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
How Is Automated Research Evaluated? A Survey of Benchmarks and Evaluation Practices Surveys benchmarks, metrics, and experimental practices used to evaluate automated research systems across ideation, coding, writing, and peer review. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on research benchmark design rather than deployment safety, security, or alignment.
Evaluating Exact Output and Checkpoint-State Prediction in Real Programs Evaluates language models on predicting program outputs and intermediate loop checkpoint states directly from source code and inputs. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Evaluates code tracing accuracy rather than safety risks, jailbreaks, or guardrails.
Evaluating Exact Output and Checkpoint-State Prediction in Real Programs Evaluates language models on predicting program outputs and intermediate loop checkpoint states directly from source code and inputs. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Measures pure code-execution prediction without tools or multi-step agent actions.
Forms of LLM-Integrated Applications from LLM-Chats to Autonomous AI Agent System Surveys and taxonomizes recurring architectural patterns across language-model-integrated software applications, ranging from chatbots to autonomous agent systems. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Taxonomy survey of application architectures, not a benchmark or evaluation methodology paper.
Forms of LLM-Integrated Applications from LLM-Chats to Autonomous AI Agent System Surveys and taxonomizes recurring architectural patterns across language-model-integrated software applications, ranging from chatbots to autonomous agent systems. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Analyzes text and tool architectures rather than multimodal vision-language representations.
Forms of LLM-Integrated Applications from LLM-Chats to Autonomous AI Agent System Surveys and taxonomizes recurring architectural patterns across language-model-integrated software applications, ranging from chatbots to autonomous agent systems. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Surveys system integration patterns rather than safety guardrails, attacks, or alignment failures.
Forms of LLM-Integrated Applications from LLM-Chats to Autonomous AI Agent System Surveys and taxonomizes recurring architectural patterns across language-model-integrated software applications, ranging from chatbots to autonomous agent systems. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Focuses on architectural patterns across systems, not model compression, distillation, or quantization.
Beyond Visual Enhancement: Adaptive Multi-Context Steering to Mitigate LVLM Hallucinations Proposes an adaptive multi-source steering decoding method to mitigate hallucinations in vision-language models by balancing visual and textual context contributions. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Addresses general LVLM hallucinations without focusing on industrial inspection or factory vision systems.
Beyond Visual Enhancement: Adaptive Multi-Context Steering to Mitigate LVLM Hallucinations Proposes an adaptive multi-source steering decoding method to mitigate hallucinations in vision-language models by balancing visual and textual context contributions. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Proposes an inference-time steering method rather than introducing LLM evaluation benchmarks or protocols.
Beyond Visual Enhancement: Adaptive Multi-Context Steering to Mitigate LVLM Hallucinations Proposes an adaptive multi-source steering decoding method to mitigate hallucinations in vision-language models by balancing visual and textual context contributions. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on multimodal decoding steering rather than agent planning, tool execution, or agentic workflows.
From Surface to Depth: Towards Cognitive Appraisal Reasoning in Multimodal Emotion Understanding Introduces a dataset and benchmark to model multimodal emotion understanding through cognitive appraisal reasoning rather than superficial affective cues. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Proposes an emotion-understanding model and dataset rather than multi-step autonomous planning agents.
From Surface to Depth: Towards Cognitive Appraisal Reasoning in Multimodal Emotion Understanding Introduces a dataset and benchmark to model multimodal emotion understanding through cognitive appraisal reasoning rather than superficial affective cues. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Deals with emotion understanding rather than deployment safety, jailbreaking, or red-teaming.
Neural Decoding as Cognitive Inference Formulates neural decoding as Bayesian cognitive inference to extract stable meta-neural semantic representations across varying neural recording modalities. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Examines cognitive inference in brain data, unrelated to AI safety, guardrails, or model hallucinations.
Neural Decoding as Cognitive Inference Formulates neural decoding as Bayesian cognitive inference to extract stable meta-neural semantic representations across varying neural recording modalities. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Presents biological neural decoding experiments, not benchmarks or evaluations for large language models.
Neural Decoding as Cognitive Inference Formulates neural decoding as Bayesian cognitive inference to extract stable meta-neural semantic representations across varying neural recording modalities. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on computational neuroscience and biological neural decoding, not LLM agents or tool use.
EliteTable: Self-Improving Program Search for Executable Control Policies Presents a program-search method using a curated elite solution table to guide language models in synthesizing executable control policies. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Studies program search performance in control tasks, not AI safety, alignment, or deployment reliability.
EliteTable: Self-Improving Program Search for Executable Control Policies Presents a program-search method using a curated elite solution table to guide language models in synthesizing executable control policies. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Evaluates a search algorithm on standard control tasks rather than contributing new LLM benchmark methodology.
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.
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	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Introduces a statistical uncertainty quantification method, not an LLM benchmark or evaluation protocol.
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	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Does not address model compression, small language models, or efficient serving.
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	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	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	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Focuses on dynamic reasoning workflows rather than retrieval-augmented generation systems.
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.
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	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	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	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	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on formal verification of neural decoders in wireless transmission, not AI agents.
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.
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.
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	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	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	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Studies general test-time training policies, not code generation or software engineering tasks.
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.
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 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.
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	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.
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.
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.
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.
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.
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.
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	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Studies social psychology attribution rather than software engineering or code generation.

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