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
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.
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.
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.
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.
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.
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	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	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	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	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focuses on retrosynthesis planning rather than retrieval-augmented generation architectures.
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	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	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	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	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	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.
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.
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	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	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.
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	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	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on clinical retrieval pipelines rather than autonomous multi-step planning 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	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	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	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 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	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	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	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on robotic world-action control policies, not LLM-based autonomous agents.
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.
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.
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 safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses memory-efficient training optimization, not deployment safety or alignment.
PATMT2: Two-stage domain adaptation for multilingual patent translation Evaluates a two-stage domain adaptation method for multilingual patent translation and analyzes failure modes in low-resource languages. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on patent translation adaptation, not autonomous planning agents.
PATMT2: Two-stage domain adaptation for multilingual patent translation Evaluates a two-stage domain adaptation method for multilingual patent translation and analyzes failure modes in low-resource languages. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Applies LoRA to fine-tune translation models, not retrieval-augmented generation.
PATMT2: Two-stage domain adaptation for multilingual patent translation Evaluates a two-stage domain adaptation method for multilingual patent translation and analyzes failure modes in low-resource languages. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on translation domain adaptation rather than novel model compression methods.
PATMT2: Two-stage domain adaptation for multilingual patent translation Evaluates a two-stage domain adaptation method for multilingual patent translation and analyzes failure modes in low-resource languages. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Examines multilingual translation errors, not AI deployment safety or guardrails.
PATMT2: Two-stage domain adaptation for multilingual patent translation Evaluates a two-stage domain adaptation method for multilingual patent translation and analyzes failure modes in low-resource languages. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Operates strictly on patent text, not multimodal vision-language models.
PATMT2: Two-stage domain adaptation for multilingual patent translation Evaluates a two-stage domain adaptation method for multilingual patent translation and analyzes failure modes in low-resource languages. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents a fine-tuned translation model, not an LLM evaluation framework.
PATMT2: Two-stage domain adaptation for multilingual patent translation Evaluates a two-stage domain adaptation method for multilingual patent translation and analyzes failure modes in low-resource languages. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Covers patent text translation, not code generation or program repair.
XAI-Guided Fourier Domain Generalisation for Pure-Simulation Sim-to-Real Deep Reinforcement Learning Develops an explainability-guided Fourier domain generalization framework to improve sim-to-real reinforcement learning for autonomous driving. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes Fourier domain domain generalisation, not LLM evaluation benchmarks.
XAI-Guided Fourier Domain Generalisation for Pure-Simulation Sim-to-Real Deep Reinforcement Learning Develops an explainability-guided Fourier domain generalization framework to improve sim-to-real reinforcement learning for autonomous driving. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Addresses sim-to-real reinforcement learning for autonomous driving, not LLM agents.
XAI-Guided Fourier Domain Generalisation for Pure-Simulation Sim-to-Real Deep Reinforcement Learning Develops an explainability-guided Fourier domain generalization framework to improve sim-to-real reinforcement learning for autonomous driving. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on sim-to-real visual domain adaptation, not LLM safety or guardrails.
XAI-Guided Fourier Domain Generalisation for Pure-Simulation Sim-to-Real Deep Reinforcement Learning Develops an explainability-guided Fourier domain generalization framework to improve sim-to-real reinforcement learning for autonomous driving. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Covers vision-based RL policy transfer, not multimodal vision-language models.
XAI-Guided Fourier Domain Generalisation for Pure-Simulation Sim-to-Real Deep Reinforcement Learning Develops an explainability-guided Fourier domain generalization framework to improve sim-to-real reinforcement learning for autonomous driving. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Focuses on autonomous driving sim-to-real transfer, explicitly excluded from industrial vision.
PXRD-Bench: How Close Are We to Determining Crystal Structures from Powder Diffraction? Introduces a benchmark and baseline model for evaluating crystal structure determination from powder X-ray diffraction data. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Studies materials science diffraction modeling, not AI safety in deployment.
PXRD-Bench: How Close Are We to Determining Crystal Structures from Powder Diffraction? Introduces a benchmark and baseline model for evaluating crystal structure determination from powder X-ray diffraction data. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Examines crystal structure determination from X-ray patterns, not autonomous agents.
PXRD-Bench: How Close Are We to Determining Crystal Structures from Powder Diffraction? Introduces a benchmark and baseline model for evaluating crystal structure determination from powder X-ray diffraction data. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Evaluates crystallography generative models, not language models or LLM benchmarks.
SLEM: Spectrum-Aware Latent Expert Merging Introduces a calibration-free latent merging framework to compress Mixture-of-Experts models and reduce memory overhead. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on MoE memory footprint reduction, not safety or reliability guardrails.
SLEM: Spectrum-Aware Latent Expert Merging Introduces a calibration-free latent merging framework to compress Mixture-of-Experts models and reduce memory overhead. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on compressing mixture-of-experts models, not autonomous planning agents.
SLEM: Spectrum-Aware Latent Expert Merging Introduces a calibration-free latent merging framework to compress Mixture-of-Experts models and reduce memory overhead. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents an expert merging compression technique, not LLM evaluation benchmarks.
Growing and Transferring Pathways for LLM Reinforcement Learning Proposes structural growth pathways to preserve pretrained weights during reinforcement learning and enable transfer across language models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Covers reinforcement learning transferability, not AI safety or deployment guardrails.

9,361–9,420 of 13,754← Previous Next →