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
Compositional Information Geometry of Probabilistic Programs Studies the compositional information geometry and Riemannian metrics of probabilistic programs. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Presents mathematical theory of probabilistic programs without LLMs.
OpenMQPI: An MLIR-based Pipeline to Offload Quantum Circuits via OpenMP Proposes an OpenMP and compiler pipeline extension to offload quantum circuits in high-performance computing. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Covers quantum compiler pipelines without involving language models.
Soliduzz: Finding Miscompilations in Smart-Contract Compilers with Assertion-Driven Equivalence Oracles Finds miscompilations in smart-contract compilers using executable assertions and standard fuzzers. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Deals with traditional compiler miscompilation rather than AI deployment safety.
Soliduzz: Finding Miscompilations in Smart-Contract Compilers with Assertion-Driven Equivalence Oracles Finds miscompilations in smart-contract compilers using executable assertions and standard fuzzers. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Focuses on compiler fuzzing rather than LLM code assistants.
Beyond Independent Values: Optimal Auctions with Correlated Valuations for LLM-Native Advertising Designs an auction mechanism that models correlated valuations among advertisers competing in conversational assistant answers. Preprint	AI agents via arxiv · 15 Oct, 06:18	Not relevant Studies advertising auction mechanics rather than language model agent systems.
sGLP: Specifying Stochastic Agents that Simulate People Presents a stochastic logic programming method to simulate human user behavior for testing decentralized grassroots apps. Preprint	AI agents via arxiv · 15 Oct, 06:18	Not relevant Focuses on stochastic concurrent logic programs simulating mobile app users.
sGLP: Specifying Stochastic Agents that Simulate People Presents a stochastic logic programming method to simulate human user behavior for testing decentralized grassroots apps. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Presents a stochastic simulation language, not LLMs writing code.
Persistent LLM Orchestration of Heterogeneous Robot Teams via Partial Delegation to Multi-Agent Protocols Orchestrates persistent multi-robot missions with language models by offloading ongoing tasks to classical protocols. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on robot team orchestration, not industrial visual inspection.
Do Rollups Self-Organize? Multi-Agent Reinforcement Learning in the EIP-4844 Blob Fee Market Simulates Ethereum blob fee markets with multi-agent reinforcement learning to analyze how independent rollups interact. Preprint	AI agents via arxiv · 15 Oct, 06:18	Not relevant Applies reinforcement learning to blockchain fee markets, not LLM agents.
Representation Theorems for Actual Powers over Two-Agent General Concurrent Game Frames Develops representation theorems for actual power structures in two-agent concurrent games using neighborhood frames. Peer-reviewed	AI agents via arxiv · 15 Oct, 06:18	Not relevant Studies game theory logic representations, not applied LLM agents.
Collective Benefits and Efficiency in Coupled Echo State Networks Uses coupled echo state networks to study how interaction and feedback affect joint performance and efficiency. Preprint	AI agents via arxiv · 15 Oct, 06:18	Not relevant Studies coupled reservoir networks, not language model multi-agent systems.
Mobility-Budget-Constrained Submodular Maximization for Distributed Semantic Multi-Robot Exploration Presents a distributed semantic exploration method for robot teams to balance map information gain with movement limits. Preprint	AI agents via arxiv · 15 Oct, 06:18	Not relevant Focuses on multi-robot mapping and path planning, not LLM agents.
Mobility-Budget-Constrained Submodular Maximization for Distributed Semantic Multi-Robot Exploration Presents a distributed semantic exploration method for robot teams to balance map information gain with movement limits. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Addresses autonomous exploration robotics rather than industrial visual inspection.
Strategic Multi-Agent Resource Allocation via Statistical Contracts The paper investigates statistical contracts and hypothesis testing to allocate resources strategically among competing agents. Preprint	AI agents via arxiv · 15 Oct, 06:18	Not relevant Covers theoretical statistical contracts, not language model planning or tool use.
CALM: Decentralized Control of Cooperative Aerial Manipulation with Multi-Agent Reinforcement Learning The paper presents a decentralized multi-agent reinforcement learning controller for cooperative aerial manipulation using multiple drones. Preprint	AI agents via arxiv · 15 Oct, 06:18	Not relevant Focuses on physical robot control, not language model agent workflows.
CALM: Decentralized Control of Cooperative Aerial Manipulation with Multi-Agent Reinforcement Learning The paper presents a decentralized multi-agent reinforcement learning controller for cooperative aerial manipulation using multiple drones. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Concerns cooperative aerial manipulation, not industrial defect detection.
Pristine Models The paper introduces an architecture that separates safe and unsafe visual knowledge during pretraining so unsafe branches can be disabled. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses on safe concept separation in image generation pretraining.
Pristine Models The paper introduces an architecture that separates safe and unsafe visual knowledge during pretraining so unsafe branches can be disabled. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on safety compartmentalization rather than model compression.
Pristine Models The paper introduces an architecture that separates safe and unsafe visual knowledge during pretraining so unsafe branches can be disabled. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Covers generative image safety, not manufacturing inspection or defects.
Pristine Models The paper introduces an architecture that separates safe and unsafe visual knowledge during pretraining so unsafe branches can be disabled. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Proposes an architecture for concept unlearning, not evaluation benchmarks.
A Physics-Informed Hybrid Forward-Inverse Machine Learning Framework for High-Dimensional Sparse Recipe Reconstruction The paper proposes a physics-informed forward-inverse machine learning framework for sparse recipe formulation in industrial dyeing. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Does not address small models, pruning, or efficient inference.
A Physics-Informed Hybrid Forward-Inverse Machine Learning Framework for High-Dimensional Sparse Recipe Reconstruction The paper proposes a physics-informed forward-inverse machine learning framework for sparse recipe formulation in industrial dyeing. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Concerns industrial chemical recipes, not AI deployment safety risks.
A Physics-Informed Hybrid Forward-Inverse Machine Learning Framework for High-Dimensional Sparse Recipe Reconstruction The paper proposes a physics-informed forward-inverse machine learning framework for sparse recipe formulation in industrial dyeing. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on chemical dye formulations, not evaluating language models.
From Prototype to Massing: Learning Graph Correspondence for Scalable Facade Modeling The paper develops a graph attention network to transfer prototype facade articulation patterns onto building massing targets. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on architectural design workflows, not operational AI safety.
From Prototype to Massing: Learning Graph Correspondence for Scalable Facade Modeling The paper develops a graph attention network to transfer prototype facade articulation patterns onto building massing targets. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on graph correspondence for architecture, not model efficiency.
From Prototype to Massing: Learning Graph Correspondence for Scalable Facade Modeling The paper develops a graph attention network to transfer prototype facade articulation patterns onto building massing targets. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Investigates architectural facade modeling rather than benchmarking language models.
Early Detection of Pension-Sponsor Distress from Public Filings: A Sponsor Vulnerability Index, Stress-Augmented VaR, and Dynamic Self-Investment Caps The paper develops an early-warning pipeline using public balance-sheet filings to predict defined-benefit pension sponsor distress. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Does not study model compression, distillation, or efficient model architectures.
Early Detection of Pension-Sponsor Distress from Public Filings: A Sponsor Vulnerability Index, Stress-Augmented VaR, and Dynamic Self-Investment Caps The paper develops an early-warning pipeline using public balance-sheet filings to predict defined-benefit pension sponsor distress. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on financial distress prediction, not evaluating language models.
Early Detection of Pension-Sponsor Distress from Public Filings: A Sponsor Vulnerability Index, Stress-Augmented VaR, and Dynamic Self-Investment Caps The paper develops an early-warning pipeline using public balance-sheet filings to predict defined-benefit pension sponsor distress. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Deals with financial risk modeling, not AI system deployment safety.
Hierarchical Multi-Agent Reasoning Systems for Knowledge-Grounded Material Substitution The paper introduces a hierarchical multi-agent reasoning system combining retrieval and knowledge graphs to find chemical material substitutes. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on scientific agent workflows, not model compression or efficient inference.
Hierarchical Multi-Agent Reasoning Systems for Knowledge-Grounded Material Substitution The paper introduces a hierarchical multi-agent reasoning system combining retrieval and knowledge graphs to find chemical material substitutes. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Proposes a multi-agent domain system, not an LLM evaluation benchmark.
Hierarchical Multi-Agent Reasoning Systems for Knowledge-Grounded Material Substitution The paper introduces a hierarchical multi-agent reasoning system combining retrieval and knowledge graphs to find chemical material substitutes. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Addresses materials design workflows, not deployment AI safety, red-teaming, or guardrails.
On physical implementability of reinforcement-learning-driven reasoning The paper presents a theoretical negative result on physically implementing reinforcement-learning-driven reasoning in certain physical neural networks. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Studies physical stability in neural hardware, not AI deployment safety or jailbreaks.
On physical implementability of reinforcement-learning-driven reasoning The paper presents a theoretical negative result on physically implementing reinforcement-learning-driven reasoning in certain physical neural networks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Analyzes physical neural network dynamics, not LLM benchmark evaluation.
On physical implementability of reinforcement-learning-driven reasoning The paper presents a theoretical negative result on physically implementing reinforcement-learning-driven reasoning in certain physical neural networks. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Theoretical physics paper on hardware stability, not efficient model compression or serving.
A Stochastic Analysis Perspective on Diffusion Models The paper provides a stochastic analysis perspective linking score matching, Langevin sampling, and diffusion model time reversal. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Mathematical survey of diffusion models, not LLM benchmark design.
A Stochastic Analysis Perspective on Diffusion Models The paper provides a stochastic analysis perspective linking score matching, Langevin sampling, and diffusion model time reversal. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Explores stochastic foundations of diffusion sampling, not deployment safety or guardrails.
A Stochastic Analysis Perspective on Diffusion Models The paper provides a stochastic analysis perspective linking score matching, Langevin sampling, and diffusion model time reversal. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Covers stochastic analysis of diffusion processes, not model distillation or compression.
ULPack: Reference-Conditioned Lossless Compression of Checkpoint Trajectories The paper introduces a lossless compression codec for model checkpoint trajectories conditioned on reference floating-point exponents. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Presents a checkpoint compression algorithm, not an LLM benchmark.
ULPack: Reference-Conditioned Lossless Compression of Checkpoint Trajectories The paper introduces a lossless compression codec for model checkpoint trajectories conditioned on reference floating-point exponents. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Addresses binary checkpoint file compression, not safety, guardrails, or model alignment.
Refined Grid Sampling for Efficient Implicit Neural Representations of Large-Scale Scientific Data The paper proposes a dynamic stratified grid sampling approach to train implicit neural representations on large scientific data faster. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Deals with grid coordinate sampling speed, not deployment safety or guardrails.
Refined Grid Sampling for Efficient Implicit Neural Representations of Large-Scale Scientific Data The paper proposes a dynamic stratified grid sampling approach to train implicit neural representations on large scientific data faster. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on scientific implicit neural representations, not LLM benchmark evaluation.
Embedding Graph Kernel Entropy (EGKE): Fine-grained Hallucination Detection for LLMs from Embeddings The paper proposes an embedding-based graph kernel entropy method for computationally efficient hallucination detection in language models. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Uses sentence embeddings for entropy calculation, not small model design or compression.
Embedding Graph Kernel Entropy (EGKE): Fine-grained Hallucination Detection for LLMs from Embeddings The paper proposes an embedding-based graph kernel entropy method for computationally efficient hallucination detection in language models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Proposes a hallucination detection method rather than evaluating benchmark trustworthiness.
Learning Non-Gaussian SEMs with Conditional Risk, and Why the Direction of Recursion Matters The paper extends recursive linear structural equation model identification to non-Gaussian noise using conditional prediction risk. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Deals with causal discovery in structural equation models, not LLM evaluation.
Learning Non-Gaussian SEMs with Conditional Risk, and Why the Direction of Recursion Matters The paper extends recursive linear structural equation model identification to non-Gaussian noise using conditional prediction risk. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Presents statistical causality algorithms, not small or efficient neural models.
Learning Non-Gaussian SEMs with Conditional Risk, and Why the Direction of Recursion Matters The paper extends recursive linear structural equation model identification to non-Gaussian noise using conditional prediction risk. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Analyzes causal model recovery, not AI safety or deployment monitoring.
Adaptive-Depth Is a Finishing Stage The paper proposes training looped transformers at variable depths only during a short finishing stage to reduce compute costs. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Investigates compute efficiency in transformers, not safety, guardrails, or hallucinations.
Adaptive-Depth Is a Finishing Stage The paper proposes training looped transformers at variable depths only during a short finishing stage to reduce compute costs. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on transformer training recipes, not LLM evaluation frameworks.
Do Artificial Neural Networks Need Dendritic Nanotubes? Two Reasons for Shrinking Importance Estimates in Continual Learning The paper examines importance estimate shrinkage to mitigate catastrophic forgetting in regularization-based continual learning. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Addresses continual learning parameter importance, not model compression or efficient inference.
Do Artificial Neural Networks Need Dendritic Nanotubes? Two Reasons for Shrinking Importance Estimates in Continual Learning The paper examines importance estimate shrinkage to mitigate catastrophic forgetting in regularization-based continual learning. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Studies continual learning parameter shrinkage, not deployment safety or guardrails.
Do Artificial Neural Networks Need Dendritic Nanotubes? Two Reasons for Shrinking Importance Estimates in Continual Learning The paper examines importance estimate shrinkage to mitigate catastrophic forgetting in regularization-based continual learning. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Investigates continual learning regularization techniques, not LLM evaluation benchmarks.
Variance Fingerprints: Component-Wise Identification of Content Factors by Augmentation Design The paper establishes theoretical conditions for identifying individual content factors in self-supervised learning via designed augmentation strengths. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Addresses factor identification in self-supervised models, not model efficiency or compression.
Variance Fingerprints: Component-Wise Identification of Content Factors by Augmentation Design The paper establishes theoretical conditions for identifying individual content factors in self-supervised learning via designed augmentation strengths. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Theoretical self-supervised learning identifiability paper, not an LLM benchmark.
Variance Fingerprints: Component-Wise Identification of Content Factors by Augmentation Design The paper establishes theoretical conditions for identifying individual content factors in self-supervised learning via designed augmentation strengths. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on data augmentation theory, not safety guardrails or reliability.
Learning to Sync at the Edge: Client-Side Adaptive Synchronization for Federated Learning The paper introduces a client-side reinforcement learning scheduler to optimize synchronization decisions in edge federated learning. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on federated learning communication protocols, not LLM benchmarking.
Learning to Sync at the Edge: Client-Side Adaptive Synchronization for Federated Learning The paper introduces a client-side reinforcement learning scheduler to optimize synchronization decisions in edge federated learning. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Addresses federated synchronization efficiency, not AI safety or hallucination prevention.
Layer-wise Input Recoverability in Deep Neural Networks The paper probes convolutional neural networks to measure how much layer input information can be reconstructed from layer outputs. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Investigates input recoverability in CNNs, not model compression or efficient serving.
Layer-wise Input Recoverability in Deep Neural Networks The paper probes convolutional neural networks to measure how much layer input information can be reconstructed from layer outputs. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Studies CNN layer representations, not LLM benchmarks or evaluation methods.
Layer-wise Input Recoverability in Deep Neural Networks The paper probes convolutional neural networks to measure how much layer input information can be reconstructed from layer outputs. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Analyzes feature recoverability in vision backbones, not AI safety or deployment robustness.

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