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
REPORT-OPS: Hybrid Evidence-Guided Reporting with Human-Supervised Refinement for HPC The paper presents an agent that combines language models with operational policies to generate verified operational reports for high-performance computing systems. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Presents an HPC reporting agent rather than general LLM evaluation benchmarks.
Definition Grounding in Theory-Level Auto-formalization The paper proposes definition grounding to continuously evaluate and revise formal mathematical definitions generated by language model agents. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on formal mathematical verification rather than operational AI deployment safety.
Do Syntax Heads Influence Model Behavior? The paper analyzes whether syntactic attention heads in transformers play a direct causal role in syntactic language predictions. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Analyzes linguistic syntax heads rather than code generation or program repair.
Do Syntax Heads Influence Model Behavior? The paper analyzes whether syntactic attention heads in transformers play a direct causal role in syntactic language predictions. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on linguistic interpretability rather than deployment safety, jailbreaks, or guardrails.
Do Syntax Heads Influence Model Behavior? The paper analyzes whether syntactic attention heads in transformers play a direct causal role in syntactic language predictions. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Investigates internal transformer attention heads rather than autonomous planning agents.
Do Syntax Heads Influence Model Behavior? The paper analyzes whether syntactic attention heads in transformers play a direct causal role in syntactic language predictions. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Studies attention head interpretability rather than model compression or efficient serving.
Do Syntax Heads Influence Model Behavior? The paper analyzes whether syntactic attention heads in transformers play a direct causal role in syntactic language predictions. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Studies textual syntax heads rather than multimodal vision-language understanding.
Do Syntax Heads Influence Model Behavior? The paper analyzes whether syntactic attention heads in transformers play a direct causal role in syntactic language predictions. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Studies internal syntax mechanisms rather than retrieval-augmented generation systems.
Do Syntax Heads Influence Model Behavior? The paper analyzes whether syntactic attention heads in transformers play a direct causal role in syntactic language predictions. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on mechanistic interpretability of syntax heads rather than benchmark evaluation.
Write Once, Plan Many: Building Reusable Visual Planning Systems from One Scene The paper presents a framework using vision-language models to generate reusable visual planning code from a single practice scene. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on planning program generation rather than safety guardrails or alignment.
Write Once, Plan Many: Building Reusable Visual Planning Systems from One Scene The paper presents a framework using vision-language models to generate reusable visual planning code from a single practice scene. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on visual planning synthesis rather than LLM evaluation benchmarks.
Refined Topology-Pass Scheduling: Damped Trust-Region Compensation for Dynamic k-NN Graphs in SE(3)-Equivariant Structure Prediction The paper develops an efficient two-pass scheduling method to update nearest-neighbor graphs in SE(3)-equivariant structure prediction models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on molecular structure graph optimization rather than LLM benchmarks.
Refined Topology-Pass Scheduling: Damped Trust-Region Compensation for Dynamic k-NN Graphs in SE(3)-Equivariant Structure Prediction The paper develops an efficient two-pass scheduling method to update nearest-neighbor graphs in SE(3)-equivariant structure prediction models. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Optimizes SE(3) graph prediction pipelines rather than efficient language model inference.
Refined Topology-Pass Scheduling: Damped Trust-Region Compensation for Dynamic k-NN Graphs in SE(3)-Equivariant Structure Prediction The paper develops an efficient two-pass scheduling method to update nearest-neighbor graphs in SE(3)-equivariant structure prediction models. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Deals with equivariant structure prediction pipelines, not AI planning agents.
Refined Topology-Pass Scheduling: Damped Trust-Region Compensation for Dynamic k-NN Graphs in SE(3)-Equivariant Structure Prediction The paper develops an efficient two-pass scheduling method to update nearest-neighbor graphs in SE(3)-equivariant structure prediction models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on geometric graph algorithm compensation rather than AI safety guardrails.
Copy-Paste Language Models The paper proposes adding an explicit copy-paste prediction head to language models to improve in-context retrieval and copying efficiency. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Proposes a model architecture head rather than multi-step planning or autonomous agents.
Copy-Paste Language Models The paper proposes adding an explicit copy-paste prediction head to language models to improve in-context retrieval and copying efficiency. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Proposes a general language modeling head rather than software engineering code models.
Copy-Paste Language Models The paper proposes adding an explicit copy-paste prediction head to language models to improve in-context retrieval and copying efficiency. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on model architecture for in-context copying rather than evaluation benchmarks.
Copy-Paste Language Models The paper proposes adding an explicit copy-paste prediction head to language models to improve in-context retrieval and copying efficiency. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Focuses on textual token copying heads rather than multimodal understanding.
Copy-Paste Language Models The paper proposes adding an explicit copy-paste prediction head to language models to improve in-context retrieval and copying efficiency. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Proposes architectural copying heads rather than AI safety or reliability guardrails.
MIND: Instruction-Conditioned Incremental Grounding for Robots The paper introduces a framework that incrementally updates robot spatial judgments after environmental changes without full scene recomputation. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Focuses on robotic navigation and room grounding, not industrial defect detection.
MIND: Instruction-Conditioned Incremental Grounding for Robots The paper introduces a framework that incrementally updates robot spatial judgments after environmental changes without full scene recomputation. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on robotic target maintenance efficiency rather than deployment safety guardrails.
MIND: Instruction-Conditioned Incremental Grounding for Robots The paper introduces a framework that incrementally updates robot spatial judgments after environmental changes without full scene recomputation. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on robotic instruction grounding rather than LLM benchmark methodologies.
Beyond Maliciousness: Static and Runtime Attack-Surface Profiling of Agent Skills The paper profiles the security attack surface and execution privileges of third-party agent skills across public marketplaces. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on security profiling of agent tools rather than general LLM evaluation benchmarks.
MemeTrace: A Multimodal LLM-Based Agent for Safety-Aware Meme Provenance Analysis The paper presents a multimodal agent designed to trace the origin, spread, and potential safety risks of online memes. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Focuses on meme moderation rather than software development or code generation.
MemeTrace: A Multimodal LLM-Based Agent for Safety-Aware Meme Provenance Analysis The paper presents a multimodal agent designed to trace the origin, spread, and potential safety risks of online memes. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Focuses on multimodal meme provenance analysis rather than RAG architecture.
MemeTrace: A Multimodal LLM-Based Agent for Safety-Aware Meme Provenance Analysis The paper presents a multimodal agent designed to trace the origin, spread, and potential safety risks of online memes. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Presents an agent application rather than benchmark design or general LLM evaluation.
MemeTrace: A Multimodal LLM-Based Agent for Safety-Aware Meme Provenance Analysis The paper presents a multimodal agent designed to trace the origin, spread, and potential safety risks of online memes. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on meme analysis using VLMs rather than model compression or efficiency.
Whose Data, Whose Benchmark? A User-Centric Framework for Auditing Evaluation Resources The paper introduces a framework to audit cultural, geographic, and user-representation biases embedded within popular machine learning benchmarks. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on evaluation benchmark auditing rather than model compression or efficient serving.
Whose Data, Whose Benchmark? A User-Centric Framework for Auditing Evaluation Resources The paper introduces a framework to audit cultural, geographic, and user-representation biases embedded within popular machine learning benchmarks. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Audits evaluation datasets rather than multi-step agent planning or tool use.
Whose Data, Whose Benchmark? A User-Centric Framework for Auditing Evaluation Resources The paper introduces a framework to audit cultural, geographic, and user-representation biases embedded within popular machine learning benchmarks. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Audits general evaluation benchmarks rather than code generation or software engineering tools.
Whose Data, Whose Benchmark? A User-Centric Framework for Auditing Evaluation Resources The paper introduces a framework to audit cultural, geographic, and user-representation biases embedded within popular machine learning benchmarks. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Focuses on benchmark audit methodologies rather than multimodal architecture or reasoning.
Whose Data, Whose Benchmark? A User-Centric Framework for Auditing Evaluation Resources The paper introduces a framework to audit cultural, geographic, and user-representation biases embedded within popular machine learning benchmarks. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Examines benchmark assumptions rather than retrieval-augmented generation systems.
Exploring Interpretability in Deep Learning-Based Forest Classification - Separating Similar Forest Classes using Sentinel-2 Time Series The paper evaluates recurrent neural networks and feature attribution methods for tree-species classification using satellite time-series imagery. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Applies remote sensing classification rather than general LLM evaluation or benchmarking.
Exploring Interpretability in Deep Learning-Based Forest Classification - Separating Similar Forest Classes using Sentinel-2 Time Series The paper evaluates recurrent neural networks and feature attribution methods for tree-species classification using satellite time-series imagery. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Explores remote sensing interpretability rather than AI safety guardrails or deployment reliability.
Exploring Interpretability in Deep Learning-Based Forest Classification - Separating Similar Forest Classes using Sentinel-2 Time Series The paper evaluates recurrent neural networks and feature attribution methods for tree-species classification using satellite time-series imagery. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Discusses remote sensing RNN classification rather than small language models or model compression.
Exploring Interpretability in Deep Learning-Based Forest Classification - Separating Similar Forest Classes using Sentinel-2 Time Series The paper evaluates recurrent neural networks and feature attribution methods for tree-species classification using satellite time-series imagery. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Studies tree-species classification using RNNs and Sentinel-2 imagery, not AI agents.
Dimension-Free Scaling Laws for Size Generalization: An Exponential Separation The paper establishes theoretical bounds and sample complexity laws for models generalizing across varying input sizes in structured data. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Theoretical analysis of sample complexity rather than empirical LLM evaluation methods.
Dimension-Free Scaling Laws for Size Generalization: An Exponential Separation The paper establishes theoretical bounds and sample complexity laws for models generalizing across varying input sizes in structured data. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on theoretical statistical learning theory on sets and graphs, not AI agents.
Dimension-Free Scaling Laws for Size Generalization: An Exponential Separation The paper establishes theoretical bounds and sample complexity laws for models generalizing across varying input sizes in structured data. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Discusses theoretical sample complexity for size generalization rather than practical efficient model inference.
Dimension-Free Scaling Laws for Size Generalization: An Exponential Separation The paper establishes theoretical bounds and sample complexity laws for models generalizing across varying input sizes in structured data. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Theoretical analysis of generalization bounds rather than operational safety or robustness in deployment.
Whose turn is it? Context-guided model routing for stateful agents The paper investigates context-aware model routing rules across turns in stateful multi-step agent applications. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on model routing optimization rather than benchmark design or LLM evaluation.
Whose turn is it? Context-guided model routing for stateful agents The paper investigates context-aware model routing rules across turns in stateful multi-step agent applications. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on cost reduction and routing efficiency rather than AI safety or guardrails.
Beyond ICD Codes: Semantic IDs for Generative Medical Coding The paper proposes training language models on hierarchically clustered semantic identifiers instead of standard ICD codes for clinical coding. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Proposes a representation framework for medical coding rather than general benchmark methodologies.
Beyond ICD Codes: Semantic IDs for Generative Medical Coding The paper proposes training language models on hierarchically clustered semantic identifiers instead of standard ICD codes for clinical coding. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Addresses generative medical coding rather than multi-step planning or autonomous agents.
Beyond ICD Codes: Semantic IDs for Generative Medical Coding The paper proposes training language models on hierarchically clustered semantic identifiers instead of standard ICD codes for clinical coding. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on clinical document coding accuracy rather than AI safety or jailbreak prevention.
DTDD: Divergence-Triggered Dynamic Distillation for Reliable On-Policy Supervision The paper introduces a distillation framework that triggers teacher takeovers when student policies diverge during long-horizon reasoning. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Deals with training stability in distillation rather than operational safety or guardrails.
DTDD: Divergence-Triggered Dynamic Distillation for Reliable On-Policy Supervision The paper introduces a distillation framework that triggers teacher takeovers when student policies diverge during long-horizon reasoning. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Proposes a model distillation training method rather than an evaluation benchmark.
Generalization Analysis of Biased Stochastic Gradient Methods for Minimax Problems The paper provides a theoretical generalization and stability analysis of biased stochastic gradient methods for minimax optimization problems. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Mathematical analysis of minimax optimization rather than empirical LLM benchmark evaluation.
Generalization Analysis of Biased Stochastic Gradient Methods for Minimax Problems The paper provides a theoretical generalization and stability analysis of biased stochastic gradient methods for minimax optimization problems. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Presents abstract optimization theory rather than practical deployment safety or guardrails.
Generalization Analysis of Biased Stochastic Gradient Methods for Minimax Problems The paper provides a theoretical generalization and stability analysis of biased stochastic gradient methods for minimax optimization problems. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Theoretical optimization paper on stochastic gradient methods, not AI agents.
LLM-jp-4 Technical Report The paper details the development, training data criteria, and performance of the open Japanese sovereign models, LLM-jp-4. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Introduces a general sovereign language model rather than tools for software engineering.
LLM-jp-4 Technical Report The paper details the development, training data criteria, and performance of the open Japanese sovereign models, LLM-jp-4. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on model architecture and pretraining transparency rather than safety guardrails or alignment.
LLM-jp-4 Technical Report The paper details the development, training data criteria, and performance of the open Japanese sovereign models, LLM-jp-4. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Focuses on text language models without multimodal cross-domain capabilities.
LLM-jp-4 Technical Report The paper details the development, training data criteria, and performance of the open Japanese sovereign models, LLM-jp-4. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Contains standard model benchmarks rather than proposing new evaluation methodologies.
LLM-jp-4 Technical Report The paper details the development, training data criteria, and performance of the open Japanese sovereign models, LLM-jp-4. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Covers foundational model training, not retrieval-augmented generation techniques.
LLM-jp-4 Technical Report The paper details the development, training data criteria, and performance of the open Japanese sovereign models, LLM-jp-4. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Describes base LLM pretraining rather than autonomous multi-step agents or tool use.
Can We Believe AI Peer Review? The paper evaluates the reliability, score variance, and consistency of automated peer reviews generated by large language models. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Studies LLMs used for scientific reviewing rather than multi-step tool-using agents.
Why Do Agent Teams Fail Across Boundaries? A Failure Taxonomy for Agent-to-Agent Systems with Private States The paper presents a taxonomy of failure modes in multi-agent systems where agents interact across organizational boundaries with private states. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on multi-agent failure taxonomy rather than general LLM evaluation benchmarks.
When Is Smaller Greener? Right-Sizing Language Models for Agentic AI The paper presents a decision model to evaluate language model efficiency based on energy and cost per completed agentic task. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on energy and cost optimization rather than deployment safety risks.

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