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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
When Do Learned Blocks Preserve Causal Meaning? Block Identifiability and Constructive Causal Abstractions This paper defines conditions under which learned multidimensional representation blocks preserve causal abstractions and high-level concept interventions. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on theoretical latent representations rather than safety guardrails.
When Do Learned Blocks Preserve Causal Meaning? Block Identifiability and Constructive Causal Abstractions This paper defines conditions under which learned multidimensional representation blocks preserve causal abstractions and high-level concept interventions. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies theoretical representation learning, not multi-step LLM agents.
When Do Learned Blocks Preserve Causal Meaning? Block Identifiability and Constructive Causal Abstractions This paper defines conditions under which learned multidimensional representation blocks preserve causal abstractions and high-level concept interventions. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Deals with causal representation theory rather than efficient serving.
When Do Learned Blocks Preserve Causal Meaning? Block Identifiability and Constructive Causal Abstractions This paper defines conditions under which learned multidimensional representation blocks preserve causal abstractions and high-level concept interventions. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Theoretical analysis of causal abstractions, not an LLM benchmark.
Fan Out or Relay? Ownership-Aware Routing for LLM Agents Querying Siloed Tables This paper shows data ownership determines whether agents should broadcast or relay table queries, and proposes ownership-aware routing. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses data routing efficiency rather than safety or guardrails.
Fan Out or Relay? Ownership-Aware Routing for LLM Agents Querying Siloed Tables This paper shows data ownership determines whether agents should broadcast or relay table queries, and proposes ownership-aware routing. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Studies multi-agent database coordination rather than software code generation.
Fan Out or Relay? Ownership-Aware Routing for LLM Agents Querying Siloed Tables This paper shows data ownership determines whether agents should broadcast or relay table queries, and proposes ownership-aware routing. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses purely on tabular SQL queries without multimodal data.
Fan Out or Relay? Ownership-Aware Routing for LLM Agents Querying Siloed Tables This paper shows data ownership determines whether agents should broadcast or relay table queries, and proposes ownership-aware routing. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes a routing architecture rather than an evaluation benchmark.
Fan Out or Relay? Ownership-Aware Routing for LLM Agents Querying Siloed Tables This paper shows data ownership determines whether agents should broadcast or relay table queries, and proposes ownership-aware routing. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focuses on agent routing protocols over siloed databases, not RAG.
Fan Out or Relay? Ownership-Aware Routing for LLM Agents Querying Siloed Tables This paper shows data ownership determines whether agents should broadcast or relay table queries, and proposes ownership-aware routing. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Deals with agent query orchestration rather than model compression.
When and How Order-Flow Interventions Work After Dealers Adapt This paper introduces an order-flow allocation framework and evaluation protocol to analyze how intervention rules influence adaptive trading dealers. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Analyzes market-making economic agents rather than LLM-based autonomous agents.
When and How Order-Flow Interventions Work After Dealers Adapt This paper introduces an order-flow allocation framework and evaluation protocol to analyze how intervention rules influence adaptive trading dealers. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on financial market dynamics rather than AI deployment risks.
When and How Order-Flow Interventions Work After Dealers Adapt This paper introduces an order-flow allocation framework and evaluation protocol to analyze how intervention rules influence adaptive trading dealers. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Studies order-flow mechanisms rather than LLM benchmarks.
Claim Framing as a Driver of LLM Sycophancy This paper studies how different framings of user claims induce sycophancy in large language models on factual questions. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Examines factual sycophancy rather than software engineering or code.
Claim Framing as a Driver of LLM Sycophancy This paper studies how different framings of user claims induce sycophancy in large language models on factual questions. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies question-answering bias rather than planning or tool-using agents.
Claim Framing as a Driver of LLM Sycophancy This paper studies how different framings of user claims induce sycophancy in large language models on factual questions. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not study external retrieval mechanisms or grounding systems.
Claim Framing as a Driver of LLM Sycophancy This paper studies how different framings of user claims induce sycophancy in large language models on factual questions. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Investigates text-based sycophancy without multimodal inputs.
Claim Framing as a Driver of LLM Sycophancy This paper studies how different framings of user claims induce sycophancy in large language models on factual questions. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Studies model alignment behavior across scales, not model compression.
LoOPSD: On-Policy Self-Distillation with Looped Teachers This paper introduces a self-distillation method that searches over repeated layer execution orders to create stronger teacher guidance. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses exclusively on text-based language model distillation.
LoOPSD: On-Policy Self-Distillation with Looped Teachers This paper introduces a self-distillation method that searches over repeated layer execution orders to create stronger teacher guidance. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Presents model training methods rather than safety or reliability.
LoOPSD: On-Policy Self-Distillation with Looped Teachers This paper introduces a self-distillation method that searches over repeated layer execution orders to create stronger teacher guidance. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Presents self-distillation techniques rather than multi-step autonomous agents.
LoOPSD: On-Policy Self-Distillation with Looped Teachers This paper introduces a self-distillation method that searches over repeated layer execution orders to create stronger teacher guidance. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not involve external document retrieval or grounding systems.
LoOPSD: On-Policy Self-Distillation with Looped Teachers This paper introduces a self-distillation method that searches over repeated layer execution orders to create stronger teacher guidance. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Focuses on general self-distillation rather than software code generation.
LoOPSD: On-Policy Self-Distillation with Looped Teachers This paper introduces a self-distillation method that searches over repeated layer execution orders to create stronger teacher guidance. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Introduces a distillation method rather than an evaluation benchmark.
Lifting Finite-Sample Gaussian Approximation of Wide Neural Networks to Functional Convergence This paper establishes functional convergence bounds for wide neural networks approximating Gaussian processes on arbitrary domains. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Theoretical probability bounds on neural networks, not safety guardrails.
Lifting Finite-Sample Gaussian Approximation of Wide Neural Networks to Functional Convergence This paper establishes functional convergence bounds for wide neural networks approximating Gaussian processes on arbitrary domains. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Theoretical analysis of neural networks as Gaussian processes, not agents.
Lifting Finite-Sample Gaussian Approximation of Wide Neural Networks to Functional Convergence This paper establishes functional convergence bounds for wide neural networks approximating Gaussian processes on arbitrary domains. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents mathematical convergence proofs, not empirical evaluation frameworks.
Lifting Finite-Sample Gaussian Approximation of Wide Neural Networks to Functional Convergence This paper establishes functional convergence bounds for wide neural networks approximating Gaussian processes on arbitrary domains. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on infinite-width theoretical limits, not efficient model compression.
Back to the Future: Reverse-Consequence Modeling for Long-Horizon Particle Dynamics This paper proposes predicting the final state first and using diffusion to interpolate long-horizon physical particle trajectories. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on trajectory prediction and physics modeling, not language agents.
Back to the Future: Reverse-Consequence Modeling for Long-Horizon Particle Dynamics This paper proposes predicting the final state first and using diffusion to interpolate long-horizon physical particle trajectories. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents a physical dynamics model, not an LLM benchmark.
Back to the Future: Reverse-Consequence Modeling for Long-Horizon Particle Dynamics This paper proposes predicting the final state first and using diffusion to interpolate long-horizon physical particle trajectories. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Deals with physics simulation and robotics rather than safety.
Looped Reasoning Finishes Earlier Than You Think This paper proposes a recurrent architecture that adds rotational degrees of freedom to prevent oscillatory bottlenecks in looped reasoning. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on internal dynamic convergence rather than deployment safety.
Looped Reasoning Finishes Earlier Than You Think This paper proposes a recurrent architecture that adds rotational degrees of freedom to prevent oscillatory bottlenecks in looped reasoning. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies recurrent architecture dynamics rather than multi-step tool-using agents.
Looped Reasoning Finishes Earlier Than You Think This paper proposes a recurrent architecture that adds rotational degrees of freedom to prevent oscillatory bottlenecks in looped reasoning. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes a recurrent architecture rather than an evaluation framework.
Neuro-Symbolic Graph Transformers for Hierarchical Intention Recognition from Multimodal Human Activity Sequences This paper presents a neuro-symbolic graph transformer with first-order logic loss to recognize hierarchical human intentions from multimodal activity sequences. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on action recognition models rather than LLM-based autonomous agents.
Neuro-Symbolic Graph Transformers for Hierarchical Intention Recognition from Multimodal Human Activity Sequences This paper presents a neuro-symbolic graph transformer with first-order logic loss to recognize hierarchical human intentions from multimodal activity sequences. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Introduces a computer vision action dataset, not LLM evaluation.
Neuro-Symbolic Graph Transformers for Hierarchical Intention Recognition from Multimodal Human Activity Sequences This paper presents a neuro-symbolic graph transformer with first-order logic loss to recognize hierarchical human intentions from multimodal activity sequences. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not explore model compression, quantization, or efficient serving.
Neuro-Symbolic Graph Transformers for Hierarchical Intention Recognition from Multimodal Human Activity Sequences This paper presents a neuro-symbolic graph transformer with first-order logic loss to recognize hierarchical human intentions from multimodal activity sequences. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on intention recognition algorithms rather than AI safety risks.
QORH: Learning Query-Specific Ordered Rule Hypergraphs for Inductive Knowledge Graph Link Prediction QORH models multi-relation rules as query-specific hypergraphs with message passing to improve inductive link prediction on unseen knowledge graph entities. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes a knowledge graph model, not an LLM benchmark.
QORH: Learning Query-Specific Ordered Rule Hypergraphs for Inductive Knowledge Graph Link Prediction QORH models multi-relation rules as query-specific hypergraphs with message passing to improve inductive link prediction on unseen knowledge graph entities. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on graph neural networks for link prediction, not agents.
QORH: Learning Query-Specific Ordered Rule Hypergraphs for Inductive Knowledge Graph Link Prediction QORH models multi-relation rules as query-specific hypergraphs with message passing to improve inductive link prediction on unseen knowledge graph entities. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Investigates link prediction rather than AI safety or guardrails.
A Few More Tokens Ahead: Reinforcing Parallelism in Diffusion LLMs RePara trains diffusion language models using group-relative reinforcement learning on short token blocks to improve math generation speed and accuracy. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes a training and decoding technique rather than evaluation.
A Few More Tokens Ahead: Reinforcing Parallelism in Diffusion LLMs RePara trains diffusion language models using group-relative reinforcement learning on short token blocks to improve math generation speed and accuracy. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Deals with diffusion language model decoding rather than autonomous agents.
A Few More Tokens Ahead: Reinforcing Parallelism in Diffusion LLMs RePara trains diffusion language models using group-relative reinforcement learning on short token blocks to improve math generation speed and accuracy. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on generation efficiency and reasoning speed, not safety.
How Reliable Are Large Language Models for Soil Organic Carbon Assessment? An Australian Benchmark The paper evaluates seven language models on estimating soil organic carbon concentrations, calculating carbon stock, and comparing agricultural paddock pairs. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Evaluates direct LLM question answering, not multi-step planning agents.
How Reliable Are Large Language Models for Soil Organic Carbon Assessment? An Australian Benchmark The paper evaluates seven language models on estimating soil organic carbon concentrations, calculating carbon stock, and comparing agricultural paddock pairs. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on domain-specific numerical accuracy rather than safety risks.
Reset Early, Reset Often, Eliminate Models SATResetCoP restarts connection-tableau search branches while preserving accumulated clauses in an incremental SAT solver to improve theorem proving efficiency. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Investigates symbolic logic provers rather than AI deployment safety.
Reset Early, Reset Often, Eliminate Models SATResetCoP restarts connection-tableau search branches while preserving accumulated clauses in an incremental SAT solver to improve theorem proving efficiency. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Covers automated theorem proving algorithms, not language model agents.
Reset Early, Reset Often, Eliminate Models SATResetCoP restarts connection-tableau search branches while preserving accumulated clauses in an incremental SAT solver to improve theorem proving efficiency. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Evaluates classical SAT-based provers rather than language model capabilities.
TextualRL: Textual Reinforcement Learning for Context Management TextualRL updates agent context prompts using groupwise trajectory comparisons rather than isolated feedback runs to improve policy adaptation without parameter tuning. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Deals with prompt optimization rather than model compression or inference.
TextualRL: Textual Reinforcement Learning for Context Management TextualRL updates agent context prompts using groupwise trajectory comparisons rather than isolated feedback runs to improve policy adaptation without parameter tuning. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Focuses on general prompt context management, not software engineering.
TextualRL: Textual Reinforcement Learning for Context Management TextualRL updates agent context prompts using groupwise trajectory comparisons rather than isolated feedback runs to improve policy adaptation without parameter tuning. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Does not address safety, jailbreaks, or deployment guardrails.
TextualRL: Textual Reinforcement Learning for Context Management TextualRL updates agent context prompts using groupwise trajectory comparisons rather than isolated feedback runs to improve policy adaptation without parameter tuning. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes a training method, not a general evaluation benchmark.
TextualRL: Textual Reinforcement Learning for Context Management TextualRL updates agent context prompts using groupwise trajectory comparisons rather than isolated feedback runs to improve policy adaptation without parameter tuning. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses purely on text context updates without multimodal inputs.
TextualRL: Textual Reinforcement Learning for Context Management TextualRL updates agent context prompts using groupwise trajectory comparisons rather than isolated feedback runs to improve policy adaptation without parameter tuning. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Optimizes prompt context policy rather than external document retrieval.
A Controlled Evaluation of Curvature Inspired Scalar Feature Scaling for Domain Generalization This paper audits a specific scalar feature scaling method to test whether claimed curvature benefits hold for domain generalization benchmarks. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on standard domain generalization algorithms, not AI safety.
A Controlled Evaluation of Curvature Inspired Scalar Feature Scaling for Domain Generalization This paper audits a specific scalar feature scaling method to test whether claimed curvature benefits hold for domain generalization benchmarks. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies domain generalization feature scaling, not multi-step AI agents.
A Controlled Evaluation of Curvature Inspired Scalar Feature Scaling for Domain Generalization This paper audits a specific scalar feature scaling method to test whether claimed curvature benefits hold for domain generalization benchmarks. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Evaluates general domain generalization datasets, not industrial inspection tasks.
A Controlled Evaluation of Curvature Inspired Scalar Feature Scaling for Domain Generalization This paper audits a specific scalar feature scaling method to test whether claimed curvature benefits hold for domain generalization benchmarks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Audits a specific domain generalization technique rather than LLM benchmarks.
A Controlled Evaluation of Curvature Inspired Scalar Feature Scaling for Domain Generalization This paper audits a specific scalar feature scaling method to test whether claimed curvature benefits hold for domain generalization benchmarks. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Does not investigate vision-language or multimodal multimodal foundation models.

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