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
When Missingness Follows the Graph: Beyond Feature Propagation for Missing Features This paper formalizes graph-structured missing features where missingness spreads across edges, testing existing graph propagation methods under this setting. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Addresses graph neural networks with missing features, not LLM agents.
When Missingness Follows the Graph: Beyond Feature Propagation for Missing Features This paper formalizes graph-structured missing features where missingness spreads across edges, testing existing graph propagation methods under this setting. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on GNN feature imputation rather than language model evaluation.
When Missingness Follows the Graph: Beyond Feature Propagation for Missing Features This paper formalizes graph-structured missing features where missingness spreads across edges, testing existing graph propagation methods under this setting. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not study model compression, distillation, or efficient inference.
Certifying Almost-Sure Reachability in Unknown MDPs This paper investigates theoretical limits and conditions for verifying reachability policies in unknown Markov decision processes using sample access. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on theoretical verification of Markov Decision Processes, not LLM agents.
Certifying Almost-Sure Reachability in Unknown MDPs This paper investigates theoretical limits and conditions for verifying reachability policies in unknown Markov decision processes using sample access. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Does not evaluate language models or introduce LLM benchmarks.
H-DDCM: Hadamard Codebooks for Fast Generative Image and Video Compression This paper presents a generative compression framework using implicit randomized Hadamard dictionaries to speed up diffusion-based image and video coding. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on diffusion compression, not language model agents or planning.
H-DDCM: Hadamard Codebooks for Fast Generative Image and Video Compression This paper presents a generative compression framework using implicit randomized Hadamard dictionaries to speed up diffusion-based image and video coding. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on data compression rather than AI safety or reliability.
H-DDCM: Hadamard Codebooks for Fast Generative Image and Video Compression This paper presents a generative compression framework using implicit randomized Hadamard dictionaries to speed up diffusion-based image and video coding. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Addresses generative compression, not industrial defect detection or visual quality control.
H-DDCM: Hadamard Codebooks for Fast Generative Image and Video Compression This paper presents a generative compression framework using implicit randomized Hadamard dictionaries to speed up diffusion-based image and video coding. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Does not propose benchmarks or evaluations for language models.
Fresh at Decision, Stale at Action: Routing New Information to LLM Decisions Already Made This paper introduces a timestamp theory and persistent ledger to route updated information to pending LLM decisions before action. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents a temporal decision ledger rather than an evaluation benchmark.
Monitoring latent reasoning in looped language models This paper presents an interpretability protocol to monitor latent reasoning dynamics and stability across multiple looped language model families. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not explore retrieval mechanisms or grounding on databases.
Monitoring latent reasoning in looped language models This paper presents an interpretability protocol to monitor latent reasoning dynamics and stability across multiple looped language model families. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Analyzes internal looped model mechanics, not autonomous tool-use agents.
Monitoring latent reasoning in looped language models This paper presents an interpretability protocol to monitor latent reasoning dynamics and stability across multiple looped language model families. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on mechanistic interpretability analysis rather than safety guardrails.
Monitoring latent reasoning in looped language models This paper presents an interpretability protocol to monitor latent reasoning dynamics and stability across multiple looped language model families. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on text language models rather than multimodal architectures.
Monitoring latent reasoning in looped language models This paper presents an interpretability protocol to monitor latent reasoning dynamics and stability across multiple looped language model families. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Studies model internal representations rather than software code tasks.
Mitigating Cross-Modal Hallucinations by Correcting Modality Misrouting in Omnimodal LLMs This paper proposes an inference-time intervention method to detect and correct modality misrouting that causes multimodal hallucinations. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes a hallucination mitigation technique rather than a benchmark.
Mitigating Cross-Modal Hallucinations by Correcting Modality Misrouting in Omnimodal LLMs This paper proposes an inference-time intervention method to detect and correct modality misrouting that causes multimodal hallucinations. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Investigates multimodal inference steering rather than multi-step agent planning.
RadixFlow: Filling Execution Bubbles in Prefix-Cached Hybrid LLM Serving This paper introduces a scheduler that decouples prefix matching from state readiness to eliminate execution bubbles in hybrid LLM serving. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on serving latency optimization rather than deployment safety.
RadixFlow: Filling Execution Bubbles in Prefix-Cached Hybrid LLM Serving This paper introduces a scheduler that decouples prefix matching from state readiness to eliminate execution bubbles in hybrid LLM serving. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Addresses low-level inference serving schedulers, not autonomous agents.
RadixFlow: Filling Execution Bubbles in Prefix-Cached Hybrid LLM Serving This paper introduces a scheduler that decouples prefix matching from state readiness to eliminate execution bubbles in hybrid LLM serving. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Introduces a serving scheduler rather than an evaluation benchmark.
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.
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	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	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies theoretical representation learning, not multi-step LLM agents.
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	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Deals with agent query orchestration rather than model compression.
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	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	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	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes a routing architecture rather than an evaluation benchmark.
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	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Studies order-flow mechanisms rather than LLM benchmarks.
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.
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.
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	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	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Examines factual sycophancy rather than software engineering or code.
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	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	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Introduces a distillation method rather than an evaluation benchmark.
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	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not involve external document retrieval or grounding systems.
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.
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	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.
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 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	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	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes a recurrent architecture rather than an evaluation framework.
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.
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	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	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	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.

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