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
DisCo Algebra: Why Disintegration and Combination Are Not Inverses Introduces an algebraic framework proving information loss asymmetry when decomposing and reconstructing joint probability distributions. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Deals with abstract probability compression, not efficient LLM serving or quantization.
DisCo Algebra: Why Disintegration and Combination Are Not Inverses Introduces an algebraic framework proving information loss asymmetry when decomposing and reconstructing joint probability distributions. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on theoretical probability operads, not LLM benchmarks or evaluation.
Conformal Sequential Decision Making With Cost and Sublinear Policy Regret Guarantees Develops an episodic sequential decision agent providing hard budget guarantees and sublinear regret bounds using conformal prediction. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Studies online conformal regret algorithms, not model efficiency.
Conformal Sequential Decision Making With Cost and Sublinear Policy Regret Guarantees Develops an episodic sequential decision agent providing hard budget guarantees and sublinear regret bounds using conformal prediction. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Studies formal regret bounds for agents, not deployment safety guardrails.
Conformal Sequential Decision Making With Cost and Sublinear Policy Regret Guarantees Develops an episodic sequential decision agent providing hard budget guarantees and sublinear regret bounds using conformal prediction. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on sequential decision algorithms, not LLM evaluation.
Algorithmic Fairness Over Time: Advances & Prospects Presents a taxonomy and research roadmap for measuring algorithmic fairness over time in systems with feedback loops. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Surveys algorithmic fairness taxonomy, not LLM benchmark evaluation.
Algorithmic Fairness Over Time: Advances & Prospects Presents a taxonomy and research roadmap for measuring algorithmic fairness over time in systems with feedback loops. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Addresses algorithmic fairness over time, not model compression.
PATTERN Can Be Solved from Memory: Generator-Aware Baselines for Synthetic Node Classification Shows the PATTERN graph benchmark can be solved by memorization because pattern subgraphs are reused across splits. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Critiques graph benchmarks, not model compression or efficiency.
PATTERN Can Be Solved from Memory: Generator-Aware Baselines for Synthetic Node Classification Shows the PATTERN graph benchmark can be solved by memorization because pattern subgraphs are reused across splits. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Analyzes benchmark contamination, not safety guardrails or alignment.
Sparsity Saves Compute, Not Samples: Oracle Inequalities for Dense and Sparse Mixture of Experts Provides statistical error bounds for dense and sparse mixture-of-experts models, showing sparsity reduces compute rather than sample complexity. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on statistical oracle inequalities, not AI safety risks.
Sparsity Saves Compute, Not Samples: Oracle Inequalities for Dense and Sparse Mixture of Experts Provides statistical error bounds for dense and sparse mixture-of-experts models, showing sparsity reduces compute rather than sample complexity. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Theoretical statistical analysis of MoE bounds, not model efficiency.
Sparsity Saves Compute, Not Samples: Oracle Inequalities for Dense and Sparse Mixture of Experts Provides statistical error bounds for dense and sparse mixture-of-experts models, showing sparsity reduces compute rather than sample complexity. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Statistical study of MoE estimation, not LLM benchmarks.
Information-Theoretic Foundations of Online Temperature Sampling Formulates an online sampling algorithm that adjusts temperature dynamically under cross-entropy budgets to prevent repetitive language model output. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Studies online temperature sampling theory, not LLM evaluation.
Information-Theoretic Foundations of Online Temperature Sampling Formulates an online sampling algorithm that adjusts temperature dynamically under cross-entropy budgets to prevent repetitive language model output. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Theoretical study of entropy in sampling, not model compression.
Information-Theoretic Foundations of Online Temperature Sampling Formulates an online sampling algorithm that adjusts temperature dynamically under cross-entropy budgets to prevent repetitive language model output. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Analyzes repetitive generation decoding theory, not deployment safety guardrails.
The Optimal Decoder is Tilted Temperature Derives a verifier-tilted temperature sampling formula that optimizes pass-at-k metrics under entropy diversity and plausibility constraints. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Explores sampling temperature theory, not small or compressed models.
The Optimal Decoder is Tilted Temperature Derives a verifier-tilted temperature sampling formula that optimizes pass-at-k metrics under entropy diversity and plausibility constraints. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Analyzes sampling temperature optimization, not AI safety or guardrails.
The Optimal Decoder is Tilted Temperature Derives a verifier-tilted temperature sampling formula that optimizes pass-at-k metrics under entropy diversity and plausibility constraints. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Studies decoding temperature formulations, not benchmark evaluation design.
Toward a Multimodal Foundation Model for Plasma State Reconstruction Reconstructs magnetic reconnection plasma states from sparse multimodal observations using a residual 3D U-Net architecture. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Studies plasma physics reconstruction, not deployment safety risks.
Toward a Multimodal Foundation Model for Plasma State Reconstruction Reconstructs magnetic reconnection plasma states from sparse multimodal observations using a residual 3D U-Net architecture. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Applies U-Net to plasma physics, not LLM evaluation.
Toward a Multimodal Foundation Model for Plasma State Reconstruction Reconstructs magnetic reconnection plasma states from sparse multimodal observations using a residual 3D U-Net architecture. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on plasma state reconstruction, not model compression.
On the Theoretical Limits of Learning Unconstrained Interatomic Potentials Demonstrates theoretical sampling failures and kinetic traps when training unconstrained machine-learned interatomic potentials from energy and force data. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Covers physical molecular dynamics, not AI safety and reliability.
On the Theoretical Limits of Learning Unconstrained Interatomic Potentials Demonstrates theoretical sampling failures and kinetic traps when training unconstrained machine-learned interatomic potentials from energy and force data. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Studies interatomic potentials, not model compression or efficiency.
On the Theoretical Limits of Learning Unconstrained Interatomic Potentials Demonstrates theoretical sampling failures and kinetic traps when training unconstrained machine-learned interatomic potentials from energy and force data. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Theoretical analysis of molecular potentials, not LLM benchmarks.
Swarm Telepathy: Prefill-Free KV-Cache Transfer Across Families Enables prefill-free multi-agent communication across different language model families using cross-model key-value cache transfer. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Proposes KV-cache transfer, not benchmark evaluation methodologies.
Swarm Telepathy: Prefill-Free KV-Cache Transfer Across Families Enables prefill-free multi-agent communication across different language model families using cross-model key-value cache transfer. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on inference speedup, not safety, guardrails, or hallucination.
Anchors Enhance Controllability and Reasoning of Diffusion Language Models Uses reinforcement learning to place token anchors that guide text generation in frozen diffusion language models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Proposes anchor-guided generation, not LLM benchmark evaluation.
Anchors Enhance Controllability and Reasoning of Diffusion Language Models Uses reinforcement learning to place token anchors that guide text generation in frozen diffusion language models. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Studies diffusion language model controllability, not model compression.
Anchors Enhance Controllability and Reasoning of Diffusion Language Models Uses reinforcement learning to place token anchors that guide text generation in frozen diffusion language models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on generation controllability in DLMs, not deployment safety risks.
Random Feature Pruning for Multi-Class Classification Proposes a sequential selection algorithm to prune redundant random features for multiclass classification with theoretical guarantees. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Studies feature pruning algorithms, not AI safety or alignment.
Random Feature Pruning for Multi-Class Classification Proposes a sequential selection algorithm to prune redundant random features for multiclass classification with theoretical guarantees. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Studies random kitchen sinks pruning, not LLM benchmarks.
Random Feature Pruning for Multi-Class Classification Proposes a sequential selection algorithm to prune redundant random features for multiclass classification with theoretical guarantees. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on classical random feature pruning, not small language models.
RELAGRAM: Recursive Latent Graph Model for the Capacitated Vehicle Routing Problem Introduces a recurrent latent graph transformer that solves capacitated vehicle routing problems by updating edge representations iteratively. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Covers vehicle routing optimization, not AI safety in deployment.
RELAGRAM: Recursive Latent Graph Model for the Capacitated Vehicle Routing Problem Introduces a recurrent latent graph transformer that solves capacitated vehicle routing problems by updating edge representations iteratively. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Applies latent recursion to CVRP, not LLM evaluation.
RELAGRAM: Recursive Latent Graph Model for the Capacitated Vehicle Routing Problem Introduces a recurrent latent graph transformer that solves capacitated vehicle routing problems by updating edge representations iteratively. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on combinatorial optimization, not LLM compression or inference efficiency.
Energy-Guided Learning for Heat Equation with Noisy Source: Optimal Estimation and Generalization Bounds Presents an energy-regularized regression method to learn heat equation solutions from noisy physical data and proves estimation error bounds. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Theoretical PDE learning work, not AI safety or robustness.
Energy-Guided Learning for Heat Equation with Noisy Source: Optimal Estimation and Generalization Bounds Presents an energy-regularized regression method to learn heat equation solutions from noisy physical data and proves estimation error bounds. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on solving heat equations, not LLM benchmarks.
Energy-Guided Learning for Heat Equation with Noisy Source: Optimal Estimation and Generalization Bounds Presents an energy-regularized regression method to learn heat equation solutions from noisy physical data and proves estimation error bounds. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Studies PDE mathematical learning bounds, not model efficiency.
Data-Topology Compatibility Enables Personalization in Decentralized Learning Analyzes how data and network structure alignment enables personalized model groups in decentralized learning without explicit personalization algorithms. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on theoretical decentralized learning, not efficient model architectures.
Data-Topology Compatibility Enables Personalization in Decentralized Learning Analyzes how data and network structure alignment enables personalized model groups in decentralized learning without explicit personalization algorithms. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Analyzes decentralized learning theory, not LLM benchmark evaluation.
Data-Topology Compatibility Enables Personalization in Decentralized Learning Analyzes how data and network structure alignment enables personalized model groups in decentralized learning without explicit personalization algorithms. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Theoretical study of decentralized learning, not deployment safety risks.
When Dynamic Scaling Fails: A Scale-State Feedback Governs Recurrent-State Quantization in Linear-Attention and SSM LLMs This paper investigates error compounding and scale-state feedback mechanisms during recurrent-state quantization in linear-attention and state-space language models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on quantization dynamics, not safety guardrails or jailbreaks.
When Dynamic Scaling Fails: A Scale-State Feedback Governs Recurrent-State Quantization in Linear-Attention and SSM LLMs This paper investigates error compounding and scale-state feedback mechanisms during recurrent-state quantization in linear-attention and state-space language models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Studies recurrent state quantization failure, not LLM evaluation benchmarks.
Sensor-Aware Joint Data Fusion and State Estimation under Sparse Multi-rate Distribution Grid Sensing This paper proposes a graph differential equation and transformer architecture to perform joint data fusion and state estimation in power distribution grids. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Studies electrical grid sensing, not AI safety and reliability.
Sensor-Aware Joint Data Fusion and State Estimation under Sparse Multi-rate Distribution Grid Sensing This paper proposes a graph differential equation and transformer architecture to perform joint data fusion and state estimation in power distribution grids. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Addresses power distribution sensing, not model efficiency techniques.
Sensor-Aware Joint Data Fusion and State Estimation under Sparse Multi-rate Distribution Grid Sensing This paper proposes a graph differential equation and transformer architecture to perform joint data fusion and state estimation in power distribution grids. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on power grid state estimation, not LLM benchmarks.
Beyond Adjacent Layers: Graph-Guided Layer Fusion for Compressing Large Language Models This paper introduces a graph-guided layer fusion method that identifies and merges functionally redundant non-adjacent layers to compress language models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on depth compression, not benchmark evaluation design.
Beyond Adjacent Layers: Graph-Guided Layer Fusion for Compressing Large Language Models This paper introduces a graph-guided layer fusion method that identifies and merges functionally redundant non-adjacent layers to compress language models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Evaluates model pruning and fusion, not AI safety risks.
LexiCan: Lexicographic Canonicalization of Point Clouds for Machine Learning Applications This paper presents an invariant lexicographic canonicalization method to resolve rotation and permutation ambiguities in 3D point cloud representations. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Studies 3D point cloud canonicalization, not LLM evaluation.
LexiCan: Lexicographic Canonicalization of Point Clouds for Machine Learning Applications This paper presents an invariant lexicographic canonicalization method to resolve rotation and permutation ambiguities in 3D point cloud representations. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Proposes point cloud canonicalization, not small or efficient models.
LexiCan: Lexicographic Canonicalization of Point Clouds for Machine Learning Applications This paper presents an invariant lexicographic canonicalization method to resolve rotation and permutation ambiguities in 3D point cloud representations. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Addresses 3D point clouds, not AI deployment safety.
Physics Foundation Models for Efficient Control of Fluid Flows This paper attaches reinforcement learning control heads to a pretrained physics foundation model for closed-loop fluid-flow control. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Explores fluid flow control, not model compression or efficiency.
Physics Foundation Models for Efficient Control of Fluid Flows This paper attaches reinforcement learning control heads to a pretrained physics foundation model for closed-loop fluid-flow control. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Studies fluid flow control, not LLM evaluation benchmarks.
Physics Foundation Models for Efficient Control of Fluid Flows This paper attaches reinforcement learning control heads to a pretrained physics foundation model for closed-loop fluid-flow control. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on physics control systems, not deployment safety guardrails.
Game-Theoretic Reinforcement Learning based Topology-Agnostic Defense for Virtual Power Plants This paper proposes a game-theoretic reinforcement learning framework to defend virtual power plant resources without requiring grid network topology knowledge. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Examines electrical grid cybersecurity, not LLM safety or deployment risks.
Game-Theoretic Reinforcement Learning based Topology-Agnostic Defense for Virtual Power Plants This paper proposes a game-theoretic reinforcement learning framework to defend virtual power plant resources without requiring grid network topology knowledge. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Addresses grid cyber-physical security, not model compression or efficiency.
Game-Theoretic Reinforcement Learning based Topology-Agnostic Defense for Virtual Power Plants This paper proposes a game-theoretic reinforcement learning framework to defend virtual power plant resources without requiring grid network topology knowledge. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on power grid defense, not LLM benchmarks.
DeltaNet-PC: Test-Time Memory Without Backpropagation Through Time This paper formulates sequential test-time memory updates as latent variables within a predictive coding framework without backpropagation through time. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Proposes a predictive coding architecture, not an evaluation benchmark.
DeltaNet-PC: Test-Time Memory Without Backpropagation Through Time This paper formulates sequential test-time memory updates as latent variables within a predictive coding framework without backpropagation through time. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on sequence memory architectures, not deployment safety.
Non-Euclidean Gradient Descent Has Many Edges of Stability This paper studies stability thresholds and convergence boundaries of non-Euclidean gradient descent on quadratic objectives. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Gradient descent convergence analysis, not efficient model serving.

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