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
Stochastic Grouping Conformal Prediction for Effective Subgroup Reliability Proposes a conformal prediction framework that learns stochastic groupings to improve subgroup coverage reliability without inflating overall prediction sets. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on conformal prediction for subgroup uncertainty quantification, not LLM agents.
Can LLMs Fix It Without Code? Toward Automated Verification of No-Code Bug Fixes Presents an execution-based evaluation pipeline using browser agents to verify language model solutions for no-code software bug reports. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on bug resolution benchmarking rather than safety vulnerabilities, guardrails, or alignment.
MindFlow: Mind Supernet Powered Thinking Flows for Research Idea Innovation Proposes a framework modeling research ideation as modular thinking flows sampled dynamically from a probabilistic supernet. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Focuses on scientific research ideation rather than code generation or program repair.
MindFlow: Mind Supernet Powered Thinking Flows for Research Idea Innovation Proposes a framework modeling research ideation as modular thinking flows sampled dynamically from a probabilistic supernet. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Does not explore quantization, pruning, distillation, or efficient inference.
MindFlow: Mind Supernet Powered Thinking Flows for Research Idea Innovation Proposes a framework modeling research ideation as modular thinking flows sampled dynamically from a probabilistic supernet. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Addresses creativity and plausibility in ideation rather than safety, jailbreaks, or guardrails.
MindFlow: Mind Supernet Powered Thinking Flows for Research Idea Innovation Proposes a framework modeling research ideation as modular thinking flows sampled dynamically from a probabilistic supernet. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Focuses on text-based research idea generation without multimodal vision or audio components.
MindFlow: Mind Supernet Powered Thinking Flows for Research Idea Innovation Proposes a framework modeling research ideation as modular thinking flows sampled dynamically from a probabilistic supernet. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Focuses on dynamic reasoning workflows rather than retrieval-augmented generation systems.
MetaOPD: Meta-Learned Token Weighting for On-Policy Distillation Presents a bilevel optimization framework for on-policy distillation that meta-learns dynamic token weights based on student validation loss. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on token weighting for student distillation, not multi-step agent planning or tools.
MetaOPD: Meta-Learned Token Weighting for On-Policy Distillation Presents a bilevel optimization framework for on-policy distillation that meta-learns dynamic token weights based on student validation loss. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Proposes an on-policy distillation method rather than a framework for evaluating LLMs.
MetaOPD: Meta-Learned Token Weighting for On-Policy Distillation Presents a bilevel optimization framework for on-policy distillation that meta-learns dynamic token weights based on student validation loss. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Targets training optimization and distillation loss rather than deployment safety or robustness.
DataVista: Diagnosing Multimodal LLMs on Data Video Understanding Introduces a benchmark evaluating multimodal language models on extracting data, tracking narrative flows, and answering questions from animated data videos. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Focuses on business and news data videos, not industrial inspection or manufacturing perception.
DataVista: Diagnosing Multimodal LLMs on Data Video Understanding Introduces a benchmark evaluating multimodal language models on extracting data, tracking narrative flows, and answering questions from animated data videos. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Evaluates multimodal understanding from video charts rather than external knowledge retrieval.
DataVista: Diagnosing Multimodal LLMs on Data Video Understanding Introduces a benchmark evaluating multimodal language models on extracting data, tracking narrative flows, and answering questions from animated data videos. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Evaluates multimodal video understanding, not code generation or software engineering.
DataVista: Diagnosing Multimodal LLMs on Data Video Understanding Introduces a benchmark evaluating multimodal language models on extracting data, tracking narrative flows, and answering questions from animated data videos. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Introduces a video question-answering benchmark rather than autonomous agent tool use or planning.
DataVista: Diagnosing Multimodal LLMs on Data Video Understanding Introduces a benchmark evaluating multimodal language models on extracting data, tracking narrative flows, and answering questions from animated data videos. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Focuses on multimodal benchmarking, not model compression or efficient serving.
DataVista: Diagnosing Multimodal LLMs on Data Video Understanding Introduces a benchmark evaluating multimodal language models on extracting data, tracking narrative flows, and answering questions from animated data videos. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on capability benchmarking rather than safety, red-teaming, or guardrails.
Neural Network Verification for Deep Joint Source-Channel Coding Presents a bound-propagation verification framework to bound worst-case reconstruction errors for deep joint source-channel coding decoders under noise. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Deals with communications coding verification, not software engineering or coding LLMs.
Neural Network Verification for Deep Joint Source-Channel Coding Presents a bound-propagation verification framework to bound worst-case reconstruction errors for deep joint source-channel coding decoders under noise. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Develops verification methods for wireless neural decoders rather than LLM evaluation.
Neural Network Verification for Deep Joint Source-Channel Coding Presents a bound-propagation verification framework to bound worst-case reconstruction errors for deep joint source-channel coding decoders under noise. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on formal verification of neural decoders in wireless transmission, not AI agents.
Agentic-TTT: Training test-time policy for test-time training Proposes a policy mechanism enabling language models to decide when, how, and which test-time training procedures to execute. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Studies general test-time training policies, not code generation or software engineering tasks.
Agentic-TTT: Training test-time policy for test-time training Proposes a policy mechanism enabling language models to decide when, how, and which test-time training procedures to execute. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Focuses on parameter-updating test-time training algorithms, not retrieval-augmented generation.
Agentic-TTT: Training test-time policy for test-time training Proposes a policy mechanism enabling language models to decide when, how, and which test-time training procedures to execute. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Proposes an algorithmic framework for test-time adaptation rather than LLM evaluation methodologies.
Agentic-TTT: Training test-time policy for test-time training Proposes a policy mechanism enabling language models to decide when, how, and which test-time training procedures to execute. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on performance adaptation during inference rather than safety, alignment, or guardrails.
Agentic-TTT: Training test-time policy for test-time training Proposes a policy mechanism enabling language models to decide when, how, and which test-time training procedures to execute. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Focuses on test-time adaptation policies for LLMs without multimodal components.
An Interpretable Approach to PDE Solution Discovery via Structural Experience Distillation Uses Monte Carlo tree search and structural experience distillation to identify interpretable symbolic expressions for partial differential equation solutions. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on physical equation discovery rather than AI safety, robustness, or deployment guardrails.
An Interpretable Approach to PDE Solution Discovery via Structural Experience Distillation Uses Monte Carlo tree search and structural experience distillation to identify interpretable symbolic expressions for partial differential equation solutions. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Addresses symbolic scientific discovery algorithms rather than LLM evaluation benchmarks.
An Interpretable Approach to PDE Solution Discovery via Structural Experience Distillation Uses Monte Carlo tree search and structural experience distillation to identify interpretable symbolic expressions for partial differential equation solutions. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Applies MCTS to symbolic PDE discovery, not LLM agent planning or tool orchestration.
The Polytopal Neural Network Introduces a neural network architecture enforcing layer-wise polytopal constraints to improve latent representation interpretability without degrading task performance. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Addresses model interpretability methods rather than LLM benchmarking or evaluation frameworks.
The Polytopal Neural Network Introduces a neural network architecture enforcing layer-wise polytopal constraints to improve latent representation interpretability without degrading task performance. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Studies internal representation geometry rather than deployment safety, guardrails, or jailbreaks.
The Polytopal Neural Network Introduces a neural network architecture enforcing layer-wise polytopal constraints to improve latent representation interpretability without degrading task performance. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on neural network interpretability and latent geometric representations, not AI agents.
REACT: Rolling Denoising and Dual Decoupling for Reactive Robot Control with VLA Models Proposes a rolling-denoising framework for vision-language-action models to improve robot control reactivity without sacrificing long-horizon execution consistency. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Focuses on robotic manipulation and action control, not industrial vision or visual quality control.
REACT: Rolling Denoising and Dual Decoupling for Reactive Robot Control with VLA Models Proposes a rolling-denoising framework for vision-language-action models to improve robot control reactivity without sacrificing long-horizon execution consistency. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Addresses real-time robotic latency and reactivity rather than AI safety or alignment risks.
REACT: Rolling Denoising and Dual Decoupling for Reactive Robot Control with VLA Models Proposes a rolling-denoising framework for vision-language-action models to improve robot control reactivity without sacrificing long-horizon execution consistency. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Introduces a control framework rather than benchmark evaluation of language models.
Complexity of Grounded Semantics and Preferred Semantics in Finitary Argumentation Frameworks Analyzes the computational complexity of grounded and preferred semantics decision problems within computably finitary abstract argumentation frameworks. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Pure theoretical formal logic paper without direct application to AI safety guardrails or deployment risks.
Complexity of Grounded Semantics and Preferred Semantics in Finitary Argumentation Frameworks Analyzes the computational complexity of grounded and preferred semantics decision problems within computably finitary abstract argumentation frameworks. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Theoretical analysis of abstract argumentation complexity, not LLM agents or tool-using systems.
Complexity of Grounded Semantics and Preferred Semantics in Finitary Argumentation Frameworks Analyzes the computational complexity of grounded and preferred semantics decision problems within computably finitary abstract argumentation frameworks. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Investigates formal computational complexity rather than empirical LLM evaluation methods.
PulseBound: Future-Beat State Forecasting Under an Explicit Information Boundary Develops a representation learning approach for photoplethysmography data that enforces strict causal information boundaries during future-beat forecasting. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Focuses on physiological time-series forecasting, not language model evaluation or benchmarks.
PulseBound: Future-Beat State Forecasting Under an Explicit Information Boundary Develops a representation learning approach for photoplethysmography data that enforces strict causal information boundaries during future-beat forecasting. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Addresses causal data leakage in biomedical signal representation rather than deployed AI safety risks.
PulseBound: Future-Beat State Forecasting Under an Explicit Information Boundary Develops a representation learning approach for photoplethysmography data that enforces strict causal information boundaries during future-beat forecasting. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Proposes a physiological forecasting architecture for PPG signals, unrelated to LLM agents.
Examining Social Attribution in LLM Reasoning: A Theory-Guided Probing Methodology Proposes a benchmark and probing methodology based on attribution theory to examine how language models assign responsibility and blame. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Does not address model efficiency, distillation, or quantization.
Examining Social Attribution in LLM Reasoning: A Theory-Guided Probing Methodology Proposes a benchmark and probing methodology based on attribution theory to examine how language models assign responsibility and blame. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Does not involve retrieval mechanisms or grounding on external databases.
Examining Social Attribution in LLM Reasoning: A Theory-Guided Probing Methodology Proposes a benchmark and probing methodology based on attribution theory to examine how language models assign responsibility and blame. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Focuses on text-based social reasoning rather than multimodal processing.
Examining Social Attribution in LLM Reasoning: A Theory-Guided Probing Methodology Proposes a benchmark and probing methodology based on attribution theory to examine how language models assign responsibility and blame. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Probes social reasoning in LLMs, but does not study autonomous agents, tool use, or multi-agent workflows.
Examining Social Attribution in LLM Reasoning: A Theory-Guided Probing Methodology Proposes a benchmark and probing methodology based on attribution theory to examine how language models assign responsibility and blame. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Studies social psychology attribution rather than software engineering or code generation.
InterviewPlayground: A Validated Simulation Environment for Evaluating AI Interviewers Presents a simulated participant environment to evaluate multi-turn AI interviewers across market research and social science scenarios. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on measurement of interview elicitation quality rather than safety, jailbreaks, or guardrails.
InterviewPlayground: A Validated Simulation Environment for Evaluating AI Interviewers Presents a simulated participant environment to evaluate multi-turn AI interviewers across market research and social science scenarios. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Does not investigate retrieval systems, chunking, or external document grounding.
InterviewPlayground: A Validated Simulation Environment for Evaluating AI Interviewers Presents a simulated participant environment to evaluate multi-turn AI interviewers across market research and social science scenarios. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Focuses on conversational interviewing for social research, not code generation or software engineering.
InterviewPlayground: A Validated Simulation Environment for Evaluating AI Interviewers Presents a simulated participant environment to evaluate multi-turn AI interviewers across market research and social science scenarios. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Focuses on conversational text-based interview interactions, not multimodal inputs.
InterviewPlayground: A Validated Simulation Environment for Evaluating AI Interviewers Presents a simulated participant environment to evaluate multi-turn AI interviewers across market research and social science scenarios. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Does not explore model compression, quantization, distillation, or serving efficiency.
Humanoid World Action Model With Joint State--Action Generation Introduces a world action model for humanoid robots that jointly models robot internal states and scene evolution to close the execution gap. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on humanoid low-level control and proprioceptive action generation, not conversational LLM agents.
Humanoid World Action Model With Joint State--Action Generation Introduces a world action model for humanoid robots that jointly models robot internal states and scene evolution to close the execution gap. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Addresses physical control discrepancies in robotics rather than AI safety or guardrails.
Humanoid World Action Model With Joint State--Action Generation Introduces a world action model for humanoid robots that jointly models robot internal states and scene evolution to close the execution gap. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Proposes a world action model architecture rather than LLM benchmarking or evaluation frameworks.
Humanoid World Action Model With Joint State--Action Generation Introduces a world action model for humanoid robots that jointly models robot internal states and scene evolution to close the execution gap. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Targets humanoid whole-body control and physical manipulation, not industrial inspection or defect detection.
Traceable World State: A Provenance-Aware State Representation and Deterministic Replay Framework for Robotic Systems Introduces an immutable, provenance-aware state representation framework with deterministic replay and audit logging for robotic systems. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Introduces a robotics runtime architecture rather than an LLM evaluation benchmark.
Traceable World State: A Provenance-Aware State Representation and Deterministic Replay Framework for Robotic Systems Introduces an immutable, provenance-aware state representation framework with deterministic replay and audit logging for robotic systems. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Focuses on state provenance representation and replay runtime rather than industrial computer vision.
Traceable World State: A Provenance-Aware State Representation and Deterministic Replay Framework for Robotic Systems Introduces an immutable, provenance-aware state representation framework with deterministic replay and audit logging for robotic systems. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Targets robotic state management and auditing, not LLM code generation or software engineering agents.
MPGE: A Multi-Perspective Graph Explainer for Molecular Classification Explanation Presents an explainer for molecular graph neural networks combining factual, counterfactual, and exemplar graph perturbations. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Addresses graph explainability techniques rather than language model evaluation or benchmarks.
MPGE: A Multi-Perspective Graph Explainer for Molecular Classification Explanation Presents an explainer for molecular graph neural networks combining factual, counterfactual, and exemplar graph perturbations. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on GNN explainability for molecular classification, not LLM agents or tool orchestration.
MPGE: A Multi-Perspective Graph Explainer for Molecular Classification Explanation Presents an explainer for molecular graph neural networks combining factual, counterfactual, and exemplar graph perturbations. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on local post-hoc explanations for molecular GNNs, not deployed AI safety or robustness.
MPGE: A Multi-Perspective Graph Explainer for Molecular Classification Explanation Presents an explainer for molecular graph neural networks combining factual, counterfactual, and exemplar graph perturbations. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Does not address model compression, distillation, quantization, or efficient serving.

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