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
Unconstrained CTI-REALM with Open-Weights This paper evaluates open-weight language models against closed frontier models on a cybersecurity detection benchmark using an interactive telemetry environment. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Evaluates autonomous cyber threat intelligence agents, not standard RAG pipelines.
Unconstrained CTI-REALM with Open-Weights This paper evaluates open-weight language models against closed frontier models on a cybersecurity detection benchmark using an interactive telemetry environment. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Evaluates agent performance on security tasks rather than AI safety guardrails.
Unconstrained CTI-REALM with Open-Weights This paper evaluates open-weight language models against closed frontier models on a cybersecurity detection benchmark using an interactive telemetry environment. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Focuses on security threat detection workflows rather than software engineering code.
Unconstrained CTI-REALM with Open-Weights This paper evaluates open-weight language models against closed frontier models on a cybersecurity detection benchmark using an interactive telemetry environment. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Deals with text and telemetry data rather than multimodal vision or audio.
Visualizing the World Model in an Agent's Value Function The paper uses counterfactual interventions to visualize and examine implicit environmental concepts learned inside a reinforcement learning agent's value function. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Analyzes classical RL PPO agents in games, not language model agents.
Visualizing the World Model in an Agent's Value Function The paper uses counterfactual interventions to visualize and examine implicit environmental concepts learned inside a reinforcement learning agent's value function. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Studies world representations in RL value functions, not LLM benchmarks.
Visualizing the World Model in an Agent's Value Function The paper uses counterfactual interventions to visualize and examine implicit environmental concepts learned inside a reinforcement learning agent's value function. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Examines RL representations in Crafter rather than LLM deployment safety.
Visualizing the World Model in an Agent's Value Function The paper uses counterfactual interventions to visualize and examine implicit environmental concepts learned inside a reinforcement learning agent's value function. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not address model compression, quantization, or efficient serving.
Cross-Modal Latent Influence: Patch-Level Training Sample Attribution for Vision-Language Models The authors introduce sparse autoencoders to trace multimodal training influence down to specific image regions and text phrases. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on internal latent attribution methods rather than benchmark evaluation.
Cross-Modal Latent Influence: Patch-Level Training Sample Attribution for Vision-Language Models The authors introduce sparse autoencoders to trace multimodal training influence down to specific image regions and text phrases. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Examines internal feature attribution rather than deployment safety, guardrails, or hallucination.
Cross-Modal Latent Influence: Patch-Level Training Sample Attribution for Vision-Language Models The authors introduce sparse autoencoders to trace multimodal training influence down to specific image regions and text phrases. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies model interpretability and attribution, not LLM agents or tool use.
Should SLMs be in the Driver's Seat? A Study of Local Models as Interpreters in Cloud-Written Agent Programs The paper demonstrates a hybrid architecture where cloud-generated agent code delegates localized private-data queries to edge models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Evaluates agent architectural design rather than general LLM benchmarking.
Cortex: Measurement-Gated Evolution and Pruning of Coding-Agent Skills and Rules The authors develop a system that systematically tests, evolves, and prunes skill and rule files used by coding agents. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Tool for optimizing agent harnesses rather than general LLM benchmarking.
Learnable Low-Rank Polynomial Sketch for Effective Linear Attention The paper proposes a learnable low-rank polynomial sketch to reduce approximation errors in linear attention mechanisms. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Concerns attention mechanism efficiency rather than retrieval-augmented generation.
Learnable Low-Rank Polynomial Sketch for Effective Linear Attention The paper proposes a learnable low-rank polynomial sketch to reduce approximation errors in linear attention mechanisms. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on transformer linear attention kernels, not agent systems.
Learnable Low-Rank Polynomial Sketch for Effective Linear Attention The paper proposes a learnable low-rank polynomial sketch to reduce approximation errors in linear attention mechanisms. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Addresses attention kernel math, not code generation or program repair.
Learnable Low-Rank Polynomial Sketch for Effective Linear Attention The paper proposes a learnable low-rank polynomial sketch to reduce approximation errors in linear attention mechanisms. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes attention approximations rather than LLM evaluation benchmarks.
Learnable Low-Rank Polynomial Sketch for Effective Linear Attention The paper proposes a learnable low-rank polynomial sketch to reduce approximation errors in linear attention mechanisms. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Deals with core attention complexity rather than multimodal processing.
Learnable Low-Rank Polynomial Sketch for Effective Linear Attention The paper proposes a learnable low-rank polynomial sketch to reduce approximation errors in linear attention mechanisms. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on computational speed rather than deployment safety or guardrails.
Harness^2: Recursive Agent Harnessing for an Open World The authors propose a two-level recursive self-improvement framework to evolve and refine execution harnesses for open-world agents. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Focuses on general open-world agent harnesses rather than coding assistants.
Harness^2: Recursive Agent Harnessing for an Open World The authors propose a two-level recursive self-improvement framework to evolve and refine execution harnesses for open-world agents. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Deals with capability improvement rather than safety, alignment, or guardrails.
Harness^2: Recursive Agent Harnessing for an Open World The authors propose a two-level recursive self-improvement framework to evolve and refine execution harnesses for open-world agents. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Studies agent harnesses rather than multimodal vision-language models.
Harness^2: Recursive Agent Harnessing for an Open World The authors propose a two-level recursive self-improvement framework to evolve and refine execution harnesses for open-world agents. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Addresses agent harness refinement, not retrieval-augmented generation pipelines.
Harness^2: Recursive Agent Harnessing for an Open World The authors propose a two-level recursive self-improvement framework to evolve and refine execution harnesses for open-world agents. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on recursive agent improvement rather than evaluation benchmarks.
Harness^2: Recursive Agent Harnessing for an Open World The authors propose a two-level recursive self-improvement framework to evolve and refine execution harnesses for open-world agents. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not focus on model compression, quantization, or efficient serving.
CAPTOR: a vision-language foundation model for Chest-Abdomen-Pelvis Oncologic CT The paper develops and evaluates a multimodal foundation model for interpreting oncologic chest, abdomen, and pelvis CT scans. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on clinical diagnostic representation rather than deployment safety guardrails.
CAPTOR: a vision-language foundation model for Chest-Abdomen-Pelvis Oncologic CT The paper develops and evaluates a multimodal foundation model for interpreting oncologic chest, abdomen, and pelvis CT scans. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on medical imaging foundation models, not autonomous agents.
CAPTOR: a vision-language foundation model for Chest-Abdomen-Pelvis Oncologic CT The paper develops and evaluates a multimodal foundation model for interpreting oncologic chest, abdomen, and pelvis CT scans. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Pertains to medical CT interpretation, not industrial manufacturing defect inspection.
CAPTOR: a vision-language foundation model for Chest-Abdomen-Pelvis Oncologic CT The paper develops and evaluates a multimodal foundation model for interpreting oncologic chest, abdomen, and pelvis CT scans. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Evaluates medical foundation models rather than LLM benchmarks.
Latent World Action Model for GUI Agents The paper presents a latent world action model that predicts action-induced state transitions rather than full-screen pixel reconstructions for graphical agents. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes an agent architecture rather than LLM evaluation benchmarks.
Latent World Action Model for GUI Agents The paper presents a latent world action model that predicts action-induced state transitions rather than full-screen pixel reconstructions for graphical agents. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on GUI action accuracy rather than safety risks.
Latent World Action Model for GUI Agents The paper presents a latent world action model that predicts action-induced state transitions rather than full-screen pixel reconstructions for graphical agents. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Deals with computer GUI interaction rather than industrial manufacturing inspection.
Agentic Data Engineering for LLMs: System Design and a Controlled Empirical Study The authors present a framework where autonomous agents iteratively refine data engineering strategies to improve model training outcomes. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Studies agentic data generation strategies rather than LLM evaluation benchmarking.
Agentic Data Engineering for LLMs: System Design and a Controlled Empirical Study The authors present a framework where autonomous agents iteratively refine data engineering strategies to improve model training outcomes. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Focuses on agent-driven data curation rather than coding assistants.
Agentic Data Engineering for LLMs: System Design and a Controlled Empirical Study The authors present a framework where autonomous agents iteratively refine data engineering strategies to improve model training outcomes. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Addresses text and math curricula, not multimodal models.
Agentic Data Engineering for LLMs: System Design and a Controlled Empirical Study The authors present a framework where autonomous agents iteratively refine data engineering strategies to improve model training outcomes. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Studies data engineering optimization rather than safety or guardrails.
Agentic Data Engineering for LLMs: System Design and a Controlled Empirical Study The authors present a framework where autonomous agents iteratively refine data engineering strategies to improve model training outcomes. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focuses on data engineering agents, not retrieval-augmented generation pipelines.
Agentic Data Engineering for LLMs: System Design and a Controlled Empirical Study The authors present a framework where autonomous agents iteratively refine data engineering strategies to improve model training outcomes. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on training data selection rather than model compression.
Akule: Fast and Scalable Multi-Robot Motion Planning via Sparse Interaction Diffusion The paper introduces a sparse interaction diffusion method that coordinates multi-robot trajectories using a frozen single-robot prior and modular corrections. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Studies multi-robot motion planning rather than industrial visual inspection.
Akule: Fast and Scalable Multi-Robot Motion Planning via Sparse Interaction Diffusion The paper introduces a sparse interaction diffusion method that coordinates multi-robot trajectories using a frozen single-robot prior and modular corrections. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on trajectory planning algorithms rather than AI safety guardrails.
Akule: Fast and Scalable Multi-Robot Motion Planning via Sparse Interaction Diffusion The paper introduces a sparse interaction diffusion method that coordinates multi-robot trajectories using a frozen single-robot prior and modular corrections. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Evaluates robotic trajectory planning rather than LLM evaluation benchmarks.
Echo: Error-Compensated 4-Bit Tensor-Parallel Communication for LLM Training and Inference The authors develop an error-compensated 4-bit all-reduce algorithm to reduce communication overhead during distributed model training and inference. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on distributed GPU communication, not LLM agent systems.
Echo: Error-Compensated 4-Bit Tensor-Parallel Communication for LLM Training and Inference The authors develop an error-compensated 4-bit all-reduce algorithm to reduce communication overhead during distributed model training and inference. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on tensor-parallel communication speed rather than LLM benchmarks.
Echo: Error-Compensated 4-Bit Tensor-Parallel Communication for LLM Training and Inference The authors develop an error-compensated 4-bit all-reduce algorithm to reduce communication overhead during distributed model training and inference. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Deals with distributed systems performance rather than safety or reliability.
The RNN Homunculus: Learning to Gate Long-Term Memory The paper studies gate learning in recurrent networks, showing how adding short-dependency samples helps models learn long-range temporal connections. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Explores sequence architecture memory gating, not agent workflows.
The RNN Homunculus: Learning to Gate Long-Term Memory The paper studies gate learning in recurrent networks, showing how adding short-dependency samples helps models learn long-range temporal connections. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on recurrent architecture training rather than safety guardrails.
The RNN Homunculus: Learning to Gate Long-Term Memory The paper studies gate learning in recurrent networks, showing how adding short-dependency samples helps models learn long-range temporal connections. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Studies gated RNN sequence learning rather than LLM benchmarking.
GeoFix: Geometry Fixing for 4D Consistent Generation The authors introduce a framework that uses cross-view depth consistency as a single reward to improve 4D video generation geometry. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Concerns 4D video generation rather than industrial defect detection.
GeoFix: Geometry Fixing for 4D Consistent Generation The authors introduce a framework that uses cross-view depth consistency as a single reward to improve 4D video generation geometry. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not investigate small models, distillation, or quantization.
GeoFix: Geometry Fixing for 4D Consistent Generation The authors introduce a framework that uses cross-view depth consistency as a single reward to improve 4D video generation geometry. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on generative consistency rather than LLM evaluation benchmarks.
GeoFix: Geometry Fixing for 4D Consistent Generation The authors introduce a framework that uses cross-view depth consistency as a single reward to improve 4D video generation geometry. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on video generation geometry, not autonomous multi-step agents.
GeoFix: Geometry Fixing for 4D Consistent Generation The authors introduce a framework that uses cross-view depth consistency as a single reward to improve 4D video generation geometry. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Concerns visual geometry consistency rather than deployment safety risks.
Controllable Diverse Sampling through Pullback Directions The paper proposes a training-free method leveraging pullback directions to increase sample diversity in conditional diffusion generation. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on diffusion sampling geometry rather than multimodal reasoning.
Controllable Diverse Sampling through Pullback Directions The paper proposes a training-free method leveraging pullback directions to increase sample diversity in conditional diffusion generation. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on sample diversity rather than deployment safety or guardrails.
Controllable Diverse Sampling through Pullback Directions The paper proposes a training-free method leveraging pullback directions to increase sample diversity in conditional diffusion generation. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Investigates diffusion sampling diversity rather than LLM benchmarks.
Controllable Diverse Sampling through Pullback Directions The paper proposes a training-free method leveraging pullback directions to increase sample diversity in conditional diffusion generation. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Addresses creative image generation, not industrial visual inspection.
Controllable Diverse Sampling through Pullback Directions The paper proposes a training-free method leveraging pullback directions to increase sample diversity in conditional diffusion generation. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Deals with diffusion image diversity, not autonomous multi-step agents.
RUBATO: Decoupled Clocks Allow Full-Duplex Models to Think as Fast as They Can The authors propose decoupled positional encodings enabling full-duplex speech models to run internal thinking streams at hardware-adaptive speeds. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Does not benchmark language models or evaluate evaluation methodology.
RUBATO: Decoupled Clocks Allow Full-Duplex Models to Think as Fast as They Can The authors propose decoupled positional encodings enabling full-duplex speech models to run internal thinking streams at hardware-adaptive speeds. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on speech model streaming latency rather than safety.
RUBATO: Decoupled Clocks Allow Full-Duplex Models to Think as Fast as They Can The authors propose decoupled positional encodings enabling full-duplex speech models to run internal thinking streams at hardware-adaptive speeds. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not focus on retrieval-augmented generation or document grounding.

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