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
The Delegation Dilemma: Economies of Scale, Accountability, and Polycentric Governance Models repeated games with endogenous delegation to analyze trade-offs between economies of scale and institutional accountability. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Focuses on game-theoretic governance dynamics rather than AI deployment safety.
The Delegation Dilemma: Economies of Scale, Accountability, and Polycentric Governance Models repeated games with endogenous delegation to analyze trade-offs between economies of scale and institutional accountability. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Studies organizational dynamics of agents, not model compression or efficiency.
Harmful Partial Interventions and Benign Leakage in Concept-based Models Investigates how partial interventions in concept-based models can unexpectedly reduce downstream task performance due to information leakage. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Addresses model interpretability and interventions, not model efficiency.
Harmful Partial Interventions and Benign Leakage in Concept-based Models Investigates how partial interventions in concept-based models can unexpectedly reduce downstream task performance due to information leakage. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Focuses on concept-based models, not multi-step tool-using agents.
Harmful Partial Interventions and Benign Leakage in Concept-based Models Investigates how partial interventions in concept-based models can unexpectedly reduce downstream task performance due to information leakage. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Studies concept intervention harm rather than LLM benchmarking methods.
DeciCAD: Harness-Grounded Learning of Action Policies for CAD Agents Develops a domain harness and policy learning pipeline to improve language model agent execution in computer-aided design environments. Preprint	Computer vision for industrial applications via arxiv · 17 Oct, 06:17	Not relevant Interactive CAD modeling, not industrial defect inspection on manufacturing lines.
DeciCAD: Harness-Grounded Learning of Action Policies for CAD Agents Develops a domain harness and policy learning pipeline to improve language model agent execution in computer-aided design environments. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Focuses on CAD harness policy learning, not model compression.
DeciCAD: Harness-Grounded Learning of Action Policies for CAD Agents Develops a domain harness and policy learning pipeline to improve language model agent execution in computer-aided design environments. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Focuses on CAD action harnesses, not retrieval-augmented generation.
DeciCAD: Harness-Grounded Learning of Action Policies for CAD Agents Develops a domain harness and policy learning pipeline to improve language model agent execution in computer-aided design environments. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Focuses on CAD policy optimization, not deployment safety or guardrails.
DeciCAD: Harness-Grounded Learning of Action Policies for CAD Agents Develops a domain harness and policy learning pipeline to improve language model agent execution in computer-aided design environments. Preprint	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Addresses procedural CAD modeling code, not multimodal vision-language models.
DeciCAD: Harness-Grounded Learning of Action Policies for CAD Agents Develops a domain harness and policy learning pipeline to improve language model agent execution in computer-aided design environments. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Introduces a CAD agent framework, not a general LLM benchmark.
From Research Goals to Scientific Closure: Evaluating Agents Through Evidence-Supported Physics Research Introduces a benchmark evaluating whether language model agents can independently direct and validate goal-specified physics research investigations. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Focuses on agent research competence benchmarking, not deployment guardrails.
CF-AVSS: A Coarse-to-Fine Audio-Visual Speech Separation Framework with Target-Speaker Refinement and Post-Fusion for Real-World AVSE Challenge Introduces a two-stage coarse-to-fine speech separation architecture leveraging visual streams to isolate target speaker audio in noisy environments. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Speech processing model, not an LLM evaluation benchmark.
CF-AVSS: A Coarse-to-Fine Audio-Visual Speech Separation Framework with Target-Speaker Refinement and Post-Fusion for Real-World AVSE Challenge Introduces a two-stage coarse-to-fine speech separation architecture leveraging visual streams to isolate target speaker audio in noisy environments. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Audio-visual signal separation method, not an AI agent architecture.
CF-AVSS: A Coarse-to-Fine Audio-Visual Speech Separation Framework with Target-Speaker Refinement and Post-Fusion for Real-World AVSE Challenge Introduces a two-stage coarse-to-fine speech separation architecture leveraging visual streams to isolate target speaker audio in noisy environments. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Signal processing speech enhancement, not AI safety or robustness.
DINO-FIL: Multi-Layer Transformer Fusion for Generative Inpainting Forgery Localization Combines multi-layer vision transformer representations with a fusion decoder to localize pixel-level generative image inpainting manipulations. Peer-reviewed	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Pure computer vision forensic model, not a multimodal language system.
DINO-FIL: Multi-Layer Transformer Fusion for Generative Inpainting Forgery Localization Combines multi-layer vision transformer representations with a fusion decoder to localize pixel-level generative image inpainting manipulations. Peer-reviewed	Computer vision for industrial applications via arxiv · 17 Oct, 06:17	Not relevant Detects generative image inpainting edits, not industrial defect detection.
DINO-FIL: Multi-Layer Transformer Fusion for Generative Inpainting Forgery Localization Combines multi-layer vision transformer representations with a fusion decoder to localize pixel-level generative image inpainting manipulations. Peer-reviewed	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Image forensic detection model, not an LLM benchmark.
DINO-FIL: Multi-Layer Transformer Fusion for Generative Inpainting Forgery Localization Combines multi-layer vision transformer representations with a fusion decoder to localize pixel-level generative image inpainting manipulations. Peer-reviewed	AI agents via arxiv · 17 Oct, 06:17	Not relevant Image forgery localization model, not an AI agent system.
DINO-FIL: Multi-Layer Transformer Fusion for Generative Inpainting Forgery Localization Combines multi-layer vision transformer representations with a fusion decoder to localize pixel-level generative image inpainting manipulations. Peer-reviewed	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Focuses on image editing forensics, not LLM deployment safety.
In the Guise of Correction: How Framing and Cultural Adaptation Erode LLM Safety on Real-World Criminal Cases Presents a benchmark based on real criminal cases showing how correction framings and cultural adaptations bypass safety guardrails. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Evaluates safety vulnerabilities, not retrieval-augmented generation systems.
In the Guise of Correction: How Framing and Cultural Adaptation Erode LLM Safety on Real-World Criminal Cases Presents a benchmark based on real criminal cases showing how correction framings and cultural adaptations bypass safety guardrails. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Focuses on criminal prompt compliance, not coding assistants or software repair.
In the Guise of Correction: How Framing and Cultural Adaptation Erode LLM Safety on Real-World Criminal Cases Presents a benchmark based on real criminal cases showing how correction framings and cultural adaptations bypass safety guardrails. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Studies jailbreaking and safety erosion, not autonomous multi-step agents.
In the Guise of Correction: How Framing and Cultural Adaptation Erode LLM Safety on Real-World Criminal Cases Presents a benchmark based on real criminal cases showing how correction framings and cultural adaptations bypass safety guardrails. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Investigates safety benchmarks, not model efficiency or compression.
In the Guise of Correction: How Framing and Cultural Adaptation Erode LLM Safety on Real-World Criminal Cases Presents a benchmark based on real criminal cases showing how correction framings and cultural adaptations bypass safety guardrails. Preprint	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Focuses on text safety prompts, not multimodal image/video understanding.
RSIFlow: Adaptive Self-Improvement Workflows with an Evolving Meta-Agent Develops a recursive self-improvement framework where an evolving meta-agent adapts agent workflows based on execution trajectories and feedback. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Addresses agent orchestration and self-improvement, not safety or guardrails.
RSIFlow: Adaptive Self-Improvement Workflows with an Evolving Meta-Agent Develops a recursive self-improvement framework where an evolving meta-agent adapts agent workflows based on execution trajectories and feedback. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Focuses on agent self-improvement architecture rather than LLM benchmarking.
Cross-Embodiment Causal Transfer from Human Videos to Robot Manipulation Introduces a causal-beam transfer framework that extracts relevant subtask representations from human videos to guide robotic manipulation. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Robotics policy transfer method, not an LLM benchmark.
Cross-Embodiment Causal Transfer from Human Videos to Robot Manipulation Introduces a causal-beam transfer framework that extracts relevant subtask representations from human videos to guide robotic manipulation. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Addresses cross-embodiment robotics control, not AI safety or guardrails.
Cross-Embodiment Causal Transfer from Human Videos to Robot Manipulation Introduces a causal-beam transfer framework that extracts relevant subtask representations from human videos to guide robotic manipulation. Preprint	Computer vision for industrial applications via arxiv · 17 Oct, 06:17	Not relevant Robotic manipulation imitation learning, not industrial inspection or defect detection.
Cross-Embodiment Causal Transfer from Human Videos to Robot Manipulation Introduces a causal-beam transfer framework that extracts relevant subtask representations from human videos to guide robotic manipulation. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Robotics video-to-action transfer method, not LLM agents.
Co2In: a Bi-level Memory Incremental Learning Framework with Knowledge Encoding, Consolidation, and Integration Proposes a bi-level working and long-term memory framework to balance knowledge retention and acquisition in incremental learning. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Continual learning neural architecture, not LLM agent orchestration.
Co2In: a Bi-level Memory Incremental Learning Framework with Knowledge Encoding, Consolidation, and Integration Proposes a bi-level working and long-term memory framework to balance knowledge retention and acquisition in incremental learning. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Addresses catastrophic forgetting in continual learning, not deployment safety.
Co2In: a Bi-level Memory Incremental Learning Framework with Knowledge Encoding, Consolidation, and Integration Proposes a bi-level working and long-term memory framework to balance knowledge retention and acquisition in incremental learning. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Proposes an incremental learning framework, not an evaluation benchmark.
CIM-Embed: Learning Instruction-Conditioned Multimodal Embeddings for Clustering Learns instruction-conditioned multimodal representations to dynamically cluster objects based on user-specified partition criteria without fixed cluster counts. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Instruction-conditioned embeddings, not model compression or efficient serving.
CIM-Embed: Learning Instruction-Conditioned Multimodal Embeddings for Clustering Learns instruction-conditioned multimodal representations to dynamically cluster objects based on user-specified partition criteria without fixed cluster counts. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Presents an embedding clustering benchmark, not an LLM evaluation framework.
CIM-Embed: Learning Instruction-Conditioned Multimodal Embeddings for Clustering Learns instruction-conditioned multimodal representations to dynamically cluster objects based on user-specified partition criteria without fixed cluster counts. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Embedding representation technique, not an LLM agent architecture.
CIM-Embed: Learning Instruction-Conditioned Multimodal Embeddings for Clustering Learns instruction-conditioned multimodal representations to dynamically cluster objects based on user-specified partition criteria without fixed cluster counts. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Deals with multimodal clustering embeddings, not deployment safety.
RoleQ: Role-Aligned Scales for Converting Language Models to Ternary Proposes a structural scaling method based on transformer unit roles to convert language models to ternary weights effectively. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Quantization technique, not about AI agent workflows or planning.
RoleQ: Role-Aligned Scales for Converting Language Models to Ternary Proposes a structural scaling method based on transformer unit roles to convert language models to ternary weights effectively. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Focuses on weight quantization, not retrieval-augmented generation.
RoleQ: Role-Aligned Scales for Converting Language Models to Ternary Proposes a structural scaling method based on transformer unit roles to convert language models to ternary weights effectively. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Quantization framework for LLMs, not code generation or program repair.
RoleQ: Role-Aligned Scales for Converting Language Models to Ternary Proposes a structural scaling method based on transformer unit roles to convert language models to ternary weights effectively. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Focuses on model compression efficiency, not safety or guardrails.
RoleQ: Role-Aligned Scales for Converting Language Models to Ternary Proposes a structural scaling method based on transformer unit roles to convert language models to ternary weights effectively. Preprint	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Focuses on language model weight quantization, not multimodal models.
RoleQ: Role-Aligned Scales for Converting Language Models to Ternary Proposes a structural scaling method based on transformer unit roles to convert language models to ternary weights effectively. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Introduces a quantization algorithm rather than an evaluation benchmark.
Provably Efficient Risk-Sensitive Offline Reinforcement Learning via Optimized Certainty Equivalents Develops pessimistic value-iteration algorithms with theoretical suboptimality bounds for offline risk-sensitive reinforcement learning using optimized certainty equivalents. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Theoretical offline RL mathematics, not LLM agent systems.
Provably Efficient Risk-Sensitive Offline Reinforcement Learning via Optimized Certainty Equivalents Develops pessimistic value-iteration algorithms with theoretical suboptimality bounds for offline risk-sensitive reinforcement learning using optimized certainty equivalents. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Mathematical decision-theoretic risk formulation, not AI deployment safety.
Provably Efficient Risk-Sensitive Offline Reinforcement Learning via Optimized Certainty Equivalents Develops pessimistic value-iteration algorithms with theoretical suboptimality bounds for offline risk-sensitive reinforcement learning using optimized certainty equivalents. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Theoretical statistical bounds, not an LLM evaluation benchmark.
Provably Efficient Risk-Sensitive Offline Reinforcement Learning via Optimized Certainty Equivalents Develops pessimistic value-iteration algorithms with theoretical suboptimality bounds for offline risk-sensitive reinforcement learning using optimized certainty equivalents. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Addresses offline RL convergence bounds, not model efficiency or compression.
Anvil: Scaling Embodied Spatial Intelligence through Knowledge Recirculation Presents a unified framework with synthetic data generation and benchmarks to train models on embodied spatial reasoning and actions. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Focuses on embodied spatial perception and representations, not agent orchestration.
Anvil: Scaling Embodied Spatial Intelligence through Knowledge Recirculation Presents a unified framework with synthetic data generation and benchmarks to train models on embodied spatial reasoning and actions. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Introduces a dataset/benchmark for spatial robotics, not language model evaluation.
Anvil: Scaling Embodied Spatial Intelligence through Knowledge Recirculation Presents a unified framework with synthetic data generation and benchmarks to train models on embodied spatial reasoning and actions. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Presents an embodied data and modeling framework, not AI safety guardrails.
Anvil: Scaling Embodied Spatial Intelligence through Knowledge Recirculation Presents a unified framework with synthetic data generation and benchmarks to train models on embodied spatial reasoning and actions. Preprint	Computer vision for industrial applications via arxiv · 17 Oct, 06:17	Not relevant General embodied spatial reasoning, not industrial defect or quality inspection.
In-Context Learning Before Specialisation: Theory of Learning in a Solvable Topic Model Develops a solvable topic model theory explaining when sequence models adopt in-context learning rather than specialised memorisation during training. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Theoretical analysis of in-context learning emergence, not deployment safety.
In-Context Learning Before Specialisation: Theory of Learning in a Solvable Topic Model Develops a solvable topic model theory explaining when sequence models adopt in-context learning rather than specialised memorisation during training. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Theoretical statistical physics model, not an LLM benchmark.
In-Context Learning Before Specialisation: Theory of Learning in a Solvable Topic Model Develops a solvable topic model theory explaining when sequence models adopt in-context learning rather than specialised memorisation during training. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Theoretical pretraining sequence model analysis, not AI agent workflows.
In-Context Learning Before Specialisation: Theory of Learning in a Solvable Topic Model Develops a solvable topic model theory explaining when sequence models adopt in-context learning rather than specialised memorisation during training. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Studies pretraining sample complexity theoretically, not inference efficiency.
CoWorld Reasoner: Agentic Multimodal Reasoning with the Evolving Video World Model-Driven Harness Equips multimodal vision-language models with video world model operations as tools to support dynamic spatiotemporal visual reasoning. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Focuses on multimodal reasoning capability, not safety guardrails or hallucination mitigation.
CoWorld Reasoner: Agentic Multimodal Reasoning with the Evolving Video World Model-Driven Harness Equips multimodal vision-language models with video world model operations as tools to support dynamic spatiotemporal visual reasoning. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Presents a reasoning agent framework, not a benchmark or evaluation methodology.
Learning Rewards in Strategic Games Analyzes strategic identifiability in multi-agent games, showing when inverse reinforcement learning fails to recover true underlying agent rewards. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Theoretical game theory and IRL, not practical LLM agents.
Learning Rewards in Strategic Games Analyzes strategic identifiability in multi-agent games, showing when inverse reinforcement learning fails to recover true underlying agent rewards. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Focuses on inverse RL theory, not model compression or efficiency.

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