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
BAT3R: Robust Online 3D Reconstruction with Bayesian Adaptive State Updates Introduces a training-free Bayesian state-update framework to reduce error accumulation and representational drift in streaming 3D reconstruction models. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on geometric 3D reconstruction rather than multimodal vision-language models.
WorldRoPE: Probabilistic World-Centric Rotary Embedding for Multi-View Attention Proposes a world-centric rotary positional embedding using 3D Gaussian primitives to improve cross-view attention in multi-view vision tasks. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on geometric representations rather than AI safety guardrails.
WorldRoPE: Probabilistic World-Centric Rotary Embedding for Multi-View Attention Proposes a world-centric rotary positional embedding using 3D Gaussian primitives to improve cross-view attention in multi-view vision tasks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Introduces a positional embedding method, not an evaluation benchmark.
WorldRoPE: Probabilistic World-Centric Rotary Embedding for Multi-View Attention Proposes a world-centric rotary positional embedding using 3D Gaussian primitives to improve cross-view attention in multi-view vision tasks. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Proposes a 3D attention mechanism, not autonomous planning agents.
WorldRoPE: Probabilistic World-Centric Rotary Embedding for Multi-View Attention Proposes a world-centric rotary positional embedding using 3D Gaussian primitives to improve cross-view attention in multi-view vision tasks. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Addresses multi-view 3D attention, not industrial defect inspection.
Efficient Multi-Agent Reasoning via Confidence-Guided Adaptive Debate Introduces an adaptive multi-agent debate framework that uses a leader-supporter structure to trigger debate only when simple aggregation is insufficient. Peer-reviewed	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Introduces a multi-agent debate architecture rather than an evaluation benchmark.
Efficient Multi-Agent Reasoning via Confidence-Guided Adaptive Debate Introduces an adaptive multi-agent debate framework that uses a leader-supporter structure to trigger debate only when simple aggregation is insufficient. Peer-reviewed	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on reasoning efficiency rather than deployment safety or guardrails.
ChronoScribe: Semantic-Numerical Specialization for Instruction-Driven Time Series Forecasting Presents an architecture separating natural language interpretation from numerical time series foundation models for instruction-driven forecasting. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses forecasting accuracy rather than deployment safety or jailbreak risks.
ChronoScribe: Semantic-Numerical Specialization for Instruction-Driven Time Series Forecasting Presents an architecture separating natural language interpretation from numerical time series foundation models for instruction-driven forecasting. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Evaluates a forecasting system rather than introducing an LLM evaluation benchmark.
ChronoScribe: Semantic-Numerical Specialization for Instruction-Driven Time Series Forecasting Presents an architecture separating natural language interpretation from numerical time series foundation models for instruction-driven forecasting. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Proposes a modular pipeline for forecasting, not autonomous agentic systems.
ChronoScribe: Semantic-Numerical Specialization for Instruction-Driven Time Series Forecasting Presents an architecture separating natural language interpretation from numerical time series foundation models for instruction-driven forecasting. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on hybrid model specialization rather than quantization or distillation.
Rescuing Points Trapped Inside Incorrect Decision Regions: A target for Evaluating and Improving Forward-Forward Learning Introduces a margin-based analysis metric to evaluate class boundary representations in forward-forward learning algorithms. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on representation margins rather than deployment safety mechanisms.
Rescuing Points Trapped Inside Incorrect Decision Regions: A target for Evaluating and Improving Forward-Forward Learning Introduces a margin-based analysis metric to evaluate class boundary representations in forward-forward learning algorithms. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Evaluates Forward-Forward training dynamics rather than inference compression methods.
Rescuing Points Trapped Inside Incorrect Decision Regions: A target for Evaluating and Improving Forward-Forward Learning Introduces a margin-based analysis metric to evaluate class boundary representations in forward-forward learning algorithms. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies layer-wise neural network learning algorithms, not autonomous agents.
Rescuing Points Trapped Inside Incorrect Decision Regions: A target for Evaluating and Improving Forward-Forward Learning Introduces a margin-based analysis metric to evaluate class boundary representations in forward-forward learning algorithms. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Analyzes layer-wise learning representations, not language model evaluation benchmarks.
DWI Connectome-based Sex Classification in Young using Supervised Variational Autoencoder Applies a supervised variational autoencoder to brain connectome data for sex classification in young adults. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Investigates brain connectome classification, not LLM evaluation benchmarks.
DWI Connectome-based Sex Classification in Young using Supervised Variational Autoencoder Applies a supervised variational autoencoder to brain connectome data for sex classification in young adults. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses neuroimaging analysis rather than AI safety or guardrails.
DWI Connectome-based Sex Classification in Young using Supervised Variational Autoencoder Applies a supervised variational autoencoder to brain connectome data for sex classification in young adults. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on neuroimaging classification, not language model agents.
DWI Connectome-based Sex Classification in Young using Supervised Variational Autoencoder Applies a supervised variational autoencoder to brain connectome data for sex classification in young adults. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on medical imaging classification rather than efficient model serving.
World Models Need a Physical Registry: Learning Internal Predictive States for Physically Plausible World Models Introduces internal predictive registry slots into diffusion transformers to explicitly track physical dynamics in world models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes a video architecture rather than an LLM benchmark.
World Models Need a Physical Registry: Learning Internal Predictive States for Physically Plausible World Models Introduces internal predictive registry slots into diffusion transformers to explicitly track physical dynamics in world models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Concerns video physical realism rather than deployment safety guardrails.
World Models Need a Physical Registry: Learning Internal Predictive States for Physically Plausible World Models Introduces internal predictive registry slots into diffusion transformers to explicitly track physical dynamics in world models. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on video generation world models, not task-executing agents.
LoopQuant: Accurate and Efficient Post-Training Quantization for Looped Transformers Presents a loop-aware post-training quantization method to compress looped language models without severe accuracy loss. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Concerns low-bit quantization rather than AI safety or alignment risks.
LoopQuant: Accurate and Efficient Post-Training Quantization for Looped Transformers Presents a loop-aware post-training quantization method to compress looped language models without severe accuracy loss. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on model quantization, not autonomous planning agents.
LoopQuant: Accurate and Efficient Post-Training Quantization for Looped Transformers Presents a loop-aware post-training quantization method to compress looped language models without severe accuracy loss. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents a quantization technique rather than an evaluation benchmark.
RetroPlanner: Multi-Route Expansion and Multi-Objective Reasoning for LLM-Based Retrosynthesis Planning Proposes a multi-route retrosynthesis planner that evaluates synthesizability and cost across chemical reaction options. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Applies to chemistry synthesis planning, not software code generation.
RetroPlanner: Multi-Route Expansion and Multi-Objective Reasoning for LLM-Based Retrosynthesis Planning Proposes a multi-route retrosynthesis planner that evaluates synthesizability and cost across chemical reaction options. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Does not address deployment safety, guardrails, or alignment risks.
RetroPlanner: Multi-Route Expansion and Multi-Objective Reasoning for LLM-Based Retrosynthesis Planning Proposes a multi-route retrosynthesis planner that evaluates synthesizability and cost across chemical reaction options. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Addresses chemical planning strategy rather than model compression or efficiency.
RetroPlanner: Multi-Route Expansion and Multi-Objective Reasoning for LLM-Based Retrosynthesis Planning Proposes a multi-route retrosynthesis planner that evaluates synthesizability and cost across chemical reaction options. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Operates over text and molecular graphs, not multimodal vision-language models.
RetroPlanner: Multi-Route Expansion and Multi-Objective Reasoning for LLM-Based Retrosynthesis Planning Proposes a multi-route retrosynthesis planner that evaluates synthesizability and cost across chemical reaction options. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focuses on retrosynthesis planning rather than retrieval-augmented generation architectures.
RetroPlanner: Multi-Route Expansion and Multi-Objective Reasoning for LLM-Based Retrosynthesis Planning Proposes a multi-route retrosynthesis planner that evaluates synthesizability and cost across chemical reaction options. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents a planning methodology rather than a novel LLM evaluation benchmark.
Recursive Environment Scaling for General Long-Horizon Agents Presents an environment scaling framework that alters dependencies to scale agent benchmark difficulty while preserving task objectives. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on agent task difficulty rather than deployment safety mechanisms.
EvoWeb: A Benchmark for Coding Agents on Evolving Web Applications Introduces a benchmark evaluating coding agents on evolving web applications across cumulative feature requests. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Evaluates coding functional correctness rather than safety or alignment guardrails.
Estimating the Causal Impact of Social Navigation Signals in Corridor Scenarios Applies a causal inference framework to evaluate the impact of robotic social navigation signals in corridor interactions. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on causal navigation in robotics, not LLM-based agents.
Estimating the Causal Impact of Social Navigation Signals in Corridor Scenarios Applies a causal inference framework to evaluate the impact of robotic social navigation signals in corridor interactions. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Studies robot social navigation, not industrial inspection or defect detection.
Estimating the Causal Impact of Social Navigation Signals in Corridor Scenarios Applies a causal inference framework to evaluate the impact of robotic social navigation signals in corridor interactions. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Investigates causal inference methods rather than LLM benchmarking standards.
Estimating the Causal Impact of Social Navigation Signals in Corridor Scenarios Applies a causal inference framework to evaluate the impact of robotic social navigation signals in corridor interactions. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses social robotics navigation rather than AI deployment safety guardrails.
SLS-Bench: A Benchmark for Incident Log Summarization with Synthetic Observability Data Introduces a benchmark of synthetic observability data and incident reports to evaluate automated incident log summarization. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on benchmark construction and summarization quality, not safety risks.
Chain Dimension Scaling: Communication-Efficient Loop-Style Scaling for Industrial Short-Video Ranking Presents a loop-style parameter-sharing scaling strategy to improve recommendation rankers without increasing parameter counts. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses training efficiency rather than safety or reliability risks.
Chain Dimension Scaling: Communication-Efficient Loop-Style Scaling for Industrial Short-Video Ranking Presents a loop-style parameter-sharing scaling strategy to improve recommendation rankers without increasing parameter counts. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes a recommendation model architecture, not an evaluation benchmark.
Chain Dimension Scaling: Communication-Efficient Loop-Style Scaling for Industrial Short-Video Ranking Presents a loop-style parameter-sharing scaling strategy to improve recommendation rankers without increasing parameter counts. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on recommendation ranking models, not autonomous planning agents.
ARMED: Adaptive Retrieval-Augmented Medication Recommendation Proposes an adaptive retrieval-augmented generation framework that dynamically expands clinical queries for medication recommendation. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on clinical retrieval pipelines rather than autonomous multi-step planning agents.
ARMED: Adaptive Retrieval-Augmented Medication Recommendation Proposes an adaptive retrieval-augmented generation framework that dynamically expands clinical queries for medication recommendation. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Mentions using small language models in passing, but does not advance compression techniques.
ARMED: Adaptive Retrieval-Augmented Medication Recommendation Proposes an adaptive retrieval-augmented generation framework that dynamically expands clinical queries for medication recommendation. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Addresses medication recommendation, not software coding or program repair.
ARMED: Adaptive Retrieval-Augmented Medication Recommendation Proposes an adaptive retrieval-augmented generation framework that dynamically expands clinical queries for medication recommendation. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Designs a clinical RAG method, not an LLM evaluation benchmark.
ARMED: Adaptive Retrieval-Augmented Medication Recommendation Proposes an adaptive retrieval-augmented generation framework that dynamically expands clinical queries for medication recommendation. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on clinical text and tabular EHR history, not multimodal models.
ARMED: Adaptive Retrieval-Augmented Medication Recommendation Proposes an adaptive retrieval-augmented generation framework that dynamically expands clinical queries for medication recommendation. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on clinical recommendation accuracy, not AI deployment guardrails or safety.
VisExMEM: Visual Evidence-Based Explainable Multimodal Evaluation Metric Presents a reference-free evaluation metric that decomposes captions into atomic claims and verifies them against visual evidence. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on multimodal evaluation metrics rather than deployment guardrails or safety.
VisExMEM: Visual Evidence-Based Explainable Multimodal Evaluation Metric Presents a reference-free evaluation metric that decomposes captions into atomic claims and verifies them against visual evidence. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Addresses image caption evaluation, not code generation or review.
VisExMEM: Visual Evidence-Based Explainable Multimodal Evaluation Metric Presents a reference-free evaluation metric that decomposes captions into atomic claims and verifies them against visual evidence. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Introduces an evaluation metric, not model compression or efficient serving.
VisExMEM: Visual Evidence-Based Explainable Multimodal Evaluation Metric Presents a reference-free evaluation metric that decomposes captions into atomic claims and verifies them against visual evidence. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focuses on image-text caption verification, not external database retrieval-augmented generation.
VisExMEM: Visual Evidence-Based Explainable Multimodal Evaluation Metric Presents a reference-free evaluation metric that decomposes captions into atomic claims and verifies them against visual evidence. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Deals with multimodal evaluation metrics, not multi-step tool-using agents.
VisExMEM: Visual Evidence-Based Explainable Multimodal Evaluation Metric Presents a reference-free evaluation metric that decomposes captions into atomic claims and verifies them against visual evidence. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Applies to general image captioning, not industrial inspection or defect detection.
RAP-WAM: Learning World-Action Models through Predictive Rehearsal and Action Perturbations Introduces a world-action model framework using predictive rehearsal and action perturbations to guide robotic policy learning. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Studies robotic manipulation policies, not AI deployment safety or guardrails.
RAP-WAM: Learning World-Action Models through Predictive Rehearsal and Action Perturbations Introduces a world-action model framework using predictive rehearsal and action perturbations to guide robotic policy learning. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes policy learning with world models, not LLM evaluation benchmarks.
RAP-WAM: Learning World-Action Models through Predictive Rehearsal and Action Perturbations Introduces a world-action model framework using predictive rehearsal and action perturbations to guide robotic policy learning. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on robotic world-action control policies, not LLM-based autonomous agents.
CryptoFlowBench: Benchmarking Multi-Hop Reasoning on Blockchains for Cryptocurrency Fund Tracing Introduces a benchmark and tool-augmented agent for evaluating multi-hop reasoning in cryptocurrency fund tracing. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on multi-hop graph reasoning, not small or compressed models.
CryptoFlowBench: Benchmarking Multi-Hop Reasoning on Blockchains for Cryptocurrency Fund Tracing Introduces a benchmark and tool-augmented agent for evaluating multi-hop reasoning in cryptocurrency fund tracing. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Covers financial crime tracing benchmarks, not AI safety or reliability guardrails.
StructZO: Structured Zeroth-Order Optimization for Memory-Efficient LLM Fine-Tuning Proposes a structured zeroth-order optimization framework using subspace estimation and spectral control for memory-efficient model fine-tuning. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Develops a fine-tuning optimization framework, not an evaluation benchmark.
StructZO: Structured Zeroth-Order Optimization for Memory-Efficient LLM Fine-Tuning Proposes a structured zeroth-order optimization framework using subspace estimation and spectral control for memory-efficient model fine-tuning. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Investigates zeroth-order LLM fine-tuning, not autonomous multi-step agents.

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