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
Adaptive DHiSparse: Speculative Decoding over a Load-Adaptive KV Hierarchy Combines load-adaptive hierarchical key-value caching with speculative decoding to accelerate long-context language model serving. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Deals with internal KV cache hierarchy retrieval during inference, not external knowledge RAG.
Amortized Counterfactual Prediction with Graph Neural Networks Presents a graph neural process framework to answer counterfactual queries directly from observational data without prior causal graph discovery. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Presents general causal inference without focusing on deployed AI safety, guardrails, or alignment.
Amortized Counterfactual Prediction with Graph Neural Networks Presents a graph neural process framework to answer counterfactual queries directly from observational data without prior causal graph discovery. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Proposes a prediction architecture rather than an LLM evaluation framework or benchmark.
Amortized Counterfactual Prediction with Graph Neural Networks Presents a graph neural process framework to answer counterfactual queries directly from observational data without prior causal graph discovery. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Does not address model compression, quantization, distillation, or efficient inference serving.
Amortized Counterfactual Prediction with Graph Neural Networks Presents a graph neural process framework to answer counterfactual queries directly from observational data without prior causal graph discovery. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on causal inference via GNNs, not multi-step LLM planning or tool use.
LAPT-Scale: Scaling Embodied Policies with Latent Action Pre-Training on Human Videos Pre-trains embodied robot policies on human egocentric videos using learned continuous latent action representations. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Proposes a training method for robotics rather than a benchmark or evaluation methodology.
LAPT-Scale: Scaling Embodied Policies with Latent Action Pre-Training on Human Videos Pre-trains embodied robot policies on human egocentric videos using learned continuous latent action representations. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Does not study safety, guardrails, jailbreaks, or deployment reliability risks.
LAPT-Scale: Scaling Embodied Policies with Latent Action Pre-Training on Human Videos Pre-trains embodied robot policies on human egocentric videos using learned continuous latent action representations. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Focuses on embodied robotic manipulation rather than industrial inspection, defect detection, or factory visual tasks.
Orthros: Phase-Aware Heterogeneous Attention for Efficient Transformers Proposes an attention mechanism that switches from linear complexity during prefilling to full attention during token decoding. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Optimizes computational complexity of attention rather than safety or reliability guardrails.
Orthros: Phase-Aware Heterogeneous Attention for Efficient Transformers Proposes an attention mechanism that switches from linear complexity during prefilling to full attention during token decoding. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Proposes an attention mechanism rather than multi-step tool-using language model agents.
Orthros: Phase-Aware Heterogeneous Attention for Efficient Transformers Proposes an attention mechanism that switches from linear complexity during prefilling to full attention during token decoding. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Focuses on architectural design for attention rather than LLM evaluation benchmarks.
SafeDiSTL: Decomposing Joint Constraints for Safe Multi-Agent Reinforcement Learning Decomposes joint temporal logic constraint violations to allocate credit among agents in cooperative reinforcement learning. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Proposes an RL credit assignment method rather than an LLM evaluation benchmark.
SafeDiSTL: Decomposing Joint Constraints for Safe Multi-Agent Reinforcement Learning Decomposes joint temporal logic constraint violations to allocate credit among agents in cooperative reinforcement learning. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Focuses on constrained reinforcement learning rather than small or quantized models.
A Privileged Teacher That Only Scores Cannot Search: Critique-Conditioned On-Policy Self-Distillation for Reasoning Examines error localization and credit assignment failures in critique-conditioned on-policy self-distillation for mathematical reasoning. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Deals with general mathematical/reasoning distillation rather than software engineering or code LLMs.
A Privileged Teacher That Only Scores Cannot Search: Critique-Conditioned On-Policy Self-Distillation for Reasoning Examines error localization and credit assignment failures in critique-conditioned on-policy self-distillation for mathematical reasoning. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Investigates distillation credit signals rather than LLM benchmarking or evaluation frameworks.
A Privileged Teacher That Only Scores Cannot Search: Critique-Conditioned On-Policy Self-Distillation for Reasoning Examines error localization and credit assignment failures in critique-conditioned on-policy self-distillation for mathematical reasoning. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Studies self-distillation for reasoning rather than retrieval-augmented generation systems.
A Privileged Teacher That Only Scores Cannot Search: Critique-Conditioned On-Policy Self-Distillation for Reasoning Examines error localization and credit assignment failures in critique-conditioned on-policy self-distillation for mathematical reasoning. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on training reasoning models via distillation rather than autonomous multi-step agents.
A Privileged Teacher That Only Scores Cannot Search: Critique-Conditioned On-Policy Self-Distillation for Reasoning Examines error localization and credit assignment failures in critique-conditioned on-policy self-distillation for mathematical reasoning. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Focuses strictly on language-based reasoning traces without multimodal inputs.
Matched-Context Group Policy Optimization for LLM Agent Training Constructs context-matched preference comparisons from sibling rollouts to improve credit assignment during language model agent training. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on task performance training rather than AI safety, robustness, or guardrails.
Matched-Context Group Policy Optimization for LLM Agent Training Constructs context-matched preference comparisons from sibling rollouts to improve credit assignment during language model agent training. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Introduces a reinforcement learning training method rather than an evaluation benchmark.
Matched-Context Group Policy Optimization for LLM Agent Training Constructs context-matched preference comparisons from sibling rollouts to improve credit assignment during language model agent training. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Focuses on agent policy optimization rather than model quantization or inference efficiency.
When Edge Independence Fails: Joint Graph Diffusion with Latent Sociability Priors Introduces a latent sociability prior to mitigate edge-independence issues in discrete graph diffusion models. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Deals with graph generation quality rather than model efficiency or compression.
When Edge Independence Fails: Joint Graph Diffusion with Latent Sociability Priors Introduces a latent sociability prior to mitigate edge-independence issues in discrete graph diffusion models. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Studies graph generation priors rather than language model evaluation frameworks.
When Edge Independence Fails: Joint Graph Diffusion with Latent Sociability Priors Introduces a latent sociability prior to mitigate edge-independence issues in discrete graph diffusion models. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Addresses graph generative modeling theory rather than AI safety, alignment, or guardrails.
When Edge Independence Fails: Joint Graph Diffusion with Latent Sociability Priors Introduces a latent sociability prior to mitigate edge-independence issues in discrete graph diffusion models. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on graph diffusion theory rather than LLM-based autonomous agents.
DualQuant: Dual-scale Calibration-free Weight-and-Activation Quantization for LLMs Proposes a calibration-free post-training quantization method that jointly optimizes weight and activation scaling for 4-bit model inference. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Addresses model compression and hardware speedups rather than safety or guardrails.
DualQuant: Dual-scale Calibration-free Weight-and-Activation Quantization for LLMs Proposes a calibration-free post-training quantization method that jointly optimizes weight and activation scaling for 4-bit model inference. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Develops a quantization method rather than an evaluation benchmark for language models.
DualQuant: Dual-scale Calibration-free Weight-and-Activation Quantization for LLMs Proposes a calibration-free post-training quantization method that jointly optimizes weight and activation scaling for 4-bit model inference. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on low-bit model quantization rather than multi-step agent systems.
ReplaySSM: Cache SSM Inputs, Not State Replaces frequent state memory updates in state-space models by caching inputs and periodically checkpointing state during decoding. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Proposes an inference acceleration mechanism rather than benchmark or evaluation methodologies.
ReplaySSM: Cache SSM Inputs, Not State Replaces frequent state memory updates in state-space models by caching inputs and periodically checkpointing state during decoding. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on runtime throughput optimization rather than AI safety or alignment.
ReplaySSM: Cache SSM Inputs, Not State Replaces frequent state memory updates in state-space models by caching inputs and periodically checkpointing state during decoding. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Optimizes state-space model inference rather than autonomous agent planning or actions.
UkrMeme: Multimodal Explainable Persuasion Detection in Ukrainian Memes Introduces a Ukrainian meme dataset and evaluates vision-language models on detecting persuasive and propaganda techniques. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Analyzes online multimodal memes rather than industrial vision inspection tasks.
UkrMeme: Multimodal Explainable Persuasion Detection in Ukrainian Memes Introduces a Ukrainian meme dataset and evaluates vision-language models on detecting persuasive and propaganda techniques. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Evaluates zero-shot VLM classification on memes rather than agentic planning or tools.
UkrMeme: Multimodal Explainable Persuasion Detection in Ukrainian Memes Introduces a Ukrainian meme dataset and evaluates vision-language models on detecting persuasive and propaganda techniques. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Studies meme persuasion detection without retrieval-augmented generation mechanisms.
UkrMeme: Multimodal Explainable Persuasion Detection in Ukrainian Memes Introduces a Ukrainian meme dataset and evaluates vision-language models on detecting persuasive and propaganda techniques. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Focuses on political memes, completely unrelated to code generation or software tasks.
UkrMeme: Multimodal Explainable Persuasion Detection in Ukrainian Memes Introduces a Ukrainian meme dataset and evaluates vision-language models on detecting persuasive and propaganda techniques. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Does not address small models, model compression, or inference optimization.
Your Discrete Graph Diffusion Model is Secretly Memorizing Demonstrates that discrete graph diffusion models exhibit conditional memorization when reverse trajectories are conditioned on corrupted training graphs. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Studies graph diffusion models rather than LLM-based autonomous agent architectures.
Your Discrete Graph Diffusion Model is Secretly Memorizing Demonstrates that discrete graph diffusion models exhibit conditional memorization when reverse trajectories are conditioned on corrupted training graphs. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Investigates memorization in graph diffusion rather than LLM evaluation or benchmarks.
Your Discrete Graph Diffusion Model is Secretly Memorizing Demonstrates that discrete graph diffusion models exhibit conditional memorization when reverse trajectories are conditioned on corrupted training graphs. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Deals with graph generation theory, not model compression or efficient serving.
FORTE: A Production Framework for Feature Evaluation and Deprecation in Industrial Recommender Systems Describes an industrial framework for evaluating, ablating, and deprecating features in production recommender systems without performance regressions. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Deals with feature deprecation in recommender systems rather than LLM benchmarking.
FORTE: A Production Framework for Feature Evaluation and Deprecation in Industrial Recommender Systems Describes an industrial framework for evaluating, ablating, and deprecating features in production recommender systems without performance regressions. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Focuses on feature lifecycle in recommender pipelines, not model compression or small models.
FORTE: A Production Framework for Feature Evaluation and Deprecation in Industrial Recommender Systems Describes an industrial framework for evaluating, ablating, and deprecating features in production recommender systems without performance regressions. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Presents recommender system infrastructure rather than language model planning agents.
Same Query, Different Answer: Run-to-Run Variance in Semantic Query Processing Measures output variability across repeated runs at temperature zero in semantic query engines backed by language models. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Examines database query execution engines powered by LLMs rather than autonomous agent systems.
MC-EvoBench: Auditing Experience-Driven Self-Improvement in Minecraft Agents Introduces a controlled Minecraft benchmark to evaluate whether embodied agents acquire persistent, transferrable skills across environment resets. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Tests game-environment agents in Minecraft rather than real-world industrial computer vision.
MC-EvoBench: Auditing Experience-Driven Self-Improvement in Minecraft Agents Introduces a controlled Minecraft benchmark to evaluate whether embodied agents acquire persistent, transferrable skills across environment resets. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Studies self-improvement capabilities rather than safety risks, hallucination, or guardrails.
SpatialASM: Internalizing Active Mentalization for Spatial Reasoning Trains vision-language models to perform active spatial reasoning through novel-view generation and simulated mentalization trajectories. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Introduces a training framework for LVLMs rather than a foundational evaluation methodology.
SpatialASM: Internalizing Active Mentalization for Spatial Reasoning Trains vision-language models to perform active spatial reasoning through novel-view generation and simulated mentalization trajectories. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on spatial reasoning capabilities rather than deployment safety, alignment, or guardrails.
SpatialASM: Internalizing Active Mentalization for Spatial Reasoning Trains vision-language models to perform active spatial reasoning through novel-view generation and simulated mentalization trajectories. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Focuses on general spatial reasoning in vision-language models, not industrial inspection.
SpatialASM: Internalizing Active Mentalization for Spatial Reasoning Trains vision-language models to perform active spatial reasoning through novel-view generation and simulated mentalization trajectories. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on internalizing spatial reasoning in vision-language models rather than tool-using agents.
Studying Comics at Scale with Artificial Intelligence: The PAGE Framework and Three Studies of American Comics and Japanese Manga Applies vision-language models and structured annotation to parse and analyze historical comic books and manga at scale. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Applies existing models to historical analysis rather than evaluating LLM capabilities.
Studying Comics at Scale with Artificial Intelligence: The PAGE Framework and Three Studies of American Comics and Japanese Manga Applies vision-language models and structured annotation to parse and analyze historical comic books and manga at scale. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on digital humanities document analysis rather than autonomous multi-step agents.
Studying Comics at Scale with Artificial Intelligence: The PAGE Framework and Three Studies of American Comics and Japanese Manga Applies vision-language models and structured annotation to parse and analyze historical comic books and manga at scale. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Does not address model compression, quantization, or efficient inference serving.
Studying Comics at Scale with Artificial Intelligence: The PAGE Framework and Three Studies of American Comics and Japanese Manga Applies vision-language models and structured annotation to parse and analyze historical comic books and manga at scale. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Analyzes comic books and manga, unrelated to software code generation.
Studying Comics at Scale with Artificial Intelligence: The PAGE Framework and Three Studies of American Comics and Japanese Manga Applies vision-language models and structured annotation to parse and analyze historical comic books and manga at scale. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Applies models to historical archives rather than proposing RAG retrieval techniques.
Studying Comics at Scale with Artificial Intelligence: The PAGE Framework and Three Studies of American Comics and Japanese Manga Applies vision-language models and structured annotation to parse and analyze historical comic books and manga at scale. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on cultural analytics rather than AI safety, red-teaming, or reliability.
X-AgentRed: Red-Teaming Personal Agents in Evolving Multi-Service Ecosystems Presents an automated red-teaming framework that searches for harmful compositional tool-use paths across multi-service environments. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Focuses on red-teaming tool trajectories rather than general LLM capability benchmarking.
RATED: Reliability-Aware AI Agent Tool Retrieval via Execution Dynamics Combines dual-encoder semantic retrieval with a contextual bandit to adaptively select agent tools using execution reliability feedback. Peer-reviewed	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant The primary focus is an agent tool retrieval system, not general LLM benchmarking.
RATED: Reliability-Aware AI Agent Tool Retrieval via Execution Dynamics Combines dual-encoder semantic retrieval with a contextual bandit to adaptively select agent tools using execution reliability feedback. Peer-reviewed	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Focuses specifically on tool/API retrieval for agents rather than knowledge base RAG.
Task Context Shapes Component Roles in Language Models Investigates how changing tasks redistributes attention head influence and alters internal activations in open-weight large language models. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Focuses solely on text-based language models without multimodal inputs or outputs.
Task Context Shapes Component Roles in Language Models Investigates how changing tasks redistributes attention head influence and alters internal activations in open-weight large language models. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Does not investigate code generation, software engineering, or code assistants.

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