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
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	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	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 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 safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on domain-specific numerical accuracy rather than safety risks.
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.
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	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	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	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	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	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	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Investigates feature scaling for generalization, not model compression or efficiency.
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.
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	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	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.
Holding Agents Accountable with Verifiable Agent Insurance The authors propose verifiable agent insurance using smart-contract escrow to financially compensate users when agents violate programmatically logged behavioral promises. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes an enforcement framework rather than general LLM evaluation.
Consider, Then Choose: Order-Invariant Decisions in System One Models Runoff eliminates option-order bias in multiple-choice model predictions by scoring items independently before running balanced candidate comparisons. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Studies option order invariance, not software code generation.
Consider, Then Choose: Order-Invariant Decisions in System One Models Runoff eliminates option-order bias in multiple-choice model predictions by scoring items independently before running balanced candidate comparisons. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Studies decision invariance, not quantization or model distillation.
Consider, Then Choose: Order-Invariant Decisions in System One Models Runoff eliminates option-order bias in multiple-choice model predictions by scoring items independently before running balanced candidate comparisons. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Addresses option-order bias in question answering, not agent planning.
Consider, Then Choose: Order-Invariant Decisions in System One Models Runoff eliminates option-order bias in multiple-choice model predictions by scoring items independently before running balanced candidate comparisons. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focuses on multiple-choice decision bias, not document retrieval.
Consider, Then Choose: Order-Invariant Decisions in System One Models Runoff eliminates option-order bias in multiple-choice model predictions by scoring items independently before running balanced candidate comparisons. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Investigates multiple-choice text evaluation, not multimodal models.
Heading Hierarchy Reconstruction for Complex and Long Documents with Level-Wise Multimodal Memory The authors present a framework using level-wise multimodal memory to reconstruct coherent heading hierarchies across long, multi-page documents. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focus is multimodal document parsing architecture, not evaluation methodology.
Heading Hierarchy Reconstruction for Complex and Long Documents with Level-Wise Multimodal Memory The authors present a framework using level-wise multimodal memory to reconstruct coherent heading hierarchies across long, multi-page documents. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focus is document layout understanding, not AI deployment safety.
Heading Hierarchy Reconstruction for Complex and Long Documents with Level-Wise Multimodal Memory The authors present a framework using level-wise multimodal memory to reconstruct coherent heading hierarchies across long, multi-page documents. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on document hierarchy reconstruction, not autonomous agents.
Heading Hierarchy Reconstruction for Complex and Long Documents with Level-Wise Multimodal Memory The authors present a framework using level-wise multimodal memory to reconstruct coherent heading hierarchies across long, multi-page documents. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Addresses document parsing, not industrial visual quality control.
Beyond Answer Sharing: TrueMAD for Targeted Peer Engagement in Multi-Agent Debate TrueMAD introduces one-to-one peer feedback between debate rounds to help language model agents resolve conflicting reasoning before final revision. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focus is multi-agent reasoning accuracy, not safety guardrails.
Beyond Answer Sharing: TrueMAD for Targeted Peer Engagement in Multi-Agent Debate TrueMAD introduces one-to-one peer feedback between debate rounds to help language model agents resolve conflicting reasoning before final revision. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Evaluates on benchmarks but focuses on multi-agent debate architecture.
LiFUSE: Frequency-Spatial Unified Self-Supervised Pretraining for Liver Disease Analysis on Non-contrast CT LiFUSE uses frequency-spatial self-supervised pretraining on non-contrast CT scans to improve representation learning for liver disease detection. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on medical CT pretraining, not LLM agents.
LiFUSE: Frequency-Spatial Unified Self-Supervised Pretraining for Liver Disease Analysis on Non-contrast CT LiFUSE uses frequency-spatial self-supervised pretraining on non-contrast CT scans to improve representation learning for liver disease detection. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on medical 3D CT analysis, not multimodal VLMs.
LiFUSE: Frequency-Spatial Unified Self-Supervised Pretraining for Liver Disease Analysis on Non-contrast CT LiFUSE uses frequency-spatial self-supervised pretraining on non-contrast CT scans to improve representation learning for liver disease detection. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Does not investigate AI safety guardrails or prompt risks.
LiFUSE: Frequency-Spatial Unified Self-Supervised Pretraining for Liver Disease Analysis on Non-contrast CT LiFUSE uses frequency-spatial self-supervised pretraining on non-contrast CT scans to improve representation learning for liver disease detection. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes medical self-supervised pretraining, not LLM evaluation benchmarks.
LiFUSE: Frequency-Spatial Unified Self-Supervised Pretraining for Liver Disease Analysis on Non-contrast CT LiFUSE uses frequency-spatial self-supervised pretraining on non-contrast CT scans to improve representation learning for liver disease detection. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Addresses self-supervised pretraining, not model compression or efficiency.
LiFUSE: Frequency-Spatial Unified Self-Supervised Pretraining for Liver Disease Analysis on Non-contrast CT LiFUSE uses frequency-spatial self-supervised pretraining on non-contrast CT scans to improve representation learning for liver disease detection. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Focuses on medical liver CT imaging, not industrial inspection.
FALLNEST: Dual-Domain Dataset Condensation of Imbalanced IMU Data for Wearable Fall Detection FALLNEST condenses large imbalanced wearable motion sensor datasets into small balanced synthetic sets for training fall detection models. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Focuses on wearable sensor fall detection, not industrial vision.
FALLNEST: Dual-Domain Dataset Condensation of Imbalanced IMU Data for Wearable Fall Detection FALLNEST condenses large imbalanced wearable motion sensor datasets into small balanced synthetic sets for training fall detection models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focus is wearable sensor classification, not AI deployment safety.
FALLNEST: Dual-Domain Dataset Condensation of Imbalanced IMU Data for Wearable Fall Detection FALLNEST condenses large imbalanced wearable motion sensor datasets into small balanced synthetic sets for training fall detection models. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Processes IMU time-series sensor signals, not vision-language models.
FALLNEST: Dual-Domain Dataset Condensation of Imbalanced IMU Data for Wearable Fall Detection FALLNEST condenses large imbalanced wearable motion sensor datasets into small balanced synthetic sets for training fall detection models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focus is wearable dataset condensation, not LLM evaluation.
FALLNEST: Dual-Domain Dataset Condensation of Imbalanced IMU Data for Wearable Fall Detection FALLNEST condenses large imbalanced wearable motion sensor datasets into small balanced synthetic sets for training fall detection models. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Deals with sensor data condensation, not language model agents.
Encrypted Skills Can Become New Security Exploits If AI Agents Know How to Decipher The authors demonstrate that encoding malicious instructions in agent skills bypasses scanners and causes models to execute harmful actions upon decoding. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Evaluates a security vulnerability rather than general benchmark validity.
Encrypted Skills Can Become New Security Exploits If AI Agents Know How to Decipher The authors demonstrate that encoding malicious instructions in agent skills bypasses scanners and causes models to execute harmful actions upon decoding. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not address model compression, pruning, or efficient inference.
Encrypted Skills Can Become New Security Exploits If AI Agents Know How to Decipher The authors demonstrate that encoding malicious instructions in agent skills bypasses scanners and causes models to execute harmful actions upon decoding. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Investigates text-based agent prompt injection, not multimodal models.
Encrypted Skills Can Become New Security Exploits If AI Agents Know How to Decipher The authors demonstrate that encoding malicious instructions in agent skills bypasses scanners and causes models to execute harmful actions upon decoding. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Focus is agent security vulnerabilities, not coding assistants.
Encrypted Skills Can Become New Security Exploits If AI Agents Know How to Decipher The authors demonstrate that encoding malicious instructions in agent skills bypasses scanners and causes models to execute harmful actions upon decoding. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Studies prompt injection in agent skills, not document retrieval.
JevGPT: Thinking Fast and Slow in Autonomous Research JevGPT combines fast local evidence assessments with deliberative planning to improve autonomous research agents operating under fixed retrieval budgets. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Applies to autonomous research and QA, not software engineering.
JevGPT: Thinking Fast and Slow in Autonomous Research JevGPT combines fast local evidence assessments with deliberative planning to improve autonomous research agents operating under fixed retrieval budgets. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focus is dual-system agent coordination, not model compression.
JevGPT: Thinking Fast and Slow in Autonomous Research JevGPT combines fast local evidence assessments with deliberative planning to improve autonomous research agents operating under fixed retrieval budgets. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Processes text documents and question answering, not multimodal data.
JevGPT: Thinking Fast and Slow in Autonomous Research JevGPT combines fast local evidence assessments with deliberative planning to improve autonomous research agents operating under fixed retrieval budgets. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focus is autonomous research efficiency, not safety or guardrails.
JevGPT: Thinking Fast and Slow in Autonomous Research JevGPT combines fast local evidence assessments with deliberative planning to improve autonomous research agents operating under fixed retrieval budgets. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Introduces an autonomous research system, not an evaluation benchmark.
Which Optimizer Leaks Most? Optimizer Implicit Bias as a Training-Data Privacy Controller The authors analyze how different optimizers leave distinct implicit bias fingerprints that influence training data reconstruction from model checkpoints. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on training data reconstruction, not model evaluation.
Which Optimizer Leaks Most? Optimizer Implicit Bias as a Training-Data Privacy Controller The authors analyze how different optimizers leave distinct implicit bias fingerprints that influence training data reconstruction from model checkpoints. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies privacy leakage from optimizers, not agent planning.
Which Optimizer Leaks Most? Optimizer Implicit Bias as a Training-Data Privacy Controller The authors analyze how different optimizers leave distinct implicit bias fingerprints that influence training data reconstruction from model checkpoints. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Studies optimizer privacy leakage, not model efficiency or compression.
Output Discipline Sets the Floor: A Falsification-Guided, Model-Agnostic Agent Protocol for Auditable Hypothesis-Driven Decision Making The paper introduces a falsification-guided agent protocol requiring systematic hypothesis elimination to improve decision-making consistency on complex diagnostic tasks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focus is agent decision framework, not evaluation methodology.
A dataset of formalised theorem statements from the Annals of Mathematics AnnalsChallenge provides 50 formalized mathematics theorem statements in Lean 4 drawn from papers published in the Annals of Mathematics. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Provides a formal mathematics dataset, not an agent system.
A dataset of formalised theorem statements from the Annals of Mathematics AnnalsChallenge provides 50 formalized mathematics theorem statements in Lean 4 drawn from papers published in the Annals of Mathematics. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focus is mathematical formalisation benchmarking, not deployment safety.

13,921–13,980 of 17,313← Previous Next →