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
Frustratingly Easy Majority Voting Using Length Conditioning The paper proposes weighting majority voting responses using a length-conditioned prior adapted at test time without model fine-tuning. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Evaluates general reasoning tasks, not code generation or software engineering.
DeepPV-MRF: End-to-End Partial-Volume Fraction Estimation from MR Fingerprinting Time Series The paper introduces a deep learning framework to estimate voxel-wise brain tissue fractions directly from magnetic resonance fingerprinting signals. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Covers magnetic resonance imaging signal processing, completely unrelated to agents.
DeepPV-MRF: End-to-End Partial-Volume Fraction Estimation from MR Fingerprinting Time Series The paper introduces a deep learning framework to estimate voxel-wise brain tissue fractions directly from magnetic resonance fingerprinting signals. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Does not address AI safety, jailbreaks, or deployment guardrails.
DeepPV-MRF: End-to-End Partial-Volume Fraction Estimation from MR Fingerprinting Time Series The paper introduces a deep learning framework to estimate voxel-wise brain tissue fractions directly from magnetic resonance fingerprinting signals. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Evaluates an MRI reconstruction model, not language model benchmarking.
Universal Sample-Based Embeddings of Higher-Order Objects The paper proposes a sample-based neural framework to learn fixed-size embedding representations for higher-order objects like distributions and graphs. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Presents mathematical embedding methods for higher-order mathematical objects, not agents.
Universal Sample-Based Embeddings of Higher-Order Objects The paper proposes a sample-based neural framework to learn fixed-size embedding representations for higher-order objects like distributions and graphs. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Deals with higher-order object embeddings rather than LLM compression or efficient serving.
Universal Sample-Based Embeddings of Higher-Order Objects The paper proposes a sample-based neural framework to learn fixed-size embedding representations for higher-order objects like distributions and graphs. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Does not study model safety, robustness, hallucination, or deployment risks.
Universal Sample-Based Embeddings of Higher-Order Objects The paper proposes a sample-based neural framework to learn fixed-size embedding representations for higher-order objects like distributions and graphs. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on representation learning, not LLM evaluation or benchmarking.
Multimodal Medical Image Parsing with Task-Specific Low-Rank Adapters The paper describes a vision-language model using task-specific low-rank adapters for multimodal medical image parsing. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Describes a competition submission architecture rather than general LLM evaluation methodology.
Multimodal Medical Image Parsing with Task-Specific Low-Rank Adapters The paper describes a vision-language model using task-specific low-rank adapters for multimodal medical image parsing. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on medical image parsing performance, not deployment safety or robustness.
Multimodal Medical Image Parsing with Task-Specific Low-Rank Adapters The paper describes a vision-language model using task-specific low-rank adapters for multimodal medical image parsing. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Applies standard 4-bit LoRA without developing novel efficient serving or compression methods.
Multimodal Medical Image Parsing with Task-Specific Low-Rank Adapters The paper describes a vision-language model using task-specific low-rank adapters for multimodal medical image parsing. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Adapts vision-language models for medical images, not agentic planning or tools.
Multimodal Medical Image Parsing with Task-Specific Low-Rank Adapters The paper describes a vision-language model using task-specific low-rank adapters for multimodal medical image parsing. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Focuses on medical imaging rather than manufacturing or industrial defect inspection.
Sparse Transformer: a Controlled Testbed for Mechanistic Interpretability The paper introduces a sparse transformer testbed with interpretable neurons to evaluate mechanistic interpretability methods. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on internal model interpretability rather than deployment safety, guardrails, or hallucinations.
Sparse Transformer: a Controlled Testbed for Mechanistic Interpretability The paper introduces a sparse transformer testbed with interpretable neurons to evaluate mechanistic interpretability methods. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Investigates mechanistic interpretability in small transformers, not agent systems.
MUCO: A Mani-Unit-Structured Manipulation Framework for Compositional Long-Horizon Robot Tasks The paper proposes a modular manipulation framework that sequences verified robot capability units for long-horizon tasks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Proposes a robot manipulation framework rather than an evaluation benchmark.
MUCO: A Mani-Unit-Structured Manipulation Framework for Compositional Long-Horizon Robot Tasks The paper proposes a modular manipulation framework that sequences verified robot capability units for long-horizon tasks. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Addresses general robotic manipulation, which is excluded from industrial inspection focus.
MUCO: A Mani-Unit-Structured Manipulation Framework for Compositional Long-Horizon Robot Tasks The paper proposes a modular manipulation framework that sequences verified robot capability units for long-horizon tasks. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Addresses robotics task execution rather than AI safety guardrails or reliability.
MUCO: A Mani-Unit-Structured Manipulation Framework for Compositional Long-Horizon Robot Tasks The paper proposes a modular manipulation framework that sequences verified robot capability units for long-horizon tasks. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on physical robot manipulation control rather than language model agents.
Does Machine Translation Retain Media Bias? The paper investigates whether machine translation systems preserve or neutralize media bias across languages. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Studies media bias in machine translation, not LLM agent workflows.
Does Machine Translation Retain Media Bias? The paper investigates whether machine translation systems preserve or neutralize media bias across languages. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Does not involve retrieval-augmented generation or external knowledge retrieval.
Does Machine Translation Retain Media Bias? The paper investigates whether machine translation systems preserve or neutralize media bias across languages. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Analyzes natural language machine translation, not code synthesis or repair.
Does Machine Translation Retain Media Bias? The paper investigates whether machine translation systems preserve or neutralize media bias across languages. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Covers text-only machine translation without any multimodal data.
Does Machine Translation Retain Media Bias? The paper investigates whether machine translation systems preserve or neutralize media bias across languages. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not explore small models, quantization, or efficient model architectures.
Does Machine Translation Retain Media Bias? The paper investigates whether machine translation systems preserve or neutralize media bias across languages. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on translation bias retention, not LLM evaluation benchmarks or methodologies.
Learning Graph Energies for Finite-Time Generation The paper proposes an energy-based learning method that optimizes acceptance rates for finite-step discrete graph sampling. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Proposes a generation technique rather than an evaluation benchmark or judge framework.
Learning Graph Energies for Finite-Time Generation The paper proposes an energy-based learning method that optimizes acceptance rates for finite-step discrete graph sampling. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on molecular graph generation, not agent planning or tool orchestration.
Learning Graph Energies for Finite-Time Generation The paper proposes an energy-based learning method that optimizes acceptance rates for finite-step discrete graph sampling. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Deals with energy-based sampling algorithms, not model compression or efficient LLM serving.
Learning Graph Energies for Finite-Time Generation The paper proposes an energy-based learning method that optimizes acceptance rates for finite-step discrete graph sampling. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Does not address AI safety, jailbreaks, hallucinations, or deployment guardrails.
Quiet Triggers, Loud Footprints: A Tri-Space Measurement Study of Backdoor Stealthiness The paper presents an empirical framework measuring neural network backdoor stealthiness across input, feature, and parameter spaces. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on neural network backdoor stealthiness, not LLM agents or tool use.
Quiet Triggers, Loud Footprints: A Tri-Space Measurement Study of Backdoor Stealthiness The paper presents an empirical framework measuring neural network backdoor stealthiness across input, feature, and parameter spaces. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Analyzes backdoor stealthiness metrics in vision, not general LLM evaluation methods.
When Protection Overrides Consequences: Understanding Protection Mode in Large Language Model Decision-Making The paper introduces a benchmark to study why frontier models prioritize protecting subjects over preventing larger physical property damage. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Examines prompt-based decision preferences rather than multi-step agent planning or tool use.
AtomSearch: Evidence-Grounded Tree Search for Agent Skill Optimization The paper proposes a tree search method that optimizes agent skills through contrastive atomic rule editing and execution memory. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Main contribution is an optimization framework, not a benchmark or evaluation method.
AtomSearch: Evidence-Grounded Tree Search for Agent Skill Optimization The paper proposes a tree search method that optimizes agent skills through contrastive atomic rule editing and execution memory. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Evaluates general task benchmarks, not programming or software engineering workflows.
AtomSearch: Evidence-Grounded Tree Search for Agent Skill Optimization The paper proposes a tree search method that optimizes agent skills through contrastive atomic rule editing and execution memory. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not study model compression, quantization, distillation, or efficient serving.
AtomSearch: Evidence-Grounded Tree Search for Agent Skill Optimization The paper proposes a tree search method that optimizes agent skills through contrastive atomic rule editing and execution memory. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Does not address vision, audio, or multimodal representations.
AtomSearch: Evidence-Grounded Tree Search for Agent Skill Optimization The paper proposes a tree search method that optimizes agent skills through contrastive atomic rule editing and execution memory. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Focuses on agent skill optimization rather than retrieval-augmented document generation.
AtomSearch: Evidence-Grounded Tree Search for Agent Skill Optimization The paper proposes a tree search method that optimizes agent skills through contrastive atomic rule editing and execution memory. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on execution accuracy and skill optimization rather than safety or alignment.
Neurosymbolic Learning of Interpretable World Models The paper introduces a neurosymbolic world model that learns interpretable concepts from temporal logic constraints for agent planning. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on learning world representations rather than LLM benchmarks.
Safe-FCL: Continual Risk Awareness and Fine-Grained Safety Aggregation for Federated Continual Learning of LLMs The paper proposes a federated learning framework with risk-aware aggregation to prevent malicious updates from compromising global model safety. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Addresses federated learning aggregation rather than multi-step agents.
Safe-FCL: Continual Risk Awareness and Fine-Grained Safety Aggregation for Federated Continual Learning of LLMs The paper proposes a federated learning framework with risk-aware aggregation to prevent malicious updates from compromising global model safety. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Proposes a defense framework rather than LLM evaluation benchmarks.
Safe-FCL: Continual Risk Awareness and Fine-Grained Safety Aggregation for Federated Continual Learning of LLMs The paper proposes a federated learning framework with risk-aware aggregation to prevent malicious updates from compromising global model safety. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on federated safety rather than model efficiency or pruning.
Empirical Boundaries of Reservoir Computing for Few-Shot Sequence Pattern Tasks The paper empirically analyzes echo state network variants on synthetic sequence-to-sequence pattern tasks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Studies recurrent neural net limits rather than LLM benchmarks.
Empirical Boundaries of Reservoir Computing for Few-Shot Sequence Pattern Tasks The paper empirically analyzes echo state network variants on synthetic sequence-to-sequence pattern tasks. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on synthetic sequence limits, not safety or guardrails.
Empirical Boundaries of Reservoir Computing for Few-Shot Sequence Pattern Tasks The paper empirically analyzes echo state network variants on synthetic sequence-to-sequence pattern tasks. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Evaluates Echo State Networks on synthetic sequences, not agents.
A Strong Harness Narrows the Model Gap: A Comparative Evaluation for Legislative Document-to-XML Conversion The paper evaluates harnesses for legislative document conversion, showing structured multi-agent pipelines narrow performance gaps between models. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Processes legislative text documents without image, audio, or video modalities.
A Strong Harness Narrows the Model Gap: A Comparative Evaluation for Legislative Document-to-XML Conversion The paper evaluates harnesses for legislative document conversion, showing structured multi-agent pipelines narrow performance gaps between models. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on agent harness orchestration rather than model compression.
A Strong Harness Narrows the Model Gap: A Comparative Evaluation for Legislative Document-to-XML Conversion The paper evaluates harnesses for legislative document conversion, showing structured multi-agent pipelines narrow performance gaps between models. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Evaluates harness structuring for XML parsing, not retrieval systems.
A Strong Harness Narrows the Model Gap: A Comparative Evaluation for Legislative Document-to-XML Conversion The paper evaluates harnesses for legislative document conversion, showing structured multi-agent pipelines narrow performance gaps between models. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Applies harnesses to document-to-XML conversion, not software engineering.
What Coding Agents Leave Unsaid: Measuring Task-State Gaps Between Execution and Reports The paper measures the information gap between coding agents' internal execution logs and their user-facing progress reports. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on agent oversight gaps rather than general benchmarking.
Provable Transformer Context Compression for In-Context Reinforcement Learning The paper analyzes the minimum context size required for transformer models to perform in-context reinforcement learning without parameter updates. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Theoretical context compression in RL, not multi-step agent systems.
Provable Transformer Context Compression for In-Context Reinforcement Learning The paper analyzes the minimum context size required for transformer models to perform in-context reinforcement learning without parameter updates. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on theoretical bounds rather than benchmark evaluation.
Provable Transformer Context Compression for In-Context Reinforcement Learning The paper analyzes the minimum context size required for transformer models to perform in-context reinforcement learning without parameter updates. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Theoretical memory scaling analysis, not deployment safety or robustness.
On the Convergence of Semi-Gradient TD: A Mean Field Perspective The paper provides a theoretical analysis of stability and convergence for semi-gradient temporal-difference learning using mean-field neural networks. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Addresses mathematical stability in RL, not AI safety deployment.
On the Convergence of Semi-Gradient TD: A Mean Field Perspective The paper provides a theoretical analysis of stability and convergence for semi-gradient temporal-difference learning using mean-field neural networks. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Theoretical analysis of reinforcement learning convergence, not agents.
On the Convergence of Semi-Gradient TD: A Mean Field Perspective The paper provides a theoretical analysis of stability and convergence for semi-gradient temporal-difference learning using mean-field neural networks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Mathematical proof paper rather than LLM benchmarking.
On the Convergence of Semi-Gradient TD: A Mean Field Perspective The paper provides a theoretical analysis of stability and convergence for semi-gradient temporal-difference learning using mean-field neural networks. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on RL convergence theory rather than efficient inference.
MorphAct: Robot Policies Morphing with the Worlds They Act In The paper proposes a contextual parameterization framework that dynamically adjusts robot policy parameters as interaction contexts change. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Focuses on general robot manipulation rather than industrial quality control.
MorphAct: Robot Policies Morphing with the Worlds They Act In The paper proposes a contextual parameterization framework that dynamically adjusts robot policy parameters as interaction contexts change. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Addresses policy generalization rather than safety guardrails or alignment.
MorphAct: Robot Policies Morphing with the Worlds They Act In The paper proposes a contextual parameterization framework that dynamically adjusts robot policy parameters as interaction contexts change. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Proposes a robotics parameterization framework rather than a benchmark.

4,321–4,380 of 13,754← Previous Next →