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
Equal CER, Unequal Damage: Severity-Weighted Character Error Rate for OCR Proposes a severity-weighted character error rate that charges text transcription errors based on their practical impact on reader understanding. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Evaluates general OCR transcription, not industrial visual inspection.
Equal CER, Unequal Damage: Severity-Weighted Character Error Rate for OCR Proposes a severity-weighted character error rate that charges text transcription errors based on their practical impact on reader understanding. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Presents an OCR evaluation metric, not an agent system.
Equal CER, Unequal Damage: Severity-Weighted Character Error Rate for OCR Proposes a severity-weighted character error rate that charges text transcription errors based on their practical impact on reader understanding. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Does not address retrieval-augmented generation pipelines.
Equal CER, Unequal Damage: Severity-Weighted Character Error Rate for OCR Proposes a severity-weighted character error rate that charges text transcription errors based on their practical impact on reader understanding. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Focuses on OCR transcription errors, not code generation.
Equal CER, Unequal Damage: Severity-Weighted Character Error Rate for OCR Proposes a severity-weighted character error rate that charges text transcription errors based on their practical impact on reader understanding. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not cover model compression or inference efficiency.
Equal CER, Unequal Damage: Severity-Weighted Character Error Rate for OCR Proposes a severity-weighted character error rate that charges text transcription errors based on their practical impact on reader understanding. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on transcription scoring rather than deployment safety.
Selective Growing Q-Networks (SGQN): An Analysis of Adaptive Action Grids in Decoupled Q-Learning Implements adaptive action grids in value-based reinforcement learning that selectively refine promising bins while pruning suboptimal actions. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on Q-learning grids, not small language models.
Selective Growing Q-Networks (SGQN): An Analysis of Adaptive Action Grids in Decoupled Q-Learning Implements adaptive action grids in value-based reinforcement learning that selectively refine promising bins while pruning suboptimal actions. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on RL action discretization, not deployment safety.
Selective Growing Q-Networks (SGQN): An Analysis of Adaptive Action Grids in Decoupled Q-Learning Implements adaptive action grids in value-based reinforcement learning that selectively refine promising bins while pruning suboptimal actions. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Studies continuous control reinforcement learning, not LLM agents.
Selective Growing Q-Networks (SGQN): An Analysis of Adaptive Action Grids in Decoupled Q-Learning Implements adaptive action grids in value-based reinforcement learning that selectively refine promising bins while pruning suboptimal actions. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Proposes continuous control RL grids, not LLM evaluation.
SafeAdapt-Poker: Counterfactual Risk Calibration for Budgeted Adaptation under Opponent Shift Introduces a counterfactual risk management method to calibrate adaptive decision-making against shifting opponent behaviors within fixed deviation budgets. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Presents an adaptation algorithm, not an evaluation benchmark.
SafeAdapt-Poker: Counterfactual Risk Calibration for Budgeted Adaptation under Opponent Shift Introduces a counterfactual risk management method to calibrate adaptive decision-making against shifting opponent behaviors within fixed deviation budgets. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not deal with model compression or efficient serving.
SafeAdapt-Poker: Counterfactual Risk Calibration for Budgeted Adaptation under Opponent Shift Introduces a counterfactual risk management method to calibrate adaptive decision-making against shifting opponent behaviors within fixed deviation budgets. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Poker game theory adaptation, not deployment safety or guardrails.
SafeAdapt-Poker: Counterfactual Risk Calibration for Budgeted Adaptation under Opponent Shift Introduces a counterfactual risk management method to calibrate adaptive decision-making against shifting opponent behaviors within fixed deviation budgets. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on game theory and reinforcement learning in poker.
Quantifying privacy risks in foundation model representations of brain MRI Evaluates patient linkage and attribute inference risks present in brain magnetic resonance imaging representations generated by pretrained foundation models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Privacy risk analysis rather than a general LLM benchmark.
Quantifying privacy risks in foundation model representations of brain MRI Evaluates patient linkage and attribute inference risks present in brain magnetic resonance imaging representations generated by pretrained foundation models. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Studies privacy leakage in medical representations, not autonomous agents.
Quantifying privacy risks in foundation model representations of brain MRI Evaluates patient linkage and attribute inference risks present in brain magnetic resonance imaging representations generated by pretrained foundation models. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Focuses on brain MRI encoders, not multimodal VLMs.
Quantifying privacy risks in foundation model representations of brain MRI Evaluates patient linkage and attribute inference risks present in brain magnetic resonance imaging representations generated by pretrained foundation models. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Evaluates brain MRI representations, not industrial defect inspection.
Hindsight-Privileged RLVR: Single-Rollout Token Credit Without Group Sampling Derives token-level credit from single rollouts using hindsight future context rather than sampling multiple attempts per prompt. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Addresses RL optimization rather than safety or alignment guardrails.
Hindsight-Privileged RLVR: Single-Rollout Token Credit Without Group Sampling Derives token-level credit from single rollouts using hindsight future context rather than sampling multiple attempts per prompt. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Presents an RL training algorithm rather than an evaluation benchmark.
Hindsight-Privileged RLVR: Single-Rollout Token Credit Without Group Sampling Derives token-level credit from single rollouts using hindsight future context rather than sampling multiple attempts per prompt. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on RL token-level credit assignment, not agent architectures.
VascuSR: Iterative Anatomy-Aware Diffusion for CT Super-Resolution Uses explicit three-dimensional vascular guidance in an iterative diffusion pipeline to improve anatomical accuracy in computed tomography super-resolution. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Proposes a generative method, not an evaluation framework.
VascuSR: Iterative Anatomy-Aware Diffusion for CT Super-Resolution Uses explicit three-dimensional vascular guidance in an iterative diffusion pipeline to improve anatomical accuracy in computed tomography super-resolution. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Medical CT imaging diffusion method, not an agent system.
VascuSR: Iterative Anatomy-Aware Diffusion for CT Super-Resolution Uses explicit three-dimensional vascular guidance in an iterative diffusion pipeline to improve anatomical accuracy in computed tomography super-resolution. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Focuses on medical CT scans, not industrial manufacturing inspection.
VascuSR: Iterative Anatomy-Aware Diffusion for CT Super-Resolution Uses explicit three-dimensional vascular guidance in an iterative diffusion pipeline to improve anatomical accuracy in computed tomography super-resolution. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not address efficient language model serving or compression.
VascuSR: Iterative Anatomy-Aware Diffusion for CT Super-Resolution Uses explicit three-dimensional vascular guidance in an iterative diffusion pipeline to improve anatomical accuracy in computed tomography super-resolution. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Does not cover AI safety, jailbreaks, or guardrails.
VascuSR: Iterative Anatomy-Aware Diffusion for CT Super-Resolution Uses explicit three-dimensional vascular guidance in an iterative diffusion pipeline to improve anatomical accuracy in computed tomography super-resolution. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Medical image reconstruction, not multimodal vision-language models.
VASPO: Self-Distillation with Privileged Value Estimation Combines privileged teacher signals with value estimation in policy optimization to provide denser token-level supervision for reasoning tasks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Proposes an RL training algorithm, not an evaluation method.
VASPO: Self-Distillation with Privileged Value Estimation Combines privileged teacher signals with value estimation in policy optimization to provide denser token-level supervision for reasoning tasks. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Reinforcement learning and distillation technique, not agent tool use.
VASPO: Self-Distillation with Privileged Value Estimation Combines privileged teacher signals with value estimation in policy optimization to provide denser token-level supervision for reasoning tasks. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Evaluated on mathematical reasoning benchmarks, not code generation.
VASPO: Self-Distillation with Privileged Value Estimation Combines privileged teacher signals with value estimation in policy optimization to provide denser token-level supervision for reasoning tasks. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Does not involve external retrieval or RAG systems.
VASPO: Self-Distillation with Privileged Value Estimation Combines privileged teacher signals with value estimation in policy optimization to provide denser token-level supervision for reasoning tasks. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Focuses exclusively on text-based mathematical reasoning.
VASPO: Self-Distillation with Privileged Value Estimation Combines privileged teacher signals with value estimation in policy optimization to provide denser token-level supervision for reasoning tasks. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Addresses training dynamics rather than deployment safety risks.
The Wealth of Agents: Scaling Agentic AI Across Developers Studies multi-developer agentic systems where independently built specialist agents collaborate to outperform larger systems created by single developers. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Studies agent collaboration frameworks rather than deployment safety.
The Wealth of Agents: Scaling Agentic AI Across Developers Studies multi-developer agentic systems where independently built specialist agents collaborate to outperform larger systems created by single developers. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Evaluates existing benchmarks rather than proposing evaluation methodology.
ActiveEvidence: Decision-Focused Evidence Graphs with Verifiable Acquisition Gaps Presents a graph-based framework with decision certificates to determine when multimodal agents should acquire visual evidence under cost constraints. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Theoretical active evidence framework, not industrial visual inspection.
ActiveEvidence: Decision-Focused Evidence Graphs with Verifiable Acquisition Gaps Presents a graph-based framework with decision certificates to determine when multimodal agents should acquire visual evidence under cost constraints. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Presents an acquisition algorithm rather than an evaluation benchmark.
ActiveEvidence: Decision-Focused Evidence Graphs with Verifiable Acquisition Gaps Presents a graph-based framework with decision certificates to determine when multimodal agents should acquire visual evidence under cost constraints. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on cost-sensitive acquisition rather than AI safety.
ActiveEvidence: Decision-Focused Evidence Graphs with Verifiable Acquisition Gaps Presents a graph-based framework with decision certificates to determine when multimodal agents should acquire visual evidence under cost constraints. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not study model compression or efficient serving architectures.
PrismaCoder: Pushing the Boundaries of Open Multimodal Code Intelligence through Decomposed Rubric Synthesis Introduces a decomposed rubric synthesis method to create training data for open multimodal models that generate visual code artifacts. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on data pipeline and training, not evaluation.
PrismaCoder: Pushing the Boundaries of Open Multimodal Code Intelligence through Decomposed Rubric Synthesis Introduces a decomposed rubric synthesis method to create training data for open multimodal models that generate visual code artifacts. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Visual code generation, not industrial manufacturing quality control.
PrismaCoder: Pushing the Boundaries of Open Multimodal Code Intelligence through Decomposed Rubric Synthesis Introduces a decomposed rubric synthesis method to create training data for open multimodal models that generate visual code artifacts. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not focus on model compression or inference efficiency.
PrismaCoder: Pushing the Boundaries of Open Multimodal Code Intelligence through Decomposed Rubric Synthesis Introduces a decomposed rubric synthesis method to create training data for open multimodal models that generate visual code artifacts. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Does not cover document retrieval or RAG architectures.
PrismaCoder: Pushing the Boundaries of Open Multimodal Code Intelligence through Decomposed Rubric Synthesis Introduces a decomposed rubric synthesis method to create training data for open multimodal models that generate visual code artifacts. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on code intelligence and artifact generation, not agents.
PrismaCoder: Pushing the Boundaries of Open Multimodal Code Intelligence through Decomposed Rubric Synthesis Introduces a decomposed rubric synthesis method to create training data for open multimodal models that generate visual code artifacts. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Does not address safety, jailbreaks, or guardrails.
Detection of Potato Plant Leaf Diseases using Convolutional Neural Networks Develops and tests a lightweight convolutional neural network to classify potato leaf diseases on field-collected farm images. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Does not address AI deployment safety or robustness.
Detection of Potato Plant Leaf Diseases using Convolutional Neural Networks Develops and tests a lightweight convolutional neural network to classify potato leaf diseases on field-collected farm images. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Traditional compact CNN application, not SLMs or LLM efficiency.
Detection of Potato Plant Leaf Diseases using Convolutional Neural Networks Develops and tests a lightweight convolutional neural network to classify potato leaf diseases on field-collected farm images. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Applies existing metrics to leaf disease classification.
Detection of Potato Plant Leaf Diseases using Convolutional Neural Networks Develops and tests a lightweight convolutional neural network to classify potato leaf diseases on field-collected farm images. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Studies basic CNN classification, not autonomous language agents.
PerSo: Investigating Facets of LLMs' Social Intelligence in Real-World Persuasion Scenarios Introduces a benchmark based on real online discussions to evaluate both cognitive and behavioral persuasion skills in language models. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Benchmark paper evaluating persuasion dialogue rather than agent systems.
PerSo: Investigating Facets of LLMs' Social Intelligence in Real-World Persuasion Scenarios Introduces a benchmark based on real online discussions to evaluate both cognitive and behavioral persuasion skills in language models. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Evaluates text-based dialogue, not multimodal models.
PerSo: Investigating Facets of LLMs' Social Intelligence in Real-World Persuasion Scenarios Introduces a benchmark based on real online discussions to evaluate both cognitive and behavioral persuasion skills in language models. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Does not cover coding assistants or software repair.
PerSo: Investigating Facets of LLMs' Social Intelligence in Real-World Persuasion Scenarios Introduces a benchmark based on real online discussions to evaluate both cognitive and behavioral persuasion skills in language models. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Does not involve external document retrieval or grounding.
PerSo: Investigating Facets of LLMs' Social Intelligence in Real-World Persuasion Scenarios Introduces a benchmark based on real online discussions to evaluate both cognitive and behavioral persuasion skills in language models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on persuasive capability evaluation rather than safety guardrails.
PerSo: Investigating Facets of LLMs' Social Intelligence in Real-World Persuasion Scenarios Introduces a benchmark based on real online discussions to evaluate both cognitive and behavioral persuasion skills in language models. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not study model compression or efficient serving.
PhySGA: A Physics-Space Grounded Agentic Framework for Generalizable and Interactive 3D Composition Proposes an interactive agent framework that utilizes physics simulation and spatial exploration for complex 3D object assembly and composition. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on interactive composition framework, not benchmark methodology.
PhySGA: A Physics-Space Grounded Agentic Framework for Generalizable and Interactive 3D Composition Proposes an interactive agent framework that utilizes physics simulation and spatial exploration for complex 3D object assembly and composition. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Focuses on 3D graphics composition, not industrial inspection.
PhySGA: A Physics-Space Grounded Agentic Framework for Generalizable and Interactive 3D Composition Proposes an interactive agent framework that utilizes physics simulation and spatial exploration for complex 3D object assembly and composition. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Does not address AI safety or guardrails.
Scaling Discovery Requires Forgetting Shows that periodically clearing agent search history and restarting from the top candidate prevents automated research agents from plateauing. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant General automated research optimization, not software engineering tools.
Scaling Discovery Requires Forgetting Shows that periodically clearing agent search history and restarting from the top candidate prevents automated research agents from plateauing. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Does not involve multimodal data or models.

4,561–4,620 of 13,754← Previous Next →