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
Euler: Towards Human-Centered Multi-Agent Mathematical Discovery This paper introduces a multi-agent mathematical discovery system that organizes proofs, assumptions, and failed attempts into structured research graphs. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Targets research discovery workflows rather than deployment safety or alignment.
Explorer-Zero: Improving Self-Play for Long-Horizon Tasks with Decomposable Verifiable Rewards This paper proposes a self-play reinforcement learning framework that automatically generates tasks and intermediate verifiable rewards for long-horizon agent training. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on self-play training, not model quantization, distillation, or lightweight serving.
Explorer-Zero: Improving Self-Play for Long-Horizon Tasks with Decomposable Verifiable Rewards This paper proposes a self-play reinforcement learning framework that automatically generates tasks and intermediate verifiable rewards for long-horizon agent training. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Proposes an RL training technique, not an LLM evaluation benchmark.
Explorer-Zero: Improving Self-Play for Long-Horizon Tasks with Decomposable Verifiable Rewards This paper proposes a self-play reinforcement learning framework that automatically generates tasks and intermediate verifiable rewards for long-horizon agent training. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on agent task capability rather than safety or guardrails.
Rationale Distribution Distillation via Flow Matching for Distributional Continuous Latent Reasoning This paper presents a flow matching method to distill diverse continuous latent reasoning distributions from multiple chain-of-thought rationales. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Does not cover external knowledge retrieval or grounding.
Rationale Distribution Distillation via Flow Matching for Distributional Continuous Latent Reasoning This paper presents a flow matching method to distill diverse continuous latent reasoning distributions from multiple chain-of-thought rationales. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Studies reasoning efficiency and coverage rather than AI safety or guardrails.
Rationale Distribution Distillation via Flow Matching for Distributional Continuous Latent Reasoning This paper presents a flow matching method to distill diverse continuous latent reasoning distributions from multiple chain-of-thought rationales. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Focuses on textual and latent reasoning paths, not multimodal perception.
Rationale Distribution Distillation via Flow Matching for Distributional Continuous Latent Reasoning This paper presents a flow matching method to distill diverse continuous latent reasoning distributions from multiple chain-of-thought rationales. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on latent reasoning distillation rather than autonomous agent tool use.
Rationale Distribution Distillation via Flow Matching for Distributional Continuous Latent Reasoning This paper presents a flow matching method to distill diverse continuous latent reasoning distributions from multiple chain-of-thought rationales. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Introduces a distillation method rather than an evaluation benchmark.
Rationale Distribution Distillation via Flow Matching for Distributional Continuous Latent Reasoning This paper presents a flow matching method to distill diverse continuous latent reasoning distributions from multiple chain-of-thought rationales. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Targets mathematical reasoning distillation, not software engineering or code LLMs.
Neural Wasserstein Splines: Representation-Invariant Second-Order Priors for Trajectory Inference The paper presents a continuous trajectory inference method that regularizes population dynamics using neural splines and Wasserstein smoothness. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Develops a mathematical inference algorithm, not an evaluation method.
Neural Wasserstein Splines: Representation-Invariant Second-Order Priors for Trajectory Inference The paper presents a continuous trajectory inference method that regularizes population dynamics using neural splines and Wasserstein smoothness. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on dynamic optimal transport splines rather than efficient model serving.
Neural Wasserstein Splines: Representation-Invariant Second-Order Priors for Trajectory Inference The paper presents a continuous trajectory inference method that regularizes population dynamics using neural splines and Wasserstein smoothness. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Does not cover AI safety, guardrails, or alignment.
Neural Wasserstein Splines: Representation-Invariant Second-Order Priors for Trajectory Inference The paper presents a continuous trajectory inference method that regularizes population dynamics using neural splines and Wasserstein smoothness. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Addresses continuous mathematical trajectory modeling on populations, not AI agents.
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	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on reasoning accuracy via voting rather than safety or reliability risks.
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	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Presents an inference aggregation algorithm rather than an evaluation benchmark.
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	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Applies exclusively to text-based reasoning models, not multimodal inputs.
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	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on test-time aggregation of chain-of-thought samples, not agent tool execution.
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	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Does not involve external document retrieval or grounding.
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	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Evaluates an MRI reconstruction model, not language model benchmarking.
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.
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	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	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on representation learning, not LLM evaluation or 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	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.
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.
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	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Describes a competition submission architecture rather than general LLM evaluation methodology.
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.
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.
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	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	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on physical robot manipulation control rather than language model agents.
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.
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	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	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	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on translation bias retention, not LLM evaluation benchmarks or methodologies.
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	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Analyzes natural language machine translation, not code synthesis or repair.
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	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 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	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	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Analyzes backdoor stealthiness metrics in vision, not general LLM evaluation methods.
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.
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	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	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	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	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	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	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	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	AI agents via arxiv · 15 Oct, 15:06	Not relevant Addresses federated learning aggregation rather than multi-step agents.

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