graph neural networks. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Covers graph knowledge fusion, not deployment safety or reliability risks.
DeKoR: Decomposed Knowledge Routing for Efficient GNN--LLM Fusion This paper proposes a decomposed routing framework that distills large language model knowledge into lightweight graph neural networks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on GNN-LLM fusion architectures, not LLM evaluation or benchmarking.
Multi-Token Random-Walk Learning via Meeting-Triggered Information Exchange This paper analyzes the complexity and theoretical benefits of meeting-triggered information exchange in multi-token random-walk decentralized learning. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Analyzes decentralized optimization algorithms, not LLM evaluation methodologies.
Multi-Token Random-Walk Learning via Meeting-Triggered Information Exchange This paper analyzes the complexity and theoretical benefits of meeting-triggered information exchange in multi-token random-walk decentralized learning. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Examines optimization complexity bounds, not deployment safety or robustness.
Multi-Token Random-Walk Learning via Meeting-Triggered Information Exchange This paper analyzes the complexity and theoretical benefits of meeting-triggered information exchange in multi-token random-walk decentralized learning. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Studies distributed training algorithms, not model compression or efficient serving.
VitaBench: Memory Lifecycles Beyond Retrieval This paper introduces a benchmark to evaluate agent memory lifecycle decisions, including compression, consolidation, and deletion. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on agent memory benchmarking, not safety, guardrails, or alignment.
VitaBench: Memory Lifecycles Beyond Retrieval This paper introduces a benchmark to evaluate agent memory lifecycle decisions, including compression, consolidation, and deletion. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Presents a memory evaluation benchmark, not model compression or efficient serving.
More Domains, No Gains? FLO: Fast-Slow Reciprocal Learning for Multi-Domain Graph Pre-training This paper studies why multi-domain graph pre-training fails to generalize and proposes a fast-slow reciprocal learning framework. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Addresses graph foundation pre-training, not deployment safety or monitoring.
More Domains, No Gains? FLO: Fast-Slow Reciprocal Learning for Multi-Domain Graph Pre-training This paper studies why multi-domain graph pre-training fails to generalize and proposes a fast-slow reciprocal learning framework. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on graph pre-training paradigms, not LLM evaluation benchmarks.
More Domains, No Gains? FLO: Fast-Slow Reciprocal Learning for Multi-Domain Graph Pre-training This paper studies why multi-domain graph pre-training fails to generalize and proposes a fast-slow reciprocal learning framework. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Studies multi-domain graph pre-training, not model compression or serving.
Random Fourier Features in Near-Linear Time This paper provides a tighter analysis enabling near-linear time computation of random Fourier feature maps with near-subgaussian concentration. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Studies kernel approximation theory, not LLM evaluation methods.
Random Fourier Features in Near-Linear Time This paper provides a tighter analysis enabling near-linear time computation of random Fourier feature maps with near-subgaussian concentration. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Covers kernel concentration bounds, not deployment safety or reliability.
Random Fourier Features in Near-Linear Time This paper provides a tighter analysis enabling near-linear time computation of random Fourier feature maps with near-subgaussian concentration. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on kernel feature computation, not small models or inference.
Rethinking Opinion Measurement Interfaces for Human-in-the-Loop Optimization This paper evaluates human opinion measurement interfaces and recommends listwise preference collection for human-in-the-loop optimization. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Analyzes human-in-the-loop interfaces, not model compression or efficient serving.
Rethinking Opinion Measurement Interfaces for Human-in-the-Loop Optimization This paper evaluates human opinion measurement interfaces and recommends listwise preference collection for human-in-the-loop optimization. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Studies opinion elicitation interfaces, not safety guardrails or system red-teaming.
CrossMean: Model Merging by Pairing Merged and Expert Activations This paper presents an activation-pairing method and selection criterion to improve closed-form model merging without retraining. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Studies parameter merging algorithms, not deployed AI safety or robustness.
CrossMean: Model Merging by Pairing Merged and Expert Activations This paper presents an activation-pairing method and selection criterion to improve closed-form model merging without retraining. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on model merging algorithms, not language model benchmarking.
Formalizing Forgetting: Theory of Forgetting for Online Continual Learning This paper provides theoretical upper bounds on catastrophic forgetting during single-pass online continual learning under convex objectives. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Studies theoretical continual learning, not deployment safety or guardrails.
Formalizing Forgetting: Theory of Forgetting for Online Continual Learning This paper provides theoretical upper bounds on catastrophic forgetting during single-pass online continual learning under convex objectives. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on continual learning theory, not LLM benchmark assessment.
Formalizing Forgetting: Theory of Forgetting for Online Continual Learning This paper provides theoretical upper bounds on catastrophic forgetting during single-pass online continual learning under convex objectives. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Investigates catastrophic forgetting bounds, not model compression or efficiency.
Groups Don't Move as One: A Bregman Clustering View of Linearized Group Influence This paper uses Bregman clustering to address opposing member effects within linearized group influence estimates for machine learning models. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Studies dataset attribution methods, not model compression or distillation.
Groups Don't Move as One: A Bregman Clustering View of Linearized Group Influence This paper uses Bregman clustering to address opposing member effects within linearized group influence estimates for machine learning models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Analyzes sample influence estimators, not deployment guardrails or robustness.
Groups Don't Move as One: A Bregman Clustering View of Linearized Group Influence This paper uses Bregman clustering to address opposing member effects within linearized group influence estimates for machine learning models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on theoretical influence functions, not LLM benchmark evaluation.
Machine Learning-Based Prediction of Dengue Infection Using Clinical and Hematological Data from Bangladesh: A Comparative Analysis of Benchmark and Extended Prediction Models This paper evaluates standard machine learning models to predict dengue infection from clinical and hematological data in Bangladesh. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Studies dengue prediction models, not efficient model serving techniques.
Machine Learning-Based Prediction of Dengue Infection Using Clinical and Hematological Data from Bangladesh: A Comparative Analysis of Benchmark and Extended Prediction Models This paper evaluates standard machine learning models to predict dengue infection from clinical and hematological data in Bangladesh. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Examines tabular clinical models, not language model evaluation methodologies.
Machine Learning-Based Prediction of Dengue Infection Using Clinical and Hematological Data from Bangladesh: A Comparative Analysis of Benchmark and Extended Prediction Models This paper evaluates standard machine learning models to predict dengue infection from clinical and hematological data in Bangladesh. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Evaluates clinical diagnostic performance, not AI system deployment safeguards.
Explore This! Beat and Downbeat Tracking From a Learned Tatum Grid This paper proposes a neural architecture that predicts a tatum grid to track musical beats and downbeats without dynamic Bayesian networks. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on musical beat tracking, not efficient language modeling.
Explore This! Beat and Downbeat Tracking From a Learned Tatum Grid This paper proposes a neural architecture that predicts a tatum grid to track musical beats and downbeats without dynamic Bayesian networks. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Concerns musical signal processing, not AI safety and reliability.

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