Every paper checked — nothing hidden
All papers
Every paper checked for brief #4 of 12–19 Oct 2026 — nothing is hidden. Brief papers first.
All17,313In brief24More (on-topic)3,333Not relevant13,754Not assessed87Older352Could not confirm93Blogs with no paper266All blogs284
| Paper | Topic | Relevance | Feedback |
|---|---|---|---|
| Separating Prompt Harmfulness from Interaction Safety via Partial Generation The paper proposes evaluating partial generation outputs to stop harmful model interactions early while avoiding unnecessary prompt rejections. Preprint | Small and efficient models via arxiv · 15 Oct, 06:17 | Not relevant Focuses on safety guardrails, not model compression. | |
| Separating Prompt Harmfulness from Interaction Safety via Partial Generation The paper proposes evaluating partial generation outputs to stop harmful model interactions early while avoiding unnecessary prompt rejections. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17 | Not relevant Studies early guardrail detection, not evaluation benchmarks. | |
| Separating Prompt Harmfulness from Interaction Safety via Partial Generation The paper proposes evaluating partial generation outputs to stop harmful model interactions early while avoiding unnecessary prompt rejections. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17 | Not relevant Mentions search, but focuses on guardrail generation stopping. | |
| Relational Learning as World Modeling The paper presents TabWM, a tabular world model that infers system dynamics from past executions to predict database edits from actions. Preprint | Small and efficient models via arxiv · 15 Oct, 06:17 | Not relevant Does not explore model compression or inference efficiency. | |
| Relational Learning as World Modeling The paper presents TabWM, a tabular world model that infers system dynamics from past executions to predict database edits from actions. Preprint | AI agents via arxiv · 15 Oct, 06:17 | Not relevant Focuses on relational world modeling, not agent tool-use. | |
| Relational Learning as World Modeling The paper presents TabWM, a tabular world model that infers system dynamics from past executions to predict database edits from actions. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17 | Not relevant Presents tabular world models, not LLM evaluation. | |
| Relational Learning as World Modeling The paper presents TabWM, a tabular world model that infers system dynamics from past executions to predict database edits from actions. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 06:17 | Not relevant Focuses on tabular dynamics, not safety or guardrails. | |
| Geospatial Vector Geometry as a Native Spatial Modality for Vision-Language Models The paper proposes GeoVec-VLM, integrating geospatial vector geometry as a native input modality to improve spatial reasoning in vision-language models. Preprint | AI agents via arxiv · 15 Oct, 06:17 | Not relevant Focuses on geospatial representations, not agentic planning. | |
| Geospatial Vector Geometry as a Native Spatial Modality for Vision-Language Models The paper proposes GeoVec-VLM, integrating geospatial vector geometry as a native input modality to improve spatial reasoning in vision-language models. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17 | Not relevant Presents a multimodal architecture, not benchmark evaluation. | |
| Geospatial Vector Geometry as a Native Spatial Modality for Vision-Language Models The paper proposes GeoVec-VLM, integrating geospatial vector geometry as a native input modality to improve spatial reasoning in vision-language models. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 06:17 | Not relevant Focuses on spatial multimodal reasoning, not safety. | |
| Game-Creation-Agents: Autonomous Game Production The paper introduces an autonomous multi-agent framework that translates natural language prompts into playable browser games with automated verification. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17 | Not relevant Focuses on multi-agent game production, not benchmarks. | |
| Game-Creation-Agents: Autonomous Game Production The paper introduces an autonomous multi-agent framework that translates natural language prompts into playable browser games with automated verification. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 06:17 | Not relevant Presents game production pipelines, not safety or robustness. | |
| PrivacyGuard: Source-Linked Sanitization and Locally Resolvable PII References for Cloud GUI Agents The paper presents PrivacyGuard, a framework using source-linked sanitization and local references to prevent personal data leakage in cloud GUI agents. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17 | Not relevant Proposes a privacy defense system, not evaluation methodology. | |
| LIBERO-Industry: Benchmarking Robotic Manipulation under Industrial Process Constraints The paper introduces LIBERO-Industry, a benchmark evaluating robotic manipulation policies under industrial process constraints such as moving material flows and tolerances. Preprint | AI agents via arxiv · 15 Oct, 06:17 | Not relevant Focuses on robotic manipulation benchmarks, not LLM agents. | |
| LIBERO-Industry: Benchmarking Robotic Manipulation under Industrial Process Constraints The paper introduces LIBERO-Industry, a benchmark evaluating robotic manipulation policies under industrial process constraints such as moving material flows and tolerances. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17 | Not relevant Benchmark covers robot manipulation policies, not language models. | |
| LIBERO-Industry: Benchmarking Robotic Manipulation under Industrial Process Constraints The paper introduces LIBERO-Industry, a benchmark evaluating robotic manipulation policies under industrial process constraints such as moving material flows and tolerances. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 06:17 | Not relevant Addresses robotics assembly benchmarks, not safety guardrails. | |
| InfoVis: Unified Slide Image Generation and Editing The paper introduces InfoVis, a 9B multimodal model along with a benchmark and training dataset for slide image generation and editing. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17 | Not relevant Slide benchmark is secondary to generating multimodal presentation slides. | |
| InfoVis: Unified Slide Image Generation and Editing The paper introduces InfoVis, a 9B multimodal model along with a benchmark and training dataset for slide image generation and editing. Preprint | Small and efficient models via arxiv · 15 Oct, 06:17 | Not relevant Does not focus on model compression or efficient serving. | |
| InfoVis: Unified Slide Image Generation and Editing The paper introduces InfoVis, a 9B multimodal model along with a benchmark and training dataset for slide image generation and editing. Preprint | AI agents via arxiv · 15 Oct, 06:17 | Not relevant Focuses on image generation, not multi-step agents. | |
| InfoVis: Unified Slide Image Generation and Editing The paper introduces InfoVis, a 9B multimodal model along with a benchmark and training dataset for slide image generation and editing. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 06:17 | Not relevant Focuses on visual generation, not deployment safety. | |
| InfoVis: Unified Slide Image Generation and Editing The paper introduces InfoVis, a 9B multimodal model along with a benchmark and training dataset for slide image generation and editing. Preprint | Computer vision for industrial applications via arxiv · 15 Oct, 06:17 | Not relevant Deals with presentation slides, not industrial defect detection. | |
| Logit Leakage through Floating-Point Bias Addition The paper reveals a numerical side channel in floating-point bias addition that enables recovering full logit vectors from API queries. Preprint | AI agents via arxiv · 15 Oct, 06:17 | Not relevant Investigates API logit leakage, not agent workflows. | |
| Logit Leakage through Floating-Point Bias Addition The paper reveals a numerical side channel in floating-point bias addition that enables recovering full logit vectors from API queries. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17 | Not relevant Focuses on numerical side-channel attacks, not benchmarking. | |
| StableJudge: A Lightweight Adversarial Defense Framework for LLM-as-a-Judge The paper presents StableJudge, an external lightweight sentry model that sanitizes prompt-injection attacks before LLM-as-a-judge scoring. Preprint | AI agents via arxiv · 15 Oct, 06:17 | Not relevant Focuses on LLM-as-a-judge defenses, not agent planning. | |
| Growing to See: Efficient Pretraining of Self-Supervised Vision Transformers The paper investigates progressive depth growth during self-supervised vision transformer pretraining to improve downstream accuracy while reducing computation. Preprint | Computer vision for industrial applications via arxiv · 15 Oct, 06:17 | Not relevant Evaluates standard image benchmarks, not industrial inspection. | |
| Growing to See: Efficient Pretraining of Self-Supervised Vision Transformers The paper investigates progressive depth growth during self-supervised vision transformer pretraining to improve downstream accuracy while reducing computation. Preprint | AI agents via arxiv · 15 Oct, 06:17 | Not relevant Focuses on vision transformer pretraining, not agents. | |
| Growing to See: Efficient Pretraining of Self-Supervised Vision Transformers The paper investigates progressive depth growth during self-supervised vision transformer pretraining to improve downstream accuracy while reducing computation. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17 | Not relevant Studies layer growth during training, not LLM evaluation. | |
| Growing to See: Efficient Pretraining of Self-Supervised Vision Transformers The paper investigates progressive depth growth during self-supervised vision transformer pretraining to improve downstream accuracy while reducing computation. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 06:17 | Not relevant Addresses training efficiency, not safety or guardrails. | |
| Growing to See: Efficient Pretraining of Self-Supervised Vision Transformers The paper investigates progressive depth growth during self-supervised vision transformer pretraining to improve downstream accuracy while reducing computation. Preprint | Multimodal models via arxiv · 15 Oct, 06:17 | Not relevant Focuses on self-supervised vision models, not multimodal models. | |
| DTA-GT: Direction- and Topology-Aware Graph Transformer for Neural Network Representation Learning The paper introduces a direction- and topology-aware graph transformer to predict neural network architecture attributes like accuracy and latency. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 06:17 | Not relevant Focuses on architecture search, not deployment safety. | |
| DTA-GT: Direction- and Topology-Aware Graph Transformer for Neural Network Representation Learning The paper introduces a direction- and topology-aware graph transformer to predict neural network architecture attributes like accuracy and latency. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17 | Not relevant Focuses on NAS accuracy prediction, not LLM benchmarks. | |
| DTA-GT: Direction- and Topology-Aware Graph Transformer for Neural Network Representation Learning The paper introduces a direction- and topology-aware graph transformer to predict neural network architecture attributes like accuracy and latency. Preprint | AI agents via arxiv · 15 Oct, 06:17 | Not relevant Presents neural architecture search predictors, not agents. | |
| Unsupervised Data Drift Monitoring for Deployed Deepfake Speech Detectors The paper proposes an unsupervised drift monitor using internal embedding distances to flag unfamiliar inputs in deployed deepfake speech detectors. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17 | Not relevant Focuses on run-time drift monitoring, not LLM benchmarks. | |
| Unsupervised Data Drift Monitoring for Deployed Deepfake Speech Detectors The paper proposes an unsupervised drift monitor using internal embedding distances to flag unfamiliar inputs in deployed deepfake speech detectors. Preprint | Computer vision for industrial applications via arxiv · 15 Oct, 06:17 | Not relevant Applies to deepfake speech, not industrial computer vision. | |
| Unsupervised Data Drift Monitoring for Deployed Deepfake Speech Detectors The paper proposes an unsupervised drift monitor using internal embedding distances to flag unfamiliar inputs in deployed deepfake speech detectors. Preprint | Multimodal models via arxiv · 15 Oct, 06:17 | Not relevant Evaluates audio speech detectors, not multimodal foundation models. | |
| Unsupervised Data Drift Monitoring for Deployed Deepfake Speech Detectors The paper proposes an unsupervised drift monitor using internal embedding distances to flag unfamiliar inputs in deployed deepfake speech detectors. Preprint | AI agents via arxiv · 15 Oct, 06:17 | Not relevant Covers speech anomaly detection, not language model agents. | |
| Unsupervised Data Drift Monitoring for Deployed Deepfake Speech Detectors The paper proposes an unsupervised drift monitor using internal embedding distances to flag unfamiliar inputs in deployed deepfake speech detectors. Preprint | Small and efficient models via arxiv · 15 Oct, 06:17 | Not relevant Does not address model compression or inference efficiency. | |
| A Scaled-Up Empirical Study of Syntactic Language Models The paper presents an empirical study evaluating syntactic language models at scales up to 1B parameters across linearizations and masks. Preprint | Multimodal models via arxiv · 15 Oct, 06:17 | Not relevant Focuses solely on text syntactic modeling. | |
| A Scaled-Up Empirical Study of Syntactic Language Models The paper presents an empirical study evaluating syntactic language models at scales up to 1B parameters across linearizations and masks. Preprint | Small and efficient models via arxiv · 15 Oct, 06:17 | Not relevant Investigates syntactic inductive biases up to 1B parameters. | |
| A Scaled-Up Empirical Study of Syntactic Language Models The paper presents an empirical study evaluating syntactic language models at scales up to 1B parameters across linearizations and masks. Preprint | LLMs for code via arxiv · 15 Oct, 06:17 | Not relevant Explores natural language syntax, not software engineering. | |
| A Scaled-Up Empirical Study of Syntactic Language Models The paper presents an empirical study evaluating syntactic language models at scales up to 1B parameters across linearizations and masks. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17 | Not relevant Does not cover retrieval-augmented generation. | |
| A Scaled-Up Empirical Study of Syntactic Language Models The paper presents an empirical study evaluating syntactic language models at scales up to 1B parameters across linearizations and masks. Preprint | AI agents via arxiv · 15 Oct, 06:17 | Not relevant Studies syntactic language modeling, not agentic planning. | |
| A Scaled-Up Empirical Study of Syntactic Language Models The paper presents an empirical study evaluating syntactic language models at scales up to 1B parameters across linearizations and masks. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 06:17 | Not relevant Does not address safety, jailbreaks, or hallucinations. | |
| A Scaled-Up Empirical Study of Syntactic Language Models The paper presents an empirical study evaluating syntactic language models at scales up to 1B parameters across linearizations and masks. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17 | Not relevant Analyzes syntactic architecture scaling, not evaluation methodologies. | |
| Same Operation, Different Permission: Evaluating Whether Guardrails Honor Authorization The paper introduces a benchmark to evaluate whether language-model guardrails properly distinguish authorized from unauthorized actions across identical operations. Preprint | LLMs for code via arxiv · 15 Oct, 06:17 | Not relevant Mentions agent actions, not code generation or program repair. | |
| Few-for-Many Best-Arm Identification in Multi-Objective Bandits The paper presents multi-objective bandit algorithms to identify a small set of arms that minimizes degradation across multiple objectives. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17 | Not relevant Focuses on multi-objective bandits, not LLM evaluation. | |
| Few-for-Many Best-Arm Identification in Multi-Objective Bandits The paper presents multi-objective bandit algorithms to identify a small set of arms that minimizes degradation across multiple objectives. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 06:17 | Not relevant Focuses on bandit sample complexity, not deployment safety. | |
| Few-for-Many Best-Arm Identification in Multi-Objective Bandits The paper presents multi-objective bandit algorithms to identify a small set of arms that minimizes degradation across multiple objectives. Preprint | Small and efficient models via arxiv · 15 Oct, 06:17 | Not relevant Theoretical multi-armed bandit paper, not model efficiency. | |
| Few-for-Many Best-Arm Identification in Multi-Objective Bandits The paper presents multi-objective bandit algorithms to identify a small set of arms that minimizes degradation across multiple objectives. Preprint | AI agents via arxiv · 15 Oct, 06:17 | Not relevant Theoretical bandit algorithm, not language model agents. | |
| Towards Cost-Efficient Video Dataset Construction with Large Language Models The paper presents an LLM-based metadata pre-screening method to filter candidate videos before downloading, reducing data curation costs. Preprint | Computer vision for industrial applications via arxiv · 15 Oct, 06:17 | Not relevant Focuses on general video datasets, not industrial defect detection. | |
| Towards Cost-Efficient Video Dataset Construction with Large Language Models The paper presents an LLM-based metadata pre-screening method to filter candidate videos before downloading, reducing data curation costs. Preprint | Multimodal models via arxiv · 15 Oct, 06:17 | Not relevant Applies text LLMs to video metadata, not multimodal modeling. | |
| Towards Cost-Efficient Video Dataset Construction with Large Language Models The paper presents an LLM-based metadata pre-screening method to filter candidate videos before downloading, reducing data curation costs. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17 | Not relevant Proposes dataset curation filtering, not LLM benchmark evaluation. | |
| Towards Cost-Efficient Video Dataset Construction with Large Language Models The paper presents an LLM-based metadata pre-screening method to filter candidate videos before downloading, reducing data curation costs. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 06:17 | Not relevant Addresses data curation costs, not AI safety or reliability. | |
| Towards Cost-Efficient Video Dataset Construction with Large Language Models The paper presents an LLM-based metadata pre-screening method to filter candidate videos before downloading, reducing data curation costs. Preprint | AI agents via arxiv · 15 Oct, 06:17 | Not relevant Uses LLM for data filtering, not multi-step planning. | |
| Davinci-Tool: Scaling Agentic Tool-Use Supervision with Verifiable Executable Environments The paper introduces a 30B tool-use model and an execution-grounded dataset to scale agentic tool supervision with verifiable environments. Preprint | Multimodal models via arxiv · 15 Oct, 06:17 | Not relevant Does not involve multimodal or vision models. | |
| Davinci-Tool: Scaling Agentic Tool-Use Supervision with Verifiable Executable Environments The paper introduces a 30B tool-use model and an execution-grounded dataset to scale agentic tool supervision with verifiable environments. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 06:17 | Not relevant Focuses on agent tool-use capabilities, not deployment safety. | |
| Davinci-Tool: Scaling Agentic Tool-Use Supervision with Verifiable Executable Environments The paper introduces a 30B tool-use model and an execution-grounded dataset to scale agentic tool supervision with verifiable environments. Preprint | Small and efficient models via arxiv · 15 Oct, 06:17 | Not relevant Does not study model compression, pruning, or efficiency. | |
| Davinci-Tool: Scaling Agentic Tool-Use Supervision with Verifiable Executable Environments The paper introduces a 30B tool-use model and an execution-grounded dataset to scale agentic tool supervision with verifiable environments. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17 | Not relevant Focus is on dataset synthesis and agent training. | |
| Davinci-Tool: Scaling Agentic Tool-Use Supervision with Verifiable Executable Environments The paper introduces a 30B tool-use model and an execution-grounded dataset to scale agentic tool supervision with verifiable environments. Preprint | LLMs for code via arxiv · 15 Oct, 06:17 | Not relevant Deals with tool execution environments, not software engineering. | |
| Davinci-Tool: Scaling Agentic Tool-Use Supervision with Verifiable Executable Environments The paper introduces a 30B tool-use model and an execution-grounded dataset to scale agentic tool supervision with verifiable environments. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17 | Not relevant Focuses on tool-use agents, not document retrieval. |
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