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
Feature-Context Consistency: Unsupervised Adversarial Detection with Drift Stability on Attributed Graphs Detects adversarial nodes on evolving graphs without fraud labels by measuring the consistency between node features and aggregated neighborhood contexts. Peer-reviewed	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on graph fraud detection rather than LLM safety, guardrails, or hallucination.
Audit Before You Distill: A Controlled Workflow for Reasoning Transfer into Compact Medical Language Models The paper analyzes the impact of teacher rationales before distilling medical reasoning capabilities into compact language models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on rationale distillation methods rather than general model evaluation techniques.
Audit Before You Distill: A Controlled Workflow for Reasoning Transfer into Compact Medical Language Models The paper analyzes the impact of teacher rationales before distilling medical reasoning capabilities into compact language models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on distillation accuracy rather than deployment safety, red teaming, or guardrails.
Audit Before You Distill: A Controlled Workflow for Reasoning Transfer into Compact Medical Language Models The paper analyzes the impact of teacher rationales before distilling medical reasoning capabilities into compact language models. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Applies to medical examination questions rather than software engineering or code generation.
Audit Before You Distill: A Controlled Workflow for Reasoning Transfer into Compact Medical Language Models The paper analyzes the impact of teacher rationales before distilling medical reasoning capabilities into compact language models. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focuses on model distillation rather than retrieval-augmented generation systems.
Audit Before You Distill: A Controlled Workflow for Reasoning Transfer into Compact Medical Language Models The paper analyzes the impact of teacher rationales before distilling medical reasoning capabilities into compact language models. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on distillation training rather than multi-step agent planning or tool use.
Audit Before You Distill: A Controlled Workflow for Reasoning Transfer into Compact Medical Language Models The paper analyzes the impact of teacher rationales before distilling medical reasoning capabilities into compact language models. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Examines text-based medical examination questions without any multimodal components.
Trace-to-Architecture: Capability Bottleneck Diagnosis for Harness Revision The paper introduces Trace-to-Architecture, a framework that diagnoses LLM agent execution failures to automatically revise agent harness components. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on diagnosing and repairing agent architectures rather than evaluation methodology.
Trace-to-Architecture: Capability Bottleneck Diagnosis for Harness Revision The paper introduces Trace-to-Architecture, a framework that diagnoses LLM agent execution failures to automatically revise agent harness components. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on task performance and architecture revision rather than safety or reliability guardrails.
Queue-Aware Token Communications: Bridging AI Representation and Wireless Service The paper introduces an architecture that incorporates token queue dynamics into wireless communications for foundation model inference services. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses telecommunication systems, not deployment safety.
Queue-Aware Token Communications: Bridging AI Representation and Wireless Service The paper introduces an architecture that incorporates token queue dynamics into wireless communications for foundation model inference services. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Covers wireless token queues, not LLM benchmarking.
Queue-Aware Token Communications: Bridging AI Representation and Wireless Service The paper introduces an architecture that incorporates token queue dynamics into wireless communications for foundation model inference services. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on wireless communications, not LLM agents.
VAttnPO: Visual Attention-Guided Policy Optimization The paper proposes VAttnPO, using teacher visual attention to refine token-level policy optimization for multimodal reinforcement learning. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on RL policy optimization, not LLM benchmarks.
VAttnPO: Visual Attention-Guided Policy Optimization The paper proposes VAttnPO, using teacher visual attention to refine token-level policy optimization for multimodal reinforcement learning. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on policy optimization, not deployment safety.
VAttnPO: Visual Attention-Guided Policy Optimization The paper proposes VAttnPO, using teacher visual attention to refine token-level policy optimization for multimodal reinforcement learning. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Deals with general visual reasoning, not industrial inspection.
VAttnPO: Visual Attention-Guided Policy Optimization The paper proposes VAttnPO, using teacher visual attention to refine token-level policy optimization for multimodal reinforcement learning. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on multimodal RL training, not agent planning.
Before Attention Sinks Form: Hidden-State Geometry Reveals and Modulates Their Formation The paper analyzes pre-sink hidden-state geometry to detect and modulate attention sink formation in language models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Studies attention sink mechanics, not benchmark evaluations.
Before Attention Sinks Form: Hidden-State Geometry Reveals and Modulates Their Formation The paper analyzes pre-sink hidden-state geometry to detect and modulate attention sink formation in language models. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Investigates internal transformer attention, not agents.
Before Attention Sinks Form: Hidden-State Geometry Reveals and Modulates Their Formation The paper analyzes pre-sink hidden-state geometry to detect and modulate attention sink formation in language models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Studies attention mechanisms, not safety or guardrails.
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.
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	AI agents via arxiv · 15 Oct, 06:17	Not relevant Deals with safety classification, not tool-use agents.
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	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 safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on tabular dynamics, not safety or guardrails.
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.
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	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 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	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	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.
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	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not focus on model compression or efficient serving.
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	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	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	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on self-supervised vision models, not multimodal models.
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	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Studies layer growth during training, not LLM evaluation.
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	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	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	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	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	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	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Analyzes syntactic architecture scaling, not evaluation methodologies.
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.

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